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90,ml-dl,https://github.com/tensorflow/tensorflow,[],,['1605.08695'],[],,,,tensorflow/tensorflow,tensorflow,179925,89314,7655,C++,https://tensorflow.org,An Open Source Machine Learning Framework for Everyone,tensorflow,2024-01-14,2015-11-07,429,418.9870259481038,https://avatars.githubusercontent.com/u/15658638?v=4,An Open Source Machine Learning Framework for Everyone,"['deep-learning', 'deep-neural-networks', 'distributed', 'machine-learning', 'ml', 'neural-network', 'tensorflow']","['deep-learning', 'deep-neural-networks', 'distributed', 'machine-learning', 'ml', 'neural-network', 'tensorflow']",2024-01-14,"[('mlflow/mlflow', 0.7961823344230652, 'ml-ops', 2), ('determined-ai/determined', 0.7361361384391785, 'ml-ops', 3), ('keras-team/keras', 0.7145187258720398, 'ml-dl', 3), ('horovod/horovod', 0.7118455767631531, 'ml-ops', 3), ('onnx/onnx', 0.7098345756530762, 'ml', 6), ('microsoft/onnxruntime', 0.681681215763092, 'ml', 3), ('huggingface/datasets', 0.6682099103927612, 'nlp', 3), 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('ourownstory/neural_prophet', 0.5077774524688721, 'ml', 3), ('fastai/fastcore', 0.5071895718574524, 'util', 0), ('probml/pyprobml', 0.5070176124572754, 'ml', 5), ('docarray/docarray', 0.5064188241958618, 'data', 3), ('ofa-sys/ofa', 0.5054358243942261, 'llm', 1), ('apple/ml-ane-transformers', 0.5047073364257812, 'ml', 0), ('microsoft/generative-ai-for-beginners', 0.5024099946022034, 'study', 1), ('hiyouga/llama-factory', 0.5012959241867065, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5012958645820618, 'llm', 1), ('pycaret/pycaret', 0.5007736682891846, 'ml', 1)]",2351,3.0,,76.73,2442,1872,63,0,29,28,29,2439.0,7500.0,90.0,3.1,94
77,ml-dl,https://github.com/pytorch/pytorch,[],,['1912.01703'],['torch'],,,,pytorch/pytorch,pytorch,74404,20452,1679,Python,https://pytorch.org,Tensors and Dynamic neural networks in Python with strong GPU acceleration,pytorch,2024-01-14,2016-08-13,389,191.05942773294203,https://avatars.githubusercontent.com/u/21003710?v=4,Tensors and Dynamic neural networks in Python with strong GPU acceleration,"['autograd', 'deep-learning', 'gpu', 'machine-learning', 'neural-network', 'numpy', 'tensor']","['autograd', 'deep-learning', 'gpu', 'machine-learning', 'neural-network', 'numpy', 'tensor']",2024-01-14,"[('ggerganov/ggml', 0.6398614645004272, 'ml', 1), ('google/tf-quant-finance', 0.6339718103408813, 'finance', 1), ('tensorly/tensorly', 0.6296373605728149, 'ml-dl', 3), ('arogozhnikov/einops', 0.624183177947998, 'ml-dl', 3), ('nvidia/tensorrt-llm', 0.61689293384552, 'viz', 1), ('xl0/lovely-tensors', 0.6134235858917236, 'ml-dl', 1), ('nvidia/warp', 0.6113191843032837, 'sim', 1), ('intel/intel-extension-for-pytorch', 0.6112003922462463, 'perf', 3), ('huggingface/accelerate', 0.5829792022705078, 'ml', 0), ('karpathy/micrograd', 0.5792595744132996, 'study', 0), ('cupy/cupy', 0.5749741196632385, 'math', 3), ('google/jax', 0.5702386498451233, 'ml', 2), ('rafiqhasan/auto-tensorflow', 0.5693333745002747, 'ml-dl', 1), ('jeshraghian/snntorch', 0.5666177272796631, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5660499334335327, 'ml-dl', 2), ('pytorch/ignite', 0.5628584623336792, 'ml-dl', 3), ('ageron/handson-ml2', 0.5553191304206848, 'ml', 0), ('plasma-umass/scalene', 0.5483222007751465, 'profiling', 1), ('microsoft/onnxruntime', 0.5476374626159668, 'ml', 2), ('nyandwi/modernconvnets', 0.5467002391815186, 'ml-dl', 0), ('tensorflow/addons', 0.5459487438201904, 'ml', 3), ('artemyk/dynpy', 0.5410839319229126, 'sim', 0), ('facebookincubator/aitemplate', 0.5403209328651428, 'ml-dl', 0), ('lightly-ai/lightly', 0.5388930439949036, 'ml', 2), ('ddbourgin/numpy-ml', 0.5358303189277649, 'ml', 1), ('keras-rl/keras-rl', 0.535692572593689, 'ml-rl', 1), ('xl0/lovely-numpy', 0.5318784713745117, 'util', 2), ('skorch-dev/skorch', 0.5300707817077637, 'ml-dl', 1), ('gradio-app/gradio', 0.5270141959190369, 'viz', 2), ('horovod/horovod', 0.5245367288589478, 'ml-ops', 2), ('exaloop/codon', 0.5243420600891113, 'perf', 0), ('tlkh/tf-metal-experiments', 0.5232517123222351, 'perf', 2), ('tensorlayer/tensorlayer', 0.5231267213821411, 'ml-rl', 2), ('rentruewang/koila', 0.5214821696281433, 'ml', 3), ('ray-project/ray', 0.5206745862960815, 'ml-ops', 2), ('pytorch/glow', 0.5188764929771423, 'ml', 0), ('dmlc/dgl', 0.5178238749504089, 'ml-dl', 1), ('keras-team/autokeras', 0.5157300233840942, 'ml-dl', 2), ('patrick-kidger/torchtyping', 0.5150875449180603, 'typing', 0), ('micropython/micropython', 0.5111660361289978, 'util', 0), ('salesforce/warp-drive', 0.5105277895927429, 'ml-rl', 2), ('facebookresearch/pytorch3d', 0.5076754093170166, 'ml-dl', 0), ('google/trax', 0.5070921778678894, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5070353150367737, 'ml-dl', 3), ('google/gin-config', 0.5066419243812561, 'util', 0), ('tensorflow/mesh', 0.5063491463661194, 'ml-dl', 0), ('d2l-ai/d2l-en', 0.5062558650970459, 'study', 2), ('cvxgrp/pymde', 0.5061772465705872, 'ml', 2), ('eleutherai/gpt-neox', 0.5058263540267944, 'llm', 0), ('pyg-team/pytorch_geometric', 0.503031313419342, 'ml-dl', 1), ('mrdbourke/m1-machine-learning-test', 0.5010038018226624, 'ml', 1), ('denys88/rl_games', 0.5007427930831909, 'ml-rl', 1)]",4586,4.0,,238.27,9243,6429,90,0,5,579,5,9242.0,30000.0,90.0,3.2,90
974,llm,https://github.com/hwchase17/langchain,"['langchain', 'chatbot', 'language-model']",,[],['langchain'],1.0,,,hwchase17/langchain,langchain,73828,11096,622,Python,https://python.langchain.com,⚡ Building applications with LLMs through composability ⚡,hwchase17,2024-01-14,2022-10-17,67,1099.5659574468084,https://avatars.githubusercontent.com/u/126733545?v=4,⚡ Building applications with LLMs through composability ⚡,[],"['chatbot', 'langchain', 'language-model']",2024-01-13,"[('nomic-ai/gpt4all', 0.756317138671875, 'llm', 2), ('deep-diver/llm-as-chatbot', 0.6912153363227844, 'llm', 1), ('thudm/chatglm2-6b', 0.6808525919914246, 'llm', 0), ('pathwaycom/llm-app', 0.6680853366851807, 'llm', 1), ('intel/intel-extension-for-transformers', 0.662899911403656, 'perf', 1), ('microsoft/autogen', 0.6627272963523865, 'llm', 1), ('embedchain/embedchain', 0.652535617351532, 'llm', 0), ('mlc-ai/web-llm', 0.6466255187988281, 'llm', 1), ('agenta-ai/agenta', 0.6438751816749573, 'llm', 1), ('young-geng/easylm', 0.6422194838523865, 'llm', 2), ('chainlit/chainlit', 0.6356831789016724, 'llm', 1), ('langchain-ai/langgraph', 0.6307724118232727, 'llm', 1), ('microsoft/promptflow', 0.6271064877510071, 'llm', 0), ('deepset-ai/haystack', 0.6265890598297119, 'llm', 1), ('microsoft/promptcraft-robotics', 0.6214913129806519, 'sim', 0), ('dylanhogg/llmgraph', 0.6144862174987793, 'ml', 0), ('nat/openplayground', 0.607382595539093, 'llm', 1), ('lm-sys/fastchat', 0.6048544645309448, 'llm', 2), ('run-llama/rags', 0.6003484129905701, 'llm', 1), ('eugeneyan/open-llms', 0.599155068397522, 'study', 0), ('deep-diver/pingpong', 0.589103639125824, 'llm', 0), ('citadel-ai/langcheck', 0.5860381722450256, 'llm', 1), ('chatarena/chatarena', 0.5813690423965454, 'llm', 0), ('li-plus/chatglm.cpp', 0.5806834697723389, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5801489949226379, 'llm', 1), ('shishirpatil/gorilla', 0.5784041285514832, 'llm', 0), ('berriai/litellm', 0.573574423789978, 'llm', 1), ('langchain-ai/chat-langchain', 0.5708911418914795, 'llm', 0), ('zilliztech/gptcache', 0.5693703293800354, 'llm', 2), ('guidance-ai/guidance', 0.5676143765449524, 'llm', 1), ('nvidia/nemo-guardrails', 0.5671313405036926, 'llm', 1), ('ibm/dromedary', 0.5653164386749268, 'llm', 1), ('fasteval/fasteval', 0.5635910630226135, 'llm', 0), ('eth-sri/lmql', 0.5619138479232788, 'llm', 1), ('salesforce/xgen', 0.5602133274078369, 'llm', 1), ('explosion/spacy-llm', 0.5581352710723877, 'llm', 0), ('microsoft/semantic-kernel', 0.5559676289558411, 'llm', 0), ('salesforce/codet5', 0.5549836158752441, 'nlp', 1), ('jina-ai/thinkgpt', 0.5536667704582214, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5530260801315308, 'llm', 1), ('rcgai/simplyretrieve', 0.5519610643386841, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5519071221351624, 'llm', 1), ('hiyouga/llama-factory', 0.5519071221351624, 'llm', 1), ('confident-ai/deepeval', 0.5516130328178406, 'testing', 1), ('langchain-ai/langsmith-cookbook', 0.5514335632324219, 'llm', 1), ('lchen001/llmdrift', 0.5510751008987427, 'llm', 1), ('alphasecio/langchain-examples', 0.5499154925346375, 'llm', 1), ('cheshire-cat-ai/core', 0.5408321619033813, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.5383664965629578, 'llm', 0), ('mmabrouk/chatgpt-wrapper', 0.5374903082847595, 'llm', 1), ('aiwaves-cn/agents', 0.536536693572998, 'nlp', 1), ('tigerlab-ai/tiger', 0.5362697243690491, 'llm', 0), ('next-gpt/next-gpt', 0.5355835556983948, 'llm', 0), ('microsoft/torchscale', 0.5352845788002014, 'llm', 0), ('bigscience-workshop/petals', 0.5341628789901733, 'data', 1), ('minimaxir/simpleaichat', 0.5275580883026123, 'llm', 0), ('openlmlab/moss', 0.5230026245117188, 'llm', 1), ('killianlucas/open-interpreter', 0.5187687873840332, 'llm', 0), ('conceptofmind/toolformer', 0.5181885361671448, 'llm', 1), ('argilla-io/argilla', 0.5174627304077148, 'nlp', 1), ('mlc-ai/mlc-llm', 0.5162545442581177, 'llm', 1), ('ajndkr/lanarky', 0.5159770846366882, 'llm', 0), ('prefecthq/marvin', 0.5155816078186035, 'nlp', 0), ('togethercomputer/openchatkit', 0.5152731537818909, 'nlp', 1), ('langchain-ai/langsmith-sdk', 0.5111579298973083, 'llm', 1), ('rasahq/rasa', 0.5109175443649292, 'llm', 1), ('run-llama/llama-lab', 0.5104470252990723, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5097209215164185, 'llm', 0), ('mnotgod96/appagent', 0.5082905888557434, 'llm', 0), ('logspace-ai/langflow', 0.5081093907356262, 'llm', 1), ('prefecthq/langchain-prefect', 0.5060675740242004, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5019939541816711, 'study', 0)]",1838,2.0,,122.67,5689,3714,15,0,309,251,309,5686.0,11588.0,90.0,2.0,90
190,util,https://github.com/home-assistant/core,[],,[],['homeassistant'],,,,home-assistant/core,core,65602,26660,1346,Python,https://www.home-assistant.io,:house_with_garden: Open source home automation that puts local control and privacy first.,home-assistant,2024-01-14,2013-09-17,541,121.26062846580406,https://avatars.githubusercontent.com/u/13844975?v=4,🏡 Open source home automation that puts local control and privacy first.,"['asyncio', 'home-automation', 'internet-of-things', 'iot', 'mqtt', 'raspberry-pi']","['asyncio', 'home-automation', 'internet-of-things', 'iot', 'mqtt', 'raspberry-pi']",2024-01-14,"[('blakeblackshear/frigate', 0.5823681354522705, 'util', 2)]",3981,4.0,,269.17,9465,6640,126,0,158,116,158,9463.0,30000.0,90.0,3.2,88
1519,util,https://github.com/yt-dlp/yt-dlp,[],,[],[],,,,yt-dlp/yt-dlp,yt-dlp,64022,5251,425,Python,https://discord.gg/H5MNcFW63r,A youtube-dl fork with additional features and fixes,yt-dlp,2024-01-14,2020-10-26,170,376.2837951301427,https://avatars.githubusercontent.com/u/79589310?v=4,A youtube-dl fork with additional features and fixes,"['sponskrub', 'sponsorblock', 'video-downloader', 'youtube-dl', 'youtube-dlc', 'youtube-downloader', 'yt-dlp']","['sponskrub', 'sponsorblock', 'video-downloader', 'youtube-dl', 'youtube-dlc', 'youtube-downloader', 'yt-dlp']",2024-01-09,"[('pytube/pytube', 0.5481389164924622, 'util', 0)]",1421,2.0,,15.33,1162,616,39,0,12,26,12,1164.0,2319.0,90.0,2.0,87
1174,llm,https://github.com/ggerganov/llama.cpp,"['llama', 'language-model']",,[],[],,,,ggerganov/llama.cpp,llama.cpp,48751,6952,467,C,,Port of Facebook's LLaMA model in C/C++,ggerganov,2024-01-14,2023-03-10,46,1046.8006134969326,,Port of Facebook's LLaMA model in C/C++,[],"['language-model', 'llama']",2024-01-13,"[('microsoft/llama-2-onnx', 0.6485655903816223, 'llm', 2), ('facebookresearch/llama-recipes', 0.6467329263687134, 'llm', 2), ('tloen/alpaca-lora', 0.6068325042724609, 'llm', 2), ('abetlen/llama-cpp-python', 0.5961679816246033, 'llm', 2), ('jzhang38/tinyllama', 0.5698243379592896, 'llm', 2), ('facebookresearch/llama', 0.5505987405776978, 'llm', 2), ('run-llama/llama-lab', 0.545893132686615, 'llm', 2), ('karpathy/llama2.c', 0.5367782711982727, 'llm', 2), ('lightning-ai/lit-llama', 0.5175377726554871, 'llm', 2)]",484,6.0,,35.98,1336,836,10,0,1140,1516,1140,1338.0,5491.0,90.0,4.1,87
1126,llm,https://github.com/jerryjliu/llama_index,"['llama-index', 'llama', 'language-model']",,[],[],1.0,,,jerryjliu/llama_index,llama_index,26670,3389,204,Python,https://docs.llamaindex.ai,LlamaIndex (formerly GPT Index) is a data framework for your LLM applications,jerryjliu,2024-01-14,2022-11-02,64,411.2114537444934,https://avatars.githubusercontent.com/u/130722866?v=4,LlamaIndex (formerly GPT Index) is a data framework for your LLM applications,"['agents', 'application', 'data', 'fine-tuning', 'framework', 'llamaindex', 'llm', 'rag', 'vector-database']","['agents', 'application', 'data', 'fine-tuning', 'framework', 'language-model', 'llama', 'llama-index', 'llamaindex', 'llm', 'rag', 'vector-database']",2024-01-14,"[('run-llama/llama-hub', 0.6595434546470642, 'data', 1), ('run-llama/llama-lab', 0.6514791250228882, 'llm', 3), ('zilliztech/gptcache', 0.5998677611351013, 'llm', 3), ('bentoml/openllm', 0.5906221866607666, 'ml-ops', 3), ('pathwaycom/llm-app', 0.5804813504219055, 'llm', 3), ('bobazooba/xllm', 0.5593804121017456, 'llm', 2), ('lancedb/lancedb', 0.5558105707168579, 'data', 1), ('confident-ai/deepeval', 0.5483352541923523, 'testing', 2), ('ajndkr/lanarky', 0.5451417565345764, 'llm', 0), ('vllm-project/vllm', 0.5435225963592529, 'llm', 2), ('microsoft/llama-2-onnx', 0.542698860168457, 'llm', 2), ('shishirpatil/gorilla', 0.5362586975097656, 'llm', 1), ('lightning-ai/lit-llama', 0.5328296422958374, 'llm', 2), ('predibase/lorax', 0.5294747352600098, 'llm', 3), ('opengenerativeai/genossgpt', 0.5268757939338684, 'llm', 2), ('tloen/alpaca-lora', 0.5254223942756653, 'llm', 2), ('tigerlab-ai/tiger', 0.5246336460113525, 'llm', 3), ('ray-project/llm-applications', 0.5219264030456543, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5202019214630127, 'llm', 3), ('nebuly-ai/nebullvm', 0.518968403339386, 'perf', 1), ('deepset-ai/haystack', 0.5174872279167175, 'llm', 1), ('eugeneyan/open-llms', 0.5083422660827637, 'study', 1), ('mshumer/gpt-llm-trainer', 0.5029345154762268, 'llm', 0), ('microsoft/semantic-kernel', 0.5002325773239136, 'llm', 1)]",581,2.0,,46.73,1916,1585,15,0,226,198,226,1917.0,3805.0,90.0,2.0,87
136,web,https://github.com/tiangolo/fastapi,[],,[],[],1.0,,,tiangolo/fastapi,fastapi,66943,5669,656,Python,https://fastapi.tiangolo.com/,"FastAPI framework, high performance, easy to learn, fast to code, ready for production",tiangolo,2024-01-14,2018-12-08,268,249.3885045236828,,"FastAPI framework, high performance, easy to learn, fast to code, ready for production","['api', 'async', 'asyncio', 'fastapi', 'framework', 'json', 'json-schema', 'openapi', 'openapi3', 'pydantic', 'python-types', 'redoc', 'rest', 'starlette', 'swagger', 'swagger-ui', 'uvicorn', 'web']","['api', 'async', 'asyncio', 'fastapi', 'framework', 'json', 'json-schema', 'openapi', 'openapi3', 'pydantic', 'python-types', 'redoc', 'rest', 'starlette', 'swagger', 'swagger-ui', 'uvicorn', 'web']",2024-01-13,"[('vitalik/django-ninja', 0.8338611721992493, 'web', 4), ('asacristani/fastapi-rocket-boilerplate', 0.7025260925292969, 'template', 1), ('starlite-api/starlite', 0.6928763389587402, 'web', 7), ('tiangolo/full-stack-fastapi-postgresql', 0.6879447102546692, 'template', 6), ('awtkns/fastapi-crudrouter', 0.6808283925056458, 'web', 10), ('python-restx/flask-restx', 0.6711971163749695, 'web', 4), ('hugapi/hug', 0.6468743085861206, 'util', 0), ('s3rius/fastapi-template', 0.6399540305137634, 'web', 2), ('fastai/fastcore', 0.63350909948349, 'util', 0), ('falconry/falcon', 0.608084499835968, 'web', 4), ('huge-success/sanic', 0.6080839037895203, 'web', 3), ('willmcgugan/textual', 0.6067060232162476, 'term', 1), ('fastapi-users/fastapi-users', 0.599251925945282, 'web', 4), ('rawheel/fastapi-boilerplate', 0.5945311188697815, 'web', 2), ('ml-tooling/opyrator', 0.5786353349685669, 'viz', 2), ('dmontagu/fastapi_client', 0.5745565891265869, 'web', 0), ('ajndkr/lanarky', 0.5630785822868347, 'llm', 2), ('tiangolo/sqlmodel', 0.5522589683532715, 'data', 4), ('shishirpatil/gorilla', 0.5488146543502808, 'llm', 1), ('plotly/dash', 0.5433139801025391, 'viz', 0), ('pyeve/eve', 0.5388421416282654, 'web', 1), ('alirn76/panther', 0.5322808027267456, 'web', 1), ('fastai/ghapi', 0.5305969715118408, 'util', 1), ('klen/muffin', 0.5296502709388733, 'web', 1), ('prefecthq/server', 0.5276178121566772, 'util', 0), ('tiangolo/asyncer', 0.525113582611084, 'perf', 2), ('flet-dev/flet', 0.5213461518287659, 'web', 1), ('neoteroi/blacksheep', 0.5194063782691956, 'web', 3), ('zhanymkanov/fastapi-best-practices', 0.5182605981826782, 'study', 1), ('alphasecio/langchain-examples', 0.5145138502120972, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5137580037117004, 'llm', 0), ('timofurrer/awesome-asyncio', 0.5137189030647278, 'study', 1), ('meltano/meltano', 0.5132185220718384, 'ml-ops', 0), ('fastapi-admin/fastapi-admin', 0.5084497928619385, 'web', 1), ('magicstack/uvloop', 0.5084176659584045, 'util', 2), ('pallets/flask', 0.5078809857368469, 'web', 0), ('flipkart-incubator/astra', 0.5062249302864075, 'web', 0), ('agronholm/anyio', 0.5019605159759521, 'perf', 1)]",559,2.0,,9.33,526,241,62,0,34,35,34,545.0,1751.0,90.0,3.2,86
1335,llm,https://github.com/oobabooga/text-generation-webui,"['ui', 'language-model']",,[],[],,,,oobabooga/text-generation-webui,text-generation-webui,31018,4217,283,Python,,"A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.",oobabooga,2024-01-14,2022-12-21,57,536.1135802469136,,"A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.",[],"['language-model', 'ui']",2024-01-14,"[('hannibal046/awesome-llm', 0.65085768699646, 'study', 1), ('mlc-ai/web-llm', 0.6323723793029785, 'llm', 1), ('lianjiatech/belle', 0.6288968324661255, 'llm', 0), ('microsoft/autogen', 0.6154066920280457, 'llm', 0), ('young-geng/easylm', 0.6048070788383484, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5950998663902283, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5891088843345642, 'llm', 1), ('freedomintelligence/llmzoo', 0.5807399153709412, 'llm', 1), ('lm-sys/fastchat', 0.5752599835395813, 'llm', 1), ('microsoft/llama-2-onnx', 0.5708426833152771, 'llm', 1), ('next-gpt/next-gpt', 0.5688201785087585, 'llm', 0), ('ai21labs/lm-evaluation', 0.5664257407188416, 'llm', 1), ('conceptofmind/toolformer', 0.5621170997619629, 'llm', 1), ('hiyouga/llama-factory', 0.5427017211914062, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5427016019821167, 'llm', 1), ('facebookresearch/llama-recipes', 0.5421599745750427, 'llm', 1), ('lupantech/chameleon-llm', 0.5410828590393066, 'llm', 1), ('run-llama/llama-lab', 0.5403240919113159, 'llm', 1), ('lightning-ai/lit-llama', 0.5357398986816406, 'llm', 1), ('juncongmoo/pyllama', 0.5334741473197937, 'llm', 0), ('cg123/mergekit', 0.5220274925231934, 'llm', 0), ('openlmlab/moss', 0.5163723230361938, 'llm', 1), ('microsoft/lora', 0.5137597918510437, 'llm', 1), ('xtekky/gpt4free', 0.5130376815795898, 'llm', 1), ('dylanhogg/llmgraph', 0.5120260715484619, 'ml', 0), ('langchain-ai/langgraph', 0.5098942518234253, 'llm', 0), ('salesforce/xgen', 0.5097959041595459, 'llm', 1), ('togethercomputer/redpajama-data', 0.5050533413887024, 'llm', 0), ('guidance-ai/guidance', 0.504760205745697, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5040669441223145, 'llm', 0), ('vitalik/django-ninja', 0.5028524398803711, 'web', 0), ('prefecthq/langchain-prefect', 0.5021174550056458, 'llm', 0), ('bobazooba/xllm', 0.501327633857727, 'llm', 0)]",290,2.0,,56.17,1313,1044,13,0,25,23,25,1313.0,3050.0,90.0,2.3,86
952,util,https://github.com/astral-sh/ruff,['code-quality'],,[],[],,,,astral-sh/ruff,ruff,22091,731,69,Rust,https://docs.astral.sh/ruff,"An extremely fast Python linter and code formatter, written in Rust.",astral-sh,2024-01-14,2022-08-09,77,286.8961038961039,https://avatars.githubusercontent.com/u/115962839?v=4,"An extremely fast Python linter and code formatter, written in Rust.","['linter', 'pep8', 'ruff', 'rust', 'rustpython', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide']","['code-quality', 'linter', 'pep8', 'ruff', 'rust', 'rustpython', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide']",2024-01-14,"[('google/pytype', 0.6849864721298218, 'typing', 4), ('grantjenks/blue', 0.6735846996307373, 'util', 1), ('psf/black', 0.6487815976142883, 'util', 1), ('aswinnnn/pyscan', 0.6466583609580994, 'security', 2), ('rustpython/rustpython', 0.6286622881889343, 'util', 1), ('instagram/monkeytype', 0.6186012029647827, 'typing', 1), ('google/yapf', 0.6007269024848938, 'util', 1), ('pytoolz/toolz', 0.5842194557189941, 'util', 0), ('pycqa/flake8', 0.5777422785758972, 'util', 7), ('landscapeio/prospector', 0.5776445269584656, 'util', 0), ('pypy/pypy', 0.5697559118270874, 'util', 0), ('pola-rs/polars', 0.5693628787994385, 'pandas', 1), ('pycqa/pylint', 0.569158136844635, 'util', 5), ('samuelcolvin/rtoml', 0.5641447305679321, 'data', 1), ('pyo3/maturin', 0.556878387928009, 'util', 1), ('mynameisfiber/high_performance_python_2e', 0.542158305644989, 'study', 0), ('crytic/slither', 0.5403991937637329, 'crypto', 1), ('hhatto/autopep8', 0.5378404259681702, 'util', 1), ('tiangolo/typer', 0.5375232100486755, 'term', 0), ('python/mypy', 0.533256471157074, 'typing', 2), ('rubik/radon', 0.528230607509613, 'util', 1), ('numba/llvmlite', 0.5217279195785522, 'util', 0), ('nedbat/coveragepy', 0.5197017788887024, 'testing', 0), ('pyston/pyston', 0.5196829438209534, 'util', 0), ('mdmzfzl/neetcode-solutions', 0.519241452217102, 'study', 1), ('klen/pylama', 0.5164267420768738, 'util', 1), ('facebookincubator/cinder', 0.5143499374389648, 'perf', 0), ('python/cpython', 0.5113768577575684, 'util', 0), ('mito-ds/monorepo', 0.5106766223907471, 'jupyter', 0), ('python/typeshed', 0.5103850364685059, 'typing', 1), ('pycqa/mccabe', 0.509772777557373, 'util', 0), ('klen/py-frameworks-bench', 0.5096173882484436, 'perf', 0), ('facebook/pyre-check', 0.5057628154754639, 'typing', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5036055445671082, 'study', 0), ('willmcgugan/rich', 0.5019975900650024, 'term', 0), ('ta-lib/ta-lib-python', 0.5013483166694641, 'finance', 0), ('fastai/fastcore', 0.5008984804153442, 'util', 0)]",344,3.0,,80.87,1804,1453,17,0,86,205,86,1800.0,4554.0,90.0,2.5,86
1171,llm,https://github.com/torantulino/auto-gpt,['autonomous-agents'],,[],[],,,,torantulino/auto-gpt,AutoGPT,156295,39233,1550,JavaScript,https://agpt.co,"AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.",torantulino,2024-01-14,2023-03-16,45,3418.953125,https://avatars.githubusercontent.com/u/130738209?v=4,"AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.","['ai', 'artificial-intelligence', 'autonomous-agents', 'gpt-4', 'openai']","['ai', 'artificial-intelligence', 'autonomous-agents', 'gpt-4', 'openai']",2024-01-12,"[('antonosika/gpt-engineer', 0.6554578542709351, 'llm', 3), ('assafelovic/gpt-researcher', 0.6427838802337646, 'llm', 0), ('yoheinakajima/babyagi', 0.6121024489402771, 'llm', 2), ('transformeroptimus/superagi', 0.6111651659011841, 'llm', 5), ('mindsdb/mindsdb', 0.5791462659835815, 'data', 2), ('oneil512/insight', 0.5754587054252625, 'ml', 1), ('microsoft/lmops', 0.5733479261398315, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.5608023405075073, 'ml-dl', 2), ('awslabs/autogluon', 0.5540371537208557, 'ml', 0), ('linksoul-ai/autoagents', 0.5497490167617798, 'llm', 1), ('prefecthq/marvin', 0.5470284223556519, 'nlp', 2), ('winedarksea/autots', 0.5468161702156067, 'time-series', 0), ('langchain-ai/opengpts', 0.5114303231239319, 'llm', 0), ('minimaxir/aitextgen', 0.5113155841827393, 'llm', 0), ('farizrahman4u/loopgpt', 0.5069693922996521, 'llm', 0), ('huggingface/autotrain-advanced', 0.5032193660736084, 'ml', 0), ('operand/agency', 0.5023353099822998, 'llm', 3), ('keras-team/autokeras', 0.5023127198219299, 'ml-dl', 0)]",713,1.0,,83.33,1181,1033,10,0,20,24,20,1165.0,1990.0,90.0,1.7,85
40,ml-dl,https://github.com/keras-team/keras,[],,[],[],,,,keras-team/keras,keras,60166,19523,1912,Python,http://keras.io/,Deep Learning for humans,keras-team,2024-01-14,2015-03-28,461,130.3907120743034,https://avatars.githubusercontent.com/u/34455048?v=4,Deep Learning for humans,"['data-science', 'deep-learning', 'jax', 'machine-learning', 'neural-networks', 'pytorch', 'tensorflow']","['data-science', 'deep-learning', 'jax', 'machine-learning', 'neural-networks', 'pytorch', 'tensorflow']",2024-01-10,"[('explosion/thinc', 0.7295705080032349, 'ml-dl', 5), ('tensorflow/tensorflow', 0.7145187258720398, 'ml-dl', 3), ('google/trax', 0.7093995809555054, 'ml-dl', 3), ('huggingface/transformers', 0.6673086285591125, 'nlp', 5), ('tensorlayer/tensorlayer', 0.6656548976898193, 'ml-rl', 2), ('onnx/onnx', 0.6622923612594604, 'ml', 4), ('mosaicml/composer', 0.6608446836471558, 'ml-dl', 4), ('deepmind/dm-haiku', 0.6441159844398499, 'ml-dl', 4), ('keras-rl/keras-rl', 0.64116370677948, 'ml-rl', 3), ('xl0/lovely-tensors', 0.6338521242141724, 'ml-dl', 2), ('microsoft/onnxruntime', 0.6258196234703064, 'ml', 5), ('huggingface/datasets', 0.6186720728874207, 'nlp', 4), ('d2l-ai/d2l-en', 0.6172467470169067, 'study', 6), ('nyandwi/modernconvnets', 0.6126129627227783, 'ml-dl', 2), ('alpa-projects/alpa', 0.6106972098350525, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.6089706420898438, 'ml', 2), ('aiqc/aiqc', 0.6046340465545654, 'ml-ops', 0), ('nvidia/deeplearningexamples', 0.5996661186218262, 'ml-dl', 3), ('pytorch/ignite', 0.5964016318321228, 'ml-dl', 3), ('mrdbourke/pytorch-deep-learning', 0.5936383008956909, 'study', 3), ('tensorflow/tensor2tensor', 0.5834200382232666, 'ml', 2), ('tensorly/tensorly', 0.5832876563072205, 'ml-dl', 4), ('fepegar/torchio', 0.5828122496604919, 'ml-dl', 3), ('arogozhnikov/einops', 0.580649197101593, 'ml-dl', 4), ('microsoft/deepspeed', 0.5722226500511169, 'ml-dl', 3), ('ageron/handson-ml2', 0.5698571801185608, 'ml', 0), ('keras-team/autokeras', 0.5688300728797913, 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79,ml,https://github.com/scikit-learn/scikit-learn,[],,['1201.0490'],[],,,,scikit-learn/scikit-learn,scikit-learn,57045,25029,2148,Python,https://scikit-learn.org,scikit-learn: machine learning in Python,scikit-learn,2024-01-14,2010-08-17,702,81.26068376068376,https://avatars.githubusercontent.com/u/365630?v=4,scikit-learn: machine learning in Python,"['data-analysis', 'data-science', 'machine-learning', 'statistics']","['data-analysis', 'data-science', 'machine-learning', 'statistics']",2024-01-13,"[('rasbt/mlxtend', 0.7584848403930664, 'ml', 2), ('online-ml/river', 0.6860164403915405, 'ml', 2), ('pycaret/pycaret', 0.6792936325073242, 'ml', 2), ('scikit-learn-contrib/metric-learn', 0.6777765154838562, 'ml', 1), ('gradio-app/gradio', 0.6689245700836182, 'viz', 3), ('scikit-learn-contrib/imbalanced-learn', 0.6626807451248169, 'ml', 4), ('featurelabs/featuretools', 0.6306010484695435, 'ml', 2), ('scikit-learn-contrib/lightning', 0.6304967403411865, 'ml', 1), ('sktime/sktime', 0.6196394562721252, 'time-series', 2), ('epistasislab/tpot', 0.6184797286987305, 'ml', 2), ('firmai/atspy', 0.6164392232894897, 'time-series', 0), ('statsmodels/statsmodels', 0.6116586327552795, 'ml', 3), ('awslabs/gluonts', 0.6101686358451843, 'time-series', 2), ('automl/auto-sklearn', 0.6095296144485474, 'ml', 0), ('ageron/handson-ml2', 0.6084038615226746, 'ml', 0), ('pymc-devs/pymc3', 0.6052919030189514, 'ml', 0), ('huggingface/evaluate', 0.5982382893562317, 'ml', 1), ('ddbourgin/numpy-ml', 0.5975611209869385, 'ml', 1), ('firmai/industry-machine-learning', 0.5884523987770081, 'study', 2), ('scikit-mobility/scikit-mobility', 0.5857199430465698, 'gis', 3), ('tensorflow/data-validation', 0.5854442715644836, 'ml-ops', 0), ('probml/pyprobml', 0.5849363207817078, 'ml', 1), ('districtdatalabs/yellowbrick', 0.5833300948143005, 'ml', 1), ('alkaline-ml/pmdarima', 0.5828933715820312, 'time-series', 1), ('sentinel-hub/eo-learn', 0.581174910068512, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.578446090221405, 'study', 4), ('patchy631/machine-learning', 0.575724184513092, 'ml', 0), ('google/pyglove', 0.5719713568687439, 'util', 1), ('scipy/scipy', 0.5719190835952759, 'math', 0), ('jovianml/opendatasets', 0.5696676969528198, 'data', 2), ('goldmansachs/gs-quant', 0.5592624545097351, 'finance', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5567522644996643, 'study', 0), ('google/temporian', 0.5564255714416504, 'time-series', 0), ('quantopian/pyfolio', 0.5517749786376953, 'finance', 0), ('pandas-dev/pandas', 0.5512358546257019, 'pandas', 2), ('fatiando/verde', 0.5504959225654602, 'gis', 1), ('ranaroussi/quantstats', 0.5498625636100769, 'finance', 0), ('kubeflow/fairing', 0.5472146272659302, 'ml-ops', 0), ('selfexplainml/piml-toolbox', 0.5468170642852783, 'ml-interpretability', 0), ('jeshraghian/snntorch', 0.5448082089424133, 'ml-dl', 1), ('eleutherai/pyfra', 0.543034553527832, 'ml', 0), ('polyaxon/datatile', 0.5421066284179688, 'pandas', 2), ('guyallard/markov_clustering', 0.5356784462928772, 'graph', 0), ('sympy/sympy', 0.5355417132377625, 'math', 0), ('xplainable/xplainable', 0.5327098965644836, 'ml-interpretability', 3), ('rasbt/machine-learning-book', 0.5305891036987305, 'study', 1), ('teamhg-memex/eli5', 0.5276551842689514, 'ml', 2), ('csinva/imodels', 0.525899350643158, 'ml', 3), ('numpy/numpy', 0.525807797908783, 'math', 0), ('nccr-itmo/fedot', 0.5258017778396606, 'ml-ops', 1), ('ta-lib/ta-lib-python', 0.5245856642723083, 'finance', 0), ('clips/pattern', 0.524055540561676, 'nlp', 1), ('thealgorithms/python', 0.5219714641571045, 'study', 0), ('huggingface/datasets', 0.5215214490890503, 'nlp', 1), ('feast-dev/feast', 0.5212818384170532, 'ml-ops', 2), ('crflynn/stochastic', 0.5197334885597229, 'sim', 0), ('rasbt/stat451-machine-learning-fs20', 0.5173183083534241, 'study', 0), ('lightly-ai/lightly', 0.5172454714775085, 'ml', 1), ('dagworks-inc/hamilton', 0.514525830745697, 'ml-ops', 3), ('dylanhogg/awesome-python', 0.5138348937034607, 'study', 2), ('mwaskom/seaborn', 0.5135470628738403, 'viz', 1), ('merantix-momentum/squirrel-core', 0.5131182670593262, 'ml', 2), ('yzhao062/pyod', 0.5111293196678162, 'data', 3), ('gbeced/pyalgotrade', 0.5099478363990784, 'finance', 0), ('xl0/lovely-numpy', 0.5066837072372437, 'util', 1), ('keon/algorithms', 0.5060738921165466, 'util', 0), ('ml-tooling/opyrator', 0.5052728056907654, 'viz', 1), ('wesm/pydata-book', 0.5047890543937683, 'study', 0), ('mdbloice/augmentor', 0.5044978260993958, 'ml', 1), ('sloria/textblob', 0.5042425990104675, 'nlp', 0), ('stan-dev/pystan', 0.5038464665412903, 'ml', 0), ('tensorflow/tensorflow', 0.5015178918838501, 'ml-dl', 1), ('skops-dev/skops', 0.5013717412948608, 'ml-ops', 1), ('gerdm/prml', 0.5011841058731079, 'study', 1), ('mlflow/mlflow', 0.5004898905754089, 'ml-ops', 1), ('explosion/spacy', 0.5003823637962341, 'nlp', 2), ('norvig/pytudes', 0.5002601742744446, 'util', 0)]",3042,8.0,,38.21,842,522,163,0,5,10,5,842.0,2257.0,90.0,2.7,85
1175,util,https://github.com/ggerganov/whisper.cpp,[],,[],[],,,,ggerganov/whisper.cpp,whisper.cpp,27061,2620,268,C,,Port of OpenAI's Whisper model in C/C++,ggerganov,2024-01-14,2022-09-25,70,385.0142276422764,,Port of OpenAI's Whisper model in C/C++,"['inference', 'openai', 'speech-recognition', 'speech-to-text', 'transformer', 'whisper']","['inference', 'openai', 'speech-recognition', 'speech-to-text', 'transformer', 'whisper']",2024-01-13,"[('sanchit-gandhi/whisper-jax', 0.6906029582023621, 'ml', 3), ('m-bain/whisperx', 0.582147479057312, 'nlp', 3), ('vaibhavs10/insanely-fast-whisper', 0.5495933890342712, 'llm', 1), ('huggingface/transformers', 0.5125561356544495, 'nlp', 2)]",228,5.0,,9.44,454,285,16,0,12,17,12,454.0,1023.0,90.0,2.3,85
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1639,llm,https://github.com/microsoft/autogen,['autonomous-agents'],,[],[],1.0,,,microsoft/autogen,autogen,19646,2518,253,Jupyter Notebook,https://microsoft.github.io/autogen/,Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ,microsoft,2024-01-14,2023-08-18,23,833.4666666666667,https://avatars.githubusercontent.com/u/6154722?v=4,Enable Next-Gen Large Language Model Applications. 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1288,llm,https://github.com/xtekky/gpt4free,[],,[],[],,,,xtekky/gpt4free,gpt4free,51340,12653,436,Python,https://discord.gg/XfybzPXPH5,The official gpt4free repository | various collection of powerful language models,xtekky,2024-01-14,2023-03-29,43,1170.6188925081433,,The official gpt4free repository | various collection of powerful language models,"['chatbot', 'chatbots', 'chatgpt', 'chatgpt-4', 'chatgpt-api', 'chatgpt-free', 'chatgpt4', 'free-gpt', 'gpt', 'gpt-3', 'gpt-4', 'gpt3', 'gpt4', 'gpt4-api', 'language-model', 'openai', 'openai-api', 'openai-chatgpt', 'reverse-engineering']","['chatbot', 'chatbots', 'chatgpt', 'chatgpt-4', 'chatgpt-api', 'chatgpt-free', 'chatgpt4', 'free-gpt', 'gpt', 'gpt-3', 'gpt-4', 'gpt3', 'gpt4', 'gpt4-api', 'language-model', 'openai', 'openai-api', 'openai-chatgpt', 'reverse-engineering']",2024-01-13,"[('run-llama/rags', 0.7228777408599854, 'llm', 3), ('microsoft/autogen', 0.7057386040687561, 'llm', 4), ('openai/openai-cookbook', 0.6899942755699158, 'ml', 4), ('blinkdl/chatrwkv', 0.6857205033302307, 'llm', 3), ('killianlucas/open-interpreter', 0.6677355766296387, 'llm', 2), ('next-gpt/next-gpt', 0.6495850086212158, 'llm', 2), ('mayooear/gpt4-pdf-chatbot-langchain', 0.642866313457489, 'llm', 2), ('lianjiatech/belle', 0.6404276490211487, 'llm', 0), ('shishirpatil/gorilla', 0.6333321332931519, 'llm', 2), ('hannibal046/awesome-llm', 0.6301591992378235, 'study', 2), ('embedchain/embedchain', 0.6268326640129089, 'llm', 2), ('farizrahman4u/loopgpt', 0.617682158946991, 'llm', 3), ('lm-sys/fastchat', 0.610226035118103, 'llm', 2), ('minimaxir/gpt-2-simple', 0.604681670665741, 'llm', 1), ('bobazooba/xllm', 0.6042348742485046, 'llm', 4), ('promptslab/promptify', 0.6027716398239136, 'nlp', 5), ('lupantech/chameleon-llm', 0.5982718467712402, 'llm', 4), ('mmabrouk/chatgpt-wrapper', 0.5910773277282715, 'llm', 6), ('openlmlab/moss', 0.5782349705696106, 'llm', 2), ('langchain-ai/opengpts', 0.5756799578666687, 'llm', 0), ('mlc-ai/web-llm', 0.5752508640289307, 'llm', 2), ('nomic-ai/gpt4all', 0.5740395784378052, 'llm', 2), ('guardrails-ai/guardrails', 0.5575733184814453, 'llm', 2), ('openai/tiktoken', 0.5542630553245544, 'nlp', 1), ('togethercomputer/openchatkit', 0.5519025325775146, 'nlp', 1), ('haotian-liu/llava', 0.5506402850151062, 'llm', 3), ('vision-cair/minigpt-4', 0.548042356967926, 'llm', 0), ('databrickslabs/dolly', 0.5452688932418823, 'llm', 2), ('minimaxir/simpleaichat', 0.5435710549354553, 'llm', 1), ('guidance-ai/guidance', 0.5423391461372375, 'llm', 2), ('prefecthq/marvin', 0.536582887172699, 'nlp', 3), ('eth-sri/lmql', 0.529441237449646, 'llm', 3), ('eleutherai/gpt-neo', 0.5255731344223022, 'llm', 3), ('laion-ai/open-assistant', 0.5251535177230835, 'llm', 2), ('bigscience-workshop/megatron-deepspeed', 0.5237749814987183, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5237749814987183, 'llm', 0), ('rasahq/rasa', 0.523765504360199, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5187653303146362, 'llm', 1), ('fasteval/fasteval', 0.5166919827461243, 'llm', 0), ('openai/openai-python', 0.515657901763916, 'util', 1), ('whu-zqh/chatgpt-vs.-bert', 0.5137408971786499, 'llm', 1), ('h2oai/h2ogpt', 0.5131638646125793, 'llm', 2), ('oobabooga/text-generation-webui', 0.5130376815795898, 'llm', 1), ('continuum-llms/chatgpt-memory', 0.5107817053794861, 'llm', 2), ('microsoft/lora', 0.5105166435241699, 'llm', 2), ('dylanhogg/llmgraph', 0.509717583656311, 'ml', 1), ('intel/intel-extension-for-transformers', 0.5091184973716736, 'perf', 1), ('ai4finance-foundation/fingpt', 0.5088690519332886, 'finance', 2), ('mnotgod96/appagent', 0.5081965923309326, 'llm', 2), ('mindsdb/mindsdb', 0.5054675936698914, 'data', 2), ('microsoft/chatgpt-robot-manipulation-prompts', 0.50308758020401, 'llm', 0), ('imartinez/privategpt', 0.5020104646682739, 'llm', 1)]",173,3.0,,21.31,433,363,10,0,55,73,55,432.0,907.0,90.0,2.1,83
1108,llm,https://github.com/lm-sys/fastchat,"['chatbot', 'evaluation', 'language-model']",,[],['fschat'],,,,lm-sys/fastchat,FastChat,30941,3865,324,Python,,"An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.",lm-sys,2024-01-14,2023-03-19,45,683.2397476340694,https://avatars.githubusercontent.com/u/126381704?v=4,"An open platform for training, serving, and evaluating large language models. 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0.526286244392395, 'llm', 0), ('guardrails-ai/guardrails', 0.525242269039154, 'llm', 0), ('langchain-ai/langgraph', 0.5237873792648315, 'llm', 0), ('explosion/spacy-llm', 0.5232176780700684, 'llm', 0), ('epfllm/meditron', 0.5228744149208069, 'llm', 1), ('keras-team/keras-nlp', 0.5216904878616333, 'nlp', 0), ('jalammar/ecco', 0.5213225483894348, 'ml-interpretability', 0), ('tigerlab-ai/tiger', 0.5211068987846375, 'llm', 0), ('ofa-sys/ofa', 0.5206315517425537, 'llm', 0), ('prefecthq/langchain-prefect', 0.5175867080688477, 'llm', 0), ('pathwaycom/llm-app', 0.5164425373077393, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5163758993148804, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.51581871509552, 'llm', 0), ('microsoft/megatron-deepspeed', 0.51581871509552, 'llm', 0), ('openai/tiktoken', 0.5158092975616455, 'nlp', 0), ('neulab/prompt2model', 0.5152702331542969, 'llm', 1), ('huggingface/transformers', 0.5151023864746094, 'nlp', 1), ('bigscience-workshop/promptsource', 0.514387309551239, 'nlp', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5135443210601807, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5120673179626465, 'llm', 0), ('neuml/txtai', 0.5114730000495911, 'nlp', 1), ('intel/intel-extension-for-transformers', 0.5099033117294312, 'perf', 1), ('dylanhogg/llmgraph', 0.5098823308944702, 'ml', 0), ('mooler0410/llmspracticalguide', 0.5060816407203674, 'study', 0), ('salesforce/blip', 0.5055869817733765, 'diffusion', 0), ('norskregnesentral/skweak', 0.5055734515190125, 'nlp', 0), ('allenai/allennlp', 0.5036823153495789, 'nlp', 0), ('jina-ai/finetuner', 0.5029986500740051, 'ml', 0), ('explosion/spacy-transformers', 0.5027205348014832, 'llm', 1), ('laion-ai/open-assistant', 0.5022433996200562, 'llm', 1), ('bobazooba/xllm', 0.5017418265342712, 'llm', 0), ('mlc-ai/mlc-llm', 0.5015469193458557, 'llm', 1), ('thudm/glm-130b', 0.5007140636444092, 'llm', 0)]",207,5.0,,13.92,562,312,10,0,12,26,12,561.0,800.0,90.0,1.4,83
60,pandas,https://github.com/pandas-dev/pandas,"['data-analysis', 'data-science', 'arrow', 'dataframe']",,[],[],1.0,,,pandas-dev/pandas,pandas,40904,17108,1117,Python,https://pandas.pydata.org,"Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more",pandas-dev,2024-01-14,2010-08-24,701,58.3509272467903,https://avatars.githubusercontent.com/u/21206976?v=4,"Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more","['alignment', 'data-analysis', 'data-science', 'flexible', 'pandas']","['alignment', 'arrow', 'data-analysis', 'data-science', 'dataframe', 'flexible', 'pandas']",2024-01-13,"[('unionai-oss/pandera', 0.6850487589836121, 'pandas', 1), ('krzjoa/awesome-python-data-science', 0.6641040444374084, 'study', 2), ('man-group/dtale', 0.6628751754760742, 'viz', 3), ('pytoolz/toolz', 0.6519975066184998, 'util', 0), ('rasbt/mlxtend', 0.6480407118797302, 'ml', 1), ('python-odin/odin', 0.6475261449813843, 'util', 0), ('plotly/dash', 0.6398255825042725, 'viz', 1), ('wesm/pydata-book', 0.6384697556495667, 'study', 0), ('dylanhogg/awesome-python', 0.6308945417404175, 'study', 2), ('polyaxon/datatile', 0.6224325299263, 'pandas', 2), ('eleutherai/pyfra', 0.6219991445541382, 'ml', 0), ('rapidsai/cudf', 0.6205441355705261, 'pandas', 5), ('holoviz/panel', 0.6123045682907104, 'viz', 0), ('keon/algorithms', 0.6043705940246582, 'util', 0), ('mwaskom/seaborn', 0.6021502017974854, 'viz', 2), ('pyparsing/pyparsing', 0.5993269681930542, 'util', 0), ('alkaline-ml/pmdarima', 0.5989955067634583, 'time-series', 0), ('fastai/fastcore', 0.5918874144554138, 'util', 0), ('dagworks-inc/hamilton', 0.590579092502594, 'ml-ops', 4), ('imageio/imageio', 0.5883070826530457, 'util', 0), ('pypy/pypy', 0.5845887660980225, 'util', 0), ('stan-dev/pystan', 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1540,llm,https://github.com/killianlucas/open-interpreter,[],,[],[],,,,killianlucas/open-interpreter,open-interpreter,37402,3268,283,Python,http://openinterpreter.com/,A natural language interface for computers,killianlucas,2024-01-14,2023-07-14,28,1309.07,,A natural language interface for computers,"['chatgpt', 'gpt-4', 'interpreter', 'javascript', 'nodejs']","['chatgpt', 'gpt-4', 'interpreter', 'javascript', 'nodejs']",2024-01-14,"[('run-llama/rags', 0.6841922998428345, 'llm', 1), ('xtekky/gpt4free', 0.6677355766296387, 'llm', 2), ('mayooear/gpt4-pdf-chatbot-langchain', 0.6425415277481079, 'llm', 0), ('embedchain/embedchain', 0.6413992643356323, 'llm', 1), ('minimaxir/simpleaichat', 0.6259564757347107, 'llm', 1), ('microsoft/autogen', 0.61408931016922, 'llm', 2), ('openai/openai-cookbook', 0.6033370494842529, 'ml', 2), ('mlc-ai/web-llm', 0.6023691892623901, 'llm', 1), ('guidance-ai/guidance', 0.6002689003944397, 'llm', 1), ('blinkdl/chatrwkv', 0.6001401543617249, 'llm', 1), ('openlmlab/moss', 0.5889337062835693, 'llm', 1), ('promptslab/promptify', 0.5764856338500977, 'nlp', 2), ('lm-sys/fastchat', 0.5708563923835754, 'llm', 0), ('lupantech/chameleon-llm', 0.5703567862510681, 'llm', 2), ('togethercomputer/openchatkit', 0.570201575756073, 'nlp', 0), ('databrickslabs/dolly', 0.559283971786499, 'llm', 0), ('lianjiatech/belle', 0.5514397621154785, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5495890974998474, 'llm', 1), ('hannibal046/awesome-llm', 0.5456922054290771, 'study', 0), ('next-gpt/next-gpt', 0.5455114841461182, 'llm', 2), ('farizrahman4u/loopgpt', 0.5448848605155945, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5440534949302673, 'nlp', 0), ('eth-sri/lmql', 0.5410472750663757, 'llm', 1), ('fasteval/fasteval', 0.5310702919960022, 'llm', 0), ('nomic-ai/gpt4all', 0.5259217619895935, 'llm', 0), ('pyscript/pyscript', 0.5255423784255981, 'web', 1), ('bigscience-workshop/promptsource', 0.5243964195251465, 'nlp', 0), ('rasahq/rasa', 0.523764431476593, 'llm', 0), ('prefecthq/marvin', 0.5231456756591797, 'nlp', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5223796367645264, 'llm', 0), ('cheshire-cat-ai/core', 0.5222368240356445, 'llm', 0), ('hwchase17/langchain', 0.5187687873840332, 'llm', 0), ('h2oai/h2ogpt', 0.5181115865707397, 'llm', 1), ('pndurette/gtts', 0.5171147584915161, 'util', 0), ('openai/chatgpt-retrieval-plugin', 0.5168745517730713, 'llm', 1), ('nltk/nltk', 0.5141381621360779, 'nlp', 0), ('shishirpatil/gorilla', 0.5121797323226929, 'llm', 1), ('laion-ai/open-assistant', 0.5087501406669617, 'llm', 1), ('jalammar/ecco', 0.5064843893051147, 'ml-interpretability', 0), ('rcgai/simplyretrieve', 0.5058812499046326, 'llm', 0), ('minimaxir/aitextgen', 0.5057147741317749, 'llm', 0), ('langchain-ai/chat-langchain', 0.5030243396759033, 'llm', 0), ('gventuri/pandas-ai', 0.5016472935676575, 'pandas', 1), ('microsoft/generative-ai-for-beginners', 0.5015537738800049, 'study', 1)]",74,2.0,,34.79,532,370,6,0,25,55,25,533.0,1172.0,90.0,2.2,82
54,math,https://github.com/numpy/numpy,[],,[],[],1.0,,,numpy/numpy,numpy,25443,9034,599,Python,https://numpy.org,The fundamental package for scientific computing with Python.,numpy,2024-01-13,2010-09-13,698,36.44383057090239,https://avatars.githubusercontent.com/u/288276?v=4,The fundamental package for scientific computing with Python.,['numpy'],['numpy'],2024-01-13,"[('scipy/scipy', 0.7627651691436768, 'math', 0), ('roban/cosmolopy', 0.7280952334403992, 'sim', 0), ('pyqtgraph/pyqtgraph', 0.6338181495666504, 'viz', 1), ('marcomusy/vedo', 0.6334555745124817, 'viz', 1), ('cupy/cupy', 0.6240665912628174, 'math', 1), ('google/jax', 0.6223551034927368, 'ml', 1), ('blaze/blaze', 0.6074309349060059, 'pandas', 0), ('scikit-hep/uproot5', 0.5980868935585022, 'data', 1), ('dask/dask', 0.5977861285209656, 'perf', 1), ('enthought/mayavi', 0.5977712869644165, 'viz', 0), ('fredrik-johansson/mpmath', 0.5976458191871643, 'math', 0), ('eleutherai/pyfra', 0.5739134550094604, 'ml', 0), ('jakevdp/pythondatasciencehandbook', 0.5711981654167175, 'study', 1), ('hips/autograd', 0.5689885020256042, 'ml', 0), ('pytoolz/toolz', 0.5658218860626221, 'util', 0), ('pyston/pyston', 0.5655171871185303, 'util', 0), ('sympy/sympy', 0.5636308193206787, 'math', 0), ('luispedro/mahotas', 0.5569568872451782, 'viz', 1), ('scitools/iris', 0.5556809306144714, 'gis', 0), ('xl0/lovely-numpy', 0.5545908808708191, 'util', 1), ('ageron/handson-ml2', 0.5535714626312256, 'ml', 0), ('pypy/pypy', 0.5520856380462646, 'util', 0), ('joblib/joblib', 0.5501325130462646, 'util', 0), ('hgrecco/pint', 0.5478784441947937, 'util', 0), ('python/cpython', 0.5478320121765137, 'util', 0), ('ipython/ipyparallel', 0.5446184277534485, 'perf', 0), ('cma-es/pycma', 0.5440859198570251, 'math', 0), ('scikit-geometry/scikit-geometry', 0.5440155267715454, 'gis', 0), ('pysal/pysal', 0.5434750318527222, 'gis', 0), ('wesm/pydata-book', 0.5418587327003479, 'study', 0), ('pyscf/pyscf', 0.5385183095932007, 'sim', 0), ('connorferster/handcalcs', 0.5350318551063538, 'jupyter', 0), ('altair-viz/altair', 0.5299937129020691, 'viz', 0), ('matplotlib/matplotlib', 0.5291272401809692, 'viz', 0), ('micropython/micropython', 0.5278494358062744, 'util', 0), ('pmorissette/ffn', 0.5275481343269348, 'finance', 0), ('scikit-learn-contrib/metric-learn', 0.5264557003974915, 'ml', 0), ('scikit-learn/scikit-learn', 0.525807797908783, 'ml', 0), ('plasma-umass/scalene', 0.5247296094894409, 'profiling', 0), ('intel/intel-extension-for-pytorch', 0.5218780636787415, 'perf', 0), ('numba/numba', 0.5208587050437927, 'perf', 1), ('gradio-app/gradio', 0.5195870995521545, 'viz', 0), ('nalepae/pandarallel', 0.5160078406333923, 'pandas', 0), ('scikit-image/scikit-image', 0.5151585936546326, 'util', 0), ('cython/cython', 0.5139519572257996, 'util', 0), ('pythonspeed/filprofiler', 0.5123228430747986, 'profiling', 0), ('pycaret/pycaret', 0.5097333192825317, 'ml', 0), ('nvidia/warp', 0.5096368193626404, 'sim', 0), ('earthlab/earthpy', 0.5088415741920471, 'gis', 0), ('goldmansachs/gs-quant', 0.5077769160270691, 'finance', 0), ('huggingface/huggingface_hub', 0.5030168890953064, 'ml', 0)]",1729,8.0,,65.15,950,712,162,0,12,17,12,950.0,2663.0,90.0,2.8,82
1405,llm,https://github.com/vllm-project/vllm,[],,[],[],1.0,,,vllm-project/vllm,vllm,13183,1567,138,Python,https://docs.vllm.ai,A high-throughput and memory-efficient inference and serving engine for LLMs,vllm-project,2024-01-14,2023-02-09,50,259.9464788732394,https://avatars.githubusercontent.com/u/136984999?v=4,A high-throughput and memory-efficient inference and serving engine for LLMs,"['gpt', 'inference', 'llama', 'llm', 'llm-serving', 'llmops', 'mlops', 'model-serving', 'pytorch', 'transformer']","['gpt', 'inference', 'llama', 'llm', 'llm-serving', 'llmops', 'mlops', 'model-serving', 'pytorch', 'transformer']",2024-01-12,"[('predibase/lorax', 0.7764987945556641, 'llm', 7), ('bentoml/openllm', 0.7688142657279968, 'ml-ops', 5), ('ray-project/ray-llm', 0.7471011877059937, 'llm', 3), ('bigscience-workshop/petals', 0.6905003190040588, 'data', 4), ('sjtu-ipads/powerinfer', 0.6339587569236755, 'llm', 2), ('microsoft/jarvis', 0.632617175579071, 'llm', 1), ('microsoft/llmlingua', 0.6239213347434998, 'llm', 1), ('bobazooba/xllm', 0.6193138957023621, 'llm', 4), ('iryna-kondr/scikit-llm', 0.6003293395042419, 'llm', 1), ('titanml/takeoff', 0.5988606214523315, 'llm', 3), ('intel/intel-extension-for-transformers', 0.5956281423568726, 'perf', 0), ('salesforce/xgen', 0.58912593126297, 'llm', 1), ('artidoro/qlora', 0.5857405066490173, 'llm', 0), ('microsoft/torchscale', 0.582655668258667, 'llm', 1), ('tairov/llama2.mojo', 0.5809930562973022, 'llm', 2), ('explosion/spacy-llm', 0.5805611610412598, 'llm', 2), ('lightning-ai/lit-gpt', 0.5720106959342957, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5688649415969849, 'llm', 0), ('eugeneyan/open-llms', 0.5680029988288879, 'study', 1), ('pathwaycom/llm-app', 0.5676781535148621, 'llm', 2), ('microsoft/semantic-kernel', 0.564845621585846, 'llm', 1), ('nvidia/tensorrt-llm', 0.5639804601669312, 'viz', 0), ('young-geng/easylm', 0.5622703433036804, 'llm', 2), ('truera/trulens', 0.5575326681137085, 'llm', 2), ('deepset-ai/haystack', 0.5563030242919922, 'llm', 1), ('nebuly-ai/nebullvm', 0.5560014247894287, 'perf', 1), ('squeezeailab/squeezellm', 0.5539712309837341, 'llm', 3), ('opengenerativeai/genossgpt', 0.5517637133598328, 'llm', 4), ('tigerlab-ai/tiger', 0.5497113466262817, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5451328754425049, 'llm', 3), ('neuralmagic/deepsparse', 0.5437913537025452, 'nlp', 1), ('jerryjliu/llama_index', 0.5435225963592529, 'llm', 2), ('nomic-ai/gpt4all', 0.5424882769584656, 'llm', 0), ('zilliztech/gptcache', 0.540032148361206, 'llm', 3), ('huggingface/optimum', 0.5363191962242126, 'ml', 2), ('hiyouga/llama-factory', 0.5355907678604126, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5355907082557678, 'llm', 3), ('mooler0410/llmspracticalguide', 0.5348982810974121, 'study', 0), ('optimalscale/lmflow', 0.5301596522331238, 'llm', 2), ('microsoft/autogen', 0.5299646854400635, 'llm', 2), ('citadel-ai/langcheck', 0.5279338359832764, 'llm', 0), ('shishirpatil/gorilla', 0.5265129208564758, 'llm', 1), ('huggingface/text-generation-inference', 0.5211599469184875, 'llm', 4), ('lianjiatech/belle', 0.5205905437469482, 'llm', 1), ('argilla-io/argilla', 0.5203900337219238, 'nlp', 2), ('skypilot-org/skypilot', 0.5184321403503418, 'llm', 1), ('lightning-ai/lit-llama', 0.5155295729637146, 'llm', 1), ('ludwig-ai/ludwig', 0.5144979357719421, 'ml-ops', 3), ('exaloop/codon', 0.5132838487625122, 'perf', 0), ('microsoft/lmops', 0.5128405094146729, 'llm', 2), ('night-chen/toolqa', 0.5108919143676758, 'llm', 0), ('microsoft/promptflow', 0.5073052048683167, 'llm', 2), ('lancedb/lancedb', 0.5034992694854736, 'data', 0), ('ray-project/llm-applications', 0.5022127032279968, 'llm', 0), ('opengvlab/omniquant', 0.5003920197486877, 'llm', 1)]",152,5.0,,12.31,1343,612,11,0,17,20,17,1342.0,3569.0,90.0,2.7,82
1542,llm,https://github.com/h2oai/h2ogpt,['question-answering'],,[],[],,,,h2oai/h2ogpt,h2ogpt,9412,1153,141,Python,http://h2o.ai,"Private Q&A and summarization of documents+images or chat with local GPT, 100% private, Apache 2.0. Supports Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/",h2oai,2024-01-14,2023-03-24,44,211.16666666666666,https://avatars.githubusercontent.com/u/1402695?v=4,"Private Q&A and summarization of documents+images or chat with local GPT, 100% private, Apache 2.0. Supports Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/","['ai', 'chatgpt', 'embeddings', 'generative', 'gpt', 'gpt4all', 'llama2', 'llm', 'mixtral', 'pdf', 'private', 'privategpt', 'vectorstore']","['ai', 'chatgpt', 'embeddings', 'generative', 'gpt', 'gpt4all', 'llama2', 'llm', 'mixtral', 'pdf', 'private', 'privategpt', 'question-answering', 'vectorstore']",2024-01-14,"[('imartinez/privategpt', 0.6058406233787537, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5983483195304871, 'llm', 1), ('bhaskatripathi/pdfgpt', 0.5438480377197266, 'llm', 0), ('run-llama/rags', 0.5400290489196777, 'llm', 2), ('killianlucas/open-interpreter', 0.5181115865707397, 'llm', 1), ('openai/openai-cookbook', 0.5150684714317322, 'ml', 1), ('xtekky/gpt4free', 0.5131638646125793, 'llm', 2), ('openai/chatgpt-retrieval-plugin', 0.5049951076507568, 'llm', 1)]",67,7.0,,92.04,380,305,10,0,70,86,70,380.0,861.0,90.0,2.3,82
14,web,https://github.com/django/django,[],,[],[],,,,django/django,django,75018,30779,2295,Python,https://www.djangoproject.com/,The Web framework for perfectionists with deadlines.,django,2024-01-14,2012-04-28,613,122.29296693060084,https://avatars.githubusercontent.com/u/27804?v=4,The Web framework for perfectionists with deadlines.,"['apps', 'django', 'framework', 'models', 'orm', 'templates', 'views', 'web']","['apps', 'django', 'framework', 'models', 'orm', 'templates', 'views', 'web']",2024-01-12,"[('emmett-framework/emmett', 0.6188867688179016, 'web', 0), ('wagtail/wagtail', 0.5955774188041687, 'web', 1), ('pallets/flask', 0.5927203893661499, 'web', 0), ('feincms/feincms', 0.5812693238258362, 'web', 0), ('stephenmcd/mezzanine', 0.5459080934524536, 'web', 1), ('bottlepy/bottle', 0.5293058156967163, 'web', 0), ('pylons/pyramid', 0.5236054062843323, 'web', 0), ('piccolo-orm/piccolo_admin', 0.5213547348976135, 'data', 0), ('indico/indico', 0.514659583568573, 'web', 0), ('reflex-dev/reflex', 0.5036461353302002, 'web', 1), ('willmcgugan/textual', 0.5026986598968506, 'term', 1), ('klen/muffin', 0.5025768876075745, 'web', 0), ('pallets/jinja', 0.5016000270843506, 'util', 1)]",3047,6.0,,17.88,443,366,143,1,0,34,34,443.0,968.0,90.0,2.2,81
1419,viz,https://github.com/apache/superset,[],,[],[],,,,apache/superset,superset,56148,12096,1492,TypeScript,https://superset.apache.org/,Apache Superset is a Data Visualization and Data Exploration Platform,apache,2024-01-14,2015-07-21,445,126.17528089887641,https://avatars.githubusercontent.com/u/47359?v=4,Apache Superset is a Data Visualization and Data Exploration Platform,"['analytics', 'apache', 'apache-superset', 'asf', 'bi', 'business-analytics', 'business-intelligence', 'data-analysis', 'data-analytics', 'data-engineering', 'data-science', 'data-visualization', 'data-viz', 'flask', 'react', 'sql-editor', 'superset']","['analytics', 'apache', 'apache-superset', 'asf', 'bi', 'business-analytics', 'business-intelligence', 'data-analysis', 'data-analytics', 'data-engineering', 'data-science', 'data-visualization', 'data-viz', 'flask', 'react', 'sql-editor', 'superset']",2024-01-13,"[('apache/airflow', 0.5265361070632935, 'ml-ops', 3), ('apache/spark', 0.5012211203575134, 'data', 0)]",1064,4.0,,31.83,1198,639,103,0,63,55,63,1197.0,2168.0,90.0,1.8,81
980,llm,https://github.com/laion-ai/open-assistant,[],,['2304.07327'],[],1.0,,,laion-ai/open-assistant,Open-Assistant,36115,3277,418,Python,https://open-assistant.io,"OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.",laion-ai,2024-01-14,2022-12-13,59,612.1186440677966,https://avatars.githubusercontent.com/u/92627801?v=4,"OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.","['ai', 'assistant', 'chatgpt', 'discord-bot', 'language-model', 'machine-learning', 'nextjs', 'rlhf']","['ai', 'assistant', 'chatgpt', 'discord-bot', 'language-model', 'machine-learning', 'nextjs', 'rlhf']",2024-01-06,"[('embedchain/embedchain', 0.618574321269989, 'llm', 2), ('togethercomputer/openchatkit', 0.6049430966377258, 'nlp', 0), ('rasahq/rasa', 0.5849403738975525, 'llm', 1), ('openai/openai-cookbook', 0.5750684142112732, 'ml', 1), ('deeppavlov/deeppavlov', 0.563031792640686, 'nlp', 2), ('langchain-ai/opengpts', 0.5428431034088135, 'llm', 1), ('run-llama/rags', 0.5403715968132019, 'llm', 1), ('larsbaunwall/bricky', 0.5381844639778137, 'llm', 2), ('xtekky/gpt4free', 0.5251535177230835, 'llm', 2), ('openai/tiktoken', 0.5184189677238464, 'nlp', 1), ('blinkdl/chatrwkv', 0.5142605900764465, 'llm', 2), ('krohling/bondai', 0.5133852362632751, 'llm', 0), ('killianlucas/open-interpreter', 0.5087501406669617, 'llm', 1), ('openlmlab/moss', 0.5062494874000549, 'llm', 2), ('lm-sys/fastchat', 0.5022433996200562, 'llm', 1), ('aimhubio/aim', 0.5019581913948059, 'ml-ops', 2), ('openai/openai-python', 0.5009532570838928, 'util', 0)]",305,2.0,,31.42,52,41,13,0,113,119,113,51.0,71.0,90.0,1.4,81
860,llm,https://github.com/hpcaitech/colossalai,[],,[],['colossalai'],,,,hpcaitech/colossalai,ColossalAI,36093,4117,373,Python,https://www.colossalai.org,"Making large AI models cheaper, faster and more accessible",hpcaitech,2024-01-14,2021-10-28,117,306.61529126213594,https://avatars.githubusercontent.com/u/88699314?v=4,"Making large AI models cheaper, faster and more accessible","['ai', 'big-model', 'data-parallelism', 'deep-learning', 'distributed-computing', 'foundation-models', 'heterogeneous-training', 'hpc', 'inference', 'large-scale', 'model-parallelism', 'pipeline-parallelism']","['ai', 'big-model', 'data-parallelism', 'deep-learning', 'distributed-computing', 'foundation-models', 'heterogeneous-training', 'hpc', 'inference', 'large-scale', 'model-parallelism', 'pipeline-parallelism']",2024-01-11,"[('bentoml/bentoml', 0.719754159450531, 'ml-ops', 2), ('alpa-projects/alpa', 0.6304028034210205, 'ml-dl', 2), ('pytorchlightning/pytorch-lightning', 0.6259893178939819, 'ml-dl', 2), ('googlecloudplatform/vertex-ai-samples', 0.6214262843132019, 'ml', 1), ('mlc-ai/mlc-llm', 0.6059736013412476, 'llm', 0), ('mosaicml/composer', 0.5992001891136169, 'ml-dl', 1), ('feast-dev/feast', 0.585966944694519, 'ml-ops', 0), ('mlflow/mlflow', 0.5852144360542297, 'ml-ops', 1), ('onnx/onnx', 0.5809974670410156, 'ml', 1), ('tensorflow/tensorflow', 0.5766342282295227, 'ml-dl', 1), ('uber/fiber', 0.5713132619857788, 'data', 1), ('ray-project/ray', 0.5699094533920288, 'ml-ops', 1), ('jina-ai/jina', 0.5669887065887451, 'ml', 1), ('huggingface/datasets', 0.5625196695327759, 'nlp', 1), ('explosion/thinc', 0.5544447898864746, 'ml-dl', 2), ('opentensor/bittensor', 0.552403450012207, 'ml', 2), ('qdrant/qdrant', 0.547572910785675, 'data', 0), ('microsoft/onnxruntime', 0.5474748015403748, 'ml', 1), ('netflix/metaflow', 0.5460145473480225, 'ml-ops', 1), ('google-research/google-research', 0.542206883430481, 'ml', 1), ('operand/agency', 0.5398745536804199, 'llm', 1), ('polyaxon/polyaxon', 0.5358098745346069, 'ml-ops', 1), ('paddlepaddle/paddle', 0.5353530645370483, 'ml-dl', 1), ('activeloopai/deeplake', 0.5351329445838928, 'ml-ops', 2), ('microsoft/lmops', 0.5340853929519653, 'llm', 0), ('interpretml/interpret', 0.5297350883483887, 'ml-interpretability', 1), ('mindsdb/mindsdb', 0.5289170742034912, 'data', 1), ('keras-team/keras', 0.5246325135231018, 'ml-dl', 1), ('xplainable/xplainable', 0.5240747928619385, 'ml-interpretability', 0), ('tensorflow/tensor2tensor', 0.5182396173477173, 'ml', 1), ('ludwig-ai/ludwig', 0.5150622129440308, 'ml-ops', 1), ('superduperdb/superduperdb', 0.514103353023529, 'data', 2), ('skypilot-org/skypilot', 0.5133247971534729, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5127544403076172, 'study', 2), ('nccr-itmo/fedot', 0.5125412344932556, 'ml-ops', 0), ('oegedijk/explainerdashboard', 0.5112443566322327, 'ml-interpretability', 0), ('avaiga/taipy', 0.5108974575996399, 'data', 0), ('horovod/horovod', 0.5101556181907654, 'ml-ops', 1), ('lutzroeder/netron', 0.5051214098930359, 'ml', 2), ('unity-technologies/ml-agents', 0.5021815896034241, 'ml-rl', 1)]",161,3.0,,20.25,401,293,27,0,13,15,13,401.0,437.0,90.0,1.1,81
119,ml-ops,https://github.com/apache/airflow,[],,[],['apache-airflow'],,,,apache/airflow,airflow,33075,13355,757,Python,https://airflow.apache.org/,"Apache Airflow - A platform to programmatically author, schedule, and monitor workflows",apache,2024-01-14,2015-04-13,459,72.03640323584318,https://avatars.githubusercontent.com/u/47359?v=4,"Apache Airflow - A platform to programmatically author, schedule, and monitor workflows","['airflow', 'apache', 'apache-airflow', 'automation', 'dag', 'data-engineering', 'data-integration', 'data-orchestrator', 'data-pipelines', 'data-science', 'elt', 'etl', 'machine-learning', 'mlops', 'orchestration', 'scheduler', 'workflow', 'workflow-engine', 'workflow-orchestration']","['airflow', 'apache', 'apache-airflow', 'automation', 'dag', 'data-engineering', 'data-integration', 'data-orchestrator', 'data-pipelines', 'data-science', 'elt', 'etl', 'machine-learning', 'mlops', 'orchestration', 'scheduler', 'workflow', 'workflow-engine', 'workflow-orchestration']",2024-01-13,"[('dagster-io/dagster', 0.6647198796272278, 'ml-ops', 10), ('flyteorg/flyte', 0.6581413745880127, 'ml-ops', 4), ('mage-ai/mage-ai', 0.6510021090507507, 'ml-ops', 8), ('kestra-io/kestra', 0.6468701362609863, 'ml-ops', 9), ('astronomer/astro-sdk', 0.6314418911933899, 'ml-ops', 5), ('orchest/orchest', 0.57388836145401, 'ml-ops', 6), ('polyaxon/polyaxon', 0.5673277974128723, 'ml-ops', 4), ('prefecthq/server', 0.5666928291320801, 'util', 4), ('astronomer/astronomer', 0.5602481961250305, 'ml-ops', 1), ('getindata/kedro-kubeflow', 0.5596566200256348, 'ml-ops', 1), ('zenml-io/zenml', 0.5523534417152405, 'ml-ops', 4), ('prefecthq/prefect', 0.5517212748527527, 'ml-ops', 6), ('pydoit/doit', 0.5482216477394104, 'util', 2), ('airbytehq/airbyte', 0.5474141836166382, 'data', 4), ('avaiga/taipy', 0.5394763946533203, 'data', 5), ('apache/superset', 0.5265361070632935, 'viz', 3), ('anyscale/airflow-provider-ray', 0.521160364151001, 'ml-ops', 0), ('backtick-se/cowait', 0.5194593667984009, 'util', 3), ('bodywork-ml/bodywork-core', 0.5161139965057373, 'ml-ops', 4), ('kubeflow/pipelines', 0.5132193565368652, 'ml-ops', 3), ('dagworks-inc/hamilton', 0.5085076093673706, 'ml-ops', 7)]",3136,6.0,,92.02,2213,1826,107,0,16,500,16,2210.0,4964.0,90.0,2.2,81
1434,llm,https://github.com/hiyouga/llama-efficient-tuning,[],,[],[],,,,hiyouga/llama-efficient-tuning,LLaMA-Factory,9674,1534,80,Python,,"Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)",hiyouga,2024-01-14,2023-05-28,35,274.16194331983803,,"Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)","['baichuan', 'chatglm', 'fine-tuning', 'generative-ai', 'gpt', 'instruction-tuning', 'language-model', 'large-language-models', 'llama', 'llm', 'llms', 'lora', 'mistral', 'mixture-of-experts', 'peft', 'qlora', 'quantization', 'qwen', 'rlhf', 'transformers']","['baichuan', 'chatglm', 'fine-tuning', 'generative-ai', 'gpt', 'instruction-tuning', 'language-model', 'large-language-models', 'llama', 'llm', 'llms', 'lora', 'mistral', 'mixture-of-experts', 'peft', 'qlora', 'quantization', 'qwen', 'rlhf', 'transformers']",2024-01-13,"[('hiyouga/llama-factory', 1.0000001192092896, 'llm', 20), ('bobazooba/xllm', 0.7037465572357178, 'llm', 5), ('young-geng/easylm', 0.6924943923950195, 'llm', 3), ('h2oai/h2o-llmstudio', 0.6901037096977234, 'llm', 5), ('lianjiatech/belle', 0.6741761565208435, 'llm', 2), ('tigerlab-ai/tiger', 0.6649465560913086, 'llm', 3), ('confident-ai/deepeval', 0.6391015648841858, 'testing', 2), ('artidoro/qlora', 0.6366947889328003, 'llm', 1), ('bigscience-workshop/petals', 0.6349529027938843, 'data', 3), ('next-gpt/next-gpt', 0.6337035894393921, 'llm', 3), ('bentoml/openllm', 0.6292673945426941, 'ml-ops', 4), ('intel/intel-extension-for-transformers', 0.608906626701355, 'perf', 0), ('ludwig-ai/ludwig', 0.6046748757362366, 'ml-ops', 4), ('zrrskywalker/llama-adapter', 0.6037131547927856, 'llm', 3), ('cg123/mergekit', 0.5988380312919617, 'llm', 2), ('salesforce/xgen', 0.5888428092002869, 'llm', 3), ('microsoft/autogen', 0.5876283049583435, 'llm', 1), ('lightning-ai/lit-llama', 0.5843559503555298, 'llm', 2), ('li-plus/chatglm.cpp', 0.5838775634765625, 'llm', 3), ('thudm/chatglm2-6b', 0.5761158466339111, 'llm', 3), ('huggingface/peft', 0.5760744214057922, 'llm', 3), ('mooler0410/llmspracticalguide', 0.5758503079414368, 'study', 1), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5739951133728027, 'llm', 2), ('microsoft/lora', 0.5733075141906738, 'llm', 2), ('juncongmoo/pyllama', 0.5714971423149109, 'llm', 0), ('deepset-ai/haystack', 0.5705711841583252, 'llm', 4), ('predibase/lorax', 0.5675815939903259, 'llm', 6), ('hannibal046/awesome-llm', 0.5673013925552368, 'study', 2), ('eth-sri/lmql', 0.5669613480567932, 'llm', 1), ('eugeneyan/open-llms', 0.5650503039360046, 'study', 3), ('agenta-ai/agenta', 0.5645886659622192, 'llm', 3), ('optimalscale/lmflow', 0.5642813444137573, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5635749697685242, 'llm', 3), ('microsoft/torchscale', 0.5607730746269226, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5589559078216553, 'llm', 1), ('salesforce/codet5', 0.5533753037452698, 'nlp', 2), ('nat/openplayground', 0.5530924797058105, 'llm', 1), ('hwchase17/langchain', 0.5519071221351624, 'llm', 1), ('nomic-ai/gpt4all', 0.5512845516204834, 'llm', 1), ('lm-sys/fastchat', 0.5488898754119873, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5485014319419861, 'nlp', 1), ('infinitylogesh/mutate', 0.5444034934043884, 'nlp', 1), ('mlc-ai/web-llm', 0.5438233613967896, 'llm', 2), ('dylanhogg/llmgraph', 0.5430907607078552, 'ml', 1), ('oobabooga/text-generation-webui', 0.5427016019821167, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5422529578208923, 'llm', 1), ('fasteval/fasteval', 0.5391843914985657, 'llm', 1), ('ai21labs/lm-evaluation', 0.5385950803756714, 'llm', 1), ('alphasecio/langchain-examples', 0.5362140536308289, 'llm', 1), ('vllm-project/vllm', 0.5355907082557678, 'llm', 3), ('openbmb/toolbench', 0.5351806879043579, 'llm', 1), ('declare-lab/instruct-eval', 0.5344966053962708, 'llm', 1), ('tloen/alpaca-lora', 0.5321193337440491, 'llm', 2), ('ray-project/llm-applications', 0.5320852994918823, 'llm', 2), ('yizhongw/self-instruct', 0.530853271484375, 'llm', 2), ('zilliztech/gptcache', 0.5300213694572449, 'llm', 3), ('llmware-ai/llmware', 0.5291881561279297, 'llm', 3), ('microsoft/flaml', 0.5290378928184509, 'ml', 0), ('baichuan-inc/baichuan-13b', 0.5287743806838989, 'llm', 1), ('tiger-ai-lab/mammoth', 0.5284292697906494, 'llm', 1), ('ray-project/ray-llm', 0.528387725353241, 'llm', 3), ('freedomintelligence/llmzoo', 0.5281152129173279, 'llm', 1), ('hegelai/prompttools', 0.5267438292503357, 'llm', 2), ('citadel-ai/langcheck', 0.5261443257331848, 'llm', 1), ('explosion/spacy-llm', 0.5260066986083984, 'llm', 3), ('argilla-io/argilla', 0.5245571732521057, 'nlp', 2), ('prefecthq/langchain-prefect', 0.5244019627571106, 'llm', 1), ('guidance-ai/guidance', 0.5210491418838501, 'llm', 1), ('nvidia/tensorrt-llm', 0.519806444644928, 'viz', 1), ('predibase/llm_distillation_playbook', 0.5162742733955383, 'llm', 0), ('langchain-ai/langgraph', 0.5161828994750977, 'llm', 0), ('tsinghuadatabasegroup/db-gpt', 0.5159655809402466, 'llm', 1), ('haotian-liu/llava', 0.511481523513794, 'llm', 2), ('microsoft/llama-2-onnx', 0.5092772841453552, 'llm', 2), ('cstankonrad/long_llama', 0.5056299567222595, 'llm', 2), ('microsoft/promptflow', 0.5054689645767212, 'llm', 2), ('ctlllll/llm-toolmaker', 0.5054222941398621, 'llm', 1), ('jzhang38/tinyllama', 0.505348265171051, 'llm', 2), ('opengvlab/omniquant', 0.5039510726928711, 'llm', 3), ('run-llama/rags', 0.5037953853607178, 'llm', 1), ('conceptofmind/toolformer', 0.5014779567718506, 'llm', 1), ('huggingface/transformers', 0.5012958645820618, 'nlp', 1), ('facebookresearch/llama-recipes', 0.5004853010177612, 'llm', 2)]",33,7.0,,13.65,1090,1047,8,0,17,26,17,1092.0,2458.0,90.0,2.3,81
1880,llm,https://github.com/hiyouga/llama-factory,[],,[],[],,,,hiyouga/llama-factory,LLaMA-Factory,9674,1534,80,Python,,"Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)",hiyouga,2024-01-14,2023-05-28,35,274.16194331983803,,"Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)","['baichuan', 'chatglm', 'fine-tuning', 'generative-ai', 'gpt', 'instruction-tuning', 'language-model', 'large-language-models', 'llama', 'llm', 'llms', 'lora', 'mistral', 'mixture-of-experts', 'peft', 'qlora', 'quantization', 'qwen', 'rlhf', 'transformers']","['baichuan', 'chatglm', 'fine-tuning', 'generative-ai', 'gpt', 'instruction-tuning', 'language-model', 'large-language-models', 'llama', 'llm', 'llms', 'lora', 'mistral', 'mixture-of-experts', 'peft', 'qlora', 'quantization', 'qwen', 'rlhf', 'transformers']",2024-01-13,"[('hiyouga/llama-efficient-tuning', 1.0000001192092896, 'llm', 20), ('bobazooba/xllm', 0.7037465572357178, 'llm', 5), ('young-geng/easylm', 0.6924945116043091, 'llm', 3), ('h2oai/h2o-llmstudio', 0.6901035308837891, 'llm', 5), ('lianjiatech/belle', 0.6741760969161987, 'llm', 2), ('tigerlab-ai/tiger', 0.6649466753005981, 'llm', 3), ('confident-ai/deepeval', 0.6391016840934753, 'testing', 2), ('artidoro/qlora', 0.6366949081420898, 'llm', 1), ('bigscience-workshop/petals', 0.6349529027938843, 'data', 3), ('next-gpt/next-gpt', 0.6337036490440369, 'llm', 3), ('bentoml/openllm', 0.6292675733566284, 'ml-ops', 4), ('intel/intel-extension-for-transformers', 0.6089065670967102, 'perf', 0), ('ludwig-ai/ludwig', 0.6046749949455261, 'ml-ops', 4), ('zrrskywalker/llama-adapter', 0.6037132740020752, 'llm', 3), ('cg123/mergekit', 0.5988380908966064, 'llm', 2), ('salesforce/xgen', 0.5888428688049316, 'llm', 3), ('microsoft/autogen', 0.5876283049583435, 'llm', 1), ('lightning-ai/lit-llama', 0.5843560099601746, 'llm', 2), ('li-plus/chatglm.cpp', 0.5838776230812073, 'llm', 3), ('thudm/chatglm2-6b', 0.5761158466339111, 'llm', 3), ('huggingface/peft', 0.576074481010437, 'llm', 3), ('mooler0410/llmspracticalguide', 0.575850248336792, 'study', 1), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5739949345588684, 'llm', 2), ('microsoft/lora', 0.5733075141906738, 'llm', 2), ('juncongmoo/pyllama', 0.5714971423149109, 'llm', 0), ('deepset-ai/haystack', 0.5705711245536804, 'llm', 4), ('predibase/lorax', 0.5675815343856812, 'llm', 6), ('hannibal046/awesome-llm', 0.5673015117645264, 'study', 2), ('eth-sri/lmql', 0.5669615268707275, 'llm', 1), ('eugeneyan/open-llms', 0.5650503635406494, 'study', 3), ('agenta-ai/agenta', 0.5645887851715088, 'llm', 3), ('optimalscale/lmflow', 0.5642813444137573, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5635750889778137, 'llm', 3), ('microsoft/torchscale', 0.5607731938362122, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5589560270309448, 'llm', 1), ('salesforce/codet5', 0.5533753633499146, 'nlp', 2), ('nat/openplayground', 0.5530926585197449, 'llm', 1), ('hwchase17/langchain', 0.5519071221351624, 'llm', 1), ('nomic-ai/gpt4all', 0.5512844920158386, 'llm', 1), ('lm-sys/fastchat', 0.5488899946212769, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5485014915466309, 'nlp', 1), ('infinitylogesh/mutate', 0.5444034337997437, 'nlp', 1), ('mlc-ai/web-llm', 0.5438234210014343, 'llm', 2), ('dylanhogg/llmgraph', 0.5430908203125, 'ml', 1), ('oobabooga/text-generation-webui', 0.5427017211914062, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5422530174255371, 'llm', 1), ('fasteval/fasteval', 0.5391845107078552, 'llm', 1), ('ai21labs/lm-evaluation', 0.5385951995849609, 'llm', 1), ('alphasecio/langchain-examples', 0.5362140536308289, 'llm', 1), ('vllm-project/vllm', 0.5355907678604126, 'llm', 3), ('openbmb/toolbench', 0.5351808667182922, 'llm', 1), ('declare-lab/instruct-eval', 0.5344966650009155, 'llm', 1), ('tloen/alpaca-lora', 0.5321192145347595, 'llm', 2), ('ray-project/llm-applications', 0.5320853590965271, 'llm', 2), ('yizhongw/self-instruct', 0.5308533310890198, 'llm', 2), ('zilliztech/gptcache', 0.5300214290618896, 'llm', 3), ('llmware-ai/llmware', 0.5291882157325745, 'llm', 3), ('microsoft/flaml', 0.5290379524230957, 'ml', 0), ('baichuan-inc/baichuan-13b', 0.5287744402885437, 'llm', 1), ('tiger-ai-lab/mammoth', 0.5284292697906494, 'llm', 1), ('ray-project/ray-llm', 0.5283878445625305, 'llm', 3), ('freedomintelligence/llmzoo', 0.5281153321266174, 'llm', 1), ('hegelai/prompttools', 0.5267438292503357, 'llm', 2), ('citadel-ai/langcheck', 0.5261441469192505, 'llm', 1), ('explosion/spacy-llm', 0.5260065793991089, 'llm', 3), ('argilla-io/argilla', 0.5245572328567505, 'nlp', 2), ('prefecthq/langchain-prefect', 0.5244020819664001, 'llm', 1), ('guidance-ai/guidance', 0.5210491418838501, 'llm', 1), ('nvidia/tensorrt-llm', 0.5198065042495728, 'viz', 1), ('predibase/llm_distillation_playbook', 0.5162743330001831, 'llm', 0), ('langchain-ai/langgraph', 0.5161830186843872, 'llm', 0), ('tsinghuadatabasegroup/db-gpt', 0.5159657001495361, 'llm', 1), ('haotian-liu/llava', 0.5114816427230835, 'llm', 2), ('microsoft/llama-2-onnx', 0.5092772841453552, 'llm', 2), ('cstankonrad/long_llama', 0.5056300163269043, 'llm', 2), ('microsoft/promptflow', 0.505469024181366, 'llm', 2), ('ctlllll/llm-toolmaker', 0.5054224133491516, 'llm', 1), ('jzhang38/tinyllama', 0.5053484439849854, 'llm', 2), ('opengvlab/omniquant', 0.5039510726928711, 'llm', 3), ('run-llama/rags', 0.5037953853607178, 'llm', 1), ('conceptofmind/toolformer', 0.5014780759811401, 'llm', 1), ('huggingface/transformers', 0.5012959241867065, 'nlp', 1), ('facebookresearch/llama-recipes', 0.5004853010177612, 'llm', 2)]",33,7.0,,13.65,1090,1047,8,0,17,26,17,1092.0,2458.0,90.0,2.3,81
404,study,https://github.com/thealgorithms/python,[],,[],[],,,,thealgorithms/python,Python,174693,43676,5935,Python,https://the-algorithms.com/,All Algorithms implemented in Python,thealgorithms,2024-01-14,2016-07-16,393,444.0272331154684,https://avatars.githubusercontent.com/u/20487725?v=4,All Algorithms implemented in Python,"['algorithm', 'algorithm-competitions', 'algorithms-implemented', 'algos', 'community-driven', 'education', 'interview', 'learn', 'practice', 'searches', 'sorting-algorithms', 'sorts']","['algorithm', 'algorithm-competitions', 'algorithms-implemented', 'algos', 'community-driven', 'education', 'interview', 'learn', 'practice', 'searches', 'sorting-algorithms', 'sorts']",2024-01-13,"[('keon/algorithms', 0.707886815071106, 'util', 1), ('krzjoa/awesome-python-data-science', 0.5975322127342224, 'study', 0), ('atsushisakai/pythonrobotics', 0.58967125415802, 'sim', 1), ('ranaroussi/quantstats', 0.5772646069526672, 'finance', 0), ('sympy/sympy', 0.5507904887199402, 'math', 0), ('scikit-mobility/scikit-mobility', 0.5455461740493774, 'gis', 0), ('joowani/binarytree', 0.5343596935272217, 'util', 2), ('dagworks-inc/hamilton', 0.5340306162834167, 'ml-ops', 0), ('sloria/textblob', 0.531756579875946, 'nlp', 0), ('scipy/scipy', 0.5260889530181885, 'math', 0), ('quantconnect/lean', 0.5260835886001587, 'finance', 1), ('online-ml/river', 0.5223714709281921, 'ml', 0), ('scikit-learn/scikit-learn', 0.5219714641571045, 'ml', 0), ('dylanhogg/awesome-python', 0.5201041102409363, 'study', 0), ('cython/cython', 0.5155187249183655, 'util', 0), ('gradio-app/gradio', 0.513965904712677, 'viz', 0), ('plotly/dash', 0.5107855796813965, 'viz', 0), ('gbeced/pyalgotrade', 0.5092158317565918, 'finance', 0), ('pypy/pypy', 0.5088759660720825, 'util', 0), ('networkx/networkx', 0.5088640451431274, 'graph', 0), ('quantopian/zipline', 0.5000721216201782, 'finance', 0)]",1186,7.0,,10.83,725,600,91,0,0,0,0,717.0,693.0,90.0,1.0,80
1137,llm,https://github.com/nomic-ai/gpt4all,"['chatbot', 'language-model']",,[],[],,,,nomic-ai/gpt4all,gpt4all,59720,6689,597,C++,https://gpt4all.io,gpt4all: open-source LLM chatbots that you can run anywhere,nomic-ai,2024-01-14,2023-03-27,44,1352.8802588996764,https://avatars.githubusercontent.com/u/102670180?v=4,gpt4all: open-source LLM chatbots that you can run anywhere,['llm-inference'],"['chatbot', 'language-model', 'llm-inference']",2024-01-12,"[('deep-diver/llm-as-chatbot', 0.7740846276283264, 'llm', 1), ('embedchain/embedchain', 0.7717016935348511, 'llm', 0), ('hwchase17/langchain', 0.756317138671875, 'llm', 2), ('microsoft/autogen', 0.716184139251709, 'llm', 2), ('intel/intel-extension-for-transformers', 0.7130550742149353, 'perf', 2), ('thudm/chatglm2-6b', 0.6924757957458496, 'llm', 0), ('deepset-ai/haystack', 0.6876985430717468, 'llm', 1), ('togethercomputer/openchatkit', 0.6839218139648438, 'nlp', 1), ('fasteval/fasteval', 0.6797701120376587, 'llm', 0), ('run-llama/rags', 0.679734468460083, 'llm', 1), ('lm-sys/fastchat', 0.6753419041633606, 'llm', 2), ('young-geng/easylm', 0.6724932789802551, 'llm', 2), ('aiwaves-cn/agents', 0.6714950799942017, 'nlp', 1), ('pathwaycom/llm-app', 0.6673745512962341, 'llm', 1), ('rcgai/simplyretrieve', 0.6575261950492859, 'llm', 0), ('rasahq/rasa', 0.6559364199638367, 'llm', 1), ('mlc-ai/web-llm', 0.6522467136383057, 'llm', 1), ('microsoft/promptcraft-robotics', 0.6439793109893799, 'sim', 0), ('dylanhogg/llmgraph', 0.6398856043815613, 'ml', 0), ('deeppavlov/deeppavlov', 0.6342305541038513, 'nlp', 1), ('chatarena/chatarena', 0.6325280070304871, 'llm', 0), ('blinkdl/chatrwkv', 0.6257155537605286, 'llm', 2), ('h2oai/h2o-llmstudio', 0.6245695948600769, 'llm', 1), ('bigscience-workshop/petals', 0.6160693168640137, 'data', 1), ('shishirpatil/gorilla', 0.6152714490890503, 'llm', 0), ('nebuly-ai/nebullvm', 0.6061845421791077, 'perf', 0), ('openlmlab/moss', 0.6058529615402222, 'llm', 1), ('mooler0410/llmspracticalguide', 0.6006626486778259, 'study', 0), ('tigerlab-ai/tiger', 0.5997213125228882, 'llm', 0), ('salesforce/codet5', 0.5974037051200867, 'nlp', 1), ('eugeneyan/open-llms', 0.5968722701072693, 'study', 0), ('cheshire-cat-ai/core', 0.5960567593574524, 'llm', 1), ('langchain-ai/chat-langchain', 0.594020426273346, 'llm', 0), ('salesforce/xgen', 0.590821385383606, 'llm', 1), ('gunthercox/chatterbot', 0.5897710919380188, 'nlp', 1), ('li-plus/chatglm.cpp', 0.5896359086036682, 'llm', 0), ('argilla-io/argilla', 0.5886470079421997, 'nlp', 0), ('nat/openplayground', 0.5870195627212524, 'llm', 1), ('hegelai/prompttools', 0.5853099226951599, 'llm', 0), ('berriai/litellm', 0.5837444067001343, 'llm', 0), ('nvidia/nemo-guardrails', 0.583074152469635, 'llm', 1), ('mmabrouk/chatgpt-wrapper', 0.5768630504608154, 'llm', 1), ('next-gpt/next-gpt', 0.5750716328620911, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5748793482780457, 'llm', 0), ('xtekky/gpt4free', 0.5740395784378052, 'llm', 2), ('microsoft/jarvis', 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('larsbaunwall/bricky', 0.5481459498405457, 'llm', 0), ('microsoft/promptflow', 0.5479527115821838, 'llm', 0), ('mnotgod96/appagent', 0.5476191639900208, 'llm', 0), ('confident-ai/deepeval', 0.5438401699066162, 'testing', 1), ('vllm-project/vllm', 0.5424882769584656, 'llm', 0), ('bentoml/openllm', 0.5412029027938843, 'ml-ops', 1), ('paddlepaddle/paddlenlp', 0.5397657155990601, 'llm', 0), ('bobazooba/xllm', 0.5367876887321472, 'llm', 0), ('langchain-ai/langgraph', 0.533517599105835, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5312781929969788, 'llm', 0), ('infinitylogesh/mutate', 0.5304908752441406, 'nlp', 1), ('krohling/bondai', 0.5283734798431396, 'llm', 0), ('lchen001/llmdrift', 0.5283440351486206, 'llm', 1), ('mlc-ai/mlc-llm', 0.5278238654136658, 'llm', 1), ('killianlucas/open-interpreter', 0.5259217619895935, 'llm', 0), ('llmware-ai/llmware', 0.5244438648223877, 'llm', 0), ('aws-samples/serverless-pdf-chat', 0.5228908061981201, 'llm', 0), ('microsoft/lmops', 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1524,llm,https://github.com/antonosika/gpt-engineer,['coding-assistant'],,[],[],,,,antonosika/gpt-engineer,gpt-engineer,48414,7869,494,Python,,"Specify what you want it to build, the AI asks for clarification, and then builds it.",antonosika,2024-01-14,2023-04-29,39,1227.891304347826,https://avatars.githubusercontent.com/u/153750385?v=4,"Specify what you want it to build, the AI asks for clarification, and then builds it.","['ai', 'autonomous-agent', 'codebase-generation', 'coding-assistant', 'gpt-4', 'openai']","['ai', 'autonomous-agent', 'codebase-generation', 'coding-assistant', 'gpt-4', 'openai']",2024-01-10,"[('prefecthq/marvin', 0.7385191917419434, 'nlp', 2), ('mindsdb/mindsdb', 0.6977242827415466, 'data', 1), ('transformeroptimus/superagi', 0.677440881729126, 'llm', 3), ('lastmile-ai/aiconfig', 0.6765998005867004, 'util', 1), ('torantulino/auto-gpt', 0.6554578542709351, 'llm', 3), ('smol-ai/developer', 0.6368786096572876, 'llm', 3), ('microsoft/generative-ai-for-beginners', 0.6323514580726624, 'study', 2), ('sweepai/sweep', 0.6145350933074951, 'llm', 1), ('pytorchlightning/pytorch-lightning', 0.6123632192611694, 'ml-dl', 1), ('bentoml/bentoml', 0.6053545475006104, 'ml-ops', 1), ('microsoft/promptflow', 0.5964549779891968, 'llm', 1), ('google-research/language', 0.5954734683036804, 'nlp', 0), ('pythagora-io/gpt-pilot', 0.590089738368988, 'llm', 3), ('mlc-ai/mlc-llm', 0.5844728946685791, 'llm', 0), ('microsoft/lmops', 0.5837531089782715, 'llm', 0), ('cheshire-cat-ai/core', 0.5780220031738281, 'llm', 1), ('operand/agency', 0.5778323411941528, 'llm', 2), ('yoheinakajima/babyagi', 0.5550106763839722, 'llm', 0), ('krohling/bondai', 0.5529053211212158, 'llm', 0), ('oegedijk/explainerdashboard', 0.545367956161499, 'ml-interpretability', 0), ('googlecloudplatform/vertex-ai-samples', 0.5366024374961853, 'ml', 1), ('unity-technologies/ml-agents', 0.5287721157073975, 'ml-rl', 0), ('alirezadir/machine-learning-interview-enlightener', 0.527275800704956, 'study', 1), ('jina-ai/jina', 0.5212904214859009, 'ml', 0), ('oliveirabruno01/babyagi-asi', 0.518890917301178, 'llm', 1), ('netflix/metaflow', 0.5169954299926758, 'ml-ops', 1), ('oneil512/insight', 0.5132570862770081, 'ml', 1), ('avaiga/taipy', 0.5112882852554321, 'data', 0), ('activeloopai/deeplake', 0.5061295628547668, 'ml-ops', 1), ('assafelovic/gpt-researcher', 0.5011528730392456, 'llm', 1)]",91,2.0,,17.35,201,180,9,0,16,22,16,200.0,379.0,90.0,1.9,80
712,pandas,https://github.com/pola-rs/polars,"['arrow', 'rust', 'dataframe', 'olap']",,[],[],1.0,,,pola-rs/polars,polars,23493,1364,143,Rust,https://docs.pola.rs,"Dataframes powered by a multithreaded, vectorized query engine, written in Rust",pola-rs,2024-01-14,2020-05-13,193,121.18717759764186,https://avatars.githubusercontent.com/u/83768144?v=4,"Dataframes powered by a multithreaded, vectorized query engine, written in Rust","['arrow', 'dataframe', 'dataframe-library', 'dataframes', 'out-of-core', 'polars', 'rust']","['arrow', 'dataframe', 'dataframe-library', 'dataframes', 'olap', 'out-of-core', 'polars', 'rust']",2024-01-12,"[('eventual-inc/daft', 0.7005141377449036, 'pandas', 2), ('sfu-db/connector-x', 0.6430513262748718, 'data', 2), ('delta-io/delta-rs', 0.596839427947998, 'pandas', 1), ('vaexio/vaex', 0.5824528336524963, 'perf', 1), ('astral-sh/ruff', 0.5693628787994385, 'util', 1), ('apache/arrow', 0.5664820075035095, 'data', 1), ('ibis-project/ibis', 0.5494480729103088, 'data', 1), ('tobymao/sqlglot', 0.5481932163238525, 'data', 0), ('aswinnnn/pyscan', 0.5435234308242798, 'security', 1), ('rapidsai/cudf', 0.5434831380844116, 'pandas', 2), ('pandas-dev/pandas', 0.5433159470558167, 'pandas', 2), ('ydataai/ydata-profiling', 0.5423642992973328, 'pandas', 0), ('fugue-project/fugue', 0.5422793030738831, 'pandas', 0), ('adamerose/pandasgui', 0.5391742587089539, 'pandas', 1), ('apache/spark', 0.5380522608757019, 'data', 0), ('jmcarpenter2/swifter', 0.5324315428733826, 'pandas', 0), ('rustpython/rustpython', 0.5321800112724304, 'util', 1), ('polyaxon/datatile', 0.5283935070037842, 'pandas', 1), ('qdrant/vector-db-benchmark', 0.5243891477584839, 'perf', 0), ('man-group/dtale', 0.5197016596794128, 'viz', 0), ('qdrant/fastembed', 0.5184158682823181, 'ml', 0), ('fastai/fastcore', 0.5103496313095093, 'util', 0), ('aws/aws-sdk-pandas', 0.50155109167099, 'pandas', 0)]",343,4.0,,65.37,2668,1799,45,0,92,117,92,2663.0,4358.0,90.0,1.6,80
773,diffusion,https://github.com/huggingface/diffusers,[],,[],[],,,,huggingface/diffusers,diffusers,20337,4219,175,Python,https://huggingface.co/docs/diffusers,🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch,huggingface,2024-01-14,2022-05-30,87,233.3754098360656,https://avatars.githubusercontent.com/u/25720743?v=4,🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch,"['deep-learning', 'diffusion', 'image-generation', 'image2image', 'pytorch', 'score-based-generative-modeling', 'stable-diffusion', 'stable-diffusion-diffusers', 'text2image']","['deep-learning', 'diffusion', 'image-generation', 'image2image', 'pytorch', 'score-based-generative-modeling', 'stable-diffusion', 'stable-diffusion-diffusers', 'text2image']",2024-01-12,"[('albarji/mixture-of-diffusers', 0.6519318222999573, 'diffusion', 1), ('sharonzhou/long_stable_diffusion', 0.6350945830345154, 'diffusion', 0), ('compvis/latent-diffusion', 0.6299404501914978, 'diffusion', 2), ('stability-ai/stablediffusion', 0.6299402713775635, 'diffusion', 2), ('compvis/stable-diffusion', 0.629136860370636, 'diffusion', 2), ('tanelp/tiny-diffusion', 0.5994593501091003, 'diffusion', 0), ('carson-katri/dream-textures', 0.5772011280059814, 'diffusion', 2), ('openai/glide-text2im', 0.565796971321106, 'diffusion', 0), ('nateraw/stable-diffusion-videos', 0.5594053268432617, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.552635669708252, 'diffusion', 7), ('pollinations/dance-diffusion', 0.5509036779403687, 'diffusion', 0), ('xavierxiao/dreambooth-stable-diffusion', 0.5447068810462952, 'diffusion', 2), ('lucidrains/musiclm-pytorch', 0.54345703125, 'ml', 1), ('saharmor/dalle-playground', 0.539817750453949, 'diffusion', 1), ('lucidrains/dalle2-pytorch', 0.532724142074585, 'diffusion', 1), ('lucidrains/imagen-pytorch', 0.5299099087715149, 'ml-dl', 1), ('pytorch/ignite', 0.528372049331665, 'ml-dl', 2), ('timothybrooks/instruct-pix2pix', 0.5238240957260132, 'diffusion', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5232102274894714, 'diffusion', 0), ('laion-ai/dalle2-laion', 0.5089276432991028, 'diffusion', 1), ('jina-ai/discoart', 0.5044229030609131, 'diffusion', 2), ('stability-ai/stability-sdk', 0.5022362470626831, 'diffusion', 1)]",564,1.0,,5.79,1641,1326,20,0,34,40,34,1640.0,5457.0,90.0,3.3,80
1240,llm,https://github.com/microsoft/semantic-kernel,[],,[],[],,,,microsoft/semantic-kernel,semantic-kernel,16027,2345,221,C#,https://aka.ms/semantic-kernel,Integrate cutting-edge LLM technology quickly and easily into your apps,microsoft,2024-01-14,2023-02-27,48,332.9050445103858,https://avatars.githubusercontent.com/u/6154722?v=4,Integrate cutting-edge LLM technology quickly and easily into your apps,"['ai', 'artificial-intelligence', 'llm', 'openai', 'sdk']","['ai', 'artificial-intelligence', 'llm', 'openai', 'sdk']",2024-01-12,"[('microsoft/promptflow', 0.7958884835243225, 'llm', 2), ('pathwaycom/llm-app', 0.7631767988204956, 'llm', 1), ('mlc-ai/mlc-llm', 0.6558853983879089, 'llm', 1), ('tigerlab-ai/tiger', 0.6522761583328247, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6336042284965515, 'perf', 0), ('nebuly-ai/nebullvm', 0.6294217705726624, 'perf', 3), ('alphasecio/langchain-examples', 0.6260648965835571, 'llm', 2), ('mnotgod96/appagent', 0.6219491362571716, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.6138186454772949, 'llm', 0), ('shishirpatil/gorilla', 0.612429678440094, 'llm', 1), ('h2oai/h2o-llmstudio', 0.6094576716423035, 'llm', 2), ('bigscience-workshop/petals', 0.6056543588638306, 'data', 0), ('lancedb/lancedb', 0.6036065220832825, 'data', 0), ('microsoft/lmops', 0.6015781760215759, 'llm', 1), ('deepset-ai/haystack', 0.5954805016517639, 'llm', 1), ('ludwig-ai/ludwig', 0.5949147939682007, 'ml-ops', 1), ('iryna-kondr/scikit-llm', 0.5926992893218994, 'llm', 1), ('berriai/litellm', 0.5909188985824585, 'llm', 2), ('prefecthq/marvin', 0.583898663520813, 'nlp', 3), ('chainlit/chainlit', 0.5796830654144287, 'llm', 2), ('microsoft/torchscale', 0.573233425617218, 'llm', 0), ('microsoft/jarvis', 0.572293758392334, 'llm', 0), ('lastmile-ai/aiconfig', 0.5658584237098694, 'util', 2), ('vllm-project/vllm', 0.564845621585846, 'llm', 1), ('bentoml/bentoml', 0.5644956827163696, 'ml-ops', 1), ('bentoml/openllm', 0.5633988380432129, 'ml-ops', 2), ('embedchain/embedchain', 0.555986225605011, 'llm', 2), ('hwchase17/langchain', 0.5559676289558411, 'llm', 0), ('operand/agency', 0.5540736317634583, 'llm', 3), ('nomic-ai/gpt4all', 0.5500825047492981, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5478009581565857, 'llm', 0), ('sweepai/sweep', 0.5460195541381836, 'llm', 2), ('cheshire-cat-ai/core', 0.5438644886016846, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5393573641777039, 'ml-dl', 2), ('eugeneyan/open-llms', 0.5385962128639221, 'study', 1), ('citadel-ai/langcheck', 0.5307271480560303, 'llm', 0), ('microsoft/promptcraft-robotics', 0.5297889113426208, 'sim', 1), ('mmabrouk/chatgpt-wrapper', 0.526870608329773, 'llm', 2), ('skypilot-org/skypilot', 0.5244306325912476, 'llm', 0), ('young-geng/easylm', 0.5219286680221558, 'llm', 0), ('activeloopai/deeplake', 0.5194681882858276, 'ml-ops', 2), ('argilla-io/argilla', 0.5170361399650574, 'nlp', 2), ('opengenerativeai/genossgpt', 0.5167599320411682, 'llm', 2), ('hegelai/prompttools', 0.5153470039367676, 'llm', 0), ('smol-ai/developer', 0.5124703049659729, 'llm', 1), ('streamlit/streamlit', 0.5103716254234314, 'viz', 0), ('ajndkr/lanarky', 0.5084496140480042, 'llm', 0), ('jina-ai/jina', 0.5081870555877686, 'ml', 0), ('pythagora-io/gpt-pilot', 0.5081477165222168, 'llm', 1), ('lightning-ai/lit-gpt', 0.5075657367706299, 'llm', 0), ('bobazooba/xllm', 0.5029643774032593, 'llm', 2), ('jerryjliu/llama_index', 0.5002325773239136, 'llm', 1)]",185,1.0,,38.23,1813,1379,11,0,63,284,63,1811.0,1719.0,90.0,0.9,80
1384,llm,https://github.com/mlc-ai/mlc-llm,[],,[],[],,,,mlc-ai/mlc-llm,mlc-llm,15117,1130,155,Python,https://llm.mlc.ai/docs,"Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.",mlc-ai,2024-01-14,2023-04-29,39,383.4021739130435,https://avatars.githubusercontent.com/u/106173866?v=4,"Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.","['language-model', 'llm', 'machine-learning-compilation', 'tvm']","['language-model', 'llm', 'machine-learning-compilation', 'tvm']",2024-01-13,"[('microsoft/lmops', 0.7063540816307068, 'llm', 2), ('microsoft/semantic-kernel', 0.6558853983879089, 'llm', 1), ('bentoml/bentoml', 0.6454080939292908, 'ml-ops', 0), ('pytorchlightning/pytorch-lightning', 0.6401709914207458, 'ml-dl', 0), ('microsoft/promptflow', 0.621173620223999, 'llm', 1), ('nebuly-ai/nebullvm', 0.6126720309257507, 'perf', 1), ('cheshire-cat-ai/core', 0.6107067465782166, 'llm', 1), ('prefecthq/marvin', 0.6106210350990295, 'nlp', 1), ('pathwaycom/llm-app', 0.6101855039596558, 'llm', 1), ('operand/agency', 0.6070036888122559, 'llm', 1), ('hpcaitech/colossalai', 0.6059736013412476, 'llm', 0), ('ludwig-ai/ludwig', 0.5952907800674438, 'ml-ops', 1), ('lastmile-ai/aiconfig', 0.588663637638092, 'util', 1), ('tigerlab-ai/tiger', 0.5859246850013733, 'llm', 1), ('antonosika/gpt-engineer', 0.5844728946685791, 'llm', 0), ('bigscience-workshop/petals', 0.583511233329773, 'data', 0), ('jina-ai/jina', 0.581267774105072, 'ml', 0), ('mlflow/mlflow', 0.5713922381401062, 'ml-ops', 0), ('ray-project/ray', 0.568311333656311, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.5680209994316101, 'ml', 0), ('tensorflow/tensorflow', 0.5597818493843079, 'ml-dl', 0), ('sweepai/sweep', 0.5575688481330872, 'llm', 1), ('alpa-projects/alpa', 0.5512301921844482, 'ml-dl', 1), ('transformeroptimus/superagi', 0.5498563051223755, 'llm', 1), ('mindsdb/mindsdb', 0.5494204163551331, 'data', 1), ('microsoft/autogen', 0.5481517314910889, 'llm', 0), ('microsoft/onnxruntime', 0.5396208763122559, 'ml', 0), ('llmware-ai/llmware', 0.5390260815620422, 'llm', 0), ('embedchain/embedchain', 0.5380026698112488, 'llm', 1), ('titanml/takeoff', 0.5372212529182434, 'llm', 2), ('nvidia/deeplearningexamples', 0.5367917418479919, 'ml-dl', 0), ('avaiga/taipy', 0.5353958010673523, 'data', 0), ('huggingface/datasets', 0.532573401927948, 'nlp', 0), ('microsoft/torchscale', 0.532260000705719, 'llm', 0), ('onnx/onnx', 0.5302952527999878, 'ml', 0), ('intel/intel-extension-for-transformers', 0.5288311243057251, 'perf', 0), ('guardrails-ai/guardrails', 0.527996301651001, 'llm', 1), ('nomic-ai/gpt4all', 0.5278238654136658, 'llm', 1), ('microsoft/nni', 0.5266501307487488, 'ml', 0), ('activeloopai/deeplake', 0.5259242653846741, 'ml-ops', 1), ('lucidrains/toolformer-pytorch', 0.5253450274467468, 'llm', 1), ('smol-ai/developer', 0.5239886045455933, 'llm', 0), ('uber/fiber', 0.522170901298523, 'data', 0), ('microsoft/generative-ai-for-beginners', 0.5216159820556641, 'study', 1), ('google-research/language', 0.5201672911643982, 'nlp', 0), ('rafiqhasan/auto-tensorflow', 0.5193679928779602, 'ml-dl', 0), ('nccr-itmo/fedot', 0.5192214250564575, 'ml-ops', 0), ('determined-ai/determined', 0.5185369253158569, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5173749327659607, 'viz', 0), ('hwchase17/langchain', 0.5162545442581177, 'llm', 1), ('arize-ai/phoenix', 0.5153992772102356, 'ml-interpretability', 0), ('oegedijk/explainerdashboard', 0.5132462978363037, 'ml-interpretability', 0), ('argilla-io/argilla', 0.51301109790802, 'nlp', 1), ('jina-ai/thinkgpt', 0.5101083517074585, 'llm', 1), ('aimhubio/aim', 0.509802520275116, 'ml-ops', 0), ('deepset-ai/haystack', 0.508885383605957, 'llm', 1), ('microsoft/jarvis', 0.5086725354194641, 'llm', 0), ('mnotgod96/appagent', 0.5084356665611267, 'llm', 1), ('luodian/otter', 0.5079561471939087, 'llm', 0), ('kubeflow/fairing', 0.5076168179512024, 'ml-ops', 0), ('unity-technologies/ml-agents', 0.5073363184928894, 'ml-rl', 0), ('rasahq/rasa', 0.50617915391922, 'llm', 0), ('aiwaves-cn/agents', 0.5054627060890198, 'nlp', 2), ('mosaicml/composer', 0.5053991675376892, 'ml-dl', 0), ('nat/openplayground', 0.5039643049240112, 'llm', 1), ('modularml/mojo', 0.5038229823112488, 'util', 0), ('lm-sys/fastchat', 0.5015469193458557, 'llm', 1), ('superduperdb/superduperdb', 0.5006630420684814, 'data', 0)]",86,5.0,,15.5,665,532,9,0,1,1,1,666.0,1245.0,90.0,1.9,80
1329,llm,https://github.com/logspace-ai/langflow,"['ui', 'langchain']",,[],[],1.0,,,logspace-ai/langflow,langflow,14139,2132,123,Python,http://www.langflow.org,"⛓️ Langflow is a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.",logspace-ai,2024-01-14,2023-02-08,50,278.01404494382024,https://avatars.githubusercontent.com/u/85702467?v=4,"⛓️ Langflow is a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.","['chatgpt', 'langchain', 'large-language-models', 'react-flow']","['chatgpt', 'langchain', 'large-language-models', 'react-flow', 'ui']",2024-01-11,"[('langchain-ai/langgraph', 0.6137604713439941, 'llm', 1), ('gkamradt/langchain-tutorials', 0.5531355142593384, 'study', 0), ('prefecthq/langchain-prefect', 0.548227071762085, 'llm', 2), ('hwchase17/langchain', 0.5081093907356262, 'llm', 1)]",84,2.0,,117.65,351,234,11,0,131,147,131,354.0,500.0,90.0,1.4,80
1613,llm,https://github.com/berriai/litellm,[],,[],[],,,,berriai/litellm,litellm,4435,435,38,Python,https://litellm-api.up.railway.app/,"Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)",berriai,2024-01-14,2023-07-27,26,166.01604278074868,https://avatars.githubusercontent.com/u/121462774?v=4,"Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)","['anthropic', 'langchain', 'langchain-python', 'llm', 'llmops', 'openai']","['anthropic', 'langchain', 'langchain-python', 'llm', 'llmops', 'openai']",2024-01-14,"[('opengenerativeai/genossgpt', 0.7308034896850586, 'llm', 2), ('shishirpatil/gorilla', 0.7173708081245422, 'llm', 1), ('chainlit/chainlit', 0.6367120742797852, 'llm', 3), ('eugeneyan/open-llms', 0.5926091074943542, 'study', 1), ('microsoft/semantic-kernel', 0.5909188985824585, 'llm', 2), ('ajndkr/lanarky', 0.587331235408783, 'llm', 1), ('nomic-ai/gpt4all', 0.5837444067001343, 'llm', 0), ('pathwaycom/llm-app', 0.5751776695251465, 'llm', 2), ('hwchase17/langchain', 0.573574423789978, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.5576227903366089, 'llm', 0), ('run-llama/llama-hub', 0.5469061136245728, 'data', 1), ('alphasecio/langchain-examples', 0.5404641032218933, 'llm', 3), ('zilliztech/gptcache', 0.539711594581604, 'llm', 3), ('mmabrouk/chatgpt-wrapper', 0.5316495299339294, 'llm', 2), ('citadel-ai/langcheck', 0.5299030542373657, 'llm', 0), ('openai/openai-python', 0.5291408896446228, 'util', 1), ('alpha-vllm/llama2-accessory', 0.5151165127754211, 'llm', 0), ('bentoml/openllm', 0.5140005350112915, 'ml-ops', 2), ('young-geng/easylm', 0.5113945603370667, 'llm', 0), ('ray-project/llm-applications', 0.5112382173538208, 'llm', 1), ('microsoft/promptflow', 0.5052803754806519, 'llm', 1), ('tigerlab-ai/tiger', 0.5017346143722534, 'llm', 1), ('deep-diver/pingpong', 0.5002941489219666, 'llm', 0)]",100,5.0,,104.19,933,772,6,0,31,81,31,933.0,3000.0,90.0,3.2,80
1122,util,https://github.com/localstack/localstack,[],,[],[],,,,localstack/localstack,localstack,50845,3851,524,Python,https://localstack.cloud,💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline,localstack,2024-01-14,2016-10-25,379,134.155672823219,https://avatars.githubusercontent.com/u/28732122?v=4,💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline,"['aws', 'cloud', 'continuous-integration', 'developer-tools', 'localstack', 'testing']","['aws', 'cloud', 'continuous-integration', 'developer-tools', 'localstack', 'testing']",2024-01-12,"[('nficano/python-lambda', 0.5929733514785767, 'util', 1), ('aws/chalice', 0.5926650762557983, 'web', 2), ('zenml-io/mlstacks', 0.5585765838623047, 'ml-ops', 0), ('boto/boto3', 0.5544808506965637, 'util', 2), ('skypilot-org/skypilot', 0.5336942672729492, 'llm', 0), ('lithops-cloud/lithops', 0.514224648475647, 'ml-ops', 0), ('rpgreen/apilogs', 0.5111103653907776, 'util', 1), ('orchest/orchest', 0.5089136362075806, 'ml-ops', 1), ('developmentseed/titiler', 0.5016547441482544, 'gis', 0)]",526,2.0,,27.35,1070,832,88,0,12,10,12,1070.0,2218.0,90.0,2.1,79
985,llm,https://github.com/facebookresearch/llama,"['llama', 'language-model']",,[],[],,,,facebookresearch/llama,llama,48284,8277,471,Python,,Inference code for LLaMA models,facebookresearch,2024-01-14,2023-02-14,50,965.68,https://avatars.githubusercontent.com/u/16943930?v=4,Inference code for LLaMA models,[],"['language-model', 'llama']",2023-11-14,"[('facebookresearch/codellama', 0.8860641121864319, 'llm', 2), ('karpathy/llama2.c', 0.8788975477218628, 'llm', 2), ('facebookresearch/llama-recipes', 0.7843647003173828, 'llm', 2), ('microsoft/llama-2-onnx', 0.7213297486305237, 'llm', 2), ('tairov/llama2.mojo', 0.7085148692131042, 'llm', 1), ('abetlen/llama-cpp-python', 0.700358510017395, 'llm', 2), ('tloen/alpaca-lora', 0.6226324439048767, 'llm', 2), ('jzhang38/tinyllama', 0.6092095375061035, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.6008679866790771, 'llm', 0), ('openlm-research/open_llama', 0.5816770195960999, 'llm', 2), ('lightning-ai/lit-llama', 0.5731508731842041, 'llm', 2), ('zrrskywalker/llama-adapter', 0.5667337775230408, 'llm', 2), ('run-llama/llama-lab', 0.562117874622345, 'llm', 2), ('cg123/mergekit', 0.5536092519760132, 'llm', 1), ('ggerganov/llama.cpp', 0.5505987405776978, 'llm', 2), ('juncongmoo/pyllama', 0.5485102534294128, 'llm', 0), ('hao-ai-lab/lookaheaddecoding', 0.5299458503723145, 'llm', 0), ('openai/gpt-2', 0.5193938612937927, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5126661062240601, 'llm', 1)]",41,3.0,,1.54,252,148,11,2,0,1,1,251.0,462.0,90.0,1.8,79
1414,data,https://github.com/apache/spark,[],,[],[],,,,apache/spark,spark,37581,28124,2033,Scala,https://spark.apache.org/,Apache Spark - A unified analytics engine for large-scale data processing,apache,2024-01-14,2014-02-25,518,72.55019305019304,https://avatars.githubusercontent.com/u/47359?v=4,Apache Spark - A unified analytics engine for large-scale data processing,"['big-data', 'java', 'jdbc', 'r', 'scala', 'spark', 'sql']","['big-data', 'java', 'jdbc', 'r', 'scala', 'spark', 'sql']",2024-01-13,"[('fugue-project/fugue', 0.6404576897621155, 'pandas', 2), ('airbytehq/airbyte', 0.6244115829467773, 'data', 1), ('apache/incubator-sedona', 0.5984252095222473, 'gis', 2), ('lithops-cloud/lithops', 0.5891563296318054, 'ml-ops', 1), ('vaexio/vaex', 0.5628867149353027, 'perf', 0), ('mage-ai/mage-ai', 0.5627126693725586, 'ml-ops', 2), ('ydataai/ydata-profiling', 0.546349048614502, 'pandas', 0), ('hi-primus/optimus', 0.5423697233200073, 'ml-ops', 1), ('pola-rs/polars', 0.5380522608757019, 'pandas', 0), ('spotify/luigi', 0.537491500377655, 'ml-ops', 0), ('kestra-io/kestra', 0.5336428284645081, 'ml-ops', 0), ('eventual-inc/daft', 0.5322152972221375, 'pandas', 0), ('ibis-project/ibis', 0.5312047004699707, 'data', 1), ('polyaxon/datatile', 0.529706597328186, 'pandas', 1), ('dmlc/xgboost', 0.5240032076835632, 'ml', 0), ('aws/aws-sdk-pandas', 0.5226028561592102, 'pandas', 0), ('flyteorg/flyte', 0.5193337202072144, 'ml-ops', 0), ('airbnb/omniduct', 0.5120821595191956, 'data', 0), ('tobymao/sqlglot', 0.5114732980728149, 'data', 2), ('apache/arrow', 0.5110272765159607, 'data', 0), ('streamlit/streamlit', 0.505164384841919, 'viz', 0), ('apache/superset', 0.5012211203575134, 'viz', 0), ('saulpw/visidata', 0.5010607838630676, 'term', 0)]",2996,4.0,,77.21,2781,2595,120,0,0,22,22,2781.0,4294.0,90.0,1.5,79
1494,llm,https://github.com/geekan/metagpt,[],,[],[],1.0,,,geekan/metagpt,MetaGPT,33271,3942,803,Python,https://deepwisdom.ai/,"🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo",geekan,2024-01-14,2023-06-30,30,1088.303738317757,,"🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo","['agent', 'gpt', 'llm', 'metagpt', 'multi-agent']","['agent', 'gpt', 'llm', 'metagpt', 'multi-agent']",2024-01-11,"[('assafelovic/gpt-researcher', 0.6312734484672546, 'llm', 0), ('linksoul-ai/autoagents', 0.6280576586723328, 'llm', 0), ('operand/agency', 0.6091906428337097, 'llm', 2), ('mnotgod96/appagent', 0.5650525093078613, 'llm', 2), ('transformeroptimus/superagi', 0.5468068718910217, 'llm', 1), ('microsoft/autogen', 0.5422032475471497, 'llm', 1), ('yoheinakajima/babyagi', 0.5191521644592285, 'llm', 0), ('zacwellmer/worldmodels', 0.5185655355453491, 'ml-rl', 0), ('langchain-ai/langgraph', 0.5153509974479675, 'llm', 0), ('jina-ai/thinkgpt', 0.5057692527770996, 'llm', 0)]",68,2.0,,32.58,391,320,7,0,11,21,11,391.0,401.0,90.0,1.0,79
909,ml-ops,https://github.com/ray-project/ray,[],,[],[],,,,ray-project/ray,ray,29440,5047,464,Python,https://ray.io,Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.,ray-project,2024-01-14,2016-10-25,379,77.67810026385224,https://avatars.githubusercontent.com/u/22125274?v=4,Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.,"['automl', 'data-science', 'deep-learning', 'deployment', 'distributed', 'hyperparameter-optimization', 'hyperparameter-search', 'java', 'llm-serving', 'machine-learning', 'model-selection', 'optimization', 'parallel', 'pytorch', 'ray', 'reinforcement-learning', 'rllib', 'serving', 'tensorflow']","['automl', 'data-science', 'deep-learning', 'deployment', 'distributed', 'hyperparameter-optimization', 'hyperparameter-search', 'java', 'llm-serving', 'machine-learning', 'model-selection', 'optimization', 'parallel', 'pytorch', 'ray', 'reinforcement-learning', 'rllib', 'serving', 'tensorflow']",2024-01-13,"[('ray-project/ray-educational-materials', 0.7717517614364624, 'study', 3), ('horovod/horovod', 0.6456006169319153, 'ml-ops', 5), ('alpa-projects/alpa', 0.6443263292312622, 'ml-dl', 2), ('determined-ai/determined', 0.6333746314048767, 'ml-ops', 7), ('merantix-momentum/squirrel-core', 0.6162523627281189, 'ml', 6), ('microsoft/nni', 0.6101014018058777, 'ml', 8), ('plasma-umass/scalene', 0.6044383645057678, 'profiling', 0), ('microsoft/deepspeed', 0.5907596349716187, 'ml-dl', 3), ('uber/fiber', 0.5900027751922607, 'data', 1), ('microsoft/onnxruntime', 0.5840818285942078, 'ml', 4), ('google/vizier', 0.5806607604026794, 'ml', 4), ('explosion/thinc', 0.5737190246582031, 'ml-dl', 4), ('tensorlayer/tensorlayer', 0.5710065960884094, 'ml-rl', 3), ('hpcaitech/colossalai', 0.5699094533920288, 'llm', 1), ('google/trax', 0.568706214427948, 'ml-dl', 3), ('mlc-ai/mlc-llm', 0.568311333656311, 'llm', 0), ('google/tf-quant-finance', 0.5673449039459229, 'finance', 1), ('tensorflow/tensorflow', 0.5655400156974792, 'ml-dl', 4), ('transformeroptimus/superagi', 0.5615883469581604, 'llm', 0), ('karpathy/micrograd', 0.5609050393104553, 'study', 0), ('pytorchlightning/pytorch-lightning', 0.5608101487159729, 'ml-dl', 4), ('mlflow/mlflow', 0.5602455139160156, 'ml-ops', 1), ('pytorch/rl', 0.5598548054695129, 'ml-rl', 3), ('ray-project/ray-llm', 0.5598110556602478, 'llm', 3), ('keras-team/autokeras', 0.5593129396438599, 'ml-dl', 4), ('microsoft/flaml', 0.5589168667793274, 'ml', 5), ('activeloopai/deeplake', 0.5552250146865845, 'ml-ops', 5), ('wandb/client', 0.5538603067398071, 'ml', 8), ('gradio-app/gradio', 0.5529914498329163, 'viz', 3), ('tensorflow/tensor2tensor', 0.5493798851966858, 'ml', 3), ('googlecloudplatform/vertex-ai-samples', 0.549000084400177, 'ml', 1), ('intel/intel-extension-for-pytorch', 0.5487441420555115, 'perf', 3), ('unity-technologies/ml-agents', 0.5486295223236084, 'ml-rl', 3), ('jina-ai/jina', 0.5483666062355042, 'ml', 2), ('epistasislab/tpot', 0.5462473034858704, 'ml', 5), ('polyaxon/polyaxon', 0.5454540252685547, 'ml-ops', 7), ('ml-tooling/opyrator', 0.5436031818389893, 'viz', 2), ('nvidia/deeplearningexamples', 0.5435977578163147, 'ml-dl', 3), ('bentoml/bentoml', 0.5420705080032349, 'ml-ops', 2), ('google/gin-config', 0.5417131185531616, 'util', 1), ('nccr-itmo/fedot', 0.541328489780426, 'ml-ops', 3), ('salesforce/logai', 0.5410916805267334, 'util', 1), ('superduperdb/superduperdb', 0.5404704809188843, 'data', 2), ('paddlepaddle/paddle', 0.5365186333656311, 'ml-dl', 2), ('kubeflow/fairing', 0.5362722873687744, 'ml-ops', 0), ('huggingface/transformers', 0.535620391368866, 'nlp', 4), ('dask/dask-ml', 0.5340972542762756, 'ml', 0), ('fugue-project/fugue', 0.5340755581855774, 'pandas', 2), ('denys88/rl_games', 0.5337716937065125, 'ml-rl', 3), ('apache/incubator-mxnet', 0.5308846235275269, 'ml-dl', 0), ('uber/petastorm', 0.5304347276687622, 'data', 4), ('sail-sg/envpool', 0.529890775680542, 'sim', 1), ('fastai/fastcore', 0.528685986995697, 'util', 0), ('rafiqhasan/auto-tensorflow', 0.5257235169410706, 'ml-dl', 3), ('intel/scikit-learn-intelex', 0.5234288573265076, 'perf', 1), ('huggingface/datasets', 0.5230560898780823, 'nlp', 4), ('titanml/takeoff', 0.5229300856590271, 'llm', 1), ('pytorch/pytorch', 0.5206745862960815, 'ml-dl', 2), ('optuna/optuna', 0.5129156112670898, 'ml', 4), ('oegedijk/explainerdashboard', 0.5122550129890442, 'ml-interpretability', 0), ('salesforce/warp-drive', 0.5116983652114868, 'ml-rl', 3), ('onnx/onnx', 0.5107229948043823, 'ml', 4), ('ashleve/lightning-hydra-template', 0.510144054889679, 'util', 2), ('qdrant/qdrant', 0.5096204280853271, 'data', 1), ('mosaicml/composer', 0.5085017085075378, 'ml-dl', 3), ('bigscience-workshop/petals', 0.5073845386505127, 'data', 3), ('lutzroeder/netron', 0.5069729685783386, 'ml', 4), ('huggingface/optimum', 0.5062342286109924, 'ml', 2), ('fmind/mlops-python-package', 0.5060107111930847, 'template', 0), ('ggerganov/ggml', 0.505761981010437, 'ml', 1), ('pytorch/ignite', 0.504410982131958, 'ml-dl', 3), ('mindsdb/mindsdb', 0.5041195154190063, 'data', 1), ('automl/auto-sklearn', 0.5039158463478088, 'ml', 3), ('aws/sagemaker-python-sdk', 0.5026758909225464, 'ml', 3), ('polyaxon/datatile', 0.5014405846595764, 'pandas', 3), ('ddbourgin/numpy-ml', 0.5012813210487366, 'ml', 2), ('microsoft/lmops', 0.5007473826408386, 'llm', 0)]",964,5.0,,96.75,3301,2103,88,0,14,11,14,3294.0,4063.0,90.0,1.2,79
251,ml-dl,https://github.com/pytorchlightning/pytorch-lightning,[],,[],[],1.0,,,pytorchlightning/pytorch-lightning,pytorch-lightning,25642,3131,245,Python,https://lightning.ai,"Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.",pytorchlightning,2024-01-14,2019-03-31,252,101.63873159682899,https://avatars.githubusercontent.com/u/58386951?v=4,"Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.","['ai', 'artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'pytorch']","['ai', 'artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'pytorch']",2024-01-11,"[('mlc-ai/mlc-llm', 0.6401709914207458, 'llm', 0), ('googlecloudplatform/vertex-ai-samples', 0.6309098601341248, 'ml', 2), ('hpcaitech/colossalai', 0.6259893178939819, 'llm', 2), ('antonosika/gpt-engineer', 0.6123632192611694, 'llm', 1), ('bentoml/bentoml', 0.6110719442367554, 'ml-ops', 3), ('microsoft/onnxruntime', 0.5993390083312988, 'ml', 3), ('microsoft/lmops', 0.5851262211799622, 'llm', 0), ('onnx/onnx', 0.574492335319519, 'ml', 3), ('salesforce/warp-drive', 0.5696126222610474, 'ml-rl', 2), ('nvidia/deeplearningexamples', 0.5674681067466736, 'ml-dl', 2), ('alpa-projects/alpa', 0.5670627951622009, 'ml-dl', 2), ('pytorch/pytorch', 0.5660499334335327, 'ml-dl', 2), ('prefecthq/marvin', 0.5638337135314941, 'nlp', 1), ('ray-project/ray', 0.5608101487159729, 'ml-ops', 4), ('torantulino/auto-gpt', 0.5608023405075073, 'llm', 2), ('huggingface/accelerate', 0.5604153871536255, 'ml', 0), ('sweepai/sweep', 0.557704508304596, 'llm', 1), ('tensorflow/tensorflow', 0.5566743612289429, 'ml-dl', 2), ('mindsdb/mindsdb', 0.5564075708389282, 'data', 3), ('nyandwi/modernconvnets', 0.5557078123092651, 'ml-dl', 0), ('ludwig-ai/ludwig', 0.5543079972267151, 'ml-ops', 4), ('determined-ai/determined', 0.55375736951828, 'ml-ops', 4), ('google/trax', 0.550957202911377, 'ml-dl', 2), ('rafiqhasan/auto-tensorflow', 0.5506039261817932, 'ml-dl', 1), ('microsoft/promptflow', 0.5492174029350281, 'llm', 1), ('cheshire-cat-ai/core', 0.5489669442176819, 'llm', 1), ('activeloopai/deeplake', 0.5463938117027283, 'ml-ops', 5), ('jina-ai/jina', 0.5442116260528564, 'ml', 2), ('keras-rl/keras-rl', 0.5397281050682068, 'ml-rl', 1), ('microsoft/semantic-kernel', 0.5393573641777039, 'llm', 2), ('huggingface/datasets', 0.5380210280418396, 'nlp', 3), ('oegedijk/explainerdashboard', 0.5369613766670227, 'ml-interpretability', 0), ('bigscience-workshop/petals', 0.536682665348053, 'data', 3), ('reloadware/reloadium', 0.5348438024520874, 'profiling', 2), ('google/tf-quant-finance', 0.534396767616272, 'finance', 0), ('horovod/horovod', 0.5334360599517822, 'ml-ops', 3), ('mosaicml/composer', 0.5314188599586487, 'ml-dl', 3), ('polyaxon/polyaxon', 0.529716968536377, 'ml-ops', 5), ('plasma-umass/scalene', 0.5295047760009766, 'profiling', 0), ('operand/agency', 0.526900053024292, 'llm', 3), ('transformeroptimus/superagi', 0.5248159170150757, 'llm', 2), ('microsoft/nni', 0.52174973487854, 'ml', 4), ('mlflow/mlflow', 0.5213505029678345, 'ml-ops', 2), ('google/dopamine', 0.5187596082687378, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.5169978737831116, 'ml-rl', 2), ('intel/intel-extension-for-pytorch', 0.5152040123939514, 'perf', 3), ('explosion/thinc', 0.5136130452156067, 'ml-dl', 5), ('microsoft/deepspeed', 0.5130410194396973, 'ml-dl', 3), ('lastmile-ai/aiconfig', 0.5128691792488098, 'util', 1), ('blackhc/toma', 0.5127522945404053, 'ml-dl', 3), ('uber/fiber', 0.512328028678894, 'data', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5122131109237671, 'study', 3), ('deepmind/dm_control', 0.5117486715316772, 'ml-rl', 3), ('ddbourgin/numpy-ml', 0.5105450749397278, 'ml', 1), ('tensorlayer/tensorlayer', 0.5099591612815857, 'ml-rl', 2), ('tlkh/tf-metal-experiments', 0.5083010792732239, 'perf', 1), ('lutzroeder/netron', 0.5080562829971313, 'ml', 4), ('adap/flower', 0.5078296065330505, 'ml-ops', 5), ('tensorflow/tensor2tensor', 0.5069326758384705, 'ml', 2), ('google-research/google-research', 0.5055912733078003, 'ml', 2), ('pytorch/glow', 0.5054729580879211, 'ml', 0), ('neuralmagic/deepsparse', 0.5051679015159607, 'nlp', 0), ('nvidia/tensorrt-llm', 0.5039601922035217, 'viz', 0), ('pythagora-io/gpt-pilot', 0.5030331015586853, 'llm', 1), ('skypilot-org/skypilot', 0.5027073621749878, 'llm', 3), ('aimhubio/aim', 0.5019053220748901, 'ml-ops', 4)]",921,5.0,,43.56,882,630,58,0,24,42,24,880.0,1224.0,90.0,1.4,79
228,term,https://github.com/willmcgugan/textual,[],,[],[],,,,willmcgugan/textual,textual,22568,701,163,Python,https://textual.textualize.io/,The lean application framework for Python.  Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.,willmcgugan,2024-01-14,2021-04-08,146,153.8227848101266,https://avatars.githubusercontent.com/u/93378883?v=4,The lean application framework for Python.  Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.,"['cli', 'framework', 'rich', 'terminal', 'tui']","['cli', 'framework', 'rich', 'terminal', 'tui']",2024-01-12,"[('r0x0r/pywebview', 0.6927530765533447, 'gui', 0), ('pallets/flask', 0.6732026934623718, 'web', 0), ('plotly/dash', 0.6724681854248047, 'viz', 0), ('bottlepy/bottle', 0.661994218826294, 'web', 0), ('eleutherai/pyfra', 0.6594876646995544, 'ml', 0), ('hoffstadt/dearpygui', 0.6536368131637573, 'gui', 0), ('masoniteframework/masonite', 0.6535159349441528, 'web', 1), ('flet-dev/flet', 0.6341605186462402, 'web', 0), ('kivy/kivy', 0.632539689540863, 'util', 0), ('reflex-dev/reflex', 0.6325315237045288, 'web', 1), ('pyinfra-dev/pyinfra', 0.6321539878845215, 'util', 0), ('pypy/pypy', 0.6274396181106567, 'util', 0), ('vitalik/django-ninja', 0.6247105002403259, 'web', 0), ('webpy/webpy', 0.6224280595779419, 'web', 0), ('gradio-app/gradio', 0.6202593445777893, 'viz', 0), ('amaargiru/pyroad', 0.6114891171455383, 'study', 0), ('urwid/urwid', 0.6095877885818481, 'term', 0), ('holoviz/panel', 0.6071124076843262, 'viz', 0), ('tiangolo/fastapi', 0.6067060232162476, 'web', 1), ('klen/muffin', 0.6039458513259888, 'web', 0), ('beeware/toga', 0.5924068689346313, 'gui', 0), ('pysimplegui/pysimplegui', 0.589139997959137, 'gui', 0), ('pypa/hatch', 0.5866153836250305, 'util', 1), ('parthjadhav/tkinter-designer', 0.5848771333694458, 'gui', 0), ('micropython/micropython', 0.5815902352333069, 'util', 0), ('fastai/fastcore', 0.5812305808067322, 'util', 0), ('hugapi/hug', 0.5758522152900696, 'util', 0), ('timofurrer/awesome-asyncio', 0.575556218624115, 'study', 0), ('jquast/blessed', 0.5734678506851196, 'term', 2), ('ethereum/web3.py', 0.5721923112869263, 'crypto', 0), ('alphasecio/langchain-examples', 0.570219874382019, 'llm', 0), ('google/gin-config', 0.5691657662391663, 'util', 0), ('quantconnect/lean', 0.5675188899040222, 'finance', 0), ('python-restx/flask-restx', 0.5673668384552002, 'web', 0), ('backtick-se/cowait', 0.5645186305046082, 'util', 0), ('dylanhogg/awesome-python', 0.5644351243972778, 'study', 0), ('1200wd/bitcoinlib', 0.5643294453620911, 'crypto', 0), ('kubeflow/fairing', 0.5632326006889343, 'ml-ops', 0), ('falconry/falcon', 0.562833845615387, 'web', 1), ('pympler/pympler', 0.5624990463256836, 'perf', 0), ('malloydata/malloy-py', 0.5623626708984375, 'data', 0), ('dddomodossola/remi', 0.5620313882827759, 'gui', 0), ('minimaxir/simpleaichat', 0.5615967512130737, 'llm', 0), ('ploomber/ploomber', 0.5585596561431885, 'ml-ops', 0), ('reloadware/reloadium', 0.5569230318069458, 'profiling', 0), ('buildbot/buildbot', 0.5555017590522766, 'util', 0), ('sourcery-ai/sourcery', 0.5534403920173645, 'util', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5517593026161194, 'template', 0), ('pypa/pipenv', 0.5516801476478577, 'util', 0), ('pallets/quart', 0.5513238906860352, 'web', 0), ('plotly/plotly.py', 0.5511562824249268, 'viz', 0), ('scrapy/scrapy', 0.5489547252655029, 'data', 1), ('pylons/pyramid', 0.5482103228569031, 'web', 0), ('goldmansachs/gs-quant', 0.5451081991195679, 'finance', 0), ('python/cpython', 0.542621910572052, 'util', 0), ('wxwidgets/phoenix', 0.5400227308273315, 'gui', 0), ('pygamelib/pygamelib', 0.539828896522522, 'gamedev', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5363441109657288, 'template', 0), ('pytoolz/toolz', 0.5360779166221619, 'util', 0), ('tiangolo/sqlmodel', 0.5339970588684082, 'data', 0), ('voila-dashboards/voila', 0.5331439971923828, 'jupyter', 0), ('beeware/briefcase', 0.5323150753974915, 'util', 0), ('bokeh/bokeh', 0.5320950150489807, 'viz', 0), ('pallets/werkzeug', 0.5303859114646912, 'web', 0), ('alexmojaki/snoop', 0.5284596681594849, 'debug', 0), ('cython/cython', 0.52788907289505, 'util', 0), ('polyaxon/datatile', 0.5274295806884766, 'pandas', 0), ('clips/pattern', 0.5270101428031921, 'nlp', 0), ('orchest/orchest', 0.526781439781189, 'ml-ops', 0), ('pytables/pytables', 0.5240064263343811, 'data', 0), ('pyston/pyston', 0.5220023393630981, 'util', 0), ('facebookresearch/hydra', 0.5213847160339355, 'util', 0), ('pyglet/pyglet', 0.5208635330200195, 'gamedev', 0), ('pyscript/pyscript-cli', 0.5197693109512329, 'web', 0), ('pypa/build', 0.5193080306053162, 'util', 0), ('cherrypy/cherrypy', 0.5189430117607117, 'web', 0), ('prefecthq/server', 0.5155190229415894, 'util', 0), ('faster-cpython/ideas', 0.5151427388191223, 'perf', 0), ('samuelcolvin/python-devtools', 0.5147838592529297, 'debug', 0), ('fmind/mlops-python-package', 0.5143515467643738, 'template', 0), ('simple-salesforce/simple-salesforce', 0.5137646198272705, 'data', 0), ('federicoceratto/dashing', 0.5119619369506836, 'term', 1), ('gaogaotiantian/viztracer', 0.5108093023300171, 'profiling', 0), ('ofek/pyapp', 0.5099067091941833, 'util', 1), ('huggingface/huggingface_hub', 0.5090726613998413, 'ml', 0), ('kitao/pyxel', 0.5071513056755066, 'gamedev', 0), ('landscapeio/prospector', 0.5064331889152527, 'util', 0), ('kalliope-project/kalliope', 0.5060406923294067, 'util', 0), ('google/python-fire', 0.5056248307228088, 'term', 1), ('microsoft/playwright-python', 0.5054807662963867, 'testing', 0), ('sumerc/yappi', 0.5049981474876404, 'profiling', 0), ('cohere-ai/notebooks', 0.5048252940177917, 'llm', 0), ('dosisod/refurb', 0.5046628713607788, 'util', 1), ('merantix-momentum/squirrel-core', 0.50379478931427, 'ml', 0), ('django/django', 0.5026986598968506, 'web', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5023050904273987, 'study', 0), ('avaiga/taipy', 0.5015890002250671, 'data', 0), ('pexpect/pexpect', 0.5012505054473877, 'util', 0), ('replicate/replicate-python', 0.5011894106864929, 'ml', 0), ('ianmiell/shutit', 0.5011722445487976, 'util', 0), ('rawheel/fastapi-boilerplate', 0.5006387233734131, 'web', 0)]",118,4.0,,54.0,468,373,34,0,60,29,60,468.0,1012.0,90.0,2.2,79
1744,diffusion,https://github.com/comfyanonymous/comfyui,['gui'],,[],[],,,,comfyanonymous/comfyui,ComfyUI,22243,2329,225,Python,,"The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.",comfyanonymous,2024-01-14,2023-01-17,54,411.9074074074074,,"The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.","['pytorch', 'stable-diffusion']","['gui', 'pytorch', 'stable-diffusion']",2024-01-13,"[('carson-katri/dream-textures', 0.6763654947280884, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6438121199607849, 'diffusion', 2), ('stability-ai/stability-sdk', 0.6112304925918579, 'diffusion', 1), ('mlc-ai/web-stable-diffusion', 0.6069907546043396, 'diffusion', 1), ('civitai/sd_civitai_extension', 0.6050902009010315, 'llm', 0), ('bentoml/onediffusion', 0.5863965749740601, 'diffusion', 1), ('divamgupta/diffusionbee-stable-diffusion-ui', 0.5425298810005188, 'diffusion', 1), ('xavierxiao/dreambooth-stable-diffusion', 0.5401220321655273, 'diffusion', 2), ('divamgupta/stable-diffusion-tensorflow', 0.5308429002761841, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.5260990858078003, 'diffusion', 0), ('thereforegames/unprompted', 0.5250580906867981, 'diffusion', 1), ('graphistry/pygraphistry', 0.503555953502655, 'data', 0), ('lkwq007/stablediffusion-infinity', 0.5033169388771057, 'diffusion', 2), ('westhealth/pyvis', 0.5001736283302307, 'graph', 0)]",87,1.0,,32.35,893,300,12,0,1,1,1,895.0,2009.0,90.0,2.2,79
1869,ml,https://github.com/ml-explore/mlx,"['numpy', 'apple-silicon', 'jax']","MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.",[],[],,,,ml-explore/mlx,mlx,11499,687,92,C++,,MLX: An array framework for Apple silicon,ml-explore,2024-01-14,2023-11-28,9,1277.6666666666667,https://avatars.githubusercontent.com/u/102832242?v=4,MLX: An array framework for Apple silicon,[],"['apple-silicon', 'jax', 'numpy']",2024-01-13,"[('tlkh/tf-metal-experiments', 0.6006742119789124, 'perf', 0), ('tlkh/asitop', 0.5542373061180115, 'perf', 1), ('xl0/lovely-numpy', 0.5240765810012817, 'util', 1), ('apple/ml-stable-diffusion', 0.5221449136734009, 'diffusion', 0), ('arogozhnikov/einops', 0.5031680464744568, 'ml-dl', 2), ('mrdbourke/m1-machine-learning-test', 0.5017737150192261, 'ml', 0)]",55,4.0,,3.81,426,324,2,0,4,43,4,427.0,1379.0,90.0,3.2,79
65,util,https://github.com/psf/black,['code-quality'],,[],[],,,,psf/black,black,35818,2362,226,Python,https://black.readthedocs.io/en/stable/,The uncompromising Python code formatter,psf,2024-01-14,2018-03-14,306,116.72532588454376,https://avatars.githubusercontent.com/u/50630501?v=4,The uncompromising Python code formatter,"['autopep8', 'code', 'codeformatter', 'formatter', 'gofmt', 'pre-commit-hook', 'yapf']","['autopep8', 'code', 'code-quality', 'codeformatter', 'formatter', 'gofmt', 'pre-commit-hook', 'yapf']",2024-01-11,"[('grantjenks/blue', 0.9170903563499451, 'util', 7), ('hhatto/autopep8', 0.7485063672065735, 'util', 2), ('google/yapf', 0.6890390515327454, 'util', 2), ('astral-sh/ruff', 0.6487815976142883, 'util', 1), ('rubik/radon', 0.609166145324707, 'util', 0), ('pycqa/flake8', 0.6002591848373413, 'util', 1), ('google/pytype', 0.5877479314804077, 'typing', 1), ('eugeneyan/python-collab-template', 0.5849995613098145, 'template', 0), ('instagram/monkeytype', 0.5771250128746033, 'typing', 1), ('microsoft/pycodegpt', 0.5714641213417053, 'llm', 0), ('pre-commit/pre-commit', 0.5649186968803406, 'util', 1), ('asottile/pyupgrade', 0.5597057342529297, 'util', 1), ('pycqa/pylint-django', 0.5581016540527344, 'util', 0), ('nbqa-dev/nbqa', 0.5564059019088745, 'jupyter', 2), ('nedbat/coveragepy', 0.5479041337966919, 'testing', 0), ('landscapeio/prospector', 0.5413801670074463, 'util', 0), ('numba/llvmlite', 0.5366169810295105, 'util', 0), ('agronholm/typeguard', 0.5326973795890808, 'typing', 1), ('grahamdumpleton/wrapt', 0.527543306350708, 'util', 0), ('pygments/pygments', 0.5274278521537781, 'util', 0), ('hoffstadt/dearpygui', 0.5271217226982117, 'gui', 0), ('pypy/pypy', 0.5263950228691101, 'util', 0), ('facebook/pyre-check', 0.5206736922264099, 'typing', 1), ('instagram/libcst', 0.5167094469070435, 'util', 0), ('python/mypy', 0.5155326128005981, 'typing', 1), ('sourcery-ai/sourcery', 0.5155060291290283, 'util', 1), ('dosisod/refurb', 0.514424204826355, 'util', 0), ('google/latexify_py', 0.5119499564170837, 'util', 0), ('pypa/hatch', 0.5100483894348145, 'util', 0), ('microsoft/pyright', 0.5090447068214417, 'typing', 1), ('hadialqattan/pycln', 0.5089622735977173, 'util', 0), ('pdm-project/pdm', 0.5081842541694641, 'util', 0), ('python-rope/rope', 0.504595935344696, 'util', 0), ('pytoolz/toolz', 0.5029543042182922, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5026202201843262, 'study', 0)]",430,7.0,,5.33,291,216,71,0,11,9,11,285.0,561.0,90.0,2.0,78
1385,llm,https://github.com/transformeroptimus/superagi,[],,[],[],,,,transformeroptimus/superagi,SuperAGI,13487,1650,158,Python,https://superagi.com/,"<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.",transformeroptimus,2024-01-14,2023-05-13,37,360.3396946564886,,"<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.","['agents', 'agi', 'ai', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agents', 'gpt-4', 'llm', 'llmops', 'nextjs', 'openai', 'pinecone', 'superagi']","['agents', 'agi', 'ai', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agents', 'gpt-4', 'llm', 'llmops', 'nextjs', 'openai', 'pinecone', 'superagi']",2024-01-12,"[('antonosika/gpt-engineer', 0.677440881729126, 'llm', 3), ('prefecthq/marvin', 0.6271318197250366, 'nlp', 4), ('operand/agency', 0.6139060258865356, 'llm', 8), ('torantulino/auto-gpt', 0.6111651659011841, 'llm', 5), ('oliveirabruno01/babyagi-asi', 0.5967099666595459, 'llm', 3), ('sweepai/sweep', 0.5931740403175354, 'llm', 2), ('google-research/language', 0.5880876779556274, 'nlp', 0), ('assafelovic/gpt-researcher', 0.5870476961135864, 'llm', 0), ('smol-ai/developer', 0.5681149363517761, 'llm', 1), ('superduperdb/superduperdb', 0.5671401023864746, 'data', 2), ('aimhubio/aim', 0.5632416605949402, 'ml-ops', 1), ('lastmile-ai/aiconfig', 0.5631660223007202, 'util', 2), ('ray-project/ray', 0.5615883469581604, 'ml-ops', 0), ('microsoft/lmops', 0.5605264902114868, 'llm', 2), ('mindsdb/mindsdb', 0.558373212814331, 'data', 3), ('mlc-ai/mlc-llm', 0.5498563051223755, 'llm', 1), ('krohling/bondai', 0.5482898354530334, 'llm', 2), ('geekan/metagpt', 0.5468068718910217, 'llm', 1), ('embedchain/embedchain', 0.5429883003234863, 'llm', 2), ('unity-technologies/ml-agents', 0.5418506264686584, 'ml-rl', 0), ('mlflow/mlflow', 0.5397615432739258, 'ml-ops', 1), ('cheshire-cat-ai/core', 0.5372999310493469, 'llm', 2), ('pythagora-io/gpt-pilot', 0.5368140935897827, 'llm', 2), ('yoheinakajima/babyagi', 0.5311616659164429, 'llm', 2), ('oegedijk/explainerdashboard', 0.5287240147590637, 'ml-interpretability', 0), ('googlecloudplatform/vertex-ai-samples', 0.5266525745391846, 'ml', 1), ('pytorchlightning/pytorch-lightning', 0.5248159170150757, 'ml-dl', 2), ('lucidrains/toolformer-pytorch', 0.5244137048721313, 'llm', 1), ('bentoml/bentoml', 0.5222785472869873, 'ml-ops', 2), ('netflix/metaflow', 0.5128297209739685, 'ml-ops', 1), ('aiwaves-cn/agents', 0.5044564604759216, 'nlp', 2), ('jina-ai/jina', 0.5039621591567993, 'ml', 1), ('microsoft/generative-ai-for-beginners', 0.5030859112739563, 'study', 2), ('modularml/mojo', 0.503006637096405, 'util', 1), ('invoke-ai/invokeai', 0.5029866695404053, 'diffusion', 1), ('projectmesa/mesa', 0.5027607083320618, 'sim', 0)]",68,2.0,,34.96,81,34,8,0,13,20,13,81.0,103.0,90.0,1.3,78
1765,data,https://github.com/airbytehq/airbyte,['data-engineering'],,[],[],,,,airbytehq/airbyte,airbyte,12809,3348,177,Python,https://airbyte.com,"The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.",airbytehq,2024-01-14,2020-07-27,183,69.9399375975039,https://avatars.githubusercontent.com/u/59758427?v=4,"The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.","['bigquery', 'change-data-capture', 'data', 'data-analysis', 'data-collection', 'data-engineering', 'data-integration', 'data-pipeline', 'elt', 'etl', 'java', 'mssql', 'mysql', 'pipeline', 'postgresql', 'redshift', 's3', 'self-hosted', 'snowflake']","['bigquery', 'change-data-capture', 'data', 'data-analysis', 'data-collection', 'data-engineering', 'data-integration', 'data-pipeline', 'elt', 'etl', 'java', 'mssql', 'mysql', 'pipeline', 'postgresql', 'redshift', 's3', 'self-hosted', 'snowflake']",2024-01-12,"[('mage-ai/mage-ai', 0.7392861247062683, 'ml-ops', 6), ('orchest/orchest', 0.7260224223136902, 'ml-ops', 2), ('dagster-io/dagster', 0.6772870421409607, 'ml-ops', 3), ('simonw/datasette', 0.663290798664093, 'data', 0), ('kestra-io/kestra', 0.6355409622192383, 'ml-ops', 7), ('aws/aws-sdk-pandas', 0.6343478560447693, 'pandas', 4), ('meltano/meltano', 0.6305922865867615, 'ml-ops', 3), ('flyteorg/flyte', 0.6267026662826538, 'ml-ops', 2), ('apache/spark', 0.6244115829467773, 'data', 1), ('airbnb/omniduct', 0.6164752244949341, 'data', 0), ('ploomber/ploomber', 0.6143587827682495, 'ml-ops', 1), ('dagworks-inc/hamilton', 0.5934673547744751, 'ml-ops', 3), ('featureform/embeddinghub', 0.5929150581359863, 'nlp', 0), ('netflix/metaflow', 0.5793136954307556, 'ml-ops', 0), ('fugue-project/fugue', 0.5778406858444214, 'pandas', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5710554718971252, 'template', 1), ('dbt-labs/dbt-core', 0.5581380724906921, 'ml-ops', 1), ('zenodo/zenodo', 0.5533841252326965, 'util', 1), ('databricks/dbt-databricks', 0.5521032214164734, 'data', 1), ('apache/airflow', 0.5474141836166382, 'ml-ops', 4), ('ibis-project/ibis', 0.5454192161560059, 'data', 5), ('polyaxon/datatile', 0.5435851812362671, 'pandas', 0), ('saulpw/visidata', 0.5434727668762207, 'term', 0), ('avaiga/taipy', 0.5423561334609985, 'data', 2), ('hi-primus/optimus', 0.5406291484832764, 'ml-ops', 1), ('coleifer/peewee', 0.53973788022995, 'data', 0), ('lithops-cloud/lithops', 0.5396174788475037, 'ml-ops', 0), ('airbnb/knowledge-repo', 0.5395446419715881, 'data', 2), ('darribas/gds_env', 0.5391685962677002, 'gis', 0), ('linealabs/lineapy', 0.5353677272796631, 'jupyter', 0), ('streamlit/streamlit', 0.5340747833251953, 'viz', 1), ('backtick-se/cowait', 0.5309221148490906, 'util', 1), ('superduperdb/superduperdb', 0.5290766954421997, 'data', 1), ('dlt-hub/dlt', 0.5255663394927979, 'data', 3), ('tokern/data-lineage', 0.5245165824890137, 'data', 1), ('whylabs/whylogs', 0.5231224298477173, 'util', 1), ('tobymao/sqlglot', 0.5210596919059753, 'data', 4), ('databrickslabs/dbx', 0.5199562907218933, 'data', 0), ('great-expectations/great_expectations', 0.5163770914077759, 'ml-ops', 2), ('merantix-momentum/squirrel-core', 0.5148383975028992, 'ml', 0), ('kubeflow-kale/kale', 0.5148319005966187, 'ml-ops', 0), ('intake/intake', 0.5141798853874207, 'data', 0), ('piccolo-orm/piccolo_admin', 0.5102675557136536, 'data', 1), ('astronomer/astro-sdk', 0.5056763887405396, 'ml-ops', 6), ('pathwaycom/pathway', 0.5019164681434631, 'data', 0)]",849,4.0,,116.94,4022,2543,42,0,58,127,58,4008.0,7126.0,90.0,1.8,78
1198,llm,https://github.com/haotian-liu/llava,[],,[],[],,,,haotian-liu/llava,LLaVA,12398,1289,115,Python,https://llava.hliu.cc,[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.,haotian-liu,2024-01-14,2023-04-17,41,301.34027777777777,,[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.,"['chatbot', 'chatgpt', 'foundation-models', 'gpt-4', 'instruction-tuning', 'llama', 'llama-2', 'llama2', 'llava', 'multi-modality', 'multimodal', 'vision-language-model', 'visual-language-learning']","['chatbot', 'chatgpt', 'foundation-models', 'gpt-4', 'instruction-tuning', 'llama', 'llama-2', 'llama2', 'llava', 'multi-modality', 'multimodal', 'vision-language-model', 'visual-language-learning']",2023-12-28,"[('instruction-tuning-with-gpt-4/gpt-4-llm', 0.6272305250167847, 'llm', 4), ('next-gpt/next-gpt', 0.6144143342971802, 'llm', 6), ('h2oai/h2o-llmstudio', 0.5654820203781128, 'llm', 4), ('luodian/otter', 0.5594356060028076, 'llm', 6), ('xtekky/gpt4free', 0.5506402850151062, 'llm', 3), ('lightning-ai/lit-llama', 0.5264182686805725, 'llm', 1), ('bigscience-workshop/petals', 0.5203514695167542, 'data', 3), ('bobazooba/xllm', 0.5188300609588623, 'llm', 4), ('mnotgod96/appagent', 0.5170363187789917, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5162800550460815, 'perf', 1), ('hiyouga/llama-factory', 0.5114816427230835, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.511481523513794, 'llm', 2)]",39,8.0,,6.38,528,218,9,1,5,7,5,528.0,1062.0,90.0,2.0,78
1087,llm,https://github.com/huggingface/peft,[],,[],[],,,,huggingface/peft,peft,12040,1024,96,Python,https://huggingface.co/docs/peft,🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.,huggingface,2024-01-14,2022-11-25,61,195.54524361948955,https://avatars.githubusercontent.com/u/25720743?v=4,🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.,"['adapter', 'diffusion', 'llm', 'lora', 'parameter-efficient-learning', 'pytorch', 'transformers']","['adapter', 'diffusion', 'llm', 'lora', 'parameter-efficient-learning', 'pytorch', 'transformers']",2024-01-12,"[('huggingface/optimum', 0.6415036916732788, 'ml', 2), ('hiyouga/llama-factory', 0.576074481010437, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5760744214057922, 'llm', 3), ('huggingface/transformers', 0.5402594208717346, 'nlp', 1), ('kubeflow/katib', 0.5282931327819824, 'ml', 0), ('pytorch/ignite', 0.5194254517555237, 'ml-dl', 1), ('nvidia/apex', 0.5134326219558716, 'ml-dl', 0), ('karpathy/micrograd', 0.5045297145843506, 'study', 0), ('microsoft/flaml', 0.5017809867858887, 'ml', 0)]",119,3.0,,11.65,431,384,14,0,10,10,10,429.0,1434.0,90.0,3.3,78
1316,pandas,https://github.com/gventuri/pandas-ai,[],,[],[],,,,gventuri/pandas-ai,pandas-ai,9456,820,81,Python,https://docs.pandas-ai.com,"Chat with your data (CSV, pandas, polars, etc). PandasAI makes data analysis conversational",gventuri,2024-01-13,2023-04-22,40,233.8939929328622,,"Chat with your data (CSV, pandas, polars, etc). PandasAI makes data analysis conversational","['ai', 'data-analysis', 'data-science', 'devtools', 'gpt-3', 'gpt-4', 'llm', 'pandas']","['ai', 'data-analysis', 'data-science', 'devtools', 'gpt-3', 'gpt-4', 'llm', 'pandas']",2024-01-11,"[('run-llama/rags', 0.6272019743919373, 'llm', 1), ('polyaxon/datatile', 0.5753474235534668, 'pandas', 2), ('mindsdb/mindsdb', 0.5711646676063538, 'data', 2), ('oneil512/insight', 0.5358114242553711, 'ml', 2), ('embedchain/embedchain', 0.5332342386245728, 'llm', 2), ('minimaxir/simpleaichat', 0.5319541692733765, 'llm', 1), ('ydataai/ydata-profiling', 0.5286967158317566, 'pandas', 3), ('pandas-dev/pandas', 0.5250675678253174, 'pandas', 3), ('nvidia/nemo', 0.513568639755249, 'nlp', 0), ('prefecthq/marvin', 0.5097314715385437, 'nlp', 2), ('reloadware/reloadium', 0.5095576047897339, 'profiling', 2), ('killianlucas/open-interpreter', 0.5016472935676575, 'llm', 1)]",58,5.0,,12.35,229,132,9,0,104,143,104,229.0,572.0,90.0,2.5,78
180,term,https://github.com/willmcgugan/rich,[],,[],[],,,,willmcgugan/rich,rich,46047,1700,543,Python,https://rich.readthedocs.io/en/latest/,Rich is a Python library for rich text and beautiful formatting in the terminal.,willmcgugan,2024-01-14,2019-11-10,220,209.0330739299611,https://avatars.githubusercontent.com/u/93378883?v=4,Rich is a Python library for rich text and beautiful formatting in the terminal.,"['ansi-colors', 'emoji', 'markdown', 'progress-bar', 'progress-bar-python', 'rich', 'syntax-highlighting', 'tables', 'terminal', 'terminal-color', 'traceback', 'tracebacks-rich', 'tui']","['ansi-colors', 'emoji', 'markdown', 'progress-bar', 'progress-bar-python', 'rich', 'syntax-highlighting', 'tables', 'terminal', 'terminal-color', 'traceback', 'tracebacks-rich', 'tui']",2023-11-15,"[('tartley/colorama', 0.7069451808929443, 'util', 1), ('jquast/blessed', 0.6152134537696838, 'term', 1), ('tiangolo/typer', 0.5575374364852905, 'term', 1), ('pygments/pygments', 0.5573404431343079, 'util', 1), ('urwid/urwid', 0.538774847984314, 'term', 0), ('grantjenks/blue', 0.5356051325798035, 'util', 0), ('federicoceratto/dashing', 0.5353392958641052, 'term', 1), ('rockhopper-technologies/enlighten', 0.5262578725814819, 'term', 0), ('plotly/plotly.py', 0.5253220200538635, 'viz', 0), ('google/yapf', 0.5237478017807007, 'util', 0), ('carpedm20/emoji', 0.5216153264045715, 'util', 1), ('hhatto/autopep8', 0.5127493739128113, 'util', 0), ('pygamelib/pygamelib', 0.5059705376625061, 'gamedev', 0), ('astral-sh/ruff', 0.5019975900650024, 'util', 0), ('hoffstadt/dearpygui', 0.5006871223449707, 'gui', 0)]",235,6.0,,2.65,132,59,51,2,17,38,17,132.0,218.0,90.0,1.7,77
1019,finance,https://github.com/openbb-finance/openbbterminal,[],,[],[],1.0,,,openbb-finance/openbbterminal,OpenBBTerminal,25332,2523,256,Python,https://my.openbb.co/app/terminal,"Investment Research for Everyone, Everywhere.",openbb-finance,2024-01-14,2020-12-20,162,156.0950704225352,https://avatars.githubusercontent.com/u/80064875?v=4,"Investment Research for Everyone, Everywhere.","['artificial-intelligence', 'crypto', 'cryptocurrency', 'economics', 'finance', 'investment', 'investment-research', 'machine-learning', 'openbb', 'quantitative-finance', 'stocks']","['artificial-intelligence', 'crypto', 'cryptocurrency', 'economics', 'finance', 'investment', 'investment-research', 'machine-learning', 'openbb', 'quantitative-finance', 'stocks']",2024-01-14,"[('polakowo/vectorbt', 0.6711627244949341, 'finance', 4), ('ai4finance-foundation/finrl', 0.5771132111549377, 'finance', 1), ('google-research/google-research', 0.5702900886535645, 'ml', 1), ('numerai/example-scripts', 0.5369656682014465, 'finance', 2), ('ranaroussi/quantstats', 0.53244549036026, 'finance', 2), ('zvtvz/zvt', 0.5273526310920715, 'finance', 3), ('microsoft/qlib', 0.5187370777130127, 'finance', 4), ('stefmolin/stock-analysis', 0.518646776676178, 'finance', 0), ('idanya/algo-trader', 0.5061081051826477, 'finance', 0)]",212,3.0,,29.44,429,364,37,0,17,11,17,426.0,462.0,90.0,1.1,77
254,crypto,https://github.com/freqtrade/freqtrade,[],,[],[],,,,freqtrade/freqtrade,freqtrade,24007,5552,613,Python,https://www.freqtrade.io,"Free, open source crypto trading bot",freqtrade,2024-01-14,2017-05-17,349,68.6194365046958,https://avatars.githubusercontent.com/u/37536846?v=4,"Free, open source crypto trading bot","['algorithmic-trading', 'bitcoin', 'cryptocurrencies', 'cryptocurrency', 'freqtrade', 'telegram-bot', 'trade', 'trading-bot']","['algorithmic-trading', 'bitcoin', 'cryptocurrencies', 'cryptocurrency', 'freqtrade', 'telegram-bot', 'trade', 'trading-bot']",2024-01-13,"[('idanya/algo-trader', 0.8236120939254761, 'finance', 2), ('ccxt/ccxt', 0.6369279026985168, 'crypto', 4), ('gbeced/basana', 0.6232483983039856, 'finance', 3), ('polakowo/vectorbt', 0.606457531452179, 'finance', 2), ('quantconnect/lean', 0.5934526324272156, 'finance', 1), ('blankly-finance/blankly', 0.5714588165283203, 'finance', 2), ('bmoscon/cryptofeed', 0.5561859011650085, 'crypto', 3), ('ai4finance-foundation/finrl', 0.5126928091049194, 'finance', 1), ('eternnoir/pytelegrambotapi', 0.5027558207511902, 'util', 1), ('gbeced/pyalgotrade', 0.5014805197715759, 'finance', 0), ('opentensor/bittensor', 0.5012444853782654, 'ml', 1)]",319,5.0,,71.92,410,389,81,0,13,13,13,410.0,1028.0,90.0,2.5,77
1663,data,https://github.com/mindsdb/mindsdb,[],,[],[],,,,mindsdb/mindsdb,mindsdb,19573,2599,379,Python,https://mindsdb.com,Build AI 🤖 using SQL,mindsdb,2024-01-14,2018-08-02,286,68.26656701544594,https://avatars.githubusercontent.com/u/31035808?v=4,Build AI 🤖 using SQL,"['ai', 'ai-agents', 'artificial-intelligence', 'auto-gpt', 'chatbot', 'database', 'forecasting', 'gpt', 'gpt4all', 'huggingface', 'llm', 'machine-learning', 'ml', 'mongodb', 'mysql', 'postgres', 'semantic-search', 'timeseries']","['ai', 'ai-agents', 'artificial-intelligence', 'auto-gpt', 'chatbot', 'database', 'forecasting', 'gpt', 'gpt4all', 'huggingface', 'llm', 'machine-learning', 'ml', 'mongodb', 'mysql', 'postgres', 'semantic-search', 'timeseries']",2024-01-12,"[('prefecthq/marvin', 0.7208438515663147, 'nlp', 3), ('antonosika/gpt-engineer', 0.6977242827415466, 'llm', 1), ('microsoft/generative-ai-for-beginners', 0.6579902768135071, 'study', 3), ('cheshire-cat-ai/core', 0.6340122222900391, 'llm', 3), ('superduperdb/superduperdb', 0.6333754658699036, 'data', 6), ('activeloopai/deeplake', 0.630451500415802, 'ml-ops', 4), ('netflix/metaflow', 0.594740092754364, 'ml-ops', 3), ('operand/agency', 0.5936707854270935, 'llm', 4), ('googlecloudplatform/vertex-ai-samples', 0.5918219685554504, 'ml', 2), ('bentoml/bentoml', 0.5877701044082642, 'ml-ops', 2), ('oegedijk/explainerdashboard', 0.5860490798950195, 'ml-interpretability', 0), ('run-llama/rags', 0.5839447975158691, 'llm', 2), ('torantulino/auto-gpt', 0.5791462659835815, 'llm', 2), ('microsoft/lmops', 0.5788654088973999, 'llm', 2), ('xplainable/xplainable', 0.5784251093864441, 'ml-interpretability', 1), ('microsoft/promptflow', 0.573220431804657, 'llm', 3), ('oneil512/insight', 0.5716148614883423, 'ml', 4), ('gventuri/pandas-ai', 0.5711646676063538, 'pandas', 2), ('pathwaycom/llm-app', 0.5670859217643738, 'llm', 3), ('lastmile-ai/aiconfig', 0.5651490092277527, 'util', 2), ('sweepai/sweep', 0.5614449381828308, 'llm', 2), ('qdrant/qdrant', 0.5584307312965393, 'data', 1), ('transformeroptimus/superagi', 0.558373212814331, 'llm', 3), ('embedchain/embedchain', 0.5583111643791199, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5564075708389282, 'ml-dl', 3), ('llmware-ai/llmware', 0.55570387840271, 'llm', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5548688173294067, 'study', 2), ('google-research/google-research', 0.5539237856864929, 'ml', 2), ('google-research/language', 0.5518535375595093, 'nlp', 1), ('avaiga/taipy', 0.5504463315010071, 'data', 0), ('mlc-ai/mlc-llm', 0.5494204163551331, 'llm', 1), ('deepset-ai/haystack', 0.5477265119552612, 'llm', 3), ('polyaxon/datatile', 0.5471445918083191, 'pandas', 0), ('lancedb/lancedb', 0.5393930077552795, 'data', 1), ('lupantech/chameleon-llm', 0.5369391441345215, 'llm', 2), ('chancefocus/pixiu', 0.5329363346099854, 'finance', 2), ('salesforce/logai', 0.5326856970787048, 'util', 2), ('iterative/dvc', 0.5321820974349976, 'ml-ops', 2), ('nicolas-hbt/pygraft', 0.5319455862045288, 'ml', 2), ('ml-tooling/opyrator', 0.5296940207481384, 'viz', 1), ('nebuly-ai/nebullvm', 0.529395341873169, 'perf', 3), ('hpcaitech/colossalai', 0.5289170742034912, 'llm', 1), ('marqo-ai/marqo', 0.5285826325416565, 'ml', 3), ('winedarksea/autots', 0.5278527736663818, 'time-series', 2), ('giskard-ai/giskard', 0.5253748893737793, 'data', 2), ('mlflow/mlflow', 0.5246097445487976, 'ml-ops', 3), ('rasahq/rasa', 0.5234725475311279, 'llm', 2), ('ai4finance-foundation/fingpt', 0.5226989984512329, 'finance', 2), ('larsbaunwall/bricky', 0.5211383700370789, 'llm', 1), ('explosion/thinc', 0.5208819508552551, 'ml-dl', 3), ('ourownstory/neural_prophet', 0.5187014937400818, 'ml', 4), ('thilinarajapakse/simpletransformers', 0.515864372253418, 'nlp', 0), ('databrickslabs/dolly', 0.5149961113929749, 'llm', 2), ('dylanhogg/llmgraph', 0.5112169981002808, 'ml', 1), ('microsoft/autogen', 0.5091248750686646, 'llm', 2), ('chatarena/chatarena', 0.5072094798088074, 'llm', 2), ('reloadware/reloadium', 0.5066758990287781, 'profiling', 2), ('paddlepaddle/paddlenlp', 0.5058019161224365, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5057101249694824, 'llm', 1), ('lianjiatech/belle', 0.5054676532745361, 'llm', 0), ('xtekky/gpt4free', 0.5054675936698914, 'llm', 2), ('microsoft/nni', 0.5044615864753723, 'ml', 1), ('ray-project/ray', 0.5041195154190063, 'ml-ops', 1), ('rcgai/simplyretrieve', 0.5031827688217163, 'llm', 3), ('feast-dev/feast', 0.5014446973800659, 'ml-ops', 2), ('whylabs/whylogs', 0.5011435151100159, 'util', 1), ('ludwig-ai/ludwig', 0.5004526376724243, 'ml-ops', 3)]",815,4.0,,146.96,1216,1015,66,0,64,44,64,1214.0,1692.0,90.0,1.4,77
1506,llm,https://github.com/guidance-ai/guidance,"['chatgpt', 'prompt-engineering', 'language-model', 'template']",,[],[],,,,guidance-ai/guidance,guidance,15711,1011,114,Jupyter Notebook,,A guidance language for controlling large language models.,guidance-ai,2024-01-14,2022-11-10,63,246.58520179372198,https://avatars.githubusercontent.com/u/142035062?v=4,A guidance language for controlling large language models.,[],"['chatgpt', 'language-model', 'prompt-engineering', 'template']",2024-01-12,"[('keirp/automatic_prompt_engineer', 0.7347609996795654, 'llm', 2), ('ctlllll/llm-toolmaker', 0.6901283860206604, 'llm', 1), ('microsoft/autogen', 0.6821621656417847, 'llm', 1), ('hannibal046/awesome-llm', 0.6724131107330322, 'study', 1), ('neulab/prompt2model', 0.6568854451179504, 'llm', 1), ('next-gpt/next-gpt', 0.6470729112625122, 'llm', 1), ('promptslab/promptify', 0.6470388770103455, 'nlp', 2), ('hazyresearch/ama_prompting', 0.6410424709320068, 'llm', 1), ('lianjiatech/belle', 0.6406282782554626, 'llm', 0), ('lm-sys/fastchat', 0.6356953382492065, 'llm', 1), ('eth-sri/lmql', 0.6066431999206543, 'llm', 2), ('juncongmoo/pyllama', 0.6007868051528931, 'llm', 0), ('ai21labs/lm-evaluation', 0.6006115674972534, 'llm', 1), ('killianlucas/open-interpreter', 0.6002689003944397, 'llm', 1), ('freedomintelligence/llmzoo', 0.5987256169319153, 'llm', 1), ('openlmlab/moss', 0.5859184861183167, 'llm', 2), ('kyegomez/tree-of-thoughts', 0.5812641382217407, 'llm', 2), ('guardrails-ai/guardrails', 0.578825056552887, 'llm', 0), ('agenta-ai/agenta', 0.5751789808273315, 'llm', 1), ('1rgs/jsonformer', 0.5747532248497009, 'llm', 1), ('lupantech/chameleon-llm', 0.5735355019569397, 'llm', 2), ('conceptofmind/toolformer', 0.5727112293243408, 'llm', 1), ('hwchase17/langchain', 0.5676143765449524, 'llm', 1), ('reasoning-machines/pal', 0.5653203129768372, 'llm', 1), ('yizhongw/self-instruct', 0.5607982873916626, 'llm', 1), ('srush/minichain', 0.5577107667922974, 'llm', 1), ('prefecthq/langchain-prefect', 0.5558692812919617, 'llm', 0), ('spcl/graph-of-thoughts', 0.5539388060569763, 'llm', 1), ('mlc-ai/web-llm', 0.5491284728050232, 'llm', 2), ('dylanhogg/llmgraph', 0.5485440492630005, 'ml', 1), ('stanfordnlp/dspy', 0.5456482768058777, 'llm', 0), ('xtekky/gpt4free', 0.5423391461372375, 'llm', 2), ('thudm/chatglm2-6b', 0.5406391024589539, 'llm', 0), ('bigscience-workshop/biomedical', 0.5397950410842896, 'data', 0), ('fasteval/fasteval', 0.5377461910247803, 'llm', 0), ('jalammar/ecco', 0.5360292196273804, 'ml-interpretability', 0), ('baichuan-inc/baichuan-13b', 0.5342095494270325, 'llm', 1), ('cg123/mergekit', 0.5327853560447693, 'llm', 0), ('thudm/chatglm-6b', 0.5300068259239197, 'llm', 1), ('optimalscale/lmflow', 0.5283278226852417, 'llm', 2), ('jonasgeiping/cramming', 0.5238267779350281, 'nlp', 1), ('hiyouga/llama-efficient-tuning', 0.5210491418838501, 'llm', 1), ('hiyouga/llama-factory', 0.5210491418838501, 'llm', 1), ('young-geng/easylm', 0.5203404426574707, 'llm', 1), ('facebookresearch/shepherd', 0.5177493095397949, 'llm', 1), ('databrickslabs/dolly', 0.5170300602912903, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5157712697982788, 'llm', 1), ('run-llama/rags', 0.5145743489265442, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5125293135643005, 'nlp', 0), ('bigscience-workshop/promptsource', 0.5123276114463806, 'nlp', 0), ('hazyresearch/h3', 0.5101184248924255, 'llm', 0), ('thudm/codegeex', 0.5090668201446533, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5060887932777405, 'llm', 0), ('openai/finetune-transformer-lm', 0.5050269961357117, 'llm', 0), ('oobabooga/text-generation-webui', 0.504760205745697, 'llm', 1), ('aiwaves-cn/agents', 0.5045157670974731, 'nlp', 1), ('mooler0410/llmspracticalguide', 0.5038110613822937, 'study', 0), ('confident-ai/deepeval', 0.5033418536186218, 'testing', 2), ('openbmb/toolbench', 0.5015900135040283, 'llm', 0)]",53,2.0,,17.44,253,187,14,0,32,28,32,253.0,509.0,90.0,2.0,77
1740,llm,https://github.com/microsoft/promptflow,['prompt-engineering'],,[],[],1.0,,,microsoft/promptflow,promptflow,7088,516,78,Python,https://microsoft.github.io/promptflow/,"Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.",microsoft,2024-01-14,2023-06-30,30,231.85046728971963,https://avatars.githubusercontent.com/u/6154722?v=4,"Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.","['ai', 'ai-application-development', 'ai-applications', 'chatgpt', 'gpt', 'llm', 'prompt', 'prompt-engineering']","['ai', 'ai-application-development', 'ai-applications', 'chatgpt', 'gpt', 'llm', 'prompt', 'prompt-engineering']",2024-01-12,"[('microsoft/semantic-kernel', 0.7958884835243225, 'llm', 2), ('pathwaycom/llm-app', 0.7594974040985107, 'llm', 1), ('lastmile-ai/aiconfig', 0.7025976181030273, 'util', 2), ('h2oai/h2o-llmstudio', 0.6735444664955139, 'llm', 4), ('intel/intel-extension-for-transformers', 0.6592389345169067, 'perf', 0), ('microsoft/lmops', 0.6579537987709045, 'llm', 3), ('tigerlab-ai/tiger', 0.6455790996551514, 'llm', 1), ('prefecthq/marvin', 0.6454984545707703, 'nlp', 3), ('bentoml/bentoml', 0.6441292762756348, 'ml-ops', 1), ('hwchase17/langchain', 0.6271064877510071, 'llm', 0), ('cheshire-cat-ai/core', 0.6264965534210205, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.6248592734336853, 'llm', 0), ('bentoml/openllm', 0.6225191950798035, 'ml-ops', 2), ('sweepai/sweep', 0.6218081712722778, 'llm', 2), ('mlc-ai/mlc-llm', 0.621173620223999, 'llm', 1), ('microsoft/promptcraft-robotics', 0.6192827224731445, 'sim', 3), ('agenta-ai/agenta', 0.6172305941581726, 'llm', 2), ('deepset-ai/haystack', 0.6076862812042236, 'llm', 2), ('chainlit/chainlit', 0.6072118878364563, 'llm', 2), ('shishirpatil/gorilla', 0.6032128930091858, 'llm', 2), ('pythagora-io/gpt-pilot', 0.5965003371238708, 'llm', 1), ('antonosika/gpt-engineer', 0.5964549779891968, 'llm', 1), ('ludwig-ai/ludwig', 0.5964416861534119, 'ml-ops', 1), ('mnotgod96/appagent', 0.5954347252845764, 'llm', 2), ('nebuly-ai/nebullvm', 0.5947597622871399, 'perf', 2), ('mmabrouk/chatgpt-wrapper', 0.5893070101737976, 'llm', 2), ('bigscience-workshop/petals', 0.5844665765762329, 'data', 1), ('embedchain/embedchain', 0.5782580971717834, 'llm', 3), ('mindsdb/mindsdb', 0.573220431804657, 'data', 3), ('hegelai/prompttools', 0.5713818669319153, 'llm', 1), ('confident-ai/deepeval', 0.5659092664718628, 'testing', 2), ('alphasecio/langchain-examples', 0.5629417896270752, 'llm', 1), ('run-llama/rags', 0.5604910254478455, 'llm', 2), ('eugeneyan/open-llms', 0.5602272748947144, 'study', 1), ('microsoft/generative-ai-for-beginners', 0.5575390458106995, 'study', 4), ('argilla-io/argilla', 0.555797815322876, 'nlp', 2), ('citadel-ai/langcheck', 0.5546442866325378, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.5492174029350281, 'ml-dl', 1), ('nomic-ai/gpt4all', 0.5479527115821838, 'llm', 0), ('arize-ai/phoenix', 0.5460477471351624, 'ml-interpretability', 0), ('microsoft/torchscale', 0.542982816696167, 'llm', 0), ('salesforce/codet5', 0.5427641868591309, 'nlp', 0), ('lancedb/lancedb', 0.5394440293312073, 'data', 0), ('chatarena/chatarena', 0.5370725989341736, 'llm', 2), ('microsoft/autogen', 0.5367021560668945, 'llm', 2), ('operand/agency', 0.5344325304031372, 'llm', 2), ('avaiga/taipy', 0.5334009528160095, 'data', 0), ('promptslab/promptify', 0.531336784362793, 'nlp', 2), ('llmware-ai/llmware', 0.5309350490570068, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.5296043753623962, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5263214707374573, 'llm', 2), ('bobazooba/xllm', 0.52497398853302, 'llm', 3), ('iterative/dvc', 0.514451265335083, 'ml-ops', 1), ('vllm-project/vllm', 0.5073052048683167, 'llm', 2), ('hiyouga/llama-factory', 0.505469024181366, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5054689645767212, 'llm', 2), ('berriai/litellm', 0.5052803754806519, 'llm', 1), ('dylanhogg/llmgraph', 0.5038095712661743, 'ml', 2), ('skypilot-org/skypilot', 0.5022432208061218, 'llm', 0)]",53,1.0,,19.69,1065,977,7,0,20,118,20,1065.0,3879.0,90.0,3.6,77
1885,llm,https://github.com/sjtu-ipads/powerinfer,[],,[],[],,,,sjtu-ipads/powerinfer,PowerInfer,6237,324,71,C,,High-speed Large Language Model Serving on PCs with Consumer-grade GPUs,sjtu-ipads,2024-01-14,2023-12-15,6,949.1086956521739,https://avatars.githubusercontent.com/u/10797537?v=4,High-speed Large Language Model Serving on PCs with Consumer-grade GPUs,"['falcon', 'large-language-models', 'llama', 'llm', 'llm-inference', 'local-inference']","['falcon', 'large-language-models', 'llama', 'llm', 'llm-inference', 'local-inference']",2024-01-11,"[('nvidia/tensorrt-llm', 0.6346943974494934, 'viz', 0), ('vllm-project/vllm', 0.6339587569236755, 'llm', 2), ('hannibal046/awesome-llm', 0.6129886507987976, 'study', 0), ('ray-project/ray-llm', 0.5986047387123108, 'llm', 3), ('huggingface/text-generation-inference', 0.5956083536148071, 'llm', 1), ('lianjiatech/belle', 0.5860909223556519, 'llm', 1), ('salesforce/xgen', 0.5852330327033997, 'llm', 2), ('microsoft/autogen', 0.5836211442947388, 'llm', 1), ('next-gpt/next-gpt', 0.5799466967582703, 'llm', 2), ('artidoro/qlora', 0.5710045695304871, 'llm', 0), ('freedomintelligence/llmzoo', 0.5668209195137024, 'llm', 0), ('squeezeailab/squeezellm', 0.5623429417610168, 'llm', 3), ('bobazooba/xllm', 0.5589494705200195, 'llm', 3), ('bigscience-workshop/petals', 0.5581437349319458, 'data', 3), ('predibase/lorax', 0.5567286014556885, 'llm', 3), ('hpcaitech/energonai', 0.5440476536750793, 'ml', 0), ('cg123/mergekit', 0.5398347973823547, 'llm', 2), ('lightning-ai/lit-llama', 0.5379477143287659, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5372994542121887, 'llm', 1), ('mlc-ai/web-llm', 0.5370975136756897, 'llm', 1), ('timdettmers/bitsandbytes', 0.5349003076553345, 'util', 0), ('ctlllll/llm-toolmaker', 0.5325697660446167, 'llm', 0), ('ai21labs/lm-evaluation', 0.5313429236412048, 'llm', 0), ('juncongmoo/pyllama', 0.5285684466362, 'llm', 0), ('thudm/chatglm2-6b', 0.5270649194717407, 'llm', 2), ('young-geng/easylm', 0.5250315070152283, 'llm', 2), ('microsoft/lora', 0.5186458230018616, 'llm', 0), ('optimalscale/lmflow', 0.5180608034133911, 'llm', 0), ('facebookresearch/llama', 0.5126661062240601, 'llm', 1), ('nat/openplayground', 0.5090302228927612, 'llm', 0), ('facebookresearch/codellama', 0.5052860975265503, 'llm', 1), ('jzhang38/tinyllama', 0.5038610696792603, 'llm', 1), ('mit-han-lab/streaming-llm', 0.5003539323806763, 'llm', 0)]",391,6.0,,29.92,116,68,1,0,0,0,0,116.0,183.0,90.0,1.6,77
127,ml-dl,https://github.com/microsoft/deepspeed,[],,[],[],,,,microsoft/deepspeed,DeepSpeed,30742,3726,309,Python,https://www.deepspeed.ai/,"DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.",microsoft,2024-01-14,2020-01-23,209,146.5899182561308,https://avatars.githubusercontent.com/u/6154722?v=4,"DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.","['billion-parameters', 'compression', 'data-parallelism', 'deep-learning', 'gpu', 'inference', 'machine-learning', 'mixture-of-experts', 'model-parallelism', 'pipeline-parallelism', 'pytorch', 'trillion-parameters', 'zero']","['billion-parameters', 'compression', 'data-parallelism', 'deep-learning', 'gpu', 'inference', 'machine-learning', 'mixture-of-experts', 'model-parallelism', 'pipeline-parallelism', 'pytorch', 'trillion-parameters', 'zero']",2024-01-13,"[('determined-ai/determined', 0.6881573796272278, 'ml-ops', 3), ('horovod/horovod', 0.6651274561882019, 'ml-ops', 3), ('paddlepaddle/paddle', 0.6585884094238281, 'ml-dl', 2), ('uber/petastorm', 0.6462931632995605, 'data', 3), ('neuralmagic/deepsparse', 0.643064022064209, 'nlp', 1), ('tensorflow/tensorflow', 0.6415050029754639, 'ml-dl', 2), ('apache/incubator-mxnet', 0.6390171051025391, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.6303361058235168, 'ml', 2), ('alpa-projects/alpa', 0.6055509448051453, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.6031937003135681, 'ml-dl', 2), ('google/trax', 0.5964576601982117, 'ml-dl', 2), ('ray-project/ray', 0.5907596349716187, 'ml-ops', 3), ('eleutherai/gpt-neox', 0.5843392610549927, 'llm', 0), ('optuna/optuna', 0.5794839262962341, 'ml', 1), ('keras-team/keras', 0.5722226500511169, 'ml-dl', 3), ('microsoft/nni', 0.5704576373100281, 'ml', 3), ('google/vizier', 0.5668622851371765, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.5665271878242493, 'perf', 3), ('deepchecks/deepchecks', 0.5651904940605164, 'data', 3), ('microsoft/onnxruntime', 0.5646995902061462, 'ml', 3), ('huggingface/datasets', 0.5633695721626282, 'nlp', 3), ('google/tf-quant-finance', 0.5623762011528015, 'finance', 1), ('rasbt/machine-learning-book', 0.5622684955596924, 'study', 3), ('pytorch/ignite', 0.5605365037918091, 'ml-dl', 3), ('bigscience-workshop/petals', 0.5571476817131042, 'data', 4), ('aiqc/aiqc', 0.55571448802948, 'ml-ops', 0), ('salesforce/warp-drive', 0.5514455437660217, 'ml-rl', 3), ('mosaicml/composer', 0.5493502616882324, 'ml-dl', 3), ('explosion/thinc', 0.5471163392066956, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.5429177284240723, 'ml-rl', 1), ('google-research/deeplab2', 0.5402511954307556, 'ml', 0), ('huggingface/optimum', 0.5390816926956177, 'ml', 2), ('neuralmagic/sparseml', 0.5357862114906311, 'ml-dl', 1), ('deepmind/deepmind-research', 0.5327295660972595, 'ml', 0), ('huggingface/transformers', 0.5304394364356995, 'nlp', 3), ('mlflow/mlflow', 0.5296139717102051, 'ml-ops', 1), ('keras-team/autokeras', 0.5261964201927185, 'ml-dl', 2), ('tlkh/tf-metal-experiments', 0.5253063440322876, 'perf', 2), ('rwightman/pytorch-image-models', 0.5248521566390991, 'ml-dl', 1), ('dmlc/xgboost', 0.5240546464920044, 'ml', 1), ('pytorch/glow', 0.5229150652885437, 'ml', 0), ('pyg-team/pytorch_geometric', 0.5219319462776184, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.5205578207969666, 'util', 2), ('deepmind/dm-haiku', 0.5188496112823486, 'ml-dl', 2), ('adap/flower', 0.5188344120979309, 'ml-ops', 3), ('ddbourgin/numpy-ml', 0.5183699727058411, 'ml', 1), ('polyaxon/polyaxon', 0.5181537866592407, 'ml-ops', 3), ('deepmind/dm_control', 0.5161496996879578, 'ml-rl', 2), ('fepegar/torchio', 0.5152624845504761, 'ml-dl', 3), ('denys88/rl_games', 0.5148147940635681, 'ml-rl', 2), ('deepmodeling/deepmd-kit', 0.513860821723938, 'sim', 1), ('pytorchlightning/pytorch-lightning', 0.5130410194396973, 'ml-dl', 3), ('iperov/deepfacelab', 0.5126926302909851, 'ml-dl', 2), ('lutzroeder/netron', 0.5104838609695435, 'ml', 3), ('pyro-ppl/pyro', 0.5087716579437256, 'ml-dl', 3), ('rom1504/img2dataset', 0.5075109601020813, 'data', 1), ('blackhc/toma', 0.5066729784011841, 'ml-dl', 3), ('wandb/client', 0.5063109397888184, 'ml', 3), ('onnx/onnx', 0.5063037276268005, 'ml', 3), ('plasma-umass/scalene', 0.5038928389549255, 'profiling', 1), ('karpathy/micrograd', 0.5035680532455444, 'study', 0), ('d2l-ai/d2l-en', 0.5029295682907104, 'study', 3), ('merantix-momentum/squirrel-core', 0.5016716122627258, 'ml', 3), ('mrdbourke/pytorch-deep-learning', 0.5005961656570435, 'study', 3), ('intellabs/bayesian-torch', 0.5001977682113647, 'ml', 2)]",279,2.0,,14.46,655,404,48,0,24,20,24,652.0,1081.0,90.0,1.7,76
184,ml,https://github.com/google/jax,"['numpy', 'xla', 'autograd']",,[],[],,,,google/jax,jax,26282,2457,323,Python,http://jax.readthedocs.io/,"Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more",google,2024-01-14,2018-10-25,274,95.67030681227249,https://avatars.githubusercontent.com/u/1342004?v=4,"Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more",['jax'],"['autograd', 'jax', 'numpy', 'xla']",2024-01-14,"[('numba/numba', 0.6459792256355286, 'perf', 1), ('numpy/numpy', 0.6223551034927368, 'math', 1), ('exaloop/codon', 0.6078891158103943, 'perf', 0), ('hips/autograd', 0.5986758470535278, 'ml', 0), ('pyston/pyston', 0.5706031918525696, 'util', 0), ('pytorch/pytorch', 0.5702386498451233, 'ml-dl', 2), ('micropython/micropython', 0.5700966119766235, 'util', 0), ('pypy/pypy', 0.5690382122993469, 'util', 0), ('dfki-ric/pytransform3d', 0.5666728615760803, 'math', 0), ('luispedro/mahotas', 0.5661518573760986, 'viz', 1), ('nvidia/warp', 0.5563659071922302, 'sim', 0), ('marella/ctransformers', 0.5531230568885803, 'nlp', 0), ('dosisod/refurb', 0.5519415736198425, 'util', 0), ('arogozhnikov/einops', 0.5517240762710571, 'ml-dl', 2), ('plasma-umass/scalene', 0.5463865399360657, 'profiling', 0), ('joblib/joblib', 0.5429615378379822, 'util', 0), ('google/pyglove', 0.5426806211471558, 'util', 0), ('google/gin-config', 0.5384674668312073, 'util', 0), ('artemyk/dynpy', 0.5380663871765137, 'sim', 0), ('numba/llvmlite', 0.534612774848938, 'util', 0), ('cython/cython', 0.5345306992530823, 'util', 0), ('pytoolz/toolz', 0.5340706706047058, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5305117964744568, 'perf', 0), ('stijnwoestenborghs/gradi-mojo', 0.5190989971160889, 'util', 0), ('python/cpython', 0.5174630284309387, 'util', 0), ('cupy/cupy', 0.5167858004570007, 'math', 1), ('pympler/pympler', 0.516033411026001, 'perf', 0), ('ipython/ipyparallel', 0.514338493347168, 'perf', 0), ('zerointensity/pointers.py', 0.5109991431236267, 'perf', 0), ('connorferster/handcalcs', 0.5066884160041809, 'jupyter', 0), ('nvidia/cuda-python', 0.5065921545028687, 'ml', 0), ('fredrik-johansson/mpmath', 0.5052241086959839, 'math', 0), ('lcompilers/lpython', 0.5010378956794739, 'util', 0)]",617,2.0,,84.96,1540,1198,64,0,38,42,38,1534.0,1514.0,90.0,1.0,76
349,ml-dl,https://github.com/paddlepaddle/paddle,[],,[],[],,,,paddlepaddle/paddle,Paddle,21225,5393,726,C++,http://www.paddlepaddle.org/,PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）,paddlepaddle,2024-01-13,2016-08-15,389,54.54295154185022,https://avatars.githubusercontent.com/u/23534030?v=4,PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）,"['deep-learning', 'distributed-training', 'efficiency', 'machine-learning', 'neural-network', 'paddlepaddle', 'scalability']","['deep-learning', 'distributed-training', 'efficiency', 'machine-learning', 'neural-network', 'paddlepaddle', 'scalability']",2024-01-13,"[('horovod/horovod', 0.7518745064735413, 'ml-ops', 2), ('alpa-projects/alpa', 0.7082504034042358, 'ml-dl', 3), ('uber/fiber', 0.69061678647995, 'data', 1), ('microsoft/deepspeed', 0.6585884094238281, 'ml-dl', 2), ('tensorflow/tensorflow', 0.6499528884887695, 'ml-dl', 3), ('optuna/optuna', 0.6472195386886597, 'ml', 1), ('determined-ai/determined', 0.6040452122688293, 'ml-ops', 3), ('apache/incubator-mxnet', 0.5943779349327087, 'ml-dl', 0), ('uber/petastorm', 0.5659868717193604, 'data', 2), ('onnx/onnx', 0.5445713400840759, 'ml', 3), ('ray-project/ray', 0.5365186333656311, 'ml-ops', 2), ('hpcaitech/colossalai', 0.5353530645370483, 'llm', 1), ('microsoft/jarvis', 0.5339170098304749, 'llm', 1), ('tensorflow/tensor2tensor', 0.5337570905685425, 'ml', 2), ('dask/dask-ml', 0.5259979963302612, 'ml', 0), ('eventual-inc/daft', 0.5211067199707031, 'pandas', 2), ('aiqc/aiqc', 0.5197136998176575, 'ml-ops', 0), ('nvidia/deeplearningexamples', 0.5118424296379089, 'ml-dl', 2), ('microsoft/onnxruntime', 0.5105121731758118, 'ml', 2), ('neuralmagic/deepsparse', 0.5083123445510864, 'nlp', 0), ('nevronai/metisfl', 0.5069923996925354, 'ml', 2), ('mlflow/mlflow', 0.5048314332962036, 'ml-ops', 1), ('nvidia/apex', 0.5022732019424438, 'ml-dl', 0), ('bigscience-workshop/petals', 0.5011550784111023, 'data', 2)]",1152,6.0,,132.67,3577,3079,90,0,3,10,3,3577.0,5537.0,90.0,1.5,76
1522,llm,https://github.com/pythagora-io/gpt-pilot,['coding-assistant'],,[],[],,,,pythagora-io/gpt-pilot,gpt-pilot,20513,1763,190,Python,,Dev tool that writes scalable apps from scratch while the developer oversees the implementation,pythagora-io,2024-01-14,2023-08-16,23,859.8263473053893,https://avatars.githubusercontent.com/u/123263103?v=4,Dev tool that writes scalable apps from scratch while the developer oversees the implementation,"['ai', 'codegen', 'coding-assistant', 'developer-tools', 'gpt-4', 'research-project']","['ai', 'codegen', 'coding-assistant', 'developer-tools', 'gpt-4', 'research-project']",2024-01-14,"[('sweepai/sweep', 0.7174859046936035, 'llm', 2), ('lastmile-ai/aiconfig', 0.5966111421585083, 'util', 2), ('avaiga/taipy', 0.5966109037399292, 'data', 1), ('microsoft/promptflow', 0.5965003371238708, 'llm', 1), ('antonosika/gpt-engineer', 0.590089738368988, 'llm', 3), ('iterative/dvc', 0.5759281516075134, 'ml-ops', 2), ('streamlit/streamlit', 0.5534999370574951, 'viz', 1), ('bentoml/bentoml', 0.5526854395866394, 'ml-ops', 1), ('smol-ai/developer', 0.5505355000495911, 'llm', 2), ('ploomber/ploomber', 0.5441665649414062, 'ml-ops', 0), ('netflix/metaflow', 0.5420463681221008, 'ml-ops', 1), ('transformeroptimus/superagi', 0.5368140935897827, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5301491022109985, 'llm', 0), ('prefecthq/marvin', 0.5274754762649536, 'nlp', 1), ('polyaxon/polyaxon', 0.5244739055633545, 'ml-ops', 0), ('wandb/client', 0.5149502754211426, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.513899564743042, 'ml', 1), ('zenml-io/zenml', 0.5131421089172363, 'ml-ops', 1), ('dagworks-inc/hamilton', 0.510085940361023, 'ml-ops', 0), ('microsoft/semantic-kernel', 0.5081477165222168, 'llm', 1), ('samuelcolvin/python-devtools', 0.5080005526542664, 'debug', 0), ('meltano/meltano', 0.5066982507705688, 'ml-ops', 0), ('pytorchlightning/pytorch-lightning', 0.5030331015586853, 'ml-dl', 1), ('cheshire-cat-ai/core', 0.5015438199043274, 'llm', 1), ('prefecthq/server', 0.500454843044281, 'util', 0)]",43,4.0,,14.9,340,211,5,0,0,0,0,341.0,509.0,90.0,1.5,76
1477,util,https://github.com/facebookresearch/audiocraft,"['musicgen', 'generation', 'audio']",,[],[],,,,facebookresearch/audiocraft,audiocraft,18023,1787,165,Python,,"Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.",facebookresearch,2024-01-14,2023-06-08,33,534.5805084745763,https://avatars.githubusercontent.com/u/16943930?v=4,"Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.",[],"['audio', 'generation', 'musicgen']",2024-01-11,"[('pollinations/dance-diffusion', 0.5851640105247498, 'diffusion', 1), ('libaudioflux/audioflux', 0.551815927028656, 'util', 1), ('spotify/pedalboard', 0.5256170034408569, 'util', 1), ('spotify/basic-pitch', 0.5119118094444275, 'util', 1), ('bastibe/python-soundfile', 0.5025203227996826, 'util', 0)]",30,4.0,,2.44,111,44,7,0,0,9,9,111.0,129.0,90.0,1.2,76
1249,util,https://github.com/modularml/mojo,[],,[],[],,,,modularml/mojo,mojo,16469,2132,250,Jupyter Notebook,https://docs.modular.com/mojo,The Mojo Programming Language,modularml,2024-01-14,2023-04-28,39,416.1841155234657,https://avatars.githubusercontent.com/u/39327063?v=4,The Mojo Programming Language,"['ai', 'language', 'machine-learning', 'modular', 'mojo', 'programming-language']","['ai', 'language', 'machine-learning', 'modular', 'mojo', 'programming-language']",2024-01-08,"[('google/pyglove', 0.5558887124061584, 'util', 1), ('prefecthq/marvin', 0.5523964166641235, 'nlp', 1), ('ravenscroftj/turbopilot', 0.5523777604103088, 'llm', 1), ('evhub/coconut', 0.5474485754966736, 'util', 2), ('explosion/thinc', 0.5388814806938171, 'ml-dl', 2), ('python/cpython', 0.5251497626304626, 'util', 0), ('ml-tooling/opyrator', 0.5188204050064087, 'viz', 1), ('salesforce/codet5', 0.518414318561554, 'nlp', 0), ('lupantech/chameleon-llm', 0.5143639445304871, 'llm', 1), ('google/trax', 0.5107121467590332, 'ml-dl', 1), ('lucidrains/toolformer-pytorch', 0.5094813108444214, 'llm', 0), ('embedchain/embedchain', 0.5094591975212097, 'llm', 1), ('stanfordnlp/dspy', 0.5088305473327637, 'llm', 0), ('salesforce/codegen', 0.5068298578262329, 'nlp', 0), ('sweepai/sweep', 0.5049862861633301, 'llm', 1), ('mlc-ai/mlc-llm', 0.5038229823112488, 'llm', 0), ('transformeroptimus/superagi', 0.503006637096405, 'llm', 1), ('nebuly-ai/nebullvm', 0.5025712847709656, 'perf', 1), ('thilinarajapakse/simpletransformers', 0.5019367933273315, 'nlp', 0), ('operand/agency', 0.5005974769592285, 'llm', 2)]",44,4.0,,1.98,608,320,9,0,0,0,0,607.0,1390.0,90.0,2.3,76
901,web,https://github.com/reflex-dev/reflex,[],,[],[],,,,reflex-dev/reflex,reflex,14660,924,120,Python,https://reflex.dev,🕸 Web apps in pure Python 🐍,reflex-dev,2024-01-14,2022-10-25,66,222.12121212121212,https://avatars.githubusercontent.com/u/104714959?v=4,🕸 Web apps in pure Python 🐍,"['framework', 'open-source', 'webdev', 'webdevelopment']","['framework', 'open-source', 'webdev', 'webdevelopment']",2024-01-12,"[('pallets/flask', 0.7278944849967957, 'web', 0), ('webpy/webpy', 0.702220618724823, 'web', 0), ('bottlepy/bottle', 0.6463294625282288, 'web', 0), ('willmcgugan/textual', 0.6325315237045288, 'term', 1), ('masoniteframework/masonite', 0.6244274973869324, 'web', 1), ('flet-dev/flet', 0.612991988658905, 'web', 0), ('r0x0r/pywebview', 0.6069856882095337, 'gui', 0), ('falconry/falcon', 0.5998751521110535, 'web', 1), ('scrapy/scrapy', 0.5838077664375305, 'data', 1), ('klen/muffin', 0.5825878977775574, 'web', 0), ('pylons/pyramid', 0.5728359818458557, 'web', 0), ('pallets/quart', 0.5711527466773987, 'web', 0), ('plotly/dash', 0.5687400698661804, 'viz', 0), ('encode/uvicorn', 0.5666500926017761, 'web', 0), ('pallets/werkzeug', 0.5643238425254822, 'web', 0), ('cherrypy/cherrypy', 0.553268313407898, 'web', 0), ('clips/pattern', 0.547705352306366, 'nlp', 0), ('emmett-framework/emmett', 0.5462912917137146, 'web', 0), ('voila-dashboards/voila', 0.5454056859016418, 'jupyter', 0), ('holoviz/panel', 0.5437241196632385, 'viz', 0), ('neoteroi/blacksheep', 0.5400927066802979, 'web', 1), ('huge-success/sanic', 0.5383414030075073, 'web', 1), ('pywebio/pywebio', 0.5351123213768005, 'web', 0), ('kivy/kivy', 0.5318344831466675, 'util', 0), ('encode/httpx', 0.5139403939247131, 'web', 0), ('dylanhogg/awesome-python', 0.5130147337913513, 'study', 1), ('vitalik/django-ninja', 0.512046754360199, 'web', 0), ('cobrateam/splinter', 0.5109301805496216, 'testing', 0), ('eleutherai/pyfra', 0.5102131962776184, 'ml', 0), ('pyodide/pyodide', 0.5090579986572266, 'util', 0), ('roniemartinez/dude', 0.5052448511123657, 'util', 1), ('django/django', 0.5036461353302002, 'web', 1)]",100,3.0,,16.29,522,385,15,0,39,37,39,521.0,452.0,90.0,0.9,76
1849,llm,https://github.com/sweepai/sweep,[],,[],[],,,,sweepai/sweep,sweep,6595,380,38,Python,https://sweep.dev,Sweep: AI-powered Junior Developer for small features and bug fixes.,sweepai,2024-01-13,2023-06-14,32,200.7173913043478,https://avatars.githubusercontent.com/u/127925974?v=4,Sweep: AI-powered Junior Developer for small features and bug fixes.,"['ai', 'code-assistant', 'code-search', 'developer-tools', 'gen-ai', 'github-app', 'gpt-35-turbo', 'gpt-4-32k', 'llm']","['ai', 'code-assistant', 'code-search', 'developer-tools', 'gen-ai', 'github-app', 'gpt-35-turbo', 'gpt-4-32k', 'llm']",2024-01-13,"[('pythagora-io/gpt-pilot', 0.7174859046936035, 'llm', 2), ('microsoft/promptflow', 0.6218081712722778, 'llm', 2), ('prefecthq/marvin', 0.6178321838378906, 'nlp', 2), ('antonosika/gpt-engineer', 0.6145350933074951, 'llm', 1), ('cheshire-cat-ai/core', 0.6141369342803955, 'llm', 2), ('lastmile-ai/aiconfig', 0.6068457365036011, 'util', 3), ('avaiga/taipy', 0.6026955842971802, 'data', 1), ('transformeroptimus/superagi', 0.5931740403175354, 'llm', 2), ('smol-ai/developer', 0.5850329995155334, 'llm', 1), ('googlecloudplatform/vertex-ai-samples', 0.5760075449943542, 'ml', 1), ('iterative/dvc', 0.5702275633811951, 'ml-ops', 2), ('bentoml/bentoml', 0.5681687593460083, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5614449381828308, 'data', 2), ('pytorchlightning/pytorch-lightning', 0.557704508304596, 'ml-dl', 1), ('mlc-ai/mlc-llm', 0.5575688481330872, 'llm', 1), ('microsoft/lmops', 0.5525568127632141, 'llm', 1), ('microsoft/semantic-kernel', 0.5460195541381836, 'llm', 2), ('embedchain/embedchain', 0.5455598831176758, 'llm', 2), ('aimhubio/aim', 0.5406528115272522, 'ml-ops', 1), ('microsoft/generative-ai-for-beginners', 0.5320358872413635, 'study', 1), ('pathwaycom/llm-app', 0.5312567353248596, 'llm', 1), ('oegedijk/explainerdashboard', 0.5306786298751831, 'ml-interpretability', 0), ('netflix/metaflow', 0.5298407077789307, 'ml-ops', 1), ('nebuly-ai/nebullvm', 0.5257266163825989, 'perf', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5161364078521729, 'study', 1), ('reloadware/reloadium', 0.5105124711990356, 'profiling', 1), ('modularml/mojo', 0.5049862861633301, 'util', 1), ('intel/scikit-learn-intelex', 0.502382218837738, 'perf', 0)]",31,3.0,,104.77,988,521,7,0,4,10,4,987.0,1659.0,90.0,1.7,76
1302,ml-dl,https://github.com/openai/whisper,"['speech-recognition', 'speech-to-text']",,[],[],,,,openai/whisper,whisper,54165,6200,466,Python,,Robust Speech Recognition via Large-Scale Weak Supervision,openai,2024-01-14,2022-09-16,71,756.7964071856287,https://avatars.githubusercontent.com/u/14957082?v=4,Robust Speech Recognition via Large-Scale Weak Supervision,[],"['speech-recognition', 'speech-to-text']",2023-12-18,"[('facebookresearch/seamless_communication', 0.5865522623062134, 'nlp', 1), ('cmusphinx/pocketsphinx', 0.5778233408927917, 'ml', 1), ('norskregnesentral/skweak', 0.5360553860664368, 'nlp', 0), ('m-bain/whisperx', 0.5077972412109375, 'nlp', 2)]",66,4.0,,1.23,40,18,16,1,10,8,10,40.0,36.0,90.0,0.9,75
947,ml,https://github.com/openai/openai-cookbook,[],,[],[],,,,openai/openai-cookbook,openai-cookbook,53007,8839,826,MDX,https://cookbook.openai.com,Examples and guides for using the OpenAI API,openai,2024-01-14,2022-03-11,98,537.7521739130435,https://avatars.githubusercontent.com/u/14957082?v=4,Examples and guides for using the OpenAI API,"['chatgpt', 'docs', 'gpt-3', 'gpt-4', 'openai']","['chatgpt', 'docs', 'gpt-3', 'gpt-4', 'openai']",2024-01-09,"[('run-llama/rags', 0.7131730318069458, 'llm', 2), ('langchain-ai/opengpts', 0.7003576159477234, 'llm', 0), ('xtekky/gpt4free', 0.6899942755699158, 'llm', 4), ('shishirpatil/gorilla', 0.6725669503211975, 'llm', 1), ('openai/openai-python', 0.6652215123176575, 'util', 1), ('killianlucas/open-interpreter', 0.6033370494842529, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.6022109985351562, 'llm', 3), ('mayooear/gpt4-pdf-chatbot-langchain', 0.59941166639328, 'llm', 1), ('laion-ai/open-assistant', 0.5750684142112732, 'llm', 1), ('openai/tiktoken', 0.5737849473953247, 'nlp', 1), ('continuum-llms/chatgpt-memory', 0.5522615909576416, 'llm', 1), ('minimaxir/simpleaichat', 0.5488078594207764, 'llm', 1), ('opengenerativeai/genossgpt', 0.5448883175849915, 'llm', 1), ('minimaxir/gpt-2-simple', 0.5388956665992737, 'llm', 1), ('blinkdl/chatrwkv', 0.5351440906524658, 'llm', 1), ('embedchain/embedchain', 0.5339502096176147, 'llm', 1), ('farizrahman4u/loopgpt', 0.533822238445282, 'llm', 1), ('larsbaunwall/bricky', 0.527281641960144, 'llm', 1), ('togethercomputer/openchatkit', 0.51520174741745, 'nlp', 0), ('h2oai/h2ogpt', 0.5150684714317322, 'llm', 1), ('prefecthq/marvin', 0.5121986269950867, 'nlp', 1), ('chainlit/chainlit', 0.5076209306716919, 'llm', 2)]",196,1.0,,9.71,237,184,22,0,0,0,0,237.0,316.0,90.0,1.3,75
1325,llm,https://github.com/imartinez/privategpt,"['langchain', 'llama', 'language-model']",,[],[],,,,imartinez/privategpt,privateGPT,46302,6151,425,Python,https://docs.privategpt.dev,"Interact with your documents using the power of GPT, 100% privately, no data leaks",imartinez,2024-01-14,2023-05-02,39,1187.2307692307693,,"Interact with your documents using the power of GPT, 100% privately, no data leaks",[],"['langchain', 'language-model', 'llama']",2024-01-09,"[('h2oai/h2ogpt', 0.6058406233787537, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5235142707824707, 'llm', 1), ('xtekky/gpt4free', 0.5020104646682739, 'llm', 1)]",54,1.0,,3.44,884,360,9,0,4,5,4,884.0,1066.0,90.0,1.2,75
87,viz,https://github.com/streamlit/streamlit,[],,[],[],,,,streamlit/streamlit,streamlit,29557,2690,303,Python,https://streamlit.io,Streamlit — A faster way to build and share data apps.,streamlit,2024-01-14,2019-08-24,231,127.71543209876543,https://avatars.githubusercontent.com/u/45109972?v=4,Streamlit — A faster way to build and share data apps.,"['data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'developer-tools', 'machine-learning', 'streamlit']","['data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'developer-tools', 'machine-learning', 'streamlit']",2024-01-12,"[('explosion/spacy-streamlit', 0.6851550340652466, 'nlp', 2), ('google/mediapipe', 0.6068508625030518, 'ml', 2), ('alphasecio/langchain-examples', 0.5835210084915161, 'llm', 1), ('gradio-app/gradio', 0.5769603848457336, 'viz', 5), ('meltano/meltano', 0.5685817003250122, 'ml-ops', 0), ('mage-ai/mage-ai', 0.5670653581619263, 'ml-ops', 2), ('pythagora-io/gpt-pilot', 0.5534999370574951, 'llm', 1), ('featureform/embeddinghub', 0.5492863059043884, 'nlp', 2), ('ploomber/ploomber', 0.5482877492904663, 'ml-ops', 2), ('polyaxon/datatile', 0.5420833230018616, 'pandas', 2), ('airbytehq/airbyte', 0.5340747833251953, 'data', 1), ('merantix-momentum/squirrel-core', 0.5240601897239685, 'ml', 3), ('netflix/metaflow', 0.5236718654632568, 'ml-ops', 2), ('simonw/datasette', 0.5206021666526794, 'data', 0), ('pathwaycom/pathway', 0.519947350025177, 'data', 0), ('pathwaycom/llm-app', 0.5164030194282532, 'llm', 1), ('avaiga/taipy', 0.5149668455123901, 'data', 2), ('ashleve/lightning-hydra-template', 0.5148152112960815, 'util', 1), ('orchest/orchest', 0.5133765935897827, 'ml-ops', 2), ('microsoft/semantic-kernel', 0.5103716254234314, 'llm', 0), ('activeloopai/deeplake', 0.5083068013191223, 'ml-ops', 3), ('superduperdb/superduperdb', 0.5076687932014465, 'data', 0), ('towhee-io/towhee', 0.5068960785865784, 'ml-ops', 1), ('apache/spark', 0.505164384841919, 'data', 0), ('fugue-project/fugue', 0.5049965381622314, 'pandas', 1)]",216,2.0,,15.4,865,576,53,0,20,357,20,865.0,1220.0,90.0,1.4,75
546,ml,https://github.com/open-mmlab/mmdetection,[],,[],[],,,,open-mmlab/mmdetection,mmdetection,26744,9094,376,Python,https://mmdetection.readthedocs.io,OpenMMLab Detection Toolbox and Benchmark,open-mmlab,2024-01-14,2018-08-22,283,94.21640664318068,https://avatars.githubusercontent.com/u/10245193?v=4,OpenMMLab Detection Toolbox and Benchmark,"['cascade-rcnn', 'convnext', 'detr', 'fast-rcnn', 'faster-rcnn', 'glip', 'grounding-dino', 'instance-segmentation', 'mask-rcnn', 'object-detection', 'panoptic-segmentation', 'pytorch', 'retinanet', 'rtmdet', 'semisupervised-learning', 'ssd', 'swin-transformer', 'transformer', 'vision-transformer', 'yolo']","['cascade-rcnn', 'convnext', 'detr', 'fast-rcnn', 'faster-rcnn', 'glip', 'grounding-dino', 'instance-segmentation', 'mask-rcnn', 'object-detection', 'panoptic-segmentation', 'pytorch', 'retinanet', 'rtmdet', 'semisupervised-learning', 'ssd', 'swin-transformer', 'transformer', 'vision-transformer', 'yolo']",2024-01-05,"[('open-mmlab/mmsegmentation', 0.7892122864723206, 'ml', 3), ('open-mmlab/mmcv', 0.6718772649765015, 'ml', 0), ('deci-ai/super-gradients', 0.6168127655982971, 'ml-dl', 2), ('matterport/mask_rcnn', 0.5989094376564026, 'ml-dl', 3), ('roboflow/supervision', 0.5974782109260559, 'ml', 4), ('facebookresearch/detectron2', 0.5715402960777283, 'ml-dl', 0), ('roboflow/notebooks', 0.5606690049171448, 'study', 2), ('nvlabs/gcvit', 0.559407114982605, 'diffusion', 2), ('microsoft/swin-transformer', 0.5538708567619324, 'ml', 3), ('megvii-basedetection/yolox', 0.545394778251648, 'ml', 3), ('rwightman/pytorch-image-models', 0.5427139401435852, 'ml-dl', 1), ('idea-research/groundingdino', 0.5333415269851685, 'diffusion', 1), ('google-research/deeplab2', 0.516015350818634, 'ml', 0), ('facebookresearch/detectron', 0.5015984177589417, 'ml-dl', 0)]",453,9.0,,4.4,520,178,66,0,8,11,8,520.0,673.0,90.0,1.3,75
345,viz,https://github.com/gradio-app/gradio,[],,[],[],,,,gradio-app/gradio,gradio,25521,1821,145,Python,http://www.gradio.app,"Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!",gradio-app,2024-01-14,2018-12-19,266,95.63543897216275,https://avatars.githubusercontent.com/u/51063788?v=4,"Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!","['data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'deploy', 'gradio', 'gradio-interface', 'interface', 'machine-learning', 'models', 'python-notebook', 'ui', 'ui-components']","['data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'deploy', 'gradio', 'gradio-interface', 'interface', 'machine-learning', 'models', 'python-notebook', 'ui', 'ui-components']",2024-01-11,"[('scikit-learn/scikit-learn', 0.6689245700836182, 'ml', 3), ('dylanhogg/awesome-python', 0.6672014594078064, 'study', 3), ('ageron/handson-ml2', 0.6595058441162109, 'ml', 0), ('plotly/dash', 0.6556648015975952, 'viz', 2), ('rasbt/mlxtend', 0.6547028422355652, 'ml', 2), ('kubeflow/fairing', 0.6449424624443054, 'ml-ops', 0), ('pycaret/pycaret', 0.6444831490516663, 'ml', 2), ('merantix-momentum/squirrel-core', 0.6419711709022522, 'ml', 3), ('featurelabs/featuretools', 0.6402891278266907, 'ml', 2), ('wandb/client', 0.6344223618507385, 'ml', 3), ('ml-tooling/opyrator', 0.6309301853179932, 'viz', 1), ('holoviz/panel', 0.6235347390174866, 'viz', 0), ('polyaxon/datatile', 0.6204770803451538, 'pandas', 2), ('willmcgugan/textual', 0.6202593445777893, 'term', 0), ('krzjoa/awesome-python-data-science', 0.620049238204956, 'study', 5), ('fastai/fastcore', 0.6137790679931641, 'util', 0), ('huggingface/huggingface_hub', 0.6130750775337219, 'ml', 3), ('online-ml/river', 0.6122671961784363, 'ml', 2), ('firmai/industry-machine-learning', 0.6118634939193726, 'study', 2), ('ddbourgin/numpy-ml', 0.6104564666748047, 'ml', 1), 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1203,data,https://github.com/chroma-core/chroma,['vectordb'],,[],[],,,,chroma-core/chroma,chroma,10477,856,71,Python,https://www.trychroma.com/,the AI-native open-source embedding database,chroma-core,2024-01-14,2022-10-05,68,152.15560165975103,https://avatars.githubusercontent.com/u/105881770?v=4,the AI-native open-source embedding database,"['document-retrieval', 'embeddings', 'llms']","['document-retrieval', 'embeddings', 'llms', 'vectordb']",2024-01-11,"[('qdrant/fastembed', 0.7453431487083435, 'ml', 2), ('neuml/txtai', 0.6690793633460999, 'nlp', 1), ('kagisearch/vectordb', 0.6572227478027344, 'data', 2), ('jina-ai/vectordb', 0.6463486552238464, 'data', 1), ('lancedb/lancedb', 0.6137559413909912, 'data', 1), ('activeloopai/deeplake', 0.6069470643997192, 'ml-ops', 0), ('nomic-ai/nomic', 0.5949238538742065, 'nlp', 0), ('huggingface/text-embeddings-inference', 0.5832590460777283, 'llm', 1), ('koaning/embetter', 0.5779300332069397, 'data', 0), ('plasticityai/magnitude', 0.5727373957633972, 'nlp', 1), ('sebischair/lbl2vec', 0.5661131143569946, 'nlp', 0), ('llmware-ai/llmware', 0.5633347034454346, 'llm', 1), ('milvus-io/bootcamp', 0.5605462789535522, 'data', 1), ('koaning/whatlies', 0.5464308261871338, 'nlp', 1), ('jina-ai/clip-as-service', 0.5438365340232849, 'nlp', 0), ('qdrant/qdrant', 0.5403642058372498, 'data', 0), ('ddangelov/top2vec', 0.5397540330886841, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5298216938972473, 'llm', 0), ('rom1504/clip-retrieval', 0.5229653716087341, 'ml', 0), ('weaviate/demo-text2vec-openai', 0.5036654472351074, 'util', 0), ('qdrant/vector-db-benchmark', 0.5014863610267639, 'perf', 0), ('nomic-ai/semantic-search-app-template', 0.5006744265556335, 'study', 0), ('alphasecio/langchain-examples', 0.5002412796020508, 'llm', 1)]",96,3.0,,12.94,523,312,16,0,52,52,52,523.0,1249.0,90.0,2.4,75
1892,util,https://github.com/pypy/pypy,"['cpython', 'compiler', 'rpython']",,[],[],,,,pypy/pypy,pypy,508,19,17,Python,https://pypy.org,PyPy is a very fast and compliant implementation of the Python language.,pypy,2024-01-14,2023-12-29,4,111.125,https://avatars.githubusercontent.com/u/318667?v=4,PyPy is a very fast and compliant implementation of the Python language.,[],"['compiler', 'cpython', 'rpython']",2024-01-13,"[('cython/cython', 0.8010214567184448, 'util', 1), ('pyston/pyston', 0.7871115207672119, 'util', 0), ('python/cpython', 0.7743640542030334, 'util', 1), ('pytoolz/toolz', 0.7066237330436707, 'util', 0), ('exaloop/codon', 0.6885042786598206, 'perf', 1), ('fastai/fastcore', 0.6839970946311951, 'util', 0), ('faster-cpython/tools', 0.6747263669967651, 'perf', 1), ('hoffstadt/dearpygui', 0.6707800030708313, 'gui', 0), ('micropython/micropython', 0.6625933051109314, 'util', 0), ('webpy/webpy', 0.6487240195274353, 'web', 0), ('pyglet/pyglet', 0.6459312438964844, 'gamedev', 0), 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256,crypto,https://github.com/ccxt/ccxt,[],,[],[],,,,ccxt/ccxt,ccxt,30090,7393,935,Python,https://docs.ccxt.com,A JavaScript / TypeScript / Python / C# / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges,ccxt,2024-01-14,2017-05-14,350,85.90130505709625,https://avatars.githubusercontent.com/u/31901609?v=4,A JavaScript / TypeScript / Python / C# / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges,"['altcoin', 'api', 'arbitrage', 'bitcoin', 'bot', 'btc', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'e-commerce', 'eth', 'ethereum', 'exchange', 'invest', 'market-data', 'merchant', 'strategy', 'trade', 'trading']","['altcoin', 'api', 'arbitrage', 'bitcoin', 'bot', 'btc', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'e-commerce', 'eth', 'ethereum', 'exchange', 'invest', 'market-data', 'merchant', 'strategy', 'trade', 'trading']",2024-01-13,"[('freqtrade/freqtrade', 0.6369279026985168, 'crypto', 4), ('blankly-finance/blankly', 0.6164409518241882, 'finance', 4), ('gbeced/basana', 0.6148871183395386, 'finance', 1), ('bmoscon/cryptofeed', 0.6092362999916077, 'crypto', 9), ('numerai/example-scripts', 0.6066434979438782, 'finance', 1), ('idanya/algo-trader', 0.5679965019226074, 'finance', 0), ('cyberpunkmetalhead/binance-volatility-trading-bot', 0.5664846301078796, 'crypto', 0), ('1200wd/bitcoinlib', 0.566085934638977, 'crypto', 1), ('quantconnect/lean', 0.5551174879074097, 'finance', 1), ('gbeced/pyalgotrade', 0.5324677228927612, 'finance', 0), ('pmaji/crypto-whale-watching-app', 0.5305103063583374, 'crypto', 3), ('man-c/pycoingecko', 0.5272769927978516, 'crypto', 3), ('hydrosquall/tiingo-python', 0.5252950191497803, 'finance', 0), ('polakowo/vectorbt', 0.5177646279335022, 'finance', 2), ('primal100/pybitcointools', 0.5137020945549011, 'crypto', 0)]",774,1.0,,212.31,1537,1328,81,0,1,103,1,1536.0,2138.0,90.0,1.4,74
640,util,https://github.com/python-poetry/poetry,[],,[],[],,,,python-poetry/poetry,poetry,28020,2183,188,Python,https://python-poetry.org,Python packaging and dependency management made easy,python-poetry,2024-01-14,2018-02-28,308,90.72155411655874,https://avatars.githubusercontent.com/u/48722593?v=4,Python packaging and dependency management made easy,"['dependency-manager', 'package-manager', 'packaging', 'poetry']","['dependency-manager', 'package-manager', 'packaging', 'poetry']",2024-01-10,"[('mitsuhiko/rye', 0.8581348657608032, 'util', 3), ('indygreg/pyoxidizer', 0.8143705725669861, 'util', 2), ('pypa/flit', 0.8031641244888306, 'util', 2), ('pypa/hatch', 0.7199224233627319, 'util', 2), ('pdm-project/pdm', 0.7060061097145081, 'util', 2), ('pomponchik/instld', 0.6835312843322754, 'util', 1), ('regebro/pyroma', 0.6802058219909668, 'util', 1), ('tiangolo/poetry-version-plugin', 0.6390268206596375, 'util', 1), ('tezromach/python-package-template', 0.6311818361282349, 'template', 1), ('pyodide/micropip', 0.609188437461853, 'util', 0), ('mamba-org/mamba', 0.5992787480354309, 'util', 2), ('pypi/warehouse', 0.5964840054512024, 'util', 0), ('jazzband/pip-tools', 0.5958384871482849, 'util', 1), ('thoth-station/micropipenv', 0.5704753994941711, 'util', 1), ('tox-dev/pipdeptree', 0.5638055205345154, 'util', 0), ('ofek/pyapp', 0.5570515394210815, 'util', 1), ('pypa/pipenv', 0.5455138087272644, 'util', 1), ('pyscaffold/pyscaffold', 0.5353856682777405, 'template', 0), ('ivankorobkov/python-inject', 0.534679651260376, 'util', 0), ('mamba-org/gator', 0.5295340418815613, 'jupyter', 0), ('omry/omegaconf', 0.5274845361709595, 'util', 0), ('conda/conda', 0.527414858341217, 'util', 2), ('eleutherai/pyfra', 0.5234190821647644, 'ml', 0), ('pypa/installer', 0.522213339805603, 'util', 0), ('python-injector/injector', 0.5174149870872498, 'util', 0), ('pytables/pytables', 0.517038106918335, 'data', 0), ('beeware/briefcase', 0.5169381499290466, 'util', 0), ('allrod5/injectable', 0.5134292244911194, 'util', 0), ('spack/spack', 0.5117734670639038, 'util', 1), ('amaargiru/pyroad', 0.5109917521476746, 'study', 0), ('ets-labs/python-dependency-injector', 0.510571300983429, 'util', 0), ('hoffstadt/dearpygui', 0.5035780668258667, 'gui', 0), ('grahamdumpleton/wrapt', 0.5032442212104797, 'util', 0)]",523,2.0,,6.02,462,319,72,0,9,22,9,461.0,1181.0,90.0,2.6,74
272,ml,https://github.com/google/mediapipe,[],,[],[],,,,google/mediapipe,mediapipe,24416,4890,496,C++,https://mediapipe.dev,"Cross-platform, customizable ML solutions for live and streaming media.",google,2024-01-14,2019-06-13,241,101.01182033096927,https://avatars.githubusercontent.com/u/1342004?v=4,"Cross-platform, customizable ML solutions for live and streaming media.","['android', 'audio-processing', 'c-plus-plus', 'calculator', 'computer-vision', 'deep-learning', 'framework', 'graph-based', 'graph-framework', 'inference', 'machine-learning', 'mediapipe', 'mobile-development', 'perception', 'pipeline-framework', 'stream-processing', 'video-processing']","['android', 'audio-processing', 'c-plus-plus', 'calculator', 'computer-vision', 'deep-learning', 'framework', 'graph-based', 'graph-framework', 'inference', 'machine-learning', 'mediapipe', 'mobile-development', 'perception', 'pipeline-framework', 'stream-processing', 'video-processing']",2024-01-13,"[('streamlit/streamlit', 0.6068508625030518, 'viz', 2), ('mlflow/mlflow', 0.5900856852531433, 'ml-ops', 1), ('microsoft/onnxruntime', 0.5802665948867798, 'ml', 2), ('tensorflow/tensorflow', 0.5778871178627014, 'ml-dl', 2), ('pathwaycom/pathway', 0.5712288022041321, 'data', 0), ('jina-ai/jina', 0.5477234125137329, 'ml', 3), ('online-ml/river', 0.541152834892273, 'ml', 2), ('huggingface/datasets', 0.5388594269752502, 'nlp', 3), ('onnx/onnx', 0.5367459654808044, 'ml', 2), ('polyaxon/polyaxon', 0.5345292091369629, 'ml-ops', 2), ('roboflow/supervision', 0.5272902250289917, 'ml', 4), ('googlecloudplatform/vertex-ai-samples', 0.5271955132484436, 'ml', 0), ('ml-tooling/opyrator', 0.5265260338783264, 'viz', 1), ('bentoml/bentoml', 0.5233632922172546, 'ml-ops', 2), ('horovod/horovod', 0.519681990146637, 'ml-ops', 2), ('alpa-projects/alpa', 0.5148690938949585, 'ml-dl', 2), ('feast-dev/feast', 0.5142419338226318, 'ml-ops', 1), ('mage-ai/mage-ai', 0.508601188659668, 'ml-ops', 1), ('explosion/thinc', 0.5080121159553528, 'ml-dl', 2), ('keras-team/keras', 0.5077323317527771, 'ml-dl', 2), ('merantix-momentum/squirrel-core', 0.5076141953468323, 'ml', 3), ('activeloopai/deeplake', 0.5053433775901794, 'ml-ops', 3), ('polyaxon/datatile', 0.5041832327842712, 'pandas', 0)]",54,2.0,,37.27,375,250,56,0,11,12,11,374.0,988.0,90.0,2.6,74
138,nlp,https://github.com/huggingface/datasets,[],,[],[],,,,huggingface/datasets,datasets,17866,2441,278,Python,https://huggingface.co/docs/datasets,"🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools",huggingface,2024-01-13,2020-03-26,200,89.01209964412811,https://avatars.githubusercontent.com/u/25720743?v=4,"🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools","['computer-vision', 'datasets', 'deep-learning', 'machine-learning', 'natural-language-processing', 'nlp', 'numpy', 'pandas', 'pytorch', 'speech', 'tensorflow']","['computer-vision', 'datasets', 'deep-learning', 'machine-learning', 'natural-language-processing', 'nlp', 'numpy', 'pandas', 'pytorch', 'speech', 'tensorflow']",2024-01-11,"[('mosaicml/composer', 0.6692463159561157, 'ml-dl', 3), ('tensorflow/tensorflow', 0.6682099103927612, 'ml-dl', 3), ('merantix-momentum/squirrel-core', 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0.599459171295166, 'ml-ops', 4), ('lutzroeder/netron', 0.5983027219772339, 'ml', 4), ('feast-dev/feast', 0.5980408191680908, 'ml-ops', 1), ('bentoml/bentoml', 0.596264123916626, 'ml-ops', 2), ('huggingface/huggingface_hub', 0.5904778242111206, 'ml', 4), ('xl0/lovely-tensors', 0.5892137289047241, 'ml-dl', 2), ('firmai/industry-machine-learning', 0.5870203375816345, 'study', 1), ('aws/sagemaker-python-sdk', 0.5822443962097168, 'ml', 3), ('intel/intel-extension-for-pytorch', 0.5820187926292419, 'perf', 3), ('apple/coremltools', 0.5799334049224854, 'ml', 3), ('hazyresearch/meerkat', 0.5792464017868042, 'viz', 2), ('optimalscale/lmflow', 0.578310489654541, 'llm', 2), ('towhee-io/towhee', 0.5772765278816223, 'ml-ops', 2), ('alibaba/easynlp', 0.5757067799568176, 'nlp', 4), ('neuralmagic/deepsparse', 0.5752450823783875, 'nlp', 2), ('keras-team/autokeras', 0.5751702785491943, 'ml-dl', 3), ('fepegar/torchio', 0.574800968170166, 'ml-dl', 3), ('microsoft/torchgeo', 0.570610761642456, 'gis', 4), 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0.5560346841812134, 'ml-ops', 0), ('ludwig-ai/ludwig', 0.5557738542556763, 'ml-ops', 5), ('dylanhogg/awesome-python', 0.5519663095474243, 'study', 5), ('whylabs/whylogs', 0.5516629815101624, 'util', 1), ('koaning/human-learn', 0.551318883895874, 'data', 1), ('selfexplainml/piml-toolbox', 0.5501903891563416, 'ml-interpretability', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5495465993881226, 'ml', 4), ('infinitylogesh/mutate', 0.5476288199424744, 'nlp', 0), ('google/tf-quant-finance', 0.5474328994750977, 'finance', 1), ('titanml/takeoff', 0.5470577478408813, 'llm', 0), ('aimhubio/aim', 0.5470335483551025, 'ml-ops', 3), ('xplainable/xplainable', 0.5465106964111328, 'ml-interpretability', 1), ('deci-ai/super-gradients', 0.5464844107627869, 'ml-dl', 3), ('wandb/client', 0.5461671948432922, 'ml', 4), ('neuralmagic/sparseml', 0.5456334948539734, 'ml-dl', 3), ('vaexio/vaex', 0.545326828956604, 'perf', 1), ('milvus-io/bootcamp', 0.545052170753479, 'data', 2), ('horovod/horovod', 0.5425728559494019, 'ml-ops', 4), ('doccano/doccano', 0.5418587923049927, 'nlp', 3), ('blackhc/toma', 0.5416107177734375, 'ml-dl', 2), ('ggerganov/ggml', 0.5396940112113953, 'ml', 1), ('google/mediapipe', 0.5388594269752502, 'ml', 3), ('huggingface/autotrain-advanced', 0.5383889079093933, 'ml', 3), ('pytorchlightning/pytorch-lightning', 0.5380210280418396, 'ml-dl', 3), ('alpa-projects/alpa', 0.5375392436981201, 'ml-dl', 2), ('google/trax', 0.5373858213424683, 'ml-dl', 3), ('mage-ai/mage-ai', 0.5363893508911133, 'ml-ops', 1), ('rasahq/rasa', 0.5359129905700684, 'llm', 3), ('aleju/imgaug', 0.5358902812004089, 'ml', 2), ('netflix/metaflow', 0.5358194708824158, 'ml-ops', 1), ('keras-team/keras-cv', 0.535764217376709, 'ml-dl', 1), ('dagworks-inc/hamilton', 0.5354235172271729, 'ml-ops', 3), ('ashleve/lightning-hydra-template', 0.5349999666213989, 'util', 2), ('espnet/espnet', 0.5337874889373779, 'nlp', 2), ('parallel-domain/pd-sdk', 0.5329124927520752, 'data', 1), ('kubeflow-kale/kale', 0.5326496362686157, 'ml-ops', 1), ('mlc-ai/mlc-llm', 0.532573401927948, 'llm', 0), ('tensorlayer/tensorlayer', 0.5325732827186584, 'ml-rl', 2), ('uber/petastorm', 0.5324203372001648, 'data', 4), ('ml-tooling/opyrator', 0.5313810110092163, 'viz', 1), ('tensorflow/addons', 0.5287928581237793, 'ml', 3), ('unity-technologies/ml-agents', 0.5283746719360352, 'ml-rl', 2), ('oml-team/open-metric-learning', 0.528002142906189, 'ml', 3), ('huggingface/optimum', 0.5267567038536072, 'ml', 1), ('automl/auto-sklearn', 0.5262554883956909, 'ml', 0), ('tlkh/tf-metal-experiments', 0.5242385268211365, 'perf', 2), ('pytorch/ignite', 0.523903489112854, 'ml-dl', 3), ('avaiga/taipy', 0.5236226916313171, 'data', 0), ('ray-project/ray', 0.5230560898780823, 'ml-ops', 4), ('tensorflow/lucid', 0.5218112468719482, 'ml-interpretability', 2), ('developmentseed/label-maker', 0.52162766456604, 'gis', 2), ('scikit-learn/scikit-learn', 0.5215214490890503, 'ml', 1), ('koaning/embetter', 0.5214577317237854, 'data', 0), ('rwightman/pytorch-image-models', 0.5214285254478455, 'ml-dl', 1), ('explosion/spacy', 0.5209731459617615, 'nlp', 4), ('extreme-bert/extreme-bert', 0.5209168195724487, 'llm', 5), ('microsoft/unilm', 0.5208196043968201, 'nlp', 1), ('featurelabs/featuretools', 0.5206751227378845, 'ml', 1), ('bigscience-workshop/petals', 0.5203559398651123, 'data', 4), ('allenai/allennlp', 0.5202730894088745, 'nlp', 4), ('pycaret/pycaret', 0.5196223855018616, 'ml', 1), ('deepfakes/faceswap', 0.5192609429359436, 'ml-dl', 2), ('xl0/lovely-numpy', 0.519147515296936, 'util', 2), ('superduperdb/superduperdb', 0.5187201499938965, 'data', 1), ('rasbt/mlxtend', 0.5176108479499817, 'ml', 1), ('paddlepaddle/paddlenlp', 0.5161171555519104, 'llm', 1), ('fastai/fastcore', 0.5157922506332397, 'util', 0), ('microsoft/jarvis', 0.5145260691642761, 'llm', 2), ('lightly-ai/lightly', 0.5144714713096619, 'ml', 4), ('iperov/deepfacelab', 0.5133152008056641, 'ml-dl', 2), ('microsoft/lmops', 0.5126212239265442, 'llm', 1), ('makcedward/nlpaug', 0.5126201510429382, 'nlp', 3), ('intel/scikit-learn-intelex', 0.5119993090629578, 'perf', 1), ('ageron/handson-ml2', 0.5119935870170593, 'ml', 0), ('docarray/docarray', 0.511342465877533, 'data', 3), ('kubeflow/pipelines', 0.511340856552124, 'ml-ops', 1), ('tensorflow/data-validation', 0.5105088353157043, 'ml-ops', 0), ('meltano/meltano', 0.5098901391029358, 'ml-ops', 0), ('oegedijk/explainerdashboard', 0.5090382695198059, 'ml-interpretability', 0), ('dask/dask-ml', 0.5084608197212219, 'ml', 0), ('visual-layer/fastdup', 0.5084423422813416, 'ml', 2), ('nvlabs/gcvit', 0.5082133412361145, 'diffusion', 1), ('cheshire-cat-ai/core', 0.508074939250946, 'llm', 0), ('scikit-learn-contrib/imbalanced-learn', 0.507771372795105, 'ml', 1), ('project-monai/monai', 0.5077080130577087, 'ml', 2), ('lucidrains/imagen-pytorch', 0.5067300200462341, 'ml-dl', 1), ('skorch-dev/skorch', 0.5065990686416626, 'ml-dl', 2), ('epistasislab/tpot', 0.50594562292099, 'ml', 1), ('microsoft/semi-supervised-learning', 0.505528450012207, 'ml', 5), ('winedarksea/autots', 0.5051737427711487, 'time-series', 2), ('facebookresearch/pytorch3d', 0.5045773386955261, 'ml-dl', 0), ('tensorly/tensorly', 0.50432950258255, 'ml-dl', 4), ('nebuly-ai/nebullvm', 0.5037619471549988, 'perf', 0), ('tigerlab-ai/tiger', 0.5033867955207825, 'llm', 0), ('open-mmlab/mmsegmentation', 0.5029417276382446, 'ml', 1), ('hi-primus/optimus', 0.5028654932975769, 'ml-ops', 1), ('llmware-ai/llmware', 0.5021990537643433, 'llm', 3), ('speechbrain/speechbrain', 0.5019820928573608, 'nlp', 2), ('interpretml/interpret', 0.5013808608055115, 'ml-interpretability', 1), ('reloadware/reloadium', 0.500510036945343, 'profiling', 1), ('jaidedai/easyocr', 0.5004381537437439, 'data', 3), ('featureform/embeddinghub', 0.5000424981117249, 'nlp', 1)]",540,4.0,,7.73,373,245,46,0,19,23,19,373.0,907.0,90.0,2.4,74
1,data,https://github.com/apache/arrow,"['apache-arrow', 'pandas', 'data-analysis', 'columnar']",,[],[],,,,apache/arrow,arrow,13013,3202,356,C++,https://arrow.apache.org/,Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing,apache,2024-01-13,2016-02-17,414,31.367424242424242,https://avatars.githubusercontent.com/u/47359?v=4,Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing,['arrow'],"['apache-arrow', 'arrow', 'columnar', 'data-analysis', 'pandas']",2024-01-13,"[('vaexio/vaex', 0.6739583611488342, 'perf', 0), ('pola-rs/polars', 0.5664820075035095, 'pandas', 1), ('scikit-hep/awkward-1.0', 0.559539258480072, 'data', 3), ('rapidsai/cudf', 0.5487073659896851, 'pandas', 3), ('apache/spark', 0.5110272765159607, 'data', 0), ('pandas-dev/pandas', 0.5044839382171631, 'pandas', 3)]",1186,6.0,,52.1,1814,1186,96,0,0,11,11,1814.0,5639.0,90.0,3.1,74
58,web,https://github.com/pallets/flask,[],,[],[],,,,pallets/flask,flask,65459,16119,2139,Python,https://flask.palletsprojects.com,The Python micro framework for building web applications.,pallets,2024-01-14,2010-04-06,721,90.78918169209432,https://avatars.githubusercontent.com/u/16748505?v=4,The Python micro framework for building web applications.,"['flask', 'jinja', 'pallets', 'web-framework', 'werkzeug', 'wsgi']","['flask', 'jinja', 'pallets', 'web-framework', 'werkzeug', 'wsgi']",2024-01-01,"[('bottlepy/bottle', 0.8060166835784912, 'web', 2), ('pallets/werkzeug', 0.7842201590538025, 'web', 3), ('pylons/pyramid', 0.7600159049034119, 'web', 2), ('webpy/webpy', 0.7347891926765442, 'web', 0), ('masoniteframework/masonite', 0.7340575456619263, 'web', 0), ('reflex-dev/reflex', 0.7278944849967957, 'web', 0), ('pallets/quart', 0.7176867127418518, 'web', 0), ('falconry/falcon', 0.7083405256271362, 'web', 1), ('klen/muffin', 0.6954807639122009, 'web', 0), ('willmcgugan/textual', 0.6732026934623718, 'term', 0), ('flet-dev/flet', 0.6681143045425415, 'web', 0), ('neoteroi/blacksheep', 0.6553666591644287, 'web', 0), ('r0x0r/pywebview', 0.6464259624481201, 'gui', 0), ('python-restx/flask-restx', 0.6463286280632019, 'web', 1), ('eleutherai/pyfra', 0.6199480295181274, 'ml', 0), ('cherrypy/cherrypy', 0.6162318587303162, 'web', 0), ('emmett-framework/emmett', 0.6101765632629395, 'web', 1), ('django/django', 0.5927203893661499, 'web', 0), ('scrapy/scrapy', 0.591780960559845, 'data', 0), ('encode/uvicorn', 0.5909908413887024, 'web', 0), ('voila-dashboards/voila', 0.5802770256996155, 'jupyter', 0), ('ets-labs/python-dependency-injector', 0.5796188712120056, 'util', 1), ('benoitc/gunicorn', 0.5726903676986694, 'web', 1), ('pylons/waitress', 0.5715507864952087, 'web', 0), ('plotly/dash', 0.5670453906059265, 'viz', 1), ('holoviz/panel', 0.5666216611862183, 'viz', 0), ('backtick-se/cowait', 0.5663134455680847, 'util', 0), ('huge-success/sanic', 0.5633546710014343, 'web', 1), ('sqlalchemy/mako', 0.5588720440864563, 'template', 0), ('clips/pattern', 0.5583645105361938, 'nlp', 0), ('pypy/pypy', 0.5578244924545288, 'util', 0), ('pyodide/pyodide', 0.556867241859436, 'util', 0), ('indico/indico', 0.5564385056495667, 'web', 1), ('dylanhogg/awesome-python', 0.5550673604011536, 'study', 0), ('stephenmcd/mezzanine', 0.5501194596290588, 'web', 0), ('pyeve/eve', 0.5500431656837463, 'web', 1), ('timofurrer/awesome-asyncio', 0.5479661822319031, 'study', 0), ('feincms/feincms', 0.546400785446167, 'web', 0), ('pypa/hatch', 0.5425235033035278, 'util', 0), ('alirn76/panther', 0.5419907569885254, 'web', 0), ('pyinfra-dev/pyinfra', 0.5409476161003113, 'util', 0), ('vitalik/django-ninja', 0.5399484038352966, 'web', 0), ('pyodide/micropip', 0.5379033088684082, 'util', 0), ('goldmansachs/gs-quant', 0.5355310440063477, 'finance', 0), ('kivy/kivy', 0.535079836845398, 'util', 0), ('pypa/build', 0.5296503901481628, 'util', 0), ('wagtail/wagtail', 0.5286979675292969, 'web', 0), ('starlite-api/starlite', 0.528308629989624, 'web', 0), ('hugapi/hug', 0.5281922221183777, 'util', 0), ('encode/httpx', 0.5245396494865417, 'web', 0), ('1200wd/bitcoinlib', 0.5223922729492188, 'crypto', 0), ('aws/chalice', 0.5189753770828247, 'web', 0), ('requests/toolbelt', 0.5181925296783447, 'util', 0), ('micropython/micropython', 0.5177249908447266, 'util', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5168726444244385, 'template', 0), ('hoffstadt/dearpygui', 0.5168485045433044, 'gui', 0), ('ethereum/web3.py', 0.5149424076080322, 'crypto', 0), ('cobrateam/splinter', 0.5134128332138062, 'testing', 0), ('tedivm/robs_awesome_python_template', 0.5124099254608154, 'template', 0), ('fastai/fastcore', 0.5115838050842285, 'util', 0), ('nficano/python-lambda', 0.5108464956283569, 'util', 0), ('ajndkr/lanarky', 0.510615348815918, 'llm', 0), ('eventual-inc/daft', 0.5096858143806458, 'pandas', 0), ('pywebio/pywebio', 0.5090134143829346, 'web', 0), ('tiangolo/fastapi', 0.5078809857368469, 'web', 0), ('roniemartinez/dude', 0.5063819289207458, 'util', 0), ('kubeflow/fairing', 0.5050888657569885, 'ml-ops', 0), ('google/gin-config', 0.5042587518692017, 'util', 0), ('indygreg/pyoxidizer', 0.5036942958831787, 'util', 0), ('seleniumbase/seleniumbase', 0.5030795931816101, 'testing', 0), ('amaargiru/pyroad', 0.5025720596313477, 'study', 0), ('pallets/jinja', 0.5012500882148743, 'util', 2)]",831,5.0,,3.31,82,77,168,0,8,4,8,82.0,91.0,90.0,1.1,73
104,ml,https://github.com/dmlc/xgboost,[],,[],[],,,,dmlc/xgboost,xgboost,25204,8704,910,C++,https://xgboost.readthedocs.io/en/stable/,"Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library,  for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow",dmlc,2024-01-14,2014-02-06,520,48.40274348422496,https://avatars.githubusercontent.com/u/11508361?v=4,"Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library,  for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow","['distributed-systems', 'gbdt', 'gbm', 'gbrt', 'machine-learning', 'xgboost']","['distributed-systems', 'gbdt', 'gbm', 'gbrt', 'machine-learning', 'xgboost']",2024-01-12,"[('microsoft/lightgbm', 0.8464224338531494, 'ml', 4), ('catboost/catboost', 0.7875171899795532, 'ml', 3), ('horovod/horovod', 0.5724257230758667, 'ml-ops', 1), ('dask/dask-ml', 0.5699902176856995, 'ml', 0), ('tensorflow/tensorflow', 0.5403357744216919, 'ml-dl', 1), ('uber/petastorm', 0.5380932688713074, 'data', 1), ('mlflow/mlflow', 0.5318878889083862, 'ml-ops', 1), ('google/tf-quant-finance', 0.5295057892799377, 'finance', 0), ('oml-team/open-metric-learning', 0.5269960761070251, 'ml', 0), ('microsoft/deepspeed', 0.5240546464920044, 'ml-dl', 1), ('apache/spark', 0.5240032076835632, 'data', 0), ('determined-ai/determined', 0.5239108800888062, 'ml-ops', 1), ('aws/sagemaker-python-sdk', 0.5017920732498169, 'ml', 1), ('pycaret/pycaret', 0.501174807548523, 'ml', 1)]",633,7.0,,10.58,375,286,121,0,8,6,8,375.0,893.0,90.0,2.4,73
1906,util,https://github.com/pypa/pipenv,"['venv', 'pip', 'virtualenv', 'packaging']","A virtualenv management tool that supports a multitude of systems and nicely bridges the gaps between pip, python and virtualenv.",[],[],,,,pypa/pipenv,pipenv,24385,1940,358,Python,https://pipenv.pypa.io, Python Development Workflow for Humans.,pypa,2024-01-19,2017-01-20,366,66.52182385035074,https://avatars.githubusercontent.com/u/647025?v=4, Python Development Workflow for Humans.,"['packaging', 'pip', 'pipfile', 'virtualenv']","['packaging', 'pip', 'pipfile', 'venv', 'virtualenv']",2024-01-17,"[('pypa/hatch', 0.7155768275260925, 'util', 2), ('pypa/virtualenv', 0.6951494216918945, 'util', 3), ('pyenv/pyenv', 0.6903738379478455, 'util', 2), ('thoth-station/micropipenv', 0.6586019992828369, 'util', 1), ('pomponchik/instld', 0.6500003933906555, 'util', 2), ('pypa/pipx', 0.6215226650238037, 'util', 2), ('indygreg/pyoxidizer', 0.6146742701530457, 'util', 1), ('orchest/orchest', 0.5898057818412781, 'ml-ops', 0), ('dosisod/refurb', 0.5785287618637085, 'util', 0), ('pdm-project/pdm', 0.5644690990447998, 'util', 1), ('ploomber/ploomber', 0.5622181296348572, 'ml-ops', 0), ('amaargiru/pyroad', 0.5548535585403442, 'study', 0), ('jazzband/pip-tools', 0.5539312958717346, 'util', 2), ('willmcgugan/textual', 0.5516801476478577, 'term', 0), ('bndr/pipreqs', 0.550599217414856, 'util', 0), ('ianmiell/shutit', 0.549948513507843, 'util', 0), ('backtick-se/cowait', 0.5493590235710144, 'util', 0), ('beeware/briefcase', 0.546190619468689, 'util', 0), ('python-poetry/poetry', 0.5455138087272644, 'util', 1), ('pypa/build', 0.5441533327102661, 'util', 0), ('pantsbuild/pex', 0.5425669550895691, 'util', 1), ('tezromach/python-package-template', 0.5377501249313354, 'template', 0), ('samuelcolvin/python-devtools', 0.5316519737243652, 'debug', 0), ('trailofbits/pip-audit', 0.531186044216156, 'security', 1), ('pypa/flit', 0.5302018523216248, 'util', 1), ('ofek/pyapp', 0.5295817852020264, 'util', 1), ('mitsuhiko/rye', 0.5276722311973572, 'util', 1), ('pypy/pypy', 0.5260152816772461, 'util', 0), ('skvark/opencv-python', 0.5230939984321594, 'ml', 0), ('eleutherai/pyfra', 0.5228143930435181, 'ml', 0), ('martinheinz/python-project-blueprint', 0.5226168036460876, 'template', 0), ('fmind/mlops-python-package', 0.5217861533164978, 'template', 0), ('omry/omegaconf', 0.5201141834259033, 'util', 0), ('kubeflow/fairing', 0.5174124836921692, 'ml-ops', 0), ('eugeneyan/python-collab-template', 0.5131263732910156, 'template', 0), ('pyinfra-dev/pyinfra', 0.5107925534248352, 'util', 0), ('linkedin/shiv', 0.5103631615638733, 'util', 0), ('dagworks-inc/hamilton', 0.5052401423454285, 'ml-ops', 0), ('urwid/urwid', 0.5031107664108276, 'term', 0)]",496,6.0,,8.21,122,69,85,0,34,54,34,122.0,265.0,90.0,2.2,73
1048,util,https://github.com/blakeblackshear/frigate,[],,[],[],,,,blakeblackshear/frigate,frigate,12880,1196,157,Python,https://frigate.video,NVR with realtime local object detection for IP cameras,blakeblackshear,2024-01-14,2019-01-26,261,49.26775956284153,,NVR with realtime local object detection for IP cameras,"['ai', 'camera', 'google-coral', 'home-assistant', 'home-automation', 'homeautomation', 'mqtt', 'nvr', 'object-detection', 'realtime', 'rtsp', 'tensorflow']","['ai', 'camera', 'google-coral', 'home-assistant', 'home-automation', 'homeautomation', 'mqtt', 'nvr', 'object-detection', 'realtime', 'rtsp', 'tensorflow']",2024-01-03,"[('roboflow/supervision', 0.6134657859802246, 'ml', 2), ('home-assistant/core', 0.5823681354522705, 'util', 2), ('roboflow/notebooks', 0.5778411626815796, 'study', 1), ('deci-ai/super-gradients', 0.5583767294883728, 'ml-dl', 1), ('nvlabs/gcvit', 0.5532398223876953, 'diffusion', 1), ('matterport/mask_rcnn', 0.5429755449295044, 'ml-dl', 2), ('google/automl', 0.5250091552734375, 'ml', 1), ('rwightman/pytorch-image-models', 0.5128588080406189, 'ml-dl', 0), ('nyandwi/modernconvnets', 0.5042876601219177, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5040640234947205, 'ml', 0), ('activeloopai/deeplake', 0.5021501779556274, 'ml-ops', 2)]",177,6.0,,11.87,1128,943,60,0,19,23,19,1129.0,4469.0,90.0,4.0,73
191,ml-dl,https://github.com/dmlc/dgl,[],,[],[],,,,dmlc/dgl,dgl,12638,2924,169,Python,http://dgl.ai,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",dmlc,2024-01-13,2018-04-20,301,41.90715300805306,https://avatars.githubusercontent.com/u/11508361?v=4,"Python package built to ease deep learning on graph, on top of existing DL frameworks.","['deep-learning', 'graph-neural-networks']","['deep-learning', 'graph-neural-networks']",2024-01-12,"[('pyg-team/pytorch_geometric', 0.7811087965965271, 'ml-dl', 2), ('danielegrattarola/spektral', 0.6934017539024353, 'ml-dl', 2), ('graphistry/pygraphistry', 0.6582305431365967, 'data', 0), ('stellargraph/stellargraph', 0.6574554443359375, 'graph', 2), ('accenture/ampligraph', 0.6121450066566467, 'data', 0), ('a-r-j/graphein', 0.5949839949607849, 'sim', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.593945324420929, 'study', 2), ('pygraphviz/pygraphviz', 0.5841652154922485, 'viz', 0), ('rampasek/graphgps', 0.5740870833396912, 'graph', 0), ('google-research/deeplab2', 0.5739299058914185, 'ml', 0), ('h4kor/graph-force', 0.573490560054779, 'graph', 0), ('mdbloice/augmentor', 0.5562937259674072, 'ml', 1), ('intel/intel-extension-for-pytorch', 0.5548177361488342, 'perf', 1), ('benedekrozemberczki/tigerlily', 0.5503193140029907, 'ml-dl', 1), ('hazyresearch/hgcn', 0.5476505160331726, 'ml', 0), ('ageron/handson-ml2', 0.5474348068237305, 'ml', 0), ('huggingface/huggingface_hub', 0.5465887784957886, 'ml', 1), ('gradio-app/gradio', 0.5429977774620056, 'viz', 1), ('awslabs/dgl-ke', 0.5391981601715088, 'ml', 0), ('nvidia/deeplearningexamples', 0.5359228849411011, 'ml-dl', 1), ('pytorch/ignite', 0.5350419878959656, 'ml-dl', 1), ('rasbt/machine-learning-book', 0.5301172733306885, 'study', 1), ('tensorlayer/tensorlayer', 0.5261937975883484, 'ml-rl', 1), ('facebookresearch/pytorch3d', 0.5240106582641602, 'ml-dl', 0), ('pydot/pydot', 0.5208896398544312, 'viz', 0), ('plotly/plotly.py', 0.5201095938682556, 'viz', 0), ('google-deepmind/materials_discovery', 0.5183952450752258, 'sim', 0), ('pytorch/pytorch', 0.5178238749504089, 'ml-dl', 1), ('westhealth/pyvis', 0.5159277319908142, 'graph', 0), ('mrdbourke/pytorch-deep-learning', 0.5118030905723572, 'study', 1), ('uber/petastorm', 0.5088360905647278, 'data', 1), ('tensorflow/addons', 0.5086209774017334, 'ml', 1), ('deepmodeling/deepmd-kit', 0.5051169991493225, 'sim', 1), ('fchollet/deep-learning-with-python-notebooks', 0.504264235496521, 'study', 0), ('aiqc/aiqc', 0.5034000873565674, 'ml-ops', 0), ('nicolas-chaulet/torch-points3d', 0.501939594745636, 'ml', 0), ('skorch-dev/skorch', 0.5011494755744934, 'ml-dl', 0)]",283,6.0,,20.08,581,478,70,0,8,7,8,581.0,2356.0,90.0,4.1,73
1511,ml,https://github.com/roboflow/supervision,[],,[],[],,,,roboflow/supervision,supervision,8600,636,76,Python,https://supervision.roboflow.com,We write your reusable computer vision tools. 💜,roboflow,2024-01-14,2022-11-28,61,140.65420560747663,https://avatars.githubusercontent.com/u/53104118?v=4,We write your reusable computer vision tools. 💜,"['classification', 'coco', 'computer-vision', 'deep-learning', 'image-processing', 'instance-segmentation', 'machine-learning', 'metrics', 'object-detection', 'pascal-voc', 'pytorch', 'tensorflow', 'tracking', 'video-processing', 'yolo']","['classification', 'coco', 'computer-vision', 'deep-learning', 'image-processing', 'instance-segmentation', 'machine-learning', 'metrics', 'object-detection', 'pascal-voc', 'pytorch', 'tensorflow', 'tracking', 'video-processing', 'yolo']",2024-01-13,"[('deci-ai/super-gradients', 0.6838550567626953, 'ml-dl', 4), ('roboflow/notebooks', 0.6496574878692627, 'study', 5), ('nvlabs/gcvit', 0.6431651711463928, 'diffusion', 3), ('keras-team/keras-cv', 0.640568733215332, 'ml-dl', 1), ('huggingface/datasets', 0.6294167637825012, 'nlp', 5), ('lutzroeder/netron', 0.6290085911750793, 'ml', 4), ('open-mmlab/mmediting', 0.6228029727935791, 'ml', 4), ('kevinmusgrave/pytorch-metric-learning', 0.6149064302444458, 'ml', 4), ('blakeblackshear/frigate', 0.6134657859802246, 'util', 2), ('open-mmlab/mmsegmentation', 0.6128215193748474, 'ml', 1), ('rwightman/pytorch-image-models', 0.6038311719894409, 'ml-dl', 1), ('open-mmlab/mmdetection', 0.5974782109260559, 'ml', 4), ('kornia/kornia', 0.5887291431427002, 'ml-dl', 5), ('nyandwi/modernconvnets', 0.5865331292152405, 'ml-dl', 2), ('aleju/imgaug', 0.5748479962348938, 'ml', 2), ('polyaxon/polyaxon', 0.5695512890815735, 'ml-ops', 4), ('onnx/onnx', 0.5692649483680725, 'ml', 4), ('luispedro/mahotas', 0.5504482388496399, 'viz', 1), ('oml-team/open-metric-learning', 0.5494734644889832, 'ml', 3), ('ddbourgin/numpy-ml', 0.5450491309165955, 'ml', 1), ('towhee-io/towhee', 0.544347882270813, 'ml-ops', 4), ('awslabs/autogluon', 0.54073566198349, 'ml', 5), ('hysts/pytorch_image_classification', 0.538576066493988, 'ml-dl', 2), ('microsoft/torchgeo', 0.5378150939941406, 'gis', 3), ('facebookresearch/detectron2', 0.5367303490638733, 'ml-dl', 0), ('google-research/maxvit', 0.5343255996704102, 'ml', 4), ('activeloopai/deeplake', 0.5341194868087769, 'ml-ops', 6), ('mosaicml/composer', 0.529486894607544, 'ml-dl', 3), ('neuralmagic/deepsparse', 0.5291524529457092, 'nlp', 2), ('matterport/mask_rcnn', 0.528638482093811, 'ml-dl', 3), ('tensorflow/tensorflow', 0.527900218963623, 'ml-dl', 3), ('google/mediapipe', 0.5272902250289917, 'ml', 4), ('idea-research/grounded-segment-anything', 0.525745153427124, 'llm', 0), ('lightly-ai/lightly', 0.5256815552711487, 'ml', 4), ('keras-team/keras', 0.5252509713172913, 'ml-dl', 4), ('open-mmlab/mmcv', 0.524324893951416, 'ml', 1), ('aimhubio/aim', 0.5213971138000488, 'ml-ops', 3), ('bentoml/bentoml', 0.5209812521934509, 'ml-ops', 2), ('microsoft/onnxruntime', 0.5162255167961121, 'ml', 4), ('nvidia/deeplearningexamples', 0.5154585242271423, 'ml-dl', 4), ('albumentations-team/albumentations', 0.5144950151443481, 'ml-dl', 4), ('wandb/client', 0.5144250392913818, 'ml', 4), ('deepfakes/faceswap', 0.5128257274627686, 'ml-dl', 2), ('isl-org/open3d', 0.5120977759361267, 'sim', 3), ('neuralmagic/sparseml', 0.5113904476165771, 'ml-dl', 3), ('fepegar/torchio', 0.5101572871208191, 'ml-dl', 3), ('huggingface/transformers', 0.5099833011627197, 'nlp', 4), ('xl0/lovely-tensors', 0.5090969800949097, 'ml-dl', 2), ('explosion/thinc', 0.5064694881439209, 'ml-dl', 4), ('microsoft/swin-transformer', 0.5062756538391113, 'ml', 1), ('polyaxon/datatile', 0.5057425498962402, 'pandas', 3), ('scikit-image/scikit-image', 0.5051987767219543, 'util', 2), ('google-research/deeplab2', 0.5049787163734436, 'ml', 0), ('tensorlayer/tensorlayer', 0.502228319644928, 'ml-rl', 3), ('megvii-basedetection/yolox', 0.5012127161026001, 'ml', 4), ('districtdatalabs/yellowbrick', 0.5006172060966492, 'ml', 1)]",45,4.0,,22.21,278,232,14,0,23,36,23,278.0,412.0,90.0,1.5,73
1774,llm,https://github.com/embedchain/embedchain,[],,[],[],,,,embedchain/embedchain,embedchain,7058,1226,50,Python,https://docs.embedchain.ai,The Open Source RAG framework,embedchain,2024-01-14,2023-06-20,32,220.5625,https://avatars.githubusercontent.com/u/137054526?v=4,The Open Source RAG framework,"['ai', 'chatbots', 'chatgpt', 'llm']","['ai', 'chatbots', 'chatgpt', 'llm']",2024-01-14,"[('run-llama/rags', 0.775257408618927, 'llm', 2), ('nomic-ai/gpt4all', 0.7717016935348511, 'llm', 0), ('togethercomputer/openchatkit', 0.7426310181617737, 'nlp', 0), ('rasahq/rasa', 0.7189019322395325, 'llm', 1), ('minimaxir/simpleaichat', 0.7047023773193359, 'llm', 2), ('cheshire-cat-ai/core', 0.6973840594291687, 'llm', 2), ('prefecthq/marvin', 0.6899540424346924, 'nlp', 3), ('rcgai/simplyretrieve', 0.6877291202545166, 'llm', 0), ('pathwaycom/llm-app', 0.6783232688903809, 'llm', 1), ('deepset-ai/haystack', 0.6720160841941833, 'llm', 2), ('microsoft/autogen', 0.6612349152565002, 'llm', 1), ('hwchase17/langchain', 0.652535617351532, 'llm', 0), ('chatarena/chatarena', 0.6508777141571045, 'llm', 2), ('deeppavlov/deeppavlov', 0.6491185426712036, 'nlp', 1), ('lm-sys/fastchat', 0.6472465991973877, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.6422813534736633, 'llm', 0), ('killianlucas/open-interpreter', 0.6413992643356323, 'llm', 1), ('larsbaunwall/bricky', 0.634949803352356, 'llm', 1), ('blinkdl/chatrwkv', 0.6271498203277588, 'llm', 1), ('xtekky/gpt4free', 0.6268326640129089, 'llm', 2), ('laion-ai/open-assistant', 0.618574321269989, 'llm', 2), ('krohling/bondai', 0.6167078018188477, 'llm', 0), ('intel/intel-extension-for-transformers', 0.6132449507713318, 'perf', 0), ('openlmlab/moss', 0.6122089624404907, 'llm', 1), ('fasteval/fasteval', 0.6101776957511902, 'llm', 1), ('mlc-ai/web-llm', 0.6080430746078491, 'llm', 2), ('aiwaves-cn/agents', 0.6022089123725891, 'nlp', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5969848036766052, 'llm', 0), ('microsoft/promptcraft-robotics', 0.596112847328186, 'sim', 2), ('langchain-ai/chat-langchain', 0.5953378677368164, 'llm', 0), ('shishirpatil/gorilla', 0.5869289636611938, 'llm', 2), ('tigerlab-ai/tiger', 0.5847614407539368, 'llm', 1), ('argilla-io/argilla', 0.5823776721954346, 'nlp', 2), ('microsoft/promptflow', 0.5782580971717834, 'llm', 3), ('gunthercox/chatterbot', 0.5760533809661865, 'nlp', 0), ('weaviate/verba', 0.5736026167869568, 'llm', 0), ('databrickslabs/dolly', 0.5724859237670898, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5652803182601929, 'llm', 0), ('nvidia/nemo', 0.5636201500892639, 'nlp', 0), ('mindsdb/mindsdb', 0.5583111643791199, 'data', 2), ('h2oai/h2o-llmstudio', 0.5581331253051758, 'llm', 3), ('bigscience-workshop/petals', 0.5567405819892883, 'data', 0), ('microsoft/semantic-kernel', 0.555986225605011, 'llm', 2), ('mnotgod96/appagent', 0.5549655556678772, 'llm', 2), ('nebuly-ai/nebullvm', 0.5530699491500854, 'perf', 2), ('lupantech/chameleon-llm', 0.5511299967765808, 'llm', 3), ('mmabrouk/chatgpt-wrapper', 0.5488071441650391, 'llm', 2), ('li-plus/chatglm.cpp', 0.5471507906913757, 'llm', 0), ('dylanhogg/llmgraph', 0.5462237596511841, 'ml', 2), ('sweepai/sweep', 0.5455598831176758, 'llm', 2), ('transformeroptimus/superagi', 0.5429883003234863, 'llm', 2), ('mlc-ai/mlc-llm', 0.5380026698112488, 'llm', 1), ('thudm/chatglm2-6b', 0.5360315442085266, 'llm', 1), ('openai/openai-cookbook', 0.5339502096176147, 'ml', 1), ('gventuri/pandas-ai', 0.5332342386245728, 'pandas', 2), ('alphasecio/langchain-examples', 0.532405436038971, 'llm', 1), ('next-gpt/next-gpt', 0.5254507064819336, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5248899459838867, 'study', 2), ('microsoft/lmops', 0.5240100622177124, 'llm', 1), ('llmware-ai/llmware', 0.5193879008293152, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.518301248550415, 'nlp', 0), ('young-geng/easylm', 0.5164496302604675, 'llm', 0), ('langchain-ai/langgraph', 0.5151159763336182, 'llm', 0), ('openai/gpt-discord-bot', 0.5135484933853149, 'llm', 0), ('hegelai/prompttools', 0.5133116245269775, 'llm', 0), ('salesforce/codet5', 0.5125962495803833, 'nlp', 0), ('zhudotexe/kani', 0.5120741724967957, 'llm', 1), ('zilliztech/gptcache', 0.5112298727035522, 'llm', 2), ('chainlit/chainlit', 0.5102947354316711, 'llm', 2), ('nvidia/nemo-guardrails', 0.5097015500068665, 'llm', 0), ('modularml/mojo', 0.5094591975212097, 'util', 1), ('lastmile-ai/aiconfig', 0.5092721581459045, 'util', 2), ('gunthercox/chatterbot-corpus', 0.5088998079299927, 'nlp', 0), ('iryna-kondr/scikit-llm', 0.5067012310028076, 'llm', 2), ('google-research/language', 0.5062578916549683, 'nlp', 0)]",74,2.0,,12.85,444,368,7,0,127,233,127,444.0,445.0,90.0,1.0,73
1255,llm,https://github.com/abetlen/llama-cpp-python,"['llama', 'language-model']",,[],[],,,,abetlen/llama-cpp-python,llama-cpp-python,5097,654,55,Python,https://llama-cpp-python.readthedocs.io,Python bindings for llama.cpp,abetlen,2024-01-14,2023-03-23,44,113.99041533546325,,Python bindings for llama.cpp,[],"['language-model', 'llama']",2024-01-14,"[('nomic-ai/pygpt4all', 0.7292018532752991, 'llm', 0), ('facebookresearch/llama', 0.700358510017395, 'llm', 2), ('facebookresearch/llama-recipes', 0.6769752502441406, 'llm', 2), ('karpathy/llama2.c', 0.6725909113883972, 'llm', 2), ('microsoft/llama-2-onnx', 0.6152366399765015, 'llm', 2), ('ggerganov/llama.cpp', 0.5961679816246033, 'llm', 2), ('facebookresearch/codellama', 0.5786312222480774, 'llm', 2), ('tloen/alpaca-lora', 0.572754442691803, 'llm', 2), ('run-llama/llama-lab', 0.5617009401321411, 'llm', 2), ('jzhang38/tinyllama', 0.5099416375160217, 'llm', 2)]",105,4.0,,21.35,385,195,10,0,78,103,78,385.0,811.0,90.0,2.1,73
1900,llm,https://github.com/vaibhavs10/insanely-fast-whisper,"['tts', 'cli', 'whisper']","An opinionated CLI to transcribe Audio files w/ Whisper on-device! Powered by 🤗 Transformers, Optimum & flash-attn",[],[],,,,vaibhavs10/insanely-fast-whisper,insanely-fast-whisper,5030,451,41,Jupyter Notebook,,,vaibhavs10,2024-01-17,2023-10-10,16,314.375,,"An opinionated CLI to transcribe Audio files w/ Whisper on-device! Powered by 🤗 Transformers, Optimum & flash-attn",[],"['cli', 'tts', 'whisper']",2024-01-05,"[('ggerganov/whisper.cpp', 0.5495933890342712, 'util', 1), ('m-bain/whisperx', 0.5447807312011719, 'nlp', 1), ('myshell-ai/openvoice', 0.5049346089363098, 'nlp', 1)]",15,7.0,,2.46,165,134,3,0,0,0,0,165.0,435.0,90.0,2.6,73
81,data,https://github.com/scrapy/scrapy,[],,[],[],,,,scrapy/scrapy,scrapy,49808,10379,1770,Python,https://scrapy.org,"Scrapy, a fast high-level web crawling & scraping framework for Python.",scrapy,2024-01-14,2010-02-22,727,68.49823182711198,https://avatars.githubusercontent.com/u/733635?v=4,"Scrapy, a fast high-level web crawling & scraping framework for Python.","['crawler', 'crawling', 'framework', 'scraping', 'web-scraping', 'web-scraping-python']","['crawler', 'crawling', 'framework', 'scraping', 'web-scraping', 'web-scraping-python']",2024-01-12,"[('alirezamika/autoscraper', 0.8083213567733765, 'data', 3), ('roniemartinez/dude', 0.7514920830726624, 'util', 4), ('binux/pyspider', 0.7435339093208313, 'data', 1), ('nv7-github/googlesearch', 0.7149690985679626, 'util', 0), ('clips/pattern', 0.667265772819519, 'nlp', 0), ('webpy/webpy', 0.6368110775947571, 'web', 0), ('bottlepy/bottle', 0.6345263123512268, 'web', 0), ('s0md3v/photon', 0.6149922609329224, 'data', 1), ('cherrypy/cherrypy', 0.6140583157539368, 'web', 0), ('masoniteframework/masonite', 0.6013022065162659, 'web', 1), ('falconry/falcon', 0.5992222428321838, 'web', 1), ('requests/toolbelt', 0.5953943133354187, 'util', 0), ('pallets/flask', 0.591780960559845, 'web', 0), ('klen/muffin', 0.5914323329925537, 'web', 0), ('pylons/pyramid', 0.5863667726516724, 'web', 0), ('eleutherai/pyfra', 0.5844635367393494, 'ml', 0), ('reflex-dev/reflex', 0.5838077664375305, 'web', 1), ('psf/requests', 0.5722671151161194, 'web', 0), ('pallets/werkzeug', 0.5650268197059631, 'web', 0), ('twintproject/twint', 0.5525391101837158, 'data', 0), ('pypy/pypy', 0.5505447387695312, 'util', 0), ('willmcgugan/textual', 0.5489547252655029, 'term', 1), ('erotemic/ubelt', 0.5488511323928833, 'util', 0), ('holoviz/panel', 0.5401167869567871, 'viz', 0), ('cobrateam/splinter', 0.5401042699813843, 'testing', 0), ('neoteroi/blacksheep', 0.53035569190979, 'web', 1), ('pyodide/pyodide', 0.5298803448677063, 'util', 0), ('jovianml/opendatasets', 0.5252864360809326, 'data', 0), ('fastai/fastcore', 0.5240939855575562, 'util', 0), ('plotly/dash', 0.5219252109527588, 'viz', 0), ('python/cpython', 0.52182936668396, 'util', 0), ('1200wd/bitcoinlib', 0.5212147831916809, 'crypto', 0), ('dylanhogg/awesome-python', 0.5145118832588196, 'study', 0), ('ethereum/web3.py', 0.5123814344406128, 'crypto', 0), ('timofurrer/awesome-asyncio', 0.5095760226249695, 'study', 0), ('pytoolz/toolz', 0.5080231428146362, 'util', 0), ('googleapis/google-api-python-client', 0.5074482560157776, 'util', 0), ('jiffyclub/snakeviz', 0.5070153474807739, 'profiling', 0), ('pyston/pyston', 0.5049728751182556, 'util', 0), ('seleniumbase/seleniumbase', 0.5043920278549194, 'testing', 0), ('hoffstadt/dearpygui', 0.5022242069244385, 'gui', 0)]",631,5.0,,10.87,245,141,169,0,5,8,5,245.0,348.0,90.0,1.4,72
288,ml-dl,https://github.com/rwightman/pytorch-image-models,[],,[],[],,,,rwightman/pytorch-image-models,pytorch-image-models,28366,4487,306,Python,https://huggingface.co/docs/timm,"PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more",rwightman,2024-01-14,2019-02-02,260,108.92046077893582,https://avatars.githubusercontent.com/u/25720743?v=4,"PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more","['augmix', 'cnn-classification', 'distributed-training', 'dual-path-networks', 'efficientnet', 'efficientnet-training', 'imagenet-classifier', 'mixnet', 'mnasnet', 'mobile-deep-learning', 'mobilenet-v2', 'mobilenetv3', 'nfnets', 'normalization-free-training', 'pretrained-models', 'pretrained-weights', 'pytorch', 'randaugment', 'resnet', 'vision-transformer-models']","['augmix', 'cnn-classification', 'distributed-training', 'dual-path-networks', 'efficientnet', 'efficientnet-training', 'imagenet-classifier', 'mixnet', 'mnasnet', 'mobile-deep-learning', 'mobilenet-v2', 'mobilenetv3', 'nfnets', 'normalization-free-training', 'pretrained-models', 'pretrained-weights', 'pytorch', 'randaugment', 'resnet', 'vision-transformer-models']",2024-01-13,"[('nyandwi/modernconvnets', 0.6331978440284729, 'ml-dl', 0), ('nvlabs/gcvit', 0.6296912431716919, 'diffusion', 0), ('deci-ai/super-gradients', 0.612806499004364, 'ml-dl', 3), ('roboflow/supervision', 0.6038311719894409, 'ml', 1), ('lutzroeder/netron', 0.601951003074646, 'ml', 1), ('ddbourgin/numpy-ml', 0.5986382961273193, 'ml', 1), ('hysts/pytorch_image_classification', 0.5974335074424744, 'ml-dl', 1), ('google/automl', 0.5850779414176941, 'ml', 1), ('mosaicml/composer', 0.5846765041351318, 'ml-dl', 1), ('google-research/maxvit', 0.575612485408783, 'ml', 1), ('huggingface/transformers', 0.5753883123397827, 'nlp', 2), ('roboflow/notebooks', 0.5683378577232361, 'study', 1), ('keras-team/keras', 0.5650960803031921, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5575792193412781, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5570010542869568, 'ml', 1), ('onnx/onnx', 0.5542277097702026, 'ml', 1), ('lucidrains/imagen-pytorch', 0.5502446889877319, 'ml-dl', 0), ('horovod/horovod', 0.5461047887802124, 'ml-ops', 1), ('amanchadha/coursera-deep-learning-specialization', 0.5459620952606201, 'study', 0), ('open-mmlab/mmdetection', 0.5427139401435852, 'ml', 1), ('deepfakes/faceswap', 0.5423110127449036, 'ml-dl', 0), ('aleju/imgaug', 0.5356476902961731, 'ml', 0), ('neuralmagic/deepsparse', 0.5303251147270203, 'nlp', 1), ('microsoft/torchgeo', 0.5265195965766907, 'gis', 1), ('datasystemslab/geotorch', 0.5261633992195129, 'gis', 0), ('open-mmlab/mmsegmentation', 0.5257945656776428, 'ml', 1), ('iperov/deepfacelab', 0.5256627798080444, 'ml-dl', 0), ('microsoft/deepspeed', 0.5248521566390991, 'ml-dl', 1), ('neuralmagic/sparseml', 0.5248225331306458, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5237160921096802, 'study', 0), ('microsoft/onnxruntime', 0.522943913936615, 'ml', 1), ('huggingface/datasets', 0.5214285254478455, 'nlp', 1), ('tensorflow/tensorflow', 0.5212838053703308, 'ml-dl', 0), ('blakeblackshear/frigate', 0.5128588080406189, 'util', 0), ('pytorch/ignite', 0.512000322341919, 'ml-dl', 1), ('towhee-io/towhee', 0.5081286430358887, 'ml-ops', 0), ('fepegar/torchio', 0.5073763728141785, 'ml-dl', 1), ('alpa-projects/alpa', 0.507267415523529, 'ml-dl', 1), ('explosion/thinc', 0.5064119696617126, 'ml-dl', 1), ('google/trax', 0.5016557574272156, 'ml-dl', 0), ('salesforce/blip', 0.5003149509429932, 'diffusion', 0)]",127,4.0,,7.08,91,73,60,0,15,11,15,91.0,143.0,90.0,1.6,72
1238,llm,https://github.com/vision-cair/minigpt-4,[],,[],[],,,,vision-cair/minigpt-4,MiniGPT-4,24222,2859,217,Python,https://minigpt-4.github.io,"Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)",vision-cair,2024-01-13,2023-04-15,41,584.6689655172414,https://avatars.githubusercontent.com/u/61346166?v=4,"Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)",[],[],2024-01-03,"[('karpathy/nanogpt', 0.5487928986549377, 'llm', 0), ('xtekky/gpt4free', 0.548042356967926, 'llm', 0), ('farizrahman4u/loopgpt', 0.5081561207771301, 'llm', 0)]",14,3.0,,4.71,137,41,9,0,0,0,0,137.0,200.0,90.0,1.5,72
48,viz,https://github.com/matplotlib/matplotlib,[],,[],[],,,,matplotlib/matplotlib,matplotlib,18717,7368,591,Python,https://matplotlib.org/stable/,matplotlib: plotting with Python,matplotlib,2024-01-14,2011-02-19,675,27.711294416243653,https://avatars.githubusercontent.com/u/215947?v=4,matplotlib: plotting with Python,"['data-science', 'data-visualization', 'gtk', 'matplotlib', 'plotting', 'qt', 'tk', 'wx']","['data-science', 'data-visualization', 'gtk', 'matplotlib', 'plotting', 'qt', 'tk', 'wx']",2024-01-13,"[('holoviz/hvplot', 0.7096381187438965, 'pandas', 1), ('cuemacro/chartpy', 0.6975510120391846, 'viz', 2), ('mwaskom/seaborn', 0.6802253127098083, 'viz', 3), ('bokeh/bokeh', 0.6447177529335022, 'viz', 1), ('holoviz/panel', 0.6378490328788757, 'viz', 1), ('altair-viz/altair', 0.6370353102684021, 'viz', 0), ('plotly/plotly.py', 0.6358102560043335, 'viz', 0), ('holoviz/holoviz', 0.6354397535324097, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.6332533955574036, 'viz', 1), ('maartenbreddels/ipyvolume', 0.6306770443916321, 'jupyter', 1), ('holoviz/geoviews', 0.6272979974746704, 'gis', 1), ('has2k1/plotnine', 0.6249548196792603, 'viz', 1), ('residentmario/geoplot', 0.6070235967636108, 'gis', 1), ('enthought/mayavi', 0.6048174500465393, 'viz', 0), ('dfki-ric/pytransform3d', 0.5860082507133484, 'math', 1), ('vizzuhq/ipyvizzu', 0.5835548043251038, 'jupyter', 2), ('kanaries/pygwalker', 0.5794761776924133, 'pandas', 1), ('marcomusy/vedo', 0.577059805393219, 'viz', 0), ('man-group/dtale', 0.5757876634597778, 'viz', 2), ('jakevdp/pythondatasciencehandbook', 0.5707853436470032, 'study', 1), ('scitools/cartopy', 0.566702663898468, 'gis', 1), ('scitools/iris', 0.5653036236763, 'gis', 0), ('holoviz/holoviews', 0.564393937587738, 'viz', 1), ('plotly/dash', 0.5594635605812073, 'viz', 2), ('contextlab/hypertools', 0.5552871227264404, 'ml', 1), ('westhealth/pyvis', 0.5532649159431458, 'graph', 0), ('pyvista/pyvista', 0.5509993433952332, 'viz', 1), ('csurfer/pyheat', 0.547273576259613, 'profiling', 1), ('pygraphviz/pygraphviz', 0.5422382354736328, 'viz', 0), ('federicoceratto/dashing', 0.539638102054596, 'term', 0), ('pyglet/pyglet', 0.5357295870780945, 'gamedev', 0), ('matplotlib/mplfinance', 0.5346719622612, 'finance', 1), ('numpy/numpy', 0.5291272401809692, 'math', 0), ('imageio/imageio', 0.5237370133399963, 'util', 0), ('wxwidgets/phoenix', 0.5209715366363525, 'gui', 0), ('graphistry/pygraphistry', 0.5203686356544495, 'data', 0), ('parthjadhav/tkinter-designer', 0.5160230398178101, 'gui', 0), ('nschloe/tikzplotlib', 0.5151798725128174, 'util', 1), ('pysimplegui/pysimplegui', 0.5119383931159973, 'gui', 1), ('raphaelquast/eomaps', 0.5100708603858948, 'gis', 2), ('gregorhd/mapcompare', 0.5066385269165039, 'gis', 0), ('matplotlib/basemap', 0.505860447883606, 'gis', 0), ('artelys/geonetworkx', 0.5047987103462219, 'gis', 0)]",1637,6.0,,62.79,944,616,157,0,10,10,10,947.0,2014.0,90.0,2.1,72
697,util,https://github.com/openai/openai-python,[],,[],[],,,,openai/openai-python,openai-python,17664,2403,249,Python,https://pypi.org/project/openai/,The official Python library for the OpenAI API,openai,2024-01-14,2020-10-25,170,103.73154362416108,https://avatars.githubusercontent.com/u/14957082?v=4,The official Python library for the OpenAI API,['openai'],['openai'],2024-01-12,"[('openai/openai-cookbook', 0.6652215123176575, 'ml', 1), ('langchain-ai/opengpts', 0.6509252190589905, 'llm', 0), ('shishirpatil/gorilla', 0.6225490570068359, 'llm', 0), ('googleapis/google-api-python-client', 0.5968145728111267, 'util', 0), ('fastai/ghapi', 0.5810590982437134, 'util', 0), ('fastai/fastcore', 0.5748746991157532, 'util', 0), ('urwid/urwid', 0.5712311267852783, 'term', 0), ('tebelorg/rpa-python', 0.5686379671096802, 'util', 0), ('kivy/kivy', 0.5627065300941467, 'util', 0), ('vitalik/django-ninja', 0.5567331910133362, 'web', 0), ('huggingface/huggingface_hub', 0.5553815960884094, 'ml', 0), ('pytoolz/toolz', 0.5484781861305237, 'util', 0), ('ipython/ipython', 0.5373437404632568, 'util', 0), ('man-c/pycoingecko', 0.5335339307785034, 'crypto', 0), ('simple-salesforce/simple-salesforce', 0.5326930284500122, 'data', 0), ('berriai/litellm', 0.5291408896446228, 'llm', 1), ('cohere-ai/cohere-python', 0.5279492735862732, 'util', 0), ('open-telemetry/opentelemetry-python', 0.5268572568893433, 'util', 0), ('open-telemetry/opentelemetry-python-contrib', 0.5244234204292297, 'util', 0), ('timofurrer/awesome-asyncio', 0.524164617061615, 'study', 0), ('larsbaunwall/bricky', 0.5239132642745972, 'llm', 1), ('kuimono/openapi-schema-pydantic', 0.52321857213974, 'util', 0), ('falconry/falcon', 0.5228433609008789, 'web', 0), ('kubeflow/fairing', 0.5159801244735718, 'ml-ops', 0), ('xtekky/gpt4free', 0.515657901763916, 'llm', 1), ('nasdaq/data-link-python', 0.5095574855804443, 'finance', 0), ('mitmproxy/pdoc', 0.5083382725715637, 'util', 0), ('hugapi/hug', 0.5053588151931763, 'util', 0), ('dylanhogg/awesome-python', 0.5051689147949219, 'study', 0), ('ta-lib/ta-lib-python', 0.5044464468955994, 'finance', 0), ('pypy/pypy', 0.5010403990745544, 'util', 0), ('cuemacro/findatapy', 0.5010272860527039, 'finance', 0), ('laion-ai/open-assistant', 0.5009532570838928, 'llm', 0), ('mamba-org/quetz', 0.5004561543464661, 'util', 0)]",96,3.0,,4.12,536,498,39,0,44,28,44,536.0,1076.0,90.0,2.0,72
313,util,https://github.com/pydantic/pydantic,"['serialization', 'parsing', 'typing', 'validation']",,[],[],1.0,,,pydantic/pydantic,pydantic,17235,1579,104,Python,https://docs.pydantic.dev,Data validation using Python type hints,pydantic,2024-01-14,2017-05-03,351,48.982947624847746,https://avatars.githubusercontent.com/u/110818415?v=4,Data validation using Python type hints,"['hints', 'json-schema', 'parsing', 'pydantic', 'python310', 'python311', 'python312', 'python37', 'python38', 'python39', 'validation']","['hints', 'json-schema', 'parsing', 'pydantic', 'python310', 'python311', 'python312', 'python37', 'python38', 'python39', 'serialization', 'typing', 'validation']",2024-01-12,"[('pyeve/cerberus', 0.7001333832740784, 'data', 0), ('python-odin/odin', 0.6526608467102051, 'util', 1), ('facebook/pyre-check', 0.6196001768112183, 'typing', 0), ('marshmallow-code/marshmallow', 0.606956422328949, 'util', 2), ('patrick-kidger/torchtyping', 0.5341143012046814, 'typing', 1), ('andialbrecht/sqlparse', 0.534013569355011, 'data', 0), ('agronholm/typeguard', 0.5333467721939087, 'typing', 0), ('wtforms/wtforms', 0.5329594612121582, 'web', 1), ('pylons/colander', 0.531548798084259, 'util', 2), ('collerek/ormar', 0.5280044078826904, 'data', 1), ('omry/omegaconf', 0.5237911343574524, 'util', 0), ('mkdocstrings/griffe', 0.5223087072372437, 'util', 0), ('tiangolo/sqlmodel', 0.5132742524147034, 'data', 2), ('microsoft/pyright', 0.5103999376296997, 'typing', 0), ('strawberry-graphql/strawberry', 0.5045525431632996, 'web', 0), ('instagram/libcst', 0.5014991164207458, 'util', 0)]",466,3.0,,23.17,893,695,82,0,33,19,33,893.0,2136.0,90.0,2.4,72
493,pandas,https://github.com/duckdb/duckdb,"['arrow', 'dataframe']",,[],[],1.0,,,duckdb/duckdb,duckdb,13828,1273,161,C++,http://www.duckdb.org,DuckDB is an in-process SQL OLAP Database Management System,duckdb,2024-01-14,2018-06-26,292,47.35616438356164,https://avatars.githubusercontent.com/u/82039556?v=4,DuckDB is an in-process SQL OLAP Database Management System,"['analytics', 'database', 'embedded-database', 'olap', 'sql']","['analytics', 'arrow', 'database', 'dataframe', 'embedded-database', 'olap', 'sql']",2024-01-12,"[('duckdb/dbt-duckdb', 0.6090986132621765, 'data', 0), ('mause/duckdb_engine', 0.510148286819458, 'data', 1)]",354,2.0,,265.73,1220,950,68,0,7,7,7,1220.0,2333.0,90.0,1.9,72
1237,llm,https://github.com/idea-research/grounded-segment-anything,[],,[],[],,,,idea-research/grounded-segment-anything,Grounded-Segment-Anything,12205,1094,111,Jupyter Notebook,,"Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything",idea-research,2024-01-14,2023-04-06,42,285.73578595317724,https://avatars.githubusercontent.com/u/113572103?v=4,"Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything","['3d-whole-body-pose-estimation', 'automatic-labeling-system', 'caption', 'data-generation', 'image-editing', 'open-vocabulary-detection', 'open-vocabulary-segmentation', 'speech']","['3d-whole-body-pose-estimation', 'automatic-labeling-system', 'caption', 'data-generation', 'image-editing', 'open-vocabulary-detection', 'open-vocabulary-segmentation', 'speech']",2023-12-31,"[('idea-research/groundingdino', 0.6477982997894287, 'diffusion', 0), ('roboflow/notebooks', 0.6104524731636047, 'study', 3), ('open-mmlab/mmediting', 0.5329544544219971, 'ml', 1), ('roboflow/supervision', 0.525745153427124, 'ml', 0)]",44,7.0,,4.52,79,31,9,0,0,0,0,79.0,112.0,90.0,1.4,72
1596,llm,https://github.com/paddlepaddle/paddlenlp,[],,[],[],,,,paddlepaddle/paddlenlp,PaddleNLP,10831,2753,100,Python,https://paddlenlp.readthedocs.io,"👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification,  🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.",paddlepaddle,2024-01-14,2021-02-05,155,69.62075298438934,https://avatars.githubusercontent.com/u/23534030?v=4,"👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification,  🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.","['bert', 'compression', 'distributed-training', 'document-intelligence', 'embedding', 'ernie', 'information-extraction', 'llama', 'llm', 'neural-search', 'nlp', 'paddlenlp', 'pretrained-models', 'question-answering', 'search-engine', 'semantic-analysis', 'sentiment-analysis', 'transformers', 'uie']","['bert', 'compression', 'distributed-training', 'document-intelligence', 'embedding', 'ernie', 'information-extraction', 'llama', 'llm', 'neural-search', 'nlp', 'paddlenlp', 'pretrained-models', 'question-answering', 'search-engine', 'semantic-analysis', 'sentiment-analysis', 'transformers', 'uie']",2024-01-12,"[('llmware-ai/llmware', 0.7560280561447144, 'llm', 4), ('neuml/txtai', 0.7093960642814636, 'nlp', 5), ('explosion/spacy-llm', 0.691947877407074, 'llm', 3), ('alibaba/easynlp', 0.6783795952796936, 'nlp', 4), ('deepset-ai/haystack', 0.6606729626655579, 'llm', 4), ('extreme-bert/extreme-bert', 0.6498593091964722, 'llm', 2), ('deepset-ai/farm', 0.6488863229751587, 'nlp', 4), ('young-geng/easylm', 0.6431230902671814, 'llm', 1), ('jina-ai/finetuner', 0.6416314840316772, 'ml', 3), ('huggingface/transformers', 0.6394706964492798, 'nlp', 3), ('mooler0410/llmspracticalguide', 0.632652759552002, 'study', 1), ('lianjiatech/belle', 0.6276019811630249, 'llm', 1), ('intellabs/fastrag', 0.6250779628753662, 'nlp', 4), ('ddangelov/top2vec', 0.6133404970169067, 'nlp', 1), ('freedomintelligence/llmzoo', 0.6110307574272156, 'llm', 0), ('dylanhogg/awesome-python', 0.6097233295440674, 'study', 1), ('bigscience-workshop/petals', 0.6069114804267883, 'data', 3), ('argilla-io/argilla', 0.6045259833335876, 'nlp', 2), ('maartengr/bertopic', 0.6038503050804138, 'nlp', 3), ('infinitylogesh/mutate', 0.6022129654884338, 'nlp', 0), ('bobazooba/xllm', 0.6009507775306702, 'llm', 2), ('jina-ai/clip-as-service', 0.5931671261787415, 'nlp', 2), ('norskregnesentral/skweak', 0.592394232749939, 'nlp', 0), ('explosion/spacy-models', 0.5910124182701111, 'nlp', 1), ('explosion/spacy', 0.5861974954605103, 'nlp', 1), ('tigerlab-ai/tiger', 0.5830023884773254, 'llm', 1), ('graykode/nlp-tutorial', 0.5823931097984314, 'study', 2), ('thilinarajapakse/simpletransformers', 0.5818993449211121, 'nlp', 2), ('eleutherai/the-pile', 0.5773162841796875, 'data', 1), ('salesforce/xgen', 0.5755364894866943, 'llm', 2), ('allenai/allennlp', 0.5729801058769226, 'nlp', 1), ('deeppavlov/deeppavlov', 0.5695449113845825, 'nlp', 2), ('explosion/thinc', 0.5689855217933655, 'ml-dl', 1), ('intel/intel-extension-for-transformers', 0.5665012001991272, 'perf', 0), ('lm-sys/fastchat', 0.5656995177268982, 'llm', 0), ('night-chen/toolqa', 0.5650957822799683, 'llm', 1), ('hiyouga/llama-factory', 0.5635750889778137, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5635749697685242, 'llm', 3), ('pathwaycom/llm-app', 0.5630315542221069, 'llm', 1), ('paddlepaddle/rocketqa', 0.560157835483551, 'nlp', 2), ('sloria/textblob', 0.559609591960907, 'nlp', 1), ('nltk/nltk', 0.5588723421096802, 'nlp', 1), ('eugeneyan/obsidian-copilot', 0.5580410361289978, 'llm', 1), ('confident-ai/deepeval', 0.556614100933075, 'testing', 1), ('nebuly-ai/nebullvm', 0.556330144405365, 'perf', 1), ('rasahq/rasa', 0.556032121181488, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5539929866790771, 'llm', 1), ('zilliztech/gptcache', 0.5492423176765442, 'llm', 2), ('titanml/takeoff', 0.5484669208526611, 'llm', 2), ('hannibal046/awesome-llm', 0.5444208383560181, 'study', 0), ('jonasgeiping/cramming', 0.5425843596458435, 'nlp', 0), ('alphasecio/langchain-examples', 0.5416777729988098, 'llm', 1), ('nomic-ai/gpt4all', 0.5397657155990601, 'llm', 0), ('explosion/spacy-transformers', 0.539587140083313, 'llm', 2), ('lexpredict/lexpredict-lexnlp', 0.5384023189544678, 'nlp', 1), ('dylanhogg/llmgraph', 0.5368094444274902, 'ml', 1), ('rcgai/simplyretrieve', 0.5365450978279114, 'llm', 1), ('flairnlp/flair', 0.5343381762504578, 'nlp', 1), ('huggingface/text-generation-inference', 0.531993567943573, 'llm', 1), ('ludwig-ai/ludwig', 0.5312443971633911, 'ml-ops', 2), ('chroma-core/chroma', 0.5298216938972473, 'data', 0), ('milvus-io/bootcamp', 0.528664767742157, 'data', 2), ('muennighoff/sgpt', 0.5275580286979675, 'llm', 1), ('christoschristofidis/awesome-deep-learning', 0.5255681872367859, 'study', 0), ('keras-team/keras-nlp', 0.5255599021911621, 'nlp', 1), ('cheshire-cat-ai/core', 0.5240945219993591, 'llm', 1), ('koaning/whatlies', 0.5240222215652466, 'nlp', 1), ('mlc-ai/web-llm', 0.5234134197235107, 'llm', 1), ('bigscience-workshop/megatron-deepspeed', 0.5230602622032166, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5230602622032166, 'llm', 0), ('activeloopai/deeplake', 0.5191996693611145, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5180720090866089, 'ml-dl', 1), ('docarray/docarray', 0.517856240272522, 'data', 1), ('huggingface/datasets', 0.5161171555519104, 'nlp', 1), ('databrickslabs/dolly', 0.5159105062484741, 'llm', 0), ('lancedb/lancedb', 0.5152085423469543, 'data', 1), ('microsoft/unilm', 0.5151917338371277, 'nlp', 2), ('tatsu-lab/stanford_alpaca', 0.5149617195129395, 'llm', 0), ('sebischair/lbl2vec', 0.514652669429779, 'nlp', 1), ('hegelai/prompttools', 0.514409065246582, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5144068002700806, 'llm', 1), ('cg123/mergekit', 0.5143988132476807, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5129956603050232, 'study', 1), ('reasoning-machines/pal', 0.5125070214271545, 'llm', 0), ('bytedance/lightseq', 0.5104448199272156, 'nlp', 1), ('hwchase17/langchain', 0.5097209215164185, 'llm', 0), ('ukplab/sentence-transformers', 0.5090245604515076, 'nlp', 0), ('kagisearch/vectordb', 0.5087656378746033, 'data', 1), ('franck-dernoncourt/neuroner', 0.5083341598510742, 'nlp', 1), ('shishirpatil/gorilla', 0.5080721378326416, 'llm', 1), ('maartengr/keybert', 0.5080262422561646, 'nlp', 1), ('fasteval/fasteval', 0.507324755191803, 'llm', 1), ('qdrant/fastembed', 0.5066239833831787, 'ml', 0), ('mindsdb/mindsdb', 0.5058019161224365, 'data', 1), ('plasticityai/magnitude', 0.5057362914085388, 'nlp', 1), ('baichuan-inc/baichuan-13b', 0.5055248141288757, 'llm', 0), ('squeezeailab/squeezellm', 0.5054431557655334, 'llm', 2), ('lightning-ai/lit-llama', 0.5034503936767578, 'llm', 1), ('openlmlab/moss', 0.5025514364242554, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5023376941680908, 'llm', 2), ('microsoft/autogen', 0.5008726119995117, 'llm', 0), ('ofa-sys/ofa', 0.500640332698822, 'llm', 1), ('nomic-ai/semantic-search-app-template', 0.5003646016120911, 'study', 0), ('princeton-nlp/alce', 0.5002155900001526, 'llm', 0)]",250,4.0,,28.73,858,528,36,0,7,15,7,858.0,1398.0,90.0,1.6,72
1521,nlp,https://github.com/facebookresearch/seamless_communication,"['text-to-speech', 'speech-to-text']",,[],[],,,,facebookresearch/seamless_communication,seamless_communication,9358,954,140,Jupyter Notebook,,Foundational Models for State-of-the-Art Speech and Text Translation,facebookresearch,2024-01-14,2023-08-01,26,359.9230769230769,https://avatars.githubusercontent.com/u/16943930?v=4,Foundational Models for State-of-the-Art Speech and Text Translation,[],"['speech-to-text', 'text-to-speech']",2024-01-11,"[('pndurette/gtts', 0.5938905477523804, 'util', 1), ('openai/whisper', 0.5865522623062134, 'ml-dl', 1), ('suno-ai/bark', 0.553849995136261, 'ml', 0), ('juncongmoo/pyllama', 0.5527052879333496, 'llm', 0), ('nateshmbhat/pyttsx3', 0.5521857142448425, 'util', 1), ('next-gpt/next-gpt', 0.5332794785499573, 'llm', 0), ('infinitylogesh/mutate', 0.5298645496368408, 'nlp', 0), ('m-bain/whisperx', 0.5283752083778381, 'nlp', 1), ('thudm/chatglm-6b', 0.5281906127929688, 'llm', 0), ('nvidia/nemo', 0.5158795714378357, 'nlp', 2), ('gunthercox/chatterbot-corpus', 0.5149349570274353, 'nlp', 0), ('espnet/espnet', 0.5085058212280273, 'nlp', 0), ('openlmlab/moss', 0.5070318579673767, 'llm', 0), ('ai21labs/lm-evaluation', 0.505820631980896, 'llm', 0), ('huggingface/text-generation-inference', 0.5040040612220764, 'llm', 0)]",29,6.0,,1.79,159,85,6,0,0,0,0,159.0,174.0,90.0,1.1,72
1901,util,https://github.com/mitsuhiko/rye,"['dependency-manager', 'packaging', 'package-manager']",,[],[],,,,mitsuhiko/rye,rye,7781,197,46,Rust,https://rye-up.com,An Experimental Package Management Solution for Python,mitsuhiko,2024-01-17,2023-04-22,40,192.46289752650176,,An Experimental Package Management Solution for Python,"['package-manager', 'packaging']","['dependency-manager', 'package-manager', 'packaging']",2024-01-16,"[('indygreg/pyoxidizer', 0.8726885914802551, 'util', 2), ('python-poetry/poetry', 0.8581348657608032, 'util', 3), ('pypa/flit', 0.825670599937439, 'util', 2), ('pomponchik/instld', 0.748336672782898, 'util', 1), ('pdm-project/pdm', 0.7408201694488525, 'util', 2), ('pypa/hatch', 0.7138620615005493, 'util', 2), ('regebro/pyroma', 0.661931574344635, 'util', 1), ('pyodide/micropip', 0.6547753810882568, 'util', 0), ('pypi/warehouse', 0.6509472131729126, 'util', 0), ('mamba-org/mamba', 0.6489464044570923, 'util', 2), ('tox-dev/pipdeptree', 0.5925803780555725, 'util', 0), ('tezromach/python-package-template', 0.5775073170661926, 'template', 0), ('jazzband/pip-tools', 0.5770611763000488, 'util', 1), ('ofek/pyapp', 0.575480043888092, 'util', 1), ('spack/spack', 0.5651411414146423, 'util', 1), ('tiangolo/poetry-version-plugin', 0.5633344054222107, 'util', 1), ('conda/conda', 0.5606615543365479, 'util', 2), ('ivankorobkov/python-inject', 0.5525853037834167, 'util', 0), ('pytables/pytables', 0.54390549659729, 'data', 0), ('python-injector/injector', 0.5345786213874817, 'util', 0), ('mamba-org/gator', 0.5343549847602844, 'jupyter', 0), ('beeware/briefcase', 0.5340247750282288, 'util', 0), ('pypa/pipenv', 0.5276722311973572, 'util', 1), ('pypa/installer', 0.5275384187698364, 'util', 0), ('omry/omegaconf', 0.5054756999015808, 'util', 0), ('thoth-station/micropipenv', 0.5018079280853271, 'util', 0), ('mgedmin/check-manifest', 0.5007199645042419, 'util', 0)]",52,3.0,,8.0,60,33,9,0,21,30,21,60.0,104.0,90.0,1.7,72
1193,llm,https://github.com/thudm/chatglm-6b,['language-model'],,[],[],,,,thudm/chatglm-6b,ChatGLM-6B,37146,4974,382,Python,,ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型,thudm,2024-01-14,2023-03-13,46,805.0216718266254,https://avatars.githubusercontent.com/u/48590610?v=4,ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型,[],['language-model'],2023-10-27,"[('thudm/chatglm2-6b', 0.7089318633079529, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.6088327765464783, 'nlp', 0), ('thudm/glm-130b', 0.5821515321731567, 'llm', 0), ('freedomintelligence/llmzoo', 0.5757870674133301, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5738417506217957, 'llm', 1), ('openlmlab/moss', 0.556516170501709, 'llm', 1), ('suno-ai/bark', 0.552562415599823, 'ml', 0), ('mit-han-lab/streaming-llm', 0.5384843945503235, 'llm', 0), ('ctlllll/llm-toolmaker', 0.536123514175415, 'llm', 1), ('lm-sys/fastchat', 0.5337995290756226, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5303475856781006, 'llm', 0), ('guidance-ai/guidance', 0.5300068259239197, 'llm', 1), ('facebookresearch/seamless_communication', 0.5281906127929688, 'nlp', 0), ('srush/minichain', 0.5271021127700806, 'llm', 0), ('ai21labs/lm-evaluation', 0.5155556201934814, 'llm', 1)]",49,4.0,,5.29,97,36,10,3,0,0,0,97.0,112.0,90.0,1.2,71
1104,llm,https://github.com/openai/chatgpt-retrieval-plugin,[],,[],[],,,,openai/chatgpt-retrieval-plugin,chatgpt-retrieval-plugin,20419,3796,323,Python,,The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.,openai,2024-01-13,2023-03-23,44,456.6549520766773,https://avatars.githubusercontent.com/u/14957082?v=4,The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.,"['chatgpt', 'chatgpt-plugins']","['chatgpt', 'chatgpt-plugins']",2023-12-04,"[('langchain-ai/chat-langchain', 0.5608171820640564, 'llm', 0), ('run-llama/rags', 0.5382370948791504, 'llm', 1), ('rcgai/simplyretrieve', 0.5177233815193176, 'llm', 0), ('killianlucas/open-interpreter', 0.5168745517730713, 'llm', 1), ('h2oai/h2ogpt', 0.5049951076507568, 'llm', 1)]",39,6.0,,1.73,44,18,10,1,0,0,0,44.0,33.0,90.0,0.8,71
327,security,https://github.com/aquasecurity/trivy,[],,[],[],,,,aquasecurity/trivy,trivy,19961,2005,166,Go,https://aquasecurity.github.io/trivy,"Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more",aquasecurity,2024-01-13,2019-04-11,250,79.61652421652421,https://avatars.githubusercontent.com/u/12783832?v=4,"Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more","['containers', 'devsecops', 'docker', 'go', 'golang', 'iac', 'infrastructure-as-code', 'kubernetes', 'misconfiguration', 'security', 'security-tools', 'vulnerability', 'vulnerability-detection', 'vulnerability-scanners']","['containers', 'devsecops', 'docker', 'go', 'golang', 'iac', 'infrastructure-as-code', 'kubernetes', 'misconfiguration', 'security', 'security-tools', 'vulnerability', 'vulnerability-detection', 'vulnerability-scanners']",2024-01-12,"[('gefyrahq/gefyra', 0.6041578054428101, 'util', 3), ('pytest-dev/pytest-testinfra', 0.538640558719635, 'testing', 3), ('aswinnnn/pyscan', 0.5372655987739563, 'security', 4), ('tiiuae/sbomnix', 0.5336388945579529, 'util', 2), ('chaostoolkit/chaostoolkit', 0.5057849287986755, 'util', 0)]",355,2.0,,13.77,409,306,58,0,27,28,27,409.0,603.0,90.0,1.5,71
372,ml-dl,https://github.com/pyg-team/pytorch_geometric,[],,[],[],,,,pyg-team/pytorch_geometric,pytorch_geometric,19351,3467,252,Python,https://pyg.org,Graph Neural Network Library for PyTorch,pyg-team,2024-01-14,2017-10-06,329,58.71564802774166,https://avatars.githubusercontent.com/u/89995122?v=4,Graph Neural Network Library for PyTorch,"['deep-learning', 'geometric-deep-learning', 'graph-convolutional-networks', 'graph-neural-networks', 'pytorch']","['deep-learning', 'geometric-deep-learning', 'graph-convolutional-networks', 'graph-neural-networks', 'pytorch']",2024-01-13,"[('dmlc/dgl', 0.7811087965965271, 'ml-dl', 2), ('danielegrattarola/spektral', 0.7439136505126953, 'ml-dl', 2), ('pytorch/ignite', 0.7287918925285339, 'ml-dl', 2), ('hazyresearch/hgcn', 0.6979220509529114, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.6894313097000122, 'study', 2), ('a-r-j/graphein', 0.6886248588562012, 'sim', 4), ('stellargraph/stellargraph', 0.6726529002189636, 'graph', 4), ('graphistry/pygraphistry', 0.6707996129989624, 'data', 0), ('skorch-dev/skorch', 0.6679598093032837, 'ml-dl', 1), ('chandlerbang/awesome-self-supervised-gnn', 0.6452118158340454, 'study', 2), ('rasbt/machine-learning-book', 0.6416996121406555, 'study', 2), ('intel/intel-extension-for-pytorch', 0.6297549605369568, 'perf', 2), ('rampasek/graphgps', 0.6243663430213928, 'graph', 0), ('denys88/rl_games', 0.618355929851532, 'ml-rl', 2), ('cvxgrp/pymde', 0.6165646910667419, 'ml', 1), ('tensorlayer/tensorlayer', 0.6017088294029236, 'ml-rl', 1), ('kornia/kornia', 0.5945971012115479, 'ml-dl', 2), ('facebookresearch/pytorch3d', 0.5885642766952515, 'ml-dl', 0), ('allenai/allennlp', 0.5823215246200562, 'nlp', 2), ('nicolas-chaulet/torch-points3d', 0.580845832824707, 'ml', 0), ('accenture/ampligraph', 0.58009934425354, 'data', 0), ('intellabs/bayesian-torch', 0.5782586932182312, 'ml', 2), ('pytorch/rl', 0.5780683755874634, 'ml-rl', 1), ('pytorch/torchrec', 0.570334255695343, 'ml-dl', 2), ('nvidia/apex', 0.5672152042388916, 'ml-dl', 0), ('rucaibox/recbole', 0.566724419593811, 'ml', 3), ('xl0/lovely-tensors', 0.5648797154426575, 'ml-dl', 2), ('benedekrozemberczki/tigerlily', 0.561957597732544, 'ml-dl', 1), ('keras-team/keras', 0.5601423978805542, 'ml-dl', 2), ('google-deepmind/materials_discovery', 0.5600719451904297, 'sim', 0), ('lucidrains/imagen-pytorch', 0.5598738193511963, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5547471046447754, 'ml-dl', 0), ('pytorch/data', 0.5495861768722534, 'data', 0), ('lutzroeder/netron', 0.5430858731269836, 'ml', 2), ('huggingface/transformers', 0.538887619972229, 'nlp', 2), ('karpathy/micrograd', 0.5318362712860107, 'study', 0), ('aistream-peelout/flow-forecast', 0.5282737016677856, 'time-series', 2), ('rentruewang/koila', 0.5282607674598694, 'ml', 2), ('neuralmagic/sparseml', 0.5272446274757385, 'ml-dl', 1), ('oml-team/open-metric-learning', 0.5263068079948425, 'ml', 2), ('horovod/horovod', 0.5256267189979553, 'ml-ops', 2), ('nvlabs/gcvit', 0.5232054591178894, 'diffusion', 1), ('huggingface/huggingface_hub', 0.5223245620727539, 'ml', 2), ('microsoft/deepspeed', 0.5219319462776184, 'ml-dl', 2), ('thu-ml/tianshou', 0.5205722451210022, 'ml-rl', 1), ('hysts/pytorch_image_classification', 0.5179129242897034, 'ml-dl', 1), ('explosion/thinc', 0.516636073589325, 'ml-dl', 2), ('tensorflow/tensorflow', 0.5166094899177551, 'ml-dl', 1), ('lightly-ai/lightly', 0.5151110887527466, 'ml', 2), ('ggerganov/ggml', 0.5148559212684631, 'ml', 0), ('huggingface/accelerate', 0.514308750629425, 'ml', 0), ('h4kor/graph-force', 0.5138907432556152, 'graph', 0), ('ageron/handson-ml2', 0.5134172439575195, 'ml', 0), ('google-research/deeplab2', 0.5118420720100403, 'ml', 0), ('ashleve/lightning-hydra-template', 0.5103349089622498, 'util', 2), ('arogozhnikov/einops', 0.5090392231941223, 'ml-dl', 2), ('mdbloice/augmentor', 0.5032522678375244, 'ml', 1), ('pytorch/pytorch', 0.503031313419342, 'ml-dl', 1), ('pygraphviz/pygraphviz', 0.502930760383606, 'viz', 0), ('pyro-ppl/pyro', 0.5015919208526611, 'ml-dl', 2), ('kevinmusgrave/pytorch-metric-learning', 0.501497745513916, 'ml', 2), ('uber/petastorm', 0.5014254450798035, 'data', 2)]",477,6.0,,20.87,535,427,76,0,3,6,3,535.0,785.0,90.0,1.5,71
599,web,https://github.com/pyscript/pyscript,"['pyodide', 'cpython', 'wasm', 'webassembly']","A framework that allows users to create rich Python applications in the browser using HTML's interface and the power of Pyodide, WASM, and modern web technologies.",[],[],,,,pyscript/pyscript,pyscript,17260,1434,167,Python,https://pyscript.net/,Try PyScript: https://pyscript.com  Examples: https://tinyurl.com/pyscript-examples  Community: https://discord.gg/HxvBtukrg2,pyscript,2024-01-14,2022-02-21,101,170.6497175141243,https://avatars.githubusercontent.com/u/100553281?v=4,Try PyScript: https://pyscript.com  Examples: https://tinyurl.com/pyscript-examples  Community: https://discord.gg/HxvBtukrg2,"['html', 'javascript', 'wasm']","['cpython', 'html', 'javascript', 'pyodide', 'wasm', 'webassembly']",2024-01-13,"[('masoniteframework/masonite', 0.6563617587089539, 'web', 0), ('pyscript/pyscript-cli', 0.606472909450531, 'web', 0), ('pywebio/pywebio', 0.5674092173576355, 'web', 0), ('bokeh/bokeh', 0.5267843008041382, 'viz', 1), ('killianlucas/open-interpreter', 0.5255423784255981, 'llm', 1), ('pyodide/pyodide', 0.5230824947357178, 'util', 3), ('microsoft/playwright-python', 0.5149538516998291, 'testing', 0), ('r0x0r/pywebview', 0.5112079381942749, 'gui', 2)]",116,1.0,,6.25,181,166,23,0,6,5,6,181.0,340.0,90.0,1.9,71
888,diffusion,https://github.com/apple/ml-stable-diffusion,[],,[],[],,,,apple/ml-stable-diffusion,ml-stable-diffusion,15566,835,134,Python,,Stable Diffusion with Core ML on Apple Silicon,apple,2024-01-14,2022-11-16,62,247.6409090909091,https://avatars.githubusercontent.com/u/10639145?v=4,Stable Diffusion with Core ML on Apple Silicon,[],[],2023-11-14,"[('divamgupta/diffusionbee-stable-diffusion-ui', 0.5508177876472473, 'diffusion', 0), ('ml-explore/mlx', 0.5221449136734009, 'ml', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5180650949478149, 'diffusion', 0), ('carson-katri/dream-textures', 0.5164445042610168, 'diffusion', 0)]",31,4.0,,1.37,40,17,14,2,5,5,5,40.0,104.0,90.0,2.6,71
39,jupyter,https://github.com/jupyterlab/jupyterlab,[],,[],[],,,,jupyterlab/jupyterlab,jupyterlab,13540,2973,315,TypeScript,https://jupyterlab.readthedocs.io/,JupyterLab computational environment.,jupyterlab,2024-01-14,2016-06-03,399,33.8863067572399,https://avatars.githubusercontent.com/u/22800682?v=4,JupyterLab computational environment.,"['jupyter', 'jupyterlab']","['jupyter', 'jupyterlab']",2024-01-13,"[('jupyterlab/jupyterlab-desktop', 0.7525447607040405, 'jupyter', 2), ('ipython/ipyparallel', 0.6818181872367859, 'perf', 1), ('jupyter/notebook', 0.669508159160614, 'jupyter', 1), ('ipython/ipykernel', 0.662561297416687, 'util', 1), ('jupyter/nbformat', 0.6569828987121582, 'jupyter', 0), ('jupyterlite/jupyterlite', 0.6288254261016846, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.6188204884529114, 'jupyter', 0), ('mamba-org/gator', 0.6041745543479919, 'jupyter', 0), ('jupyter/nbconvert', 0.5970379710197449, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5963035821914673, 'jupyter', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5912031531333923, 'jupyter', 2), ('mwouts/jupytext', 0.5835863947868347, 'jupyter', 1), ('chaoleili/jupyterlab_tensorboard', 0.5774864554405212, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5746160745620728, 'study', 0), ('jupyterlab/jupyter-ai', 0.5743774771690369, 'jupyter', 2), ('voila-dashboards/voila', 0.5699175596237183, 'jupyter', 1), ('koaning/drawdata', 0.569868266582489, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.5636622309684753, 'jupyter', 1), ('quantopian/qgrid', 0.5634688138961792, 'jupyter', 0), ('nteract/testbook', 0.5517867207527161, 'jupyter', 0), ('computationalmodelling/nbval', 0.5462405681610107, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5447031259536743, 'study', 0), ('cohere-ai/notebooks', 0.5374837517738342, 'llm', 0), ('jupyter-widgets/ipyleaflet', 0.523621678352356, 'gis', 1), ('aws/graph-notebook', 0.5233699083328247, 'jupyter', 1), ('bloomberg/ipydatagrid', 0.518395185470581, 'jupyter', 0), ('ageron/handson-ml2', 0.5005123019218445, 'ml', 0)]",577,8.0,,22.79,1042,801,93,0,35,4883,35,1042.0,1691.0,90.0,1.6,71
1560,ml,https://github.com/ggerganov/ggml,[],,[],[],,,,ggerganov/ggml,ggml,8536,822,109,C,,Tensor library for machine learning,ggerganov,2024-01-14,2022-09-18,71,119.7434869739479,,Tensor library for machine learning,"['automatic-differentiation', 'large-language-models', 'machine-learning', 'tensor-algebra']","['automatic-differentiation', 'large-language-models', 'machine-learning', 'tensor-algebra']",2024-01-13,"[('tensorly/tensorly', 0.7053402066230774, 'ml-dl', 2), ('arogozhnikov/einops', 0.674818754196167, 'ml-dl', 0), ('xl0/lovely-tensors', 0.6742641925811768, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.6585282683372498, 'ml-dl', 1), ('pytorch/pytorch', 0.6398614645004272, 'ml-dl', 1), ('nvidia/tensorrt-llm', 0.6161227822303772, 'viz', 0), ('google/tf-quant-finance', 0.6140791773796082, 'finance', 0), ('keras-team/autokeras', 0.6085128784179688, 'ml-dl', 1), ('tensorflow/tensorflow', 0.6057413816452026, 'ml-dl', 1), ('karpathy/micrograd', 0.6055065393447876, 'study', 0), ('pytorch/ignite', 0.6042031645774841, 'ml-dl', 1), ('huggingface/transformers', 0.6029016375541687, 'nlp', 1), ('skorch-dev/skorch', 0.5978483557701111, 'ml-dl', 1), ('tensorflow/similarity', 0.5696033239364624, 'ml-dl', 1), ('patrick-kidger/torchtyping', 0.5691167712211609, 'typing', 0), ('horovod/horovod', 0.5658283829689026, 'ml-ops', 1), ('ageron/handson-ml2', 0.5649846196174622, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.5619326233863831, 'perf', 1), ('aws/sagemaker-python-sdk', 0.5583137273788452, 'ml', 1), ('d2l-ai/d2l-en', 0.5564393997192383, 'study', 1), ('rasbt/machine-learning-book', 0.5550714135169983, 'study', 1), ('tensorflow/mesh', 0.5549049973487854, 'ml-dl', 0), ('tensorlayer/tensorlayer', 0.548178493976593, 'ml-rl', 0), ('neuralmagic/sparseml', 0.5481603741645813, 'ml-dl', 0), ('rasbt/mlxtend', 0.5479773283004761, 'ml', 1), ('oml-team/open-metric-learning', 0.5477278232574463, 'ml', 0), ('huggingface/exporters', 0.5472779870033264, 'ml', 1), ('microsoft/flaml', 0.5470587611198425, 'ml', 1), ('uber/petastorm', 0.5469512939453125, 'data', 1), ('mrdbourke/pytorch-deep-learning', 0.5461394190788269, 'study', 1), ('microsoft/nni', 0.5407408475875854, 'ml', 1), ('huggingface/datasets', 0.5396940112113953, 'nlp', 1), ('probml/pyprobml', 0.536136269569397, 'ml', 1), ('lightly-ai/lightly', 0.5310502052307129, 'ml', 1), ('explosion/thinc', 0.5289695858955383, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5287749171257019, 'ml-dl', 1), ('tensorflow/data-validation', 0.525627076625824, 'ml-ops', 0), ('tensorflow/addons', 0.525490939617157, 'ml', 1), ('hannibal046/awesome-llm', 0.5245451927185059, 'study', 0), ('timdettmers/bitsandbytes', 0.5234319567680359, 'util', 0), ('huggingface/huggingface_hub', 0.5206968188285828, 'ml', 1), ('tensorflow/tensor2tensor', 0.519944965839386, 'ml', 1), ('pycaret/pycaret', 0.5191986560821533, 'ml', 1), ('scikit-learn-contrib/lightning', 0.5173808336257935, 'ml', 1), ('keras-team/keras-nlp', 0.5159798860549927, 'nlp', 1), ('pyg-team/pytorch_geometric', 0.5148559212684631, 'ml-dl', 0), ('nccr-itmo/fedot', 0.5140013098716736, 'ml-ops', 1), ('huggingface/evaluate', 0.5103128552436829, 'ml', 1), ('keras-rl/keras-rl', 0.5087230205535889, 'ml-rl', 1), ('optimalscale/lmflow', 0.5078336596488953, 'llm', 0), ('determined-ai/determined', 0.5071011185646057, 'ml-ops', 1), ('microsoft/lora', 0.5070888996124268, 'llm', 0), ('keras-team/keras', 0.5067704319953918, 'ml-dl', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5067695379257202, 'study', 0), ('ray-project/ray', 0.505761981010437, 'ml-ops', 1), ('rasbt/deeplearning-models', 0.5030370354652405, 'ml-dl', 0), ('pytorch/rl', 0.5019291043281555, 'ml-rl', 1)]",112,7.0,,9.9,129,85,16,0,0,0,0,129.0,422.0,90.0,3.3,71
1196,llm,https://github.com/mlc-ai/web-llm,[],,[],[],,,,mlc-ai/web-llm,web-llm,8186,493,92,TypeScript,https://mlc.ai/web-llm,Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.,mlc-ai,2024-01-13,2023-04-13,41,196.23972602739727,https://avatars.githubusercontent.com/u/106173866?v=4,Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.,"['chatgpt', 'deep-learning', 'language-model', 'llm', 'tvm', 'webgpu', 'webml']","['chatgpt', 'deep-learning', 'language-model', 'llm', 'tvm', 'webgpu', 'webml']",2024-01-04,"[('mlc-ai/web-stable-diffusion', 0.6759905219078064, 'diffusion', 4), ('microsoft/autogen', 0.661967396736145, 'llm', 1), ('next-gpt/next-gpt', 0.6524577140808105, 'llm', 2), ('nomic-ai/gpt4all', 0.6522467136383057, 'llm', 1), ('hwchase17/langchain', 0.6466255187988281, 'llm', 1), ('young-geng/easylm', 0.6428095102310181, 'llm', 2), ('thudm/chatglm2-6b', 0.6331812143325806, 'llm', 1), ('oobabooga/text-generation-webui', 0.6323723793029785, 'llm', 1), ('bigscience-workshop/petals', 0.6224874258041382, 'data', 1), ('lm-sys/fastchat', 0.6169783473014832, 'llm', 1), ('embedchain/embedchain', 0.6080430746078491, 'llm', 2), ('killianlucas/open-interpreter', 0.6023691892623901, 'llm', 1), ('run-llama/rags', 0.5972565412521362, 'llm', 2), ('fasteval/fasteval', 0.5912193655967712, 'llm', 1), ('nat/openplayground', 0.589094340801239, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5826047658920288, 'perf', 0), ('xtekky/gpt4free', 0.5752508640289307, 'llm', 2), ('openlmlab/moss', 0.5738821029663086, 'llm', 3), ('lianjiatech/belle', 0.5615787506103516, 'llm', 0), ('encode/starlette', 0.5606010556221008, 'web', 0), ('salesforce/xgen', 0.5496227741241455, 'llm', 2), ('guidance-ai/guidance', 0.5491284728050232, 'llm', 2), ('titanml/takeoff', 0.548348069190979, 'llm', 2), ('hannibal046/awesome-llm', 0.5478937029838562, 'study', 1), ('hiyouga/llama-factory', 0.5438234210014343, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5438233613967896, 'llm', 2), ('alphasecio/langchain-examples', 0.5424495339393616, 'llm', 1), ('li-plus/chatglm.cpp', 0.542007565498352, 'llm', 0), ('bobazooba/xllm', 0.5402325987815857, 'llm', 3), ('blinkdl/chatrwkv', 0.5393033623695374, 'llm', 2), ('sjtu-ipads/powerinfer', 0.5370975136756897, 'llm', 1), ('dylanhogg/llmgraph', 0.5363897085189819, 'ml', 2), ('shishirpatil/gorilla', 0.533381462097168, 'llm', 2), ('pathwaycom/llm-app', 0.5320335030555725, 'llm', 1), ('deepset-ai/haystack', 0.5301832556724548, 'llm', 2), ('langchain-ai/langgraph', 0.5244644284248352, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5234134197235107, 'llm', 1), ('langchain-ai/chat-langchain', 0.5200137495994568, 'llm', 0), ('pallets/werkzeug', 0.5196402668952942, 'web', 0), ('ctlllll/llm-toolmaker', 0.5185114145278931, 'llm', 1), ('freedomintelligence/llmzoo', 0.517315149307251, 'llm', 1), ('rasahq/rasa', 0.5135403275489807, 'llm', 0), ('aiqc/aiqc', 0.5105668306350708, 'ml-ops', 0), ('lightning-ai/lit-llama', 0.508145809173584, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5051769614219666, 'llm', 2), ('databrickslabs/dolly', 0.5030844807624817, 'llm', 0), ('explosion/spacy-llm', 0.5018036961555481, 'llm', 1), ('mayooear/gpt4-pdf-chatbot-langchain', 0.50135737657547, 'llm', 0), ('deeppavlov/deeppavlov', 0.5006988644599915, 'nlp', 1), ('baichuan-inc/baichuan-13b', 0.5006130933761597, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5002791881561279, 'llm', 2)]",25,8.0,,2.58,75,61,9,0,1,1,1,75.0,174.0,90.0,2.3,71
1712,ml-ops,https://github.com/bentoml/openllm,[],,[],[],,,,bentoml/openllm,OpenLLM,7553,519,49,Python,https://bentoml.com,Operating LLMs in production,bentoml,2024-01-14,2023-04-19,40,184.86363636363637,https://avatars.githubusercontent.com/u/49176046?v=4,Operating LLMs in production,"['ai', 'bentoml', 'falcon', 'fine-tuning', 'llama', 'llama2', 'llm', 'llm-inference', 'llm-ops', 'llm-serving', 'llmops', 'mistral', 'ml', 'mlops', 'model-inference', 'mpt', 'open-source-llm', 'openllm', 'stablelm', 'vicuna']","['ai', 'bentoml', 'falcon', 'fine-tuning', 'llama', 'llama2', 'llm', 'llm-inference', 'llm-ops', 'llm-serving', 'llmops', 'mistral', 'ml', 'mlops', 'model-inference', 'mpt', 'open-source-llm', 'openllm', 'stablelm', 'vicuna']",2024-01-12,"[('vllm-project/vllm', 0.7688142657279968, 'llm', 5), ('predibase/lorax', 0.7207032442092896, 'llm', 6), ('eugeneyan/open-llms', 0.7019832134246826, 'study', 1), ('bigscience-workshop/petals', 0.6789776682853699, 'data', 3), ('ray-project/ray-llm', 0.6621026396751404, 'llm', 4), ('h2oai/h2o-llmstudio', 0.6586171388626099, 'llm', 5), ('tigerlab-ai/tiger', 0.6545884013175964, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.6482536792755127, 'llm', 1), ('hiyouga/llama-factory', 0.6292675733566284, 'llm', 4), ('hiyouga/llama-efficient-tuning', 0.6292673945426941, 'llm', 4), ('agenta-ai/agenta', 0.6264759302139282, 'llm', 2), ('microsoft/promptflow', 0.6225191950798035, 'llm', 2), ('ray-project/llm-applications', 0.6087220311164856, 'llm', 2), ('microsoft/torchscale', 0.5949733853340149, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5912082195281982, 'perf', 1), ('jerryjliu/llama_index', 0.5906221866607666, 'llm', 3), ('bobazooba/xllm', 0.5858603119850159, 'llm', 5), ('truera/trulens', 0.5837077498435974, 'llm', 2), ('ludwig-ai/ludwig', 0.5829933881759644, 'ml-ops', 6), ('microsoft/lmops', 0.581097424030304, 'llm', 1), ('confident-ai/deepeval', 0.575341522693634, 'testing', 2), ('tairov/llama2.mojo', 0.5740443468093872, 'llm', 2), ('pathwaycom/llm-app', 0.5679009556770325, 'llm', 2), ('hegelai/prompttools', 0.565089225769043, 'llm', 0), ('microsoft/semantic-kernel', 0.5633988380432129, 'llm', 2), ('titanml/takeoff', 0.5552429556846619, 'llm', 2), ('bentoml/bentoml', 0.5529214143753052, 'ml-ops', 5), ('young-geng/easylm', 0.5489903688430786, 'llm', 1), ('salesforce/xgen', 0.5479778051376343, 'llm', 1), ('microsoft/jarvis', 0.5471473336219788, 'llm', 0), ('openai/evals', 0.5440770387649536, 'llm', 0), ('deepset-ai/haystack', 0.543747067451477, 'llm', 1), ('argilla-io/argilla', 0.5431055426597595, 'nlp', 3), ('nomic-ai/gpt4all', 0.5412029027938843, 'llm', 1), ('run-llama/llama-hub', 0.5397924184799194, 'data', 1), ('lightning-ai/lit-gpt', 0.5373291373252869, 'llm', 1), ('skypilot-org/skypilot', 0.536402702331543, 'llm', 1), ('run-llama/llama-lab', 0.5358179211616516, 'llm', 1), ('iryna-kondr/scikit-llm', 0.5351253151893616, 'llm', 1), ('nebuly-ai/nebullvm', 0.5305408835411072, 'perf', 2), ('citadel-ai/langcheck', 0.5283021926879883, 'llm', 0), ('salesforce/codet5', 0.5279268622398376, 'nlp', 0), ('tloen/alpaca-lora', 0.5242617726325989, 'llm', 1), ('zrrskywalker/llama-adapter', 0.5195848345756531, 'llm', 1), ('microsoft/llama-2-onnx', 0.5186194181442261, 'llm', 1), ('mooler0410/llmspracticalguide', 0.516433596611023, 'study', 0), ('ajndkr/lanarky', 0.5158595442771912, 'llm', 1), ('berriai/litellm', 0.5140005350112915, 'llm', 2), ('polyaxon/polyaxon', 0.5105753540992737, 'ml-ops', 2), ('explosion/spacy-llm', 0.5071743726730347, 'llm', 3), ('shishirpatil/gorilla', 0.5051870942115784, 'llm', 1), ('determined-ai/determined', 0.5048798322677612, 'ml-ops', 1), ('microsoft/llmlingua', 0.5037171244621277, 'llm', 0), ('zilliztech/gptcache', 0.5029482245445251, 'llm', 2)]",23,2.0,,26.02,385,341,9,0,105,184,105,384.0,275.0,90.0,0.7,71
1464,llm,https://github.com/huggingface/text-generation-inference,[],,[],[],,,,huggingface/text-generation-inference,text-generation-inference,6605,714,88,Python,http://hf.co/docs/text-generation-inference,Large Language Model Text Generation Inference,huggingface,2024-01-14,2022-10-08,68,96.52400835073068,https://avatars.githubusercontent.com/u/25720743?v=4,Large Language Model Text Generation Inference,"['bloom', 'deep-learning', 'falcon', 'gpt', 'inference', 'nlp', 'pytorch', 'starcoder', 'transformer']","['bloom', 'deep-learning', 'falcon', 'gpt', 'inference', 'nlp', 'pytorch', 'starcoder', 'transformer']",2024-01-11,"[('infinitylogesh/mutate', 0.6840097904205322, 'nlp', 0), ('lianjiatech/belle', 0.6746523380279541, 'llm', 1), ('bytedance/lightseq', 0.672805666923523, 'nlp', 3), ('google-research/electra', 0.648766815662384, 'ml-dl', 2), ('minimaxir/gpt-2-simple', 0.6420865058898926, 'llm', 0), ('minimaxir/textgenrnn', 0.6332951188087463, 'nlp', 1), ('hannibal046/awesome-llm', 0.616241455078125, 'study', 1), ('google/sentencepiece', 0.607265830039978, 'nlp', 0), ('optimalscale/lmflow', 0.6065784692764282, 'llm', 3), ('princeton-nlp/alce', 0.6029461622238159, 'llm', 0), ('squeezeailab/squeezellm', 0.6000766158103943, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5956083536148071, 'llm', 1), ('bigcode-project/starcoder', 0.5950606465339661, 'llm', 0), ('databrickslabs/dolly', 0.5939000248908997, 'llm', 1), ('yueyu1030/attrprompt', 0.5905746221542358, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5844063758850098, 'llm', 1), ('sharonzhou/long_stable_diffusion', 0.5840281844139099, 'diffusion', 0), ('bigscience-workshop/megatron-deepspeed', 0.5798518061637878, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5798518061637878, 'llm', 0), ('togethercomputer/redpajama-data', 0.5783564448356628, 'llm', 0), ('deepset-ai/farm', 0.5736597180366516, 'nlp', 3), ('explosion/spacy-llm', 0.5718468427658081, 'llm', 2), ('allenai/allennlp', 0.5714684128761292, 'nlp', 3), ('jonasgeiping/cramming', 0.5711867213249207, 'nlp', 0), ('openlmlab/moss', 0.5647656917572021, 'llm', 1), ('microsoft/autogen', 0.5635223984718323, 'llm', 1), ('minimaxir/aitextgen', 0.563347339630127, 'llm', 0), ('ai21labs/lm-evaluation', 0.5604375004768372, 'llm', 0), ('huggingface/transformers', 0.557208240032196, 'nlp', 4), ('ctlllll/llm-toolmaker', 0.5557337403297424, 'llm', 0), ('salesforce/xgen', 0.5519874691963196, 'llm', 1), ('next-gpt/next-gpt', 0.5511967539787292, 'llm', 0), ('bobazooba/xllm', 0.5498690605163574, 'llm', 3), ('alibaba/easynlp', 0.5467448234558105, 'nlp', 3), ('baichuan-inc/baichuan-13b', 0.546048104763031, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5448298454284668, 'llm', 2), ('facebookresearch/shepherd', 0.5433861017227173, 'llm', 0), ('cg123/mergekit', 0.541591465473175, 'llm', 0), ('llmware-ai/llmware', 0.5404112339019775, 'llm', 2), ('lm-sys/fastchat', 0.538799524307251, 'llm', 0), ('microsoft/lora', 0.5352991223335266, 'llm', 2), ('intellabs/fastrag', 0.5351554155349731, 'nlp', 1), ('bigscience-workshop/biomedical', 0.5347291231155396, 'data', 0), ('norskregnesentral/skweak', 0.5326890349388123, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.531993567943573, 'llm', 1), ('lupantech/chameleon-llm', 0.531190812587738, 'llm', 0), ('maartengr/bertopic', 0.5277509093284607, 'nlp', 1), ('eugeneyan/obsidian-copilot', 0.5260320901870728, 'llm', 0), ('juncongmoo/pyllama', 0.5241082310676575, 'llm', 0), ('lvwerra/trl', 0.523781955242157, 'llm', 0), ('freedomintelligence/llmzoo', 0.523625373840332, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5219200253486633, 'llm', 0), ('vllm-project/vllm', 0.5211599469184875, 'llm', 4), ('muennighoff/sgpt', 0.519349992275238, 'llm', 1), ('reasoning-machines/pal', 0.5183734893798828, 'llm', 0), ('huggingface/text-embeddings-inference', 0.5171723365783691, 'llm', 0), ('nvidia/deeplearningexamples', 0.5152902007102966, 'ml-dl', 3), ('openai/finetune-transformer-lm', 0.5149126648902893, 'llm', 0), ('graykode/nlp-tutorial', 0.5133894681930542, 'study', 3), ('nltk/nltk', 0.5107032060623169, 'nlp', 1), ('srush/minichain', 0.5085004568099976, 'llm', 0), ('microsoft/unilm', 0.5068984627723694, 'nlp', 1), ('eleutherai/the-pile', 0.5064693093299866, 'data', 0), ('thudm/cogvideo', 0.5058658719062805, 'ml', 0), ('facebookresearch/seamless_communication', 0.5040040612220764, 'nlp', 0), ('keras-team/keras-nlp', 0.5031775236129761, 'nlp', 2), ('bigscience-workshop/petals', 0.5030701160430908, 'data', 7), ('extreme-bert/extreme-bert', 0.5023447871208191, 'llm', 4), ('openbmb/toolbench', 0.5014093518257141, 'llm', 0)]",59,4.0,,9.02,365,244,15,0,32,26,32,365.0,867.0,90.0,2.4,71
1848,llm,https://github.com/assafelovic/gpt-researcher,"['researcher', 'autonomous-agent']",,[],[],,,,assafelovic/gpt-researcher,gpt-researcher,6496,770,70,Python,https://tavily.com,GPT based autonomous agent that does online comprehensive research on any given topic,assafelovic,2024-01-14,2023-05-12,37,172.89733840304183,,GPT based autonomous agent that does online comprehensive research on any given topic,[],"['autonomous-agent', 'researcher']",2024-01-07,"[('yoheinakajima/babyagi', 0.6752115488052368, 'llm', 0), ('linksoul-ai/autoagents', 0.6728865504264832, 'llm', 0), ('torantulino/auto-gpt', 0.6427838802337646, 'llm', 0), ('geekan/metagpt', 0.6312734484672546, 'llm', 0), ('transformeroptimus/superagi', 0.5870476961135864, 'llm', 0), ('oneil512/insight', 0.5495372414588928, 'ml', 0), ('google-research/google-research', 0.545631468296051, 'ml', 0), ('operand/agency', 0.5227841734886169, 'llm', 1), ('langchain-ai/opengpts', 0.511093020439148, 'llm', 0), ('antonosika/gpt-engineer', 0.5011528730392456, 'llm', 1)]",25,5.0,,6.81,121,88,8,0,13,20,13,120.0,139.0,90.0,1.2,71
178,web,https://github.com/psf/requests,[],,[],[],,,,psf/requests,requests,50848,9298,1340,Python,https://requests.readthedocs.io/en/latest/,"A simple, yet elegant, HTTP library.",psf,2024-01-14,2011-02-13,676,75.18715673848753,https://avatars.githubusercontent.com/u/50630501?v=4,"A simple, yet elegant, HTTP library.","['client', 'cookies', 'forhumans', 'http', 'humans', 'python-requests', 'requests']","['client', 'cookies', 'forhumans', 'http', 'humans', 'python-requests', 'requests']",2024-01-08,"[('requests/toolbelt', 0.7315183281898499, 'util', 2), ('encode/httpx', 0.7030969858169556, 'web', 1), ('encode/uvicorn', 0.6152986884117126, 'web', 1), ('getsentry/responses', 0.6119535565376282, 'testing', 1), ('simple-salesforce/simple-salesforce', 0.5815314650535583, 'data', 0), ('hugapi/hug', 0.5775818228721619, 'util', 1), ('cherrypy/cherrypy', 0.5770725607872009, 'web', 1), ('scrapy/scrapy', 0.5722671151161194, 'data', 0), ('falconry/falcon', 0.5613847970962524, 'web', 1), ('pallets/werkzeug', 0.559667706489563, 'web', 1), ('magicstack/httptools', 0.5530205965042114, 'web', 0), ('googleapis/google-api-python-client', 0.552095353603363, 'util', 0), ('neoteroi/blacksheep', 0.551236629486084, 'web', 1), ('roniemartinez/dude', 0.5494452118873596, 'util', 0), ('dsdanielpark/bard-api', 0.5480201244354248, 'llm', 0), ('pallets/quart', 0.5377407670021057, 'web', 0), ('binux/pyspider', 0.5332942605018616, 'data', 0), ('aio-libs/aiohttp', 0.5318570137023926, 'web', 1), ('jiffyclub/snakeviz', 0.5308777093887329, 'profiling', 0), ('alirezamika/autoscraper', 0.5295840501785278, 'data', 0), ('tedivm/robs_awesome_python_template', 0.5218177437782288, 'template', 0), ('snyk-labs/pysnyk', 0.518162727355957, 'security', 0), ('masoniteframework/masonite', 0.5165953040122986, 'web', 0), ('klen/muffin', 0.5109789967536926, 'web', 0), ('lyz-code/cookiecutter-python-project', 0.5095129013061523, 'template', 0), ('nv7-github/googlesearch', 0.5044116973876953, 'util', 0), ('webpy/webpy', 0.5032954812049866, 'web', 0)]",748,7.0,,1.27,148,115,157,0,3,12,3,148.0,152.0,90.0,1.0,70
261,web,https://github.com/sherlock-project/sherlock,[],,[],[],,,,sherlock-project/sherlock,sherlock,46128,5887,1063,Python,http://sherlock-project.github.io,🔎 Hunt down social media accounts by username across social networks,sherlock-project,2024-01-14,2018-12-24,266,173.32045088566827,https://avatars.githubusercontent.com/u/48293496?v=4,🔎 Hunt down social media accounts by username across social networks,"['cli', 'information-gathering', 'linux', 'macos', 'osint', 'reconnaissance', 'redteam', 'sherlock', 'tools', 'windows']","['cli', 'information-gathering', 'linux', 'macos', 'osint', 'reconnaissance', 'redteam', 'sherlock', 'tools', 'windows']",2024-01-10,"[('twintproject/twint', 0.5957009196281433, 'data', 1)]",225,4.0,,2.71,78,51,62,0,0,0,0,78.0,62.0,90.0,0.8,70
220,util,https://github.com/pyenv/pyenv,"['pip', 'venv']",pyenv lets you easily switch between multiple versions of Python.,[],[],,,,pyenv/pyenv,pyenv,34980,2948,386,Roff,,Simple Python version management,pyenv,2024-01-14,2012-08-31,595,58.733509234828496,https://avatars.githubusercontent.com/u/16530698?v=4,Simple Python version management,['shell'],"['pip', 'shell', 'venv']",2023-12-31,"[('pypa/pipenv', 0.6903738379478455, 'util', 2), ('pypa/hatch', 0.6283936500549316, 'util', 0), ('pypa/virtualenv', 0.6280038952827454, 'util', 2), ('pypa/pipx', 0.6269603371620178, 'util', 2), ('pomponchik/instld', 0.6027212738990784, 'util', 2), ('thoth-station/micropipenv', 0.5758981704711914, 'util', 1), ('pantsbuild/pex', 0.5503339171409607, 'util', 1), ('pdm-project/pdm', 0.5091418027877808, 'util', 0)]",437,5.0,,3.21,86,70,138,0,25,14,25,86.0,163.0,90.0,1.9,70
954,security,https://github.com/certbot/certbot,[],,[],[],,,,certbot/certbot,certbot,30525,3449,761,Python,,Certbot is EFF's tool to obtain certs from Let's Encrypt and (optionally) auto-enable HTTPS on your server.  It can also act as a client for any other CA that uses the ACME protocol.,certbot,2024-01-14,2014-11-12,480,63.48039215686274,https://avatars.githubusercontent.com/u/17889013?v=4,Certbot is EFF's tool to obtain certs from Let's Encrypt and (optionally) auto-enable HTTPS on your server.  It can also act as a client for any other CA that uses the ACME protocol.,"['acme', 'acme-client', 'certbot', 'certificate', 'letsencrypt']","['acme', 'acme-client', 'certbot', 'certificate', 'letsencrypt']",2024-01-06,[],525,4.0,,4.79,137,79,112,0,10,14,10,138.0,214.0,90.0,1.6,70
17,nlp,https://github.com/explosion/spacy,[],,[],[],,,,explosion/spacy,spaCy,27972,4354,557,Python,https://spacy.io,💫 Industrial-strength Natural Language Processing (NLP) in Python,explosion,2024-01-14,2014-07-03,499,55.97598627787307,https://avatars.githubusercontent.com/u/20011530?v=4,💫 Industrial-strength Natural Language Processing (NLP) in Python,"['ai', 'artificial-intelligence', 'cython', 'data-science', 'deep-learning', 'entity-linking', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'neural-network', 'neural-networks', 'nlp', 'nlp-library', 'spacy', 'text-classification', 'tokenization']","['ai', 'artificial-intelligence', 'cython', 'data-science', 'deep-learning', 'entity-linking', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'neural-network', 'neural-networks', 'nlp', 'nlp-library', 'spacy', 'text-classification', 'tokenization']",2024-01-02,"[('sloria/textblob', 0.7043011784553528, 'nlp', 2), ('nltk/nltk', 0.6628869771957397, 'nlp', 3), ('flairnlp/flair', 0.6546562910079956, 'nlp', 4), ('allenai/allennlp', 0.6516764163970947, 'nlp', 4), ('keras-team/keras-nlp', 0.6470063924789429, 'nlp', 4), ('explosion/spacy-models', 0.6409233212471008, 'nlp', 4), ('norskregnesentral/skweak', 0.6230126619338989, 'nlp', 4), ('rasahq/rasa', 0.6100810170173645, 'llm', 4), ('huggingface/transformers', 0.6091046929359436, 'nlp', 5), ('deepset-ai/farm', 0.6052958965301514, 'nlp', 3), ('alibaba/easynlp', 0.6040478348731995, 'nlp', 4), ('pemistahl/lingua-py', 0.5869116187095642, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.5861974954605103, 'llm', 1), ('makcedward/nlpaug', 0.5835755467414856, 'nlp', 6), ('explosion/thinc', 0.5825142860412598, 'ml-dl', 7), ('explosion/spacy-llm', 0.573280930519104, 'llm', 6), ('lexpredict/lexpredict-lexnlp', 0.5584388971328735, 'nlp', 1), ('thilinarajapakse/simpletransformers', 0.5573221445083618, 'nlp', 2), ('llmware-ai/llmware', 0.5547406077384949, 'llm', 3), ('ddbourgin/numpy-ml', 0.5528154373168945, 'ml', 2), ('clips/pattern', 0.5512140989303589, 'nlp', 2), ('nvidia/deeplearningexamples', 0.5494264364242554, 'ml-dl', 2), ('graykode/nlp-tutorial', 0.5477665066719055, 'study', 2), ('dylanhogg/awesome-python', 0.5455989241600037, 'study', 5), ('nvidia/nemo', 0.541452169418335, 'nlp', 3), ('gradio-app/gradio', 0.5368600487709045, 'viz', 3), ('lukaszahradnik/pyneuralogic', 0.5333031415939331, 'math', 2), ('merantix-momentum/squirrel-core', 0.5305957198143005, 'ml', 6), ('franck-dernoncourt/neuroner', 0.527627170085907, 'nlp', 5), ('minimaxir/aitextgen', 0.5253174901008606, 'llm', 0), ('huggingface/datasets', 0.5209731459617615, 'nlp', 4), ('lucidrains/toolformer-pytorch', 0.5190293192863464, 'llm', 2), ('explosion/spacy-stanza', 0.5188982486724854, 'nlp', 5), ('explosion/spacy-transformers', 0.5188299417495728, 'llm', 4), ('tensorly/tensorly', 0.5184281468391418, 'ml-dl', 1), ('jalammar/ecco', 0.5182880163192749, 'ml-interpretability', 2), ('fastai/fastcore', 0.5157615542411804, 'util', 0), ('ibm/transition-amr-parser', 0.5131350159645081, 'nlp', 2), ('databrickslabs/dolly', 0.5090692043304443, 'llm', 0), ('huggingface/neuralcoref', 0.5070719122886658, 'nlp', 4), ('kagisearch/vectordb', 0.506615936756134, 'data', 3), ('deeppavlov/deeppavlov', 0.5052632689476013, 'nlp', 6), ('scikit-learn/scikit-learn', 0.5003823637962341, 'ml', 2), ('quantconnect/lean', 0.5002435445785522, 'finance', 0)]",746,3.0,,6.94,97,76,116,0,12,16,12,97.0,177.0,90.0,1.8,70
1064,diffusion,https://github.com/lllyasviel/controlnet,[],,[],[],,,,lllyasviel/controlnet,ControlNet,26084,2427,202,Python,,Let us control diffusion models!,lllyasviel,2024-01-14,2023-02-01,51,502.9972451790634,,Let us control diffusion models!,[],[],2023-09-09,"[('bentoml/onediffusion', 0.6226494908332825, 'diffusion', 0), ('openai/improved-diffusion', 0.5762985944747925, 'diffusion', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5530334115028381, 'diffusion', 0), ('carson-katri/dream-textures', 0.5427067279815674, 'diffusion', 0)]",6,5.0,,3.52,110,20,12,4,0,0,0,109.0,175.0,90.0,1.6,70
1102,llm,https://github.com/tloen/alpaca-lora,"['llama', 'language-model']",,[],[],,,,tloen/alpaca-lora,alpaca-lora,17713,2161,156,Jupyter Notebook,,Instruct-tune LLaMA on consumer hardware,tloen,2024-01-14,2023-03-13,46,383.8730650154799,,Instruct-tune LLaMA on consumer hardware,[],"['language-model', 'llama']",2023-04-18,"[('zrrskywalker/llama-adapter', 0.7604994773864746, 'llm', 2), ('microsoft/llama-2-onnx', 0.7532107830047607, 'llm', 2), ('facebookresearch/llama-recipes', 0.6903901100158691, 'llm', 2), ('jzhang38/tinyllama', 0.6666224598884583, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.663759171962738, 'llm', 0), ('run-llama/llama-lab', 0.6453814506530762, 'llm', 2), ('lightning-ai/lit-llama', 0.6250495314598083, 'llm', 2), ('facebookresearch/llama', 0.6226324439048767, 'llm', 2), ('ggerganov/llama.cpp', 0.6068325042724609, 'llm', 2), ('karpathy/llama2.c', 0.5766897201538086, 'llm', 2), ('abetlen/llama-cpp-python', 0.572754442691803, 'llm', 2), ('facebookresearch/codellama', 0.5405111908912659, 'llm', 2), ('run-llama/llama-hub', 0.5322900414466858, 'data', 0), ('hiyouga/llama-efficient-tuning', 0.5321193337440491, 'llm', 2), ('hiyouga/llama-factory', 0.5321192145347595, 'llm', 2), ('jerryjliu/llama_index', 0.5254223942756653, 'llm', 2), ('bentoml/openllm', 0.5242617726325989, 'ml-ops', 1), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5178021788597107, 'llm', 1), ('ray-project/llm-applications', 0.5138564109802246, 'llm', 0), ('predibase/lorax', 0.507797122001648, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5027761459350586, 'perf', 0), ('tairov/llama2.mojo', 0.5015569925308228, 'llm', 1)]",41,9.0,,2.85,57,12,10,9,0,0,0,57.0,76.0,90.0,1.3,70
120,ml-ops,https://github.com/mlflow/mlflow,[],,[],[],,,,mlflow/mlflow,mlflow,16234,3772,292,Python,https://mlflow.org,Open source platform for the machine learning lifecycle,mlflow,2024-01-13,2018-06-05,295,55.03050847457627,https://avatars.githubusercontent.com/u/39938107?v=4,Open source platform for the machine learning lifecycle,"['ai', 'apache-spark', 'machine-learning', 'ml', 'mlflow', 'model-management']","['ai', 'apache-spark', 'machine-learning', 'ml', 'mlflow', 'model-management']",2024-01-12,"[('tensorflow/tensorflow', 0.7961823344230652, 'ml-dl', 2), ('polyaxon/polyaxon', 0.7237421870231628, 'ml-ops', 2), ('microsoft/nni', 0.6970524191856384, 'ml', 1), ('determined-ai/determined', 0.6798075437545776, 'ml-ops', 1), ('onnx/onnx', 0.6629000902175903, 'ml', 2), ('huggingface/datasets', 0.6532941460609436, 'nlp', 1), ('googlecloudplatform/vertex-ai-samples', 0.6433287262916565, 'ml', 2), ('netflix/metaflow', 0.6375054717063904, 'ml-ops', 4), ('bentoml/bentoml', 0.6369062662124634, 'ml-ops', 3), ('merantix-momentum/squirrel-core', 0.63669753074646, 'ml', 3), ('feast-dev/feast', 0.6303765773773193, 'ml-ops', 2), ('doccano/doccano', 0.6210007667541504, 'nlp', 1), ('aws/sagemaker-python-sdk', 0.6176590919494629, 'ml', 1), ('activeloopai/deeplake', 0.6163042187690735, 'ml-ops', 3), ('horovod/horovod', 0.612357497215271, 'ml-ops', 1), ('polyaxon/datatile', 0.6103001236915588, 'pandas', 0), ('firmai/industry-machine-learning', 0.6037585735321045, 'study', 1), ('csinva/imodels', 0.5987911224365234, 'ml', 3), ('kubeflow/pipelines', 0.5984587669372559, 'ml-ops', 1), ('unity-technologies/ml-agents', 0.5918627381324768, 'ml-rl', 1), ('adap/flower', 0.5906698107719421, 'ml-ops', 2), ('google/mediapipe', 0.5900856852531433, 'ml', 1), ('google-research/language', 0.5879070162773132, 'nlp', 1), ('rasahq/rasa', 0.5874723792076111, 'llm', 1), ('hpcaitech/colossalai', 0.5852144360542297, 'llm', 1), ('aimhubio/aim', 0.5842969417572021, 'ml-ops', 4), ('nevronai/metisfl', 0.5837588310241699, 'ml', 1), ('microsoft/onnxruntime', 0.5830215811729431, 'ml', 1), ('nccr-itmo/fedot', 0.5817329287528992, 'ml-ops', 1), ('ml-tooling/opyrator', 0.58051598072052, 'viz', 1), ('patchy631/machine-learning', 0.5804417729377747, 'ml', 0), ('whylabs/whylogs', 0.5756088495254517, 'util', 1), ('mlc-ai/mlc-llm', 0.5713922381401062, 'llm', 0), ('gradio-app/gradio', 0.569076657295227, 'viz', 1), ('pycaret/pycaret', 0.5679655075073242, 'ml', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5668816566467285, 'study', 2), ('explosion/thinc', 0.5667772889137268, 'ml-dl', 2), ('wandb/client', 0.5661569833755493, 'ml', 1), ('tensorflow/data-validation', 0.56364506483078, 'ml-ops', 0), ('aiqc/aiqc', 0.5631386637687683, 'ml-ops', 0), ('tensorflow/tensor2tensor', 0.5625280737876892, 'ml', 1), ('rasbt/machine-learning-book', 0.5623690485954285, 'study', 1), ('mosaicml/composer', 0.5622864365577698, 'ml-dl', 1), ('ray-project/ray', 0.5602455139160156, 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1269,data,https://github.com/qdrant/qdrant,['vector-search'],,[],[],,,,qdrant/qdrant,qdrant,15465,909,107,Rust,https://qdrant.tech,"Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/",qdrant,2024-01-14,2020-05-30,191,80.78731343283582,https://avatars.githubusercontent.com/u/73504361?v=4,"Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/","['approximate-nearest-neighbor-search', 'embeddings-similarity', 'hnsw', 'image-search', 'knn-algorithm', 'machine-learning', 'matching', 'mlops', 'nearest-neighbor-search', 'neural-network', 'neural-search', 'recommender-system', 'search', 'search-engine', 'search-engines', 'similarity-search', 'vector-database', 'vector-search', 'vector-search-engine']","['approximate-nearest-neighbor-search', 'embeddings-similarity', 'hnsw', 'image-search', 'knn-algorithm', 'machine-learning', 'matching', 'mlops', 'nearest-neighbor-search', 'neural-network', 'neural-search', 'recommender-system', 'search', 'search-engine', 'search-engines', 'similarity-search', 'vector-database', 'vector-search', 'vector-search-engine']",2023-12-19,"[('marqo-ai/marqo', 0.7367421984672546, 'ml', 4), ('activeloopai/deeplake', 0.6835601329803467, 'ml-ops', 4), ('lancedb/lancedb', 0.6593559384346008, 'data', 7), ('jina-ai/vectordb', 0.5856242775917053, 'data', 3), ('qdrant/vector-db-benchmark', 0.582831621170044, 'perf', 3), ('googlecloudplatform/vertex-ai-samples', 0.5779662132263184, 'ml', 1), ('milvus-io/bootcamp', 0.5713267922401428, 'data', 2), ('qdrant/qdrant-haystack', 0.5697789788246155, 'data', 0), ('neuml/txtai', 0.5641199350357056, 'nlp', 7), ('mindsdb/mindsdb', 0.5584307312965393, 'data', 1), ('superduperdb/superduperdb', 0.5490362644195557, 'data', 2), ('hpcaitech/colossalai', 0.547572910785675, 'llm', 0), ('jina-ai/jina', 0.546108603477478, 'ml', 3), ('qdrant/quaterion', 0.5457815527915955, 'ml', 3), ('chroma-core/chroma', 0.5403642058372498, 'data', 0), ('docarray/docarray', 0.5395107865333557, 'data', 3), ('criteo/autofaiss', 0.5368368625640869, 'ml', 1), ('cheshire-cat-ai/core', 0.5352751016616821, 'llm', 1), ('bentoml/bentoml', 0.5326546430587769, 'ml-ops', 2), ('microsoft/nni', 0.526932418346405, 'ml', 3), ('tensorflow/similarity', 0.522907555103302, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5152298808097839, 'ml-dl', 2), ('feast-dev/feast', 0.5136101245880127, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5117506384849548, 'study', 1), ('mlflow/mlflow', 0.5098550915718079, 'ml-ops', 1), ('ray-project/ray', 0.5096204280853271, 'ml-ops', 1), ('qdrant/qdrant-client', 0.5091877579689026, 'util', 3)]",78,4.0,,20.4,682,550,44,1,24,17,24,682.0,880.0,90.0,1.3,70
121,ml-ops,https://github.com/prefecthq/prefect,[],,[],[],,,,prefecthq/prefect,prefect,13774,1409,159,Python,https://prefect.io,"Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines",prefecthq,2024-01-13,2018-06-29,291,47.240568348848605,https://avatars.githubusercontent.com/u/39270919?v=4,"Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines","['automation', 'data', 'data-engineering', 'data-ops', 'data-science', 'infrastructure', 'ml-ops', 'observability', 'orchestration', 'pipeline', 'prefect', 'workflow', 'workflow-engine']","['automation', 'data', 'data-engineering', 'data-ops', 'data-science', 'infrastructure', 'ml-ops', 'observability', 'orchestration', 'pipeline', 'prefect', 'workflow', 'workflow-engine']",2024-01-13,"[('prefecthq/server', 0.7347409129142761, 'util', 5), ('kestra-io/kestra', 0.6422749757766724, 'ml-ops', 6), ('flyteorg/flyte', 0.6408464312553406, 'ml-ops', 3), ('dagster-io/dagster', 0.5984505414962769, 'ml-ops', 4), ('prefecthq/prefect-dbt', 0.5904790759086609, 'ml-ops', 1), ('mage-ai/mage-ai', 0.5904417037963867, 'ml-ops', 5), ('getindata/kedro-kubeflow', 0.5550775527954102, 'ml-ops', 0), ('apache/airflow', 0.5517212748527527, 'ml-ops', 6), ('astronomer/astro-sdk', 0.5360534191131592, 'ml-ops', 1), ('allegroai/clearml', 0.5174835324287415, 'ml-ops', 0), ('spotify/luigi', 0.5162726640701294, 'ml-ops', 0), ('ploomber/ploomber', 0.5095990896224976, 'ml-ops', 3), ('orchest/orchest', 0.5009967684745789, 'ml-ops', 1)]",218,3.0,,45.83,810,636,67,0,67,43,67,809.0,1285.0,90.0,1.6,70
1896,nlp,https://github.com/myshell-ai/openvoice,[],,[],[],,,,myshell-ai/openvoice,OpenVoice,12210,983,111,Python,https://research.myshell.ai/open-voice,Instant voice cloning by MyShell.,myshell-ai,2024-01-14,2023-11-29,8,1378.5483870967741,https://avatars.githubusercontent.com/u/127754094?v=4,Instant voice cloning by MyShell.,"['text-to-speech', 'tts', 'voice-clone', 'zero-shot-tts']","['text-to-speech', 'tts', 'voice-clone', 'zero-shot-tts']",2024-01-09,"[('plachtaa/vall-e-x', 0.6065437197685242, 'llm', 3), ('neonbjb/tortoise-tts', 0.57065349817276, 'ml', 1), ('vaibhavs10/insanely-fast-whisper', 0.5049346089363098, 'llm', 1)]",8,4.0,,1.25,103,45,2,0,0,0,0,103.0,244.0,90.0,2.4,70
532,profiling,https://github.com/bloomberg/memray,[],,[],[],1.0,,,bloomberg/memray,memray,11750,383,58,Python,https://bloomberg.github.io/memray/,Memray is a memory profiler for Python,bloomberg,2024-01-14,2022-04-08,94,124.24471299093656,https://avatars.githubusercontent.com/u/1416818?v=4,Memray is a memory profiler for Python,"['memory', 'memory-leak', 'memory-leak-detection', 'memory-profiler', 'profiler']","['memory', 'memory-leak', 'memory-leak-detection', 'memory-profiler', 'profiler']",2024-01-12,"[('pythonspeed/filprofiler', 0.7156088352203369, 'profiling', 4), ('pympler/pympler', 0.5725077390670776, 'perf', 0), ('pythonprofilers/memory_profiler', 0.5463239550590515, 'profiling', 0)]",42,5.0,,4.33,45,38,22,0,8,11,8,45.0,94.0,90.0,2.1,70
1802,ml,https://github.com/microsoft/onnxruntime,[],,[],[],,,,microsoft/onnxruntime,onnxruntime,11513,2468,229,C++,https://onnxruntime.ai,"ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator",microsoft,2024-01-14,2018-11-10,272,42.2606187729418,https://avatars.githubusercontent.com/u/6154722?v=4,"ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator","['ai-framework', 'deep-learning', 'hardware-acceleration', 'machine-learning', 'neural-networks', 'onnx', 'pytorch', 'scikit-learn', 'tensorflow']","['ai-framework', 'deep-learning', 'hardware-acceleration', 'machine-learning', 'neural-networks', 'onnx', 'pytorch', 'scikit-learn', 'tensorflow']",2024-01-14,"[('onnx/onnx', 0.727009654045105, 'ml', 6), ('tensorflow/tensorflow', 0.681681215763092, 'ml-dl', 3), ('tlkh/tf-metal-experiments', 0.6584108471870422, 'perf', 2), ('horovod/horovod', 0.6534867882728577, 'ml-ops', 4), ('determined-ai/determined', 0.643320620059967, 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('mlc-ai/mlc-llm', 0.5396208763122559, 'llm', 0), ('activeloopai/deeplake', 0.5389828681945801, 'ml-ops', 4), ('project-monai/monai', 0.5315036773681641, 'ml', 2), ('opentensor/bittensor', 0.5280240774154663, 'ml', 4), ('koaning/human-learn', 0.5241006016731262, 'data', 2), ('rwightman/pytorch-image-models', 0.522943913936615, 'ml-dl', 1), ('lucidrains/imagen-pytorch', 0.519209623336792, 'ml-dl', 1), ('salesforce/warp-drive', 0.5185619592666626, 'ml-rl', 2), ('deci-ai/super-gradients', 0.5170263051986694, 'ml-dl', 2), ('roboflow/supervision', 0.5162255167961121, 'ml', 4), ('deepfakes/faceswap', 0.5149832963943481, 'ml-dl', 3), ('deepmind/dm_control', 0.5139978528022766, 'ml-rl', 3), ('aws/sagemaker-python-sdk', 0.513953447341919, 'ml', 3), ('xl0/lovely-tensors', 0.5118368864059448, 'ml-dl', 2), ('arogozhnikov/einops', 0.5109891295433044, 'ml-dl', 3), ('paddlepaddle/paddle', 0.5105121731758118, 'ml-dl', 2), ('cheshire-cat-ai/core', 0.5098522901535034, 'llm', 0), ('titanml/takeoff', 0.5081208348274231, 'llm', 0), ('keras-team/autokeras', 0.507604718208313, 'ml-dl', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5041807293891907, 'study', 2), ('microsoft/jarvis', 0.5037975907325745, 'llm', 2), ('denys88/rl_games', 0.5028592348098755, 'ml-rl', 2), ('huggingface/huggingface_hub', 0.5021616816520691, 'ml', 3), ('neuralmagic/sparseml', 0.5017151236534119, 'ml-dl', 3)]",579,1.0,,53.08,1524,1068,63,0,8,9,8,1521.0,4180.0,90.0,2.7,70
1014,pandas,https://github.com/kanaries/pygwalker,[],,[],[],1.0,,,kanaries/pygwalker,pygwalker,8574,403,52,Python,https://kanaries.net/home/pygwalker,PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis,kanaries,2024-01-14,2023-02-16,49,172.4655172413793,https://avatars.githubusercontent.com/u/57262471?v=4,PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis,"['data-analysis', 'data-exploration', 'dataframe', 'matplotlib', 'pandas', 'plotly', 'tableau', 'tableau-alternative', 'visualization']","['data-analysis', 'data-exploration', 'dataframe', 'matplotlib', 'pandas', 'plotly', 'tableau', 'tableau-alternative', 'visualization']",2024-01-11,"[('lux-org/lux', 0.7356677055358887, 'viz', 2), ('man-group/dtale', 0.7181293368339539, 'viz', 3), ('holoviz/panel', 0.6920881271362305, 'viz', 2), ('adamerose/pandasgui', 0.6812300682067871, 'pandas', 2), ('mwaskom/seaborn', 0.6736312508583069, 'viz', 2), ('plotly/plotly.py', 0.6529722809791565, 'viz', 2), ('bokeh/bokeh', 0.6463139653205872, 'viz', 1), ('tkrabel/bamboolib', 0.6401150822639465, 'pandas', 1), ('holoviz/holoviz', 0.6368023753166199, 'viz', 0), ('cuemacro/chartpy', 0.6271337270736694, 'viz', 2), ('holoviz/hvplot', 0.6253153085708618, 'pandas', 0), ('residentmario/geoplot', 0.6120375394821167, 'gis', 1), ('vizzuhq/ipyvizzu', 0.5989693403244019, 'jupyter', 0), ('altair-viz/altair', 0.5906645655632019, 'viz', 1), ('matplotlib/matplotlib', 0.5794761776924133, 'viz', 1), ('plotly/dash', 0.5757370591163635, 'viz', 1), ('federicoceratto/dashing', 0.569820761680603, 'term', 0), ('pyqtgraph/pyqtgraph', 0.5670520663261414, 'viz', 1), ('pandas-dev/pandas', 0.5644711256027222, 'pandas', 3), ('wesm/pydata-book', 0.5609186887741089, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.5599751472473145, 'study', 2), ('enthought/mayavi', 0.5563317537307739, 'viz', 1), ('holoviz/geoviews', 0.5505314469337463, 'gis', 0), ('scitools/iris', 0.5480643510818481, 'gis', 1), ('contextlab/hypertools', 0.5455514192581177, 'ml', 1), ('graphistry/pygraphistry', 0.5422446727752686, 'data', 2), ('quantopian/qgrid', 0.5393330454826355, 'jupyter', 0), ('datapane/datapane', 0.5378132462501526, 'viz', 0), ('opengeos/leafmap', 0.5377007722854614, 'gis', 1), ('maartenbreddels/ipyvolume', 0.5359721779823303, 'jupyter', 0), ('giswqs/geemap', 0.5327471494674683, 'gis', 0), ('alexmojaki/heartrate', 0.5303294658660889, 'debug', 1), ('rapidsai/cudf', 0.5281388759613037, 'pandas', 3), ('twopirllc/pandas-ta', 0.5264495611190796, 'finance', 2), ('vaexio/vaex', 0.5217480063438416, 'perf', 2), ('scitools/cartopy', 0.5204256772994995, 'gis', 1), ('matplotlib/mplfinance', 0.517193078994751, 'finance', 1), ('has2k1/plotnine', 0.5138752460479736, 'viz', 1), ('pyglet/pyglet', 0.5040571689605713, 'gamedev', 0), ('geopandas/geopandas', 0.5026245713233948, 'gis', 1), ('albahnsen/pycircular', 0.5010564923286438, 'math', 0)]",13,5.0,,7.79,131,114,11,0,46,53,46,131.0,85.0,90.0,0.6,70
1515,llm,https://github.com/facebookresearch/llama-recipes,"['llama', 'language-model']",,[],[],,,,facebookresearch/llama-recipes,llama-recipes,6547,906,55,Jupyter Notebook,,Examples and recipes for Llama 2 model,facebookresearch,2024-01-14,2023-07-17,28,232.63451776649745,https://avatars.githubusercontent.com/u/16943930?v=4,Examples and recipes for Llama 2 model,[],"['language-model', 'llama']",2024-01-12,"[('microsoft/llama-2-onnx', 0.820441722869873, 'llm', 2), ('facebookresearch/llama', 0.7843647003173828, 'llm', 2), ('karpathy/llama2.c', 0.7288556098937988, 'llm', 2), ('run-llama/llama-lab', 0.7252361178398132, 'llm', 2), ('tloen/alpaca-lora', 0.6903901100158691, 'llm', 2), ('jzhang38/tinyllama', 0.6900231242179871, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.6829999685287476, 'llm', 0), ('abetlen/llama-cpp-python', 0.6769752502441406, 'llm', 2), ('facebookresearch/codellama', 0.6740383505821228, 'llm', 2), ('ggerganov/llama.cpp', 0.6467329263687134, 'llm', 2), ('zrrskywalker/llama-adapter', 0.6457222700119019, 'llm', 2), ('tairov/llama2.mojo', 0.6153814792633057, 'llm', 1), ('lightning-ai/lit-llama', 0.5484828352928162, 'llm', 2), ('young-geng/easylm', 0.5477281212806702, 'llm', 2), ('cg123/mergekit', 0.5452256202697754, 'llm', 1), ('oobabooga/text-generation-webui', 0.5421599745750427, 'llm', 1), ('openlm-research/open_llama', 0.536719799041748, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5004853010177612, 'llm', 2), ('hiyouga/llama-factory', 0.5004853010177612, 'llm', 2)]",46,4.0,,8.65,139,73,6,0,0,0,0,139.0,189.0,90.0,1.4,70
587,util,https://github.com/mingrammer/diagrams,[],,[],[],,,,mingrammer/diagrams,diagrams,32318,2122,377,Python,https://diagrams.mingrammer.com,:art: Diagram as Code for prototyping cloud system architectures,mingrammer,2024-01-14,2020-02-02,208,155.16186556927298,,:art: Diagram as Code for prototyping cloud system architectures,"['architecture', 'diagram', 'diagram-as-code', 'graphviz']","['architecture', 'diagram', 'diagram-as-code', 'graphviz']",2024-01-05,"[('zenml-io/mlstacks', 0.5006417036056519, 'ml-ops', 0)]",137,7.0,,0.42,56,28,48,0,1,10,1,56.0,46.0,90.0,0.8,69
1201,ml,https://github.com/suno-ai/bark,"['multilingual', 'audio']",,[],[],,,,suno-ai/bark,bark,29862,3523,289,Jupyter Notebook,,🔊 Text-Prompted Generative Audio Model,suno-ai,2024-01-14,2023-04-07,42,701.4563758389262,https://avatars.githubusercontent.com/u/99442120?v=4,🔊 Text-Prompted Generative Audio Model,[],"['audio', 'multilingual']",2023-09-28,"[('pollinations/dance-diffusion', 0.6367303729057312, 'diffusion', 1), ('facebookresearch/seamless_communication', 0.553849995136261, 'nlp', 0), ('thudm/chatglm-6b', 0.552562415599823, 'llm', 0), ('openai/finetune-transformer-lm', 0.5396863222122192, 'llm', 0), ('hazyresearch/ama_prompting', 0.5254032015800476, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5169947743415833, 'nlp', 0), ('keirp/automatic_prompt_engineer', 0.5167652368545532, 'llm', 0)]",17,5.0,,1.42,67,14,9,4,0,0,0,67.0,91.0,90.0,1.4,69
672,ml-dl,https://github.com/facebookresearch/detectron2,[],,[],[],,,,facebookresearch/detectron2,detectron2,27792,7230,380,Python,https://detectron2.readthedocs.io/en/latest/,"Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.",facebookresearch,2024-01-14,2019-09-05,229,120.98507462686567,https://avatars.githubusercontent.com/u/16943930?v=4,"Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.",[],[],2024-01-09,"[('open-mmlab/mmdetection', 0.5715402960777283, 'ml', 0), ('roboflow/supervision', 0.5367303490638733, 'ml', 0), ('deci-ai/super-gradients', 0.5197049379348755, 'ml-dl', 0), ('nvlabs/gcvit', 0.504278838634491, 'diffusion', 0)]",263,3.0,,1.44,134,65,53,0,0,3,3,133.0,157.0,90.0,1.2,69
348,ml,https://github.com/jina-ai/jina,[],,[],[],,,,jina-ai/jina,jina,19552,2201,205,Python,https://docs.jina.ai,☁️ Build multimodal AI applications with cloud-native stack,jina-ai,2024-01-14,2020-02-13,206,94.58465791292329,https://avatars.githubusercontent.com/u/60539444?v=4,☁️ Build multimodal AI applications with cloud-native stack,"['cloud-native', 'cncf', 'deep-learning', 'docker', 'fastapi', 'framework', 'generative-ai', 'grpc', 'jaeger', 'kubernetes', 'llmops', 'machine-learning', 'microservice', 'mlops', 'multimodal', 'neural-search', 'opentelemetry', 'orchestration', 'pipeline', 'prometheus']","['cloud-native', 'cncf', 'deep-learning', 'docker', 'fastapi', 'framework', 'generative-ai', 'grpc', 'jaeger', 'kubernetes', 'llmops', 'machine-learning', 'microservice', 'mlops', 'multimodal', 'neural-search', 'opentelemetry', 'orchestration', 'pipeline', 'prometheus']",2024-01-10,"[('bentoml/bentoml', 0.692993700504303, 'ml-ops', 6), ('cheshire-cat-ai/core', 0.6241676807403564, 'llm', 1), ('googlecloudplatform/vertex-ai-samples', 0.6033614873886108, 'ml', 1), ('skypilot-org/skypilot', 0.5877841711044312, 'llm', 2), ('mlc-ai/mlc-llm', 0.581267774105072, 'llm', 0), ('microsoft/onnxruntime', 0.57099848985672, 'ml', 2), ('pathwaycom/llm-app', 0.567528486251831, 'llm', 2), ('hpcaitech/colossalai', 0.5669887065887451, 'llm', 1), ('netflix/metaflow', 0.5668376088142395, 'ml-ops', 3), ('lithops-cloud/lithops', 0.5622606873512268, 'ml-ops', 1), ('prefecthq/marvin', 0.5576962828636169, 'nlp', 0), ('polyaxon/polyaxon', 0.5560136437416077, 'ml-ops', 4), ('orchest/orchest', 0.5534289479255676, 'ml-ops', 3), ('uber/fiber', 0.5508025884628296, 'data', 1), ('horovod/horovod', 0.5503325462341309, 'ml-ops', 2), ('ray-project/ray', 0.5483666062355042, 'ml-ops', 2), ('google/mediapipe', 0.5477234125137329, 'ml', 3), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5461900234222412, 'web', 1), ('qdrant/qdrant', 0.546108603477478, 'data', 3), ('pytorchlightning/pytorch-lightning', 0.5442116260528564, 'ml-dl', 2), ('marqo-ai/marqo', 0.5435925126075745, 'ml', 2), ('alpa-projects/alpa', 0.5415318012237549, 'ml-dl', 2), ('zenml-io/zenml', 0.5385584831237793, 'ml-ops', 4), ('ml-tooling/opyrator', 0.5350887775421143, 'viz', 2), ('tensorflow/tensorflow', 0.5316888093948364, 'ml-dl', 2), ('activeloopai/deeplake', 0.5312637686729431, 'ml-ops', 3), ('docarray/docarray', 0.5284995436668396, 'data', 5), ('merantix-momentum/squirrel-core', 0.5257314443588257, 'ml', 2), ('stability-ai/stability-sdk', 0.524762749671936, 'diffusion', 1), ('luodian/otter', 0.5234236121177673, 'llm', 2), ('bodywork-ml/bodywork-core', 0.523324728012085, 'ml-ops', 6), ('antonosika/gpt-engineer', 0.5212904214859009, 'llm', 0), ('ludwig-ai/ludwig', 0.5165185332298279, 'ml-ops', 2), ('mlflow/mlflow', 0.5164972543716431, 'ml-ops', 1), ('determined-ai/determined', 0.5140607953071594, 'ml-ops', 4), ('zenml-io/mlstacks', 0.514028012752533, 'ml-ops', 1), ('tensorlayer/tensorlayer', 0.5102136135101318, 'ml-rl', 1), ('microsoft/semantic-kernel', 0.5081870555877686, 'llm', 0), ('adap/flower', 0.5048282146453857, 'ml-ops', 4), ('transformeroptimus/superagi', 0.5039621591567993, 'llm', 1), ('gradio-app/gradio', 0.5035788416862488, 'viz', 2), ('superduperdb/superduperdb', 0.5026459097862244, 'data', 2), ('eventual-inc/daft', 0.5019222497940063, 'pandas', 2), ('deepmind/dm_control', 0.5015859603881836, 'ml-rl', 2), ('lastmile-ai/aiconfig', 0.5008789300918579, 'util', 1)]",176,2.0,,6.81,198,181,48,0,25,99,25,198.0,172.0,90.0,0.9,69
280,util,https://github.com/squidfunk/mkdocs-material,[],,[],[],,,,squidfunk/mkdocs-material,mkdocs-material,16958,3239,123,HTML,https://squidfunk.github.io/mkdocs-material/,Documentation that simply works,squidfunk,2024-01-14,2016-01-28,417,40.597127222982216,,Documentation that simply works,"['documentation', 'framework', 'material-design', 'mkdocs', 'plugins', 'theme']","['documentation', 'framework', 'material-design', 'mkdocs', 'plugins', 'theme']",2024-01-10,"[('mkdocstrings/mkdocstrings', 0.6913550496101379, 'util', 1), ('sphinx-doc/sphinx', 0.6544510126113892, 'util', 1), ('mitmproxy/pdoc', 0.6519834399223328, 'util', 1), ('mkdocs/mkdocs', 0.6341920495033264, 'util', 2), ('executablebooks/jupyter-book', 0.5147403478622437, 'jupyter', 0)]",249,3.0,,17.6,316,282,97,0,68,46,68,316.0,870.0,90.0,2.8,69
1611,llm,https://github.com/thudm/chatglm2-6b,[],,[],[],,,,thudm/chatglm2-6b,ChatGLM2-6B,14854,2347,130,Python,,ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型,thudm,2024-01-14,2023-06-24,31,472.6272727272727,https://avatars.githubusercontent.com/u/48590610?v=4,ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型,"['chatglm', 'chatglm-6b', 'large-language-models', 'llm']","['chatglm', 'chatglm-6b', 'large-language-models', 'llm']",2023-10-27,"[('li-plus/chatglm.cpp', 0.7250033617019653, 'llm', 2), ('thudm/chatglm-6b', 0.7089318633079529, 'llm', 0), ('nomic-ai/gpt4all', 0.6924757957458496, 'llm', 0), ('hwchase17/langchain', 0.6808525919914246, 'llm', 0), ('fasteval/fasteval', 0.6485232710838318, 'llm', 1), ('microsoft/autogen', 0.6360533237457275, 'llm', 0), ('mlc-ai/web-llm', 0.6331812143325806, 'llm', 1), ('next-gpt/next-gpt', 0.6282567381858826, 'llm', 2), ('lm-sys/fastchat', 0.6237083673477173, 'llm', 0), ('salesforce/xgen', 0.6184314489364624, 'llm', 2), ('young-geng/easylm', 0.601020097732544, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6007229685783386, 'perf', 0), ('freedomintelligence/llmzoo', 0.5987119078636169, 'llm', 0), ('nat/openplayground', 0.5894226431846619, 'llm', 0), ('lchen001/llmdrift', 0.5880559086799622, 'llm', 0), ('cg123/mergekit', 0.58427494764328, 'llm', 1), ('dylanhogg/llmgraph', 0.580618679523468, 'ml', 1), ('openlmlab/moss', 0.5803049802780151, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.5782683491706848, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5761158466339111, 'llm', 3), ('hiyouga/llama-factory', 0.5761158466339111, 'llm', 3), ('baichuan-inc/baichuan-13b', 0.5701199173927307, 'llm', 1), ('bobazooba/xllm', 0.5669134855270386, 'llm', 2), ('lingjzhu/charsiug2p', 0.5563612580299377, 'nlp', 0), ('zhudotexe/kani', 0.5553779602050781, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5543924570083618, 'nlp', 0), ('eugeneyan/open-llms', 0.5501125454902649, 'study', 2), ('run-llama/rags', 0.547817587852478, 'llm', 1), ('hannibal046/awesome-llm', 0.5458086133003235, 'study', 0), ('infinitylogesh/mutate', 0.5451502203941345, 'nlp', 0), ('guidance-ai/guidance', 0.5406391024589539, 'llm', 0), ('explosion/spacy-llm', 0.5399860739707947, 'llm', 2), ('lianjiatech/belle', 0.5378761887550354, 'llm', 0), ('embedchain/embedchain', 0.5360315442085266, 'llm', 1), ('eleutherai/the-pile', 0.5338200330734253, 'data', 1), ('deepset-ai/haystack', 0.5324009656906128, 'llm', 1), ('confident-ai/deepeval', 0.5323322415351868, 'testing', 1), ('ctlllll/llm-toolmaker', 0.5311850309371948, 'llm', 0), ('ai21labs/lm-evaluation', 0.5304546356201172, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5270649194717407, 'llm', 2), ('thudm/glm-130b', 0.5260385274887085, 'llm', 0), ('salesforce/codet5', 0.524960994720459, 'nlp', 1), ('langchain-ai/langgraph', 0.5230793356895447, 'llm', 0), ('aiwaves-cn/agents', 0.5184250473976135, 'nlp', 1), ('ray-project/ray-llm', 0.5179040431976318, 'llm', 2), ('whu-zqh/chatgpt-vs.-bert', 0.5137940645217896, 'llm', 0), ('night-chen/toolqa', 0.5134639143943787, 'llm', 1), ('chainlit/chainlit', 0.5131312608718872, 'llm', 1), ('cstankonrad/long_llama', 0.5114611387252808, 'llm', 0), ('pathwaycom/llm-app', 0.5088384747505188, 'llm', 1), ('zilliztech/gptcache', 0.507978081703186, 'llm', 1), ('microsoft/lora', 0.5071834921836853, 'llm', 0), ('databrickslabs/dolly', 0.506598949432373, 'llm', 0), ('juncongmoo/pyllama', 0.5058130621910095, 'llm', 0), ('bigscience-workshop/petals', 0.5052765607833862, 'data', 1), ('langchain-ai/chat-langchain', 0.5037407875061035, 'llm', 0), ('artidoro/qlora', 0.5035903453826904, 'llm', 0), ('blinkdl/chatrwkv', 0.5033798813819885, 'llm', 0), ('ibm/dromedary', 0.50322425365448, 'llm', 0), ('chatarena/chatarena', 0.5024993419647217, 'llm', 1), ('lupantech/chameleon-llm', 0.5024386644363403, 'llm', 1)]",11,9.0,,1.1,123,18,7,3,0,0,0,123.0,118.0,90.0,1.0,69
1307,study,https://github.com/hannibal046/awesome-llm,"['awesome', 'language-model', 'gpt']",,[],[],,,,hannibal046/awesome-llm,Awesome-LLM,11253,847,293,,,Awesome-LLM: a curated list of Large Language Model,hannibal046,2024-01-14,2023-02-17,49,227.0057636887608,,Awesome-LLM: a curated list of Large Language Model,[],"['awesome', 'gpt', 'language-model']",2024-01-04,"[('lianjiatech/belle', 0.8117328882217407, 'llm', 0), ('freedomintelligence/llmzoo', 0.7315229177474976, 'llm', 1), ('ai21labs/lm-evaluation', 0.725335955619812, 'llm', 1), ('ctlllll/llm-toolmaker', 0.708982527256012, 'llm', 1), ('juncongmoo/pyllama', 0.6920731067657471, 'llm', 0), ('next-gpt/next-gpt', 0.6860809922218323, 'llm', 0), ('guidance-ai/guidance', 0.6724131107330322, 'llm', 1), ('microsoft/autogen', 0.662517249584198, 'llm', 1), ('togethercomputer/redpajama-data', 0.6528944969177246, 'llm', 0), ('cg123/mergekit', 0.6510716080665588, 'llm', 0), ('oobabooga/text-generation-webui', 0.65085768699646, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.6444519758224487, 'llm', 0), ('prefecthq/langchain-prefect', 0.6407128572463989, 'llm', 0), ('lm-sys/fastchat', 0.6385653614997864, 'llm', 1), ('xtekky/gpt4free', 0.6301591992378235, 'llm', 2), ('bigscience-workshop/megatron-deepspeed', 0.6277967095375061, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6277967095375061, 'llm', 0), ('bobazooba/xllm', 0.6244999766349792, 'llm', 1), ('microsoft/lora', 0.6244948506355286, 'llm', 1), ('lupantech/chameleon-llm', 0.6220220327377319, 'llm', 1), ('huggingface/text-generation-inference', 0.616241455078125, 'llm', 1), ('sjtu-ipads/powerinfer', 0.6129886507987976, 'llm', 0), ('eleutherai/the-pile', 0.6086982488632202, 'data', 0), ('guardrails-ai/guardrails', 0.6082257032394409, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.6053584218025208, 'llm', 1), ('mooler0410/llmspracticalguide', 0.6038196682929993, 'study', 1), ('jonasgeiping/cramming', 0.603162944316864, 'nlp', 1), ('bigscience-workshop/biomedical', 0.6027185916900635, 'data', 0), ('salesforce/xgen', 0.6004391312599182, 'llm', 1), ('reasoning-machines/pal', 0.5971046090126038, 'llm', 1), ('databrickslabs/dolly', 0.5911993384361267, 'llm', 1), ('infinitylogesh/mutate', 0.5907807350158691, 'nlp', 1), ('optimalscale/lmflow', 0.5771458745002747, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5770836472511292, 'llm', 1), ('srush/minichain', 0.5760703086853027, 'llm', 0), ('spcl/graph-of-thoughts', 0.5740511417388916, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5738133192062378, 'llm', 1), ('hiyouga/llama-factory', 0.5673015117645264, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5673013925552368, 'llm', 2), ('conceptofmind/toolformer', 0.5643236041069031, 'llm', 1), ('yueyu1030/attrprompt', 0.5640091896057129, 'llm', 0), ('openai/gpt-2', 0.5629648566246033, 'llm', 0), ('paperswithcode/galai', 0.5614985823631287, 'llm', 1), ('young-geng/easylm', 0.5593286752700806, 'llm', 1), ('yizhongw/self-instruct', 0.5532945394515991, 'llm', 1), ('explosion/spacy-models', 0.5515271425247192, 'nlp', 0), ('mlc-ai/web-llm', 0.5478937029838562, 'llm', 1), ('thudm/chatglm2-6b', 0.5458086133003235, 'llm', 0), ('killianlucas/open-interpreter', 0.5456922054290771, 'llm', 0), ('epfllm/meditron', 0.5448260307312012, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5444208383560181, 'llm', 0), ('openbmb/toolbench', 0.5423779487609863, 'llm', 0), ('jalammar/ecco', 0.5404885411262512, 'ml-interpretability', 0), ('openlmlab/leval', 0.540010392665863, 'llm', 1), ('openlmlab/moss', 0.5380090475082397, 'llm', 1), ('dylanhogg/llmgraph', 0.5356044769287109, 'ml', 0), ('minimaxir/gpt-2-simple', 0.5346177220344543, 'llm', 0), ('hazyresearch/h3', 0.5333759784698486, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5322656035423279, 'llm', 0), ('openai/finetune-transformer-lm', 0.527682363986969, 'llm', 0), ('facebookresearch/shepherd', 0.5266714692115784, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5266475677490234, 'llm', 1), ('aiwaves-cn/agents', 0.5250198245048523, 'nlp', 1), ('neulab/prompt2model', 0.525016188621521, 'llm', 1), ('ggerganov/ggml', 0.5245451927185059, 'ml', 0), ('nvlabs/prismer', 0.5241163969039917, 'diffusion', 1), ('princeton-nlp/alce', 0.5235013365745544, 'llm', 0), ('muennighoff/sgpt', 0.5209395885467529, 'llm', 2), ('fasteval/fasteval', 0.5191826820373535, 'llm', 0), ('lingjzhu/charsiug2p', 0.5186607241630554, 'nlp', 0), ('huawei-noah/pretrained-language-model', 0.5183480381965637, 'nlp', 0), ('langchain-ai/langgraph', 0.5179132223129272, 'llm', 0), ('timdettmers/bitsandbytes', 0.5172896385192871, 'util', 0), ('explosion/spacy-llm', 0.5151101350784302, 'llm', 0), ('microsoft/pycodegpt', 0.51466304063797, 'llm', 0), ('bytedance/lightseq', 0.5142962336540222, 'nlp', 1), ('nvidia/tensorrt-llm', 0.5133680105209351, 'viz', 1), ('eleutherai/gpt-neo', 0.5118930339813232, 'llm', 2), ('nat/openplayground', 0.5065643787384033, 'llm', 1), ('1rgs/jsonformer', 0.5063848495483398, 'llm', 0), ('gkamradt/langchain-tutorials', 0.5057912468910217, 'study', 0), ('llmware-ai/llmware', 0.5052445530891418, 'llm', 0), ('cstankonrad/long_llama', 0.5006245970726013, 'llm', 1)]",64,6.0,,5.29,17,14,11,0,0,0,0,17.0,21.0,90.0,1.2,69
122,ml-ops,https://github.com/dagster-io/dagster,[],,[],[],,,,dagster-io/dagster,dagster,9380,1160,108,Python,https://dagster.io,"An orchestration platform for the development, production, and observation of data assets.",dagster-io,2024-01-14,2018-04-30,300,31.25178486435031,https://avatars.githubusercontent.com/u/40032576?v=4,"An orchestration platform for the development, production, and observation of data assets.","['analytics', 'dagster', 'data-engineering', 'data-integration', 'data-orchestrator', 'data-pipelines', 'data-science', 'etl', 'metadata', 'mlops', 'orchestration', 'scheduler', 'workflow', 'workflow-automation']","['analytics', 'dagster', 'data-engineering', 'data-integration', 'data-orchestrator', 'data-pipelines', 'data-science', 'etl', 'metadata', 'mlops', 'orchestration', 'scheduler', 'workflow', 'workflow-automation']",2024-01-13,"[('flyteorg/flyte', 0.7850085496902466, 'ml-ops', 3), ('kestra-io/kestra', 0.7322535514831543, 'ml-ops', 7), ('airbytehq/airbyte', 0.6772870421409607, 'data', 3), ('mage-ai/mage-ai', 0.6750965714454651, 'ml-ops', 6), ('apache/airflow', 0.6647198796272278, 'ml-ops', 10), ('orchest/orchest', 0.6582160592079163, 'ml-ops', 3), ('avaiga/taipy', 0.6245914101600647, 'data', 4), ('netflix/metaflow', 0.6058613657951355, 'ml-ops', 2), ('meltano/meltano', 0.60458904504776, 'ml-ops', 2), ('ploomber/ploomber', 0.6021184325218201, 'ml-ops', 4), ('prefecthq/prefect', 0.5984505414962769, 'ml-ops', 4), ('dagworks-inc/hamilton', 0.5917893052101135, 'ml-ops', 5), ('polyaxon/polyaxon', 0.5835554003715515, 'ml-ops', 3), ('pydoit/doit', 0.5810200572013855, 'util', 3), ('bodywork-ml/bodywork-core', 0.5800526142120361, 'ml-ops', 3), ('backtick-se/cowait', 0.5563982725143433, 'util', 2), ('simonw/datasette', 0.5505277514457703, 'data', 0), ('prefecthq/server', 0.5430384278297424, 'util', 2), ('spotify/luigi', 0.5423729419708252, 'ml-ops', 0), ('astronomer/astro-sdk', 0.5415989756584167, 'ml-ops', 2), ('polyaxon/datatile', 0.5342531800270081, 'pandas', 2), ('fugue-project/fugue', 0.5329538583755493, 'pandas', 0), ('chaostoolkit/chaostoolkit', 0.5302633047103882, 'util', 0), ('dbt-labs/dbt-core', 0.5295764803886414, 'ml-ops', 1), ('databrickslabs/dbx', 0.5274744033813477, 'data', 1), ('saulpw/visidata', 0.515415370464325, 'term', 0), ('merantix-momentum/squirrel-core', 0.5139255523681641, 'ml', 1), ('featureform/embeddinghub', 0.512913703918457, 'nlp', 2), ('lithops-cloud/lithops', 0.5072967410087585, 'ml-ops', 0), ('kedro-org/kedro', 0.5058962106704712, 'ml-ops', 1)]",380,4.0,,78.85,1651,1070,70,0,66,125,66,1649.0,2509.0,90.0,1.5,69
83,data,https://github.com/sqlalchemy/sqlalchemy,[],,[],[],,,,sqlalchemy/sqlalchemy,sqlalchemy,8261,1314,90,Python,https://www.sqlalchemy.org,The Database Toolkit for Python,sqlalchemy,2024-01-14,2018-11-27,270,30.596296296296295,https://avatars.githubusercontent.com/u/6043126?v=4,The Database Toolkit for Python,"['sql', 'sqlalchemy']","['sql', 'sqlalchemy']",2024-01-13,"[('sqlalchemy/alembic', 0.8273295164108276, 'data', 2), ('tiangolo/sqlmodel', 0.8052466511726379, 'data', 2), ('agronholm/sqlacodegen', 0.7499924302101135, 'data', 0), ('ibis-project/ibis', 0.741746723651886, 'data', 2), ('mause/duckdb_engine', 0.6847227215766907, 'data', 2), ('mcfunley/pugsql', 0.6739656329154968, 'data', 1), ('andialbrecht/sqlparse', 0.6582309007644653, 'data', 0), ('simonw/sqlite-utils', 0.6196154952049255, 'data', 0), ('aminalaee/sqladmin', 0.6173149347305298, 'data', 1), ('tobymao/sqlglot', 0.6049283742904663, 'data', 1), ('collerek/ormar', 0.6017918586730957, 'data', 1), ('kayak/pypika', 0.5847384333610535, 'data', 1), ('malloydata/malloy-py', 0.5755621790885925, 'data', 1), ('macbre/sql-metadata', 0.5708635449409485, 'data', 1), ('aio-libs/aiopg', 0.5661270618438721, 'data', 1), ('tconbeer/harlequin', 0.5578516721725464, 'term', 1), ('aio-libs/aiomysql', 0.5567764043807983, 'data', 1), ('accenture/cymple', 0.5566608309745789, 'data', 0), ('machow/siuba', 0.5513966679573059, 'pandas', 1), ('eleutherai/pyfra', 0.5482377409934998, 'ml', 0), ('strawberry-graphql/strawberry', 0.5367457866668701, 'web', 0), ('pytables/pytables', 0.531848132610321, 'data', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5284538269042969, 'web', 1), ('pytoolz/toolz', 0.5267731547355652, 'util', 0), ('geopandas/geopandas', 0.5259225964546204, 'gis', 0), ('goldmansachs/gs-quant', 0.5246463418006897, 'finance', 0), ('sfu-db/connector-x', 0.5162760019302368, 'data', 1), ('jazzband/tablib', 0.5141265392303467, 'data', 0), ('python-cachier/cachier', 0.5100117921829224, 'perf', 0), ('falconry/falcon', 0.509283185005188, 'web', 0), ('nasdaq/data-link-python', 0.5085864067077637, 'finance', 0)]",677,5.0,,17.87,234,158,62,0,32,59,32,234.0,797.0,90.0,3.4,69
1894,nlp,https://github.com/m-bain/whisperx,[],,[],[],,,,m-bain/whisperx,whisperX,7397,703,106,Python,,WhisperX:  Automatic Speech Recognition with Word-level Timestamps (& Diarization),m-bain,2024-01-14,2022-12-09,59,124.17026378896882,,WhisperX:  Automatic Speech Recognition with Word-level Timestamps (& Diarization),"['asr', 'speech', 'speech-recognition', 'speech-to-text', 'whisper']","['asr', 'speech', 'speech-recognition', 'speech-to-text', 'whisper']",2024-01-03,"[('uberi/speech_recognition', 0.6138349771499634, 'ml', 2), ('cmusphinx/pocketsphinx', 0.58738112449646, 'ml', 1), ('espnet/espnet', 0.5822861194610596, 'nlp', 1), ('ggerganov/whisper.cpp', 0.582147479057312, 'util', 3), ('speechbrain/speechbrain', 0.5771469473838806, 'nlp', 3), ('vaibhavs10/insanely-fast-whisper', 0.5447807312011719, 'llm', 1), ('nvidia/nemo', 0.5402535796165466, 'nlp', 3), ('facebookresearch/seamless_communication', 0.5283752083778381, 'nlp', 1), ('sanchit-gandhi/whisper-jax', 0.5212621688842773, 'ml', 3), ('nateshmbhat/pyttsx3', 0.5181179046630859, 'util', 0), ('pndurette/gtts', 0.509675920009613, 'util', 1), ('openai/whisper', 0.5077972412109375, 'ml-dl', 2), ('neonbjb/tortoise-tts', 0.5033969283103943, 'ml', 0), ('rasahq/rasa', 0.5019509792327881, 'llm', 0)]",67,4.0,,3.71,187,61,13,0,7,7,7,186.0,273.0,90.0,1.5,69
1365,llm,https://github.com/chainlit/chainlit,[],,[],[],,,,chainlit/chainlit,chainlit,4315,504,43,TypeScript,https://docs.chainlit.io,Build Python LLM apps in minutes ⚡️,chainlit,2024-01-14,2023-03-14,46,93.80434782608695,https://avatars.githubusercontent.com/u/128686189?v=4,Build Python LLM apps in minutes ⚡️,"['chatgpt', 'langchain', 'llm', 'openai', 'openai-chatgpt', 'ui']","['chatgpt', 'langchain', 'llm', 'openai', 'openai-chatgpt', 'ui']",2024-01-12,"[('berriai/litellm', 0.6367120742797852, 'llm', 3), ('hwchase17/langchain', 0.6356831789016724, 'llm', 1), ('shishirpatil/gorilla', 0.6185116171836853, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.6109486222267151, 'llm', 3), ('run-llama/rags', 0.61057049036026, 'llm', 3), ('microsoft/promptflow', 0.6072118878364563, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5824191570281982, 'perf', 0), ('microsoft/semantic-kernel', 0.5796830654144287, 'llm', 2), ('pathwaycom/llm-app', 0.5761844515800476, 'llm', 1), ('minimaxir/simpleaichat', 0.5655502080917358, 'llm', 1), ('nomic-ai/gpt4all', 0.5640503168106079, 'llm', 0), ('alphasecio/langchain-examples', 0.5578954815864563, 'llm', 3), ('citadel-ai/langcheck', 0.5534338355064392, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.548169732093811, 'llm', 0), ('opengenerativeai/genossgpt', 0.5370033979415894, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5345412492752075, 'llm', 2), ('microsoft/promptcraft-robotics', 0.5212441086769104, 'sim', 2), ('microsoft/autogen', 0.5154480338096619, 'llm', 1), ('exaloop/codon', 0.5148961544036865, 'perf', 0), ('thudm/chatglm2-6b', 0.5131312608718872, 'llm', 1), ('embedchain/embedchain', 0.5102947354316711, 'llm', 2), ('openai/openai-cookbook', 0.5076209306716919, 'ml', 2)]",28,5.0,,11.1,218,153,10,0,59,72,59,218.0,429.0,90.0,2.0,69
1402,security,https://github.com/swisskyrepo/payloadsallthethings,[],,[],[],,,,swisskyrepo/payloadsallthethings,PayloadsAllTheThings,54665,13867,1762,Python,https://swisskyrepo.github.io/PayloadsAllTheThings/,A list of useful payloads and bypass for Web Application Security and Pentest/CTF,swisskyrepo,2024-01-14,2016-10-18,380,143.85526315789474,,A list of useful payloads and bypass for Web Application Security and Pentest/CTF,"['bounty', 'bugbounty', 'bypass', 'cheatsheet', 'enumeration', 'hacking', 'methodology', 'payload', 'payloads', 'penetration-testing', 'pentest', 'privilege-escalation', 'redteam', 'security', 'vulnerability', 'web-application']","['bounty', 'bugbounty', 'bypass', 'cheatsheet', 'enumeration', 'hacking', 'methodology', 'payload', 'payloads', 'penetration-testing', 'pentest', 'privilege-escalation', 'redteam', 'security', 'vulnerability', 'web-application']",2024-01-12,"[('flipkart-incubator/astra', 0.5459146499633789, 'web', 2), ('rhinosecuritylabs/pacu', 0.5338615775108337, 'security', 2), ('sqlmapproject/sqlmap', 0.5263312458992004, 'security', 0)]",288,5.0,,2.88,21,21,88,0,0,1,1,21.0,6.0,90.0,0.3,68
1165,ml-dl,https://github.com/facebookresearch/segment-anything,"['instance-segmentation', 'object-detection']",,[],[],,,,facebookresearch/segment-anything,segment-anything,41500,4868,287,Jupyter Notebook,,"The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.",facebookresearch,2024-01-14,2023-03-23,44,928.1150159744409,https://avatars.githubusercontent.com/u/16943930?v=4,"The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.",[],"['instance-segmentation', 'object-detection']",2023-05-02,"[('matterport/mask_rcnn', 0.5424768328666687, 'ml-dl', 2), ('roboflow/notebooks', 0.51060950756073, 'study', 1)]",16,5.0,,0.67,159,29,10,9,0,0,0,159.0,259.0,90.0,1.6,68
1381,ml,https://github.com/google-research/google-research,[],This repository contains code released by Google Research,[],[],,,,google-research/google-research,google-research,31889,7754,749,Jupyter Notebook,https://research.google,Google Research,google-research,2024-01-14,2018-10-04,277,114.82664609053498,https://avatars.githubusercontent.com/u/43830688?v=4,Google Research,"['ai', 'machine-learning', 'research']","['ai', 'machine-learning', 'research']",2024-01-09,"[('google-research/language', 0.6985517740249634, 'nlp', 2), ('googlecloudplatform/vertex-ai-samples', 0.6550525426864624, 'ml', 1), ('alirezadir/machine-learning-interview-enlightener', 0.6368386745452881, 'study', 2), ('google-research/byt5', 0.6097835898399353, 'nlp', 0), ('google-research/t5x', 0.6067818999290466, 'ml', 0), ('tensorflow/tensor2tensor', 0.5845038890838623, 'ml', 1), ('xplainable/xplainable', 0.5756439566612244, 'ml-interpretability', 1), ('openbb-finance/openbbterminal', 0.5702900886535645, 'finance', 1), ('mindsdb/mindsdb', 0.5539237856864929, 'data', 2), ('bentoml/bentoml', 0.5528449416160583, 'ml-ops', 2), ('assafelovic/gpt-researcher', 0.545631468296051, 'llm', 0), ('hpcaitech/colossalai', 0.542206883430481, 'llm', 1), ('oneil512/insight', 0.5356377363204956, 'ml', 1), ('oegedijk/explainerdashboard', 0.5351460576057434, 'ml-interpretability', 0), ('google/dopamine', 0.5344747304916382, 'ml-rl', 1), ('firmai/industry-machine-learning', 0.5328598618507385, 'study', 1), ('mlflow/mlflow', 0.5312324166297913, 'ml-ops', 2), ('patchy631/machine-learning', 0.5150724053382874, 'ml', 0), ('iterative/dvc', 0.5124830603599548, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5109012126922607, 'study', 1), ('drivendata/cookiecutter-data-science', 0.5077729225158691, 'template', 2), ('pytorchlightning/pytorch-lightning', 0.5055912733078003, 'ml-dl', 2), ('seldonio/alibi', 0.5054081678390503, 'ml-interpretability', 1), ('automl/auto-sklearn', 0.5043892860412598, 'ml', 0), ('feast-dev/feast', 0.5038433074951172, 'ml-ops', 1)]",763,3.0,,10.79,136,26,64,0,0,0,0,136.0,96.0,90.0,0.7,68
1183,ml,https://github.com/facebookresearch/faiss,"['similarity', 'embeddings', 'vector-search']",,[],[],1.0,,,facebookresearch/faiss,faiss,26250,3274,470,C++,https://faiss.ai,A library for efficient similarity search and clustering of dense vectors.,facebookresearch,2024-01-14,2017-02-07,364,72.11538461538461,https://avatars.githubusercontent.com/u/16943930?v=4,A library for efficient similarity search and clustering of dense vectors.,[],"['embeddings', 'similarity', 'vector-search']",2024-01-13,"[('criteo/autofaiss', 0.629601776599884, 'ml', 3), ('qdrant/quaterion', 0.5449787378311157, 'ml', 0), ('qdrant/fastembed', 0.5384175777435303, 'ml', 2), ('plasticityai/magnitude', 0.5381598472595215, 'nlp', 1), ('qdrant/vector-db-benchmark', 0.5264250636100769, 'perf', 1), ('qdrant/qdrant-haystack', 0.518367350101471, 'data', 0), ('spotify/annoy', 0.5121656060218811, 'ml', 0), ('pinecone-io/pinecone-python-client', 0.5026112794876099, 'data', 1), ('nmslib/hnswlib', 0.5014215111732483, 'ml', 0)]",150,6.0,,4.33,163,82,84,0,1,3,1,163.0,287.0,90.0,1.8,68
45,testing,https://github.com/locustio/locust,[],,[],[],1.0,,,locustio/locust,locust,22905,2864,434,Python,,Write scalable load tests in plain Python 🚗💨,locustio,2024-01-13,2011-02-17,675,33.897463002114165,https://avatars.githubusercontent.com/u/2641063?v=4,Write scalable load tests in plain Python 🚗💨,"['benchmarking', 'http', 'load-generator', 'load-test', 'load-testing', 'load-tests', 'locust', 'performance', 'performance-testing']","['benchmarking', 'http', 'load-generator', 'load-test', 'load-testing', 'load-tests', 'locust', 'performance', 'performance-testing']",2024-01-11,"[('ionelmc/pytest-benchmark', 0.6405083537101746, 'testing', 2), ('klen/py-frameworks-bench', 0.6128438115119934, 'perf', 0), ('wolever/parameterized', 0.5832534432411194, 'testing', 0), ('neoteroi/blacksheep', 0.5740907192230225, 'web', 1), ('pmorissette/bt', 0.5575364828109741, 'finance', 0), ('sumerc/yappi', 0.5397162437438965, 'profiling', 1), ('encode/httpx', 0.5382368564605713, 'web', 1), ('joblib/joblib', 0.534275233745575, 'util', 0), ('requests/toolbelt', 0.5336475968360901, 'util', 1), ('falconry/falcon', 0.5264105200767517, 'web', 1), ('pympler/pympler', 0.5212385058403015, 'perf', 0), ('nedbat/coveragepy', 0.5198348760604858, 'testing', 0), ('pytest-dev/pytest', 0.5194878578186035, 'testing', 0), ('klen/muffin', 0.5192926526069641, 'web', 0), ('fastai/fastcore', 0.5126659870147705, 'util', 0), ('pallets/quart', 0.5093166828155518, 'web', 0), ('python-cachier/cachier', 0.5075251460075378, 'perf', 0), ('getsentry/responses', 0.5024783611297607, 'testing', 0)]",312,6.0,,6.92,163,154,157,0,14,9,14,163.0,376.0,90.0,2.3,68
1168,llm,https://github.com/microsoft/jarvis,[],,[],[],,,,microsoft/jarvis,JARVIS,22433,1941,376,Python,,"JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf",microsoft,2024-01-14,2023-03-30,43,513.1732026143791,https://avatars.githubusercontent.com/u/6154722?v=4,"JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf","['deep-learning', 'platform', 'pytorch']","['deep-learning', 'platform', 'pytorch']",2024-01-12,"[('iryna-kondr/scikit-llm', 0.6588683128356934, 'llm', 1), ('vllm-project/vllm', 0.632617175579071, 'llm', 1), ('microsoft/torchscale', 0.5797202587127686, 'llm', 0), ('ludwig-ai/ludwig', 0.5734522342681885, 'ml-ops', 2), ('microsoft/semantic-kernel', 0.572293758392334, 'llm', 0), ('nomic-ai/gpt4all', 0.5687279105186462, 'llm', 0), ('bigscience-workshop/petals', 0.5665638446807861, 'data', 2), ('predibase/lorax', 0.5660221576690674, 'llm', 1), ('horovod/horovod', 0.5595570206642151, 'ml-ops', 2), ('bobazooba/xllm', 0.5591229200363159, 'llm', 2), ('nebuly-ai/nebullvm', 0.5589395761489868, 'perf', 0), ('tensorflow/tensor2tensor', 0.5555034279823303, 'ml', 1), ('alpha-vllm/llama2-accessory', 0.5535730719566345, 'llm', 0), ('tigerlab-ai/tiger', 0.552547812461853, 'llm', 0), ('aiqc/aiqc', 0.5510006546974182, 'ml-ops', 0), ('bentoml/openllm', 0.5471473336219788, 'ml-ops', 0), ('ray-project/ray-llm', 0.5456799268722534, 'llm', 0), ('tensorflow/tensorflow', 0.5449208617210388, 'ml-dl', 1), ('pathwaycom/llm-app', 0.544485330581665, 'llm', 0), ('rasbt/deeplearning-models', 0.5421392321586609, 'ml-dl', 0), ('truera/trulens', 0.5391638278961182, 'llm', 0), ('determined-ai/determined', 0.5367454290390015, 'ml-ops', 2), ('eugeneyan/open-llms', 0.5364652276039124, 'study', 0), ('mooler0410/llmspracticalguide', 0.5358785390853882, 'study', 0), ('paddlepaddle/paddle', 0.5339170098304749, 'ml-dl', 1), ('microsoft/lmops', 0.5317671895027161, 'llm', 0), ('young-geng/easylm', 0.5291217565536499, 'llm', 1), ('deepset-ai/haystack', 0.5287731885910034, 'llm', 1), ('argilla-io/argilla', 0.5283769369125366, 'nlp', 0), ('dylanhogg/llmgraph', 0.5276885032653809, 'ml', 0), ('uber/petastorm', 0.5265276432037354, 'data', 2), ('mrdbourke/pytorch-deep-learning', 0.5219099521636963, 'study', 2), ('deep-diver/llm-as-chatbot', 0.5197903513908386, 'llm', 0), ('mlflow/mlflow', 0.517335832118988, 'ml-ops', 0), ('intel/intel-extension-for-transformers', 0.5154780149459839, 'perf', 0), ('huggingface/datasets', 0.5145260691642761, 'nlp', 2), ('night-chen/toolqa', 0.5131213068962097, 'llm', 0), ('salesforce/xgen', 0.5117494463920593, 'llm', 0), ('titanml/takeoff', 0.5107346773147583, 'llm', 0), ('nvidia/tensorrt-llm', 0.5096875429153442, 'viz', 0), ('mlc-ai/mlc-llm', 0.5086725354194641, 'llm', 0), ('ddbourgin/numpy-ml', 0.5069154500961304, 'ml', 0), ('lamini-ai/llm-classifier', 0.5053083300590515, 'llm', 0), ('microsoft/onnxruntime', 0.5037975907325745, 'ml', 2), ('apache/incubator-mxnet', 0.5016826391220093, 'ml-dl', 0)]",23,5.0,,1.42,25,1,10,0,0,0,0,25.0,9.0,90.0,0.4,68
6,viz,https://github.com/bokeh/bokeh,[],,[],[],,,,bokeh/bokeh,bokeh,18477,4202,443,Python,https://bokeh.org,"Interactive Data Visualization in the browser, from  Python",bokeh,2024-01-13,2012-03-26,618,29.891148601802634,https://avatars.githubusercontent.com/u/8440965?v=4,"Interactive Data Visualization in the browser, from  Python","['bokeh', 'data-visualisation', 'interactive-plots', 'javascript', 'jupyter', 'notebooks', 'numfocus', 'plots', 'plotting', 'visualisation', 'visualization']","['bokeh', 'data-visualisation', 'interactive-plots', 'javascript', 'jupyter', 'notebooks', 'numfocus', 'plots', 'plotting', 'visualisation', 'visualization']",2024-01-09,"[('holoviz/panel', 0.7603949308395386, 'viz', 2), ('plotly/plotly.py', 0.7521082758903503, 'viz', 1), ('plotly/dash', 0.7066522240638733, 'viz', 1), ('man-group/dtale', 0.6867046356201172, 'viz', 1), ('vizzuhq/ipyvizzu', 0.6746152639389038, 'jupyter', 2), ('maartenbreddels/ipyvolume', 0.6592708230018616, 'jupyter', 3), ('holoviz/holoviz', 0.6567468047142029, 'viz', 0), ('cuemacro/chartpy', 0.6531518697738647, 'viz', 2), ('kanaries/pygwalker', 0.6463139653205872, 'pandas', 1), ('matplotlib/matplotlib', 0.6447177529335022, 'viz', 1), ('giswqs/geemap', 0.6420087218284607, 'gis', 1), ('altair-viz/altair', 0.6335301399230957, 'viz', 1), ('residentmario/geoplot', 0.6318153738975525, 'gis', 0), ('mwaskom/seaborn', 0.6307612061500549, 'viz', 0), ('holoviz/hvplot', 0.6104094982147217, 'pandas', 1), ('opengeos/leafmap', 0.5971899032592773, 'gis', 1), ('nomic-ai/deepscatter', 0.594667911529541, 'viz', 1), ('python-visualization/folium', 0.5900196433067322, 'gis', 1), ('visgl/deck.gl', 0.5892252326011658, 'viz', 2), ('holoviz/geoviews', 0.5875713229179382, 'gis', 1), ('pyqtgraph/pyqtgraph', 0.5844684839248657, 'viz', 1), ('raphaelquast/eomaps', 0.5836126804351807, 'gis', 2), ('r0x0r/pywebview', 0.5825904607772827, 'gui', 1), ('has2k1/plotnine', 0.5768696069717407, 'viz', 1), ('alexmojaki/heartrate', 0.571494460105896, 'debug', 1), ('lux-org/lux', 0.5685267448425293, 'viz', 2), ('federicoceratto/dashing', 0.5679104924201965, 'term', 0), ('jiffyclub/snakeviz', 0.5664529204368591, 'profiling', 0), ('masoniteframework/masonite', 0.5602609515190125, 'web', 0), ('pyvista/pyvista', 0.559531569480896, 'viz', 2), ('seleniumbase/seleniumbase', 0.5535537004470825, 'testing', 0), ('westhealth/pyvis', 0.55198734998703, 'graph', 0), ('enthought/mayavi', 0.550900936126709, 'viz', 1), ('polyaxon/datatile', 0.5476343631744385, 'pandas', 0), ('gaogaotiantian/viztracer', 0.5466111302375793, 'profiling', 1), ('giswqs/mapwidget', 0.5409641861915588, 'gis', 1), ('contextlab/hypertools', 0.5384601950645447, 'ml', 1), ('mckinsey/vizro', 0.5383272171020508, 'viz', 1), ('hoffstadt/dearpygui', 0.5377543568611145, 'gui', 0), ('voila-dashboards/voila', 0.5361472368240356, 'jupyter', 1), ('webpy/webpy', 0.5360361933708191, 'web', 0), ('brandtbucher/specialist', 0.5352582931518555, 'perf', 0), ('pyglet/pyglet', 0.5342501401901245, 'gamedev', 0), ('klen/muffin', 0.5334693193435669, 'web', 0), ('willmcgugan/textual', 0.5320950150489807, 'term', 0), ('ranaroussi/quantstats', 0.5271790027618408, 'finance', 2), ('pyscript/pyscript', 0.5267843008041382, 'web', 1), ('jakevdp/pythondatasciencehandbook', 0.5242840051651001, 'study', 0), ('hazyresearch/meerkat', 0.5242655277252197, 'viz', 0), ('graphistry/pygraphistry', 0.5225502252578735, 'data', 2), ('pysimplegui/pysimplegui', 0.5222162008285522, 'gui', 0), ('vaexio/vaex', 0.5212001800537109, 'perf', 1), ('jalammar/ecco', 0.5211085081100464, 'ml-interpretability', 1), ('wesm/pydata-book', 0.5152010321617126, 'study', 0), ('matplotlib/mplfinance', 0.5142570734024048, 'finance', 0), ('imageio/imageio', 0.5136635303497314, 'util', 0), ('parthjadhav/tkinter-designer', 0.5127165913581848, 'gui', 0), ('jupyter-widgets/ipywidgets', 0.5115343928337097, 'jupyter', 0), ('vispy/vispy', 0.5114392638206482, 'viz', 1), ('eleutherai/pyfra', 0.5110460519790649, 'ml', 0), ('microsoft/playwright-python', 0.510855495929718, 'testing', 0), ('scitools/iris', 0.5092189908027649, 'gis', 1), ('pygraphviz/pygraphviz', 0.5085844993591309, 'viz', 0), ('gregorhd/mapcompare', 0.5074924826622009, 'gis', 1), ('urwid/urwid', 0.5072845816612244, 'term', 0), ('wxwidgets/phoenix', 0.5054352879524231, 'gui', 0), ('scitools/cartopy', 0.5045799612998962, 'gis', 0), ('roniemartinez/dude', 0.5035302042961121, 'util', 0), ('adamerose/pandasgui', 0.5031503438949585, 'pandas', 0), ('wandb/client', 0.5021753907203674, 'ml', 0)]",680,5.0,,8.69,330,228,144,0,0,9,9,330.0,712.0,90.0,2.2,68
1172,llm,https://github.com/yoheinakajima/babyagi,"['autonomous-agents', 'artificial-intelligence']",GPT-4 powered task-driven autonomous agent,[],[],,,,yoheinakajima/babyagi,babyagi,18298,2473,280,Python,,,yoheinakajima,2024-01-14,2023-04-03,43,424.12582781456956,,GPT-4 powered task-driven autonomous agent,[],"['artificial-intelligence', 'autonomous-agents']",2023-09-07,"[('assafelovic/gpt-researcher', 0.6752115488052368, 'llm', 0), ('linksoul-ai/autoagents', 0.6577290892601013, 'llm', 1), ('torantulino/auto-gpt', 0.6121024489402771, 'llm', 2), ('antonosika/gpt-engineer', 0.5550106763839722, 'llm', 0), ('operand/agency', 0.53130042552948, 'llm', 2), ('transformeroptimus/superagi', 0.5311616659164429, 'llm', 2), ('geekan/metagpt', 0.5191521644592285, 'llm', 0)]",87,5.0,,5.17,6,2,10,4,0,1,1,6.0,1.0,90.0,0.2,68
75,typing,https://github.com/python/mypy,['code-quality'],,[],[],,,,python/mypy,mypy,17020,2765,236,Python,https://www.mypy-lang.org/,Optional static typing for Python,python,2024-01-13,2012-12-07,581,29.26553672316384,https://avatars.githubusercontent.com/u/1525981?v=4,Optional static typing for Python,"['linter', 'typechecker', 'types', 'typing']","['code-quality', 'linter', 'typechecker', 'types', 'typing']",2024-01-14,"[('microsoft/pyright', 0.8244960904121399, 'typing', 2), ('google/pytype', 0.7456424236297607, 'typing', 5), ('agronholm/typeguard', 0.7131239771842957, 'typing', 2), ('instagram/monkeytype', 0.7129145264625549, 'typing', 1), ('python/typeshed', 0.6911789178848267, 'typing', 3), ('facebook/pyre-check', 0.628227949142456, 'typing', 2), ('instagram/fixit', 0.5660220384597778, 'util', 1), ('patrick-kidger/torchtyping', 0.5514424443244934, 'typing', 1), ('tiangolo/typer', 0.5458303093910217, 'term', 0), ('grantjenks/blue', 0.5406695604324341, 'util', 1), ('landscapeio/prospector', 0.5345390439033508, 'util', 0), ('astral-sh/ruff', 0.533256471157074, 'util', 2), ('pytoolz/toolz', 0.5248755216598511, 'util', 0), ('psf/black', 0.5155326128005981, 'util', 1), ('pycqa/pylint', 0.5039985179901123, 'util', 2), ('pycqa/pyflakes', 0.5037577748298645, 'util', 1)]",690,4.0,,16.1,739,373,135,0,0,8,8,737.0,1435.0,90.0,1.9,68
766,ml-dl,https://github.com/sanster/lama-cleaner,[],,[],[],,,,sanster/lama-cleaner,lama-cleaner,14764,1530,119,Python,https://lama-cleaner-docs.vercel.app/,"Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.",sanster,2024-01-14,2021-11-15,115,128.22332506203475,,"Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.","['inpainting', 'lama', 'latent-diffusion', 'mat', 'pytorch', 'stable-diffusion', 'zits']","['inpainting', 'lama', 'latent-diffusion', 'mat', 'pytorch', 'stable-diffusion', 'zits']",2024-01-08,"[('open-mmlab/mmediting', 0.5644564032554626, 'ml', 2), ('invoke-ai/invokeai', 0.551865816116333, 'diffusion', 3), ('lkwq007/stablediffusion-infinity', 0.5446968078613281, 'diffusion', 2), ('carson-katri/dream-textures', 0.5392587780952454, 'diffusion', 1), ('timothybrooks/instruct-pix2pix', 0.5218861699104309, 'diffusion', 0), ('automatic1111/stable-diffusion-webui', 0.5176366567611694, 'diffusion', 2), ('albumentations-team/albumentations', 0.515965461730957, 'ml-dl', 0), ('roboflow/notebooks', 0.5101523995399475, 'study', 1)]",14,5.0,,3.37,31,12,26,0,11,23,11,31.0,39.0,90.0,1.3,68
407,web,https://github.com/aio-libs/aiohttp,[],,[],[],,,,aio-libs/aiohttp,aiohttp,14277,1996,222,Python,https://docs.aiohttp.org,Asynchronous HTTP client/server framework for asyncio and Python,aio-libs,2024-01-14,2013-10-01,539,26.487940630797773,https://avatars.githubusercontent.com/u/7049303?v=4,Asynchronous HTTP client/server framework for asyncio and Python,"['aiohttp', 'async', 'asyncio', 'http', 'http-client', 'http-server']","['aiohttp', 'async', 'asyncio', 'http', 'http-client', 'http-server']",2024-01-12,"[('encode/httpx', 0.8457793593406677, 'web', 2), ('encode/uvicorn', 0.7939640879631042, 'web', 3), ('pallets/quart', 0.7800691723823547, 'web', 2), ('neoteroi/blacksheep', 0.7317296862602234, 'web', 3), ('alirn76/panther', 0.7044265270233154, 'web', 0), ('magicstack/uvloop', 0.6718955636024475, 'util', 2), ('miguelgrinberg/python-socketio', 0.6701440215110779, 'util', 1), ('agronholm/anyio', 0.6634643077850342, 'perf', 1), ('timofurrer/awesome-asyncio', 0.6443581581115723, 'study', 1), ('aio-libs/aiobotocore', 0.6436625719070435, 'util', 2), ('python-trio/trio', 0.6344983577728271, 'perf', 1), ('samuelcolvin/aioaws', 0.6308304667472839, 'data', 1), ('encode/starlette', 0.6300008296966553, 'web', 2), ('samuelcolvin/arq', 0.6253662705421448, 'data', 2), ('geeogi/async-python-lambda-template', 0.5915722250938416, 'template', 0), ('ets-labs/python-dependency-injector', 0.5857105255126953, 'util', 2), ('tiangolo/asyncer', 0.5814606547355652, 'perf', 2), ('alex-sherman/unsync', 0.5807369351387024, 'util', 0), ('klen/muffin', 0.5731057524681091, 'web', 1), ('pytest-dev/pytest-asyncio', 0.5665972828865051, 'testing', 1), ('aio-libs/aiokafka', 0.5572373270988464, 'data', 1), ('airtai/faststream', 0.5545108914375305, 'perf', 1), ('cherrypy/cherrypy', 0.5461557507514954, 'web', 2), ('huge-success/sanic', 0.5445988774299622, 'web', 1), ('sumerc/yappi', 0.5396091341972351, 'profiling', 1), ('tornadoweb/tornado', 0.5350281596183777, 'web', 0), ('psf/requests', 0.5318570137023926, 'web', 1), ('websocket-client/websocket-client', 0.5178495049476624, 'web', 0), ('pylons/waitress', 0.5173540711402893, 'web', 1), ('falconry/falcon', 0.512401819229126, 'web', 1), ('requests/toolbelt', 0.5097265839576721, 'util', 1), ('simple-salesforce/simple-salesforce', 0.5096431970596313, 'data', 0), ('jordaneremieff/mangum', 0.5083812475204468, 'web', 1), ('hugapi/hug', 0.5045132040977478, 'util', 2), ('replicate/replicate-python', 0.5043667554855347, 'ml', 0)]",708,5.0,,8.79,385,315,125,0,8,24,8,381.0,1111.0,90.0,2.9,68
381,llm,https://github.com/deepset-ai/haystack,['haystack'],,[],[],1.0,,,deepset-ai/haystack,haystack,12318,1539,121,Python,https://haystack.deepset.ai,":mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.",deepset-ai,2024-01-14,2019-11-14,219,56.06371911573472,https://avatars.githubusercontent.com/u/51827949?v=4,":mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.","['ai', 'bert', 'chatgpt', 'generative-ai', 'gpt-3', 'information-retrieval', 'language-model', 'large-language-models', 'machine-learning', 'nlp', 'pytorch', 'question-answering', 'semantic-search', 'squad', 'summarization', 'transformers']","['ai', 'bert', 'chatgpt', 'generative-ai', 'gpt-3', 'haystack', 'information-retrieval', 'language-model', 'large-language-models', 'machine-learning', 'nlp', 'pytorch', 'question-answering', 'semantic-search', 'squad', 'summarization', 'transformers']",2024-01-12,"[('pathwaycom/llm-app', 0.7468881607055664, 'llm', 1), ('llmware-ai/llmware', 0.6901717185974121, 'llm', 11), ('nomic-ai/gpt4all', 0.6876985430717468, 'llm', 1), ('embedchain/embedchain', 0.6720160841941833, 'llm', 2), ('rcgai/simplyretrieve', 0.67171311378479, 'llm', 4), ('paddlepaddle/paddlenlp', 0.6606729626655579, 'llm', 4), ('young-geng/easylm', 0.6550629734992981, 'llm', 2), ('cheshire-cat-ai/core', 0.6507551074028015, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6430325508117676, 'perf', 0), ('night-chen/toolqa', 0.6367813944816589, 'llm', 2), ('neuml/txtai', 0.635899007320404, 'nlp', 7), ('microsoft/autogen', 0.6323454976081848, 'llm', 1), ('run-llama/rags', 0.6311088800430298, 'llm', 1), ('nebuly-ai/nebullvm', 0.6285997033119202, 'perf', 2), ('hwchase17/langchain', 0.6265890598297119, 'llm', 1), ('lm-sys/fastchat', 0.6203048825263977, 'llm', 1), ('bigscience-workshop/petals', 0.6126344203948975, 'data', 4), ('deep-diver/llm-as-chatbot', 0.6116864085197449, 'llm', 0), ('h2oai/h2o-llmstudio', 0.6111219525337219, 'llm', 3), ('microsoft/promptflow', 0.6076862812042236, 'llm', 2), ('zilliztech/gptcache', 0.6036768555641174, 'llm', 2), ('microsoft/semantic-kernel', 0.5954805016517639, 'llm', 1), ('explosion/spacy-llm', 0.5950032472610474, 'llm', 4), ('confident-ai/deepeval', 0.5944290161132812, 'testing', 2), ('agenta-ai/agenta', 0.5929967761039734, 'llm', 1), ('microsoft/generative-ai-for-beginners', 0.5903522372245789, 'study', 6), ('thilinarajapakse/simpletransformers', 0.5894189476966858, 'nlp', 2), ('dylanhogg/llmgraph', 0.5867999792098999, 'ml', 1), ('shishirpatil/gorilla', 0.5841556787490845, 'llm', 1), ('rasahq/rasa', 0.5835498571395874, 'llm', 2), ('intellabs/fastrag', 0.5783014297485352, 'nlp', 8), ('lucidrains/toolformer-pytorch', 0.5719588398933411, 'llm', 2), ('mooler0410/llmspracticalguide', 0.5716323256492615, 'study', 2), ('hiyouga/llama-efficient-tuning', 0.5705711841583252, 'llm', 4), ('hiyouga/llama-factory', 0.5705711245536804, 'llm', 4), ('deeppavlov/deeppavlov', 0.5695079565048218, 'nlp', 4), ('tigerlab-ai/tiger', 0.5681328773498535, 'llm', 1), ('argilla-io/argilla', 0.5678386688232422, 'nlp', 3), ('alpha-vllm/llama2-accessory', 0.5652121305465698, 'llm', 0), ('chatarena/chatarena', 0.563785970211029, 'llm', 3), ('iryna-kondr/scikit-llm', 0.5621213912963867, 'llm', 3), ('openlmlab/moss', 0.5615432262420654, 'llm', 3), ('microsoft/lmops', 0.5590223073959351, 'llm', 2), ('vllm-project/vllm', 0.5563030242919922, 'llm', 1), ('fasteval/fasteval', 0.5561326742172241, 'llm', 0), ('lancedb/lancedb', 0.5541740655899048, 'data', 1), ('superduperdb/superduperdb', 0.5523344278335571, 'data', 4), ('hegelai/prompttools', 0.5512160658836365, 'llm', 2), ('mindsdb/mindsdb', 0.5477265119552612, 'data', 3), ('microsoft/torchscale', 0.546305775642395, 'llm', 1), ('togethercomputer/openchatkit', 0.5454091429710388, 'nlp', 0), ('databrickslabs/dolly', 0.5437489151954651, 'llm', 0), ('bentoml/openllm', 0.543747067451477, 'ml-ops', 1), ('mnotgod96/appagent', 0.5397396087646484, 'llm', 2), ('aiwaves-cn/agents', 0.5381879806518555, 'nlp', 1), ('infinitylogesh/mutate', 0.5364488959312439, 'nlp', 1), ('nvidia/nemo', 0.5352417826652527, 'nlp', 2), ('gunthercox/chatterbot', 0.5347136855125427, 'nlp', 1), ('operand/agency', 0.5344579815864563, 'llm', 2), ('thudm/chatglm2-6b', 0.5324009656906128, 'llm', 1), ('mlc-ai/web-llm', 0.5301832556724548, 'llm', 2), ('ajndkr/lanarky', 0.5300788283348083, 'llm', 0), ('activeloopai/deeplake', 0.5295435786247253, 'ml-ops', 4), ('eleutherai/the-pile', 0.5290297269821167, 'data', 0), ('microsoft/jarvis', 0.5287731885910034, 'llm', 1), ('jina-ai/thinkgpt', 0.527265191078186, 'llm', 1), ('mmabrouk/chatgpt-wrapper', 0.525952935218811, 'llm', 2), ('salesforce/xgen', 0.525870144367218, 'llm', 3), ('ray-project/ray-llm', 0.5249264240264893, 'llm', 2), ('prefecthq/marvin', 0.524747908115387, 'nlp', 1), ('eugeneyan/open-llms', 0.5231071710586548, 'study', 1), ('alphasecio/langchain-examples', 0.5227251648902893, 'llm', 0), ('langchain-ai/langgraph', 0.5211980938911438, 'llm', 0), ('ludwig-ai/ludwig', 0.520433783531189, 'ml-ops', 2), ('jerryjliu/llama_index', 0.5174872279167175, 'llm', 1), ('whitead/paper-qa', 0.5140941143035889, 'llm', 3), ('larsbaunwall/bricky', 0.5118600130081177, 'llm', 2), ('ray-project/llm-applications', 0.5103896260261536, 'llm', 1), ('krohling/bondai', 0.5101301074028015, 'llm', 0), ('mlc-ai/mlc-llm', 0.508885383605957, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5081061124801636, 'llm', 2), ('next-gpt/next-gpt', 0.5068576335906982, 'llm', 2), ('bobazooba/xllm', 0.5065814852714539, 'llm', 3), ('cg123/mergekit', 0.5054006576538086, 'llm', 0), ('lupantech/chameleon-llm', 0.505305290222168, 'llm', 3), ('run-llama/llama-hub', 0.5051847100257874, 'data', 0), ('citadel-ai/langcheck', 0.5050056576728821, 'llm', 1)]",226,2.0,,23.33,980,749,51,0,40,26,40,978.0,1043.0,90.0,1.1,68
80,math,https://github.com/scipy/scipy,[],,[],[],,,,scipy/scipy,scipy,12076,4979,349,Python,https://scipy.org,SciPy library main repository,scipy,2024-01-13,2011-03-09,672,17.947346072186836,https://avatars.githubusercontent.com/u/288277?v=4,SciPy library main repository,"['algorithms', 'scientific-computing', 'scipy']","['algorithms', 'scientific-computing', 'scipy']",2024-01-14,"[('numpy/numpy', 0.7627651691436768, 'math', 0), ('scikit-geometry/scikit-geometry', 0.6300176382064819, 'gis', 0), ('sympy/sympy', 0.6143859028816223, 'math', 0), ('roban/cosmolopy', 0.6138890385627747, 'sim', 0), ('dask/dask', 0.6020888686180115, 'perf', 1), ('cma-es/pycma', 0.5867729187011719, 'math', 0), ('scikit-learn/scikit-learn', 0.5719190835952759, 'ml', 0), ('scikit-hep/uproot5', 0.5634987354278564, 'data', 0), ('gbeced/pyalgotrade', 0.5615862011909485, 'finance', 0), ('fredrik-johansson/mpmath', 0.5546449422836304, 'math', 0), ('pysal/pysal', 0.5544923543930054, 'gis', 0), ('cupy/cupy', 0.5399492979049683, 'math', 1), ('scikit-optimize/scikit-optimize', 0.5384607911109924, 'ml', 1), ('pycaret/pycaret', 0.5348325967788696, 'ml', 0), ('jakevdp/pythondatasciencehandbook', 0.5299432873725891, 'study', 0), ('thealgorithms/python', 0.5260889530181885, 'study', 0), ('scikit-learn-contrib/metric-learn', 0.5236490368843079, 'ml', 0), ('pyston/pyston', 0.5205775499343872, 'util', 0), ('eleutherai/pyfra', 0.5191806554794312, 'ml', 0), ('pypy/pypy', 0.5184841156005859, 'util', 0), ('pytoolz/toolz', 0.5162753462791443, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5127685070037842, 'study', 0), ('marcomusy/vedo', 0.5084977746009827, 'viz', 0), ('joblib/joblib', 0.508256196975708, 'util', 0), ('probml/pyprobml', 0.5070646405220032, 'ml', 0), ('life4/textdistance', 0.5068709850311279, 'nlp', 1), ('crflynn/stochastic', 0.5048527121543884, 'sim', 0), ('krzjoa/awesome-python-data-science', 0.5031641721725464, 'study', 0), ('pmorissette/ffn', 0.502606987953186, 'finance', 0), ('networkx/networkx', 0.5018213391304016, 'graph', 0), ('python/cpython', 0.500981867313385, 'util', 0), ('quantopian/zipline', 0.5005958080291748, 'finance', 0), ('rasbt/mlxtend', 0.5000782012939453, 'ml', 0)]",1572,4.0,,42.25,891,449,156,0,10,12,10,891.0,2493.0,90.0,2.8,68
1390,finance,https://github.com/ai4finance-foundation/fingpt,"['language-model', 'llm']",,[],[],,,,ai4finance-foundation/fingpt,FinGPT,10239,2162,209,Jupyter Notebook,https://discord.gg/trsr8SXpW5,Data-Centric FinGPT.  Open-source for open finance!  Revolutionize 🔥    We release the trained model on HuggingFace.,ai4finance-foundation,2024-01-14,2023-02-11,50,203.03966005665723,https://avatars.githubusercontent.com/u/68813910?v=4,Data-Centric FinGPT.  Open-source for open finance!  Revolutionize 🔥    We release the trained model on HuggingFace.,"['chatgpt', 'finance', 'fingpt', 'fintech', 'large-language-models', 'machine-learning', 'nlp', 'prompt-engineering', 'pytorch', 'reinforcement-learning', 'robo-advisor', 'sentiment-analysis', 'technical-analysis']","['chatgpt', 'finance', 'fingpt', 'fintech', 'language-model', 'large-language-models', 'llm', 'machine-learning', 'nlp', 'prompt-engineering', 'pytorch', 'reinforcement-learning', 'robo-advisor', 'sentiment-analysis', 'technical-analysis']",2023-12-26,"[('chancefocus/pixiu', 0.7477177381515503, 'finance', 8), ('ai4finance-foundation/finrl', 0.6304659247398376, 'finance', 3), ('argilla-io/argilla', 0.5461266040802002, 'nlp', 3), ('doccano/doccano', 0.5407078862190247, 'nlp', 1), ('lianjiatech/belle', 0.5230826735496521, 'llm', 0), ('mindsdb/mindsdb', 0.5226989984512329, 'data', 2), ('ta-lib/ta-lib-python', 0.5139918327331543, 'finance', 2), ('xtekky/gpt4free', 0.5088690519332886, 'llm', 2), ('bobazooba/xllm', 0.5081082582473755, 'llm', 4), ('goldmansachs/gs-quant', 0.5048463344573975, 'finance', 0)]",27,8.0,,10.44,51,27,11,1,0,0,0,51.0,55.0,90.0,1.1,68
200,ml,https://github.com/huggingface/accelerate,[],,[],[],,,,huggingface/accelerate,accelerate,6340,710,95,Python,https://huggingface.co/docs/accelerate,"🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision",huggingface,2024-01-14,2020-10-30,169,37.38837405223252,https://avatars.githubusercontent.com/u/25720743?v=4,"🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision",[],[],2024-01-12,"[('nvidia/apex', 0.7648141980171204, 'ml-dl', 0), ('pytorch/ignite', 0.6613282561302185, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.6329745650291443, 'perf', 0), ('skorch-dev/skorch', 0.6029394268989563, 'ml-dl', 0), ('pytorch/pytorch', 0.5829792022705078, 'ml-dl', 0), ('laekov/fastmoe', 0.5827643275260925, 'ml', 0), ('blackhc/toma', 0.5751364827156067, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5736592411994934, 'study', 0), ('hysts/pytorch_image_classification', 0.5682789087295532, 'ml-dl', 0), ('eleutherai/gpt-neo', 0.5675502419471741, 'llm', 0), ('arogozhnikov/einops', 0.5633874535560608, 'ml-dl', 0), ('tensorflow/mesh', 0.5616391897201538, 'ml-dl', 0), ('pytorchlightning/pytorch-lightning', 0.5604153871536255, 'ml-dl', 0), ('pytorch/data', 0.5591470003128052, 'data', 0), ('nicolas-chaulet/torch-points3d', 0.5572007894515991, 'ml', 0), ('xl0/lovely-tensors', 0.557181715965271, 'ml-dl', 0), ('karpathy/mingpt', 0.5470962524414062, 'llm', 0), ('denys88/rl_games', 0.5460307002067566, 'ml-rl', 0), ('rentruewang/koila', 0.5446726083755493, 'ml', 0), ('cupy/cupy', 0.5428050756454468, 'math', 0), ('ashleve/lightning-hydra-template', 0.5420671105384827, 'util', 0), ('pytorch/captum', 0.5386924743652344, 'ml-interpretability', 0), ('nvidia/tensorrt-llm', 0.5342060923576355, 'viz', 0), ('salesforce/warp-drive', 0.5263185501098633, 'ml-rl', 0), ('rasbt/machine-learning-book', 0.5255182385444641, 'study', 0), ('karpathy/micrograd', 0.5209835767745972, 'study', 0), ('cvxgrp/pymde', 0.5172093510627747, 'ml', 0), ('rasbt/deeplearning-models', 0.5161780714988708, 'ml-dl', 0), ('nvidia/warp', 0.5148420333862305, 'sim', 0), ('pyg-team/pytorch_geometric', 0.514308750629425, 'ml-dl', 0), ('google/tf-quant-finance', 0.5086351633071899, 'finance', 0), ('timdettmers/bitsandbytes', 0.5063002705574036, 'util', 0), ('pytorch/rl', 0.5024619102478027, 'ml-rl', 0)]",209,5.0,,11.67,394,338,39,0,17,13,17,394.0,1100.0,90.0,2.8,68
1751,llm,https://github.com/dsdanielpark/bard-api,[],,[],[],,,,dsdanielpark/bard-api,Bard-API,5245,661,46,Python,https://pypi.org/project/bardapi/,The unofficial python package that returns response of Google Bard through cookie value.,dsdanielpark,2024-01-14,2023-05-11,37,139.0719696969697,,The unofficial python package that returns response of Google Bard through cookie value.,"['ai-api', 'api', 'bard', 'bard-api', 'chatbot', 'google', 'google-bard', 'google-bard-api', 'google-bard-python', 'google-maps-api', 'googlebard', 'llm', 'nlp']","['ai-api', 'api', 'bard', 'bard-api', 'chatbot', 'google', 'google-bard', 'google-bard-api', 'google-bard-python', 'google-maps-api', 'googlebard', 'llm', 'nlp']",2024-01-14,"[('googleapis/google-api-python-client', 0.6036682724952698, 'util', 0), ('psf/requests', 0.5480201244354248, 'web', 0), ('googleapis/python-speech', 0.5438737273216248, 'ml', 0), ('pndurette/gtts', 0.5274245738983154, 'util', 0)]",26,1.0,,12.79,59,35,8,0,37,58,37,59.0,168.0,90.0,2.8,68
689,ml-dl,https://github.com/deepfakes/faceswap,[],,[],[],,,,deepfakes/faceswap,faceswap,48131,12869,1526,Python,https://www.faceswap.dev,Deepfakes Software For All,deepfakes,2024-01-14,2017-12-19,319,150.8808777429467,,Deepfakes Software For All,"['deep-face-swap', 'deep-learning', 'deep-neural-networks', 'deepface', 'deepfakes', 'deeplearning', 'face-swap', 'faceswap', 'fakeapp', 'machine-learning', 'myfakeapp', 'neural-nets', 'neural-networks', 'openfaceswap']","['deep-face-swap', 'deep-learning', 'deep-neural-networks', 'deepface', 'deepfakes', 'deeplearning', 'face-swap', 'faceswap', 'fakeapp', 'machine-learning', 'myfakeapp', 'neural-nets', 'neural-networks', 'openfaceswap']",2024-01-12,"[('iperov/deepfacelab', 0.8627434968948364, 'ml-dl', 12), ('huggingface/exporters', 0.5523707270622253, 'ml', 2), ('huggingface/transformers', 0.5490606427192688, 'nlp', 2), ('huggingface/huggingface_hub', 0.5489972233772278, 'ml', 2), ('explosion/thinc', 0.5478455424308777, 'ml-dl', 2), ('christoschristofidis/awesome-deep-learning', 0.5433439612388611, 'study', 2), ('rwightman/pytorch-image-models', 0.5423110127449036, 'ml-dl', 0), ('tencentarc/gfpgan', 0.5402413606643677, 'ml', 1), ('neuralmagic/sparseml', 0.5372126698493958, 'ml-dl', 0), ('fepegar/torchio', 0.5368450880050659, 'ml-dl', 2), ('keras-team/keras', 0.5320166945457458, 'ml-dl', 3), ('alpa-projects/alpa', 0.5312798619270325, 'ml-dl', 2), ('neuralmagic/deepsparse', 0.5284426212310791, 'nlp', 0), ('huggingface/autotrain-advanced', 0.5193936228752136, 'ml', 2), ('huggingface/datasets', 0.5192609429359436, 'nlp', 2), ('microsoft/onnxruntime', 0.5149832963943481, 'ml', 3), ('roboflow/supervision', 0.5128257274627686, 'ml', 2), ('nvidia/deeplearningexamples', 0.5123569965362549, 'ml-dl', 1), ('mosaicml/composer', 0.5090040564537048, 'ml-dl', 3), ('awslabs/autogluon', 0.5015328526496887, 'ml', 2), ('onnx/onnx', 0.5000324845314026, 'ml', 3)]",97,2.0,,1.52,20,19,74,0,2,1,2,20.0,22.0,90.0,1.1,67
1054,llm,https://github.com/moymix/taskmatrix,['chatgpt'],Connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.,[],[],,,,moymix/taskmatrix,TaskMatrix,34397,3381,310,Python,,,moymix,2024-01-14,2023-03-02,47,720.8952095808384,,Connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.,[],['chatgpt'],2023-06-29,"[('next-gpt/next-gpt', 0.5018583536148071, 'llm', 1)]",20,5.0,,1.73,21,6,11,7,0,0,0,21.0,23.0,90.0,1.1,67
76,nlp,https://github.com/pytorch/fairseq,[],,[],[],,,,pytorch/fairseq,fairseq,28464,6285,417,Python,,Facebook AI Research Sequence-to-Sequence Toolkit written in Python.,pytorch,2024-01-14,2017-08-29,335,84.96716417910447,https://avatars.githubusercontent.com/u/16943930?v=4,Facebook AI Research Sequence-to-Sequence Toolkit written in Python.,"['artificial-intelligence', 'pytorch']","['artificial-intelligence', 'pytorch']",2024-01-08,"[('facebookresearch/mmf', 0.6375062465667725, 'ml-dl', 1), ('deepmind/deepmind-research', 0.5041647553443909, 'ml', 0)]",421,4.0,,0.77,134,40,78,0,0,3,3,134.0,116.0,90.0,0.9,67
8,perf,https://github.com/celery/celery,[],,[],[],,,,celery/celery,celery,22871,4604,473,Python,https://docs.celeryq.dev,Distributed Task Queue (development branch),celery,2024-01-13,2009-04-24,770,29.68057100482017,https://avatars.githubusercontent.com/u/319983?v=4,Distributed Task Queue (development branch),"['amqp', 'queue-tasks', 'queue-workers', 'queued-jobs', 'redis', 'redis-queue', 'sqs', 'sqs-queue', 'task-manager', 'task-runner', 'task-scheduler']","['amqp', 'queue-tasks', 'queue-workers', 'queued-jobs', 'redis', 'redis-queue', 'sqs', 'sqs-queue', 'task-manager', 'task-runner', 'task-scheduler']",2024-01-11,"[('mher/flower', 0.6660754680633545, 'perf', 1), ('samuelcolvin/arq', 0.5360067486763, 'data', 1)]",1327,6.0,,6.65,278,167,179,0,8,16,8,280.0,513.0,90.0,1.8,67
1121,llm,https://github.com/rasahq/rasa,[],,[],['rasa'],,,,rasahq/rasa,rasa,17448,4498,350,Python,https://rasa.com/docs/rasa/,"💬   Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants",rasahq,2024-01-14,2016-10-14,380,45.846846846846844,https://avatars.githubusercontent.com/u/21214473?v=4,"💬   Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants","['bot', 'bot-framework', 'botkit', 'bots', 'chatbot', 'chatbots', 'chatbots-framework', 'conversation-driven-development', 'conversational-agents', 'conversational-ai', 'conversational-bots', 'machine-learning', 'machine-learning-library', 'mitie', 'natural-language-processing', 'nlp', 'nlu', 'rasa', 'spacy', 'wit']","['bot', 'bot-framework', 'botkit', 'bots', 'chatbot', 'chatbots', 'chatbots-framework', 'conversation-driven-development', 'conversational-agents', 'conversational-ai', 'conversational-bots', 'machine-learning', 'machine-learning-library', 'mitie', 'natural-language-processing', 'nlp', 'nlu', 'rasa', 'spacy', 'wit']",2023-11-29,"[('deeppavlov/deeppavlov', 0.7903884053230286, 'nlp', 4), ('nvidia/nemo', 0.7352750301361084, 'nlp', 1), ('krohling/bondai', 0.7222825288772583, 'llm', 0), ('embedchain/embedchain', 0.7189019322395325, 'llm', 1), ('gunthercox/chatterbot', 0.6935678720474243, 'nlp', 3), ('togethercomputer/openchatkit', 0.6932374835014343, 'nlp', 1), ('openlmlab/moss', 0.6797881126403809, 'llm', 1), ('lm-sys/fastchat', 0.6786299347877502, 'llm', 1), ('rcgai/simplyretrieve', 0.660693347454071, 'llm', 3), ('aiwaves-cn/agents', 0.6561240553855896, 'nlp', 0), ('nomic-ai/gpt4all', 0.6559364199638367, 'llm', 1), ('databrickslabs/dolly', 0.6492189168930054, 'llm', 1), ('nltk/nltk', 0.6307575106620789, 'nlp', 3), ('argilla-io/argilla', 0.6305766105651855, 'nlp', 3), ('cheshire-cat-ai/core', 0.6253566741943359, 'llm', 1), ('run-llama/rags', 0.6235318779945374, 'llm', 1), ('allenai/allennlp', 0.6140093207359314, 'nlp', 2), ('explosion/spacy', 0.6100810170173645, 'nlp', 4), ('keras-team/keras-nlp', 0.6086421012878418, 'nlp', 3), ('gunthercox/chatterbot-corpus', 0.6020722985267639, 'nlp', 0), ('prefecthq/marvin', 0.6008431315422058, 'nlp', 1), ('facebookresearch/parlai', 0.5971487164497375, 'nlp', 0), ('tensorflow/tensorflow', 0.5951974391937256, 'ml-dl', 1), ('larsbaunwall/bricky', 0.5906454920768738, 'llm', 0), ('google-research/language', 0.5900284051895142, 'nlp', 2), ('flairnlp/flair', 0.5888329744338989, 'nlp', 3), ('bigscience-workshop/promptsource', 0.5880197882652283, 'nlp', 3), ('mlflow/mlflow', 0.5874723792076111, 'ml-ops', 1), ('laion-ai/open-assistant', 0.5849403738975525, 'llm', 1), ('deepset-ai/haystack', 0.5835498571395874, 'llm', 2), ('doccano/doccano', 0.5829582214355469, 'nlp', 2), ('minimaxir/simpleaichat', 0.5821955800056458, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.5802013874053955, 'nlp', 1), ('explosion/spacy-models', 0.5728164315223694, 'nlp', 4), ('fasteval/fasteval', 0.5695826411247253, 'llm', 0), ('kalliope-project/kalliope', 0.5666339993476868, 'util', 1), ('explosion/spacy-llm', 0.5661826729774475, 'llm', 4), ('pathwaycom/llm-app', 0.5657259821891785, 'llm', 2), ('blinkdl/chatrwkv', 0.5604526400566101, 'llm', 1), ('microsoft/autogen', 0.5599412322044373, 'llm', 1), ('llmware-ai/llmware', 0.5576108694076538, 'llm', 2), ('huggingface/transformers', 0.5568842887878418, 'nlp', 3), ('alibaba/easynlp', 0.5567002892494202, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.556032121181488, 'llm', 1), ('microsoft/unilm', 0.5554572939872742, 'nlp', 1), ('lucidrains/toolformer-pytorch', 0.5501740574836731, 'llm', 0), ('openai/gpt-discord-bot', 0.5486682057380676, 'llm', 0), ('graykode/nlp-tutorial', 0.5484858155250549, 'study', 2), ('espnet/espnet', 0.5472486615180969, 'nlp', 0), ('infinitylogesh/mutate', 0.5458459258079529, 'nlp', 0), ('facebookresearch/mmf', 0.5447145104408264, 'ml-dl', 0), ('microsoft/generative-ai-for-beginners', 0.541526734828949, 'study', 0), ('dylanhogg/awesome-python', 0.536875307559967, 'study', 3), ('huggingface/datasets', 0.5359129905700684, 'nlp', 3), ('jbesomi/texthero', 0.5357376337051392, 'nlp', 2), ('nvidia/deeplearningexamples', 0.5313621163368225, 'ml-dl', 1), ('microsoft/lmops', 0.5303319096565247, 'llm', 1), ('norskregnesentral/skweak', 0.5265606641769409, 'nlp', 2), ('makcedward/nlpaug', 0.5259739756584167, 'nlp', 3), ('explosion/thinc', 0.5251515507698059, 'ml-dl', 5), ('xtekky/gpt4free', 0.523765504360199, 'llm', 2), ('killianlucas/open-interpreter', 0.523764431476593, 'llm', 0), ('mindsdb/mindsdb', 0.5234725475311279, 'data', 2), ('sloria/textblob', 0.5231644511222839, 'nlp', 2), ('nevronai/metisfl', 0.5226208567619324, 'ml', 1), ('speechbrain/speechbrain', 0.5202128291130066, 'nlp', 0), ('franck-dernoncourt/neuroner', 0.5182371735572815, 'nlp', 2), ('ddbourgin/numpy-ml', 0.5173048377037048, 'ml', 1), ('tigerlab-ai/tiger', 0.5164182186126709, 'llm', 0), ('merantix-momentum/squirrel-core', 0.5163630247116089, 'ml', 3), ('mlc-ai/web-llm', 0.5135403275489807, 'llm', 0), ('hwchase17/langchain', 0.5109175443649292, 'llm', 1), ('nebuly-ai/nebullvm', 0.5099035501480103, 'perf', 0), ('microsoft/nni', 0.5092196464538574, 'ml', 1), ('young-geng/easylm', 0.5090000033378601, 'llm', 2), ('langchain-ai/chat-langchain', 0.5066604614257812, 'llm', 0), ('mlc-ai/mlc-llm', 0.50617915391922, 'llm', 0), ('chatarena/chatarena', 0.5054959058761597, 'llm', 1), ('deepset-ai/farm', 0.505393922328949, 'nlp', 1), ('gradio-app/gradio', 0.5041884183883667, 'viz', 1), ('googlecloudplatform/vertex-ai-samples', 0.5028780102729797, 'ml', 0), ('pemistahl/lingua-py', 0.5019610524177551, 'nlp', 2), ('m-bain/whisperx', 0.5019509792327881, 'nlp', 0), ('uberi/speech_recognition', 0.5011873245239258, 'ml', 0)]",592,5.0,,29.4,138,109,88,2,70,93,70,138.0,103.0,90.0,0.7,67
1780,web,https://github.com/wagtail/wagtail,[],,[],[],,,,wagtail/wagtail,wagtail,16554,3542,341,Python,https://wagtail.org,A Django content management system focused on flexibility and user experience,wagtail,2024-01-14,2014-02-03,521,31.76480263157895,https://avatars.githubusercontent.com/u/23708009?v=4,A Django content management system focused on flexibility and user experience,"['cms', 'django', 'wagtail']","['cms', 'django', 'wagtail']",2024-01-11,"[('feincms/feincms', 0.7559544444084167, 'web', 1), ('stephenmcd/mezzanine', 0.7118169665336609, 'web', 2), ('django/django', 0.5955774188041687, 'web', 1), ('brettkromkamp/contextualise', 0.5667254328727722, 'data', 0), ('pallets/flask', 0.5286979675292969, 'web', 0), ('fastapi-admin/fastapi-admin', 0.5146538019180298, 'web', 0), ('indico/indico', 0.5091932415962219, 'web', 0), ('piccolo-orm/piccolo_admin', 0.5042932033538818, 'data', 1)]",880,3.0,,40.79,622,368,121,4,30,24,30,623.0,1539.0,90.0,2.5,67
1510,llm,https://github.com/facebookresearch/codellama,"['llama', 'language-model']",,[],[],,,,facebookresearch/codellama,codellama,11969,1192,125,Python,,Inference code for CodeLlama models,facebookresearch,2024-01-14,2023-08-24,22,526.937106918239,https://avatars.githubusercontent.com/u/16943930?v=4,Inference code for CodeLlama models,[],"['language-model', 'llama']",2023-09-26,"[('facebookresearch/llama', 0.8860641121864319, 'llm', 2), ('karpathy/llama2.c', 0.7492680549621582, 'llm', 2), ('facebookresearch/llama-recipes', 0.6740383505821228, 'llm', 2), ('microsoft/llama-2-onnx', 0.6238771080970764, 'llm', 2), ('tairov/llama2.mojo', 0.599827229976654, 'llm', 1), ('jzhang38/tinyllama', 0.5828747749328613, 'llm', 2), ('abetlen/llama-cpp-python', 0.5786312222480774, 'llm', 2), ('openai/gpt-2', 0.550112247467041, 'llm', 0), ('juncongmoo/pyllama', 0.5494055151939392, 'llm', 0), ('lightning-ai/lit-llama', 0.5444297790527344, 'llm', 2), ('tloen/alpaca-lora', 0.5405111908912659, 'llm', 2), ('openlm-research/open_llama', 0.5370882153511047, 'llm', 2), ('salesforce/codet5', 0.5207399725914001, 'nlp', 1), ('cg123/mergekit', 0.51674485206604, 'llm', 1), ('cstankonrad/long_llama', 0.5162266492843628, 'llm', 2), ('hazyresearch/h3', 0.513182520866394, 'llm', 0), ('mshumer/gpt-llm-trainer', 0.509406328201294, 'llm', 0), ('freedomintelligence/llmzoo', 0.508786141872406, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5052860975265503, 'llm', 1), ('bigcode-project/starcoder', 0.5031982064247131, 'llm', 0), ('hao-ai-lab/lookaheaddecoding', 0.5007719397544861, 'llm', 0)]",11,6.0,,0.27,58,19,5,4,0,0,0,58.0,69.0,90.0,1.2,67
72,testing,https://github.com/pytest-dev/pytest,[],,[],[],,,,pytest-dev/pytest,pytest,10981,2471,192,Python,https://pytest.org,"The pytest framework makes it easy to write small tests, yet scales to support complex functional testing",pytest-dev,2024-01-13,2015-06-15,450,24.394477943509997,https://avatars.githubusercontent.com/u/8897583?v=4,"The pytest framework makes it easy to write small tests, yet scales to support complex functional testing","['test', 'testing', 'unit-testing']","['test', 'testing', 'unit-testing']",2024-01-14,"[('ionelmc/pytest-benchmark', 0.6786613464355469, 'testing', 0), ('samuelcolvin/dirty-equals', 0.6580618023872375, 'util', 1), ('pytest-dev/pytest-xdist', 0.6530086994171143, 'testing', 0), ('pytest-dev/pytest-mock', 0.6347528100013733, 'testing', 0), ('samuelcolvin/pytest-pretty', 0.5827073454856873, 'testing', 0), ('wolever/parameterized', 0.5712449550628662, 'testing', 0), ('pytest-dev/pytest-asyncio', 0.5557228922843933, 'testing', 1), ('pytest-dev/pytest-testinfra', 0.5438085794448853, 'testing', 1), ('teemu/pytest-sugar', 0.5432929992675781, 'testing', 1), ('pytest-dev/pytest-cov', 0.5345095992088318, 'testing', 0), ('taverntesting/tavern', 0.5295860767364502, 'testing', 1), ('locustio/locust', 0.5194878578186035, 'testing', 0), ('computationalmodelling/nbval', 0.5131306648254395, 'jupyter', 1), ('pmorissette/bt', 0.5103425979614258, 'finance', 0), ('hypothesisworks/hypothesis', 0.505795955657959, 'testing', 1)]",934,5.0,,13.98,384,265,104,0,10,23,10,384.0,745.0,90.0,1.9,67
1654,util,https://github.com/pyo3/pyo3,['rust'],,[],[],,,,pyo3/pyo3,pyo3,10178,669,84,Rust,https://pyo3.rs,Rust bindings for the Python interpreter,pyo3,2024-01-14,2017-05-13,350,29.04443538524256,https://avatars.githubusercontent.com/u/28156855?v=4,Rust bindings for the Python interpreter,"['binding', 'ffi', 'python-c-api', 'rust']","['binding', 'ffi', 'python-c-api', 'rust']",2024-01-13,"[('pyo3/rust-numpy', 0.6885672211647034, 'util', 1), ('pyo3/maturin', 0.6540318131446838, 'util', 1), ('rustpython/rustpython', 0.6475306749343872, 'util', 1), ('delta-io/delta-rs', 0.5582752227783203, 'pandas', 1), ('pybind/pybind11', 0.5454217791557312, 'perf', 0), ('samuelcolvin/rtoml', 0.5289204716682434, 'data', 1)]",301,5.0,,10.88,229,174,81,0,10,12,10,228.0,662.0,90.0,2.9,67
534,nlp,https://github.com/nvidia/nemo,[],,[],[],,,,nvidia/nemo,NeMo,8946,1977,180,Python,https://nvidia.github.io/NeMo/,NeMo: a toolkit for conversational AI,nvidia,2024-01-14,2019-08-05,234,38.20744356314826,https://avatars.githubusercontent.com/u/1728152?v=4,NeMo: a toolkit for conversational AI,"['asr', 'deep-learning', 'language-model', 'machine-translation', 'neural-network', 'nlp', 'nlp-machine-learning', 'nmt', 'speaker-diarization', 'speaker-recognition', 'speech-recognition', 'speech-synthesis', 'speech-to-text', 'text-normalization', 'text-to-speech', 'tts']","['asr', 'deep-learning', 'language-model', 'machine-translation', 'neural-network', 'nlp', 'nlp-machine-learning', 'nmt', 'speaker-diarization', 'speaker-recognition', 'speech-recognition', 'speech-synthesis', 'speech-to-text', 'text-normalization', 'text-to-speech', 'tts']",2024-01-13,"[('deeppavlov/deeppavlov', 0.7471092939376831, 'nlp', 3), ('rasahq/rasa', 0.7352750301361084, 'llm', 1), ('krohling/bondai', 0.7121801972389221, 'llm', 0), ('facebookresearch/parlai', 0.6826277375221252, 'nlp', 0), ('thilinarajapakse/simpletransformers', 0.6740487813949585, 'nlp', 0), ('openlmlab/moss', 0.640605628490448, 'llm', 2), ('espnet/espnet', 0.63554447889328, 'nlp', 5), ('speechbrain/speechbrain', 0.6236434578895569, 'nlp', 7), ('rcgai/simplyretrieve', 0.6101846098899841, 'llm', 1), ('gunthercox/chatterbot', 0.5878262519836426, 'nlp', 0), ('databrickslabs/dolly', 0.5758463144302368, 'llm', 0), ('cheshire-cat-ai/core', 0.5682287216186523, 'llm', 0), ('lm-sys/fastchat', 0.5640174150466919, 'llm', 1), ('embedchain/embedchain', 0.5636201500892639, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5635098218917847, 'nlp', 0), ('prefecthq/marvin', 0.560585081577301, 'nlp', 0), ('keras-team/keras-nlp', 0.5545443892478943, 'nlp', 2), ('huggingface/transformers', 0.5480668544769287, 'nlp', 4), ('fasteval/fasteval', 0.5468846559524536, 'llm', 0), ('minimaxir/aitextgen', 0.5452271699905396, 'llm', 0), ('minimaxir/simpleaichat', 0.5450947284698486, 'llm', 0), ('explosion/spacy', 0.541452169418335, 'nlp', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5406086444854736, 'study', 1), ('m-bain/whisperx', 0.5402535796165466, 'nlp', 3), ('togethercomputer/openchatkit', 0.5391964912414551, 'nlp', 0), ('allenai/allennlp', 0.5374099016189575, 'nlp', 2), ('deepset-ai/haystack', 0.5352417826652527, 'llm', 2), ('alibaba/easynlp', 0.527443528175354, 'nlp', 2), ('chatarena/chatarena', 0.5267899036407471, 'llm', 0), ('nvidia/deeplearningexamples', 0.5229525566101074, 'ml-dl', 4), ('facebookresearch/mmf', 0.5225995779037476, 'ml-dl', 1), ('microsoft/generative-ai-for-beginners', 0.5218424201011658, 'study', 1), ('nltk/nltk', 0.5199841260910034, 'nlp', 1), ('blinkdl/chatrwkv', 0.5173482894897461, 'llm', 1), ('graykode/nlp-tutorial', 0.5165086388587952, 'study', 1), ('facebookresearch/seamless_communication', 0.5158795714378357, 'nlp', 2), ('gventuri/pandas-ai', 0.513568639755249, 'pandas', 0), ('microsoft/lmops', 0.5125614404678345, 'llm', 2), ('bentoml/bentoml', 0.5116295218467712, 'ml-ops', 1), ('lucidrains/toolformer-pytorch', 0.5109055042266846, 'llm', 2), ('ddbourgin/numpy-ml', 0.5102659463882446, 'ml', 0), ('nomic-ai/gpt4all', 0.5101531744003296, 'llm', 1), ('explosion/thinc', 0.5100991129875183, 'ml-dl', 2), ('franck-dernoncourt/neuroner', 0.5075932145118713, 'nlp', 2), ('llmware-ai/llmware', 0.503142237663269, 'llm', 1), ('minimaxir/textgenrnn', 0.5010759234428406, 'nlp', 1)]",270,2.0,,18.33,527,432,54,0,10,11,10,526.0,1411.0,90.0,2.7,67
1162,web,https://github.com/flet-dev/flet,[],,[],[],,,,flet-dev/flet,flet,7928,303,110,Python,https://flet.dev,"Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.",flet-dev,2024-01-14,2022-03-24,96,81.97341211225996,https://avatars.githubusercontent.com/u/102273996?v=4,"Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.","['android', 'flutter', 'ios', 'server-driven-ui', 'web']","['android', 'flutter', 'ios', 'server-driven-ui', 'web']",2024-01-12,"[('pallets/flask', 0.6681143045425415, 'web', 0), ('willmcgugan/textual', 0.6341605186462402, 'term', 0), ('reflex-dev/reflex', 0.612991988658905, 'web', 0), ('kivy/kivy', 0.6066066026687622, 'util', 2), ('python-restx/flask-restx', 0.6032120585441589, 'web', 0), ('r0x0r/pywebview', 0.5987342000007629, 'gui', 0), ('klen/muffin', 0.5684182643890381, 'web', 0), ('pallets/quart', 0.5679628252983093, 'web', 0), ('webpy/webpy', 0.5566127896308899, 'web', 0), ('bottlepy/bottle', 0.5521423816680908, 'web', 0), ('plotly/dash', 0.5519179105758667, 'viz', 0), ('falconry/falcon', 0.5503961443901062, 'web', 1), ('huge-success/sanic', 0.5318177342414856, 'web', 1), ('masoniteframework/masonite', 0.5303350687026978, 'web', 1), ('starlite-api/starlite', 0.5249883532524109, 'web', 0), ('tiangolo/fastapi', 0.5213461518287659, 'web', 1), ('vitalik/django-ninja', 0.5206228494644165, 'web', 0), ('alphasecio/langchain-examples', 0.5201708078384399, 'llm', 0), ('pallets/werkzeug', 0.5143693089485168, 'web', 0), ('timofurrer/awesome-asyncio', 0.5139799118041992, 'study', 0), ('hoffstadt/dearpygui', 0.5117893815040588, 'gui', 0), ('neoteroi/blacksheep', 0.5072730183601379, 'web', 1), ('pyinfra-dev/pyinfra', 0.5072339773178101, 'util', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5064703822135925, 'template', 0), ('holoviz/panel', 0.5036301016807556, 'viz', 0)]",47,3.0,,4.54,399,254,22,0,34,60,34,400.0,677.0,90.0,1.7,67
1290,llm,https://github.com/optimalscale/lmflow,"['instruction-following', 'language-model']",,[],[],,,,optimalscale/lmflow,LMFlow,7551,1077,72,Python,https://optimalscale.github.io/LMFlow/,An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.,optimalscale,2024-01-13,2023-03-27,44,171.05825242718447,https://avatars.githubusercontent.com/u/128913633?v=4,An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.,"['chatgpt', 'deep-learning', 'instruction-following', 'language-model', 'pretrained-models', 'pytorch', 'transformer']","['chatgpt', 'deep-learning', 'instruction-following', 'language-model', 'pretrained-models', 'pytorch', 'transformer']",2023-12-02,"[('juncongmoo/pyllama', 0.6721161007881165, 'llm', 0), ('guardrails-ai/guardrails', 0.6163129210472107, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.6111303567886353, 'llm', 3), ('huggingface/text-generation-inference', 0.6065784692764282, 'llm', 3), ('hpcaitech/energonai', 0.6059041619300842, 'ml', 0), ('next-gpt/next-gpt', 0.5973320603370667, 'llm', 1), ('lianjiatech/belle', 0.5924107432365417, 'llm', 0), ('nvidia/deeplearningexamples', 0.5852581858634949, 'ml-dl', 2), ('huggingface/datasets', 0.578310489654541, 'nlp', 2), ('hannibal046/awesome-llm', 0.5771458745002747, 'study', 1), ('cg123/mergekit', 0.5650473237037659, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5642813444137573, 'llm', 1), ('hiyouga/llama-factory', 0.5642813444137573, 'llm', 1), ('huggingface/transformers', 0.5635979771614075, 'nlp', 5), ('ctlllll/llm-toolmaker', 0.5597103238105774, 'llm', 1), ('openbmb/toolbench', 0.5591222643852234, 'llm', 0), ('rafiqhasan/auto-tensorflow', 0.5579131841659546, 'ml-dl', 0), ('microsoft/torchscale', 0.5554569959640503, 'llm', 1), ('young-geng/easylm', 0.5390665531158447, 'llm', 3), ('microsoft/unilm', 0.5366694331169128, 'nlp', 0), ('microsoft/autogen', 0.5350280404090881, 'llm', 1), ('databrickslabs/dolly', 0.5335168242454529, 'llm', 0), ('vllm-project/vllm', 0.5301596522331238, 'llm', 2), ('guidance-ai/guidance', 0.5283278226852417, 'llm', 2), ('bobazooba/xllm', 0.5273640751838684, 'llm', 3), ('ai21labs/lm-evaluation', 0.5266632437705994, 'llm', 1), ('lm-sys/fastchat', 0.5264976620674133, 'llm', 1), ('infinitylogesh/mutate', 0.524427056312561, 'nlp', 1), ('pytorch/ignite', 0.5235216617584229, 'ml-dl', 2), ('squeezeailab/squeezellm', 0.5209859609603882, 'llm', 1), ('llmware-ai/llmware', 0.5209429264068604, 'llm', 1), ('yizhongw/self-instruct', 0.5196613073348999, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5180608034133911, 'llm', 0), ('allenai/allennlp', 0.5176029801368713, 'nlp', 2), ('nvidia/tensorrt-llm', 0.5172522068023682, 'viz', 1), ('huawei-noah/pretrained-language-model', 0.5153259038925171, 'nlp', 1), ('jonasgeiping/cramming', 0.514919102191925, 'nlp', 1), ('keras-team/keras-nlp', 0.5128912329673767, 'nlp', 1), ('iryna-kondr/scikit-llm', 0.5123019814491272, 'llm', 2), ('reasoning-machines/pal', 0.5115619897842407, 'llm', 1), ('lupantech/chameleon-llm', 0.509954571723938, 'llm', 2), ('ggerganov/ggml', 0.5078336596488953, 'ml', 0), ('koaning/scikit-lego', 0.5066888928413391, 'ml', 0), ('eleutherai/the-pile', 0.5063433647155762, 'data', 0), ('salesforce/blip', 0.5060290694236755, 'diffusion', 0), ('mrdbourke/pytorch-deep-learning', 0.503092348575592, 'study', 2), ('tensorflow/tensor2tensor', 0.5030723810195923, 'ml', 1), ('unstructured-io/unstructured-inference', 0.5030722618103027, 'data', 0), ('ludwig-ai/ludwig', 0.5029340386390686, 'ml-ops', 2), ('togethercomputer/redpajama-data', 0.5022752285003662, 'llm', 0), ('d2l-ai/d2l-en', 0.5011979341506958, 'study', 2), ('artidoro/qlora', 0.5009582042694092, 'llm', 1), ('stanfordnlp/dspy', 0.5005626082420349, 'llm', 0), ('titanml/takeoff', 0.5003492832183838, 'llm', 1)]",38,3.0,,14.75,43,29,10,1,3,6,3,43.0,48.0,90.0,1.1,67
1166,llm,https://github.com/lvwerra/trl,[],,[],[],,,,lvwerra/trl,trl,7051,803,67,Python,http://hf.co/docs/trl,Train transformer language models with reinforcement learning.,lvwerra,2024-01-14,2020-03-27,200,35.15455840455841,https://avatars.githubusercontent.com/u/25720743?v=4,Train transformer language models with reinforcement learning.,[],[],2024-01-12,"[('bigscience-workshop/megatron-deepspeed', 0.6662755608558655, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6662755608558655, 'llm', 0), ('kzl/decision-transformer', 0.5943164229393005, 'ml-rl', 0), ('huggingface/transformers', 0.5739136338233948, 'nlp', 0), ('eleutherai/knowledge-neurons', 0.5679098963737488, 'ml-interpretability', 0), ('thilinarajapakse/simpletransformers', 0.5451595783233643, 'nlp', 0), ('hazyresearch/h3', 0.5428241491317749, 'llm', 0), ('nvidia/megatron-lm', 0.5321753025054932, 'llm', 0), ('jonasgeiping/cramming', 0.5296485424041748, 'nlp', 0), ('facebookresearch/shepherd', 0.5294312834739685, 'llm', 0), ('huggingface/text-generation-inference', 0.523781955242157, 'llm', 0), ('nvlabs/prismer', 0.509940505027771, 'diffusion', 0), ('deepset-ai/farm', 0.5094181299209595, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5060834288597107, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5007656812667847, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5007056593894958, 'llm', 0)]",133,5.0,,8.81,452,370,46,0,24,6,24,452.0,1328.0,90.0,2.9,67
1638,llm,https://github.com/mistralai/mistral-src,[],,[],[],,,,mistralai/mistral-src,mistral-src,6753,509,81,Jupyter Notebook,https://mistral.ai/,Reference implementation of Mistral AI 7B v0.1 model.,mistralai,2024-01-14,2023-09-27,17,378.168,https://avatars.githubusercontent.com/u/132372032?v=4,Reference implementation of Mistral AI 7B v0.1 model.,"['llm', 'llm-inference', 'mistralai']","['llm', 'llm-inference', 'mistralai']",2023-12-13,[],13,6.0,,0.62,86,21,4,1,0,0,0,86.0,70.0,90.0,0.8,67
1826,llm,https://github.com/langchain-ai/opengpts,"['assistant', 'api']",An open source effort to create a similar experience to OpenAI's GPTs and Assistants API.,[],[],,,,langchain-ai/opengpts,opengpts,5104,584,54,Rich Text Format,,,langchain-ai,2024-01-13,2023-11-04,12,410.6666666666667,https://avatars.githubusercontent.com/u/126733545?v=4,An open source effort to create a similar experience to OpenAI's GPTs and Assistants API.,[],"['api', 'assistant']",2024-01-02,"[('openai/openai-cookbook', 0.7003576159477234, 'ml', 0), ('openai/openai-python', 0.6509252190589905, 'util', 0), ('shishirpatil/gorilla', 0.6241956353187561, 'llm', 1), ('xtekky/gpt4free', 0.5756799578666687, 'llm', 0), ('fastai/ghapi', 0.5734665393829346, 'util', 0), ('minimaxir/gpt-2-simple', 0.5654781460762024, 'llm', 0), ('laion-ai/open-assistant', 0.5428431034088135, 'llm', 1), ('linksoul-ai/autoagents', 0.5377379655838013, 'llm', 0), ('run-llama/rags', 0.5282418131828308, 'llm', 0), ('opengenerativeai/genossgpt', 0.5244327783584595, 'llm', 1), ('vitalik/django-ninja', 0.5144355297088623, 'web', 0), ('torantulino/auto-gpt', 0.5114303231239319, 'llm', 0), ('assafelovic/gpt-researcher', 0.511093020439148, 'llm', 0), ('openai/tiktoken', 0.5110874176025391, 'nlp', 0), ('prefecthq/marvin', 0.507490336894989, 'nlp', 0), ('lucidrains/toolformer-pytorch', 0.5033729672431946, 'llm', 0)]",5,2.0,,1.77,127,71,2,0,0,0,0,127.0,178.0,90.0,1.4,67
1396,llm,https://github.com/lightning-ai/lit-gpt,"['fine-tuning', 'quantization', 'nanogpt']",,[],[],1.0,,,lightning-ai/lit-gpt,lit-gpt,4759,485,60,Python,,"Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.",lightning-ai,2024-01-13,2023-05-04,38,122.92619926199262,https://avatars.githubusercontent.com/u/58386951?v=4,"Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.",[],"['fine-tuning', 'nanogpt', 'quantization']",2024-01-12,"[('lightning-ai/lit-llama', 0.721184253692627, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.6060934066772461, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5745608806610107, 'perf', 0), ('vllm-project/vllm', 0.5720106959342957, 'llm', 0), ('microsoft/llmlingua', 0.5566024780273438, 'llm', 0), ('run-llama/llama-hub', 0.5486422181129456, 'data', 0), ('opengvlab/omniquant', 0.5481755137443542, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5436835289001465, 'llm', 1), ('bentoml/openllm', 0.5373291373252869, 'ml-ops', 1), ('bigscience-workshop/petals', 0.5297286510467529, 'data', 0), ('artidoro/qlora', 0.5197663903236389, 'llm', 0), ('tigerlab-ai/tiger', 0.5195765495300293, 'llm', 1), ('bobazooba/xllm', 0.5175870656967163, 'llm', 0), ('microsoft/semantic-kernel', 0.5075657367706299, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5048611164093018, 'llm', 0), ('predibase/lorax', 0.5018265247344971, 'llm', 1), ('eugeneyan/open-llms', 0.501139223575592, 'study', 0)]",54,6.0,,12.56,338,238,8,0,0,0,0,337.0,441.0,90.0,1.3,67
1337,nlp,https://github.com/prefecthq/marvin,[],,[],[],,,,prefecthq/marvin,marvin,4193,412,36,Python,https://askmarvin.ai,✨ Build AI interfaces that spark joy,prefecthq,2024-01-14,2023-03-10,46,90.03374233128834,https://avatars.githubusercontent.com/u/39270919?v=4,✨ Build AI interfaces that spark joy,"['agents', 'ai', 'ai-functions', 'ambient-ai', 'chatbots', 'gpt', 'llm', 'nli', 'openai']","['agents', 'ai', 'ai-functions', 'ambient-ai', 'chatbots', 'gpt', 'llm', 'nli', 'openai']",2024-01-13,"[('antonosika/gpt-engineer', 0.7385191917419434, 'llm', 2), ('cheshire-cat-ai/core', 0.7232251167297363, 'llm', 2), ('mindsdb/mindsdb', 0.7208438515663147, 'data', 3), ('embedchain/embedchain', 0.6899540424346924, 'llm', 3), ('lastmile-ai/aiconfig', 0.6677407622337341, 'util', 2), ('krohling/bondai', 0.6641014814376831, 'llm', 1), ('microsoft/promptflow', 0.6454984545707703, 'llm', 3), ('microsoft/lmops', 0.6434080600738525, 'llm', 2), ('chatarena/chatarena', 0.6427308917045593, 'llm', 1), ('microsoft/generative-ai-for-beginners', 0.6387981176376343, 'study', 3), ('smol-ai/developer', 0.6331506967544556, 'llm', 1), ('transformeroptimus/superagi', 0.6271318197250366, 'llm', 4), ('operand/agency', 0.6231884956359863, 'llm', 3), ('sweepai/sweep', 0.6178321838378906, 'llm', 2), ('run-llama/rags', 0.6145691275596619, 'llm', 2), ('mlc-ai/mlc-llm', 0.6106210350990295, 'llm', 1), ('minimaxir/simpleaichat', 0.6048039197921753, 'llm', 1), ('rasahq/rasa', 0.6008431315422058, 'llm', 1), ('pathwaycom/llm-app', 0.5963694453239441, 'llm', 1), ('bentoml/bentoml', 0.591640830039978, 'ml-ops', 1), ('thilinarajapakse/simpletransformers', 0.5893362760543823, 'nlp', 0), ('oegedijk/explainerdashboard', 0.584237813949585, 'ml-interpretability', 0), ('microsoft/semantic-kernel', 0.583898663520813, 'llm', 3), ('facebookresearch/habitat-lab', 0.5811783671379089, 'sim', 1), ('rcgai/simplyretrieve', 0.5794979929924011, 'llm', 0), ('larsbaunwall/bricky', 0.577139675617218, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5638337135314941, 'ml-dl', 1), ('deeppavlov/deeppavlov', 0.5636438131332397, 'nlp', 1), ('togethercomputer/openchatkit', 0.5622385144233704, 'nlp', 0), ('humanoidagents/humanoidagents', 0.5612106919288635, 'sim', 1), ('nvidia/nemo', 0.560585081577301, 'nlp', 0), ('microsoft/autogen', 0.5600186586380005, 'llm', 1), ('lupantech/chameleon-llm', 0.5581182837486267, 'llm', 3), ('jina-ai/jina', 0.5576962828636169, 'ml', 0), ('modularml/mojo', 0.5523964166641235, 'util', 1), ('nomic-ai/gpt4all', 0.5499098896980286, 'llm', 0), ('google/dopamine', 0.547942042350769, 'ml-rl', 1), ('torantulino/auto-gpt', 0.5470284223556519, 'llm', 2), ('googlecloudplatform/vertex-ai-samples', 0.5464761853218079, 'ml', 1), ('lucidrains/toolformer-pytorch', 0.5462851524353027, 'llm', 0), ('oliveirabruno01/babyagi-asi', 0.5443987846374512, 'llm', 1), ('aimhubio/aim', 0.541201114654541, 'ml-ops', 1), ('unity-technologies/ml-agents', 0.5411503911018372, 'ml-rl', 0), ('lm-sys/fastchat', 0.5400978922843933, 'llm', 0), ('avaiga/taipy', 0.5397293567657471, 'data', 0), ('xtekky/gpt4free', 0.536582887172699, 'llm', 3), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5360046029090881, 'llm', 0), ('mnotgod96/appagent', 0.5343064665794373, 'llm', 1), ('explosion/thinc', 0.5329232811927795, 'ml-dl', 1), ('oneil512/insight', 0.5314114093780518, 'ml', 3), ('pythagora-io/gpt-pilot', 0.5274754762649536, 'llm', 1), ('deepset-ai/haystack', 0.524747908115387, 'llm', 1), ('killianlucas/open-interpreter', 0.5231456756591797, 'llm', 0), ('microsoft/promptcraft-robotics', 0.5221617817878723, 'sim', 1), ('kitao/pyxel', 0.520916759967804, 'gamedev', 0), ('google-research/language', 0.5198028087615967, 'nlp', 0), ('activeloopai/deeplake', 0.5178278088569641, 'ml-ops', 2), ('minimaxir/aitextgen', 0.5176252126693726, 'llm', 0), ('hwchase17/langchain', 0.5155816078186035, 'llm', 0), ('luodian/otter', 0.5130387544631958, 'llm', 0), ('openai/openai-cookbook', 0.5121986269950867, 'ml', 1), ('intel/intel-extension-for-transformers', 0.5106049180030823, 'perf', 0), ('gventuri/pandas-ai', 0.5097314715385437, 'pandas', 2), ('mlflow/mlflow', 0.5080140233039856, 'ml-ops', 1), ('langchain-ai/opengpts', 0.507490336894989, 'llm', 0), ('eugeneyan/obsidian-copilot', 0.5069007277488708, 'llm', 1), ('aiwaves-cn/agents', 0.506673276424408, 'nlp', 1), ('guardrails-ai/guardrails', 0.5055996179580688, 'llm', 3), ('databrickslabs/dolly', 0.5005607008934021, 'llm', 1), ('noahshinn/reflexion', 0.5003121495246887, 'llm', 2)]",29,5.0,,33.62,136,89,10,0,43,52,43,136.0,92.0,90.0,0.7,67
1231,llm,https://github.com/karpathy/nanogpt,['nanogpt'],,[],[],,,,karpathy/nanogpt,nanoGPT,28200,4040,320,Python,,"The simplest, fastest repository for training/finetuning medium-sized GPTs.",karpathy,2024-01-14,2022-12-28,56,495.97989949748745,,"The simplest, fastest repository for training/finetuning medium-sized GPTs.",[],['nanogpt'],2023-06-22,"[('karpathy/mingpt', 0.5497898459434509, 'llm', 0), ('vision-cair/minigpt-4', 0.5487928986549377, 'llm', 0), ('farizrahman4u/loopgpt', 0.5409718155860901, 'llm', 0), ('openai/gpt-2-output-dataset', 0.5051628351211548, 'llm', 0), ('eleutherai/gpt-neo', 0.5039609670639038, 'llm', 0)]",31,3.0,,1.44,53,14,13,7,0,0,0,52.0,43.0,90.0,0.8,66
408,perf,https://github.com/google/flatbuffers,[],,[],[],,,,google/flatbuffers,flatbuffers,21620,3213,641,C++,https://flatbuffers.dev/,FlatBuffers: Memory Efficient Serialization Library,google,2024-01-14,2014-05-19,506,42.71521309624612,https://avatars.githubusercontent.com/u/1342004?v=4,FlatBuffers: Memory Efficient Serialization Library,"['c', 'c-plus-plus', 'c-sharp', 'cross-platform', 'flatbuffers', 'go', 'grpc', 'java', 'javascript', 'json-parser', 'marshalling', 'mmap', 'protobuf', 'rpc', 'rust', 'serialization', 'serialization-library', 'typescript', 'zero-copy']","['c', 'c-plus-plus', 'c-sharp', 'cross-platform', 'flatbuffers', 'go', 'grpc', 'java', 'javascript', 'json-parser', 'marshalling', 'mmap', 'protobuf', 'rpc', 'rust', 'serialization', 'serialization-library', 'typescript', 'zero-copy']",2023-12-19,"[('yukinarit/pyserde', 0.6145598292350769, 'util', 1), ('pylons/colander', 0.5039762258529663, 'util', 1)]",670,5.0,,3.85,184,102,118,1,6,4,6,184.0,252.0,90.0,1.4,66
188,ml-interpretability,https://github.com/slundberg/shap,[],,[],[],,,,slundberg/shap,shap,20940,3099,252,Jupyter Notebook,https://shap.readthedocs.io,A game theoretic approach to explain the output of any machine learning model.,slundberg,2024-01-14,2016-11-22,375,55.84,https://avatars.githubusercontent.com/u/60805229?v=4,A game theoretic approach to explain the output of any machine learning model.,"['deep-learning', 'explainability', 'gradient-boosting', 'interpretability', 'machine-learning', 'shap', 'shapley']","['deep-learning', 'explainability', 'gradient-boosting', 'interpretability', 'machine-learning', 'shap', 'shapley']",2024-01-09,"[('maif/shapash', 0.67304927110672, 'ml', 4), ('seldonio/alibi', 0.6683449745178223, 'ml-interpretability', 2), ('marcotcr/lime', 0.6387524604797363, 'ml-interpretability', 0), ('interpretml/interpret', 0.5977214574813843, 'ml-interpretability', 4), ('linkedin/fasttreeshap', 0.5951489806175232, 'ml', 3), ('xplainable/xplainable', 0.5842406153678894, 'ml-interpretability', 2), ('oegedijk/explainerdashboard', 0.5450636148452759, 'ml-interpretability', 1), ('csinva/imodels', 0.5444343090057373, 'ml', 2), ('mosaicml/composer', 0.5438055396080017, 'ml-dl', 2), ('unity-technologies/ml-agents', 0.5413389205932617, 'ml-rl', 2), ('pair-code/lit', 0.532273530960083, 'ml-interpretability', 1), ('ddbourgin/numpy-ml', 0.5084296464920044, 'ml', 2), ('tensorflow/lucid', 0.5016194581985474, 'ml-interpretability', 2)]",229,4.0,,5.5,691,90,87,0,4,7,4,690.0,688.0,90.0,1.0,66
1053,ml,https://github.com/microsoft/lightgbm,[],,[],[],,,,microsoft/lightgbm,LightGBM,15787,3793,437,C++,https://lightgbm.readthedocs.io/en/latest/,"A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.",microsoft,2024-01-14,2016-08-05,390,40.420263350402344,https://avatars.githubusercontent.com/u/6154722?v=4,"A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.","['data-mining', 'decision-trees', 'distributed', 'gbdt', 'gbm', 'gbrt', 'gradient-boosting', 'kaggle', 'lightgbm', 'machine-learning', 'microsoft', 'parallel', 'r']","['data-mining', 'decision-trees', 'distributed', 'gbdt', 'gbm', 'gbrt', 'gradient-boosting', 'kaggle', 'lightgbm', 'machine-learning', 'microsoft', 'parallel', 'r']",2024-01-12,"[('dmlc/xgboost', 0.8464224338531494, 'ml', 4), ('catboost/catboost', 0.8396483659744263, 'ml', 8)]",295,5.0,,4.75,338,259,91,0,3,5,3,339.0,598.0,90.0,1.8,66
1440,llm,https://github.com/karpathy/llama2.c,"['llama', 'language-model']",,[],[],,,,karpathy/llama2.c,llama2.c,13860,1471,159,C,,Inference Llama 2 in one file of pure C,karpathy,2024-01-14,2023-07-23,27,507.9581151832461,,Inference Llama 2 in one file of pure C,[],"['language-model', 'llama']",2023-10-09,"[('facebookresearch/llama', 0.8788975477218628, 'llm', 2), ('tairov/llama2.mojo', 0.8035979270935059, 'llm', 1), ('facebookresearch/codellama', 0.7492680549621582, 'llm', 2), ('facebookresearch/llama-recipes', 0.7288556098937988, 'llm', 2), ('microsoft/llama-2-onnx', 0.6777373552322388, 'llm', 2), ('abetlen/llama-cpp-python', 0.6725909113883972, 'llm', 2), ('tloen/alpaca-lora', 0.5766897201538086, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.5627032518386841, 'llm', 0), ('cg123/mergekit', 0.541799783706665, 'llm', 1), ('hao-ai-lab/lookaheaddecoding', 0.5412218570709229, 'llm', 0), ('openlm-research/open_llama', 0.5405749678611755, 'llm', 2), ('ggerganov/llama.cpp', 0.5367782711982727, 'llm', 2), ('jzhang38/tinyllama', 0.5350930094718933, 'llm', 2), ('juncongmoo/pyllama', 0.5219447016716003, 'llm', 0), ('run-llama/llama-lab', 0.5159832835197449, 'llm', 2), ('zrrskywalker/llama-adapter', 0.5048466920852661, 'llm', 2)]",81,1.0,,6.5,74,11,6,3,0,0,0,74.0,51.0,90.0,0.7,66
123,ml-ops,https://github.com/iterative/dvc,[],,[],[],,,,iterative/dvc,dvc,12755,1141,137,Python,https://dvc.org,🦉 ML Experiments Management with Git,iterative,2024-01-13,2017-03-04,360,35.38842647641697,https://avatars.githubusercontent.com/u/39572954?v=4,🦉 ML Experiments Management with Git,"['ai', 'collaboration', 'data-science', 'data-version-control', 'developer-tools', 'git', 'machine-learning', 'reproducibility']","['ai', 'collaboration', 'data-science', 'data-version-control', 'developer-tools', 'git', 'machine-learning', 'reproducibility']",2024-01-12,"[('netflix/metaflow', 0.6912345886230469, 'ml-ops', 3), ('allegroai/clearml', 0.618588924407959, 'ml-ops', 2), ('wandb/client', 0.6113179922103882, 'ml', 4), ('aimhubio/aim', 0.5769718885421753, 'ml-ops', 3), ('pythagora-io/gpt-pilot', 0.5759281516075134, 'llm', 2), ('sweepai/sweep', 0.5702275633811951, 'llm', 2), ('polyaxon/polyaxon', 0.5695563554763794, 'ml-ops', 2), ('bentoml/bentoml', 0.5435565710067749, 'ml-ops', 2), ('googlecloudplatform/vertex-ai-samples', 0.5397675633430481, 'ml', 2), ('mindsdb/mindsdb', 0.5321820974349976, 'data', 2), ('lastmile-ai/aiconfig', 0.522969663143158, 'util', 2), ('avaiga/taipy', 0.5210314989089966, 'data', 1), ('google-research/language', 0.5162245631217957, 'nlp', 1), ('microsoft/promptflow', 0.514451265335083, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5138351917266846, 'study', 2), ('google-research/google-research', 0.5124830603599548, 'ml', 2), ('feast-dev/feast', 0.5111587047576904, 'ml-ops', 2), ('determined-ai/determined', 0.5081852674484253, 'ml-ops', 2), ('ploomber/ploomber', 0.5037345886230469, 'ml-ops', 2), ('google/ml-metadata', 0.5009445548057556, 'ml-ops', 0), ('mlflow/mlflow', 0.5006384253501892, 'ml-ops', 2)]",290,2.0,,15.94,347,271,84,0,101,76,101,347.0,751.0,90.0,2.2,66
514,typing,https://github.com/microsoft/pyright,"['typechecker', 'code-quality']",,[],[],,,,microsoft/pyright,pyright,11459,1267,113,Python,,Static Type Checker for Python,microsoft,2024-01-14,2019-03-12,255,44.937254901960785,https://avatars.githubusercontent.com/u/6154722?v=4,Static Type Checker for Python,[],"['code-quality', 'typechecker']",2024-01-14,"[('agronholm/typeguard', 0.9137517213821411, 'typing', 2), ('python/mypy', 0.8244960904121399, 'typing', 2), ('google/pytype', 0.8127192258834839, 'typing', 2), ('facebook/pyre-check', 0.7650810480117798, 'typing', 2), ('instagram/monkeytype', 0.7293500900268555, 'typing', 1), ('python/typeshed', 0.6651108264923096, 'typing', 1), ('patrick-kidger/torchtyping', 0.5727251172065735, 'typing', 0), ('pycqa/mccabe', 0.5418702960014343, 'util', 0), ('jendrikseipp/vulture', 0.536612331867218, 'util', 1), ('rubik/radon', 0.533168375492096, 'util', 0), ('landscapeio/prospector', 0.5327200293540955, 'util', 0), ('google/yapf', 0.5311009287834167, 'util', 1), ('grantjenks/blue', 0.529589056968689, 'util', 1), ('pycqa/pycodestyle', 0.5176783204078674, 'util', 0), ('pycqa/pyflakes', 0.511178195476532, 'util', 0), ('pydantic/pydantic', 0.5103999376296997, 'util', 0), ('psf/black', 0.5090447068214417, 'util', 1)]",103,1.0,,26.13,789,758,59,0,57,88,57,787.0,1674.0,90.0,2.1,66
225,util,https://github.com/pyodide/pyodide,"['cpython', 'wasm', 'emscripten', 'webassembly']",,[],[],,,,pyodide/pyodide,pyodide,10896,738,129,Python,https://pyodide.org/en/stable/,Pyodide is a Python distribution for the browser and Node.js based on WebAssembly,pyodide,2024-01-14,2018-02-23,309,35.19704660821412,https://avatars.githubusercontent.com/u/77002075?v=4,Pyodide is a Python distribution for the browser and Node.js based on WebAssembly,['webassembly'],"['cpython', 'emscripten', 'wasm', 'webassembly']",2024-01-13,"[('pyodide/micropip', 0.7593028545379639, 'util', 1), ('webpy/webpy', 0.6373624205589294, 'web', 0), ('bottlepy/bottle', 0.6117225289344788, 'web', 0), ('cherrypy/cherrypy', 0.5807443857192993, 'web', 0), ('libtcod/python-tcod', 0.5775824785232544, 'gamedev', 0), ('pypy/pypy', 0.5725159645080566, 'util', 1), ('jupyterlite/jupyterlite', 0.5671476721763611, 'jupyter', 2), ('pallets/flask', 0.556867241859436, 'web', 0), ('masoniteframework/masonite', 0.5539262294769287, 'web', 0), ('seleniumbase/seleniumbase', 0.540057897567749, 'testing', 0), ('pylons/pyramid', 0.5352792739868164, 'web', 0), ('reactive-python/reactpy', 0.5306482911109924, 'web', 0), ('scrapy/scrapy', 0.5298803448677063, 'data', 0), ('r0x0r/pywebview', 0.5263348817825317, 'gui', 0), ('pyscript/pyscript', 0.5230824947357178, 'web', 3), ('microsoft/playwright-python', 0.5146863460540771, 'testing', 0), ('klen/muffin', 0.5121115446090698, 'web', 0), ('hoffstadt/dearpygui', 0.5109636187553406, 'gui', 0), ('pyglet/pyglet', 0.5096766948699951, 'gamedev', 0), ('reflex-dev/reflex', 0.5090579986572266, 'web', 0), ('clips/pattern', 0.5079214572906494, 'nlp', 0), ('pytest-dev/pytest-bdd', 0.5067077279090881, 'testing', 0), ('pyo3/maturin', 0.504960834980011, 'util', 1)]",207,5.0,,9.38,180,113,72,0,12,13,12,180.0,385.0,90.0,2.1,66
29,ml-ops,https://github.com/great-expectations/great_expectations,[],,[],[],,,,great-expectations/great_expectations,great_expectations,9157,1443,82,Python,https://docs.greatexpectations.io/,Always know what to expect from your data.,great-expectations,2024-01-13,2017-09-11,333,27.486706689536877,https://avatars.githubusercontent.com/u/31670619?v=4,Always know what to expect from your data.,"['cleandata', 'data-engineering', 'data-profilers', 'data-profiling', 'data-quality', 'data-science', 'data-unit-tests', 'datacleaner', 'datacleaning', 'dataquality', 'dataunittest', 'eda', 'exploratory-analysis', 'exploratory-data-analysis', 'exploratorydataanalysis', 'mlops', 'pipeline', 'pipeline-debt', 'pipeline-testing', 'pipeline-tests']","['cleandata', 'data-engineering', 'data-profilers', 'data-profiling', 'data-quality', 'data-science', 'data-unit-tests', 'datacleaner', 'datacleaning', 'dataquality', 'dataunittest', 'eda', 'exploratory-analysis', 'exploratory-data-analysis', 'exploratorydataanalysis', 'mlops', 'pipeline', 'pipeline-debt', 'pipeline-testing', 'pipeline-tests']",2024-01-12,"[('ydataai/ydata-profiling', 0.606023907661438, 'pandas', 5), ('hi-primus/optimus', 0.6027732491493225, 'ml-ops', 2), ('mage-ai/mage-ai', 0.5621156692504883, 'ml-ops', 3), ('ploomber/ploomber', 0.5521277785301208, 'ml-ops', 3), ('datafold/data-diff', 0.5429127216339111, 'data', 4), ('polyaxon/datatile', 0.5381271839141846, 'pandas', 4), ('ydataai/ydata-quality', 0.5358034372329712, 'data', 0), ('unionai-oss/pandera', 0.5351150035858154, 'pandas', 0), ('feast-dev/feast', 0.5238198041915894, 'ml-ops', 4), ('orchest/orchest', 0.5189650058746338, 'ml-ops', 1), ('dagworks-inc/hamilton', 0.5179693102836609, 'ml-ops', 3), ('google/ml-metadata', 0.5165597200393677, 'ml-ops', 0), ('airbytehq/airbyte', 0.5163770914077759, 'data', 2), ('dbt-labs/dbt-core', 0.5088808536529541, 'ml-ops', 0), ('krzjoa/awesome-python-data-science', 0.5083687901496887, 'study', 1), ('linealabs/lineapy', 0.5049693584442139, 'jupyter', 0), ('whylabs/whylogs', 0.5034822821617126, 'util', 3), ('meltano/meltano', 0.5034080147743225, 'ml-ops', 1)]",423,3.0,,45.29,909,826,77,0,55,44,55,907.0,922.0,90.0,1.0,66
117,ml-ops,https://github.com/kedro-org/kedro,[],,[],[],,From: https://github.com/quantumblacklabs/kedro,,kedro-org/kedro,kedro,9091,860,107,Python,https://kedro.org,"Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.",kedro-org,2024-01-14,2019-04-18,249,36.40560640732266,https://avatars.githubusercontent.com/u/93382166?v=4,"Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.","['experiment-tracking', 'kedro', 'machine-learning', 'machine-learning-engineering', 'mlops', 'pipeline']","['experiment-tracking', 'kedro', 'machine-learning', 'machine-learning-engineering', 'mlops', 'pipeline']",2024-01-12,"[('kedro-org/kedro-viz', 0.7322895526885986, 'ml-ops', 2), ('getindata/kedro-kubeflow', 0.660918653011322, 'ml-ops', 2), ('meltano/meltano', 0.5286129117012024, 'ml-ops', 0), ('kubeflow-kale/kale', 0.5225927233695984, 'ml-ops', 1), ('ploomber/ploomber', 0.5186499357223511, 'ml-ops', 2), ('mage-ai/mage-ai', 0.5148924589157104, 'ml-ops', 2), ('dagster-io/dagster', 0.5058962106704712, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5058719515800476, 'ml-ops', 2), ('zenml-io/zenml', 0.5034431219100952, 'ml-ops', 2)]",218,4.0,,9.52,583,417,58,0,12,10,12,583.0,1135.0,90.0,1.9,66
1368,llm,https://github.com/shishirpatil/gorilla,[],,[],[],,,,shishirpatil/gorilla,gorilla,8848,682,97,Python,https://gorilla.cs.berkeley.edu/,Gorilla: An API store for LLMs,shishirpatil,2024-01-14,2023-05-19,36,241.9375,,Gorilla: An API store for LLMs,"['api', 'api-documentation', 'chatgpt', 'claude-api', 'gpt-4-api', 'llm', 'openai-api', 'openai-functions']","['api', 'api-documentation', 'chatgpt', 'claude-api', 'gpt-4-api', 'llm', 'openai-api', 'openai-functions']",2023-11-20,"[('berriai/litellm', 0.7173708081245422, 'llm', 1), ('openai/openai-cookbook', 0.6725669503211975, 'ml', 1), ('opengenerativeai/genossgpt', 0.6697072982788086, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.6682367324829102, 'llm', 2), ('zilliztech/gptcache', 0.6505623459815979, 'llm', 2), ('run-llama/rags', 0.6374510526657104, 'llm', 2), ('xtekky/gpt4free', 0.6333321332931519, 'llm', 2), ('alphasecio/langchain-examples', 0.6303489804267883, 'llm', 1), ('langchain-ai/opengpts', 0.6241956353187561, 'llm', 1), ('openai/openai-python', 0.6225490570068359, 'util', 0), ('chainlit/chainlit', 0.6185116171836853, 'llm', 2), ('pathwaycom/llm-app', 0.6179982423782349, 'llm', 1), ('nomic-ai/gpt4all', 0.6152714490890503, 'llm', 0), ('microsoft/semantic-kernel', 0.612429678440094, 'llm', 1), ('microsoft/promptflow', 0.6032128930091858, 'llm', 2), ('dylanhogg/llmgraph', 0.5875014662742615, 'ml', 2), ('embedchain/embedchain', 0.5869289636611938, 'llm', 2), ('vitalik/django-ninja', 0.5843652486801147, 'web', 0), ('deepset-ai/haystack', 0.5841556787490845, 'llm', 1), ('hwchase17/langchain', 0.5784041285514832, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5774163603782654, 'perf', 0), ('eugeneyan/open-llms', 0.5759145021438599, 'study', 1), ('deep-diver/llm-as-chatbot', 0.5617109537124634, 'llm', 0), ('ajndkr/lanarky', 0.5609325766563416, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5525274872779846, 'llm', 2), ('hugapi/hug', 0.5522815585136414, 'util', 0), ('tiangolo/fastapi', 0.5488146543502808, 'web', 1), ('fastai/ghapi', 0.5438264012336731, 'util', 0), ('tigerlab-ai/tiger', 0.5373891592025757, 'llm', 1), ('jerryjliu/llama_index', 0.5362586975097656, 'llm', 1), ('microsoft/autogen', 0.5344410538673401, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.5339228510856628, 'llm', 0), ('mlc-ai/web-llm', 0.533381462097168, 'llm', 2), ('salesforce/codet5', 0.5298489332199097, 'nlp', 0), ('young-geng/easylm', 0.5281785130500793, 'llm', 0), ('bobazooba/xllm', 0.5277217626571655, 'llm', 2), ('vllm-project/vllm', 0.5265129208564758, 'llm', 1), ('bigscience-workshop/petals', 0.5255513191223145, 'data', 0), ('microsoft/promptcraft-robotics', 0.5253349542617798, 'sim', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.5247654914855957, 'template', 0), ('googleapis/google-api-python-client', 0.524009108543396, 'util', 0), ('snyk-labs/pysnyk', 0.5202717185020447, 'security', 1), ('mooler0410/llmspracticalguide', 0.5199465751647949, 'study', 0), ('farizrahman4u/loopgpt', 0.5184142589569092, 'llm', 1), ('starlite-api/starlite', 0.5183838605880737, 'web', 1), ('run-llama/llama-hub', 0.5163030028343201, 'data', 1), ('salesforce/xgen', 0.5161482691764832, 'llm', 1), ('nebuly-ai/nebullvm', 0.512731671333313, 'perf', 1), ('killianlucas/open-interpreter', 0.5121797323226929, 'llm', 1), ('confident-ai/deepeval', 0.5115599036216736, 'testing', 2), ('skypilot-org/skypilot', 0.5099743008613586, 'llm', 0), ('citadel-ai/langcheck', 0.5088066458702087, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5080721378326416, 'llm', 1), ('bentoml/openllm', 0.5051870942115784, 'ml-ops', 1), ('larsbaunwall/bricky', 0.5044655799865723, 'llm', 0), ('openai/tiktoken', 0.5037037134170532, 'nlp', 1), ('hegelai/prompttools', 0.5024935603141785, 'llm', 0), ('pomponchik/instld', 0.5003833174705505, 'util', 0), ('falconry/falcon', 0.500187873840332, 'web', 1)]",15,6.0,,0.75,27,13,8,2,1,2,1,27.0,39.0,90.0,1.4,66
533,nlp,https://github.com/espnet/espnet,[],,[],[],,,,espnet/espnet,espnet,7522,2046,179,Python,https://espnet.github.io/espnet/,End-to-End Speech Processing Toolkit,espnet,2024-01-14,2017-12-13,319,23.516748548459134,https://avatars.githubusercontent.com/u/34493687?v=4,End-to-End Speech Processing Toolkit,"['chainer', 'deep-learning', 'end-to-end', 'kaldi', 'machine-translation', 'pytorch', 'singing-voice-synthesis', 'speaker-diarization', 'speech-enhancement', 'speech-recognition', 'speech-separation', 'speech-synthesis', 'speech-translation', 'spoken-language-understanding', 'voice-conversion']","['chainer', 'deep-learning', 'end-to-end', 'kaldi', 'machine-translation', 'pytorch', 'singing-voice-synthesis', 'speaker-diarization', 'speech-enhancement', 'speech-recognition', 'speech-separation', 'speech-synthesis', 'speech-translation', 'spoken-language-understanding', 'voice-conversion']",2024-01-10,"[('speechbrain/speechbrain', 0.822684109210968, 'nlp', 7), ('nvidia/nemo', 0.63554447889328, 'nlp', 5), ('nateshmbhat/pyttsx3', 0.6220706105232239, 'util', 0), ('uberi/speech_recognition', 0.6095865964889526, 'ml', 1), ('huggingface/transformers', 0.5976542234420776, 'nlp', 3), ('m-bain/whisperx', 0.5822861194610596, 'nlp', 1), ('pndurette/gtts', 0.5670027732849121, 'util', 0), ('deeppavlov/deeppavlov', 0.5584684610366821, 'nlp', 1), ('rasahq/rasa', 0.5472486615180969, 'llm', 0), ('huggingface/datasets', 0.5337874889373779, 'nlp', 2), ('spotify/pedalboard', 0.5202059745788574, 'util', 0), ('lucidrains/toolformer-pytorch', 0.5174741744995117, 'llm', 1), ('spotify/basic-pitch', 0.5158132910728455, 'util', 0), ('nvidia/deeplearningexamples', 0.5145514607429504, 'ml-dl', 4), ('open-mmlab/mmediting', 0.5127143859863281, 'ml', 2), ('facebookresearch/seamless_communication', 0.5085058212280273, 'nlp', 0), ('alibaba/easynlp', 0.5063716173171997, 'nlp', 2), ('thilinarajapakse/simpletransformers', 0.5008228421211243, 'nlp', 0)]",398,5.0,,69.46,207,108,74,0,4,8,4,207.0,532.0,90.0,2.6,66
871,time-series,https://github.com/sktime/sktime,[],,[],[],1.0,,,sktime/sktime,sktime,7149,1191,102,Python,https://www.sktime.net,A unified framework for machine learning with time series,sktime,2024-01-14,2018-11-06,273,26.186813186813186,https://avatars.githubusercontent.com/u/56396127?v=4,A unified framework for machine learning with time series,"['data-mining', 'data-science', 'forecasting', 'machine-learning', 'scikit-learn', 'time-series', 'time-series-analysis', 'time-series-classification', 'time-series-regression']","['data-mining', 'data-science', 'forecasting', 'machine-learning', 'scikit-learn', 'time-series', 'time-series-analysis', 'time-series-classification', 'time-series-regression']",2024-01-13,"[('salesforce/merlion', 0.7518776059150696, 'time-series', 3), ('winedarksea/autots', 0.7021391987800598, 'time-series', 3), ('scikit-learn/scikit-learn', 0.6196394562721252, 'ml', 2), ('blue-yonder/tsfresh', 0.5950053930282593, 'time-series', 2), ('firmai/atspy', 0.5919238924980164, 'time-series', 3), ('alkaline-ml/pmdarima', 0.5908213257789612, 'time-series', 3), ('automl/auto-sklearn', 0.5886750817298889, 'ml', 1), ('unit8co/darts', 0.5841982960700989, 'time-series', 4), ('google/temporian', 0.5772302150726318, 'time-series', 1), ('awslabs/autogluon', 0.5768527984619141, 'ml', 5), ('xplainable/xplainable', 0.5621213912963867, 'ml-interpretability', 2), ('awslabs/gluonts', 0.5607674717903137, 'time-series', 4), ('salesforce/deeptime', 0.5567091107368469, 'time-series', 3), ('tensorflow/tensorflow', 0.5532556176185608, 'ml-dl', 1), ('rasbt/mlxtend', 0.5530063509941101, 'ml', 3), ('aistream-peelout/flow-forecast', 0.5518553256988525, 'time-series', 4), ('nixtla/statsforecast', 0.5418822169303894, 'time-series', 4), ('feast-dev/feast', 0.5417338609695435, 'ml-ops', 2), ('firmai/industry-machine-learning', 0.541723370552063, 'study', 2), ('pycaret/pycaret', 0.5355731844902039, 'ml', 3), ('microsoft/nni', 0.5316794514656067, 'ml', 2), ('facebookresearch/kats', 0.5305997729301453, 'time-series', 1), ('online-ml/river', 0.5300047993659973, 'ml', 2), ('ourownstory/neural_prophet', 0.529460608959198, 'ml', 3), ('linkedin/greykite', 0.5227073431015015, 'ml', 0), ('nccr-itmo/fedot', 0.520081639289856, 'ml-ops', 1), ('mlflow/mlflow', 0.5184756517410278, 'ml-ops', 1), ('microsoft/flaml', 0.5142588019371033, 'ml', 3), ('patchy631/machine-learning', 0.5128931403160095, 'ml', 0), ('koaning/human-learn', 0.5112270712852478, 'data', 2), ('districtdatalabs/yellowbrick', 0.5111024975776672, 'ml', 2), ('gradio-app/gradio', 0.5101883411407471, 'viz', 2), ('microprediction/microprediction', 0.5038242340087891, 'time-series', 1), ('polyaxon/datatile', 0.5024852752685547, 'pandas', 1)]",315,6.0,,16.44,360,240,63,0,22,12,22,360.0,796.0,90.0,2.2,66
1643,llm,https://github.com/plachtaa/vall-e-x,[],,[],[],,,,plachtaa/vall-e-x,VALL-E-X,6692,638,73,Python,,An open source implementation of Microsoft's VALL-E X zero-shot TTS model. Demo is available in https://plachtaa.github.io,plachtaa,2024-01-14,2023-07-29,26,253.2108108108108,,An open source implementation of Microsoft's VALL-E X zero-shot TTS model. Demo is available in https://plachtaa.github.io,"['emotional-speech', 'gpt', 'text-to-speech', 'transformer-architecture', 'tts', 'vall-e', 'voice-clone']","['emotional-speech', 'gpt', 'text-to-speech', 'transformer-architecture', 'tts', 'vall-e', 'voice-clone']",2023-11-03,"[('neonbjb/tortoise-tts', 0.6283189058303833, 'ml', 1), ('myshell-ai/openvoice', 0.6065437197685242, 'nlp', 3), ('pndurette/gtts', 0.5036769509315491, 'util', 2)]",9,6.0,,2.25,53,12,6,2,0,0,0,53.0,90.0,90.0,1.7,66
1856,llm,https://github.com/pytorch-labs/gpt-fast,"['pytorch', 'transformer']",,[],[],,,,pytorch-labs/gpt-fast,gpt-fast,4442,465,52,Python,,Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.,pytorch-labs,2024-01-14,2023-10-17,15,296.1333333333333,https://avatars.githubusercontent.com/u/107212512?v=4,Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.,[],"['pytorch', 'transformer']",2024-01-06,"[('karpathy/mingpt', 0.5887901782989502, 'llm', 0), ('pytorch/data', 0.5759943723678589, 'data', 0), ('huggingface/text-generation-inference', 0.5448298454284668, 'llm', 2), ('nateshmbhat/pyttsx3', 0.537563145160675, 'util', 0), ('lucidrains/dalle2-pytorch', 0.5372596383094788, 'diffusion', 0), ('minimaxir/gpt-2-simple', 0.5367690920829773, 'llm', 0), ('nvidia/apex', 0.5303529500961304, 'ml-dl', 0), ('nielsrogge/transformers-tutorials', 0.5239831805229187, 'study', 1), ('intel/intel-extension-for-pytorch', 0.5219361782073975, 'perf', 1), ('huggingface/transformers', 0.5157749056816101, 'nlp', 2), ('salesforce/blip', 0.5148594379425049, 'diffusion', 0), ('lucidrains/vit-pytorch', 0.5130720138549805, 'ml-dl', 0), ('faster-cpython/tools', 0.5083655118942261, 'perf', 0), ('lucidrains/imagen-pytorch', 0.5046398639678955, 'ml-dl', 0)]",7,4.0,,0.27,83,27,3,0,0,0,0,83.0,178.0,90.0,2.1,66
1738,viz,https://github.com/nvidia/tensorrt-llm,"['tensorrt', 'gpu', 'language-model']",,[],[],,,,nvidia/tensorrt-llm,TensorRT-LLM,4276,370,50,C++,https://nvidia.github.io/TensorRT-LLM,TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.,nvidia,2024-01-14,2023-08-16,23,179.23353293413174,https://avatars.githubusercontent.com/u/1728152?v=4,TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.,[],"['gpu', 'language-model', 'tensorrt']",2024-01-09,"[('sjtu-ipads/powerinfer', 0.6346943974494934, 'llm', 0), ('exaloop/codon', 0.6340402364730835, 'perf', 0), ('pytorch/pytorch', 0.61689293384552, 'ml-dl', 1), ('ggerganov/ggml', 0.6161227822303772, 'ml', 0), ('google/tf-quant-finance', 0.6080841422080994, 'finance', 1), ('bobazooba/xllm', 0.5863513350486755, 'llm', 0), ('google/gin-config', 0.5778404474258423, 'util', 0), ('rafiqhasan/auto-tensorflow', 0.5734004378318787, 'ml-dl', 0), ('arogozhnikov/einops', 0.5731545686721802, 'ml-dl', 0), ('numba/numba', 0.5688282251358032, 'perf', 0), ('vllm-project/vllm', 0.5639804601669312, 'llm', 0), ('cupy/cupy', 0.5631754994392395, 'math', 1), ('tensorly/tensorly', 0.5586928129196167, 'ml-dl', 0), ('tigerlab-ai/tiger', 0.5560727119445801, 'llm', 0), ('nvidia/warp', 0.5474246740341187, 'sim', 1), ('facebookincubator/aitemplate', 0.5429112315177917, 'ml-dl', 0), ('eleutherai/gpt-neo', 0.5397517681121826, 'llm', 1), ('plasma-umass/scalene', 0.53840172290802, 'profiling', 1), ('next-gpt/next-gpt', 0.5343412160873413, 'llm', 0), ('huggingface/accelerate', 0.5342060923576355, 'ml', 0), ('citadel-ai/langcheck', 0.5307314395904541, 'llm', 1), ('microsoft/torchscale', 0.5302456617355347, 'llm', 0), ('guardrails-ai/guardrails', 0.5297648906707764, 'llm', 0), ('alphasecio/langchain-examples', 0.5265845060348511, 'llm', 0), ('microsoft/autogen', 0.525221586227417, 'llm', 0), ('eth-sri/lmql', 0.524734616279602, 'llm', 1), ('blackhc/toma', 0.5219219923019409, 'ml-dl', 1), ('xl0/lovely-tensors', 0.5217703580856323, 'ml-dl', 0), ('hiyouga/llama-factory', 0.5198065042495728, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.519806444644928, 'llm', 1), ('tensorflow/addons', 0.5190548300743103, 'ml', 0), ('nomic-ai/gpt4all', 0.5173569321632385, 'llm', 1), ('optimalscale/lmflow', 0.5172522068023682, 'llm', 1), ('minimaxir/gpt-2-simple', 0.5171143412590027, 'llm', 0), ('hannibal046/awesome-llm', 0.5133680105209351, 'study', 1), ('microsoft/jarvis', 0.5096875429153442, 'llm', 0), ('pypy/pypy', 0.5079455375671387, 'util', 0), ('juncongmoo/pyllama', 0.5075156092643738, 'llm', 0), ('intel/intel-extension-for-pytorch', 0.505849301815033, 'perf', 0), ('pytorchlightning/pytorch-lightning', 0.5039601922035217, 'ml-dl', 0), ('eleutherai/gpt-neox', 0.5021648406982422, 'llm', 1)]",4,1.0,,0.83,857,578,5,0,3,12,3,858.0,2931.0,90.0,3.4,66
788,web,https://github.com/starlite-api/starlite,[],,[],[],,,,starlite-api/starlite,litestar,3806,298,33,Python,https://litestar.dev/,"Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs",starlite-api,2024-01-14,2021-12-06,112,33.93885350318471,https://avatars.githubusercontent.com/u/97250344?v=4,"Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs","['api', 'asgi', 'asyncio', 'litestar', 'litestar-api', 'litestar-framework', 'msgspec', 'openapi', 'pydantic', 'rapidoc', 'redoc', 'rest', 'starlite', 'starlite-api', 'swagger']","['api', 'asgi', 'asyncio', 'litestar', 'litestar-api', 'litestar-framework', 'msgspec', 'openapi', 'pydantic', 'rapidoc', 'redoc', 'rest', 'starlite', 'starlite-api', 'swagger']",2024-01-14,"[('vitalik/django-ninja', 0.7026381492614746, 'web', 3), ('tiangolo/fastapi', 0.6928763389587402, 'web', 7), ('huge-success/sanic', 0.6891716122627258, 'web', 2), ('falconry/falcon', 0.6774393916130066, 'web', 3), ('neoteroi/blacksheep', 0.6740537881851196, 'web', 2), ('python-restx/flask-restx', 0.6593479514122009, 'web', 3), ('pallets/quart', 0.6257473230361938, 'web', 2), ('encode/uvicorn', 0.6137245893478394, 'web', 2), ('asacristani/fastapi-rocket-boilerplate', 0.5981463193893433, 'template', 0), ('jordaneremieff/mangum', 0.5975977182388306, 'web', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.5915384292602539, 'template', 2), ('hugapi/hug', 0.573395848274231, 'util', 0), ('timofurrer/awesome-asyncio', 0.5622597932815552, 'study', 1), ('alirn76/panther', 0.5622544288635254, 'web', 0), ('encode/starlette', 0.5545772314071655, 'web', 0), ('aminalaee/sqladmin', 0.5524939894676208, 'data', 2), ('awtkns/fastapi-crudrouter', 0.550251841545105, 'web', 5), ('s3rius/fastapi-template', 0.5500728487968445, 'web', 1), ('klen/muffin', 0.548690915107727, 'web', 2), ('fastai/ghapi', 0.5345987677574158, 'util', 1), ('rawheel/fastapi-boilerplate', 0.533804178237915, 'web', 1), ('pallets/flask', 0.528308629989624, 'web', 0), ('simonw/datasette', 0.526665985584259, 'data', 1), ('aeternalis-ingenium/fastapi-backend-template', 0.5256733298301697, 'web', 0), ('flet-dev/flet', 0.5249883532524109, 'web', 0), ('shishirpatil/gorilla', 0.5183838605880737, 'llm', 1), ('fastai/fastcore', 0.5158090591430664, 'util', 0), ('emmett-framework/emmett', 0.5148096084594727, 'web', 2), ('pylons/pyramid', 0.5109481811523438, 'web', 0), ('encode/httpx', 0.5098319053649902, 'web', 1), ('sumerc/yappi', 0.5070291757583618, 'profiling', 2), ('pallets/werkzeug', 0.5068367123603821, 'web', 0), ('janetech-inc/fast-api-admin-template', 0.504593551158905, 'template', 0), ('prefecthq/server', 0.5044628381729126, 'util', 0), ('lucidrains/toolformer-pytorch', 0.5022919774055481, 'llm', 0), ('fastapi-users/fastapi-users', 0.5006967186927795, 'web', 1)]",170,4.0,,21.63,572,455,26,0,47,80,47,572.0,1394.0,90.0,2.4,66
1847,data,https://github.com/run-llama/llama-hub,"['data-loader', 'llm']",,[],[],,,,run-llama/llama-hub,llama-hub,2978,649,43,Jupyter Notebook,https://llamahub.ai/,A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain,run-llama,2024-01-14,2023-02-01,51,57.42699724517907,https://avatars.githubusercontent.com/u/130722866?v=4,A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain,[],"['data-loader', 'llm']",2024-01-13,"[('jerryjliu/llama_index', 0.6595434546470642, 'llm', 1), ('zilliztech/gptcache', 0.5969860553741455, 'llm', 1), ('eugeneyan/open-llms', 0.586604654788971, 'study', 1), ('run-llama/llama-lab', 0.5685200095176697, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5623946189880371, 'perf', 0), ('alpha-vllm/llama2-accessory', 0.561208963394165, 'llm', 0), ('ajndkr/lanarky', 0.5594974160194397, 'llm', 0), ('ray-project/llm-applications', 0.5521401166915894, 'llm', 0), ('lightning-ai/lit-gpt', 0.5486422181129456, 'llm', 0), ('berriai/litellm', 0.5469061136245728, 'llm', 1), ('salesforce/xgen', 0.542940616607666, 'llm', 1), ('bentoml/openllm', 0.5397924184799194, 'ml-ops', 1), ('predibase/lorax', 0.5340386629104614, 'llm', 1), ('pathwaycom/llm-app', 0.5336598753929138, 'llm', 1), ('tloen/alpaca-lora', 0.5322900414466858, 'llm', 0), ('microsoft/llama-2-onnx', 0.5180464386940002, 'llm', 0), ('jzhang38/tinyllama', 0.5164508819580078, 'llm', 0), ('shishirpatil/gorilla', 0.5163030028343201, 'llm', 1), ('bigscience-workshop/petals', 0.5156326293945312, 'data', 0), ('alphasecio/langchain-examples', 0.5134257674217224, 'llm', 1), ('mshumer/gpt-llm-trainer', 0.5054514408111572, 'llm', 0), ('deepset-ai/haystack', 0.5051847100257874, 'llm', 0), ('nomic-ai/gpt4all', 0.5034949779510498, 'llm', 0), ('lancedb/lancedb', 0.5028201937675476, 'data', 0)]",224,4.0,,14.58,286,240,12,0,0,59,59,285.0,330.0,90.0,1.2,66
1188,diffusion,https://github.com/stability-ai/stablediffusion,"['diffusion', 'image-generation']",,[],[],,,,stability-ai/stablediffusion,stablediffusion,33628,4452,417,Python,,High-Resolution Image Synthesis with Latent Diffusion Models,stability-ai,2024-01-14,2022-11-23,61,543.6397228637413,https://avatars.githubusercontent.com/u/100950301?v=4,High-Resolution Image Synthesis with Latent Diffusion Models,[],"['diffusion', 'image-generation']",2023-03-25,"[('compvis/latent-diffusion', 1.0000004768371582, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.690674364566803, 'diffusion', 0), ('compvis/stable-diffusion', 0.6550443768501282, 'diffusion', 2), ('openai/glide-text2im', 0.6436024308204651, 'diffusion', 0), ('huggingface/diffusers', 0.6299402713775635, 'diffusion', 2), ('sharonzhou/long_stable_diffusion', 0.5753588676452637, 'diffusion', 0), ('openai/point-e', 0.5376940965652466, 'util', 0), ('timothybrooks/instruct-pix2pix', 0.5207132697105408, 'diffusion', 0), ('kakaobrain/rq-vae-transformer', 0.5072489380836487, 'ml-dl', 0)]",18,4.0,,0.58,34,7,14,10,0,0,0,34.0,34.0,90.0,1.0,65
756,ml-dl,https://github.com/xinntao/real-esrgan,[],,[],[],,,,xinntao/real-esrgan,Real-ESRGAN,24620,3149,216,Python,,Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.,xinntao,2024-01-14,2021-07-19,132,186.31351351351353,,Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.,"['amine', 'denoise', 'esrgan', 'image-restoration', 'jpeg-compression', 'pytorch', 'real-esrgan', 'super-resolution']","['amine', 'denoise', 'esrgan', 'image-restoration', 'jpeg-compression', 'pytorch', 'real-esrgan', 'super-resolution']",2022-09-20,"[('xpixelgroup/basicsr', 0.6734346151351929, 'ml-dl', 3), ('tencentarc/gfpgan', 0.5772732496261597, 'ml', 3)]",11,7.0,,0.0,66,14,30,16,0,8,8,66.0,85.0,90.0,1.3,65
827,util,https://github.com/micropython/micropython,[],,[],[],,,,micropython/micropython,micropython,17769,7083,735,C,https://micropython.org,MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems,micropython,2024-01-14,2013-12-20,527,33.680747359870026,https://avatars.githubusercontent.com/u/6298560?v=4,MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems,"['embedded', 'microcontroller', 'micropython']","['embedded', 'microcontroller', 'micropython']",2024-01-10,"[('adafruit/circuitpython', 0.7091054916381836, 'util', 3), ('pypy/pypy', 0.6625933051109314, 'util', 0), ('pyston/pyston', 0.6496843695640564, 'util', 0), ('plasma-umass/scalene', 0.6017813086509705, 'profiling', 0), ('exaloop/codon', 0.5922623872756958, 'perf', 0), ('cython/cython', 0.5850077271461487, 'util', 0), ('ipython/ipyparallel', 0.5837920904159546, 'perf', 0), ('willmcgugan/textual', 0.5815902352333069, 'term', 0), ('google/gin-config', 0.578527569770813, 'util', 0), ('fastai/fastcore', 0.5780920386314392, 'util', 0), ('google/jax', 0.5700966119766235, 'ml', 0), ('python/cpython', 0.5564913153648376, 'util', 0), ('pallets/quart', 0.5557764768600464, 'web', 0), ('ethereum/py-evm', 0.5545228719711304, 'crypto', 0), ('intel/intel-extension-for-pytorch', 0.5541401505470276, 'perf', 0), ('pympler/pympler', 0.5511940121650696, 'perf', 0), ('artemyk/dynpy', 0.5470327734947205, 'sim', 0), ('dddomodossola/remi', 0.5408753752708435, 'gui', 0), ('hoffstadt/dearpygui', 0.5389625430107117, 'gui', 0), ('agronholm/apscheduler', 0.5366904139518738, 'util', 0), ('kubeflow/fairing', 0.5345472693443298, 'ml-ops', 0), ('joblib/joblib', 0.5310302376747131, 'util', 0), ('pyinfra-dev/pyinfra', 0.5298929214477539, 'util', 0), ('numpy/numpy', 0.5278494358062744, 'math', 0), ('numba/numba', 0.5256375670433044, 'perf', 0), ('python-trio/trio', 0.5247344970703125, 'perf', 0), ('wxwidgets/phoenix', 0.5213609933853149, 'gui', 0), ('pytoolz/toolz', 0.5203608870506287, 'util', 0), ('aws/chalice', 0.5198830366134644, 'web', 0), ('pallets/flask', 0.5177249908447266, 'web', 0), ('eleutherai/pyfra', 0.5175337195396423, 'ml', 0), ('py4j/py4j', 0.5158247947692871, 'util', 0), ('pythonspeed/filprofiler', 0.5140711069107056, 'profiling', 0), ('pytables/pytables', 0.5130558013916016, 'data', 0), ('nvidia/warp', 0.512362003326416, 'sim', 0), ('pytorch/pytorch', 0.5111660361289978, 'ml-dl', 0), ('erotemic/ubelt', 0.5087762475013733, 'util', 0), ('allrod5/injectable', 0.5082573294639587, 'util', 0), ('falconry/falcon', 0.507040798664093, 'web', 0), ('beeware/toga', 0.5063884258270264, 'gui', 0), ('alexmojaki/snoop', 0.505521297454834, 'debug', 0), ('1200wd/bitcoinlib', 0.5035257935523987, 'crypto', 0), ('numba/llvmlite', 0.5028732419013977, 'util', 0)]",613,3.0,,19.92,536,284,123,5,4,6,4,537.0,1681.0,90.0,3.1,65
30,web,https://github.com/huge-success/sanic,[],,[],[],,,,huge-success/sanic,sanic,17531,1545,408,Python,https://sanic.dev, Accelerate your web app development  | Build fast. Run fast.,huge-success,2024-01-13,2016-05-26,400,43.749376114082,https://avatars.githubusercontent.com/u/25215992?v=4, Accelerate your web app development  | Build fast. Run fast.,"['api-server', 'asgi', 'asyncio', 'framework', 'sanic', 'web', 'web-framework', 'web-server']","['api-server', 'asgi', 'asyncio', 'framework', 'sanic', 'web', 'web-framework', 'web-server']",2024-01-01,"[('neoteroi/blacksheep', 0.7380421161651611, 'web', 4), ('pallets/quart', 0.7020831108093262, 'web', 2), ('starlite-api/starlite', 0.6891716122627258, 'web', 2), ('encode/uvicorn', 0.633416473865509, 'web', 2), ('tiangolo/fastapi', 0.6080839037895203, 'web', 3), ('alirn76/panther', 0.6031531095504761, 'web', 1), ('encode/starlette', 0.5865360498428345, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5729467868804932, 'template', 0), ('klen/muffin', 0.5701971054077148, 'web', 2), ('pallets/flask', 0.5633546710014343, 'web', 1), ('tiangolo/asyncer', 0.5628356337547302, 'perf', 1), ('vitalik/django-ninja', 0.5478051900863647, 'web', 0), ('emmett-framework/emmett', 0.5460001230239868, 'web', 3), ('aio-libs/aiohttp', 0.5445988774299622, 'web', 1), ('reflex-dev/reflex', 0.5383414030075073, 'web', 1), ('falconry/falcon', 0.5344210267066956, 'web', 3), ('flet-dev/flet', 0.5318177342414856, 'web', 1), ('encode/httpx', 0.5279699563980103, 'web', 1), ('python-restx/flask-restx', 0.5204745531082153, 'web', 0), ('jordaneremieff/mangum', 0.511349081993103, 'web', 3), ('magicstack/uvloop', 0.5082210898399353, 'util', 1)]",340,6.0,,2.02,93,57,93,0,4,11,4,93.0,149.0,90.0,1.6,65
36,data,https://github.com/joke2k/faker,[],,[],[],,,,joke2k/faker,faker,16741,1851,220,Python,https://faker.readthedocs.io,Faker is a Python package that generates fake data for you.,joke2k,2024-01-14,2012-11-12,585,28.610107421875,,Faker is a Python package that generates fake data for you.,"['dataset', 'fake', 'fake-data', 'faker', 'faker-generator', 'test-data', 'test-data-generator', 'testing']","['dataset', 'fake', 'fake-data', 'faker', 'faker-generator', 'test-data', 'test-data-generator', 'testing']",2024-01-10,"[('snyk/faker-security', 0.7436192631721497, 'security', 0), ('lk-geimfari/mimesis', 0.6268561482429504, 'data', 3)]",555,7.0,,5.96,67,48,136,0,70,30,70,67.0,109.0,90.0,1.6,65
125,ml,https://github.com/onnx/onnx,[],,[],[],,,,onnx/onnx,onnx,16209,3620,438,Python,https://onnx.ai/,Open standard for machine learning interoperability,onnx,2024-01-14,2017-09-07,333,48.571489726027394,https://avatars.githubusercontent.com/u/31675368?v=4,Open standard for machine learning interoperability,"['deep-learning', 'deep-neural-networks', 'dnn', 'keras', 'machine-learning', 'ml', 'mxnet', 'neural-network', 'onnx', 'pytorch', 'scikit-learn', 'tensorflow']","['deep-learning', 'deep-neural-networks', 'dnn', 'keras', 'machine-learning', 'ml', 'mxnet', 'neural-network', 'onnx', 'pytorch', 'scikit-learn', 'tensorflow']",2024-01-12,"[('microsoft/onnxruntime', 0.727009654045105, 'ml', 6), ('tensorflow/tensorflow', 0.7098345756530762, 'ml-dl', 6), ('polyaxon/polyaxon', 0.6912744641304016, 'ml-ops', 7), ('explosion/thinc', 0.6653851866722107, 'ml-dl', 5), ('mlflow/mlflow', 0.6629000902175903, 'ml-ops', 2), ('keras-team/keras', 0.6622923612594604, 'ml-dl', 4), ('huggingface/datasets', 0.6597679853439331, 'nlp', 4), ('microsoft/nni', 0.6350607872009277, 'ml', 5), ('feast-dev/feast', 0.6326718330383301, 'ml-ops', 2), ('alpa-projects/alpa', 0.6325767636299133, 'ml-dl', 2), ('mosaicml/composer', 0.6314774751663208, 'ml-dl', 4), ('ddbourgin/numpy-ml', 0.6284279227256775, 'ml', 1), ('bentoml/bentoml', 0.6247395277023315, 'ml-ops', 2), ('lutzroeder/netron', 0.616361141204834, 'ml', 9), ('nyandwi/modernconvnets', 0.6126094460487366, 'ml-dl', 2), ('horovod/horovod', 0.5999342203140259, 'ml-ops', 6), ('huggingface/transformers', 0.5913053750991821, 'nlp', 4), ('determined-ai/determined', 0.5901457667350769, 'ml-ops', 5), ('keras-team/autokeras', 0.5896108746528625, 'ml-dl', 4), ('firmai/industry-machine-learning', 0.5830479264259338, 'study', 1), ('hpcaitech/colossalai', 0.5809974670410156, 'llm', 1), ('kubeflow/pipelines', 0.5777002573013306, 'ml-ops', 1), ('google/trax', 0.5766066908836365, 'ml-dl', 2), ('keras-rl/keras-rl', 0.5747138261795044, 'ml-rl', 3), ('pytorchlightning/pytorch-lightning', 0.574492335319519, 'ml-dl', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5712395906448364, 'study', 2), ('roboflow/supervision', 0.5692649483680725, 'ml', 4), ('adap/flower', 0.5646280646324158, 'ml-ops', 5), ('nvidia/deeplearningexamples', 0.5635833144187927, 'ml-dl', 4), ('aiqc/aiqc', 0.5626868605613708, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5625835061073303, 'viz', 1), ('merantix-momentum/squirrel-core', 0.5616537928581238, 'ml', 5), ('tensorlayer/tensorlayer', 0.5610949397087097, 'ml-rl', 3), ('neuralmagic/deepsparse', 0.5601222515106201, 'nlp', 1), ('fepegar/torchio', 0.5600517392158508, 'ml-dl', 3), ('googlecloudplatform/vertex-ai-samples', 0.558918833732605, 'ml', 1), ('amanchadha/coursera-deep-learning-specialization', 0.5587916970252991, 'study', 2), ('automl/auto-sklearn', 0.5575015544891357, 'ml', 1), ('rwightman/pytorch-image-models', 0.5542277097702026, 'ml-dl', 1), ('tensorflow/tensor2tensor', 0.5511443018913269, 'ml', 2), ('activeloopai/deeplake', 0.550166130065918, 'ml-ops', 5), ('paddlepaddle/paddle', 0.5445713400840759, 'ml-dl', 3), ('nccr-itmo/fedot', 0.5438551306724548, 'ml-ops', 1), ('rasbt/machine-learning-book', 0.5438082814216614, 'study', 4), ('nevronai/metisfl', 0.543806254863739, 'ml', 2), ('ludwig-ai/ludwig', 0.5428158640861511, 'ml-ops', 5), ('deepmind/dm-haiku', 0.542344868183136, 'ml-dl', 3), ('tensorflow/lucid', 0.5405921936035156, 'ml-interpretability', 2), ('xplainable/xplainable', 0.5391144156455994, 'ml-interpretability', 1), ('google/mediapipe', 0.5367459654808044, 'ml', 2), ('aleju/imgaug', 0.5340151786804199, 'ml', 2), ('doccano/doccano', 0.5324045419692993, 'nlp', 1), ('keras-team/keras-nlp', 0.531283974647522, 'nlp', 4), ('mlc-ai/mlc-llm', 0.5302952527999878, 'llm', 0), ('unity-technologies/ml-agents', 0.5283321142196655, 'ml-rl', 2), ('gradio-app/gradio', 0.5277537107467651, 'viz', 2), ('neuralmagic/sparseml', 0.5271431803703308, 'ml-dl', 4), ('danielegrattarola/spektral', 0.5261642932891846, 'ml-dl', 3), ('tensorly/tensorly', 0.5244054198265076, 'ml-dl', 4), ('koaning/human-learn', 0.5241888761520386, 'data', 2), ('wandb/client', 0.5230202674865723, 'ml', 5), ('kevinmusgrave/pytorch-metric-learning', 0.5226200222969055, 'ml', 3), ('stellargraph/stellargraph', 0.5222296714782715, 'graph', 2), ('pytorch/ignite', 0.521939218044281, 'ml-dl', 4), ('awslabs/autogluon', 0.5217620134353638, 'ml', 4), ('drivendata/cookiecutter-data-science', 0.5203287601470947, 'template', 1), ('intel/intel-extension-for-pytorch', 0.5180797576904297, 'perf', 4), ('csinva/imodels', 0.5179754495620728, 'ml', 3), ('winedarksea/autots', 0.516512930393219, 'time-series', 2), ('interpretml/interpret', 0.5163048505783081, 'ml-interpretability', 2), ('tensorflow/addons', 0.515902578830719, 'ml', 4), ('deepmind/dm_control', 0.5158249139785767, 'ml-rl', 2), ('tlkh/tf-metal-experiments', 0.5157482028007507, 'perf', 2), ('opentensor/bittensor', 0.5157052874565125, 'ml', 3), ('microsoft/semi-supervised-learning', 0.5151509642601013, 'ml', 3), ('microsoft/lmops', 0.5136048793792725, 'llm', 0), ('ray-project/ray', 0.5107229948043823, 'ml-ops', 4), ('mage-ai/mage-ai', 0.5106418132781982, 'ml-ops', 1), ('iryna-kondr/scikit-llm', 0.5105788111686707, 'llm', 3), ('apple/coremltools', 0.5105490684509277, 'ml', 3), ('milvus-io/bootcamp', 0.5098041892051697, 'data', 1), ('unionai-oss/unionml', 0.5090394020080566, 'ml-ops', 1), ('bodywork-ml/bodywork-core', 0.5088913440704346, 'ml-ops', 1), ('arogozhnikov/einops', 0.5081114172935486, 'ml-dl', 4), ('polyaxon/datatile', 0.5065220594406128, 'pandas', 2), ('microsoft/deepspeed', 0.5063037276268005, 'ml-dl', 3), ('mrdbourke/zero-to-mastery-ml', 0.5012384057044983, 'study', 2), ('keras-team/keras-cv', 0.5009073615074158, 'ml-dl', 1), ('operand/agency', 0.5004202127456665, 'llm', 1), ('deepfakes/faceswap', 0.5000324845314026, 'ml-dl', 3)]",305,2.0,,9.48,272,168,77,0,4,4,4,272.0,391.0,90.0,1.4,65
153,nlp,https://github.com/flairnlp/flair,[],,[],[],,,,flairnlp/flair,flair,13342,2054,204,Python,https://flairnlp.github.io/flair/,A very simple framework for state-of-the-art Natural Language Processing (NLP),flairnlp,2024-01-13,2018-06-11,294,45.35891209324915,https://avatars.githubusercontent.com/u/59021421?v=4,A very simple framework for state-of-the-art Natural Language Processing (NLP),"['machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'pytorch', 'semantic-role-labeling', 'sequence-labeling', 'word-embeddings']","['machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'pytorch', 'semantic-role-labeling', 'sequence-labeling', 'word-embeddings']",2023-12-18,"[('franck-dernoncourt/neuroner', 0.7272414565086365, 'nlp', 3), ('allenai/allennlp', 0.6880818605422974, 'nlp', 3), ('nltk/nltk', 0.6725092530250549, 'nlp', 3), ('explosion/spacy', 0.6546562910079956, 'nlp', 4), ('explosion/spacy-models', 0.6428545117378235, 'nlp', 3), ('sloria/textblob', 0.6279769539833069, 'nlp', 2), ('keras-team/keras-nlp', 0.6184731125831604, 'nlp', 3), ('norskregnesentral/skweak', 0.602255642414093, 'nlp', 1), ('explosion/spacy-llm', 0.6013752222061157, 'llm', 4), ('rasahq/rasa', 0.5888329744338989, 'llm', 3), ('alibaba/easynlp', 0.5845892429351807, 'nlp', 3), ('deepset-ai/farm', 0.5659800171852112, 'nlp', 2), ('graykode/nlp-tutorial', 0.561720073223114, 'study', 3), ('huggingface/transformers', 0.5504770874977112, 'nlp', 4), ('pemistahl/lingua-py', 0.5448145270347595, 'nlp', 2), ('llmware-ai/llmware', 0.5408936738967896, 'llm', 3), ('koaning/whatlies', 0.5374947190284729, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5343381762504578, 'llm', 1), ('plasticityai/magnitude', 0.5328223705291748, 'nlp', 4), ('jonasgeiping/cramming', 0.5266839265823364, 'nlp', 1), ('koaning/embetter', 0.5184030532836914, 'data', 0), ('ibm/transition-amr-parser', 0.5182547569274902, 'nlp', 2), ('maartengr/bertopic', 0.5175113677978516, 'nlp', 2), ('jalammar/ecco', 0.517105758190155, 'ml-interpretability', 3), ('explosion/spacy-streamlit', 0.5156773328781128, 'nlp', 4), ('bigscience-workshop/promptsource', 0.5140012502670288, 'nlp', 3), ('infinitylogesh/mutate', 0.5080118775367737, 'nlp', 0), ('qanastek/drbert', 0.5062006711959839, 'llm', 2), ('sebischair/lbl2vec', 0.5040925145149231, 'nlp', 4), ('argilla-io/argilla', 0.5028916597366333, 'nlp', 3)]",251,5.0,,11.87,85,62,68,1,5,5,5,85.0,119.0,90.0,1.4,65
143,nlp,https://github.com/ukplab/sentence-transformers,"['sentence-embeddings', 'semantic-search', 'information-retrieval']",,[],[],,,,ukplab/sentence-transformers,sentence-transformers,12848,2226,130,Python,https://www.SBERT.net,Multilingual Sentence & Image Embeddings with BERT,ukplab,2024-01-14,2019-07-24,235,54.47365233192005,https://avatars.githubusercontent.com/u/9532046?v=4,Multilingual Sentence & Image Embeddings with BERT,[],"['information-retrieval', 'semantic-search', 'sentence-embeddings']",2024-01-10,"[('jina-ai/clip-as-service', 0.7321420311927795, 'nlp', 0), ('jina-ai/finetuner', 0.5992518663406372, 'ml', 0), ('amansrivastava17/embedding-as-service', 0.5738234519958496, 'nlp', 0), ('ddangelov/top2vec', 0.5732832551002502, 'nlp', 1), ('alibaba/easynlp', 0.551360011100769, 'nlp', 0), ('muennighoff/sgpt', 0.5446346402168274, 'llm', 3), ('neuml/txtai', 0.5237442255020142, 'nlp', 3), ('ai21labs/in-context-ralm', 0.5168312788009644, 'llm', 0), ('qdrant/fastembed', 0.5122830867767334, 'ml', 0), ('extreme-bert/extreme-bert', 0.50993812084198, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5090245604515076, 'llm', 0), ('koaning/whatlies', 0.5054284334182739, 'nlp', 0), ('intellabs/fastrag', 0.5005632042884827, 'nlp', 2)]",131,4.0,,1.44,196,125,54,0,0,7,7,196.0,300.0,90.0,1.5,65
798,web,https://github.com/encode/httpx,[],,[],[],,,,encode/httpx,httpx,11723,816,113,Python,https://www.python-httpx.org/,A next generation HTTP client for Python. 🦋,encode,2024-01-14,2019-04-04,251,46.572644721906926,https://avatars.githubusercontent.com/u/19159390?v=4,A next generation HTTP client for Python. 🦋,"['asyncio', 'http', 'trio']","['asyncio', 'http', 'trio']",2024-01-12,"[('encode/uvicorn', 0.8501601815223694, 'web', 2), ('aio-libs/aiohttp', 0.8457793593406677, 'web', 2), ('pallets/quart', 0.748035192489624, 'web', 1), ('neoteroi/blacksheep', 0.7281930446624756, 'web', 2), ('psf/requests', 0.7030969858169556, 'web', 1), ('alirn76/panther', 0.6786492466926575, 'web', 0), ('requests/toolbelt', 0.6686779856681824, 'util', 1), ('cherrypy/cherrypy', 0.65858393907547, 'web', 1), ('simple-salesforce/simple-salesforce', 0.6496359705924988, 'data', 0), ('timofurrer/awesome-asyncio', 0.640356183052063, 'study', 1), ('klen/muffin', 0.6186836957931519, 'web', 2), ('falconry/falcon', 0.6106772422790527, 'web', 1), ('websocket-client/websocket-client', 0.6006039381027222, 'web', 0), ('encode/starlette', 0.5981119275093079, 'web', 1), ('miguelgrinberg/python-socketio', 0.5959307551383972, 'util', 1), ('masoniteframework/masonite', 0.5954501628875732, 'web', 0), ('python-trio/trio', 0.5947321057319641, 'perf', 1), ('pylons/waitress', 0.594509482383728, 'web', 0), ('hugapi/hug', 0.5931265950202942, 'util', 1), ('replicate/replicate-python', 0.5832346677780151, 'ml', 0), ('samuelcolvin/aioaws', 0.5720120072364807, 'data', 1), ('pallets/werkzeug', 0.5651618838310242, 'web', 1), ('agronholm/anyio', 0.558357298374176, 'perf', 2), ('airtai/faststream', 0.5473020672798157, 'perf', 1), ('webpy/webpy', 0.5403591394424438, 'web', 0), ('benoitc/gunicorn', 0.5388432145118713, 'web', 1), ('locustio/locust', 0.5382368564605713, 'testing', 1), ('aio-libs/aiobotocore', 0.536460280418396, 'util', 1), ('snyk-labs/pysnyk', 0.5362645983695984, 'security', 0), ('radiantearth/radiant-mlhub', 0.5330476760864258, 'gis', 0), ('bottlepy/bottle', 0.5296392440795898, 'web', 0), ('huge-success/sanic', 0.5279699563980103, 'web', 1), ('magicstack/uvloop', 0.5275475978851318, 'util', 1), ('samuelcolvin/arq', 0.525906503200531, 'data', 1), ('paramiko/paramiko', 0.5251051187515259, 'util', 0), ('pallets/flask', 0.5245396494865417, 'web', 0), ('pylons/pyramid', 0.5153858661651611, 'web', 0), ('tornadoweb/tornado', 0.5143762230873108, 'web', 0), ('reflex-dev/reflex', 0.5139403939247131, 'web', 0), ('pytest-dev/pytest-asyncio', 0.5099304914474487, 'testing', 1), ('starlite-api/starlite', 0.5098319053649902, 'web', 1), ('amzn/ion-python', 0.5086729526519775, 'data', 0), ('geeogi/async-python-lambda-template', 0.5077229142189026, 'template', 0), ('nasdaq/data-link-python', 0.5050049424171448, 'finance', 0), ('getsentry/responses', 0.5034039616584778, 'testing', 0), ('ethereum/web3.py', 0.5025968551635742, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5019758939743042, 'finance', 0), ('pyston/pyston', 0.5002996325492859, 'util', 0)]",212,5.0,,2.94,113,90,58,0,6,17,6,113.0,208.0,90.0,1.8,65
11,perf,https://github.com/dask/dask,[],,[],[],,,,dask/dask,dask,11689,1665,213,Python,https://dask.org,Parallel computing with task scheduling,dask,2024-01-14,2015-01-04,473,24.69755508602475,https://avatars.githubusercontent.com/u/17131925?v=4,Parallel computing with task scheduling,"['dask', 'numpy', 'pandas', 'pydata', 'scikit-learn', 'scipy']","['dask', 'numpy', 'pandas', 'pydata', 'scikit-learn', 'scipy']",2024-01-12,"[('nalepae/pandarallel', 0.7318655252456665, 'pandas', 1), ('dask/distributed', 0.719694972038269, 'perf', 2), ('agronholm/apscheduler', 0.6700900197029114, 'util', 0), ('ipython/ipyparallel', 0.64837247133255, 'perf', 0), ('joblib/joblib', 0.6337271332740784, 'util', 0), ('scipy/scipy', 0.6020888686180115, 'math', 1), ('jmcarpenter2/swifter', 0.60169517993927, 'pandas', 2), ('numpy/numpy', 0.5977861285209656, 'math', 1), ('geopandas/dask-geopandas', 0.5673131942749023, 'gis', 0), ('backtick-se/cowait', 0.5500026941299438, 'util', 1), ('samuelcolvin/arq', 0.5444415211677551, 'data', 0), ('dbader/schedule', 0.5425211191177368, 'util', 0), ('hyperopt/hyperopt', 0.5399159789085388, 'ml', 0), ('blaze/blaze', 0.5343106985092163, 'pandas', 0), ('joblib/loky', 0.5317108035087585, 'perf', 0), ('eventual-inc/daft', 0.5266561508178711, 'pandas', 0), ('dask/dask-ml', 0.526610255241394, 'ml', 0), ('eventlet/eventlet', 0.5214966535568237, 'perf', 0), ('bogdanp/dramatiq', 0.5178118944168091, 'util', 0), ('fugue-project/fugue', 0.5079086422920227, 'pandas', 2), ('python-trio/trio', 0.5067204236984253, 'perf', 0), ('parallel-domain/pd-sdk', 0.5014457702636719, 'data', 0)]",592,6.0,,9.31,292,175,110,0,0,31,31,292.0,454.0,90.0,1.6,65
38,jupyter,https://github.com/jupyter/notebook,[],,[],[],,,,jupyter/notebook,notebook,10849,4462,321,Jupyter Notebook,https://jupyter-notebook.readthedocs.io/,Jupyter Interactive Notebook,jupyter,2024-01-13,2015-04-09,459,23.5994406463642,https://avatars.githubusercontent.com/u/7388996?v=4,Jupyter Interactive Notebook,"['jupyter', 'jupyter-notebook', 'notebook']","['jupyter', 'jupyter-notebook', 'notebook']",2024-01-02,"[('jupyter-widgets/ipywidgets', 0.8538753390312195, 'jupyter', 0), ('jupyter/nbformat', 0.7493022680282593, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.7161470651626587, 'jupyter', 2), ('jupyter/nbconvert', 0.7077092528343201, 'jupyter', 0), ('voila-dashboards/voila', 0.6948908567428589, 'jupyter', 2), ('ipython/ipykernel', 0.6881211400032043, 'util', 2), ('mwouts/jupytext', 0.6791135668754578, 'jupyter', 1), ('computationalmodelling/nbval', 0.671747624874115, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.669508159160614, 'jupyter', 1), ('cohere-ai/notebooks', 0.6436967253684998, 'llm', 0), ('xiaohk/stickyland', 0.6330485939979553, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.6270691156387329, 'jupyter', 2), ('ipython/ipyparallel', 0.6222683191299438, 'perf', 1), ('nteract/testbook', 0.6220455765724182, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.6165135502815247, 'jupyter', 2), ('quantopian/qgrid', 0.613193154335022, 'jupyter', 0), ('jupyterlite/jupyterlite', 0.6035483479499817, 'jupyter', 1), ('aws/graph-notebook', 0.5949639081954956, 'jupyter', 2), ('jupyter-lsp/jupyterlab-lsp', 0.5912204384803772, 'jupyter', 3), ('mamba-org/gator', 0.5904126763343811, 'jupyter', 1), ('jupyter/nbgrader', 0.5747985243797302, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.5674871206283569, 'study', 1), ('bloomberg/ipydatagrid', 0.5550519824028015, 'jupyter', 0), ('jupyter-widgets/ipyleaflet', 0.5543774366378784, 'gis', 1), ('koaning/drawdata', 0.5532419085502625, 'jupyter', 1), ('nteract/papermill', 0.5524148344993591, 'jupyter', 2), ('jupyter/nbviewer', 0.547979474067688, 'jupyter', 2), ('jupyter/nbdime', 0.5463511943817139, 'jupyter', 2), ('chaoleili/jupyterlab_tensorboard', 0.5390121340751648, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5353759527206421, 'study', 0), ('tkrabel/bamboolib', 0.5267034769058228, 'pandas', 1), ('ageron/handson-ml2', 0.5203615427017212, 'ml', 0)]",645,8.0,,4.96,244,177,107,0,31,75,31,244.0,362.0,90.0,1.5,65
1304,study,https://github.com/eugeneyan/open-llms,['awesome'],,[],[],,,,eugeneyan/open-llms,open-llms,9154,538,204,,,📋 A list of open LLMs available for commercial use.,eugeneyan,2024-01-14,2023-05-05,38,237.32592592592593,,📋 A list of open LLMs available for commercial use.,"['commercial', 'large-language-models', 'llm', 'llms']","['awesome', 'commercial', 'large-language-models', 'llm', 'llms']",2024-01-10,"[('bentoml/openllm', 0.7019832134246826, 'ml-ops', 1), ('salesforce/xgen', 0.6812090873718262, 'llm', 2), ('mooler0410/llmspracticalguide', 0.6558331847190857, 'study', 2), ('agenta-ai/agenta', 0.6486678719520569, 'llm', 3), ('microsoft/torchscale', 0.633047878742218, 'llm', 0), ('young-geng/easylm', 0.6238888502120972, 'llm', 1), ('nat/openplayground', 0.6209505796432495, 'llm', 0), ('ibm/dromedary', 0.6077130436897278, 'llm', 0), ('confident-ai/deepeval', 0.6072402596473694, 'testing', 1), ('alpha-vllm/llama2-accessory', 0.6008363962173462, 'llm', 0), ('salesforce/codet5', 0.6007983088493347, 'nlp', 1), ('hwchase17/langchain', 0.599155068397522, 'llm', 0), ('nomic-ai/gpt4all', 0.5968722701072693, 'llm', 0), ('berriai/litellm', 0.5926091074943542, 'llm', 1), ('hegelai/prompttools', 0.5875352621078491, 'llm', 2), ('run-llama/llama-hub', 0.586604654788971, 'data', 1), ('ray-project/ray-llm', 0.5834382772445679, 'llm', 2), ('shishirpatil/gorilla', 0.5759145021438599, 'llm', 1), ('tigerlab-ai/tiger', 0.5710621476173401, 'llm', 2), ('vllm-project/vllm', 0.5680029988288879, 'llm', 1), ('hiyouga/llama-factory', 0.5650503635406494, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5650503039360046, 'llm', 3), ('citadel-ai/langcheck', 0.5613651275634766, 'llm', 0), ('microsoft/promptflow', 0.5602272748947144, 'llm', 1), ('predibase/lorax', 0.5560584664344788, 'llm', 1), ('thudm/chatglm2-6b', 0.5501125454902649, 'llm', 2), ('langchain-ai/langsmith-cookbook', 0.5495509505271912, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5479944944381714, 'perf', 0), ('ray-project/llm-applications', 0.5464956760406494, 'llm', 1), ('bigscience-workshop/petals', 0.5463467836380005, 'data', 1), ('microsoft/semantic-kernel', 0.5385962128639221, 'llm', 1), ('deep-diver/pingpong', 0.5374715328216553, 'llm', 0), ('alphasecio/langchain-examples', 0.5366406440734863, 'llm', 1), ('microsoft/jarvis', 0.5364652276039124, 'llm', 0), ('dylanhogg/llmgraph', 0.5341588258743286, 'ml', 1), ('bobazooba/xllm', 0.5326270461082458, 'llm', 2), ('openai/evals', 0.5292133092880249, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5287415385246277, 'llm', 1), ('deepset-ai/haystack', 0.5231071710586548, 'llm', 1), ('ajndkr/lanarky', 0.5215867757797241, 'llm', 0), ('nebuly-ai/nebullvm', 0.5213199257850647, 'perf', 2), ('epfllm/meditron', 0.5213027596473694, 'llm', 0), ('explosion/spacy-llm', 0.5212215185165405, 'llm', 2), ('pathwaycom/llm-app', 0.5204359889030457, 'llm', 1), ('jzhang38/tinyllama', 0.515006959438324, 'llm', 0), ('ludwig-ai/ludwig', 0.5101239681243896, 'ml-ops', 1), ('lianjiatech/belle', 0.5089041590690613, 'llm', 0), ('jerryjliu/llama_index', 0.5083422660827637, 'llm', 1), ('run-llama/llama-lab', 0.5069587826728821, 'llm', 0), ('artidoro/qlora', 0.5061990022659302, 'llm', 0), ('langchain-ai/langgraph', 0.5050806403160095, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.502346932888031, 'llm', 0), ('lightning-ai/lit-gpt', 0.501139223575592, 'llm', 0), ('argilla-io/argilla', 0.5009057521820068, 'nlp', 1)]",25,5.0,,1.56,8,6,8,0,0,0,0,8.0,9.0,90.0,1.1,65
543,ml,https://github.com/optuna/optuna,[],,[],[],,,,optuna/optuna,optuna,9124,917,121,Python,https://optuna.org,A hyperparameter optimization framework,optuna,2024-01-13,2018-02-21,309,29.445827570308897,https://avatars.githubusercontent.com/u/57251745?v=4,A hyperparameter optimization framework,"['distributed', 'hyperparameter-optimization', 'machine-learning', 'parallel']","['distributed', 'hyperparameter-optimization', 'machine-learning', 'parallel']",2024-01-10,"[('hyperopt/hyperopt', 0.7380708456039429, 'ml', 0), ('paddlepaddle/paddle', 0.6472195386886597, 'ml-dl', 1), ('kubeflow/katib', 0.6405646800994873, 'ml', 0), ('determined-ai/determined', 0.6228476166725159, 'ml-ops', 2), ('microsoft/deepspeed', 0.5794839262962341, 'ml-dl', 1), ('automl/auto-sklearn', 0.5508047938346863, 'ml', 1), ('dask/dask-ml', 0.5394331216812134, 'ml', 0), ('horovod/horovod', 0.5364230871200562, 'ml-ops', 1), ('eleutherai/oslo', 0.5354965925216675, 'ml', 0), ('uber/fiber', 0.5304052233695984, 'data', 1), ('google/vizier', 0.5277183055877686, 'ml', 2), ('alpa-projects/alpa', 0.5211974382400513, 'ml-dl', 1), ('microsoft/flaml', 0.5139668583869934, 'ml', 2), ('ray-project/ray', 0.5129156112670898, 'ml-ops', 4), ('ray-project/tune-sklearn', 0.5114966034889221, 'ml', 0), ('microsoft/nni', 0.5085669159889221, 'ml', 3), ('scikit-optimize/scikit-optimize', 0.5056672096252441, 'ml', 2)]",255,3.0,,38.81,208,166,72,0,7,10,7,208.0,407.0,90.0,2.0,65
1110,nlp,https://github.com/openai/tiktoken,"['chatgpt', 'word-segmentation', 'tokeniser']",,[],[],1.0,,,openai/tiktoken,tiktoken,8112,563,143,Python,,tiktoken is a fast BPE tokeniser for use with OpenAI's models.,openai,2024-01-14,2022-12-01,60,133.6094117647059,https://avatars.githubusercontent.com/u/14957082?v=4,tiktoken is a fast BPE tokeniser for use with OpenAI's models.,[],"['chatgpt', 'tokeniser', 'word-segmentation']",2023-12-03,"[('run-llama/rags', 0.5739045143127441, 'llm', 1), ('openai/openai-cookbook', 0.5737849473953247, 'ml', 1), ('blinkdl/chatrwkv', 0.5641686320304871, 'llm', 1), ('xtekky/gpt4free', 0.5542630553245544, 'llm', 1), ('alphasecio/langchain-examples', 0.5489485263824463, 'llm', 0), ('zhudotexe/kani', 0.528551459312439, 'llm', 1), ('laion-ai/open-assistant', 0.5184189677238464, 'llm', 1), ('lm-sys/fastchat', 0.5158092975616455, 'llm', 0), ('langchain-ai/opengpts', 0.5110874176025391, 'llm', 0), ('langchain-ai/langsmith-sdk', 0.5109971761703491, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5088499784469604, 'llm', 0), ('explosion/spacy-streamlit', 0.5080421566963196, 'nlp', 0), ('guardrails-ai/guardrails', 0.5039165019989014, 'llm', 0), ('shishirpatil/gorilla', 0.5037037134170532, 'llm', 1)]",11,4.0,,0.48,55,35,14,1,7,8,7,55.0,92.0,90.0,1.7,65
214,nlp,https://github.com/speechbrain/speechbrain,[],,[],[],,,,speechbrain/speechbrain,speechbrain,7089,1212,123,Python,http://speechbrain.github.io,A PyTorch-based Speech Toolkit,speechbrain,2024-01-14,2020-04-28,196,36.16836734693877,https://avatars.githubusercontent.com/u/54749030?v=4,A PyTorch-based Speech Toolkit,"['asr', 'audio', 'audio-processing', 'deep-learning', 'huggingface', 'language-model', 'pytorch', 'speaker-diarization', 'speaker-recognition', 'speaker-verification', 'speech-enhancement', 'speech-processing', 'speech-recognition', 'speech-separation', 'speech-to-text', 'speech-toolkit', 'speechrecognition', 'spoken-language-understanding', 'transformers', 'voice-recognition']","['asr', 'audio', 'audio-processing', 'deep-learning', 'huggingface', 'language-model', 'pytorch', 'speaker-diarization', 'speaker-recognition', 'speaker-verification', 'speech-enhancement', 'speech-processing', 'speech-recognition', 'speech-separation', 'speech-to-text', 'speech-toolkit', 'speechrecognition', 'spoken-language-understanding', 'transformers', 'voice-recognition']",2024-01-07,"[('espnet/espnet', 0.822684109210968, 'nlp', 7), ('uberi/speech_recognition', 0.6739375591278076, 'ml', 3), ('huggingface/transformers', 0.6348034739494324, 'nlp', 4), ('nvidia/nemo', 0.6236434578895569, 'nlp', 7), ('allenai/allennlp', 0.5886344313621521, 'nlp', 2), ('spotify/pedalboard', 0.5878923535346985, 'util', 2), ('nateshmbhat/pyttsx3', 0.5789477229118347, 'util', 0), ('m-bain/whisperx', 0.5771469473838806, 'nlp', 3), ('skorch-dev/skorch', 0.5581321120262146, 'ml-dl', 2), ('cmusphinx/pocketsphinx', 0.5464542508125305, 'ml', 1), ('intel/intel-extension-for-pytorch', 0.5442495942115784, 'perf', 2), ('pytorch/ignite', 0.5351871848106384, 'ml-dl', 2), ('pndurette/gtts', 0.5305692553520203, 'util', 0), ('kalliope-project/kalliope', 0.5283302664756775, 'util', 2), ('rasbt/machine-learning-book', 0.5237654447555542, 'study', 2), ('microsoft/semi-supervised-learning', 0.5235595107078552, 'ml', 2), ('rasahq/rasa', 0.5202128291130066, 'llm', 0), ('libaudioflux/audioflux', 0.5162292718887329, 'util', 3), ('pytorch/captum', 0.5150958895683289, 'ml-interpretability', 0), ('alibaba/easynlp', 0.5123403072357178, 'nlp', 3), ('blinkdl/chatrwkv', 0.5112695693969727, 'llm', 2), ('bastibe/python-soundfile', 0.5037903189659119, 'util', 0), ('deeppavlov/deeppavlov', 0.5031290054321289, 'nlp', 1), ('huggingface/huggingface_hub', 0.5026688575744629, 'ml', 2), ('huggingface/datasets', 0.5019820928573608, 'nlp', 2)]",225,5.0,,27.48,144,96,45,0,3,3,3,144.0,335.0,90.0,2.3,65
1476,ml,https://github.com/facebookresearch/xformers,['transformers'],,[],[],,,,facebookresearch/xformers,xformers,6815,466,70,Python,https://facebookresearch.github.io/xformers/,"Hackable and optimized Transformers building blocks, supporting a composable construction.",facebookresearch,2024-01-14,2021-10-13,119,56.85935637663886,https://avatars.githubusercontent.com/u/16943930?v=4,"Hackable and optimized Transformers building blocks, supporting a composable construction.",[],['transformers'],2024-01-05,"[('abertsch72/unlimiformer', 0.5475772023200989, 'nlp', 1)]",71,3.0,,5.54,92,29,27,0,13,15,13,92.0,239.0,90.0,2.6,65
858,ml-ops,https://github.com/mage-ai/mage-ai,[],,[],[],1.0,,,mage-ai/mage-ai,mage-ai,6239,579,55,Python,https://www.mage.ai/,"🧙 The modern replacement for Airflow. Build, run, and manage data pipelines for integrating and transforming data.",mage-ai,2024-01-14,2022-05-16,89,69.98878205128206,https://avatars.githubusercontent.com/u/69371472?v=4,"🧙 The modern replacement for Airflow. Build, run, and manage data pipelines for integrating and transforming data.","['artificial-intelligence', 'data', 'data-engineering', 'data-integration', 'data-pipelines', 'data-science', 'dbt', 'elt', 'etl', 'machine-learning', 'orchestration', 'pipeline', 'pipelines', 'reverse-etl', 'spark', 'sql', 'transformation']","['artificial-intelligence', 'data', 'data-engineering', 'data-integration', 'data-pipelines', 'data-science', 'dbt', 'elt', 'etl', 'machine-learning', 'orchestration', 'pipeline', 'pipelines', 'reverse-etl', 'spark', 'sql', 'transformation']",2024-01-14,"[('airbytehq/airbyte', 0.7392861247062683, 'data', 6), ('orchest/orchest', 0.7309496402740479, 'ml-ops', 5), ('ploomber/ploomber', 0.7089954614639282, 'ml-ops', 4), ('kestra-io/kestra', 0.6831650137901306, 'ml-ops', 8), ('dagster-io/dagster', 0.6750965714454651, 'ml-ops', 6), ('flyteorg/flyte', 0.6692841053009033, 'ml-ops', 3), ('meltano/meltano', 0.6557142734527588, 'ml-ops', 5), ('apache/airflow', 0.6510021090507507, 'ml-ops', 8), ('astronomer/astro-sdk', 0.6467173099517822, 'ml-ops', 4), ('linealabs/lineapy', 0.6465555429458618, 'jupyter', 0), ('avaiga/taipy', 0.6288012266159058, 'data', 4), ('kubeflow/pipelines', 0.622243344783783, 'ml-ops', 3), ('dagworks-inc/hamilton', 0.6080176830291748, 'ml-ops', 5), ('dgarnitz/vectorflow', 0.6055392622947693, 'data', 2), ('hi-primus/optimus', 0.5950447916984558, 'ml-ops', 3), ('netflix/metaflow', 0.5949897766113281, 'ml-ops', 2), ('featureform/embeddinghub', 0.5914458632469177, 'nlp', 2), ('prefecthq/prefect', 0.5904417037963867, 'ml-ops', 5), ('bodywork-ml/bodywork-core', 0.5791720151901245, 'ml-ops', 4), ('dbt-labs/dbt-core', 0.5732520818710327, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5694826245307922, 'ml-ops', 4), ('fugue-project/fugue', 0.5678737163543701, 'pandas', 3), ('pathwaycom/pathway', 0.5671728849411011, 'data', 0), ('streamlit/streamlit', 0.5670653581619263, 'viz', 2), ('apache/spark', 0.5627126693725586, 'data', 2), ('great-expectations/great_expectations', 0.5621156692504883, 'ml-ops', 3), ('polyaxon/datatile', 0.5572016835212708, 'pandas', 2), ('superduperdb/superduperdb', 0.5560696125030518, 'data', 1), ('getindata/kedro-kubeflow', 0.5537773370742798, 'ml-ops', 0), ('pydoit/doit', 0.551956057548523, 'util', 1), ('feast-dev/feast', 0.5513089299201965, 'ml-ops', 3), ('whylabs/whylogs', 0.5448665022850037, 'util', 2), ('allegroai/clearml', 0.5418257713317871, 'ml-ops', 1), ('kubeflow-kale/kale', 0.5401076674461365, 'ml-ops', 1), ('huggingface/datasets', 0.5363893508911133, 'nlp', 1), ('merantix-momentum/squirrel-core', 0.5338592529296875, 'ml', 2), ('zenml-io/zenml', 0.5322885513305664, 'ml-ops', 3), ('mlflow/mlflow', 0.5320558547973633, 'ml-ops', 1), ('google/ml-metadata', 0.524271547794342, 'ml-ops', 0), ('prefecthq/server', 0.5205321311950684, 'util', 1), ('simonw/datasette', 0.5153981447219849, 'data', 1), ('kedro-org/kedro', 0.5148924589157104, 'ml-ops', 2), ('databrickslabs/dbx', 0.5128315091133118, 'data', 0), ('onnx/onnx', 0.5106418132781982, 'ml', 1), ('google/mediapipe', 0.508601188659668, 'ml', 1), ('tobymao/sqlglot', 0.5064417123794556, 'data', 2)]",82,1.0,,48.85,641,541,20,0,43,27,43,640.0,372.0,90.0,0.6,65
1562,llm,https://github.com/jzhang38/tinyllama,"['llama', 'language-model']",,[],[],,,,jzhang38/tinyllama,TinyLlama,5287,264,110,Python,,The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.,jzhang38,2024-01-14,2023-09-02,21,246.72666666666666,,The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.,[],"['language-model', 'llama']",2024-01-10,"[('microsoft/llama-2-onnx', 0.7340604066848755, 'llm', 2), ('facebookresearch/llama-recipes', 0.6900231242179871, 'llm', 2), ('lightning-ai/lit-llama', 0.6785302758216858, 'llm', 2), ('tloen/alpaca-lora', 0.6666224598884583, 'llm', 2), ('zrrskywalker/llama-adapter', 0.6362742185592651, 'llm', 2), ('run-llama/llama-lab', 0.6213663816452026, 'llm', 2), ('facebookresearch/llama', 0.6092095375061035, 'llm', 2), ('young-geng/easylm', 0.597965657711029, 'llm', 2), ('bigscience-workshop/petals', 0.588677167892456, 'data', 1), ('mshumer/gpt-llm-trainer', 0.5883111953735352, 'llm', 0), ('facebookresearch/codellama', 0.5828747749328613, 'llm', 2), ('openlm-research/open_llama', 0.5819482207298279, 'llm', 2), ('cg123/mergekit', 0.5811179876327515, 'llm', 1), ('salesforce/xgen', 0.57785564661026, 'llm', 1), ('ggerganov/llama.cpp', 0.5698243379592896, 'llm', 2), ('karpathy/llama2.c', 0.5350930094718933, 'llm', 2), ('tairov/llama2.mojo', 0.5322071313858032, 'llm', 1), ('bobazooba/xllm', 0.5300989151000977, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5206746459007263, 'perf', 0), ('predibase/lorax', 0.5169039964675903, 'llm', 1), ('run-llama/llama-hub', 0.5164508819580078, 'data', 0), ('titanml/takeoff', 0.5162729620933533, 'llm', 2), ('eugeneyan/open-llms', 0.515006959438324, 'study', 0), ('abetlen/llama-cpp-python', 0.5099416375160217, 'llm', 2), ('huawei-noah/pretrained-language-model', 0.5081942081451416, 'nlp', 0), ('hiyouga/llama-factory', 0.5053484439849854, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.505348265171051, 'llm', 2), ('sjtu-ipads/powerinfer', 0.5038610696792603, 'llm', 1)]",8,2.0,,2.19,93,80,4,0,0,0,0,93.0,143.0,90.0,1.5,65
821,ml-rl,https://github.com/farama-foundation/gymnasium,[],,[],[],,,,farama-foundation/gymnasium,Gymnasium,4727,568,36,Python,https://gymnasium.farama.org,"An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)",farama-foundation,2024-01-14,2022-09-08,72,65.00785854616896,https://avatars.githubusercontent.com/u/62961550?v=4,"An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)","['api', 'gym', 'reinforcement-learning']","['api', 'gym', 'reinforcement-learning']",2024-01-13,"[('pettingzoo-team/pettingzoo', 0.947864830493927, 'ml-rl', 3), ('nvidia-omniverse/isaacgymenvs', 0.6873985528945923, 'sim', 1), ('unity-technologies/ml-agents', 0.6212126612663269, 'ml-rl', 1), ('pytorch/rl', 0.6115036010742188, 'ml-rl', 1), ('nvidia-omniverse/omniisaacgymenvs', 0.6047082543373108, 'sim', 0), ('google/dopamine', 0.5846539735794067, 'ml-rl', 0), ('openai/baselines', 0.5754907131195068, 'ml-rl', 0), ('thu-ml/tianshou', 0.57296222448349, 'ml-rl', 0), ('deepmind/acme', 0.5646693110466003, 'ml-rl', 1), ('facebookresearch/reagent', 0.5644688606262207, 'ml-rl', 0), ('humancompatibleai/imitation', 0.5637737512588501, 'ml-rl', 0), ('facebookresearch/habitat-lab', 0.5635895729064941, 'sim', 1), ('inspirai/timechamber', 0.5623535513877869, 'sim', 1), ('kzl/decision-transformer', 0.5532186031341553, 'ml-rl', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5474168062210083, 'study', 1), ('huggingface/deep-rl-class', 0.5383160710334778, 'study', 1), ('openai/gym', 0.533967137336731, 'ml-rl', 1), ('openai/spinningup', 0.5327202081680298, 'study', 0), ('deepmind/pysc2', 0.5224155187606812, 'ml-rl', 1), ('operand/agency', 0.5203875303268433, 'llm', 1), ('tensorlayer/tensorlayer', 0.5202954411506653, 'ml-rl', 1), ('salesforce/warp-drive', 0.5065247416496277, 'ml-rl', 1), ('ai4finance-foundation/finrl', 0.5031803250312805, 'finance', 1)]",459,2.0,,4.96,171,134,16,0,5,8,5,171.0,315.0,90.0,1.8,65
1854,gui,https://github.com/samuelcolvin/fastui,[],FastUI is a new way to build web application user interfaces defined by declarative Python code.,[],[],,,,samuelcolvin/fastui,FastUI,3211,273,28,TypeScript,https://fastui-demo.onrender.com,Build better UIs faster.,samuelcolvin,2024-01-14,2023-09-18,19,167.73880597014926,https://avatars.githubusercontent.com/u/110818415?v=4,Build better UIs faster.,"['fastapi', 'pydantic', 'react']","['fastapi', 'pydantic', 'react']",2023-12-29,"[('fastai/fastcore', 0.546245813369751, 'util', 0), ('dmontagu/fastapi_client', 0.5091067552566528, 'web', 0)]",18,3.0,,1.96,151,95,4,1,2,6,2,151.0,281.0,90.0,1.9,65
26,term,https://github.com/google/python-fire,[],,[],[],,,,google/python-fire,python-fire,25802,1487,375,Python,,Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.,google,2024-01-14,2017-02-21,362,71.27624309392266,https://avatars.githubusercontent.com/u/1342004?v=4,Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.,['cli'],['cli'],2024-01-09,"[('python-poetry/cleo', 0.6746692657470703, 'term', 1), ('kellyjonbrazil/jc', 0.6432965397834778, 'util', 1), ('pyscript/pyscript-cli', 0.6401512026786804, 'web', 0), ('urwid/urwid', 0.6262136101722717, 'term', 0), ('pallets/click', 0.6196329593658447, 'term', 1), ('tiangolo/typer', 0.6071035265922546, 'term', 1), ('jquast/blessed', 0.5881098508834839, 'term', 1), ('pexpect/pexpect', 0.5853911638259888, 'util', 0), ('pytoolz/toolz', 0.5839331150054932, 'util', 0), ('hoffstadt/dearpygui', 0.5763037800788879, 'gui', 0), ('prompt-toolkit/ptpython', 0.5544407367706299, 'util', 1), ('hugovk/pypistats', 0.5451176166534424, 'util', 1), ('tmbo/questionary', 0.5444633960723877, 'term', 1), ('kellyjonbrazil/jello', 0.5335484743118286, 'util', 1), ('pypy/pypy', 0.5175889730453491, 'util', 0), ('pypa/hatch', 0.5173959732055664, 'util', 1), ('samuelcolvin/python-devtools', 0.5147319436073303, 'debug', 0), ('landscapeio/prospector', 0.5145845413208008, 'util', 0), ('python/cpython', 0.5122049450874329, 'util', 0), ('beeware/toga', 0.5118656754493713, 'gui', 0), ('hhatto/autopep8', 0.5113070011138916, 'util', 0), ('xonsh/xonsh', 0.5076464414596558, 'util', 1), ('getsentry/responses', 0.5072945952415466, 'testing', 0), ('pygamelib/pygamelib', 0.5067519545555115, 'gamedev', 0), ('willmcgugan/textual', 0.5056248307228088, 'term', 1), ('pdm-project/pdm', 0.5054830312728882, 'util', 0), ('pympler/pympler', 0.5053215622901917, 'perf', 0), ('nedbat/coveragepy', 0.5045384764671326, 'testing', 0), ('omry/omegaconf', 0.5032675862312317, 'util', 0)]",63,4.0,,0.25,34,13,84,0,0,2,2,34.0,74.0,90.0,2.2,64
1056,study,https://github.com/d2l-ai/d2l-en,[],,[],[],,,,d2l-ai/d2l-en,d2l-en,20540,4016,394,Python,https://D2L.ai,"Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.",d2l-ai,2024-01-14,2018-10-09,277,74.15162454873646,https://avatars.githubusercontent.com/u/43974506?v=4,"Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.","['book', 'computer-vision', 'data-science', 'deep-learning', 'gaussian-processes', 'hyperparameter-optimization', 'jax', 'kaggle', 'keras', 'machine-learning', 'mxnet', 'natural-language-processing', 'notebook', 'pytorch', 'recommender-system', 'reinforcement-learning', 'tensorflow']","['book', 'computer-vision', 'data-science', 'deep-learning', 'gaussian-processes', 'hyperparameter-optimization', 'jax', 'kaggle', 'keras', 'machine-learning', 'mxnet', 'natural-language-processing', 'notebook', 'pytorch', 'recommender-system', 'reinforcement-learning', 'tensorflow']",2023-12-11,"[('tensorlayer/tensorlayer', 0.6486682891845703, 'ml-rl', 3), ('keras-team/keras', 0.6172467470169067, 'ml-dl', 6), ('mrdbourke/pytorch-deep-learning', 0.6161856055259705, 'study', 3), ('tensorflow/tensor2tensor', 0.5922254920005798, 'ml', 3), ('nvidia/deeplearningexamples', 0.5897380709648132, 'ml-dl', 5), ('google/trax', 0.587719202041626, 'ml-dl', 4), ('ageron/handson-ml2', 0.5854299664497375, 'ml', 0), ('tensorflow/tensorflow', 0.5832685828208923, 'ml-dl', 3), ('explosion/thinc', 0.5795356035232544, 'ml-dl', 7), ('keras-rl/keras-rl', 0.5773378610610962, 'ml-rl', 4), ('fchollet/deep-learning-with-python-notebooks', 0.5766072273254395, 'study', 0), ('udlbook/udlbook', 0.573790431022644, 'study', 2), ('mrdbourke/tensorflow-deep-learning', 0.5674564242362976, 'study', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5661432147026062, 'study', 2), ('horovod/horovod', 0.5628776550292969, 'ml-ops', 6), ('xl0/lovely-tensors', 0.5590056777000427, 'ml-dl', 2), ('udacity/deep-learning-v2-pytorch', 0.5573995113372803, 'study', 2), ('ggerganov/ggml', 0.5564393997192383, 'ml', 1), ('probml/pyprobml', 0.5489851236343384, 'ml', 4), ('huggingface/transformers', 0.5483046174049377, 'nlp', 6), ('deepmodeling/deepmd-kit', 0.5467190146446228, 'sim', 2), ('christoschristofidis/awesome-deep-learning', 0.5462198853492737, 'study', 2), ('microsoft/onnxruntime', 0.5409604907035828, 'ml', 4), ('davidadsp/generative_deep_learning_2nd_edition', 0.5406649708747864, 'study', 4), ('salesforce/warp-drive', 0.5405166745185852, 'ml-rl', 3), ('pytorch/ignite', 0.54007488489151, 'ml-dl', 3), ('keras-team/autokeras', 0.5389357805252075, 'ml-dl', 4), ('rasbt/machine-learning-book', 0.5370543003082275, 'study', 3), ('rasbt/stat453-deep-learning-ss20', 0.5362268090248108, 'study', 0), ('atcold/nyu-dlsp21', 0.5323129296302795, 'study', 1), ('graykode/nlp-tutorial', 0.5312547087669373, 'study', 3), ('google/tf-quant-finance', 0.5287189483642578, 'finance', 1), ('openai/spinningup', 0.5276309251785278, 'study', 0), ('rafiqhasan/auto-tensorflow', 0.5260531306266785, 'ml-dl', 2), ('tatsu-lab/stanford_alpaca', 0.5226318836212158, 'llm', 1), ('denys88/rl_games', 0.5216466188430786, 'ml-rl', 3), ('tensorly/tensorly', 0.5216432213783264, 'ml-dl', 5), ('firmai/industry-machine-learning', 0.5169808268547058, 'study', 2), ('thu-ml/tianshou', 0.5155929923057556, 'ml-rl', 1), ('deepmind/dm-haiku', 0.5113567113876343, 'ml-dl', 3), ('mrdbourke/zero-to-mastery-ml', 0.5092582702636719, 'study', 3), ('tlkh/tf-metal-experiments', 0.508011519908905, 'perf', 2), ('pytorch/pytorch', 0.5062558650970459, 'ml-dl', 2), ('adap/flower', 0.505675733089447, 'ml-ops', 4), ('pytorch/rl', 0.5053571462631226, 'ml-rl', 3), ('microsoft/deepspeed', 0.5029295682907104, 'ml-dl', 3), ('whitead/dmol-book', 0.5019837021827698, 'ml-dl', 1), ('optimalscale/lmflow', 0.5011979341506958, 'llm', 2), ('determined-ai/determined', 0.5009527802467346, 'ml-ops', 7)]",319,7.0,,4.15,36,7,64,1,1,6,1,36.0,27.0,90.0,0.8,64
468,nlp,https://github.com/microsoft/unilm,[],,[],[],,,,microsoft/unilm,unilm,16868,2249,278,Python,https://aka.ms/GeneralAI,"Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities",microsoft,2024-01-14,2019-07-23,236,71.47457627118644,https://avatars.githubusercontent.com/u/6154722?v=4,"Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities","['beit', 'beit-3', 'deepnet', 'document-ai', 'foundation-models', 'kosmos', 'kosmos-1', 'layoutlm', 'layoutxlm', 'llm', 'minilm', 'mllm', 'multimodal', 'nlp', 'pre-trained-model', 'textdiffuser', 'trocr', 'unilm', 'xlm-e']","['beit', 'beit-3', 'deepnet', 'document-ai', 'foundation-models', 'kosmos', 'kosmos-1', 'layoutlm', 'layoutxlm', 'llm', 'minilm', 'mllm', 'multimodal', 'nlp', 'pre-trained-model', 'textdiffuser', 'trocr', 'unilm', 'xlm-e']",2024-01-11,"[('qanastek/drbert', 0.5781573057174683, 'llm', 1), ('yueyu1030/attrprompt', 0.5726070404052734, 'llm', 0), ('google-research/electra', 0.5669850707054138, 'ml-dl', 1), ('ofa-sys/ofa', 0.5649746060371399, 'llm', 1), ('openai/finetune-transformer-lm', 0.5644842982292175, 'llm', 0), ('openai/clip', 0.5614645481109619, 'ml-dl', 0), ('rasahq/rasa', 0.5554572939872742, 'llm', 1), ('thudm/glm-130b', 0.5527222156524658, 'llm', 0), ('young-geng/easylm', 0.5508254170417786, 'llm', 0), ('deepset-ai/farm', 0.5462345480918884, 'nlp', 1), ('huawei-noah/pretrained-language-model', 0.5461228489875793, 'nlp', 0), ('explosion/spacy-llm', 0.5390931367874146, 'llm', 2), ('alibaba/easynlp', 0.5370928645133972, 'nlp', 1), ('optimalscale/lmflow', 0.5366694331169128, 'llm', 0), ('lm-sys/fastchat', 0.5365498661994934, 'llm', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5360260605812073, 'study', 0), ('infinitylogesh/mutate', 0.5307291746139526, 'nlp', 0), ('next-gpt/next-gpt', 0.5269091129302979, 'llm', 3), ('huggingface/datasets', 0.5208196043968201, 'nlp', 1), ('cg123/mergekit', 0.5200158357620239, 'llm', 1), ('llmware-ai/llmware', 0.5189810991287231, 'llm', 1), ('salesforce/blip', 0.5181792378425598, 'diffusion', 0), ('databrickslabs/dolly', 0.5165247917175293, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5151917338371277, 'llm', 2), ('minimaxir/textgenrnn', 0.5143858194351196, 'nlp', 0), ('thudm/p-tuning-v2', 0.5134304761886597, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.511536180973053, 'llm', 0), ('extreme-bert/extreme-bert', 0.5103119015693665, 'llm', 1), ('argilla-io/argilla', 0.5100960731506348, 'nlp', 2), ('togethercomputer/redpajama-data', 0.5096539855003357, 'llm', 0), ('huggingface/autotrain-advanced', 0.5079038143157959, 'ml', 0), ('microsoft/lmops', 0.5072470903396606, 'llm', 2), ('huggingface/text-generation-inference', 0.5068984627723694, 'llm', 1), ('bigscience-workshop/biomedical', 0.5054998397827148, 'data', 0), ('jbesomi/texthero', 0.5031306147575378, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5029410719871521, 'ml-dl', 1), ('bigscience-workshop/petals', 0.502390444278717, 'data', 1), ('mooler0410/llmspracticalguide', 0.5018056631088257, 'study', 1), ('openbmb/toolbench', 0.5009614825248718, 'llm', 0)]",64,4.0,,3.52,133,43,55,0,0,1,1,133.0,192.0,90.0,1.4,64
1690,util,https://github.com/rustpython/rustpython,['rust'],,[],[],,,,rustpython/rustpython,RustPython,16107,1153,161,Rust,https://rustpython.github.io,A Python Interpreter written in Rust,rustpython,2024-01-14,2018-05-28,296,54.38929088277858,https://avatars.githubusercontent.com/u/39710557?v=4,A Python Interpreter written in Rust,"['compiler', 'interpreter', 'jit', 'language', 'python-language', 'rust', 'wasm']","['compiler', 'interpreter', 'jit', 'language', 'python-language', 'rust', 'wasm']",2024-01-12,"[('pyo3/pyo3', 0.6475306749343872, 'util', 1), ('numba/llvmlite', 0.638462483882904, 'util', 0), ('aswinnnn/pyscan', 0.6372954249382019, 'security', 1), ('pyo3/maturin', 0.6316028237342834, 'util', 1), ('astral-sh/ruff', 0.6286622881889343, 'util', 1), ('python/cpython', 0.5933462977409363, 'util', 0), ('pyo3/rust-numpy', 0.5917856097221375, 'util', 1), ('pypy/pypy', 0.5846704244613647, 'util', 1), ('facebookincubator/cinder', 0.5831960439682007, 'perf', 3), ('samuelcolvin/rtoml', 0.5515300035476685, 'data', 1), ('pyston/pyston', 0.543964684009552, 'util', 0), ('cython/cython', 0.5352054238319397, 'util', 0), ('exaloop/codon', 0.5343741774559021, 'perf', 1), ('brandtbucher/specialist', 0.5334833860397339, 'perf', 0), ('pola-rs/polars', 0.5321800112724304, 'pandas', 1), ('pytoolz/toolz', 0.5320417881011963, 'util', 0), ('delta-io/delta-rs', 0.5259521007537842, 'pandas', 1), ('eventual-inc/daft', 0.5145300626754761, 'pandas', 1), ('fastai/fastcore', 0.5066707134246826, 'util', 0), ('mdmzfzl/neetcode-solutions', 0.5018560886383057, 'study', 1)]",404,2.0,,20.56,69,44,69,0,0,1,1,68.0,100.0,90.0,1.5,64
808,ml-dl,https://github.com/danielgatis/rembg,[],,[],[],,,,danielgatis/rembg,rembg,12793,1537,136,Python,,Rembg is a tool to remove images background,danielgatis,2024-01-14,2020-08-10,181,70.62381703470031,,Rembg is a tool to remove images background,"['background-removal', 'image-processing']","['background-removal', 'image-processing']",2023-12-16,[],48,5.0,,1.81,55,44,42,1,22,16,22,55.0,73.0,90.0,1.3,64
237,data,https://github.com/tiangolo/sqlmodel,[],,[],[],1.0,,,tiangolo/sqlmodel,sqlmodel,11999,554,151,Python,https://sqlmodel.tiangolo.com/,"SQL databases in Python, designed for simplicity, compatibility, and robustness.",tiangolo,2024-01-14,2021-08-24,127,94.48031496062993,,"SQL databases in Python, designed for simplicity, compatibility, and robustness.","['fastapi', 'json', 'json-schema', 'pydantic', 'sql', 'sqlalchemy']","['fastapi', 'json', 'json-schema', 'pydantic', 'sql', 'sqlalchemy']",2024-01-09,"[('ibis-project/ibis', 0.8095237612724304, 'data', 2), ('sqlalchemy/sqlalchemy', 0.8052466511726379, 'data', 2), ('mcfunley/pugsql', 0.6844363808631897, 'data', 1), ('andialbrecht/sqlparse', 0.6739121079444885, 'data', 0), ('tobymao/sqlglot', 0.6654618382453918, 'data', 1), ('collerek/ormar', 0.6308665871620178, 'data', 3), ('sqlalchemy/alembic', 0.6223205924034119, 'data', 2), ('kayak/pypika', 0.6139056086540222, 'data', 1), ('macbre/sql-metadata', 0.6107516288757324, 'data', 1), ('malloydata/malloy-py', 0.6022137403488159, 'data', 1), ('simonw/sqlite-utils', 0.5946550965309143, 'data', 0), ('falconry/falcon', 0.5907256007194519, 'web', 0), ('aminalaee/sqladmin', 0.5850541591644287, 'data', 2), ('plotly/dash', 0.5841458439826965, 'viz', 0), ('coleifer/peewee', 0.5841237306594849, 'data', 0), ('aio-libs/aiomysql', 0.5758809447288513, 'data', 1), ('machow/siuba', 0.5724479556083679, 'pandas', 1), ('python-odin/odin', 0.5694354176521301, 'util', 1), ('tconbeer/harlequin', 0.569147527217865, 'term', 1), ('krzjoa/awesome-python-data-science', 0.5662049651145935, 'study', 0), ('rawheel/fastapi-boilerplate', 0.5594669580459595, 'web', 3), ('datafold/data-diff', 0.5563005805015564, 'data', 1), ('nasdaq/data-link-python', 0.5559763312339783, 'finance', 0), ('fastai/fastcore', 0.5534236431121826, 'util', 0), ('tiangolo/fastapi', 0.5522589683532715, 'web', 4), ('strawberry-graphql/strawberry', 0.5469264984130859, 'web', 0), ('aio-libs/aiopg', 0.546112596988678, 'data', 1), ('agronholm/sqlacodegen', 0.542255163192749, 'data', 0), ('simonw/datasette', 0.5402882695198059, 'data', 2), ('marshmallow-code/marshmallow', 0.539881706237793, 'util', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5365174412727356, 'web', 2), ('pandas-dev/pandas', 0.5365090370178223, 'pandas', 0), ('willmcgugan/textual', 0.5339970588684082, 'term', 0), ('1200wd/bitcoinlib', 0.5315213799476624, 'crypto', 0), ('pytoolz/toolz', 0.5314618945121765, 'util', 0), ('sfu-db/connector-x', 0.5301145911216736, 'data', 1), ('mause/duckdb_engine', 0.529965877532959, 'data', 2), ('piccolo-orm/piccolo_admin', 0.5287275910377502, 'data', 1), ('aws/aws-sdk-pandas', 0.5277955532073975, 'pandas', 0), ('eleutherai/pyfra', 0.5258613228797913, 'ml', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5256924629211426, 'template', 3), ('jsonpickle/jsonpickle', 0.5255077481269836, 'data', 1), ('pytables/pytables', 0.5233675241470337, 'data', 0), ('jazzband/tablib', 0.5218853950500488, 'data', 0), ('jina-ai/vectordb', 0.52092444896698, 'data', 0), ('pyston/pyston', 0.5203248858451843, 'util', 0), ('fugue-project/fugue', 0.5192634463310242, 'pandas', 1), ('bottlepy/bottle', 0.5186842679977417, 'web', 0), ('s3rius/fastapi-template', 0.5149979591369629, 'web', 1), ('saulpw/visidata', 0.513899564743042, 'term', 1), ('pydantic/pydantic', 0.5132742524147034, 'util', 2), ('keon/algorithms', 0.5121207237243652, 'util', 0), ('kellyjonbrazil/jello', 0.5081574320793152, 'util', 1), ('python-cachier/cachier', 0.5081299543380737, 'perf', 0), ('pyparsing/pyparsing', 0.5065826773643494, 'util', 0), ('pypy/pypy', 0.5045502185821533, 'util', 0), ('lk-geimfari/mimesis', 0.5006476640701294, 'data', 1)]",71,1.0,,1.44,170,105,29,0,5,5,5,170.0,264.0,90.0,1.6,64
88,math,https://github.com/sympy/sympy,[],,[],[],1.0,,,sympy/sympy,sympy,11751,4180,291,Python,https://sympy.org/,A computer algebra system written in pure Python,sympy,2024-01-14,2010-04-30,717,16.376070077642844,https://avatars.githubusercontent.com/u/260832?v=4,A computer algebra system written in pure Python,"['computer-algebra', 'math', 'science']","['computer-algebra', 'math', 'science']",2024-01-14,"[('pyston/pyston', 0.6676157712936401, 'util', 0), ('python/cpython', 0.6439580321311951, 'util', 0), ('scipy/scipy', 0.6143859028816223, 'math', 0), ('fredrik-johansson/mpmath', 0.6130483150482178, 'math', 0), ('artemyk/dynpy', 0.6068984270095825, 'sim', 0), ('pyomo/pyomo', 0.5923652052879333, 'math', 0), ('pytoolz/toolz', 0.5918540358543396, 'util', 0), ('gbeced/pyalgotrade', 0.5751481652259827, 'finance', 0), ('norvig/pytudes', 0.5708892941474915, 'util', 0), ('pypy/pypy', 0.5672004222869873, 'util', 0), ('numpy/numpy', 0.5636308193206787, 'math', 0), ('google/latexify_py', 0.5628668069839478, 'util', 0), ('joblib/joblib', 0.5607999563217163, 'util', 0), ('connorferster/handcalcs', 0.5598889589309692, 'jupyter', 0), ('eleutherai/pyfra', 0.5521600246429443, 'ml', 0), ('thealgorithms/python', 0.5507904887199402, 'study', 0), ('has2k1/plotnine', 0.5483768582344055, 'viz', 0), ('mynameisfiber/high_performance_python_2e', 0.5473185777664185, 'study', 0), ('scikit-geometry/scikit-geometry', 0.5399286150932312, 'gis', 0), ('scikit-learn/scikit-learn', 0.5355417132377625, 'ml', 0), ('hgrecco/pint', 0.534096896648407, 'util', 1), ('adafruit/circuitpython', 0.5332902669906616, 'util', 0), ('keon/algorithms', 0.5286232233047485, 'util', 0), ('pytransitions/transitions', 0.523476243019104, 'util', 0), ('rasbt/mlxtend', 0.5225716829299927, 'ml', 0), ('pyparsing/pyparsing', 0.521645188331604, 'util', 0), ('quantopian/zipline', 0.5198596119880676, 'finance', 0), ('pyca/cryptography', 0.518089234828949, 'util', 0), ('probml/pyprobml', 0.5161436200141907, 'ml', 0), ('pymc-devs/pymc3', 0.5158290863037109, 'ml', 0), ('legrandin/pycryptodome', 0.5150558352470398, 'util', 0), ('google/pyglove', 0.5143080353736877, 'util', 0), ('julienpalard/pipe', 0.5134634971618652, 'util', 0), ('brandon-rhodes/python-patterns', 0.5133131742477417, 'util', 0), ('scikit-mobility/scikit-mobility', 0.5121610164642334, 'gis', 0), ('wesm/pydata-book', 0.511038601398468, 'study', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5073643326759338, 'study', 0), ('ipython/ipyparallel', 0.5034547448158264, 'perf', 0), ('ljvmiranda921/seagull', 0.5007748603820801, 'sim', 0)]",1267,3.0,,71.48,446,259,167,0,2,6,2,449.0,1316.0,90.0,2.9,64
1523,llm,https://github.com/smol-ai/developer,"['ai', 'coding-assistant', 'autonomous-agent']",,[],[],1.0,,,smol-ai/developer,developer,11435,1120,154,Python,https://twitter.com/SmolModels,the first library to let you embed a developer agent in your own app!,smol-ai,2024-01-12,2023-05-13,37,305.51526717557255,https://avatars.githubusercontent.com/u/132172705?v=4,the first library to let you embed a developer agent in your own app!,[],"['ai', 'autonomous-agent', 'coding-assistant']",2023-09-25,"[('antonosika/gpt-engineer', 0.6368786096572876, 'llm', 3), ('prefecthq/marvin', 0.6331506967544556, 'nlp', 1), ('sweepai/sweep', 0.5850329995155334, 'llm', 1), ('transformeroptimus/superagi', 0.5681149363517761, 'llm', 1), ('pythagora-io/gpt-pilot', 0.5505355000495911, 'llm', 2), ('lastmile-ai/aiconfig', 0.5501940846443176, 'util', 1), ('cheshire-cat-ai/core', 0.5464283227920532, 'llm', 1), ('operand/agency', 0.5302104949951172, 'llm', 2), ('krohling/bondai', 0.5269186496734619, 'llm', 0), ('mlc-ai/mlc-llm', 0.5239886045455933, 'llm', 0), ('microsoft/semantic-kernel', 0.5124703049659729, 'llm', 1), ('facebookresearch/habitat-lab', 0.5017600059509277, 'sim', 1)]",21,9.0,,1.79,7,1,8,4,0,0,0,7.0,3.0,90.0,0.4,64
751,diffusion,https://github.com/divamgupta/diffusionbee-stable-diffusion-ui,[],,[],[],,,,divamgupta/diffusionbee-stable-diffusion-ui,diffusionbee-stable-diffusion-ui,11411,558,92,JavaScript,https://diffusionbee.com,Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.,divamgupta,2024-01-14,2022-09-06,73,156.31506849315068,,Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.,"['electron-app', 'macos', 'stable-diffusion']","['electron-app', 'macos', 'stable-diffusion']",2023-12-28,"[('apple/ml-stable-diffusion', 0.5508177876472473, 'diffusion', 0), ('comfyanonymous/comfyui', 0.5425298810005188, 'diffusion', 1), ('carson-katri/dream-textures', 0.534104585647583, 'diffusion', 1), ('bentoml/onediffusion', 0.5153639316558838, 'diffusion', 1)]",18,3.0,,0.54,36,5,16,1,6,16,6,36.0,51.0,90.0,1.4,64
73,util,https://github.com/python-pillow/pillow,[],,[],[],,,,python-pillow/pillow,Pillow,11402,2157,223,Python,https://python-pillow.org,Python Imaging Library (Fork),python-pillow,2024-01-13,2012-07-24,601,18.97171381031614,https://avatars.githubusercontent.com/u/2036701?v=4,Python Imaging Library (Fork),"['c', 'cross-platform', 'image', 'image-processing', 'pil', 'pillow']","['c', 'cross-platform', 'image', 'image-processing', 'pil', 'pillow']",2024-01-12,"[('imageio/imageio', 0.6861987113952637, 'util', 0), ('scikit-image/scikit-image', 0.6156206130981445, 'util', 1), ('rhettbull/osxphotos', 0.5487352609634399, 'util', 0), ('lightly-ai/lightly', 0.5426392555236816, 'ml', 0), ('mdbloice/augmentor', 0.5397363305091858, 'ml', 0), ('luispedro/mahotas', 0.5384292602539062, 'viz', 0), ('scitools/cartopy', 0.5182700157165527, 'gis', 0), ('pypy/pypy', 0.5135209560394287, 'util', 0), ('pyglet/pyglet', 0.505748450756073, 'gamedev', 0), ('radiantearth/radiant-mlhub', 0.5047886967658997, 'gis', 0)]",458,4.0,,35.9,305,269,140,0,5,8,5,305.0,743.0,90.0,2.4,64
393,web,https://github.com/encode/starlette,[],,[],[],,,,encode/starlette,starlette,9054,805,105,Python,https://www.starlette.io/,The little ASGI framework that shines. 🌟,encode,2024-01-13,2018-06-25,292,30.99168704156479,https://avatars.githubusercontent.com/u/19159390?v=4,The little ASGI framework that shines. 🌟,"['async', 'http', 'websockets']","['async', 'http', 'websockets']",2024-01-13,"[('pallets/quart', 0.7032433152198792, 'web', 0), ('neoteroi/blacksheep', 0.6760811805725098, 'web', 1), ('encode/uvicorn', 0.6668508052825928, 'web', 1), ('aio-libs/aiohttp', 0.6300008296966553, 'web', 2), ('miguelgrinberg/python-socketio', 0.6212427616119385, 'util', 0), ('encode/httpx', 0.5981119275093079, 'web', 1), ('tiangolo/asyncer', 0.5958313345909119, 'perf', 1), ('alirn76/panther', 0.5935547351837158, 'web', 0), ('huge-success/sanic', 0.5865360498428345, 'web', 0), ('tornadoweb/tornado', 0.57993084192276, 'web', 0), ('agronholm/anyio', 0.5785104036331177, 'perf', 0), ('mlc-ai/web-llm', 0.5606010556221008, 'llm', 0), ('starlite-api/starlite', 0.5545772314071655, 'web', 0), ('emmett-framework/emmett', 0.5443409085273743, 'web', 0), ('airtai/faststream', 0.5320852398872375, 'perf', 0), ('python-trio/trio', 0.5227438807487488, 'perf', 1), ('klen/muffin', 0.5224829912185669, 'web', 0), ('jordaneremieff/mangum', 0.5116714239120483, 'web', 0), ('magicstack/uvloop', 0.5062094926834106, 'util', 1), ('pallets/werkzeug', 0.5027870535850525, 'web', 1)]",255,6.0,,3.44,125,106,68,0,17,25,17,125.0,210.0,90.0,1.7,64
1120,math,https://github.com/cupy/cupy,[],,[],[],,,,cupy/cupy,cupy,7446,735,126,Python,https://cupy.dev,NumPy & SciPy for GPU,cupy,2024-01-13,2016-11-01,378,19.6984126984127,https://avatars.githubusercontent.com/u/23187665?v=4,NumPy & SciPy for GPU,"['cublas', 'cuda', 'cudnn', 'cupy', 'curand', 'cusolver', 'cusparse', 'cusparselt', 'cutensor', 'gpu', 'nccl', 'numpy', 'nvrtc', 'nvtx', 'rocm', 'scipy', 'tensor']","['cublas', 'cuda', 'cudnn', 'cupy', 'curand', 'cusolver', 'cusparse', 'cusparselt', 'cutensor', 'gpu', 'nccl', 'numpy', 'nvrtc', 'nvtx', 'rocm', 'scipy', 'tensor']",2024-01-12,"[('rapidsai/cudf', 0.6393476724624634, 'pandas', 2), ('numpy/numpy', 0.6240665912628174, 'math', 1), ('nvidia/cuda-python', 0.5955917835235596, 'ml', 0), ('pytorch/pytorch', 0.5749741196632385, 'ml-dl', 3), ('nvidia/tensorrt-llm', 0.5631754994392395, 'viz', 1), ('numba/numba', 0.555233895778656, 'perf', 2), ('cvxgrp/pymde', 0.5531930327415466, 'ml', 2), ('arogozhnikov/einops', 0.5499424934387207, 'ml-dl', 3), ('roban/cosmolopy', 0.5431826710700989, 'sim', 0), ('huggingface/accelerate', 0.5428050756454468, 'ml', 0), ('scipy/scipy', 0.5399492979049683, 'math', 1), ('pytorch/torchrec', 0.5291244387626648, 'ml-dl', 2), ('blackhc/toma', 0.5264122486114502, 'ml-dl', 1), ('google/tf-quant-finance', 0.5244703888893127, 'finance', 1), ('nvidia/warp', 0.518425464630127, 'sim', 1), ('marcomusy/vedo', 0.5182294845581055, 'viz', 1), ('rentruewang/koila', 0.5168486833572388, 'ml', 0), ('luispedro/mahotas', 0.5168439745903015, 'viz', 1), ('google/jax', 0.5167858004570007, 'ml', 1), ('tensorly/tensorly', 0.5093604922294617, 'ml-dl', 3), ('hips/autograd', 0.5063801407814026, 'ml', 0), ('xl0/lovely-numpy', 0.5024915933609009, 'util', 1), ('isl-org/open3d', 0.5012453198432922, 'sim', 2), ('pyo3/rust-numpy', 0.5001054406166077, 'util', 1)]",354,4.0,,31.12,236,156,88,0,12,19,12,236.0,544.0,90.0,2.3,64
351,ml-ops,https://github.com/activeloopai/deeplake,['vector-search'],,[],[],,,,activeloopai/deeplake,deeplake,7381,570,84,Python,https://activeloop.ai,"Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai",activeloopai,2024-01-13,2019-08-09,233,31.600611620795107,https://avatars.githubusercontent.com/u/34816118?v=4,"Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai","['ai', 'computer-vision', 'cv', 'data-science', 'data-version-control', 'datalake', 'datasets', 'deep-learning', 'image-processing', 'langchain', 'large-language-models', 'llm', 'machine-learning', 'ml', 'mlops', 'pytorch', 'tensorflow', 'vector-database', 'vector-search']","['ai', 'computer-vision', 'cv', 'data-science', 'data-version-control', 'datalake', 'datasets', 'deep-learning', 'image-processing', 'langchain', 'large-language-models', 'llm', 'machine-learning', 'ml', 'mlops', 'pytorch', 'tensorflow', 'vector-database', 'vector-search']",2024-01-13,"[('lancedb/lancedb', 0.7212356925010681, 'data', 1), ('superduperdb/superduperdb', 0.7059310674667358, 'data', 5), ('qdrant/qdrant', 0.6835601329803467, 'data', 4), ('jina-ai/vectordb', 0.639483630657196, 'data', 2), ('mindsdb/mindsdb', 0.630451500415802, 'data', 4), ('milvus-io/bootcamp', 0.6203997731208801, 'data', 2), ('dgarnitz/vectorflow', 0.6199436187744141, 'data', 2), ('mlflow/mlflow', 0.6163042187690735, 'ml-ops', 3), ('huggingface/datasets', 0.6126352548599243, 'nlp', 6), ('pathwaycom/llm-app', 0.6084655523300171, 'llm', 3), ('chroma-core/chroma', 0.6069470643997192, 'data', 0), ('featureform/embeddinghub', 0.6054278016090393, 'nlp', 5), ('marqo-ai/marqo', 0.6008663177490234, 'ml', 4), ('googlecloudplatform/vertex-ai-samples', 0.5941661596298218, 'ml', 4), ('bentoml/bentoml', 0.5921602249145508, 'ml-ops', 4), ('cheshire-cat-ai/core', 0.586743950843811, 'llm', 3), ('alphasecio/langchain-examples', 0.5867215991020203, 'llm', 2), ('lutzroeder/netron', 0.5856375694274902, 'ml', 6), ('polyaxon/datatile', 0.585174024105072, 'pandas', 4), ('feast-dev/feast', 0.5807369947433472, 'ml-ops', 4), ('docarray/docarray', 0.5793482661247253, 'data', 3), ('neuml/txtai', 0.575560450553894, 'nlp', 5), ('keras-team/autokeras', 0.5705196857452393, 'ml-dl', 3), ('nebuly-ai/nebullvm', 0.5655378103256226, 'perf', 3), ('kagisearch/vectordb', 0.5621834397315979, 'data', 3), ('explosion/thinc', 0.5618358254432678, 'ml-dl', 5), ('polyaxon/polyaxon', 0.5602334141731262, 'ml-ops', 7), ('aimhubio/aim', 0.5577347874641418, 'ml-ops', 7), ('ray-project/ray', 0.5552250146865845, 'ml-ops', 5), ('tensorflow/tensorflow', 0.5541912913322449, 'ml-dl', 4), ('netflix/metaflow', 0.5537254214286804, 'ml-ops', 5), ('merantix-momentum/squirrel-core', 0.5520520210266113, 'ml', 10), ('whylabs/whylogs', 0.5506848096847534, 'util', 3), ('onnx/onnx', 0.550166130065918, 'ml', 5), ('lucidrains/imagen-pytorch', 0.5490372776985168, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5463938117027283, 'ml-dl', 5), ('open-mmlab/mmediting', 0.5429714918136597, 'ml', 4), ('nvidia/deeplearningexamples', 0.5426178574562073, 'ml-dl', 5), ('pathwaycom/pathway', 0.5419109463691711, 'data', 0), ('xl0/lovely-tensors', 0.5410858392715454, 'ml-dl', 2), ('nomic-ai/nomic', 0.5401791930198669, 'nlp', 0), ('awslabs/autogluon', 0.5394940376281738, 'ml', 5), ('microsoft/onnxruntime', 0.5389828681945801, 'ml', 4), ('towhee-io/towhee', 0.5378854870796204, 'ml-ops', 4), ('horovod/horovod', 0.5363553166389465, 'ml-ops', 4), ('microsoft/nni', 0.5356760025024414, 'ml', 6), ('tensorlayer/tensorlayer', 0.535557210445404, 'ml-rl', 2), ('hpcaitech/colossalai', 0.5351329445838928, 'llm', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5346347689628601, 'study', 3), ('roboflow/supervision', 0.5341194868087769, 'ml', 6), ('ludwig-ai/ludwig', 0.5339651107788086, 'ml-ops', 7), ('microsoft/generative-ai-for-beginners', 0.5314587354660034, 'study', 1), ('jina-ai/jina', 0.5312637686729431, 'ml', 3), ('huggingface/transformers', 0.5302114486694336, 'nlp', 4), ('llmware-ai/llmware', 0.529674768447876, 'llm', 4), ('deepset-ai/haystack', 0.5295435786247253, 'llm', 4), ('determined-ai/determined', 0.5291877388954163, 'ml-ops', 6), ('lastmile-ai/aiconfig', 0.5284830331802368, 'util', 2), ('google/tf-quant-finance', 0.5263845324516296, 'finance', 1), ('mlc-ai/mlc-llm', 0.5259242653846741, 'llm', 1), ('ourownstory/neural_prophet', 0.524020791053772, 'ml', 3), ('aws/sagemaker-python-sdk', 0.5221602320671082, 'ml', 3), ('koaning/embetter', 0.5196226239204407, 'data', 0), ('microsoft/semantic-kernel', 0.5194681882858276, 'llm', 2), ('paddlepaddle/paddlenlp', 0.5191996693611145, 'llm', 1), ('oegedijk/explainerdashboard', 0.5188158750534058, 'ml-interpretability', 0), ('prefecthq/marvin', 0.5178278088569641, 'nlp', 2), ('rafiqhasan/auto-tensorflow', 0.5172686576843262, 'ml-dl', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5164815783500671, 'web', 0), ('kornia/kornia', 0.516307532787323, 'ml-dl', 5), ('kubeflow/fairing', 0.5122445821762085, 'ml-ops', 0), ('gradio-app/gradio', 0.5118150115013123, 'viz', 3), ('tensorflow/tensor2tensor', 0.5102148652076721, 'ml', 2), ('qdrant/fastembed', 0.5088616609573364, 'ml', 1), ('xplainable/xplainable', 0.5084322094917297, 'ml-interpretability', 2), ('streamlit/streamlit', 0.5083068013191223, 'viz', 3), ('hegelai/prompttools', 0.5075566172599792, 'llm', 4), ('karpathy/micrograd', 0.5072367191314697, 'study', 0), ('avaiga/taipy', 0.5062883496284485, 'data', 1), ('antonosika/gpt-engineer', 0.5061295628547668, 'llm', 1), ('google/mediapipe', 0.5053433775901794, 'ml', 3), ('ddbourgin/numpy-ml', 0.505265474319458, 'ml', 1), ('deci-ai/super-gradients', 0.5050042867660522, 'ml-dl', 3), ('microsoft/lmops', 0.504906177520752, 'llm', 1), ('mosaicml/composer', 0.5032862424850464, 'ml-dl', 3), ('tensorly/tensorly', 0.5025835037231445, 'ml-dl', 3), ('keras-team/keras', 0.5025708675384521, 'ml-dl', 5), ('stability-ai/stability-sdk', 0.5024917125701904, 'diffusion', 0), ('blakeblackshear/frigate', 0.5021501779556274, 'util', 2)]",125,4.0,,27.31,86,74,54,0,83,43,83,86.0,174.0,90.0,2.0,64
776,diffusion,https://github.com/carson-katri/dream-textures,[],,[],[],,,,carson-katri/dream-textures,dream-textures,7370,394,108,Python,,Stable Diffusion built-in to Blender,carson-katri,2024-01-14,2022-09-08,72,101.35559921414539,,Stable Diffusion built-in to Blender,"['ai', 'blender', 'blender-addon', 'image-generation', 'stable-diffusion']","['ai', 'blender', 'blender-addon', 'image-generation', 'stable-diffusion']",2023-11-07,"[('automatic1111/stable-diffusion-webui', 0.7420854568481445, 'diffusion', 3), ('stability-ai/stability-sdk', 0.7153577208518982, 'diffusion', 1), ('bentoml/onediffusion', 0.6899959444999695, 'diffusion', 2), ('comfyanonymous/comfyui', 0.6763654947280884, 'diffusion', 1), ('albarji/mixture-of-diffusers', 0.6328878998756409, 'diffusion', 2), ('invoke-ai/invokeai', 0.6296207904815674, 'diffusion', 2), ('nateraw/stable-diffusion-videos', 0.6127192378044128, 'diffusion', 1), ('divamgupta/stable-diffusion-tensorflow', 0.6051181554794312, 'diffusion', 0), ('xavierxiao/dreambooth-stable-diffusion', 0.5931910872459412, 'diffusion', 1), ('mlc-ai/web-stable-diffusion', 0.5855890512466431, 'diffusion', 1), ('thereforegames/unprompted', 0.5799620747566223, 'diffusion', 1), ('jina-ai/discoart', 0.5772116184234619, 'diffusion', 1), ('huggingface/diffusers', 0.5772011280059814, 'diffusion', 2), ('ashawkey/stable-dreamfusion', 0.5740616917610168, 'diffusion', 1), ('timothybrooks/instruct-pix2pix', 0.5679231286048889, 'diffusion', 0), ('lunarring/latentblending', 0.5618883967399597, 'diffusion', 1), ('lkwq007/stablediffusion-infinity', 0.5580441951751709, 'diffusion', 1), ('civitai/sd_civitai_extension', 0.5569747686386108, 'llm', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5474333763122559, 'web', 0), ('lllyasviel/controlnet', 0.5427067279815674, 'diffusion', 0), ('sanster/lama-cleaner', 0.5392587780952454, 'ml-dl', 1), ('divamgupta/diffusionbee-stable-diffusion-ui', 0.534104585647583, 'diffusion', 1), ('open-mmlab/mmediting', 0.5198704600334167, 'ml', 1), ('saharmor/dalle-playground', 0.5181798934936523, 'diffusion', 1), ('apple/ml-stable-diffusion', 0.5164445042610168, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.5146340727806091, 'diffusion', 0)]",11,5.0,,0.54,65,31,16,0,4,10,4,65.0,113.0,90.0,1.7,64
1881,pandas,https://github.com/rapidsai/cudf,"['pandas', 'gpu', 'dataframe']","cuDF is a GPU DataFrame library for loading joining, aggregating, filtering, and otherwise manipulating data",[],[],,,,rapidsai/cudf,cudf,6898,797,146,C++,https://docs.rapids.ai/api/cudf/stable/,cuDF - GPU DataFrame Library ,rapidsai,2024-01-13,2017-05-07,351,19.636437576250508,https://avatars.githubusercontent.com/u/43887749?v=4,cuDF - GPU DataFrame Library ,"['arrow', 'cpp', 'cuda', 'cudf', 'dask', 'data-analysis', 'data-science', 'dataframe', 'gpu', 'pandas', 'pydata', 'rapids']","['arrow', 'cpp', 'cuda', 'cudf', 'dask', 'data-analysis', 'data-science', 'dataframe', 'gpu', 'pandas', 'pydata', 'rapids']",2024-01-12,"[('cupy/cupy', 0.6393476724624634, 'math', 2), ('hi-primus/optimus', 0.6309337019920349, 'ml-ops', 4), ('pandas-dev/pandas', 0.6205441355705261, 'pandas', 5), ('man-group/dtale', 0.5755491256713867, 'viz', 3), ('jmcarpenter2/swifter', 0.5688180923461914, 'pandas', 2), ('vaexio/vaex', 0.56623375415802, 'perf', 2), ('mito-ds/monorepo', 0.5557620525360107, 'jupyter', 3), ('apache/arrow', 0.5487073659896851, 'data', 3), ('pola-rs/polars', 0.5434831380844116, 'pandas', 2), ('twopirllc/pandas-ta', 0.542910099029541, 'finance', 2), ('eventual-inc/daft', 0.5403209924697876, 'pandas', 2), ('nvidia/cuda-python', 0.5373781323432922, 'ml', 0), ('ydataai/ydata-profiling', 0.5349627733230591, 'pandas', 3), ('polyaxon/datatile', 0.5338151454925537, 'pandas', 3), ('lux-org/lux', 0.5294429063796997, 'viz', 2), ('kanaries/pygwalker', 0.5281388759613037, 'pandas', 3), ('google/tf-quant-finance', 0.5263254046440125, 'finance', 1), ('graphistry/pygraphistry', 0.5241773724555969, 'data', 4), ('holoviz/spatialpandas', 0.5126527547836304, 'pandas', 1), ('nalepae/pandarallel', 0.5124971866607666, 'pandas', 1), ('holoviz/hvplot', 0.5121793746948242, 'pandas', 0), ('mementum/bta-lib', 0.5101376175880432, 'finance', 0), ('mwaskom/seaborn', 0.5099605917930603, 'viz', 2), ('adamerose/pandasgui', 0.5097667574882507, 'pandas', 2), ('has2k1/plotnine', 0.5067694783210754, 'viz', 1), ('tkrabel/bamboolib', 0.5010843276977539, 'pandas', 1), ('dylanhogg/awesome-python', 0.5004510283470154, 'study', 2), ('pytorch/torchrec', 0.5002225637435913, 'ml-dl', 2)]",269,3.0,,33.96,609,405,81,0,12,15,12,606.0,1425.0,90.0,2.4,64
1285,llm,https://github.com/zilliztech/gptcache,[],,[],[],,,,zilliztech/gptcache,GPTCache,5883,417,56,Python,https://gptcache.readthedocs.io,Semantic cache for LLMs. Fully integrated with LangChain and llama_index. ,zilliztech,2024-01-14,2023-03-24,44,131.9903846153846,https://avatars.githubusercontent.com/u/18416694?v=4,Semantic cache for LLMs. Fully integrated with LangChain and llama_index. ,"['aigc', 'autogpt', 'babyagi', 'chatbot', 'chatgpt', 'chatgpt-api', 'dolly', 'gpt', 'langchain', 'llama', 'llama-index', 'llm', 'memcache', 'milvus', 'openai', 'redis', 'semantic-search', 'similarity-search', 'vector-search']","['aigc', 'autogpt', 'babyagi', 'chatbot', 'chatgpt', 'chatgpt-api', 'dolly', 'gpt', 'langchain', 'llama', 'llama-index', 'llm', 'memcache', 'milvus', 'openai', 'redis', 'semantic-search', 'similarity-search', 'vector-search']",2023-11-28,"[('shishirpatil/gorilla', 0.6505623459815979, 'llm', 2), ('intel/intel-extension-for-transformers', 0.6376039981842041, 'perf', 1), ('deepset-ai/haystack', 0.6036768555641174, 'llm', 2), ('jerryjliu/llama_index', 0.5998677611351013, 'llm', 3), ('run-llama/rags', 0.5993092060089111, 'llm', 4), ('run-llama/llama-hub', 0.5969860553741455, 'data', 1), ('bobazooba/xllm', 0.5947780013084412, 'llm', 5), ('dylanhogg/llmgraph', 0.5875337719917297, 'ml', 2), ('pathwaycom/llm-app', 0.5837850570678711, 'llm', 2), ('hwchase17/langchain', 0.5693703293800354, 'llm', 2), ('nomic-ai/gpt4all', 0.5606859922409058, 'llm', 1), ('langchain-ai/langgraph', 0.5597312450408936, 'llm', 1), ('bigscience-workshop/petals', 0.5577248334884644, 'data', 3), ('microsoft/autogen', 0.5542406439781189, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5492423176765442, 'llm', 2), ('neuml/txtai', 0.5468195080757141, 'nlp', 3), ('h2oai/h2o-llmstudio', 0.5407289266586304, 'llm', 5), ('dgilland/cacheout', 0.540678858757019, 'perf', 0), ('vllm-project/vllm', 0.540032148361206, 'llm', 3), ('berriai/litellm', 0.539711594581604, 'llm', 3), ('hiyouga/llama-factory', 0.5300214290618896, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5300213694572449, 'llm', 3), ('li-plus/chatglm.cpp', 0.5297065377235413, 'llm', 0), ('lancedb/lancedb', 0.5283774137496948, 'data', 2), ('alphasecio/langchain-examples', 0.5278271436691284, 'llm', 3), ('deep-diver/llm-as-chatbot', 0.5247036218643188, 'llm', 1), ('tigerlab-ai/tiger', 0.5229496955871582, 'llm', 1), ('explosion/spacy-llm', 0.5211718082427979, 'llm', 4), ('salesforce/xgen', 0.5199717283248901, 'llm', 1), ('python-cachier/cachier', 0.5158444046974182, 'perf', 0), ('embedchain/embedchain', 0.5112298727035522, 'llm', 2), ('confident-ai/deepeval', 0.5101267099380493, 'testing', 2), ('continuum-llms/chatgpt-memory', 0.5097682476043701, 'llm', 3), ('nomic-ai/semantic-search-app-template', 0.5088302493095398, 'study', 2), ('thudm/chatglm2-6b', 0.507978081703186, 'llm', 1), ('long2ice/fastapi-cache', 0.506112813949585, 'web', 1), ('mooler0410/llmspracticalguide', 0.5039639472961426, 'study', 0), ('bentoml/openllm', 0.5029482245445251, 'ml-ops', 2), ('young-geng/easylm', 0.5008785724639893, 'llm', 2)]",41,1.0,,9.54,45,16,10,2,40,49,40,45.0,68.0,90.0,1.5,64
1834,llm,https://github.com/run-llama/rags,['rag'],,[],[],,,,run-llama/rags,rags,5280,640,46,Python,,"Build ChatGPT over your data, all with natural language",run-llama,2024-01-14,2023-11-16,10,492.8,https://avatars.githubusercontent.com/u/130722866?v=4,"Build ChatGPT over your data, all with natural language","['agent', 'chatbot', 'chatgpt', 'gpts', 'llamaindex', 'llm', 'openai', 'rag', 'streamlit']","['agent', 'chatbot', 'chatgpt', 'gpts', 'llamaindex', 'llm', 'openai', 'rag', 'streamlit']",2023-12-05,"[('embedchain/embedchain', 0.775257408618927, 'llm', 2), ('xtekky/gpt4free', 0.7228777408599854, 'llm', 3), ('microsoft/autogen', 0.713228166103363, 'llm', 2), ('openai/openai-cookbook', 0.7131730318069458, 'ml', 2), ('killianlucas/open-interpreter', 0.6841922998428345, 'llm', 1), ('nomic-ai/gpt4all', 0.679734468460083, 'llm', 1), ('blinkdl/chatrwkv', 0.678069531917572, 'llm', 2), ('minimaxir/simpleaichat', 0.6775078177452087, 'llm', 1), ('togethercomputer/openchatkit', 0.6478996872901917, 'nlp', 1), ('mmabrouk/chatgpt-wrapper', 0.644103467464447, 'llm', 4), ('shishirpatil/gorilla', 0.6374510526657104, 'llm', 2), ('lm-sys/fastchat', 0.6331033110618591, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6312287449836731, 'perf', 1), ('deepset-ai/haystack', 0.6311088800430298, 'llm', 1), ('gventuri/pandas-ai', 0.6272019743919373, 'pandas', 1), ('mayooear/gpt4-pdf-chatbot-langchain', 0.6243221163749695, 'llm', 1), ('rasahq/rasa', 0.6235318779945374, 'llm', 1), ('prefecthq/marvin', 0.6145691275596619, 'nlp', 2), ('chainlit/chainlit', 0.61057049036026, 'llm', 3), ('rcgai/simplyretrieve', 0.6102337837219238, 'llm', 0), ('chatarena/chatarena', 0.6069520711898804, 'llm', 1), ('fasteval/fasteval', 0.6045003533363342, 'llm', 1), ('openlmlab/moss', 0.6012157797813416, 'llm', 1), ('hwchase17/langchain', 0.6003484129905701, 'llm', 1), ('dylanhogg/llmgraph', 0.5997524857521057, 'ml', 2), ('zilliztech/gptcache', 0.5993092060089111, 'llm', 4), ('larsbaunwall/bricky', 0.5981244444847107, 'llm', 1), ('mlc-ai/web-llm', 0.5972565412521362, 'llm', 2), ('pathwaycom/llm-app', 0.5891484618186951, 'llm', 3), ('databrickslabs/dolly', 0.5840879678726196, 'llm', 1), ('mindsdb/mindsdb', 0.5839447975158691, 'data', 2), ('openai/tiktoken', 0.5739045143127441, 'nlp', 1), ('cheshire-cat-ai/core', 0.5686349868774414, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5668851137161255, 'study', 2), ('microsoft/promptflow', 0.5604910254478455, 'llm', 2), ('deep-diver/llm-as-chatbot', 0.5595909953117371, 'llm', 1), ('lupantech/chameleon-llm', 0.5582309365272522, 'llm', 3), ('continuum-llms/chatgpt-memory', 0.5546180605888367, 'llm', 1), ('next-gpt/next-gpt', 0.5521267652511597, 'llm', 2), ('mnotgod96/appagent', 0.5510104894638062, 'llm', 3), ('promptslab/promptify', 0.5508096814155579, 'nlp', 2), ('alphasecio/langchain-examples', 0.5506168007850647, 'llm', 3), ('thudm/chatglm2-6b', 0.547817587852478, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5412566065788269, 'nlp', 0), ('farizrahman4u/loopgpt', 0.5412454605102539, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5412412881851196, 'llm', 0), ('laion-ai/open-assistant', 0.5403715968132019, 'llm', 1), ('h2oai/h2ogpt', 0.5400290489196777, 'llm', 2), ('deeppavlov/deeppavlov', 0.5382777452468872, 'nlp', 1), ('openai/chatgpt-retrieval-plugin', 0.5382370948791504, 'llm', 1), ('li-plus/chatglm.cpp', 0.5372152328491211, 'llm', 0), ('bobazooba/xllm', 0.5356626510620117, 'llm', 3), ('langchain-ai/opengpts', 0.5282418131828308, 'llm', 0), ('lianjiatech/belle', 0.5264316201210022, 'llm', 0), ('argilla-io/argilla', 0.5243651866912842, 'nlp', 1), ('microsoft/promptcraft-robotics', 0.5236408710479736, 'sim', 2), ('krohling/bondai', 0.5235958099365234, 'llm', 0), ('bhaskatripathi/pdfgpt', 0.5228466987609863, 'llm', 0), ('aiwaves-cn/agents', 0.5222904682159424, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5192126035690308, 'llm', 1), ('bigscience-workshop/petals', 0.5189594626426697, 'data', 1), ('explosion/spacy-llm', 0.5166354775428772, 'llm', 2), ('langchain-ai/langgraph', 0.5150389671325684, 'llm', 0), ('guidance-ai/guidance', 0.5145743489265442, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5137847065925598, 'llm', 3), ('langchain-ai/chat-langchain', 0.5135616064071655, 'llm', 1), ('openai/gpt-discord-bot', 0.5125691890716553, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5091956257820129, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5037953853607178, 'llm', 1), ('hiyouga/llama-factory', 0.5037953853607178, 'llm', 1), ('explosion/spacy-streamlit', 0.5017317533493042, 'nlp', 1), ('young-geng/easylm', 0.5016451478004456, 'llm', 1)]",5,2.0,,0.33,58,31,2,1,0,0,0,58.0,68.0,90.0,1.2,64
1592,llm,https://github.com/skypilot-org/skypilot,"['ml-platform', 'ml-infrastructure']",,[],[],,,,skypilot-org/skypilot,skypilot,4870,313,61,Python,https://skypilot.readthedocs.io,"SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.",skypilot-org,2024-01-13,2021-08-11,128,37.79379157427938,https://avatars.githubusercontent.com/u/109387420?v=4,"SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.","['cloud-computing', 'cloud-management', 'cost-management', 'cost-optimization', 'data-science', 'deep-learning', 'distributed-training', 'finops', 'gpu', 'hyperparameter-tuning', 'job-queue', 'job-scheduler', 'llm-serving', 'llm-training', 'machine-learning', 'ml-infrastructure', 'ml-platform', 'multicloud', 'spot-instances', 'tpu']","['cloud-computing', 'cloud-management', 'cost-management', 'cost-optimization', 'data-science', 'deep-learning', 'distributed-training', 'finops', 'gpu', 'hyperparameter-tuning', 'job-queue', 'job-scheduler', 'llm-serving', 'llm-training', 'machine-learning', 'ml-infrastructure', 'ml-platform', 'multicloud', 'spot-instances', 'tpu']",2024-01-14,"[('polyaxon/polyaxon', 0.6188631057739258, 'ml-ops', 3), ('lithops-cloud/lithops', 0.6185680031776428, 'ml-ops', 2), ('googlecloudplatform/vertex-ai-samples', 0.6106062531471252, 'ml', 1), ('netflix/metaflow', 0.6102747321128845, 'ml-ops', 4), ('jina-ai/jina', 0.5877841711044312, 'ml', 2), ('bigscience-workshop/petals', 0.5853170156478882, 'data', 2), ('flyteorg/flyte', 0.5598949790000916, 'ml-ops', 2), ('pathwaycom/llm-app', 0.554336428642273, 'llm', 1), ('zenml-io/mlstacks', 0.5491843819618225, 'ml-ops', 0), ('bentoml/bentoml', 0.5428783297538757, 'ml-ops', 3), ('bentoml/openllm', 0.536402702331543, 'ml-ops', 1), ('bodywork-ml/bodywork-core', 0.5361641049385071, 'ml-ops', 2), ('localstack/localstack', 0.5336942672729492, 'util', 0), ('alpa-projects/alpa', 0.5307222008705139, 'ml-dl', 3), ('determined-ai/determined', 0.5281897783279419, 'ml-ops', 7), ('orchest/orchest', 0.5275050401687622, 'ml-ops', 2), ('microsoft/semantic-kernel', 0.5244306325912476, 'llm', 0), ('fugue-project/fugue', 0.5239272117614746, 'pandas', 1), ('vllm-project/vllm', 0.5184321403503418, 'llm', 1), ('mlflow/mlflow', 0.514528214931488, 'ml-ops', 1), ('predibase/lorax', 0.5144446492195129, 'llm', 2), ('hpcaitech/colossalai', 0.5133247971534729, 'llm', 1), ('cheshire-cat-ai/core', 0.510378360748291, 'llm', 0), ('shishirpatil/gorilla', 0.5099743008613586, 'llm', 0), ('superduperdb/superduperdb', 0.5078701376914978, 'data', 1), ('backtick-se/cowait', 0.5072562098503113, 'util', 1), ('pytorchlightning/pytorch-lightning', 0.5027073621749878, 'ml-dl', 3), ('microsoft/promptflow', 0.5022432208061218, 'llm', 0), ('uber/fiber', 0.5006906390190125, 'data', 1)]",61,5.0,,11.79,532,288,30,0,9,6,9,533.0,790.0,90.0,1.5,64
1525,llm,https://github.com/stanfordnlp/dspy,"['reasoning', 'prompting', 'fine-tuning', 'retrieval']",,[],[],1.0,,,stanfordnlp/dspy,dspy,4712,290,90,Python,,Stanford DSPy: The framework for programming—not prompting—foundation models,stanfordnlp,2024-01-14,2023-01-09,55,85.45077720207254,https://avatars.githubusercontent.com/u/3046006?v=4,Stanford DSPy: The framework for programming—not prompting—foundation models,[],"['fine-tuning', 'prompting', 'reasoning', 'retrieval']",2024-01-13,"[('srush/minichain', 0.6080040335655212, 'llm', 0), ('python/cpython', 0.5970360040664673, 'util', 0), ('kyegomez/tree-of-thoughts', 0.5961645245552063, 'llm', 0), ('reasoning-machines/pal', 0.5881893038749695, 'llm', 1), ('eleutherai/pyfra', 0.5839141607284546, 'ml', 0), ('keirp/automatic_prompt_engineer', 0.5835668444633484, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5695632696151733, 'llm', 0), ('pytoolz/toolz', 0.5550901889801025, 'util', 0), ('google/pyglove', 0.5547299385070801, 'util', 0), ('bigscience-workshop/promptsource', 0.5486031770706177, 'nlp', 0), ('guidance-ai/guidance', 0.5456482768058777, 'llm', 0), ('hazyresearch/ama_prompting', 0.540431022644043, 'llm', 0), ('hazyresearch/manifest', 0.5367062091827393, 'llm', 0), ('evhub/coconut', 0.5305084586143494, 'util', 0), ('juncongmoo/pyllama', 0.5301841497421265, 'llm', 0), ('llmware-ai/llmware', 0.5297998785972595, 'llm', 0), ('pyston/pyston', 0.5251495838165283, 'util', 0), ('pexpect/pexpect', 0.5249006152153015, 'util', 0), ('promptslab/promptify', 0.5224989056587219, 'nlp', 1), ('lupantech/chameleon-llm', 0.5166156888008118, 'llm', 1), ('eth-sri/lmql', 0.516359806060791, 'llm', 0), ('databrickslabs/dolly', 0.5139095783233643, 'llm', 0), ('modularml/mojo', 0.5088305473327637, 'util', 0), ('facebookresearch/reagent', 0.5081221461296082, 'ml-rl', 0), ('norvig/pytudes', 0.5077176690101624, 'util', 0), ('microsoft/pycodegpt', 0.5060772895812988, 'llm', 0), ('optimalscale/lmflow', 0.5005626082420349, 'llm', 0), ('dylanhogg/awesome-python', 0.5004346966743469, 'study', 0)]",44,4.0,,8.67,140,90,12,0,0,0,0,138.0,302.0,90.0,2.2,64
1890,llm,https://github.com/mnotgod96/appagent,[],,[],[],,,,mnotgod96/appagent,AppAgent,3223,312,40,Python,https://appagent-official.github.io/,"AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.",mnotgod96,2024-01-14,2023-12-20,5,550.2682926829268,,"AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.","['agent', 'chatgpt', 'generative-ai', 'gpt4', 'gpt4v', 'llm']","['agent', 'chatgpt', 'generative-ai', 'gpt4', 'gpt4v', 'llm']",2024-01-03,"[('pathwaycom/llm-app', 0.6296766400337219, 'llm', 1), ('microsoft/semantic-kernel', 0.6219491362571716, 'llm', 1), ('microsoft/promptflow', 0.5954347252845764, 'llm', 2), ('microsoft/autogen', 0.5801144242286682, 'llm', 1), ('geekan/metagpt', 0.5650525093078613, 'llm', 2), ('embedchain/embedchain', 0.5549655556678772, 'llm', 2), ('next-gpt/next-gpt', 0.5528967976570129, 'llm', 2), ('run-llama/rags', 0.5510104894638062, 'llm', 3), ('nomic-ai/gpt4all', 0.5476191639900208, 'llm', 0), ('chatarena/chatarena', 0.5439862012863159, 'llm', 1), ('deepset-ai/haystack', 0.5397396087646484, 'llm', 2), ('deep-diver/llm-as-chatbot', 0.5356595516204834, 'llm', 0), ('prefecthq/marvin', 0.5343064665794373, 'nlp', 1), ('luodian/otter', 0.5246831774711609, 'llm', 1), ('farizrahman4u/loopgpt', 0.5192574858665466, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5174366235733032, 'llm', 3), ('haotian-liu/llava', 0.5170363187789917, 'llm', 1), ('operand/agency', 0.51549232006073, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.5122302174568176, 'llm', 3), ('microsoft/promptcraft-robotics', 0.5117555260658264, 'sim', 2), ('humanoidagents/humanoidagents', 0.5099534392356873, 'sim', 1), ('mlc-ai/mlc-llm', 0.5084356665611267, 'llm', 1), ('hwchase17/langchain', 0.5082905888557434, 'llm', 0), ('xtekky/gpt4free', 0.5081965923309326, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5081307888031006, 'perf', 0), ('langchain-ai/langgraph', 0.5023365020751953, 'llm', 0), ('minimaxir/simpleaichat', 0.501400351524353, 'llm', 1)]",6,3.0,,0.42,32,5,1,0,0,0,0,32.0,41.0,90.0,1.3,64
1101,llm,https://github.com/tatsu-lab/stanford_alpaca,[],,[],[],,,,tatsu-lab/stanford_alpaca,stanford_alpaca,28052,3980,335,Python,https://crfm.stanford.edu/2023/03/13/alpaca.html,"Code and documentation to train Stanford's Alpaca models, and generate the data.",tatsu-lab,2024-01-13,2023-03-10,46,602.3435582822086,https://avatars.githubusercontent.com/u/61893194?v=4,"Code and documentation to train Stanford's Alpaca models, and generate the data.","['deep-learning', 'instruction-following', 'language-model']","['deep-learning', 'instruction-following', 'language-model']",2023-05-30,"[('optimalscale/lmflow', 0.6111303567886353, 'llm', 3), ('hannibal046/awesome-llm', 0.6053584218025208, 'study', 1), ('yizhongw/self-instruct', 0.5977901816368103, 'llm', 1), ('jonasgeiping/cramming', 0.5919219851493835, 'nlp', 1), ('huggingface/text-generation-inference', 0.5844063758850098, 'llm', 1), ('juncongmoo/pyllama', 0.5793993473052979, 'llm', 0), ('togethercomputer/redpajama-data', 0.5778451561927795, 'llm', 0), ('tiger-ai-lab/mammoth', 0.5743323564529419, 'llm', 0), ('paperswithcode/galai', 0.5739641785621643, 'llm', 1), ('stanfordnlp/dspy', 0.5695632696151733, 'llm', 0), ('infinitylogesh/mutate', 0.5620525479316711, 'nlp', 1), ('freedomintelligence/llmzoo', 0.5588739514350891, 'llm', 1), ('openai/gpt-2', 0.5557239055633545, 'llm', 0), ('graykode/nlp-tutorial', 0.5533468723297119, 'study', 0), ('nvidia/deeplearningexamples', 0.5533385872840881, 'ml-dl', 1), ('yueyu1030/attrprompt', 0.5511519312858582, 'llm', 0), ('eleutherai/the-pile', 0.5507524013519287, 'data', 0), ('cgpotts/cs224u', 0.5506641268730164, 'study', 0), ('lianjiatech/belle', 0.5461639761924744, 'llm', 0), ('facebookresearch/shepherd', 0.5445735454559326, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5441707372665405, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.5418017506599426, 'study', 0), ('lm-sys/fastchat', 0.5374524593353271, 'llm', 1), ('young-geng/easylm', 0.536756694316864, 'llm', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5365834832191467, 'study', 0), ('cg123/mergekit', 0.5332975387573242, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5329034924507141, 'llm', 1), ('rafiqhasan/auto-tensorflow', 0.5314496755599976, 'ml-dl', 0), ('ofa-sys/ofa', 0.5313153266906738, 'llm', 0), ('llmware-ai/llmware', 0.5306206345558167, 'llm', 0), ('reasoning-machines/pal', 0.5286970734596252, 'llm', 1), ('openai/finetune-transformer-lm', 0.5281931757926941, 'llm', 0), ('keras-team/keras-nlp', 0.5273944735527039, 'nlp', 1), ('openbmb/toolbench', 0.5260429382324219, 'llm', 0), ('srush/minichain', 0.5253647565841675, 'llm', 0), ('salesforce/xgen', 0.52476966381073, 'llm', 1), ('huggingface/transformers', 0.5228704214096069, 'nlp', 2), ('d2l-ai/d2l-en', 0.5226318836212158, 'study', 1), ('tensorflow/tensor2tensor', 0.5224913954734802, 'ml', 1), ('ai21labs/lm-evaluation', 0.5184262990951538, 'llm', 1), ('deepset-ai/farm', 0.5168743133544922, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5149617195129395, 'llm', 0), ('conceptofmind/toolformer', 0.5121525526046753, 'llm', 1), ('microsoft/unilm', 0.511536180973053, 'nlp', 0), ('keirp/automatic_prompt_engineer', 0.511340320110321, 'llm', 1), ('databrickslabs/dolly', 0.5099220275878906, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5094295740127563, 'llm', 2), ('rasbt/stat453-deep-learning-ss20', 0.5087552666664124, 'study', 0), ('titanml/takeoff', 0.507792055606842, 'llm', 1), ('neulab/prompt2model', 0.5068913102149963, 'llm', 1), ('ageron/handson-ml2', 0.5068382620811462, 'ml', 0), ('rasbt/deeplearning-models', 0.5054455399513245, 'ml-dl', 0), ('microsoft/generative-ai-for-beginners', 0.5033739805221558, 'study', 1), ('explosion/spacy-models', 0.5031586289405823, 'nlp', 0), ('declare-lab/instruct-eval', 0.5018754005432129, 'llm', 0)]",5,3.0,,0.6,19,4,10,8,0,0,0,19.0,20.0,90.0,1.1,63
62,viz,https://github.com/plotly/dash,[],,[],[],,,,plotly/dash,dash,19929,2030,413,Python,https://plotly.com/dash,Data Apps & Dashboards for Python. No JavaScript Required.,plotly,2024-01-14,2015-04-10,459,43.364314578800126,https://avatars.githubusercontent.com/u/5997976?v=4,Data Apps & Dashboards for Python. No JavaScript Required.,"['bioinformatics', 'charting', 'dash', 'data-science', 'data-visualization', 'finance', 'flask', 'gui-framework', 'julia', 'jupyter', 'modeling', 'plotly', 'plotly-dash', 'productivity', 'r', 'react', 'rstats', 'technical-computing', 'web-app']","['bioinformatics', 'charting', 'dash', 'data-science', 'data-visualization', 'finance', 'flask', 'gui-framework', 'julia', 'jupyter', 'modeling', 'plotly', 'plotly-dash', 'productivity', 'r', 'react', 'rstats', 'technical-computing', 'web-app']",2024-01-09,"[('holoviz/panel', 0.7759690284729004, 'viz', 2), ('plotly/plotly.py', 0.714753270149231, 'viz', 2), ('bokeh/bokeh', 0.7066522240638733, 'viz', 1), ('krzjoa/awesome-python-data-science', 0.6923890113830566, 'study', 2), ('polyaxon/datatile', 0.6874310970306396, 'pandas', 3), ('ranaroussi/quantstats', 0.6857461333274841, 'finance', 1), ('man-group/dtale', 0.6759928464889526, 'viz', 5), ('willmcgugan/textual', 0.6724681854248047, 'term', 0), 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0.5458297729492188, 'data', 0), ('mito-ds/monorepo', 0.5446365475654602, 'jupyter', 3), ('fastai/fastcore', 0.5438128113746643, 'util', 0), ('tiangolo/fastapi', 0.5433139801025391, 'web', 0), ('saulpw/visidata', 0.5414975881576538, 'term', 0), ('maartenbreddels/ipyvolume', 0.5407821536064148, 'jupyter', 1), ('ploomber/ploomber', 0.5395582914352417, 'ml-ops', 2), ('mwaskom/seaborn', 0.5341764092445374, 'viz', 2), ('zenodo/zenodo', 0.5337997078895569, 'util', 1), ('vitalik/django-ninja', 0.5318993926048279, 'web', 0), ('klen/muffin', 0.5317702889442444, 'web', 0), ('quantconnect/lean', 0.529013454914093, 'finance', 1), ('sloria/textblob', 0.5282818078994751, 'nlp', 0), ('seleniumbase/seleniumbase', 0.5277555584907532, 'testing', 0), ('rstudio/py-shiny', 0.5270806550979614, 'web', 0), ('unionai-oss/pandera', 0.5268102288246155, 'pandas', 0), ('ydataai/ydata-profiling', 0.526740312576294, 'pandas', 2), ('imageio/imageio', 0.5265047550201416, 'util', 0), ('python-restx/flask-restx', 0.5263261795043945, 'web', 1), ('webpy/webpy', 0.5255879163742065, 'web', 0), ('geopandas/geopandas', 0.525221586227417, 'gis', 0), ('lux-org/lux', 0.5251437425613403, 'viz', 2), ('ta-lib/ta-lib-python', 0.522579550743103, 'finance', 1), ('bottlepy/bottle', 0.5221378803253174, 'web', 0), ('scrapy/scrapy', 0.5219252109527588, 'data', 0), ('firmai/atspy', 0.5219200849533081, 'time-series', 1), ('simonw/datasette', 0.5204837918281555, 'data', 0), ('timofurrer/awesome-asyncio', 0.5202446579933167, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.5179597735404968, 'study', 0), ('holoviz/hvplot', 0.5148411393165588, 'pandas', 0), ('roniemartinez/dude', 0.512108325958252, 'util', 0), ('alphasecio/langchain-examples', 0.5120916962623596, 'llm', 0), ('thealgorithms/python', 0.5107855796813965, 'study', 0), ('avaiga/taipy', 0.5096752047538757, 'data', 1), ('keon/algorithms', 0.5095182657241821, 'util', 0), ('alkaline-ml/pmdarima', 0.5089923143386841, 'time-series', 0), ('hoffstadt/dearpygui', 0.5085344314575195, 'gui', 0), ('pyqtgraph/pyqtgraph', 0.5079091191291809, 'viz', 0), ('pysimplegui/pysimplegui', 0.5075579285621643, 'gui', 1), ('scitools/iris', 0.5075518488883972, 'gis', 0), ('pypy/pypy', 0.5047568678855896, 'util', 0), ('reloadware/reloadium', 0.5045337080955505, 'profiling', 1), ('urwid/urwid', 0.5028428435325623, 'term', 0), ('wandb/client', 0.5018703937530518, 'ml', 1), ('amaargiru/pyroad', 0.5013555884361267, 'study', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5003242492675781, 'template', 0), ('vaexio/vaex', 0.5000450611114502, 'perf', 1)]",143,1.0,,6.75,91,40,107,0,17,11,17,91.0,162.0,90.0,1.8,63
1699,util,https://github.com/mkdocs/mkdocs,[],,[],[],,,,mkdocs/mkdocs,mkdocs,17718,2353,231,Python,https://www.mkdocs.org,Project documentation with Markdown.,mkdocs,2024-01-14,2014-01-11,524,33.785344592754015,https://avatars.githubusercontent.com/u/9692741?v=4,Project documentation with Markdown.,"['documentation', 'markdown', 'mkdocs', 'static-site-generator']","['documentation', 'markdown', 'mkdocs', 'static-site-generator']",2023-12-23,"[('mkdocstrings/mkdocstrings', 0.6345070600509644, 'util', 1), ('squidfunk/mkdocs-material', 0.6341920495033264, 'util', 2), ('sphinx-doc/sphinx', 0.6297897100448608, 'util', 2), ('getpelican/pelican', 0.5778642296791077, 'web', 1), ('mitmproxy/pdoc', 0.5321336984634399, 'util', 1), ('pdoc3/pdoc', 0.5143741369247437, 'util', 1)]",246,5.0,,3.58,122,94,122,1,5,6,5,122.0,224.0,90.0,1.8,63
1046,ml-dl,https://github.com/lucidrains/vit-pytorch,[],,[],[],,,,lucidrains/vit-pytorch,vit-pytorch,16580,2635,137,Python,,"Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch",lucidrains,2024-01-14,2020-10-03,173,95.60131795716639,,"Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch","['artificial-intelligence', 'attention-mechanism', 'computer-vision', 'image-classification', 'transformers']","['artificial-intelligence', 'attention-mechanism', 'computer-vision', 'image-classification', 'transformers']",2023-12-23,"[('nvlabs/gcvit', 0.6527162790298462, 'diffusion', 0), ('roboflow/notebooks', 0.6355553865432739, 'study', 2), ('google-research/maxvit', 0.6311172246932983, 'ml', 1), ('deci-ai/super-gradients', 0.62143474817276, 'ml-dl', 2), ('lucidrains/imagen-pytorch', 0.5685757994651794, 'ml-dl', 1), ('microsoft/swin-transformer', 0.5670905113220215, 'ml', 1), ('hysts/pytorch_image_classification', 0.5473499298095703, 'ml-dl', 1), ('facebookresearch/vissl', 0.5331419110298157, 'ml', 0), ('huggingface/transformers', 0.532448410987854, 'nlp', 0), ('karpathy/mingpt', 0.5179693102836609, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5130720138549805, 'llm', 0), ('intel/intel-extension-for-pytorch', 0.502902626991272, 'perf', 0), ('salesforce/blip', 0.500484049320221, 'diffusion', 0)]",20,5.0,,0.77,16,6,40,1,30,57,30,16.0,15.0,90.0,0.9,63
865,util,https://github.com/ipython/ipython,[],,[],['<hide>'],,,,ipython/ipython,ipython,16051,4488,755,Python,https://ipython.readthedocs.org,"Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.",ipython,2024-01-13,2010-05-10,716,22.413125872730898,https://avatars.githubusercontent.com/u/230453?v=4,"Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.","['data-science', 'ipython', 'jupyter', 'notebook', 'repl', 'spec-0']","['data-science', 'ipython', 'jupyter', 'notebook', 'repl', 'spec-0']",2024-01-10,"[('ipython/ipykernel', 0.6446179747581482, 'util', 2), ('wesm/pydata-book', 0.6048235893249512, 'study', 0), ('python/cpython', 0.5930864810943604, 'util', 0), ('pypy/pypy', 0.583660364151001, 'util', 0), ('faster-cpython/ideas', 0.5474236607551575, 'perf', 0), ('urwid/urwid', 0.5444232821464539, 'term', 0), ('rasbt/watermark', 0.5411689281463623, 'util', 2), ('openai/openai-python', 0.5373437404632568, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.525562584400177, 'study', 0), ('pypi/warehouse', 0.5227155685424805, 'util', 0), ('faster-cpython/tools', 0.5140804052352905, 'perf', 0), ('cython/cython', 0.5134088397026062, 'util', 0), ('maartenbreddels/ipyvolume', 0.5083285570144653, 'jupyter', 1), ('pyo3/maturin', 0.5035890936851501, 'util', 0)]",978,7.0,,9.31,191,68,167,0,0,12,12,222.0,162.0,90.0,0.7,63
63,viz,https://github.com/plotly/plotly.py,[],,[],[],,,,plotly/plotly.py,plotly.py,14686,2467,280,Python,https://plotly.com/python/,The interactive graphing library for Python :sparkles: This project now includes Plotly Express!,plotly,2024-01-14,2013-11-21,531,27.62009672219237,https://avatars.githubusercontent.com/u/5997976?v=4,The interactive graphing library for Python ✨ This project now includes Plotly Express!,"['d3', 'dashboard', 'declarative', 'graph-library', 'interactive', 'jupyter-notebook', 'plotly', 'plotly-dash', 'plotlyjs', 'regl', 'sparkles', 'visualization', 'webgl']","['d3', 'dashboard', 'declarative', 'graph-library', 'interactive', 'jupyter-notebook', 'plotly', 'plotly-dash', 'plotlyjs', 'regl', 'sparkles', 'visualization', 'webgl']",2023-12-21,"[('bokeh/bokeh', 0.7521082758903503, 'viz', 1), ('vizzuhq/ipyvizzu', 0.7362504601478577, 'jupyter', 1), ('holoviz/panel', 0.7203231453895569, 'viz', 1), ('plotly/dash', 0.714753270149231, 'viz', 2), ('cuemacro/chartpy', 0.6865983605384827, 'viz', 1), ('man-group/dtale', 0.6799153089523315, 'viz', 3), ('maartenbreddels/ipyvolume', 0.6658064723014832, 'jupyter', 2), ('kanaries/pygwalker', 0.6529722809791565, 'pandas', 2), ('pygraphviz/pygraphviz', 0.6489458680152893, 'viz', 0), ('holoviz/hvplot', 0.6457611918449402, 'pandas', 0), ('graphistry/pygraphistry', 0.6395604610443115, 'data', 2), ('matplotlib/matplotlib', 0.6358102560043335, 'viz', 0), ('federicoceratto/dashing', 0.6343661546707153, 'term', 1), ('westhealth/pyvis', 0.6326491832733154, 'graph', 0), ('holoviz/holoviz', 0.6298113465309143, 'viz', 0), ('altair-viz/altair', 0.6128622889518738, 'viz', 1), ('has2k1/plotnine', 0.6062517762184143, 'viz', 0), ('opengeos/leafmap', 0.5990200638771057, 'gis', 2), ('giswqs/geemap', 0.5846720933914185, 'gis', 1), ('residentmario/geoplot', 0.5845940709114075, 'gis', 0), ('artelys/geonetworkx', 0.5817664861679077, 'gis', 0), ('raphaelquast/eomaps', 0.581186056137085, 'gis', 1), ('mwaskom/seaborn', 0.5750998854637146, 'viz', 0), ('holoviz/geoviews', 0.5691211223602295, 'gis', 0), ('pandas-dev/pandas', 0.5662622451782227, 'pandas', 0), ('scitools/cartopy', 0.5586792826652527, 'gis', 0), ('pydot/pydot', 0.5558724403381348, 'viz', 0), ('polyaxon/datatile', 0.554128885269165, 'pandas', 1), ('wesm/pydata-book', 0.5537784099578857, 'study', 0), ('willmcgugan/textual', 0.5511562824249268, 'term', 0), ('dylanhogg/awesome-python', 0.549170970916748, 'study', 0), ('lux-org/lux', 0.5476740598678589, 'viz', 1), ('voila-dashboards/voila', 0.5468341112136841, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5420818328857422, 'study', 1), ('pypy/pypy', 0.5398237705230713, 'util', 0), ('masoniteframework/masonite', 0.5396803021430969, 'web', 0), ('facultyai/dash-bootstrap-components', 0.5394929051399231, 'viz', 1), ('pyqtgraph/pyqtgraph', 0.5377339720726013, 'viz', 1), ('hoffstadt/dearpygui', 0.5339773297309875, 'gui', 0), ('nomic-ai/deepscatter', 0.5329233407974243, 'viz', 2), ('pytoolz/toolz', 0.5307526588439941, 'util', 0), ('aws/graph-notebook', 0.5306247472763062, 'jupyter', 1), ('pyvista/pyvista', 0.5286508798599243, 'viz', 1), ('urwid/urwid', 0.5260567665100098, 'term', 0), ('willmcgugan/rich', 0.5253220200538635, 'term', 0), ('strawberry-graphql/strawberry', 0.5247325897216797, 'web', 0), ('a-r-j/graphein', 0.5239959955215454, 'sim', 0), ('vispy/vispy', 0.523070216178894, 'viz', 1), ('python-visualization/folium', 0.5229291319847107, 'gis', 0), ('scitools/iris', 0.5217393040657043, 'gis', 0), ('ranaroussi/quantstats', 0.5214852094650269, 'finance', 1), ('visgl/deck.gl', 0.5213139057159424, 'viz', 2), ('gboeing/pynamical', 0.5207886099815369, 'sim', 1), ('dmlc/dgl', 0.5201095938682556, 'ml-dl', 0), ('enthought/mayavi', 0.5172713398933411, 'viz', 1), ('r0x0r/pywebview', 0.5167466402053833, 'gui', 0), ('jsonpickle/jsonpickle', 0.5167441964149475, 'data', 0), ('wxwidgets/phoenix', 0.5154394507408142, 'gui', 0), ('networkx/networkx', 0.5152801275253296, 'graph', 0), ('brandtbucher/specialist', 0.5150900483131409, 'perf', 0), ('python/cpython', 0.5148897171020508, 'util', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5129841566085815, 'jupyter', 0), ('imageio/imageio', 0.5120216012001038, 'util', 0), ('jupyter-widgets/ipywidgets', 0.5096937417984009, 'jupyter', 0), ('deeplook/sparklines', 0.5089108943939209, 'term', 0), ('h4kor/graph-force', 0.5063482522964478, 'graph', 0), ('alexmojaki/heartrate', 0.5061368942260742, 'debug', 1), ('giswqs/mapwidget', 0.504154622554779, 'gis', 0), ('pyglet/pyglet', 0.5040709972381592, 'gamedev', 0), ('jalammar/ecco', 0.5030951499938965, 'ml-interpretability', 1), ('gaogaotiantian/viztracer', 0.5023961067199707, 'profiling', 1), ('tkrabel/bamboolib', 0.5021097660064697, 'pandas', 1), ('timofurrer/awesome-asyncio', 0.5011566877365112, 'study', 0), ('klen/muffin', 0.5007705092430115, 'web', 0)]",252,6.0,,6.21,139,54,124,1,9,14,9,136.0,208.0,90.0,1.5,63
571,perf,https://github.com/pybind/pybind11,[],,[],[],,,,pybind/pybind11,pybind11,14217,2075,250,C++,https://pybind11.readthedocs.io/,Seamless operability between C++11 and Python,pybind,2024-01-14,2015-07-05,447,31.78505269881827,https://avatars.githubusercontent.com/u/17565521?v=4,Seamless operability between C++11 and Python,['bindings'],['bindings'],2024-01-13,"[('nvidia/cuda-python', 0.6058850288391113, 'ml', 0), ('marella/ctransformers', 0.5483258962631226, 'nlp', 0), ('pyo3/pyo3', 0.5454217791557312, 'util', 0), ('pyston/pyston', 0.5205539464950562, 'util', 0)]",341,6.0,,2.79,148,95,104,0,3,7,3,148.0,273.0,90.0,1.8,63
1164,llm,https://github.com/mayooear/gpt4-pdf-chatbot-langchain,[],,[],[],,,,mayooear/gpt4-pdf-chatbot-langchain,gpt4-pdf-chatbot-langchain,14031,3030,150,TypeScript,https://www.youtube.com/watch?v=ih9PBGVVOO4,GPT4 & LangChain Chatbot for large PDF docs,mayooear,2024-01-14,2023-03-17,45,307.8902821316614,,GPT4 & LangChain Chatbot for large PDF docs,"['gpt4', 'langchain', 'nextjs', 'openai', 'pdf', 'typescript']","['gpt4', 'langchain', 'nextjs', 'openai', 'pdf', 'typescript']",2023-11-13,"[('bhaskatripathi/pdfgpt', 0.6926714777946472, 'llm', 0), ('xtekky/gpt4free', 0.642866313457489, 'llm', 2), ('killianlucas/open-interpreter', 0.6425415277481079, 'llm', 0), ('run-llama/rags', 0.6243221163749695, 'llm', 1), ('openai/openai-cookbook', 0.59941166639328, 'ml', 1), ('h2oai/h2ogpt', 0.5983483195304871, 'llm', 1), ('langchain-ai/chat-langchain', 0.5839036107063293, 'llm', 0), ('embedchain/embedchain', 0.5652803182601929, 'llm', 0), ('microsoft/autogen', 0.5650250911712646, 'llm', 0), ('togethercomputer/openchatkit', 0.5483038425445557, 'nlp', 0), ('lm-sys/fastchat', 0.5297297239303589, 'llm', 0), ('imartinez/privategpt', 0.5235142707824707, 'llm', 1), ('minimaxir/simpleaichat', 0.5190370678901672, 'llm', 0), ('blinkdl/chatrwkv', 0.5159780979156494, 'llm', 0), ('larsbaunwall/bricky', 0.5141124725341797, 'llm', 2), ('mlc-ai/web-llm', 0.50135737657547, 'llm', 0)]",3,2.0,,0.4,64,40,10,2,0,0,0,64.0,87.0,90.0,1.4,63
902,perf,https://github.com/exaloop/codon,[],,[],[],,,,exaloop/codon,codon,13597,491,128,C++,https://docs.exaloop.io/codon,"A high-performance, zero-overhead, extensible Python compiler using LLVM",exaloop,2024-01-14,2021-09-27,122,111.32046783625731,https://avatars.githubusercontent.com/u/89494599?v=4,"A high-performance, zero-overhead, extensible Python compiler using LLVM","['compiler', 'gpu-programming', 'high-performance', 'llvm', 'parallel-programming']","['compiler', 'gpu-programming', 'high-performance', 'llvm', 'parallel-programming']",2024-01-13,"[('numba/numba', 0.7364824414253235, 'perf', 2), ('lcompilers/lpython', 0.7257847189903259, 'util', 2), ('cython/cython', 0.7081640958786011, 'util', 0), ('pypy/pypy', 0.6885042786598206, 'util', 1), ('numba/llvmlite', 0.6857039332389832, 'util', 0), ('pyston/pyston', 0.6812407970428467, 'util', 0), ('nvidia/tensorrt-llm', 0.6340402364730835, 'viz', 0), ('fastai/fastcore', 0.6250977516174316, 'util', 0), ('google/jax', 0.6078891158103943, 'ml', 0), ('nvidia/warp', 0.603724479675293, 'sim', 0), ('joblib/joblib', 0.6015269160270691, 'util', 0), ('micropython/micropython', 0.5922623872756958, 'util', 0), ('oracle/graalpython', 0.5912636518478394, 'util', 0), ('citadel-ai/langcheck', 0.5882013440132141, 'llm', 0), ('ethereum/py-evm', 0.5648453235626221, 'crypto', 0), ('pytorch/glow', 0.5580657720565796, 'ml', 0), ('pympler/pympler', 0.5520622730255127, 'perf', 0), ('plasma-umass/scalene', 0.5468367338180542, 'profiling', 1), ('klen/py-frameworks-bench', 0.5465443730354309, 'perf', 0), ('ipython/ipyparallel', 0.5431182980537415, 'perf', 0), ('rustpython/rustpython', 0.5343741774559021, 'util', 1), ('pypa/hatch', 0.5342748761177063, 'util', 0), ('dosisod/refurb', 0.5304907560348511, 'util', 0), ('sail-sg/envpool', 0.5291399359703064, 'sim', 0), ('pytorch/pytorch', 0.5243420600891113, 'ml-dl', 0), ('python/cpython', 0.5238469839096069, 'util', 0), ('faster-cpython/tools', 0.518226146697998, 'perf', 0), ('hoffstadt/dearpygui', 0.5152654647827148, 'gui', 0), ('eth-sri/lmql', 0.5149834752082825, 'llm', 0), ('chainlit/chainlit', 0.5148961544036865, 'llm', 0), ('pytoolz/toolz', 0.514833390712738, 'util', 0), ('google/gin-config', 0.5136995911598206, 'util', 0), ('vllm-project/vllm', 0.5132838487625122, 'llm', 0), ('panda3d/panda3d', 0.5107043981552124, 'gamedev', 0), ('google/tf-quant-finance', 0.5077102780342102, 'finance', 1), ('pythonspeed/filprofiler', 0.5046871900558472, 'profiling', 0), ('microsoft/pycodegpt', 0.5030719041824341, 'llm', 0), ('intel/intel-extension-for-pytorch', 0.5024413466453552, 'perf', 0), ('facebookincubator/aitemplate', 0.5021023750305176, 'ml-dl', 0)]",13,3.0,,1.0,45,21,28,0,6,4,6,45.0,34.0,90.0,0.8,63
1319,llm,https://github.com/openlmlab/moss,['language-model'],,[],[],,,,openlmlab/moss,MOSS,11710,1151,123,Python,https://txsun1997.github.io/blogs/moss.html,An open-source tool-augmented conversational language model from Fudan University,openlmlab,2024-01-14,2023-04-15,41,282.6551724137931,https://avatars.githubusercontent.com/u/127190579?v=4,An open-source tool-augmented conversational language model from Fudan University,"['chatgpt', 'deep-learning', 'dialogue-systems', 'large-language-models', 'natural-language-processing', 'text-generation']","['chatgpt', 'deep-learning', 'dialogue-systems', 'language-model', 'large-language-models', 'natural-language-processing', 'text-generation']",2023-09-08,"[('lm-sys/fastchat', 0.6857122778892517, 'llm', 1), ('rasahq/rasa', 0.6797881126403809, 'llm', 1), ('deeppavlov/deeppavlov', 0.6669769287109375, 'nlp', 2), ('rcgai/simplyretrieve', 0.6414903998374939, 'llm', 2), ('nvidia/nemo', 0.640605628490448, 'nlp', 2), ('microsoft/autogen', 0.6363752484321594, 'llm', 1), ('next-gpt/next-gpt', 0.6183462738990784, 'llm', 2), ('embedchain/embedchain', 0.6122089624404907, 'llm', 1), ('krohling/bondai', 0.6103507876396179, 'llm', 0), ('aiwaves-cn/agents', 0.6080291271209717, 'nlp', 1), ('nomic-ai/gpt4all', 0.6058529615402222, 'llm', 1), ('run-llama/rags', 0.6012157797813416, 'llm', 1), ('fasteval/fasteval', 0.593492329120636, 'llm', 0), ('lupantech/chameleon-llm', 0.5891066789627075, 'llm', 2), ('killianlucas/open-interpreter', 0.5889337062835693, 'llm', 1), ('guidance-ai/guidance', 0.5859184861183167, 'llm', 2), ('thudm/chatglm2-6b', 0.5803049802780151, 'llm', 1), ('xtekky/gpt4free', 0.5782349705696106, 'llm', 2), ('whu-zqh/chatgpt-vs.-bert', 0.5750217437744141, 'llm', 1), ('mlc-ai/web-llm', 0.5738821029663086, 'llm', 3), ('argilla-io/argilla', 0.5668735504150391, 'nlp', 1), ('blinkdl/chatrwkv', 0.5662409067153931, 'llm', 2), ('huggingface/text-generation-inference', 0.5647656917572021, 'llm', 1), ('conceptofmind/toolformer', 0.5642003417015076, 'llm', 1), ('deepset-ai/haystack', 0.5615432262420654, 'llm', 3), ('facebookresearch/parlai', 0.5612495541572571, 'nlp', 0), ('infinitylogesh/mutate', 0.5608090758323669, 'nlp', 2), ('thudm/chatglm-6b', 0.556516170501709, 'llm', 1), ('reasoning-machines/pal', 0.5542696714401245, 'llm', 2), ('chatarena/chatarena', 0.5541568398475647, 'llm', 3), ('databrickslabs/dolly', 0.5508648157119751, 'llm', 0), ('ai21labs/lm-evaluation', 0.5499148368835449, 'llm', 1), ('lianjiatech/belle', 0.5496982336044312, 'llm', 0), ('night-chen/toolqa', 0.549114465713501, 'llm', 1), ('minimaxir/gpt-2-simple', 0.5483125448226929, 'llm', 1), ('allenai/allennlp', 0.548026978969574, 'nlp', 2), ('baichuan-inc/baichuan-13b', 0.5460556149482727, 'llm', 3), ('gunthercox/chatterbot-corpus', 0.5456989407539368, 'nlp', 0), ('microsoft/generative-ai-for-beginners', 0.5399401783943176, 'study', 2), ('weaviate/verba', 0.5387402772903442, 'llm', 0), ('eugeneyan/obsidian-copilot', 0.5385111570358276, 'llm', 1), ('hannibal046/awesome-llm', 0.5380090475082397, 'study', 1), ('bigscience-workshop/promptsource', 0.5367189049720764, 'nlp', 1), ('togethercomputer/openchatkit', 0.5363262295722961, 'nlp', 0), ('ctlllll/llm-toolmaker', 0.5362043976783752, 'llm', 1), ('llmware-ai/llmware', 0.5356535911560059, 'llm', 1), ('young-geng/easylm', 0.5339535474777222, 'llm', 4), ('minimaxir/simpleaichat', 0.5309569835662842, 'llm', 1), ('openbmb/toolbench', 0.5276908278465271, 'llm', 0), ('langchain-ai/chat-langchain', 0.5243722200393677, 'llm', 0), ('hwchase17/langchain', 0.5230026245117188, 'llm', 1), ('ai21labs/in-context-ralm', 0.5220905542373657, 'llm', 1), ('srush/minichain', 0.5213707089424133, 'llm', 0), ('oobabooga/text-generation-webui', 0.5163723230361938, 'llm', 1), ('minimaxir/aitextgen', 0.5120126008987427, 'llm', 0), ('yueyu1030/attrprompt', 0.5103434920310974, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.508660614490509, 'llm', 2), ('freedomintelligence/llmzoo', 0.5085688233375549, 'llm', 1), ('jalammar/ecco', 0.5079621076583862, 'ml-interpretability', 1), ('facebookresearch/seamless_communication', 0.5070318579673767, 'nlp', 0), ('laion-ai/open-assistant', 0.5062494874000549, 'llm', 2), ('explosion/spacy-llm', 0.5049393773078918, 'llm', 2), ('ofa-sys/ofa', 0.5047501921653748, 'llm', 0), ('nvidia/nemo-guardrails', 0.504492461681366, 'llm', 1), ('promptslab/awesome-prompt-engineering', 0.5036895871162415, 'study', 2), ('paddlepaddle/paddlenlp', 0.5025514364242554, 'llm', 0)]",17,3.0,,3.42,13,2,9,4,0,0,0,13.0,13.0,90.0,1.0,63
644,profiling,https://github.com/plasma-umass/scalene,[],,[],[],1.0,,,plasma-umass/scalene,scalene,10488,362,87,JavaScript,,"Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals",plasma-umass,2024-01-14,2019-12-17,215,48.78139534883721,https://avatars.githubusercontent.com/u/1880823?v=4,"Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals","['cpu', 'cpu-profiling', 'gpu', 'gpu-programming', 'memory-allocation', 'memory-consumption', 'performance-analysis', 'performance-cpu', 'profiler', 'profiles-memory', 'profiling', 'python-profilers', 'scalene']","['cpu', 'cpu-profiling', 'gpu', 'gpu-programming', 'memory-allocation', 'memory-consumption', 'performance-analysis', 'performance-cpu', 'profiler', 'profiles-memory', 'profiling', 'python-profilers', 'scalene']",2024-01-11,"[('ray-project/ray', 0.6044383645057678, 'ml-ops', 0), ('pythonspeed/filprofiler', 0.6020299196243286, 'profiling', 0), ('micropython/micropython', 0.6017813086509705, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5931693315505981, 'perf', 0), ('cython/cython', 0.5566608905792236, 'util', 0), ('pytorch/glow', 0.5508031249046326, 'ml', 0), ('sumerc/yappi', 0.549071192741394, 'profiling', 1), ('pytorch/pytorch', 0.5483222007751465, 'ml-dl', 1), ('exaloop/codon', 0.5468367338180542, 'perf', 1), ('google/jax', 0.5463865399360657, 'ml', 0), ('blackhc/toma', 0.545809268951416, 'ml-dl', 1), ('nvidia/tensorrt-llm', 0.53840172290802, 'viz', 1), ('fastai/fastcore', 0.5373175144195557, 'util', 0), ('benfred/py-spy', 0.533981204032898, 'profiling', 3), ('joblib/joblib', 0.5323008298873901, 'util', 0), ('gradio-app/gradio', 0.5309175252914429, 'viz', 0), ('pytorchlightning/pytorch-lightning', 0.5295047760009766, 'ml-dl', 0), ('spotify/annoy', 0.5279016494750977, 'ml', 0), ('google/vizier', 0.5277619957923889, 'ml', 0), ('numpy/numpy', 0.5247296094894409, 'math', 0), ('determined-ai/determined', 0.524320125579834, 'ml-ops', 0), ('wandb/client', 0.5181810855865479, 'ml', 0), ('google/tf-quant-finance', 0.5155574083328247, 'finance', 1), ('pyston/pyston', 0.5100093483924866, 'util', 0), ('google/gin-config', 0.5083065032958984, 'util', 0), ('facebookincubator/aitemplate', 0.5074851512908936, 'ml-dl', 0), ('karpathy/micrograd', 0.5055549740791321, 'study', 0), ('microsoft/deepspeed', 0.5038928389549255, 'ml-dl', 1), ('microsoft/olive', 0.503036618232727, 'ml', 1), ('reloadware/reloadium', 0.5005995035171509, 'profiling', 0), ('klen/py-frameworks-bench', 0.500389039516449, 'perf', 0), ('mrdbourke/m1-machine-learning-test', 0.5002366900444031, 'ml', 0)]",44,6.0,,3.65,52,27,50,0,19,15,19,52.0,55.0,90.0,1.1,63
93,ml-ops,https://github.com/ludwig-ai/ludwig,['llm-training'],,[],[],,,,ludwig-ai/ludwig,ludwig,10390,1150,190,Python,http://ludwig.ai,"Low-code framework for building custom LLMs, neural networks, and other AI models",ludwig-ai,2024-01-13,2018-12-27,265,39.10215053763441,https://avatars.githubusercontent.com/u/65477820?v=4,"Low-code framework for building custom LLMs, neural networks, and other AI models","['computer-vision', 'data-centric', 'data-science', 'deep', 'deep-learning', 'deeplearning', 'fine-tuning', 'learning', 'llama', 'llama2', 'llm', 'llm-training', 'machine-learning', 'machinelearning', 'mistral', 'ml', 'natural-language', 'natural-language-processing', 'neural-network', 'pytorch']","['computer-vision', 'data-centric', 'data-science', 'deep', 'deep-learning', 'deeplearning', 'fine-tuning', 'learning', 'llama', 'llama2', 'llm', 'llm-training', 'machine-learning', 'machinelearning', 'mistral', 'ml', 'natural-language', 'natural-language-processing', 'neural-network', 'pytorch']",2024-01-12,"[('microsoft/torchscale', 0.6496773958206177, 'llm', 3), ('salesforce/codet5', 0.6258037686347961, 'nlp', 0), ('rafiqhasan/auto-tensorflow', 0.6223335266113281, 'ml-dl', 3), ('tigerlab-ai/tiger', 0.6120554804801941, 'llm', 3), ('bentoml/bentoml', 0.6104065775871277, 'ml-ops', 2), ('h2oai/h2o-llmstudio', 0.6097002625465393, 'llm', 5), ('bigscience-workshop/petals', 0.6096014976501465, 'data', 5), ('hiyouga/llama-factory', 0.6046749949455261, 'llm', 4), ('hiyouga/llama-efficient-tuning', 0.6046748757362366, 'llm', 4), ('tensorflow/tensorflow', 0.5990430116653442, 'ml-dl', 4), ('pathwaycom/llm-app', 0.5985682010650635, 'llm', 2), ('microsoft/promptflow', 0.5964416861534119, 'llm', 1), ('mlc-ai/mlc-llm', 0.5952907800674438, 'llm', 1), ('microsoft/semantic-kernel', 0.5949147939682007, 'llm', 1), ('bobazooba/xllm', 0.5947787761688232, 'llm', 6), ('operand/agency', 0.5899900197982788, 'llm', 2), ('bentoml/openllm', 0.5829933881759644, 'ml-ops', 6), ('lancedb/lancedb', 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0.5272180438041687, 'nlp', 3), ('nebuly-ai/nebullvm', 0.5271685123443604, 'perf', 2), ('towhee-io/towhee', 0.5253190398216248, 'ml-ops', 3), ('deepset-ai/haystack', 0.520433783531189, 'llm', 2), ('tensorlayer/tensorlayer', 0.5189732909202576, 'ml-rl', 2), ('ddbourgin/numpy-ml', 0.5178036689758301, 'ml', 1), ('polyaxon/polyaxon', 0.5176489949226379, 'ml-ops', 5), ('jina-ai/jina', 0.5165185332298279, 'ml', 2), ('keras-team/keras', 0.515795111656189, 'ml-dl', 4), ('hpcaitech/colossalai', 0.5150622129440308, 'llm', 1), ('vllm-project/vllm', 0.5144979357719421, 'llm', 3), ('google/trax', 0.5144282579421997, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.5141164064407349, 'util', 2), ('next-gpt/next-gpt', 0.5110254287719727, 'llm', 1), ('karpathy/micrograd', 0.5106154680252075, 'study', 0), ('hegelai/prompttools', 0.5103862881660461, 'llm', 2), ('eugeneyan/open-llms', 0.5101239681243896, 'study', 1), ('determined-ai/determined', 0.5082186460494995, 'ml-ops', 4), ('deci-ai/super-gradients', 0.5076872706413269, 'ml-dl', 4), ('salesforce/xgen', 0.5066179037094116, 'llm', 1), ('salesforce/codegen', 0.5055090188980103, 'nlp', 1), ('microsoft/generative-ai-for-beginners', 0.5046253800392151, 'study', 0), ('explosion/spacy-llm', 0.5036592483520508, 'llm', 4), ('optimalscale/lmflow', 0.5029340386390686, 'llm', 2), ('gradio-app/gradio', 0.5020084381103516, 'viz', 3), ('neuralmagic/deepsparse', 0.5014819502830505, 'nlp', 2), ('pycaret/pycaret', 0.5007109045982361, 'ml', 3), ('mindsdb/mindsdb', 0.5004526376724243, 'data', 3), ('rasbt/machine-learning-book', 0.5003655552864075, 'study', 3)]",151,2.0,,11.25,184,148,61,0,17,11,17,183.0,277.0,90.0,1.5,63
1624,util,https://github.com/nuitka/nuitka,[],,[],[],,,,nuitka/nuitka,Nuitka,10305,563,135,Python,http://nuitka.net,"Nuitka is a Python compiler written in Python.  It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module. ",nuitka,2024-01-14,2013-04-23,562,18.33629893238434,https://avatars.githubusercontent.com/u/43496036?v=4,"Nuitka is a Python compiler written in Python.  It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module. ","['compiler', 'nuitka', 'packaging-tool', 'performance', 'programming', 'python-compiler']","['compiler', 'nuitka', 'packaging-tool', 'performance', 'programming', 'python-compiler']",2024-01-12,"[('chrismattmann/tika-python', 0.5718207955360413, 'nlp', 0), ('numerai/numerox', 0.5074256062507629, 'finance', 0), ('pypy/pypy', 0.5013567209243774, 'util', 1)]",166,4.0,,89.88,1753,1685,131,0,0,34,34,1753.0,851.0,90.0,0.5,63
1066,data,https://github.com/bigscience-workshop/petals,[],,[],[],,,,bigscience-workshop/petals,petals,8241,423,87,Python,https://petals.dev,"🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading",bigscience-workshop,2024-01-14,2022-06-12,85,96.62814070351759,https://avatars.githubusercontent.com/u/82455566?v=4,"🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading","['bloom', 'chatbot', 'deep-learning', 'distributed-systems', 'falcon', 'gpt', 'guanaco', 'language-models', 'large-language-models', 'llama', 'llama2', 'machine-learning', 'neural-networks', 'nlp', 'pipeline-parallelism', 'pretrained-models', 'pytorch', 'tensor-parallelism', 'transformer', 'volunteer-computing']","['bloom', 'chatbot', 'deep-learning', 'distributed-systems', 'falcon', 'gpt', 'guanaco', 'language-models', 'large-language-models', 'llama', 'llama2', 'machine-learning', 'neural-networks', 'nlp', 'pipeline-parallelism', 'pretrained-models', 'pytorch', 'tensor-parallelism', 'transformer', 'volunteer-computing']",2023-11-16,"[('intel/intel-extension-for-transformers', 0.7305524945259094, 'perf', 1), ('predibase/lorax', 0.6941356062889099, 'llm', 3), ('vllm-project/vllm', 0.6905003190040588, 'llm', 4), ('bentoml/openllm', 0.6789776682853699, 'ml-ops', 3), ('young-geng/easylm', 0.6689819693565369, 'llm', 5), 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457,ml-ops,https://github.com/dbt-labs/dbt-core,[],,[],[],,,,dbt-labs/dbt-core,dbt-core,8100,1422,134,Python,https://getdbt.com,dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.,dbt-labs,2024-01-14,2016-03-10,411,19.67383761276891,https://avatars.githubusercontent.com/u/18339788?v=4,dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.,"['analytics', 'business-intelligence', 'data-modeling', 'dbt-viewpoint', 'elt', 'pypa', 'slack']","['analytics', 'business-intelligence', 'data-modeling', 'dbt-viewpoint', 'elt', 'pypa', 'slack']",2024-01-12,"[('mage-ai/mage-ai', 0.5732520818710327, 'ml-ops', 1), ('airbytehq/airbyte', 0.5581380724906921, 'data', 1), ('datafold/data-diff', 0.550411581993103, 'data', 0), ('dlt-hub/dlt', 0.5388659834861755, 'data', 1), ('databricks/dbt-databricks', 0.5302814841270447, 'data', 0), ('dagster-io/dagster', 0.5295764803886414, 'ml-ops', 1), ('saulpw/visidata', 0.5234029293060303, 'term', 0), ('great-expectations/great_expectations', 0.5088808536529541, 'ml-ops', 0), ('google/ml-metadata', 0.5018987655639648, 'ml-ops', 0)]",302,3.0,,12.71,777,483,96,0,73,28,73,777.0,1463.0,90.0,1.9,63
66,ml,https://github.com/pycaret/pycaret,[],,[],[],,,,pycaret/pycaret,pycaret,8084,1715,133,Jupyter Notebook,https://www.pycaret.org,"An open-source, low-code machine learning library in Python",pycaret,2024-01-14,2019-11-23,218,37.00981033355134,https://avatars.githubusercontent.com/u/58118658?v=4,"An open-source, low-code machine learning library in Python","['anomaly-detection', 'citizen-data-scientists', 'classification', 'clustering', 'data-science', 'gpu', 'machine-learning', 'ml', 'pycaret', 'regression', 'time-series']","['anomaly-detection', 'citizen-data-scientists', 'classification', 'clustering', 'data-science', 'gpu', 'machine-learning', 'ml', 'pycaret', 'regression', 'time-series']",2023-12-14,"[('yzhao062/pyod', 0.7633078694343567, 'data', 3), ('unit8co/darts', 0.7233750820159912, 'time-series', 4), ('rasbt/mlxtend', 0.7207165956497192, 'ml', 2), ('featurelabs/featuretools', 0.6861603856086731, 'ml', 2), ('scikit-learn/scikit-learn', 0.6792936325073242, 'ml', 2), 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162,ml,https://github.com/wandb/client,[],,[],[],,,,wandb/client,wandb,7706,594,55,Python,https://wandb.ai,🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.,wandb,2024-01-14,2017-03-24,357,21.550938873351978,https://avatars.githubusercontent.com/u/26401354?v=4,🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.,"['collaboration', 'data-science', 'data-versioning', 'deep-learning', 'experiment-track', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'jax', 'keras', 'machine-learning', 'ml-platform', 'mlops', 'model-versioning', 'pytorch', 'reinforcement-learning', 'reproducibility', 'tensorflow']","['collaboration', 'data-science', 'data-versioning', 'deep-learning', 'experiment-track', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'jax', 'keras', 'machine-learning', 'ml-platform', 'mlops', 'model-versioning', 'pytorch', 'reinforcement-learning', 'reproducibility', 'tensorflow']",2024-01-12,"[('aimhubio/aim', 0.696733832359314, 'ml-ops', 5), ('determined-ai/determined', 0.6777682900428772, 'ml-ops', 11), ('polyaxon/polyaxon', 0.6643576622009277, 'ml-ops', 9), ('polyaxon/datatile', 0.6632310748100281, 'pandas', 4), ('gradio-app/gradio', 0.6344223618507385, 'viz', 3), 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1571,nlp,https://github.com/facebookresearch/nougat,"['documents', 'pdf-parser', 'academic', 'latex']",,[],[],,,,facebookresearch/nougat,nougat,7391,462,60,Python,https://facebookresearch.github.io/nougat/,Implementation of Nougat Neural Optical Understanding for Academic Documents,facebookresearch,2024-01-14,2023-06-07,33,218.29957805907173,https://avatars.githubusercontent.com/u/16943930?v=4,Implementation of Nougat Neural Optical Understanding for Academic Documents,[],"['academic', 'documents', 'latex', 'pdf-parser']",2023-10-04,[],15,2.0,,1.12,59,16,7,3,2,3,2,59.0,89.0,90.0,1.5,63
1537,llm,https://github.com/lianjiatech/belle,[],,[],[],,,,lianjiatech/belle,BELLE,7155,711,105,HTML,,BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）,lianjiatech,2024-01-14,2023-03-17,45,157.00626959247649,https://avatars.githubusercontent.com/u/14540911?v=4,BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）,"['bloom', 'chinese-nlp', 'gpt-evaluation', 'gpt-q', 'instruct-finetune', 'instruct-gpt', 'instruction-set', 'llama', 'lora', 'open-models']","['bloom', 'chinese-nlp', 'gpt-evaluation', 'gpt-q', 'instruct-finetune', 'instruct-gpt', 'instruction-set', 'llama', 'lora', 'open-models']",2023-12-29,"[('hannibal046/awesome-llm', 0.8117328882217407, 'study', 0), ('microsoft/autogen', 0.6912463903427124, 'llm', 0), ('ctlllll/llm-toolmaker', 0.6910099983215332, 'llm', 0), ('ai21labs/lm-evaluation', 0.6819735169410706, 'llm', 0), ('bobazooba/xllm', 0.6800654530525208, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.6785591840744019, 'llm', 0), ('next-gpt/next-gpt', 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1449,util,https://github.com/pdm-project/pdm,"['package-manager', 'packaging']",,[],[],,,,pdm-project/pdm,pdm,5927,302,31,Python,https://pdm-project.org,A modern Python package and dependency manager supporting the latest PEP standards,pdm-project,2024-01-14,2019-12-27,213,27.75183946488294,https://avatars.githubusercontent.com/u/59549022?v=4,A modern Python package and dependency manager supporting the latest PEP standards,"['package-manager', 'packaging', 'pep582', 'pep621', 'workflow']","['package-manager', 'packaging', 'pep582', 'pep621', 'workflow']",2024-01-12,"[('mitsuhiko/rye', 0.7408201694488525, 'util', 2), ('indygreg/pyoxidizer', 0.7249577045440674, 'util', 2), ('python-poetry/poetry', 0.7060061097145081, 'util', 2), ('pypa/hatch', 0.6953819394111633, 'util', 2), ('pomponchik/instld', 0.6940727829933167, 'util', 1), ('pypi/warehouse', 0.6904469728469849, 'util', 0), ('pypa/flit', 0.6415485143661499, 'util', 2), ('jazzband/pip-tools', 0.6316167116165161, 'util', 1), ('pyodide/micropip', 0.6069954037666321, 'util', 0), ('mamba-org/mamba', 0.6015645861625671, 'util', 2), ('thoth-station/micropipenv', 0.5940836071968079, 'util', 0), ('pytoolz/toolz', 0.592634379863739, 'util', 0), ('hhatto/autopep8', 0.5722745656967163, 'util', 0), ('regebro/pyroma', 0.568504810333252, 'util', 1), ('pypa/pipenv', 0.5644690990447998, 'util', 1), ('tezromach/python-package-template', 0.5643466114997864, 'template', 0), ('urwid/urwid', 0.5620359778404236, 'term', 0), ('dosisod/refurb', 0.5603682398796082, 'util', 0), ('spack/spack', 0.5586650371551514, 'util', 1), ('tox-dev/pipdeptree', 0.5571689605712891, 'util', 0), ('pypa/installer', 0.5561202168464661, 'util', 0), ('hoffstadt/dearpygui', 0.5533838868141174, 'gui', 0), ('trailofbits/pip-audit', 0.54820716381073, 'security', 0), ('pypy/pypy', 0.5482062101364136, 'util', 0), ('conda/conda', 0.5474826097488403, 'util', 2), ('tiangolo/poetry-version-plugin', 0.54221111536026, 'util', 1), ('pyupio/safety', 0.5419641733169556, 'security', 0), ('libtcod/python-tcod', 0.5411107540130615, 'gamedev', 0), ('malloydata/malloy-py', 0.5352970957756042, 'data', 0), ('eleutherai/pyfra', 0.5340529680252075, 'ml', 0), ('omry/omegaconf', 0.5336859822273254, 'util', 0), ('pyo3/maturin', 0.5319200754165649, 'util', 1), ('pyscaffold/pyscaffold', 0.531360924243927, 'template', 0), ('pyston/pyston', 0.530032217502594, 'util', 0), ('prompt-toolkit/ptpython', 0.518380880355835, 'util', 0), ('allrod5/injectable', 0.5183610320091248, 'util', 0), ('ofek/pyapp', 0.514813244342804, 'util', 1), ('python-injector/injector', 0.5129967927932739, 'util', 0), ('bndr/pipreqs', 0.5109939575195312, 'util', 0), ('pyenv/pyenv', 0.5091418027877808, 'util', 0), ('psf/black', 0.5081842541694641, 'util', 0), ('grahamdumpleton/wrapt', 0.5066951513290405, 'util', 0), ('google/python-fire', 0.5054830312728882, 'term', 0), ('primal100/pybitcointools', 0.505200207233429, 'crypto', 0), ('sqlalchemy/mako', 0.5048580765724182, 'template', 0), ('eugeneyan/python-collab-template', 0.5047013163566589, 'template', 0), ('ethtx/ethtx', 0.5046972632408142, 'crypto', 0), ('python/cpython', 0.5036362409591675, 'util', 0), ('python-rope/rope', 0.5023258328437805, 'util', 0), ('tedivm/robs_awesome_python_template', 0.5013588070869446, 'template', 0), ('linkedin/shiv', 0.5008074641227722, 'util', 0)]",161,4.0,,8.96,241,219,49,0,41,46,41,241.0,508.0,90.0,2.1,63
1259,util,https://github.com/timdettmers/bitsandbytes,['cuda'],,[],[],,,,timdettmers/bitsandbytes,bitsandbytes,4678,503,43,Python,,Accessible large language models via k-bit quantization for PyTorch.,timdettmers,2024-01-14,2021-06-04,138,33.758762886597935,,Accessible large language models via k-bit quantization for PyTorch.,[],['cuda'],2024-01-12,"[('artidoro/qlora', 0.6154015064239502, 'llm', 0), ('squeezeailab/squeezellm', 0.6107233166694641, 'llm', 0), ('allenai/allennlp', 0.5652725100517273, 'nlp', 0), ('cqcl/lambeq', 0.5420622229576111, 'nlp', 0), ('rentruewang/koila', 0.5362823009490967, 'ml', 0), ('sjtu-ipads/powerinfer', 0.5349003076553345, 'llm', 0), ('intel/intel-extension-for-pytorch', 0.5321269631385803, 'perf', 0), ('opengvlab/omniquant', 0.5289829969406128, 'llm', 0), ('juncongmoo/pyllama', 0.5255274772644043, 'llm', 0), ('ggerganov/ggml', 0.5234319567680359, 'ml', 0), ('jonasgeiping/cramming', 0.5229673981666565, 'nlp', 0), ('nvidia/apex', 0.5227283239364624, 'ml-dl', 0), ('pytorch/ignite', 0.5188927054405212, 'ml-dl', 0), ('huggingface/transformers', 0.5178513526916504, 'nlp', 0), ('hannibal046/awesome-llm', 0.5172896385192871, 'study', 0), ('baichuan-inc/baichuan-13b', 0.5144120454788208, 'llm', 0), ('cvxgrp/pymde', 0.509398341178894, 'ml', 1), ('salesforce/blip', 0.5067077875137329, 'diffusion', 0), ('huggingface/accelerate', 0.5063002705574036, 'ml', 0), ('pytorch/data', 0.5031000375747681, 'data', 0), ('bytedance/lightseq', 0.5029526948928833, 'nlp', 1), ('arogozhnikov/einops', 0.5005117654800415, 'ml-dl', 0)]",56,6.0,,5.06,719,616,32,0,6,5,6,719.0,1336.0,90.0,1.9,63
1769,data,https://github.com/tobymao/sqlglot,[],,[],[],1.0,,,tobymao/sqlglot,sqlglot,4500,479,33,Python,https://sqlglot.com/,Python SQL Parser and Transpiler,tobymao,2024-01-14,2021-03-13,150,29.914529914529915,,Python SQL Parser and Transpiler,"['bigquery', 'clickhouse', 'databricks', 'duckdb', 'hive', 'mysql', 'optimizer', 'parser', 'postgres', 'presto', 'redshift', 'snowflake', 'spark', 'sql', 'sqlite', 'sqlparser', 'transpiler', 'trino', 'tsql']","['bigquery', 'clickhouse', 'databricks', 'duckdb', 'hive', 'mysql', 'optimizer', 'parser', 'postgres', 'presto', 'redshift', 'snowflake', 'spark', 'sql', 'sqlite', 'sqlparser', 'transpiler', 'trino', 'tsql']",2024-01-14,"[('ibis-project/ibis', 0.7856696248054504, 'data', 8), ('macbre/sql-metadata', 0.6693900227546692, 'data', 3), ('tiangolo/sqlmodel', 0.6654618382453918, 'data', 1), ('andialbrecht/sqlparse', 0.6332684755325317, 'data', 0), ('pyparsing/pyparsing', 0.6120659708976746, 'util', 0), ('machow/siuba', 0.6064596176147461, 'pandas', 1), ('sqlalchemy/sqlalchemy', 0.6049283742904663, 'data', 1), ('fastai/fastcore', 0.5786949396133423, 'util', 0), ('malloydata/malloy-py', 0.5748046040534973, 'data', 1), ('aws/aws-sdk-pandas', 0.5701399445533752, 'pandas', 2), ('kayak/pypika', 0.5631858706474304, 'data', 1), ('datafold/data-diff', 0.5574495196342468, 'data', 5), ('mcfunley/pugsql', 0.5487765073776245, 'data', 1), ('pola-rs/polars', 0.5481932163238525, 'pandas', 0), ('sfu-db/connector-x', 0.5419861674308777, 'data', 1), ('coleifer/peewee', 0.5414004325866699, 'data', 1), ('fugue-project/fugue', 0.5296240448951721, 'pandas', 3), ('strawberry-graphql/strawberry', 0.5215062499046326, 'web', 0), ('airbytehq/airbyte', 0.5210596919059753, 'data', 4), ('dagworks-inc/hamilton', 0.5160204768180847, 'ml-ops', 0), ('pytoolz/toolz', 0.5141063928604126, 'util', 0), ('astronomer/astro-sdk', 0.5131558179855347, 'ml-ops', 5), ('ploomber/ploomber', 0.5114932656288147, 'ml-ops', 0), ('apache/spark', 0.5114732980728149, 'data', 2), ('pandas-dev/pandas', 0.5091575384140015, 'pandas', 0), ('databricks/dbt-databricks', 0.5069236755371094, 'data', 2), ('mage-ai/mage-ai', 0.5064417123794556, 'ml-ops', 2), ('unionai-oss/pandera', 0.5052539706230164, 'pandas', 0), ('cython/cython', 0.5022580623626709, 'util', 0), ('vaexio/vaex', 0.5004581809043884, 'perf', 0)]",117,3.0,,34.85,410,406,35,0,0,163,163,411.0,470.0,90.0,1.1,63
785,ml-dl,https://github.com/facebookincubator/aitemplate,[],,[],[],,,,facebookincubator/aitemplate,AITemplate,4354,349,84,Python,,AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.,facebookincubator,2024-01-13,2022-07-15,80,54.03900709219858,https://avatars.githubusercontent.com/u/19538647?v=4,AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.,[],[],2024-01-06,"[('nvidia/tensorrt-llm', 0.5429112315177917, 'viz', 0), ('pytorch/glow', 0.542003870010376, 'ml', 0), ('pytorch/pytorch', 0.5403209328651428, 'ml-dl', 0), ('plasma-umass/scalene', 0.5074851512908936, 'profiling', 0), ('nvidia/warp', 0.502746045589447, 'sim', 0), ('exaloop/codon', 0.5021023750305176, 'perf', 0)]",89,4.0,,12.44,42,28,18,0,1,1,1,42.0,127.0,90.0,3.0,63
1583,nlp,https://github.com/aiwaves-cn/agents,[],,[],[],,,,aiwaves-cn/agents,agents,4205,307,58,Python,http://www.aiwaves-agents.com/,An Open-source Framework for Autonomous Language Agents,aiwaves-cn,2024-01-13,2023-07-18,28,150.17857142857142,https://avatars.githubusercontent.com/u/129118469?v=4,An Open-source Framework for Autonomous Language Agents,"['autonomous-agents', 'language-model', 'llm']","['autonomous-agents', 'language-model', 'llm']",2023-12-04,"[('nomic-ai/gpt4all', 0.6714950799942017, 'llm', 1), ('krohling/bondai', 0.6704409718513489, 'llm', 1), ('rasahq/rasa', 0.6561240553855896, 'llm', 0), ('jina-ai/thinkgpt', 0.6464569568634033, 'llm', 1), ('lm-sys/fastchat', 0.6372155547142029, 'llm', 1), ('microsoft/autogen', 0.6282492280006409, 'llm', 1), ('openlmlab/moss', 0.6080291271209717, 'llm', 1), ('embedchain/embedchain', 0.6022089123725891, 'llm', 1), ('argilla-io/argilla', 0.5958381295204163, 'nlp', 1), ('noahshinn/reflexion', 0.5806130170822144, 'llm', 1), ('operand/agency', 0.5783196687698364, 'llm', 2), ('minedojo/voyager', 0.5751522183418274, 'llm', 0), ('explosion/spacy-llm', 0.5685261487960815, 'llm', 1), ('tigerlab-ai/tiger', 0.5588130950927734, 'llm', 1), ('juncongmoo/pyllama', 0.5563294887542725, 'llm', 0), ('infinitylogesh/mutate', 0.5541864037513733, 'nlp', 1), ('langchain-ai/langgraph', 0.5516238808631897, 'llm', 0), ('young-geng/easylm', 0.5498647689819336, 'llm', 1), ('nebuly-ai/nebullvm', 0.5476635098457336, 'perf', 1), ('conceptofmind/toolformer', 0.5445042252540588, 'llm', 1), ('lupantech/chameleon-llm', 0.5420612692832947, 'llm', 2), ('chatarena/chatarena', 0.5419332385063171, 'llm', 0), ('humanoidagents/humanoidagents', 0.5400265455245972, 'sim', 1), ('deepset-ai/haystack', 0.5381879806518555, 'llm', 1), ('hwchase17/langchain', 0.536536693572998, 'llm', 1), ('salesforce/xgen', 0.5333874225616455, 'llm', 2), ('google-research/language', 0.5324529409408569, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.5291845202445984, 'study', 0), ('guardrails-ai/guardrails', 0.528048038482666, 'llm', 1), ('deeppavlov/deeppavlov', 0.5271100997924805, 'nlp', 0), ('hannibal046/awesome-llm', 0.5250198245048523, 'study', 1), ('cg123/mergekit', 0.5241085886955261, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5234825611114502, 'llm', 1), ('run-llama/rags', 0.5222904682159424, 'llm', 1), ('ibm/dromedary', 0.5198720097541809, 'llm', 1), ('fasteval/fasteval', 0.5190715789794922, 'llm', 1), ('thudm/chatglm2-6b', 0.5184250473976135, 'llm', 1), ('nvidia/nemo-guardrails', 0.5130923986434937, 'llm', 1), ('zacwellmer/worldmodels', 0.5116593241691589, 'ml-rl', 0), ('databrickslabs/dolly', 0.5108225345611572, 'llm', 0), ('eleutherai/the-pile', 0.5101150274276733, 'data', 1), ('allenai/allennlp', 0.5092148184776306, 'nlp', 0), ('night-chen/toolqa', 0.5083682537078857, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5078116059303284, 'nlp', 0), ('prefecthq/marvin', 0.506673276424408, 'nlp', 1), ('mlc-ai/mlc-llm', 0.5054627060890198, 'llm', 2), ('guidance-ai/guidance', 0.5045157670974731, 'llm', 1), ('transformeroptimus/superagi', 0.5044564604759216, 'llm', 2), ('freedomintelligence/llmzoo', 0.5030314922332764, 'llm', 1), ('reasoning-machines/pal', 0.5023298263549805, 'llm', 1), ('openlm-research/open_llama', 0.5005034804344177, 'llm', 1), ('rcgai/simplyretrieve', 0.5003217458724976, 'llm', 0)]",23,3.0,,20.21,47,37,6,1,0,0,0,47.0,50.0,90.0,1.1,63
1246,ml-dl,https://github.com/deci-ai/super-gradients,[],,[],[],,,,deci-ai/super-gradients,super-gradients,4073,452,41,Jupyter Notebook,https://www.supergradients.com,Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.,deci-ai,2024-01-14,2021-11-28,113,35.95334174022699,https://avatars.githubusercontent.com/u/56918593?v=4,Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.,"['computer-vision', 'deep-learning', 'image-classification', 'imagenet', 'neural-network', 'object-detection', 'pretrained-models', 'pretrained-weights', 'pytorch', 'semantic-segmentation', 'transfer-learning']","['computer-vision', 'deep-learning', 'image-classification', 'imagenet', 'neural-network', 'object-detection', 'pretrained-models', 'pretrained-weights', 'pytorch', 'semantic-segmentation', 'transfer-learning']",2024-01-12,"[('roboflow/notebooks', 0.8082399368286133, 'study', 5), ('roboflow/supervision', 0.6838550567626953, 'ml', 4), ('facebookresearch/vissl', 0.6394485831260681, 'ml', 0), ('google-research/maxvit', 0.6301395893096924, 'ml', 2), ('nvlabs/gcvit', 0.6253355145454407, 'diffusion', 4), ('lucidrains/vit-pytorch', 0.62143474817276, 'ml-dl', 2), ('open-mmlab/mmdetection', 0.6168127655982971, 'ml', 2), ('kornia/kornia', 0.6162040829658508, 'ml-dl', 4), ('rwightman/pytorch-image-models', 0.612806499004364, 'ml-dl', 3), ('lightly-ai/lightly', 0.610939621925354, 'ml', 3), ('open-mmlab/mmsegmentation', 0.5933340787887573, 'ml', 2), ('tensorflow/tensorflow', 0.5904759764671326, 'ml-dl', 2), ('megvii-basedetection/yolox', 0.5785552859306335, 'ml', 3), ('keras-team/keras-cv', 0.5779148936271667, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5777014493942261, 'ml-dl', 2), ('open-mmlab/mmcv', 0.5693687796592712, 'ml', 1), ('lutzroeder/netron', 0.5625087022781372, 'ml', 3), ('blakeblackshear/frigate', 0.5583767294883728, 'util', 1), ('mdbloice/augmentor', 0.5568101406097412, 'ml', 1), ('salesforce/blip', 0.5560441613197327, 'diffusion', 0), ('albumentations-team/albumentations', 0.5558651685714722, 'ml-dl', 3), ('microsoft/swin-transformer', 0.5506076812744141, 'ml', 4), ('azavea/raster-vision', 0.5499731302261353, 'gis', 5), ('kevinmusgrave/pytorch-metric-learning', 0.5495540499687195, 'ml', 3), ('huggingface/datasets', 0.5464844107627869, 'nlp', 3), ('pytorch/ignite', 0.5459545850753784, 'ml-dl', 3), ('matterport/mask_rcnn', 0.5377986431121826, 'ml-dl', 1), ('hysts/pytorch_image_classification', 0.5351361632347107, 'ml-dl', 3), ('towhee-io/towhee', 0.5340181589126587, 'ml-ops', 1), ('microsoft/torchgeo', 0.5279808640480042, 'gis', 3), ('lucidrains/imagen-pytorch', 0.5232393145561218, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5221906900405884, 'ml', 3), ('google/automl', 0.5212241411209106, 'ml', 1), ('facebookresearch/detectron2', 0.5197049379348755, 'ml-dl', 0), ('idea-research/groundingdino', 0.5182757377624512, 'diffusion', 1), ('microsoft/onnxruntime', 0.5170263051986694, 'ml', 2), ('oml-team/open-metric-learning', 0.5170210599899292, 'ml', 3), ('neuralmagic/sparseml', 0.5168516039848328, 'ml-dl', 4), ('aleju/imgaug', 0.5129750370979309, 'ml', 1), ('ludwig-ai/ludwig', 0.5076872706413269, 'ml-ops', 4), ('mlflow/mlflow', 0.505067765712738, 'ml-ops', 0), ('activeloopai/deeplake', 0.5050042867660522, 'ml-ops', 3), ('mosaicml/composer', 0.5038636326789856, 'ml-dl', 3), ('horovod/horovod', 0.5012701749801636, 'ml-ops', 2), ('christoschristofidis/awesome-deep-learning', 0.5002260804176331, 'study', 2)]",52,5.0,,9.79,289,256,26,0,14,457,14,289.0,401.0,90.0,1.4,63
1111,llm,https://github.com/mmabrouk/chatgpt-wrapper,[],,[],[],,,,mmabrouk/chatgpt-wrapper,llm-workflow-engine,3541,463,42,Python,,Power CLI and Workflow manager for LLMs (core package),mmabrouk,2024-01-14,2022-12-03,60,58.59810874704492,https://avatars.githubusercontent.com/u/134339574?v=4,Power CLI and Workflow manager for LLMs (core package),"['chatbot', 'chatgpt', 'gpt-3', 'gpt3', 'gpt4', 'llm', 'openai']","['chatbot', 'chatgpt', 'gpt-3', 'gpt3', 'gpt4', 'llm', 'openai']",2024-01-09,"[('shishirpatil/gorilla', 0.6682367324829102, 'llm', 2), ('run-llama/rags', 0.644103467464447, 'llm', 4), ('farizrahman4u/loopgpt', 0.6415730118751526, 'llm', 2), ('chainlit/chainlit', 0.6109486222267151, 'llm', 3), ('openai/openai-cookbook', 0.6022109985351562, 'ml', 3), ('h2oai/h2o-llmstudio', 0.5947935581207275, 'llm', 3), ('xtekky/gpt4free', 0.5910773277282715, 'llm', 6), ('microsoft/promptflow', 0.5893070101737976, 'llm', 2), ('nomic-ai/gpt4all', 0.5768630504608154, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5764015913009644, 'perf', 1), ('deep-diver/llm-as-chatbot', 0.5747789144515991, 'llm', 1), ('microsoft/promptcraft-robotics', 0.5717716217041016, 'sim', 2), ('opengenerativeai/genossgpt', 0.56969153881073, 'llm', 2), ('pathwaycom/llm-app', 0.5610566735267639, 'llm', 2), ('microsoft/autogen', 0.5563012361526489, 'llm', 2), ('embedchain/embedchain', 0.5488071441650391, 'llm', 2), ('hwchase17/langchain', 0.5374903082847595, 'llm', 1), ('berriai/litellm', 0.5316495299339294, 'llm', 2), ('microsoft/semantic-kernel', 0.526870608329773, 'llm', 2), ('deepset-ai/haystack', 0.525952935218811, 'llm', 2), ('mnotgod96/appagent', 0.5122302174568176, 'llm', 3), ('alpha-vllm/llama2-accessory', 0.5011169910430908, 'llm', 0)]",25,3.0,,19.67,7,7,14,0,80,79,80,7.0,19.0,90.0,2.7,63
1199,llm,https://github.com/h2oai/h2o-llmstudio,[],,[],[],,,,h2oai/h2o-llmstudio,h2o-llmstudio,3215,347,45,Python,https://gpt-gm.h2o.ai,H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/,h2oai,2024-01-13,2023-04-17,41,78.14236111111111,https://avatars.githubusercontent.com/u/1402695?v=4,H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/,"['ai', 'chatbot', 'chatgpt', 'fine-tuning', 'finetuning', 'generative', 'generative-ai', 'gpt', 'llama', 'llama2', 'llm', 'llm-training']","['ai', 'chatbot', 'chatgpt', 'fine-tuning', 'finetuning', 'generative', 'generative-ai', 'gpt', 'llama', 'llama2', 'llm', 'llm-training']",2023-12-22,"[('hiyouga/llama-efficient-tuning', 0.6901037096977234, 'llm', 5), ('hiyouga/llama-factory', 0.6901035308837891, 'llm', 5), ('alpha-vllm/llama2-accessory', 0.6861495971679688, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6861023902893066, 'perf', 1), ('microsoft/promptflow', 0.6735444664955139, 'llm', 4), ('bentoml/openllm', 0.6586171388626099, 'ml-ops', 5), ('tigerlab-ai/tiger', 0.6549601554870605, 'llm', 3), ('pathwaycom/llm-app', 0.6417025327682495, 'llm', 2), ('nomic-ai/gpt4all', 0.6245695948600769, 'llm', 1), ('bigscience-workshop/petals', 0.6163656115531921, 'data', 4), ('deepset-ai/haystack', 0.6111219525337219, 'llm', 3), ('ludwig-ai/ludwig', 0.6097002625465393, 'ml-ops', 5), ('microsoft/semantic-kernel', 0.6094576716423035, 'llm', 2), ('agenta-ai/agenta', 0.6011561751365662, 'llm', 1), ('young-geng/easylm', 0.6009706258773804, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.5947935581207275, 'llm', 3), ('iryna-kondr/scikit-llm', 0.5840808749198914, 'llm', 2), ('hwchase17/langchain', 0.5801489949226379, 'llm', 1), ('argilla-io/argilla', 0.5786020159721375, 'nlp', 2), ('confident-ai/deepeval', 0.5734099745750427, 'testing', 2), ('deep-diver/llm-as-chatbot', 0.5731402635574341, 'llm', 1), ('salesforce/codet5', 0.5726329684257507, 'nlp', 0), ('haotian-liu/llava', 0.5654820203781128, 'llm', 4), ('ray-project/llm-applications', 0.5635949373245239, 'llm', 2), ('predibase/lorax', 0.5625096559524536, 'llm', 4), ('bobazooba/xllm', 0.5602803230285645, 'llm', 5), ('microsoft/torchscale', 0.5583703517913818, 'llm', 0), ('embedchain/embedchain', 0.5581331253051758, 'llm', 3), ('shishirpatil/gorilla', 0.5525274872779846, 'llm', 2), ('microsoft/autogen', 0.5479278564453125, 'llm', 3), ('vllm-project/vllm', 0.5451328754425049, 'llm', 3), ('cheshire-cat-ai/core', 0.5447829365730286, 'llm', 3), ('lightning-ai/lit-gpt', 0.5436835289001465, 'llm', 1), ('microsoft/promptcraft-robotics', 0.5414144992828369, 'sim', 2), ('zilliztech/gptcache', 0.5407289266586304, 'llm', 5), ('lastmile-ai/aiconfig', 0.5382325053215027, 'util', 3), ('chainlit/chainlit', 0.5345412492752075, 'llm', 2), ('zrrskywalker/llama-adapter', 0.5344659686088562, 'llm', 1), ('hegelai/prompttools', 0.5304824709892273, 'llm', 0), ('eugeneyan/open-llms', 0.5287415385246277, 'study', 1), ('nebuly-ai/nebullvm', 0.5265906453132629, 'perf', 2), ('jerryjliu/llama_index', 0.5202019214630127, 'llm', 3), ('mnotgod96/appagent', 0.5174366235733032, 'llm', 3), ('run-llama/rags', 0.5137847065925598, 'llm', 3), ('citadel-ai/langcheck', 0.5101147890090942, 'llm', 0), ('dylanhogg/llmgraph', 0.5098133087158203, 'ml', 3), ('chatarena/chatarena', 0.5097920894622803, 'llm', 2), ('microsoft/lmops', 0.5096468329429626, 'llm', 2), ('next-gpt/next-gpt', 0.5090202689170837, 'llm', 2), ('lightning-ai/lit-llama', 0.5055845975875854, 'llm', 1), ('mlc-ai/web-llm', 0.5051769614219666, 'llm', 2), ('run-llama/llama-lab', 0.5041861534118652, 'llm', 1), ('eth-sri/lmql', 0.5039780139923096, 'llm', 1), ('alphasecio/langchain-examples', 0.5006608366966248, 'llm', 1)]",24,5.0,,5.33,133,102,9,1,15,20,15,133.0,119.0,90.0,0.9,63
729,diffusion,https://github.com/compvis/stable-diffusion,"['diffusion', 'image-generation']",,[],[],,,,compvis/stable-diffusion,stable-diffusion,62816,9600,543,Jupyter Notebook,https://ommer-lab.com/research/latent-diffusion-models/,A latent text-to-image diffusion model,compvis,2024-01-14,2022-08-10,76,817.3085501858736,https://avatars.githubusercontent.com/u/30233788?v=4,A latent text-to-image diffusion model,[],"['diffusion', 'image-generation']",2022-11-16,"[('sharonzhou/long_stable_diffusion', 0.6926607489585876, 'diffusion', 0), ('openai/glide-text2im', 0.6833638548851013, 'diffusion', 0), ('compvis/latent-diffusion', 0.655044674873352, 'diffusion', 2), ('stability-ai/stablediffusion', 0.6550443768501282, 'diffusion', 2), ('saharmor/dalle-playground', 0.6419358253479004, 'diffusion', 0), ('nateraw/stable-diffusion-videos', 0.6355494856834412, 'diffusion', 0), ('huggingface/diffusers', 0.629136860370636, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.5530049204826355, 'diffusion', 0), ('jina-ai/discoart', 0.5315988659858704, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.5289968252182007, 'diffusion', 2), ('openai/clip', 0.5251467227935791, 'ml-dl', 0)]",8,1.0,,0.0,58,10,17,14,0,0,0,58.0,105.0,90.0,1.8,62
244,ml,https://github.com/tencentarc/gfpgan,[],,[],[],,,,tencentarc/gfpgan,GFPGAN,33415,5481,481,Python,,GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.,tencentarc,2024-01-14,2021-03-19,149,223.40496657115568,https://avatars.githubusercontent.com/u/83739826?v=4,GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.,"['deep-learning', 'face-restoration', 'gan', 'gfpgan', 'image-restoration', 'pytorch', 'super-resolution']","['deep-learning', 'face-restoration', 'gan', 'gfpgan', 'image-restoration', 'pytorch', 'super-resolution']",2022-09-16,"[('xpixelgroup/basicsr', 0.6321846842765808, 'ml-dl', 2), ('xinntao/real-esrgan', 0.5772732496261597, 'ml-dl', 3), ('deepfakes/faceswap', 0.5402413606643677, 'ml-dl', 1), ('mchong6/jojogan', 0.5290706753730774, 'data', 0)]",11,3.0,,0.0,45,4,34,16,0,5,5,45.0,31.0,90.0,0.7,62
232,template,https://github.com/cookiecutter/cookiecutter,[],,[],[],,,,cookiecutter/cookiecutter,cookiecutter,20987,1986,227,Python,https://pypi.org/project/cookiecutter/,"A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.",cookiecutter,2024-01-14,2013-07-14,550,38.138369678089305,https://avatars.githubusercontent.com/u/12502901?v=4,"A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.",['cookiecutter'],['cookiecutter'],2023-11-27,"[('lyz-code/cookiecutter-python-project', 0.8542928099632263, 'template', 1), ('tedivm/robs_awesome_python_template', 0.8344708681106567, 'template', 0), ('giswqs/pypackage', 0.7356756329536438, 'template', 1), ('ionelmc/cookiecutter-pylibrary', 0.7163523435592651, 'template', 1), ('buuntu/fastapi-react', 0.6335521340370178, 'template', 1), ('crmne/cookiecutter-modern-datascience', 0.5783246755599976, 'template', 1), ('tezromach/python-package-template', 0.5202876925468445, 'template', 1), ('pypa/hatch', 0.5061737895011902, 'util', 0)]",318,6.0,,1.56,90,43,128,2,7,4,7,90.0,105.0,90.0,1.2,62
1055,study,https://github.com/microsoft/recommenders,[],,[],[],,,,microsoft/recommenders,recommenders,17261,2965,270,Python,https://microsoft-recommenders.readthedocs.io/en/latest/,Best Practices on Recommendation Systems,microsoft,2024-01-14,2018-09-19,279,61.67789688616641,https://avatars.githubusercontent.com/u/142452264?v=4,Best Practices on Recommendation Systems,"['artificial-intelligence', 'azure', 'data-science', 'deep-learning', 'jupyter-notebook', 'kubernetes', 'machine-learning', 'microsoft', 'operationalization', 'ranking', 'rating', 'recommendation', 'recommendation-algorithm', 'recommendation-engine', 'recommendation-system', 'recommender', 'tutorial']","['artificial-intelligence', 'azure', 'data-science', 'deep-learning', 'jupyter-notebook', 'kubernetes', 'machine-learning', 'microsoft', 'operationalization', 'ranking', 'rating', 'recommendation', 'recommendation-algorithm', 'recommendation-engine', 'recommendation-system', 'recommender', 'tutorial']",2023-12-23,"[('rucaibox/recbole', 0.5956623554229736, 'ml', 3), ('nicolashug/surprise', 0.5881096720695496, 'ml', 3), ('pytorch/torchrec', 0.5602710247039795, 'ml-dl', 2), ('jacopotagliabue/reclist', 0.5187485218048096, 'ml', 1)]",128,2.0,,6.81,52,27,65,1,0,2,2,52.0,79.0,90.0,1.5,62
41,util,https://github.com/kivy/kivy,[],,[],[],,,,kivy/kivy,kivy,16614,3096,606,Python,https://kivy.org,"Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS",kivy,2024-01-14,2010-11-03,690,24.048387096774192,https://avatars.githubusercontent.com/u/1266152?v=4,"Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS","['android', 'app', 'ios', 'kivy', 'linux', 'macos', 'ui', 'windows']","['android', 'app', 'ios', 'kivy', 'linux', 'macos', 'ui', 'windows']",2024-01-05,"[('beeware/toga', 0.692936360836029, 'gui', 0), ('r0x0r/pywebview', 0.6854493618011475, 'gui', 2), ('willmcgugan/textual', 0.632539689540863, 'term', 0), ('hoffstadt/dearpygui', 0.6317042112350464, 'gui', 4), ('flet-dev/flet', 0.6066066026687622, 'web', 2), ('pysimplegui/pysimplegui', 0.600288450717926, 'gui', 0), ('alphasecio/langchain-examples', 0.5670594573020935, 'llm', 0), ('wxwidgets/phoenix', 0.5668678879737854, 'gui', 2), ('dddomodossola/remi', 0.5648102164268494, 'gui', 1), ('urwid/urwid', 0.5640290975570679, 'term', 0), ('openai/openai-python', 0.5627065300941467, 'util', 0), ('parthjadhav/tkinter-designer', 0.554401695728302, 'gui', 0), ('vitalik/django-ninja', 0.5482510328292847, 'web', 0), ('pypy/pypy', 0.540271520614624, 'util', 0), ('gradio-app/gradio', 0.5393166542053223, 'viz', 1), ('pyglet/pyglet', 0.5390869975090027, 'gamedev', 0), ('pallets/flask', 0.535079836845398, 'web', 0), ('reflex-dev/reflex', 0.5318344831466675, 'web', 0), ('holoviz/panel', 0.5100237131118774, 'viz', 0), ('panda3d/panda3d', 0.5057852268218994, 'gamedev', 0), ('masoniteframework/masonite', 0.5023773312568665, 'web', 0), ('fastai/ghapi', 0.5002898573875427, 'util', 0)]",604,6.0,,3.19,827,437,161,1,3,4,3,827.0,1087.0,90.0,1.3,62
314,gui,https://github.com/hoffstadt/dearpygui,[],,[],[],,,,hoffstadt/dearpygui,DearPyGui,11691,639,150,C++,https://dearpygui.readthedocs.io/en/latest/,Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies,hoffstadt,2024-01-14,2020-05-28,191,60.981371087928466,,Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies,"['cpp', 'cross-platform', 'dearpygui', 'graphics', 'gui', 'imgui', 'linux', 'macos', 'native', 'python-gui', 'toolkit', 'tools', 'ui', 'windows']","['cpp', 'cross-platform', 'dearpygui', 'graphics', 'gui', 'imgui', 'linux', 'macos', 'native', 'python-gui', 'toolkit', 'tools', 'ui', 'windows']",2024-01-11,"[('beeware/toga', 0.7974780201911926, 'gui', 2), ('parthjadhav/tkinter-designer', 0.6940200924873352, 'gui', 1), ('pysimplegui/pysimplegui', 0.6711195111274719, 'gui', 2), ('pypy/pypy', 0.6707800030708313, 'util', 0), ('urwid/urwid', 0.6700049042701721, 'term', 0), ('r0x0r/pywebview', 0.6651965379714966, 'gui', 3), ('pyglet/pyglet', 0.6631710529327393, 'gamedev', 0), ('willmcgugan/textual', 0.6536368131637573, 'term', 0), ('wxwidgets/phoenix', 0.6336509585380554, 'gui', 4), ('kivy/kivy', 0.6317042112350464, 'util', 4), ('pyston/pyston', 0.6223757863044739, 'util', 0), ('jquast/blessed', 0.6031554937362671, 'term', 0), ('python/cpython', 0.6008025407791138, 'util', 0), ('dddomodossola/remi', 0.5960089564323425, 'gui', 2), ('holoviz/panel', 0.5953347086906433, 'viz', 1), ('alexmojaki/snoop', 0.5897102952003479, 'debug', 0), ('pytoolz/toolz', 0.5808964371681213, 'util', 0), ('eleutherai/pyfra', 0.5808343887329102, 'ml', 0), ('google/python-fire', 0.5763037800788879, 'term', 0), ('webpy/webpy', 0.5751984119415283, 'web', 0), ('fastai/fastcore', 0.5673369765281677, 'util', 0), ('pyscript/pyscript-cli', 0.5643460750579834, 'web', 0), ('holoviz/holoviz', 0.5614981651306152, 'viz', 0), ('huggingface/huggingface_hub', 0.5539893507957458, 'ml', 0), ('pallets/click', 0.5539004802703857, 'term', 0), ('pdm-project/pdm', 0.5533838868141174, 'util', 0), ('secdev/scapy', 0.5505430698394775, 'util', 0), ('pexpect/pexpect', 0.5491853952407837, 'util', 0), ('pypa/hatch', 0.5478973388671875, 'util', 0), ('ethereum/web3.py', 0.5474371314048767, 'crypto', 0), ('minimaxir/simpleaichat', 0.5460814237594604, 'llm', 0), ('cython/cython', 0.5458163619041443, 'util', 1), ('libtcod/python-tcod', 0.5448720455169678, 'gamedev', 0), ('masoniteframework/masonite', 0.5427703261375427, 'web', 0), ('faster-cpython/tools', 0.5421075820922852, 'perf', 0), ('pympler/pympler', 0.5417070388793945, 'perf', 0), ('bottlepy/bottle', 0.541211724281311, 'web', 0), ('tqdm/tqdm', 0.5399507284164429, 'term', 1), ('erotemic/ubelt', 0.5396130681037903, 'util', 1), ('landscapeio/prospector', 0.5395547747612, 'util', 0), ('tmbo/questionary', 0.5394479632377625, 'term', 0), ('klen/muffin', 0.5390740036964417, 'web', 0), ('micropython/micropython', 0.5389625430107117, 'util', 0), ('bokeh/bokeh', 0.5377543568611145, 'viz', 0), ('hhatto/autopep8', 0.5373272895812988, 'util', 0), ('gradio-app/gradio', 0.5356285572052002, 'viz', 1), ('plotly/plotly.py', 0.5339773297309875, 'viz', 0), ('dylanhogg/awesome-python', 0.5330666899681091, 'study', 0), ('grantjenks/blue', 0.5329349040985107, 'util', 0), ('pygments/pygments', 0.5314857959747314, 'util', 0), ('google/gin-config', 0.5313805341720581, 'util', 0), ('psf/black', 0.5271217226982117, 'util', 0), ('pyodide/micropip', 0.5266197919845581, 'util', 0), ('indygreg/pyoxidizer', 0.5257773995399475, 'util', 0), ('jiffyclub/snakeviz', 0.5256134867668152, 'profiling', 0), ('imageio/imageio', 0.5239776968955994, 'util', 0), ('pygame/pygame', 0.5225834846496582, 'gamedev', 0), ('pyinfra-dev/pyinfra', 0.5215190649032593, 'util', 0), ('faster-cpython/ideas', 0.5212865471839905, 'perf', 0), ('pygamelib/pygamelib', 0.5179816484451294, 'gamedev', 0), ('sqlalchemy/mako', 0.5178695917129517, 'template', 0), ('pallets/flask', 0.5168485045433044, 'web', 0), ('exaloop/codon', 0.5152654647827148, 'perf', 0), ('rstudio/py-shiny', 0.5147618651390076, 'web', 0), ('pypa/virtualenv', 0.5124236941337585, 'util', 0), ('renpy/renpy', 0.5120179057121277, 'viz', 0), ('adafruit/circuitpython', 0.511806309223175, 'util', 0), ('flet-dev/flet', 0.5117893815040588, 'web', 0), ('klen/py-frameworks-bench', 0.511713981628418, 'perf', 0), ('pyodide/pyodide', 0.5109636187553406, 'util', 0), ('ipython/ipyparallel', 0.5102288126945496, 'perf', 0), ('allrod5/injectable', 0.508715033531189, 'util', 0), ('plotly/dash', 0.5085344314575195, 'viz', 0), ('goldmansachs/gs-quant', 0.5079163908958435, 'finance', 0), ('beeware/briefcase', 0.5077422261238098, 'util', 0), ('pyqtgraph/pyqtgraph', 0.5063014626502991, 'viz', 0), ('tkrabel/bamboolib', 0.5062575936317444, 'pandas', 0), ('xonsh/xonsh', 0.504555881023407, 'util', 0), ('timofurrer/awesome-asyncio', 0.5044320225715637, 'study', 0), ('python-poetry/cleo', 0.5041629672050476, 'term', 0), ('python-poetry/poetry', 0.5035780668258667, 'util', 0), ('microsoft/playwright-python', 0.5034270882606506, 'testing', 0), ('asweigart/pyperclip', 0.5023604035377502, 'util', 0), ('py4j/py4j', 0.5022397041320801, 'util', 0), ('scrapy/scrapy', 0.5022242069244385, 'data', 0), ('1200wd/bitcoinlib', 0.5015420913696289, 'crypto', 0), ('intel/intel-extension-for-pytorch', 0.5012747645378113, 'perf', 0), ('requests/toolbelt', 0.5012592077255249, 'util', 0), ('willmcgugan/rich', 0.5006871223449707, 'term', 0), ('gaogaotiantian/viztracer', 0.5000486373901367, 'profiling', 0)]",65,3.0,,1.02,88,48,44,0,3,7,3,88.0,139.0,90.0,1.6,62
989,finance,https://github.com/ranaroussi/yfinance,[],,[],['yfinance'],1.0,,,ranaroussi/yfinance,yfinance,11035,2104,235,Python,https://aroussi.com/post/python-yahoo-finance,Download market data from Yahoo! Finance's API,ranaroussi,2024-01-14,2017-05-21,349,31.593047034764826,,Download market data from Yahoo! Finance's API,"['financial-data', 'fix-yahoo-finance', 'market-data', 'pandas', 'stock-data', 'yahoo-finance', 'yahoo-finance-api']","['financial-data', 'fix-yahoo-finance', 'market-data', 'pandas', 'stock-data', 'yahoo-finance', 'yahoo-finance-api']",2024-01-11,"[('cuemacro/findatapy', 0.6290664672851562, 'finance', 1), ('pydata/pandas-datareader', 0.6126816868782043, 'pandas', 3), ('hydrosquall/tiingo-python', 0.5750361084938049, 'finance', 0), ('matplotlib/mplfinance', 0.5448886156082153, 'finance', 1), ('mega-barrel/yfin-etl', 0.535876452922821, 'finance', 1), ('stefmolin/stock-analysis', 0.5187216401100159, 'finance', 1)]",102,1.0,,4.56,142,94,81,0,37,13,37,142.0,547.0,90.0,3.9,62
639,util,https://github.com/pyinstaller/pyinstaller,[],,[],[],,,,pyinstaller/pyinstaller,pyinstaller,10966,1970,232,Python,http://www.pyinstaller.org,Freeze (package) Python programs into stand-alone executables,pyinstaller,2024-01-14,2011-11-23,635,17.246012132105143,https://avatars.githubusercontent.com/u/1215332?v=4,Freeze (package) Python programs into stand-alone executables,"['bundle', 'package', 'py2app', 'py2exe', 'pyinstaller', 'python-to-exe']","['bundle', 'package', 'py2app', 'py2exe', 'pyinstaller', 'python-to-exe']",2024-01-13,"[('beeware/briefcase', 0.6962027549743652, 'util', 3), ('ofek/pyapp', 0.5914837121963501, 'util', 1), ('indygreg/pyoxidizer', 0.5304632782936096, 'util', 0), ('pyodide/micropip', 0.5264869332313538, 'util', 0), ('pypa/pipx', 0.5007230639457703, 'util', 0)]",461,4.0,,8.67,312,280,148,0,13,6,13,312.0,563.0,90.0,1.8,62
1776,ml,https://github.com/neonbjb/tortoise-tts,"['text-to-speech', 'voice']",,[],[],,,,neonbjb/tortoise-tts,tortoise-tts,10615,1544,159,Jupyter Notebook,,A multi-voice TTS system trained with an emphasis on quality,neonbjb,2024-01-14,2022-01-28,104,101.50956284153006,,A multi-voice TTS system trained with an emphasis on quality,[],"['text-to-speech', 'voice']",2023-11-22,"[('plachtaa/vall-e-x', 0.6283189058303833, 'llm', 1), ('myshell-ai/openvoice', 0.57065349817276, 'nlp', 1), ('m-bain/whisperx', 0.5033969283103943, 'nlp', 0)]",34,4.0,,1.75,118,42,24,2,0,2,2,118.0,151.0,90.0,1.3,62
1334,ml,https://github.com/facebookresearch/animateddrawings,['animation'],,[],[],,,,facebookresearch/animateddrawings,AnimatedDrawings,9893,837,84,Python,,"Code to accompany ""A Method for Animating Children's Drawings of the Human Figure""",facebookresearch,2024-01-14,2022-11-30,60,162.56103286384976,https://avatars.githubusercontent.com/u/16943930?v=4,"Code to accompany ""A Method for Animating Children's Drawings of the Human Figure""",[],['animation'],2023-12-10,[],14,2.0,,1.56,35,24,14,1,1,1,1,35.0,55.0,90.0,1.6,62
126,perf,https://github.com/modin-project/modin,[],,[],[],1.0,,,modin-project/modin,modin,9219,631,116,Python,http://modin.readthedocs.io,Modin: Scale your Pandas workflows by changing a single line of code,modin-project,2024-01-14,2018-06-21,292,31.494875549048317,https://avatars.githubusercontent.com/u/40475955?v=4,Modin: Scale your Pandas workflows by changing a single line of code,"['analytics', 'data-science', 'dataframe', 'datascience', 'distributed', 'modin', 'pandas', 'sql']","['analytics', 'data-science', 'dataframe', 'datascience', 'distributed', 'modin', 'pandas', 'sql']",2024-01-13,"[('jmcarpenter2/swifter', 0.6068665385246277, 'pandas', 2), ('nalepae/pandarallel', 0.6011874675750732, 'pandas', 1), ('lux-org/lux', 0.5601602792739868, 'viz', 2), ('ydataai/ydata-profiling', 0.5375041365623474, 'pandas', 2), ('kestra-io/kestra', 0.5311633348464966, 'ml-ops', 0), ('hi-primus/optimus', 0.5256850123405457, 'ml-ops', 1), ('flyteorg/flyte', 0.5223826169967651, 'ml-ops', 1), ('adamerose/pandasgui', 0.5174034237861633, 'pandas', 2), ('pytables/pytables', 0.5136765837669373, 'data', 0), ('tkrabel/bamboolib', 0.5066853761672974, 'pandas', 1)]",124,3.0,,10.35,332,274,68,0,19,15,19,331.0,319.0,90.0,1.0,62
438,ml-dl,https://github.com/kornia/kornia,[],,[],[],,,,kornia/kornia,kornia,9004,916,127,Python,https://kornia.readthedocs.io,Geometric Computer Vision Library for SpatialAI,kornia,2024-01-14,2018-08-22,283,31.720181177654755,https://avatars.githubusercontent.com/u/56968752?v=4,Geometric Computer Vision Library for SpatialAI,"['artificial-intelligence', 'computer-vision', 'deep-learning', 'image-processing', 'machine-learning', 'neural-network', 'pytorch', 'robotics', 'spatial-ai']","['artificial-intelligence', 'computer-vision', 'deep-learning', 'image-processing', 'machine-learning', 'neural-network', 'pytorch', 'robotics', 'spatial-ai']",2024-01-13,"[('deci-ai/super-gradients', 0.6162040829658508, 'ml-dl', 4), ('isl-org/open3d', 0.6128343939781189, 'sim', 2), ('pyg-team/pytorch_geometric', 0.5945971012115479, 'ml-dl', 2), ('roboflow/supervision', 0.5887291431427002, 'ml', 5), ('microsoft/torchgeo', 0.5526466369628906, 'gis', 3), ('pytorch/rl', 0.5435752272605896, 'ml-rl', 3), ('earthlab/earthpy', 0.5408483743667603, 'gis', 0), ('lightly-ai/lightly', 0.5407032370567322, 'ml', 4), ('tensorlayer/tensorlayer', 0.5397638082504272, 'ml-rl', 3), ('nvlabs/gcvit', 0.5384776592254639, 'diffusion', 1), ('scikit-geometry/scikit-geometry', 0.5317684412002563, 'gis', 0), ('geomstats/geomstats', 0.5275140404701233, 'math', 2), ('azavea/raster-vision', 0.5268778204917908, 'gis', 4), ('roboflow/notebooks', 0.5266382098197937, 'study', 4), ('lucidrains/imagen-pytorch', 0.5201680064201355, 'ml-dl', 2), ('activeloopai/deeplake', 0.516307532787323, 'ml-ops', 5), ('kevinmusgrave/pytorch-metric-learning', 0.5158526301383972, 'ml', 4), ('remotesensinglab/raster4ml', 0.5086722373962402, 'gis', 1), ('facebookresearch/pytorch3d', 0.5057213306427002, 'ml-dl', 0), ('facebookresearch/habitat-lab', 0.502574622631073, 'sim', 3), ('pytorch/ignite', 0.5025068521499634, 'ml-dl', 4), ('albumentations-team/albumentations', 0.5021526217460632, 'ml-dl', 3)]",245,6.0,,6.13,143,95,66,0,5,8,5,144.0,110.0,90.0,0.8,62
129,viz,https://github.com/altair-viz/altair,['visualization'],,[],[],,,,altair-viz/altair,altair,8655,785,141,Python,https://altair-viz.github.io/,Declarative statistical visualization library for Python,altair-viz,2024-01-13,2015-09-19,436,19.831423895253682,https://avatars.githubusercontent.com/u/22396732?v=4,Declarative statistical visualization library for Python,[],['visualization'],2024-01-09,"[('mwaskom/seaborn', 0.8215170502662659, 'viz', 0), ('enthought/mayavi', 0.7132139801979065, 'viz', 1), ('holoviz/holoviz', 0.7110622525215149, 'viz', 0), ('has2k1/plotnine', 0.6832043528556824, 'viz', 0), ('residentmario/geoplot', 0.6802260875701904, 'gis', 0), ('alexmojaki/heartrate', 0.6655700206756592, 'debug', 1), ('pyqtgraph/pyqtgraph', 0.6555560827255249, 'viz', 1), ('man-group/dtale', 0.6516591310501099, 'viz', 1), ('holoviz/geoviews', 0.6453191041946411, 'gis', 0), ('matplotlib/matplotlib', 0.6370353102684021, 'viz', 0), ('bokeh/bokeh', 0.6335301399230957, 'viz', 1), ('scitools/iris', 0.6287659406661987, 'gis', 0), ('contextlab/hypertools', 0.6141685843467712, 'ml', 1), ('plotly/plotly.py', 0.6128622889518738, 'viz', 1), ('scitools/cartopy', 0.6086525917053223, 'gis', 0), ('holoviz/hvplot', 0.5933358669281006, 'pandas', 0), ('kanaries/pygwalker', 0.5906645655632019, 'pandas', 1), ('gaogaotiantian/viztracer', 0.5886167287826538, 'profiling', 1), ('holoviz/panel', 0.5875522494316101, 'viz', 0), ('dfki-ric/pytransform3d', 0.5856305360794067, 'math', 1), ('westhealth/pyvis', 0.5836617946624756, 'graph', 0), ('pysal/pysal', 0.5805773735046387, 'gis', 0), ('vispy/vispy', 0.5802785158157349, 'viz', 1), ('pandas-dev/pandas', 0.5787591338157654, 'pandas', 0), ('gregorhd/mapcompare', 0.5779780149459839, 'gis', 0), ('wesm/pydata-book', 0.5764631032943726, 'study', 0), ('giswqs/geemap', 0.5686102509498596, 'gis', 0), ('nschloe/perfplot', 0.5681192278862, 'perf', 0), ('graphistry/pygraphistry', 0.5656019449234009, 'data', 1), ('jakevdp/pythondatasciencehandbook', 0.5655259490013123, 'study', 0), ('pyglet/pyglet', 0.5653769969940186, 'gamedev', 0), ('eleutherai/pyfra', 0.5628342032432556, 'ml', 0), ('artelys/geonetworkx', 0.5582142472267151, 'gis', 0), ('datapane/datapane', 0.556462287902832, 'viz', 0), ('marcomusy/vedo', 0.5546357035636902, 'viz', 1), ('brandtbucher/specialist', 0.5505017042160034, 'perf', 0), ('cuemacro/chartpy', 0.5500401258468628, 'viz', 0), ('vizzuhq/ipyvizzu', 0.5488370656967163, 'jupyter', 0), ('opengeos/leafmap', 0.5457186698913574, 'gis', 0), ('lux-org/lux', 0.5405464172363281, 'viz', 1), ('imageio/imageio', 0.5385008454322815, 'util', 0), ('csurfer/pyheat', 0.5379376411437988, 'profiling', 0), ('albahnsen/pycircular', 0.5373349189758301, 'math', 0), ('gboeing/pynamical', 0.5344410538673401, 'sim', 1), ('nomic-ai/deepscatter', 0.5337156653404236, 'viz', 1), ('connorferster/handcalcs', 0.531620442867279, 'jupyter', 0), ('numpy/numpy', 0.5299937129020691, 'math', 0), ('pyutils/line_profiler', 0.5267626643180847, 'profiling', 0), ('python/cpython', 0.526507556438446, 'util', 0), ('earthlab/earthpy', 0.5252060294151306, 'gis', 0), ('makepath/xarray-spatial', 0.5251989364624023, 'gis', 0), ('pyston/pyston', 0.5246127843856812, 'util', 0), ('raphaelquast/eomaps', 0.5242673754692078, 'gis', 1), ('pytoolz/toolz', 0.5240060687065125, 'util', 0), ('maartenbreddels/ipyvolume', 0.5231117010116577, 'jupyter', 0), ('mckinsey/vizro', 0.5226520895957947, 'viz', 1), ('rasbt/mlxtend', 0.5204195976257324, 'ml', 0), ('rjt1990/pyflux', 0.5199966430664062, 'time-series', 0), ('holoviz/datashader', 0.5193212032318115, 'gis', 0), ('holoviz/holoviews', 0.5143608450889587, 'viz', 0), ('bmabey/pyldavis', 0.5086315274238586, 'ml', 0), ('pygraphviz/pygraphviz', 0.5070845484733582, 'viz', 0), ('stan-dev/pystan', 0.506462574005127, 'ml', 0)]",160,7.0,,4.88,115,90,101,0,8,4,8,115.0,302.0,90.0,2.6,62
172,ml-dl,https://github.com/facebookresearch/pytorch3d,[],,[],[],,,,facebookresearch/pytorch3d,pytorch3d,7977,1236,146,Python,https://pytorch3d.org/,PyTorch3D is FAIR's library of reusable components for deep learning with 3D data,facebookresearch,2024-01-14,2019-10-25,222,35.84017971758665,https://avatars.githubusercontent.com/u/16943930?v=4,PyTorch3D is FAIR's library of reusable components for deep learning with 3D data,[],[],2024-01-04,"[('isl-org/open3d', 0.634926438331604, 'sim', 0), ('pytorch/ignite', 0.6142131686210632, 'ml-dl', 0), ('nicolas-chaulet/torch-points3d', 0.6138631701469421, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.5983558893203735, 'perf', 0), ('mrdbourke/pytorch-deep-learning', 0.5950154066085815, 'study', 0), ('pyg-team/pytorch_geometric', 0.5885642766952515, 'ml-dl', 0), ('skorch-dev/skorch', 0.5811278223991394, 'ml-dl', 0), ('xl0/lovely-tensors', 0.5774106383323669, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.574776828289032, 'study', 0), ('ashleve/lightning-hydra-template', 0.565503716468811, 'util', 0), ('arogozhnikov/einops', 0.5348989963531494, 'ml-dl', 0), ('pytorch/data', 0.5338151454925537, 'data', 0), ('cvxgrp/pymde', 0.5327015519142151, 'ml', 0), ('lightly-ai/lightly', 0.5310624241828918, 'ml', 0), ('tensorlayer/tensorlayer', 0.530800461769104, 'ml-rl', 0), ('denys88/rl_games', 0.5304609537124634, 'ml-rl', 0), ('google-research/deeplab2', 0.5299793481826782, 'ml', 0), ('nvidia/apex', 0.5290184020996094, 'ml-dl', 0), ('thu-ml/tianshou', 0.5248837471008301, 'ml-rl', 0), ('dmlc/dgl', 0.5240106582641602, 'ml-dl', 0), ('karpathy/micrograd', 0.5227355360984802, 'study', 0), ('huggingface/huggingface_hub', 0.5197669863700867, 'ml', 0), ('weecology/deepforest', 0.5166642069816589, 'gis', 0), ('horovod/horovod', 0.5143802165985107, 'ml-ops', 0), ('uber/petastorm', 0.5134731531143188, 'data', 0), ('nvlabs/gcvit', 0.5093544125556946, 'diffusion', 0), ('huggingface/transformers', 0.508711040019989, 'nlp', 0), ('pytorch/pytorch', 0.5076754093170166, 'ml-dl', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5057374238967896, 'study', 0), ('kornia/kornia', 0.5057213306427002, 'ml-dl', 0), ('pytorch/torchrec', 0.5049778819084167, 'ml-dl', 0), ('huggingface/datasets', 0.5045773386955261, 'nlp', 0), ('pytorch/rl', 0.5044132471084595, 'ml-rl', 0), ('mdbloice/augmentor', 0.5032729506492615, 'ml', 0), ('neuralmagic/deepsparse', 0.5011691451072693, 'nlp', 0)]",133,5.0,,2.08,95,66,51,0,3,4,3,95.0,215.0,90.0,2.3,62
548,ml,https://github.com/open-mmlab/mmsegmentation,[],,[],[],,,,open-mmlab/mmsegmentation,mmsegmentation,6943,2405,54,Python,https://mmsegmentation.readthedocs.io/en/latest/,OpenMMLab Semantic Segmentation Toolbox and Benchmark.,open-mmlab,2024-01-14,2020-06-14,189,36.68,https://avatars.githubusercontent.com/u/10245193?v=4,OpenMMLab Semantic Segmentation Toolbox and Benchmark.,"['deeplabv3', 'image-segmentation', 'medical-image-segmentation', 'pspnet', 'pytorch', 'realtime-segmentation', 'retinal-vessel-segmentation', 'semantic-segmentation', 'swin-transformer', 'transformer', 'vessel-segmentation']","['deeplabv3', 'image-segmentation', 'medical-image-segmentation', 'pspnet', 'pytorch', 'realtime-segmentation', 'retinal-vessel-segmentation', 'semantic-segmentation', 'swin-transformer', 'transformer', 'vessel-segmentation']",2023-12-14,"[('open-mmlab/mmdetection', 0.7892122864723206, 'ml', 3), ('open-mmlab/mmcv', 0.6504673361778259, 'ml', 0), ('roboflow/supervision', 0.6128215193748474, 'ml', 1), ('nvlabs/gcvit', 0.5941908359527588, 'diffusion', 1), ('deci-ai/super-gradients', 0.5933340787887573, 'ml-dl', 2), ('google-research/deeplab2', 0.5815913081169128, 'ml', 0), ('fepegar/torchio', 0.5752494931221008, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5589426755905151, 'ml', 1), ('microsoft/swin-transformer', 0.5363519191741943, 'ml', 2), ('aleju/imgaug', 0.5349984169006348, 'ml', 0), ('rwightman/pytorch-image-models', 0.5257945656776428, 'ml-dl', 1), ('project-monai/monai', 0.5131828188896179, 'ml', 1), ('albumentations-team/albumentations', 0.5108982920646667, 'ml-dl', 1), ('lutzroeder/netron', 0.5060480237007141, 'ml', 1), ('huggingface/datasets', 0.5029417276382446, 'nlp', 1)]",165,6.0,,3.88,189,57,44,1,10,13,10,188.0,207.0,90.0,1.1,62
3,ml,https://github.com/awslabs/autogluon,[],,[],[],,,,awslabs/autogluon,autogluon,6696,816,100,Python,https://auto.gluon.ai/,"AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data",awslabs,2024-01-14,2019-07-29,235,28.476306196840827,https://avatars.githubusercontent.com/u/92389699?v=4,"AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data","['autogluon', 'automated-machine-learning', 'automl', 'computer-vision', 'data-science', 'deep-learning', 'ensemble-learning', 'forecasting', 'gluon', 'hyperparameter-optimization', 'image-classification', 'machine-learning', 'natural-language-processing', 'object-detection', 'pytorch', 'scikit-learn', 'structured-data', 'tabular-data', 'time-series', 'transfer-learning']","['autogluon', 'automated-machine-learning', 'automl', 'computer-vision', 'data-science', 'deep-learning', 'ensemble-learning', 'forecasting', 'gluon', 'hyperparameter-optimization', 'image-classification', 'machine-learning', 'natural-language-processing', 'object-detection', 'pytorch', 'scikit-learn', 'structured-data', 'tabular-data', 'time-series', 'transfer-learning']",2024-01-08,"[('mljar/mljar-supervised', 0.7640585899353027, 'ml', 6), ('keras-team/autokeras', 0.7593497633934021, 'ml-dl', 4), ('winedarksea/autots', 0.7558371424674988, 'time-series', 5), ('microsoft/flaml', 0.7324180603027344, 'ml', 9), ('microsoft/nni', 0.7000966668128967, 'ml', 7), ('automl/auto-sklearn', 0.672629177570343, 'ml', 4), ('huggingface/autotrain-advanced', 0.6629055142402649, 'ml', 3), ('nccr-itmo/fedot', 0.6268003582954407, 'ml-ops', 4), ('ourownstory/neural_prophet', 0.6234149932861328, 'ml', 5), ('ydataai/ydata-synthetic', 0.5872030258178711, 'data', 4), ('alpa-projects/alpa', 0.5790343284606934, 'ml-dl', 2), ('sktime/sktime', 0.5768527984619141, 'time-series', 5), ('huggingface/datasets', 0.5675216913223267, 'nlp', 5), ('salesforce/merlion', 0.5632773637771606, 'time-series', 5), ('sdv-dev/sdv', 0.562446117401123, 'data', 3), ('mosaicml/composer', 0.554071307182312, 'ml-dl', 3), ('torantulino/auto-gpt', 0.5540371537208557, 'llm', 0), ('aleju/imgaug', 0.5523484349250793, 'ml', 2), ('featurelabs/featuretools', 0.5521774291992188, 'ml', 5), ('milvus-io/bootcamp', 0.5507424473762512, 'data', 2), ('xplainable/xplainable', 0.5499810576438904, 'ml-interpretability', 2), ('towhee-io/towhee', 0.5480068325996399, 'ml-ops', 2), ('neuralmagic/sparseml', 0.5449380278587341, 'ml-dl', 5), ('open-mmlab/mmediting', 0.5436397194862366, 'ml', 3), ('shankarpandala/lazypredict', 0.5431965589523315, 'ml', 2), ('nixtla/statsforecast', 0.5410817265510559, 'time-series', 5), ('roboflow/supervision', 0.54073566198349, 'ml', 5), ('microsoft/torchgeo', 0.5401058197021484, 'gis', 3), ('activeloopai/deeplake', 0.5394940376281738, 'ml-ops', 5), ('lutzroeder/netron', 0.5327269434928894, 'ml', 3), ('epistasislab/tpot', 0.5319231748580933, 'ml', 6), ('firmai/atspy', 0.53136146068573, 'time-series', 2), ('polyaxon/datatile', 0.5291432738304138, 'pandas', 2), ('explosion/thinc', 0.5290007591247559, 'ml-dl', 4), ('gradio-app/gradio', 0.5276086926460266, 'viz', 3), ('fatiando/verde', 0.5272731781005859, 'gis', 1), ('alibaba/easynlp', 0.5265514850616455, 'nlp', 4), ('onnx/onnx', 0.5217620134353638, 'ml', 4), ('google/automl', 0.5180317163467407, 'ml', 2), ('salesforce/deeptime', 0.5168805718421936, 'time-series', 3), ('huggingface/transformers', 0.5144530534744263, 'nlp', 4), ('ddbourgin/numpy-ml', 0.5125497579574585, 'ml', 1), ('alkaline-ml/pmdarima', 0.5124308466911316, 'time-series', 3), ('kevinmusgrave/pytorch-metric-learning', 0.5119850635528564, 'ml', 4), ('iperov/deepfacelab', 0.5111877918243408, 'ml-dl', 2), ('lucidrains/imagen-pytorch', 0.5101648569107056, 'ml-dl', 1), ('google/trax', 0.5100558996200562, 'ml-dl', 2), ('rafiqhasan/auto-tensorflow', 0.5091307163238525, 'ml-dl', 2), ('developmentseed/label-maker', 0.5088804364204407, 'gis', 2), ('docarray/docarray', 0.5075685381889343, 'data', 3), ('polyaxon/polyaxon', 0.5069301724433899, 'ml-ops', 5), ('google/pyglove', 0.5035163164138794, 'util', 2), ('aistream-peelout/flow-forecast', 0.5033537149429321, 'time-series', 5), ('fepegar/torchio', 0.5024552941322327, 'ml-dl', 3), ('deepfakes/faceswap', 0.5015328526496887, 'ml-dl', 2), ('jindongwang/transferlearning', 0.5002632141113281, 'ml', 3)]",114,5.0,,11.42,414,252,54,0,5,7,5,413.0,511.0,90.0,1.2,62
1883,llm,https://github.com/apple/ml-ferret,"['ferret', 'mllm']",Ferret: Refer and Ground Anything Anywhere at Any Granularity,[],[],,,,apple/ml-ferret,ml-ferret,6445,331,111,Python,,,apple,2024-01-14,2023-10-06,16,388.92241379310343,https://avatars.githubusercontent.com/u/10639145?v=4,Ferret: Refer and Ground Anything Anywhere at Any Granularity,[],"['ferret', 'mllm']",2023-12-15,[],1,1.0,,0.06,8,1,3,1,0,0,0,8.0,27.0,90.0,3.4,62
1806,llm,https://github.com/mit-han-lab/streaming-llm,"['attention', 'long-dialogue']",,[],[],,,,mit-han-lab/streaming-llm,streaming-llm,5807,335,61,Python,https://arxiv.org/abs/2309.17453,Efficient Streaming Language Models with Attention Sinks,mit-han-lab,2024-01-14,2023-09-29,17,330.479674796748,https://avatars.githubusercontent.com/u/39571499?v=4,Efficient Streaming Language Models with Attention Sinks,[],"['attention', 'long-dialogue']",2023-10-25,"[('freedomintelligence/llmzoo', 0.5750225782394409, 'llm', 0), ('juncongmoo/pyllama', 0.5574493408203125, 'llm', 0), ('bytedance/lightseq', 0.5517858862876892, 'nlp', 0), ('srush/minichain', 0.5470554232597351, 'llm', 0), ('thudm/chatglm-6b', 0.5384843945503235, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5352895259857178, 'llm', 0), ('hannibal046/awesome-llm', 0.5322656035423279, 'study', 0), ('ai21labs/lm-evaluation', 0.5273094773292542, 'llm', 0), ('fasteval/fasteval', 0.5244007110595703, 'llm', 0), ('huggingface/text-generation-inference', 0.5219200253486633, 'llm', 0), ('lm-sys/fastchat', 0.5120673179626465, 'llm', 0), ('jonasgeiping/cramming', 0.5054171681404114, 'nlp', 0), ('lvwerra/trl', 0.5007056593894958, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5003539323806763, 'llm', 0)]",5,3.0,,0.48,41,16,4,3,0,0,0,41.0,49.0,90.0,1.2,62
538,util,https://github.com/mamba-org/mamba,"['package-manager', 'packaging']",,[],[],,,,mamba-org/mamba,mamba,5690,322,45,C++,https://mamba.readthedocs.io,The Fast Cross-Platform Package Manager,mamba-org,2024-01-13,2019-03-05,256,22.2265625,https://avatars.githubusercontent.com/u/66118895?v=4,The Fast Cross-Platform Package Manager,"['conda', 'cpp', 'package-manager']","['conda', 'cpp', 'package-manager', 'packaging']",2024-01-11,"[('conda/conda', 0.752036988735199, 'util', 3), ('mamba-org/boa', 0.7310133576393127, 'util', 1), ('mamba-org/quetz', 0.699140191078186, 'util', 1), ('indygreg/pyoxidizer', 0.6980676651000977, 'util', 2), ('conda/conda-build', 0.6777679920196533, 'util', 1), ('spack/spack', 0.6774295568466187, 'util', 1), ('pomponchik/instld', 0.6645695567131042, 'util', 1), ('mitsuhiko/rye', 0.6489464044570923, 'util', 2), ('conda/conda-pack', 0.6462101340293884, 'util', 1), ('pdm-project/pdm', 0.6015645861625671, 'util', 2), ('python-poetry/poetry', 0.5992787480354309, 'util', 2), ('pypa/flit', 0.5987750291824341, 'util', 2), ('conda/constructor', 0.5558754801750183, 'util', 1), ('pypa/hatch', 0.5550651550292969, 'util', 2), ('pyodide/micropip', 0.5484160780906677, 'util', 0), ('tiiuae/sbomnix', 0.5184597373008728, 'util', 0), ('conda-forge/conda-smithy', 0.5094754695892334, 'util', 0), ('mamba-org/gator', 0.5042513012886047, 'jupyter', 1), ('fastai/fastcore', 0.5001938343048096, 'util', 0)]",146,5.0,,8.25,287,195,59,0,21,61,21,285.0,513.0,90.0,1.8,62
1878,llm,https://github.com/microsoft/promptbase,['prompt-engineering'],"promptbase is an evolving collection of resources, best practices, and example scripts for eliciting the best performance from foundation models.",[],[],,,,microsoft/promptbase,promptbase,4433,302,51,Python,,All things prompt engineering,microsoft,2024-01-14,2023-12-12,7,633.2857142857143,https://avatars.githubusercontent.com/u/6154722?v=4,All things prompt engineering,[],['prompt-engineering'],2024-01-13,"[('hazyresearch/ama_prompting', 0.7119161486625671, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.6507399082183838, 'llm', 1), ('hazyresearch/manifest', 0.629546046257019, 'llm', 1), ('promptslab/awesome-prompt-engineering', 0.56931471824646, 'study', 1), ('promptslab/promptify', 0.5429252982139587, 'nlp', 1)]",6,2.0,,0.69,34,25,1,0,0,0,0,34.0,14.0,90.0,0.4,62
1899,data,https://github.com/superduperdb/superduperdb,[],,[],[],,,,superduperdb/superduperdb,superduperdb,3863,500,34,Python,https://superduperdb.com,"🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.",superduperdb,2024-01-17,2022-08-30,74,52.2027027027027,https://avatars.githubusercontent.com/u/120034956?v=4,"🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.","['ai', 'chatbot', 'data', 'database', 'distributed-ml', 'inference', 'llm-inference', 'llm-serving', 'llmops', 'ml', 'mlops', 'mongodb', 'pretrained-models', 'pytorch', 'semantic-search', 'sklearn', 'torch', 'transformers', 'vector-search']","['ai', 'chatbot', 'data', 'database', 'distributed-ml', 'inference', 'llm-inference', 'llm-serving', 'llmops', 'ml', 'mlops', 'mongodb', 'pretrained-models', 'pytorch', 'semantic-search', 'sklearn', 'torch', 'transformers', 'vector-search']",2024-01-16,"[('activeloopai/deeplake', 0.7059310674667358, 'ml-ops', 5), ('mindsdb/mindsdb', 0.6333754658699036, 'data', 6), ('featureform/embeddinghub', 0.6010522246360779, 'nlp', 2), ('lancedb/lancedb', 0.6009349226951599, 'data', 1), ('pathwaycom/llm-app', 0.595395028591156, 'llm', 2), ('mosaicml/composer', 0.5755331516265869, 'ml-dl', 1), ('transformeroptimus/superagi', 0.5671401023864746, 'llm', 2), ('kubeflow-kale/kale', 0.5663890838623047, 'ml-ops', 0), ('avaiga/taipy', 0.5622673034667969, 'data', 1), ('mage-ai/mage-ai', 0.5560696125030518, 'ml-ops', 1), ('bigscience-workshop/petals', 0.5544981360435486, 'data', 3), ('deepset-ai/haystack', 0.5523344278335571, 'llm', 4), ('qdrant/qdrant', 0.5490362644195557, 'data', 2), ('bentoml/bentoml', 0.5483355522155762, 'ml-ops', 3), ('cheshire-cat-ai/core', 0.547654926776886, 'llm', 3), ('netflix/metaflow', 0.542826235294342, 'ml-ops', 3), ('googlecloudplatform/vertex-ai-samples', 0.5415635704994202, 'ml', 3), ('ray-project/ray', 0.5404704809188843, 'ml-ops', 2), ('feast-dev/feast', 0.530910313129425, 'ml-ops', 2), ('airbytehq/airbyte', 0.5290766954421997, 'data', 1), ('mlflow/mlflow', 0.522727370262146, 'ml-ops', 2), ('huggingface/datasets', 0.5187201499938965, 'nlp', 1), ('hpcaitech/colossalai', 0.514103353023529, 'llm', 2), ('pathwaycom/pathway', 0.5140187740325928, 'data', 1), ('dgarnitz/vectorflow', 0.5114015340805054, 'data', 1), ('nebuly-ai/nebullvm', 0.5111740827560425, 'perf', 1), ('polyaxon/datatile', 0.5108543634414673, 'pandas', 2), ('merantix-momentum/squirrel-core', 0.5102357268333435, 'ml', 3), ('skypilot-org/skypilot', 0.5078701376914978, 'llm', 1), ('streamlit/streamlit', 0.5076687932014465, 'viz', 0), ('milvus-io/bootcamp', 0.5059596300125122, 'data', 0), ('jina-ai/jina', 0.5026459097862244, 'ml', 2), ('mlc-ai/mlc-llm', 0.5006630420684814, 'llm', 0), ('polyaxon/polyaxon', 0.5004454851150513, 'ml-ops', 3)]",37,1.0,,25.54,712,579,17,0,18,14,18,712.0,558.0,90.0,0.8,62
1407,llm,https://github.com/luodian/otter,['multi-modality'],,[],[],,,,luodian/otter,Otter,3311,276,101,Python,https://otter-ntu.github.io/,"🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.",luodian,2024-01-13,2023-04-01,43,76.24013157894737,,"🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.","['apple-vision-pro', 'artificial-inteligence', 'chatgpt', 'deep-learning', 'egocentric-vision', 'embodied', 'embodied-ai', 'foundation-models', 'gpt-4', 'instruction-tuning', 'large-scale-models', 'machine-learning', 'multi-modality', 'visual-language-learning']","['apple-vision-pro', 'artificial-inteligence', 'chatgpt', 'deep-learning', 'egocentric-vision', 'embodied', 'embodied-ai', 'foundation-models', 'gpt-4', 'instruction-tuning', 'large-scale-models', 'machine-learning', 'multi-modality', 'visual-language-learning']",2023-12-30,"[('facebookresearch/habitat-lab', 0.5644159913063049, 'sim', 1), ('haotian-liu/llava', 0.5594356060028076, 'llm', 6), ('minedojo/voyager', 0.5513647794723511, 'llm', 0), ('nvlabs/prismer', 0.5447686314582825, 'diffusion', 0), ('open-mmlab/mmediting', 0.53682941198349, 'ml', 1), ('christoschristofidis/awesome-deep-learning', 0.5302937030792236, 'study', 2), ('jina-ai/finetuner', 0.5296557545661926, 'ml', 0), ('mnotgod96/appagent', 0.5246831774711609, 'llm', 1), ('nvidia/deeplearningexamples', 0.52414870262146, 'ml-dl', 1), ('jina-ai/jina', 0.5234236121177673, 'ml', 2), ('extreme-bert/extreme-bert', 0.5146451592445374, 'llm', 2), ('prefecthq/marvin', 0.5130387544631958, 'nlp', 0), ('huggingface/autotrain-advanced', 0.5102346539497375, 'ml', 2), ('ofa-sys/ofa', 0.5090562701225281, 'llm', 0), ('mlc-ai/mlc-llm', 0.5079561471939087, 'llm', 0), ('facebookresearch/mmf', 0.5072057843208313, 'ml-dl', 1), ('unity-technologies/ml-agents', 0.5054410099983215, 'ml-rl', 2), ('jina-ai/clip-as-service', 0.5049297213554382, 'nlp', 2), ('next-gpt/next-gpt', 0.501610517501831, 'llm', 5)]",12,3.0,,9.17,47,30,10,0,3,4,3,47.0,104.0,90.0,2.2,62
1287,llm,https://github.com/eth-sri/lmql,[],,[],[],,,,eth-sri/lmql,lmql,2903,157,20,Python,https://lmql.ai,A language for constraint-guided and efficient LLM programming.,eth-sri,2024-01-14,2022-11-24,61,47.039351851851855,https://avatars.githubusercontent.com/u/5363413?v=4,A language for constraint-guided and efficient LLM programming.,"['chatgpt', 'gpt-3', 'huggingface', 'language-model', 'programming-language']","['chatgpt', 'gpt-3', 'huggingface', 'language-model', 'programming-language']",2024-01-09,"[('guidance-ai/guidance', 0.6066431999206543, 'llm', 2), ('microsoft/autogen', 0.5932376384735107, 'llm', 1), ('bobazooba/xllm', 0.5887289047241211, 'llm', 1), ('next-gpt/next-gpt', 0.5689839124679565, 'llm', 1), ('hiyouga/llama-factory', 0.5669615268707275, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5669613480567932, 'llm', 1), ('dylanhogg/llmgraph', 0.5644213557243347, 'ml', 1), ('hwchase17/langchain', 0.5619138479232788, 'llm', 1), ('li-plus/chatglm.cpp', 0.5538818836212158, 'llm', 0), ('salesforce/codet5', 0.5481631755828857, 'nlp', 1), ('lupantech/chameleon-llm', 0.5429258942604065, 'llm', 2), ('farizrahman4u/loopgpt', 0.5417187809944153, 'llm', 1), ('killianlucas/open-interpreter', 0.5410472750663757, 'llm', 1), ('xtekky/gpt4free', 0.529441237449646, 'llm', 3), ('lianjiatech/belle', 0.5279043316841125, 'llm', 0), ('citadel-ai/langcheck', 0.5266656279563904, 'llm', 1), ('nvidia/tensorrt-llm', 0.524734616279602, 'viz', 1), ('stanfordnlp/dspy', 0.516359806060791, 'llm', 0), ('nomic-ai/gpt4all', 0.5151371955871582, 'llm', 1), ('exaloop/codon', 0.5149834752082825, 'perf', 0), ('pyomo/pyomo', 0.5141502022743225, 'math', 0), ('reasoning-machines/pal', 0.5124557018280029, 'llm', 1), ('juncongmoo/pyllama', 0.5070822834968567, 'llm', 0), ('promptslab/promptify', 0.5044764876365662, 'nlp', 2), ('h2oai/h2o-llmstudio', 0.5039780139923096, 'llm', 1), ('confident-ai/deepeval', 0.5033408403396606, 'testing', 2), ('explosion/spacy-llm', 0.502147376537323, 'llm', 1), ('langchain-ai/langgraph', 0.5018105506896973, 'llm', 0)]",34,3.0,,15.31,78,32,14,0,9,17,9,78.0,156.0,90.0,2.0,62
1232,llm,https://github.com/nvidia/nemo-guardrails,"['language-model', 'guardrails']",,[],[],,,,nvidia/nemo-guardrails,NeMo-Guardrails,2823,228,31,Python,,NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.,nvidia,2024-01-14,2023-04-18,41,68.85365853658537,https://avatars.githubusercontent.com/u/1728152?v=4,NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.,[],"['guardrails', 'language-model']",2024-01-12,"[('guardrails-ai/guardrails', 0.654189944267273, 'llm', 0), ('nomic-ai/gpt4all', 0.583074152469635, 'llm', 1), ('hwchase17/langchain', 0.5671313405036926, 'llm', 1), ('llm-tuning-safety/llms-finetuning-safety', 0.5286512970924377, 'llm', 0), ('tigerlab-ai/tiger', 0.527998149394989, 'llm', 0), ('nat/openplayground', 0.5180691480636597, 'llm', 1), ('aiwaves-cn/agents', 0.5130923986434937, 'nlp', 1), ('embedchain/embedchain', 0.5097015500068665, 'llm', 0), ('openlmlab/moss', 0.504492461681366, 'llm', 1), ('microsoft/autogen', 0.5011264681816101, 'llm', 0)]",30,2.0,,12.65,105,45,9,0,6,12,6,105.0,212.0,90.0,2.0,62
1711,llm,https://github.com/guardrails-ai/guardrails,[],,[],[],,,,guardrails-ai/guardrails,guardrails,2693,179,23,Python,https://docs.guardrailsai.com/,Adding guardrails to large language models.,guardrails-ai,2024-01-14,2023-01-29,52,51.505464480874316,https://avatars.githubusercontent.com/u/140440022?v=4,Adding guardrails to large language models.,"['ai', 'foundation-model', 'gpt-3', 'llm', 'openai']","['ai', 'foundation-model', 'gpt-3', 'llm', 'openai']",2024-01-10,"[('nvidia/nemo-guardrails', 0.654189944267273, 'llm', 0), ('juncongmoo/pyllama', 0.6344968676567078, 'llm', 0), ('optimalscale/lmflow', 0.6163129210472107, 'llm', 0), ('llm-tuning-safety/llms-finetuning-safety', 0.6136285066604614, 'llm', 1), ('lianjiatech/belle', 0.6092329621315002, 'llm', 0), ('hannibal046/awesome-llm', 0.6082257032394409, 'study', 0), ('microsoft/autogen', 0.6005452871322632, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5916648507118225, 'llm', 0), ('guidance-ai/guidance', 0.578825056552887, 'llm', 0), ('lupantech/chameleon-llm', 0.5615499019622803, 'llm', 3), ('tigerlab-ai/tiger', 0.5576193928718567, 'llm', 1), ('xtekky/gpt4free', 0.5575733184814453, 'llm', 2), ('bobazooba/xllm', 0.5430307984352112, 'llm', 2), ('minimaxir/gpt-2-simple', 0.5327882766723633, 'llm', 1), ('next-gpt/next-gpt', 0.5325504541397095, 'llm', 1), ('nvidia/tensorrt-llm', 0.5297648906707764, 'viz', 0), ('aiwaves-cn/agents', 0.528048038482666, 'nlp', 1), ('mlc-ai/mlc-llm', 0.527996301651001, 'llm', 1), ('lm-sys/fastchat', 0.525242269039154, 'llm', 0), ('explosion/spacy-llm', 0.5183983445167542, 'llm', 3), ('microsoft/lmops', 0.5142502784729004, 'llm', 1), ('cg123/mergekit', 0.511229395866394, 'llm', 1), ('rafiqhasan/auto-tensorflow', 0.5081257224082947, 'ml-dl', 0), ('prefecthq/marvin', 0.5055996179580688, 'nlp', 3), ('langchain-ai/langgraph', 0.5052242875099182, 'llm', 0), ('openai/tiktoken', 0.5039165019989014, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5022191405296326, 'llm', 0)]",36,3.0,,11.83,202,167,12,0,23,41,23,202.0,211.0,90.0,1.0,62
1750,data,https://github.com/giskard-ai/giskard,[],,[],[],,,,giskard-ai/giskard,giskard,2471,171,19,Python,https://docs.giskard.ai,"🐢 The testing framework for ML models, from tabular to LLMs",giskard-ai,2024-01-12,2022-03-06,99,24.88776978417266,https://avatars.githubusercontent.com/u/71782571?v=4,"🐢 The testing framework for ML models, from tabular to LLMs","['ai-safety', 'ai-testing', 'artificial-intelligence', 'cicd', 'ethical-artificial-intelligence', 'explainable-ai', 'fairness-ai', 'llmops', 'machine-learning', 'machine-learning-testing', 'ml-safety', 'ml-testing', 'ml-validation', 'mlops', 'model-testing', 'model-validation', 'quality-assurance', 'responsible-ai', 'responsible-ml', 'trustworthy-ai']","['ai-safety', 'ai-testing', 'artificial-intelligence', 'cicd', 'ethical-artificial-intelligence', 'explainable-ai', 'fairness-ai', 'llmops', 'machine-learning', 'machine-learning-testing', 'ml-safety', 'ml-testing', 'ml-validation', 'mlops', 'model-testing', 'model-validation', 'quality-assurance', 'responsible-ai', 'responsible-ml', 'trustworthy-ai']",2024-01-12,"[('arize-ai/phoenix', 0.6145030856132507, 'ml-interpretability', 2), ('confident-ai/deepeval', 0.5690423250198364, 'testing', 1), ('ludwig-ai/ludwig', 0.5647245049476624, 'ml-ops', 1), ('interpretml/interpret', 0.5559372305870056, 'ml-interpretability', 3), ('microsoft/lmops', 0.5501245260238647, 'llm', 0), ('deepchecks/deepchecks', 0.550081193447113, 'data', 3), ('csinva/imodels', 0.5474556088447571, 'ml', 3), ('nccr-itmo/fedot', 0.5448036789894104, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5253748893737793, 'data', 2), ('googlecloudplatform/vertex-ai-samples', 0.5234065651893616, 'ml', 1), ('bentoml/bentoml', 0.5216226577758789, 'ml-ops', 3), ('lastmile-ai/aiconfig', 0.5168980360031128, 'util', 0), ('tigerlab-ai/tiger', 0.5133479237556458, 'llm', 1), ('eugeneyan/testing-ml', 0.5097730755805969, 'testing', 1), ('llmware-ai/llmware', 0.5087332725524902, 'llm', 1), ('feast-dev/feast', 0.507591962814331, 'ml-ops', 2), ('mlflow/mlflow', 0.5059633851051331, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5021538734436035, 'ml-ops', 3)]",40,4.0,,95.15,437,411,23,0,36,27,36,437.0,627.0,90.0,1.4,62
1721,viz,https://github.com/mckinsey/vizro,[],,[],[],,,,mckinsey/vizro,vizro,2136,91,14,Python,https://vizro.readthedocs.io/en/stable/,Vizro is a toolkit for creating modular data visualization applications.,mckinsey,2024-01-14,2023-09-04,21,101.02702702702703,https://avatars.githubusercontent.com/u/4265350?v=4,Vizro is a toolkit for creating modular data visualization applications.,"['dashboard', 'data-visualization', 'plotly', 'plotly-dash', 'pydantic', 'visualization']","['dashboard', 'data-visualization', 'plotly', 'plotly-dash', 'pydantic', 'visualization']",2024-01-11,"[('man-group/dtale', 0.584793746471405, 'viz', 3), ('holoviz/panel', 0.5707153081893921, 'viz', 1), ('visgl/deck.gl', 0.5679866671562195, 'viz', 2), ('pyvista/pyvista', 0.5597613453865051, 'viz', 1), ('gaogaotiantian/viztracer', 0.5495272874832153, 'profiling', 1), ('holoviz/holoviz', 0.5490374565124512, 'viz', 0), ('bokeh/bokeh', 0.5383272171020508, 'viz', 1), ('pyqtgraph/pyqtgraph', 0.5374338626861572, 'viz', 1), ('mwaskom/seaborn', 0.535768449306488, 'viz', 1), ('nomic-ai/deepscatter', 0.5346618294715881, 'viz', 2), ('altair-viz/altair', 0.5226520895957947, 'viz', 1), ('polyaxon/datatile', 0.5210312008857727, 'pandas', 2), ('saulpw/visidata', 0.517253577709198, 'term', 0), ('mitvis/vistext', 0.5145730972290039, 'data', 0), ('gregorhd/mapcompare', 0.506889283657074, 'gis', 0), ('residentmario/geoplot', 0.5022686719894409, 'gis', 0)]",15,2.0,,3.54,161,134,4,0,11,33,11,161.0,194.0,90.0,1.2,62
55,ml-rl,https://github.com/openai/gym,['reinforcement-learning'],,[],[],,,,openai/gym,gym,33375,8704,1061,Python,https://www.gymlibrary.dev,A toolkit for developing and comparing reinforcement learning algorithms.,openai,2024-01-14,2016-04-27,404,82.43648553281581,https://avatars.githubusercontent.com/u/14957082?v=4,A toolkit for developing and comparing reinforcement learning algorithms.,[],['reinforcement-learning'],2023-01-30,"[('shangtongzhang/reinforcement-learning-an-introduction', 0.6223573088645935, 'study', 1), ('deepmind/acme', 0.6127493381500244, 'ml-rl', 1), ('pytorch/rl', 0.5818626880645752, 'ml-rl', 1), ('thu-ml/tianshou', 0.5749742984771729, 'ml-rl', 0), ('facebookresearch/reagent', 0.573969841003418, 'ml-rl', 0), ('google/dopamine', 0.5587803721427917, 'ml-rl', 0), ('openai/baselines', 0.5574614405632019, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5399578213691711, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.5394145846366882, 'ml-rl', 1), ('humancompatibleai/imitation', 0.5389503836631775, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.533967137336731, 'ml-rl', 1), ('kzl/decision-transformer', 0.5008352994918823, 'ml-rl', 0), ('denys88/rl_games', 0.5007779598236084, 'ml-rl', 1), ('arise-initiative/robosuite', 0.500554084777832, 'ml-rl', 1)]",384,3.0,,0.04,23,10,94,12,0,7,7,23.0,29.0,90.0,1.3,61
1082,security,https://github.com/sqlmapproject/sqlmap,[],,[],[],,,,sqlmapproject/sqlmap,sqlmap,29583,5602,1090,Python,http://sqlmap.org,Automatic SQL injection and database takeover tool,sqlmapproject,2024-01-14,2012-06-26,605,48.897520661157024,https://avatars.githubusercontent.com/u/735289?v=4,Automatic SQL injection and database takeover tool,"['database', 'detection', 'exploitation', 'pentesting', 'sql-injection', 'sqlmap', 'takeover', 'vulnerability-scanner']","['database', 'detection', 'exploitation', 'pentesting', 'sql-injection', 'sqlmap', 'takeover', 'vulnerability-scanner']",2024-01-11,"[('swisskyrepo/payloadsallthethings', 0.5263312458992004, 'security', 0)]",133,3.0,,2.38,73,64,141,0,1,9,1,73.0,84.0,90.0,1.2,61
467,util,https://github.com/alievk/avatarify-python,[],,[],[],,,,alievk/avatarify-python,avatarify-python,15989,3217,317,Python,,"Avatars for Zoom, Skype and other video-conferencing apps.",alievk,2024-01-13,2020-04-06,199,80.28909612625539,,"Avatars for Zoom, Skype and other video-conferencing apps.",[],[],2023-09-29,[],24,5.0,,0.08,54,14,46,4,0,1,1,54.0,165.0,90.0,3.1,61
331,ml-rl,https://github.com/unity-technologies/ml-agents,[],,[],[],,,,unity-technologies/ml-agents,ml-agents,15866,4039,551,C#,https://unity.com/products/machine-learning-agents,The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.,unity-technologies,2024-01-14,2017-09-08,333,47.56402569593148,https://avatars.githubusercontent.com/u/426196?v=4,The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.,"['deep-learning', 'deep-reinforcement-learning', 'machine-learning', 'neural-networks', 'reinforcement-learning', 'unity', 'unity3d']","['deep-learning', 'deep-reinforcement-learning', 'machine-learning', 'neural-networks', 'reinforcement-learning', 'unity', 'unity3d']",2023-12-02,"[('facebookresearch/habitat-lab', 0.6679417490959167, 'sim', 3), ('salesforce/warp-drive', 0.6577045321464539, 'ml-rl', 2), ('google/dopamine', 0.6415248513221741, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.6379924416542053, 'ml', 3), ('inspirai/timechamber', 0.6286770105361938, 'sim', 2), ('keras-rl/keras-rl', 0.6218107342720032, 'ml-rl', 3), ('pytorch/rl', 0.6213619709014893, 'ml-rl', 2), ('farama-foundation/gymnasium', 0.6212126612663269, 'ml-rl', 1), ('tensorlayer/tensorlayer', 0.61866694688797, 'ml-rl', 2), ('pettingzoo-team/pettingzoo', 0.6170148849487305, 'ml-rl', 1), ('deepmind/dm_control', 0.6150110363960266, 'ml-rl', 4), ('google/trax', 0.5988940596580505, 'ml-dl', 4), ('thu-ml/tianshou', 0.598300576210022, 'ml-rl', 0), ('operand/agency', 0.596383810043335, 'llm', 1), ('mlflow/mlflow', 0.5918627381324768, 'ml-ops', 1), ('arise-initiative/robosuite', 0.58687424659729, 'ml-rl', 1), ('openai/spinningup', 0.5850274562835693, 'study', 0), ('tensorflow/tensorflow', 0.5806676149368286, 'ml-dl', 2), ('nvidia-omniverse/orbit', 0.5719271898269653, 'sim', 0), ('bentoml/bentoml', 0.5712394714355469, 'ml-ops', 2), ('googlecloudplatform/vertex-ai-samples', 0.5682862997055054, 'ml', 0), ('openai/baselines', 0.5591613054275513, 'ml-rl', 0), ('denys88/rl_games', 0.5579639077186584, 'ml-rl', 2), ('microsoft/onnxruntime', 0.5510517358779907, 'ml', 3), ('microsoft/nni', 0.5489663481712341, 'ml', 2), ('ray-project/ray', 0.5486295223236084, 'ml-ops', 3), ('oegedijk/explainerdashboard', 0.5465452671051025, 'ml-interpretability', 0), ('polyaxon/polyaxon', 0.5450904965400696, 'ml-ops', 3), ('ddbourgin/numpy-ml', 0.5443470478057861, 'ml', 3), ('determined-ai/determined', 0.5427035689353943, 'ml-ops', 2), ('transformeroptimus/superagi', 0.5418506264686584, 'llm', 0), ('slundberg/shap', 0.5413389205932617, 'ml-interpretability', 2), ('prefecthq/marvin', 0.5411503911018372, 'nlp', 0), ('openai/gym', 0.5394145846366882, 'ml-rl', 1), ('google-research/language', 0.5368104577064514, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5343484878540039, 'ml-dl', 1), ('nvidia-omniverse/omniisaacgymenvs', 0.5333084464073181, 'sim', 0), ('deepchecks/deepchecks', 0.5319340825080872, 'data', 2), ('antonosika/gpt-engineer', 0.5287721157073975, 'llm', 0), ('huggingface/datasets', 0.5283746719360352, 'nlp', 2), ('onnx/onnx', 0.5283321142196655, 'ml', 2), ('facebookresearch/reagent', 0.5251111388206482, 'ml-rl', 0), ('humancompatibleai/imitation', 0.5231532454490662, 'ml-rl', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5230525732040405, 'study', 2), ('projectmesa/mesa', 0.5214616060256958, 'sim', 0), ('deepmind/pysc2', 0.5204218029975891, 'ml-rl', 2), ('ai4finance-foundation/finrl', 0.5189048647880554, 'finance', 2), ('explosion/thinc', 0.5183029174804688, 'ml-dl', 2), ('pytorchlightning/pytorch-lightning', 0.5169978737831116, 'ml-dl', 2), ('deeppavlov/deeppavlov', 0.5153900980949402, 'nlp', 2), ('microsoft/lmops', 0.5131751298904419, 'llm', 0), ('krohling/bondai', 0.5124642252922058, 'llm', 0), ('zacwellmer/worldmodels', 0.5110092163085938, 'ml-rl', 0), ('keras-team/keras', 0.5099013447761536, 'ml-dl', 3), ('koaning/human-learn', 0.5095399618148804, 'data', 1), ('deepmind/acme', 0.5082459449768066, 'ml-rl', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5079880356788635, 'study', 2), ('mlc-ai/mlc-llm', 0.5073363184928894, 'llm', 0), ('aiqc/aiqc', 0.5059369802474976, 'ml-ops', 0), ('luodian/otter', 0.5054410099983215, 'llm', 2), ('rasbt/machine-learning-book', 0.5040963292121887, 'study', 3), ('hpcaitech/colossalai', 0.5021815896034241, 'llm', 1), ('ml-tooling/opyrator', 0.5017167925834656, 'viz', 1), ('rasbt/deeplearning-models', 0.501521110534668, 'ml-dl', 0)]",161,5.0,,0.81,68,49,77,1,1,22,1,68.0,154.0,90.0,2.3,61
550,ml-ops,https://github.com/horovod/horovod,[],,[],['horovod'],,,,horovod/horovod,horovod,13750,2227,331,Python,http://horovod.ai,"Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.",horovod,2024-01-13,2017-08-09,337,40.69767441860465,https://avatars.githubusercontent.com/u/46361271?v=4,"Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.","['baidu', 'deep-learning', 'deeplearning', 'keras', 'machine-learning', 'machinelearning', 'mpi', 'mxnet', 'pytorch', 'ray', 'spark', 'tensorflow', 'uber']","['baidu', 'deep-learning', 'deeplearning', 'keras', 'machine-learning', 'machinelearning', 'mpi', 'mxnet', 'pytorch', 'ray', 'spark', 'tensorflow', 'uber']",2024-01-05,"[('paddlepaddle/paddle', 0.7518745064735413, 'ml-dl', 2), ('tensorflow/tensorflow', 0.7118455767631531, 'ml-dl', 3), ('determined-ai/determined', 0.6985735297203064, 'ml-ops', 5), ('microsoft/deepspeed', 0.6651274561882019, 'ml-dl', 3), ('alpa-projects/alpa', 0.6610665321350098, 'ml-dl', 2), ('microsoft/onnxruntime', 0.6534867882728577, 'ml', 4), ('ray-project/ray', 0.6456006169319153, 'ml-ops', 5), ('eventual-inc/daft', 0.6405372023582458, 'pandas', 2), ('uber/petastorm', 0.6362690329551697, 'data', 4), ('adap/flower', 0.6284599304199219, 'ml-ops', 4), ('apache/incubator-mxnet', 0.6248153448104858, 'ml-dl', 1), ('uber/fiber', 0.6222132444381714, 'data', 1), ('mlflow/mlflow', 0.612357497215271, 'ml-ops', 1), ('arogozhnikov/einops', 0.6111495494842529, 'ml-dl', 4), ('pytorch/ignite', 0.6100185513496399, 'ml-dl', 3), ('huggingface/transformers', 0.6071690917015076, 'nlp', 4), ('merantix-momentum/squirrel-core', 0.6060230135917664, 'ml', 4), ('google/tf-quant-finance', 0.6027436852455139, 'finance', 1), ('onnx/onnx', 0.5999342203140259, 'ml', 6), ('aws/sagemaker-python-sdk', 0.5958705544471741, 'ml', 4), ('intel/intel-extension-for-pytorch', 0.5949805974960327, 'perf', 3), ('tensorly/tensorly', 0.5948300361633301, 'ml-dl', 4), ('bigscience-workshop/petals', 0.5874497294425964, 'data', 3), ('rasbt/machine-learning-book', 0.5868951082229614, 'study', 3), ('nvidia/apex', 0.5866835117340088, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5849502086639404, 'ml-dl', 4), ('nevronai/metisfl', 0.5837339162826538, 'ml', 2), ('polyaxon/polyaxon', 0.5797879695892334, 'ml-ops', 6), ('ashleve/lightning-hydra-template', 0.5749367475509644, 'util', 2), ('dmlc/xgboost', 0.5724257230758667, 'ml', 1), ('explosion/thinc', 0.569107711315155, 'ml-dl', 5), ('ggerganov/ggml', 0.5658283829689026, 'ml', 1), ('nyandwi/modernconvnets', 0.5646082162857056, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5628776550292969, 'study', 6), ('ludwig-ai/ludwig', 0.5625487565994263, 'ml-ops', 5), ('microsoft/jarvis', 0.5595570206642151, 'llm', 2), ('keras-team/keras-nlp', 0.5583081245422363, 'nlp', 4), ('backtick-se/cowait', 0.5568536520004272, 'util', 1), ('tlkh/tf-metal-experiments', 0.5538284182548523, 'perf', 2), ('karpathy/micrograd', 0.552807092666626, 'study', 0), ('tensorflow/similarity', 0.5510625839233398, 'ml-dl', 3), ('jina-ai/jina', 0.5503325462341309, 'ml', 2), ('mrdbourke/pytorch-deep-learning', 0.548136830329895, 'study', 3), ('rwightman/pytorch-image-models', 0.5461047887802124, 'ml-dl', 1), ('keras-team/keras', 0.5442795157432556, 'ml-dl', 4), ('tensorflow/addons', 0.5429335832595825, 'ml', 3), ('huggingface/datasets', 0.5425728559494019, 'nlp', 4), ('ray-project/ray-educational-materials', 0.5418562293052673, 'study', 2), ('neuralmagic/sparseml', 0.5365691781044006, 'ml-dl', 3), ('optuna/optuna', 0.5364230871200562, 'ml', 1), ('activeloopai/deeplake', 0.5363553166389465, 'ml-ops', 4), ('deepmind/dm-haiku', 0.5358175039291382, 'ml-dl', 2), ('pytorchlightning/pytorch-lightning', 0.5334360599517822, 'ml-dl', 3), ('skorch-dev/skorch', 0.5322457551956177, 'ml-dl', 2), ('denys88/rl_games', 0.5315641164779663, 'ml-rl', 2), ('titanml/takeoff', 0.529777467250824, 'llm', 0), ('aiqc/aiqc', 0.5296958684921265, 'ml-ops', 0), ('neuralmagic/deepsparse', 0.5292316675186157, 'nlp', 1), ('rafiqhasan/auto-tensorflow', 0.5287712216377258, 'ml-dl', 4), ('lutzroeder/netron', 0.5276727080345154, 'ml', 8), ('pyg-team/pytorch_geometric', 0.5256267189979553, 'ml-dl', 2), ('pytorch/pytorch', 0.5245367288589478, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5228431820869446, 'ml-rl', 2), ('google/mediapipe', 0.519681990146637, 'ml', 2), ('iryna-kondr/scikit-llm', 0.5187541842460632, 'llm', 2), ('xl0/lovely-tensors', 0.5179717540740967, 'ml-dl', 2), ('ageron/handson-ml2', 0.5166817307472229, 'ml', 0), ('huggingface/exporters', 0.5157522559165955, 'ml', 4), ('ddbourgin/numpy-ml', 0.5155518651008606, 'ml', 1), ('facebookresearch/pytorch3d', 0.5143802165985107, 'ml-dl', 0), ('nccr-itmo/fedot', 0.5143430829048157, 'ml-ops', 1), ('keras-team/autokeras', 0.5139292478561401, 'ml-dl', 4), ('christoschristofidis/awesome-deep-learning', 0.5127199292182922, 'study', 2), ('fugue-project/fugue', 0.5121609568595886, 'pandas', 2), ('googlecloudplatform/vertex-ai-samples', 0.5119903683662415, 'ml', 0), ('tensorflow/mesh', 0.5112833976745605, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5111513733863831, 'ml-dl', 3), ('google/gin-config', 0.5107054710388184, 'util', 1), ('megvii-basedetection/yolox', 0.5106202363967896, 'ml', 2), ('mosaicml/composer', 0.5103276371955872, 'ml-dl', 3), ('hpcaitech/colossalai', 0.5101556181907654, 'llm', 1), ('keras-rl/keras-rl', 0.509486973285675, 'ml-rl', 3), ('salesforce/warp-drive', 0.5066569447517395, 'ml-rl', 2), ('tensorflow/tensor2tensor', 0.5053731799125671, 'ml', 2), ('microsoft/nni', 0.5024771094322205, 'ml', 4), ('deci-ai/super-gradients', 0.5012701749801636, 'ml-dl', 2), ('pytorch/rl', 0.5009804368019104, 'ml-rl', 2)]",172,5.0,,0.94,48,20,78,0,3,12,3,48.0,64.0,90.0,1.3,61
1021,finance,https://github.com/microsoft/qlib,[],,[],[],1.0,,,microsoft/qlib,qlib,13247,2302,275,Python,https://qlib.readthedocs.io/en/latest/,"Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.",microsoft,2024-01-14,2020-08-14,180,73.36155063291139,https://avatars.githubusercontent.com/u/6154722?v=4,"Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.","['algorithmic-trading', 'auto-quant', 'deep-learning', 'finance', 'fintech', 'investment', 'machine-learning', 'paper', 'platform', 'quant', 'quant-dataset', 'quant-models', 'quantitative-finance', 'quantitative-trading', 'research', 'research-paper', 'stock-data']","['algorithmic-trading', 'auto-quant', 'deep-learning', 'finance', 'fintech', 'investment', 'machine-learning', 'paper', 'platform', 'quant', 'quant-dataset', 'quant-models', 'quantitative-finance', 'quantitative-trading', 'research', 'research-paper', 'stock-data']",2023-11-21,"[('ai4finance-foundation/finrl', 0.5603201389312744, 'finance', 3), ('zvtvz/zvt', 0.5502883791923523, 'finance', 6), ('goldmansachs/gs-quant', 0.5478851199150085, 'finance', 0), ('google/tf-quant-finance', 0.5447477698326111, 'finance', 2), ('chancefocus/pixiu', 0.5296194553375244, 'finance', 2), ('quantconnect/lean', 0.5189212560653687, 'finance', 1), ('openbb-finance/openbbterminal', 0.5187370777130127, 'finance', 4), ('xplainable/xplainable', 0.5183536410331726, 'ml-interpretability', 1), ('ranaroussi/quantstats', 0.5154274702072144, 'finance', 5), ('tensorflow/tensor2tensor', 0.5105899572372437, 'ml', 2), ('numerai/example-scripts', 0.5105475187301636, 'finance', 1), ('quantecon/quantecon.py', 0.5021764636039734, 'sim', 0)]",122,3.0,,1.12,178,118,42,2,3,6,3,178.0,107.0,90.0,0.6,61
849,viz,https://github.com/visgl/deck.gl,[],,[],[],,,,visgl/deck.gl,deck.gl,11453,2061,1693,TypeScript,https://deck.gl,WebGL2 powered visualization framework,visgl,2024-01-13,2015-12-15,424,27.01179245283019,https://avatars.githubusercontent.com/u/46735142?v=4,WebGL2 powered visualization framework,"['data-visualization', 'geospatial-analysis', 'javascript', 'maps', 'visualization', 'webgl']","['data-visualization', 'geospatial-analysis', 'javascript', 'maps', 'visualization', 'webgl']",2024-01-12,"[('nomic-ai/deepscatter', 0.6989966630935669, 'viz', 3), ('giswqs/geemap', 0.6095970869064331, 'gis', 0), ('raphaelquast/eomaps', 0.6003251075744629, 'gis', 1), ('bokeh/bokeh', 0.5892252326011658, 'viz', 2), ('residentmario/geoplot', 0.5753275156021118, 'gis', 0), ('mckinsey/vizro', 0.5679866671562195, 'viz', 2), ('vispy/vispy', 0.5518122911453247, 'viz', 1), ('pyvista/pyvista', 0.5255587100982666, 'viz', 1), ('holoviz/datashader', 0.521821916103363, 'gis', 0), ('plotly/plotly.py', 0.5213139057159424, 'viz', 2), ('holoviz/holoviz', 0.5192416310310364, 'viz', 0), ('man-group/dtale', 0.5085725784301758, 'viz', 2), ('holoviz/panel', 0.5066721439361572, 'viz', 0)]",243,3.0,,6.06,207,137,98,0,57,72,57,207.0,193.0,90.0,0.9,61
411,util,https://github.com/pre-commit/pre-commit,['code-quality'],,[],[],,,,pre-commit/pre-commit,pre-commit,11446,788,86,Python,https://pre-commit.com,A framework for managing and maintaining multi-language pre-commit hooks.,pre-commit,2024-01-14,2014-03-13,515,22.194459833795015,https://avatars.githubusercontent.com/u/6943086?v=4,A framework for managing and maintaining multi-language pre-commit hooks.,"['git', 'linter', 'pre-commit', 'refactoring']","['code-quality', 'git', 'linter', 'pre-commit', 'refactoring']",2024-01-12,"[('asottile/pyupgrade', 0.6998698115348816, 'util', 3), ('psf/black', 0.5649186968803406, 'util', 1), ('thudm/codegeex', 0.5082143545150757, 'llm', 0)]",156,4.0,,2.46,105,97,120,0,17,19,17,105.0,234.0,90.0,2.2,61
1250,util,https://github.com/openai/triton,[],,[],[],,,,openai/triton,triton,9513,1050,171,C++,https://triton-lang.org/,Development repository for the Triton language and compiler,openai,2024-01-14,2014-08-30,491,19.357848837209303,https://avatars.githubusercontent.com/u/14957082?v=4,Development repository for the Triton language and compiler,[],[],2024-01-12,"[('scikit-build/scikit-build', 0.5731392502784729, 'ml', 0), ('python/cpython', 0.5405258536338806, 'util', 0), ('pytorch/glow', 0.5112419724464417, 'ml', 0), ('cython/cython', 0.502590537071228, 'util', 0), ('pypy/pypy', 0.500663697719574, 'util', 0)]",202,6.0,,18.96,516,379,114,0,0,1,1,517.0,1005.0,90.0,1.9,61
243,util,https://github.com/aws/serverless-application-model,[],,[],[],,,,aws/serverless-application-model,serverless-application-model,9181,2406,289,Python,https://aws.amazon.com/serverless/sam,The AWS Serverless Application Model (AWS SAM) transform is a AWS CloudFormation macro that transforms SAM templates into CloudFormation templates.,aws,2024-01-12,2016-10-10,381,24.08808095952024,https://avatars.githubusercontent.com/u/2232217?v=4,The AWS Serverless Application Model (AWS SAM) transform is a AWS CloudFormation macro that transforms SAM templates into CloudFormation templates.,"['aws', 'aws-sam', 'lambda', 'sam', 'sam-specification', 'serverless', 'serverless-application-model', 'serverless-applications']","['aws', 'aws-sam', 'lambda', 'sam', 'sam-specification', 'serverless', 'serverless-application-model', 'serverless-applications']",2024-01-10,"[('nficano/python-lambda', 0.5357404947280884, 'util', 2), ('aws/chalice', 0.5237606167793274, 'web', 3)]",268,4.0,,8.02,155,144,88,0,28,13,28,154.0,173.0,90.0,1.1,61
1289,llm,https://github.com/blinkdl/chatrwkv,[],,[],[],,,,blinkdl/chatrwkv,ChatRWKV,9013,670,90,Python,,"ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.",blinkdl,2024-01-14,2023-01-13,54,165.15968586387436,,"ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.","['chatbot', 'chatgpt', 'language-model', 'pytorch', 'rnn', 'rwkv']","['chatbot', 'chatgpt', 'language-model', 'pytorch', 'rnn', 'rwkv']",2023-12-27,"[('xtekky/gpt4free', 0.6857205033302307, 'llm', 3), ('run-llama/rags', 0.678069531917572, 'llm', 2), ('blinkdl/rwkv-lm', 0.6400032043457031, 'llm', 5), ('embedchain/embedchain', 0.6271498203277588, 'llm', 1), ('nomic-ai/gpt4all', 0.6257155537605286, 'llm', 2), ('lm-sys/fastchat', 0.6127018928527832, 'llm', 2), ('togethercomputer/openchatkit', 0.6018038392066956, 'nlp', 1), ('killianlucas/open-interpreter', 0.6001401543617249, 'llm', 1), ('microsoft/autogen', 0.5980231761932373, 'llm', 2), ('fasteval/fasteval', 0.5970721244812012, 'llm', 0), ('minimaxir/simpleaichat', 0.5946717262268066, 'llm', 1), ('databrickslabs/dolly', 0.592911422252655, 'llm', 1), ('next-gpt/next-gpt', 0.5798339247703552, 'llm', 1), ('openlmlab/moss', 0.5662409067153931, 'llm', 2), ('openai/tiktoken', 0.5641686320304871, 'nlp', 1), ('rasahq/rasa', 0.5604526400566101, 'llm', 1), ('deeppavlov/deeppavlov', 0.5481441617012024, 'nlp', 1), ('mlc-ai/web-llm', 0.5393033623695374, 'llm', 2), ('openai/openai-cookbook', 0.5351440906524658, 'ml', 1), ('allenai/allennlp', 0.5303969979286194, 'nlp', 1), ('gunthercox/chatterbot', 0.5218847393989563, 'nlp', 1), ('langchain-ai/chat-langchain', 0.521385908126831, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5196929574012756, 'nlp', 0), ('nvidia/nemo', 0.5173482894897461, 'nlp', 1), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5159780979156494, 'llm', 0), ('laion-ai/open-assistant', 0.5142605900764465, 'llm', 2), ('chatarena/chatarena', 0.5140795111656189, 'llm', 1), ('rcgai/simplyretrieve', 0.5114522576332092, 'llm', 0), ('speechbrain/speechbrain', 0.5112695693969727, 'nlp', 2), ('thudm/chatglm2-6b', 0.5033798813819885, 'llm', 0)]",17,4.0,,5.88,14,7,12,1,0,0,0,14.0,7.0,90.0,0.5,61
1074,nlp,https://github.com/togethercomputer/openchatkit,['chatbot'],"OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots",[],[],,,,togethercomputer/openchatkit,OpenChatKit,8958,1024,123,Python,,,togethercomputer,2024-01-14,2023-03-03,47,188.3063063063063,https://avatars.githubusercontent.com/u/109101822?v=4,"OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots",[],['chatbot'],2023-08-17,"[('embedchain/embedchain', 0.7426310181617737, 'llm', 0), ('gunthercox/chatterbot', 0.6964467167854309, 'nlp', 1), ('rasahq/rasa', 0.6932374835014343, 'llm', 1), ('nomic-ai/gpt4all', 0.6839218139648438, 'llm', 1), ('minimaxir/simpleaichat', 0.6666284203529358, 'llm', 0), ('deeppavlov/deeppavlov', 0.6648150086402893, 'nlp', 1), ('run-llama/rags', 0.6478996872901917, 'llm', 1), ('rcgai/simplyretrieve', 0.6417977213859558, 'llm', 0), ('larsbaunwall/bricky', 0.6274131536483765, 'llm', 0), ('laion-ai/open-assistant', 0.6049430966377258, 'llm', 0), ('blinkdl/chatrwkv', 0.6018038392066956, 'llm', 1), ('langchain-ai/chat-langchain', 0.6002198457717896, 'llm', 0), ('krohling/bondai', 0.596705973148346, 'llm', 0), ('lm-sys/fastchat', 0.5938577651977539, 'llm', 1), ('cheshire-cat-ai/core', 0.5846368074417114, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5749940872192383, 'llm', 0), ('killianlucas/open-interpreter', 0.570201575756073, 'llm', 0), ('prefecthq/marvin', 0.5622385144233704, 'nlp', 0), ('xtekky/gpt4free', 0.5519025325775146, 'llm', 1), ('kalliope-project/kalliope', 0.5493156909942627, 'util', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5483038425445557, 'llm', 0), ('deepset-ai/haystack', 0.5454091429710388, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5419533848762512, 'llm', 1), ('nvidia/nemo', 0.5391964912414551, 'nlp', 0), ('errbotio/errbot', 0.5380634665489197, 'nlp', 1), ('openlmlab/moss', 0.5363262295722961, 'llm', 0), ('openai/gpt-discord-bot', 0.5345660448074341, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5263169407844543, 'nlp', 0), ('weaviate/verba', 0.5258392691612244, 'llm', 0), ('fasteval/fasteval', 0.519807755947113, 'llm', 0), ('hwchase17/langchain', 0.5152731537818909, 'llm', 1), ('openai/openai-cookbook', 0.51520174741745, 'ml', 0), ('pathwaycom/llm-app', 0.5052575469017029, 'llm', 1), ('eternnoir/pytelegrambotapi', 0.5013675093650818, 'util', 0)]",19,6.0,,2.15,8,2,11,5,0,2,2,8.0,4.0,90.0,0.5,61
1361,llm,https://github.com/artidoro/qlora,['language-model'],,[],[],,,,artidoro/qlora,qlora,8702,762,81,Jupyter Notebook,https://arxiv.org/abs/2305.14314,QLoRA: Efficient Finetuning of Quantized LLMs,artidoro,2024-01-14,2023-05-11,37,230.7348484848485,,QLoRA: Efficient Finetuning of Quantized LLMs,[],['language-model'],2023-07-24,"[('opengvlab/omniquant', 0.7345274090766907, 'llm', 0), ('squeezeailab/squeezellm', 0.6456478834152222, 'llm', 0), ('hiyouga/llama-factory', 0.6366949081420898, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.6366947889328003, 'llm', 1), ('bobazooba/xllm', 0.6219983100891113, 'llm', 0), ('timdettmers/bitsandbytes', 0.6154015064239502, 'util', 0), ('ray-project/ray-llm', 0.6143306493759155, 'llm', 0), ('salesforce/xgen', 0.6102448105812073, 'llm', 1), ('juncongmoo/pyllama', 0.5865978002548218, 'llm', 0), ('vllm-project/vllm', 0.5857405066490173, 'llm', 0), ('young-geng/easylm', 0.574920654296875, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5710045695304871, 'llm', 0), ('amazon-science/dq-bart', 0.5504317283630371, 'nlp', 0), ('predibase/llm_distillation_playbook', 0.5419549345970154, 'llm', 0), ('cg123/mergekit', 0.5330007076263428, 'llm', 0), ('vahe1994/spqr', 0.5260251760482788, 'llm', 0), ('lightning-ai/lit-gpt', 0.5197663903236389, 'llm', 0), ('hao-ai-lab/lookaheaddecoding', 0.5174603462219238, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5163824558258057, 'nlp', 0), ('microsoft/torchscale', 0.5133755207061768, 'llm', 0), ('lightning-ai/lit-llama', 0.5125903487205505, 'llm', 1), ('lianjiatech/belle', 0.5086693167686462, 'llm', 0), ('li-plus/chatglm.cpp', 0.5086432099342346, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5075502395629883, 'perf', 0), ('mooler0410/llmspracticalguide', 0.5064951181411743, 'study', 0), ('eugeneyan/open-llms', 0.5061990022659302, 'study', 0), ('ibm/dromedary', 0.5037577748298645, 'llm', 1), ('thudm/chatglm2-6b', 0.5035903453826904, 'llm', 0), ('optimalscale/lmflow', 0.5009582042694092, 'llm', 1)]",16,4.0,,1.37,34,12,8,6,0,0,0,33.0,31.0,90.0,0.9,61
609,testing,https://github.com/robotframework/robotframework,[],,[],[],,,,robotframework/robotframework,robotframework,8634,2217,475,Python,http://robotframework.org,Generic automation framework for acceptance testing and RPA,robotframework,2024-01-13,2014-06-27,500,17.248287671232877,https://avatars.githubusercontent.com/u/574284?v=4,Generic automation framework for acceptance testing and RPA,"['attd', 'automation', 'bdd', 'robotframework', 'rpa', 'testautomation', 'testing']","['attd', 'automation', 'bdd', 'robotframework', 'rpa', 'testautomation', 'testing']",2024-01-11,"[('vedro-universe/vedro', 0.5252175331115723, 'testing', 1), ('seleniumbase/seleniumbase', 0.5185773968696594, 'testing', 0)]",193,4.0,,13.5,280,216,116,0,12,15,12,280.0,408.0,90.0,1.5,61
312,util,https://github.com/pypa/pipx,[],,[],[],,,,pypa/pipx,pipx,7789,361,75,Python,https://pipx.pypa.io,Install and Run Python Applications in Isolated Environments,pypa,2024-01-14,2018-10-06,277,28.075695159629248,https://avatars.githubusercontent.com/u/647025?v=4,Install and Run Python Applications in Isolated Environments,"['cli', 'pip', 'venv']","['cli', 'pip', 'venv']",2024-01-13,"[('pyenv/pyenv', 0.6269603371620178, 'util', 2), ('pypa/virtualenv', 0.6216922998428345, 'util', 2), ('pypa/pipenv', 0.6215226650238037, 'util', 2), ('ofek/pyapp', 0.604620099067688, 'util', 1), ('beeware/briefcase', 0.5395711064338684, 'util', 0), ('pantsbuild/pex', 0.5113623738288879, 'util', 1), ('pomponchik/instld', 0.5103548765182495, 'util', 2), ('pypa/hatch', 0.5080375671386719, 'util', 1), ('pyinstaller/pyinstaller', 0.5007230639457703, 'util', 0)]",124,4.0,,2.52,264,199,64,0,9,12,9,264.0,474.0,90.0,1.8,61
1047,ml-dl,https://github.com/lucidrains/imagen-pytorch,[],,[],[],,,,lucidrains/imagen-pytorch,imagen-pytorch,7563,714,111,Python,,"Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch",lucidrains,2024-01-13,2022-05-23,88,85.80388978930308,,"Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch","['artificial-intelligence', 'deep-learning', 'imagination-machine', 'text-to-image', 'text-to-video']","['artificial-intelligence', 'deep-learning', 'imagination-machine', 'text-to-image', 'text-to-video']",2024-01-12,"[('lucidrains/dalle2-pytorch', 0.7950653433799744, 'diffusion', 3), ('hysts/pytorch_image_classification', 0.6431624889373779, 'ml-dl', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.6227165460586548, 'web', 1), ('pytorch/ignite', 0.6169648766517639, 'ml-dl', 1), ('nvlabs/gcvit', 0.6131131052970886, 'diffusion', 1), ('saharmor/dalle-playground', 0.5938807725906372, 'diffusion', 2), ('skorch-dev/skorch', 0.5915490388870239, 'ml-dl', 0), ('alibaba/easynlp', 0.5900583267211914, 'nlp', 1), ('lucidrains/deep-daze', 0.588476300239563, 'ml', 3), ('salesforce/blip', 0.579346776008606, 'diffusion', 0), ('open-mmlab/mmediting', 0.5784053206443787, 'ml', 1), ('minimaxir/textgenrnn', 0.5763087272644043, 'nlp', 1), ('lucidrains/vit-pytorch', 0.5685757994651794, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5683515667915344, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5663982033729553, 'perf', 1), ('pyg-team/pytorch_geometric', 0.5598738193511963, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5554819703102112, 'study', 1), ('allenai/allennlp', 0.5545222163200378, 'nlp', 1), ('rwightman/pytorch-image-models', 0.5502446889877319, 'ml-dl', 0), ('activeloopai/deeplake', 0.5490372776985168, 'ml-ops', 1), ('karpathy/micrograd', 0.5438899993896484, 'study', 0), ('automatic1111/stable-diffusion-webui', 0.5401318073272705, 'diffusion', 1), ('lightly-ai/lightly', 0.5377395153045654, 'ml', 1), ('openai/clip', 0.5373484492301941, 'ml-dl', 1), ('jina-ai/clip-as-service', 0.5368449091911316, 'nlp', 1), ('huggingface/diffusers', 0.5299099087715149, 'diffusion', 1), ('huggingface/transformers', 0.5293651223182678, 'nlp', 1), ('ddbourgin/numpy-ml', 0.5289348363876343, 'ml', 0), ('nomic-ai/nomic', 0.5279374718666077, 'nlp', 0), ('nicolas-chaulet/torch-points3d', 0.5233572125434875, 'ml', 0), ('deci-ai/super-gradients', 0.5232393145561218, 'ml-dl', 1), ('lutzroeder/netron', 0.52129065990448, 'ml', 1), ('kornia/kornia', 0.5201680064201355, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.5199929475784302, 'study', 1), ('microsoft/onnxruntime', 0.519209623336792, 'ml', 1), ('denys88/rl_games', 0.5174486041069031, 'ml-rl', 1), ('jaidedai/easyocr', 0.5161765217781067, 'data', 1), ('davidadsp/generative_deep_learning_2nd_edition', 0.5147601962089539, 'study', 1), ('nvidia/deeplearningexamples', 0.5135546326637268, 'ml-dl', 1), ('sharonzhou/long_stable_diffusion', 0.5128797292709351, 'diffusion', 0), ('neuralmagic/sparseml', 0.512857973575592, 'ml-dl', 0), ('tensorlayer/tensorlayer', 0.5119508504867554, 'ml-rl', 2), ('awslabs/autogluon', 0.5101648569107056, 'ml', 1), ('deepmind/deepmind-research', 0.5075194835662842, 'ml', 0), ('huggingface/datasets', 0.5067300200462341, 'nlp', 1), ('pytorch-labs/gpt-fast', 0.5046398639678955, 'llm', 0), ('fepegar/torchio', 0.5043638348579407, 'ml-dl', 1), ('xl0/lovely-tensors', 0.5031198263168335, 'ml-dl', 1), ('nvlabs/prismer', 0.5019423961639404, 'diffusion', 0)]",20,3.0,,1.0,13,3,20,0,40,212,40,13.0,16.0,90.0,1.2,61
344,jupyter,https://github.com/mwouts/jupytext,[],,[],[],,,,mwouts/jupytext,jupytext,6301,387,69,Python,https://jupytext.readthedocs.io,"Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts",mwouts,2024-01-14,2018-06-15,293,21.463260340632605,,"Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts","['hydrogen', 'jupyter-notebook', 'jupyterlab', 'jupyterlab-extension', 'knitr', 'markdown', 'notebooks', 'rmarkdown', 'rstudio', 'version-control']","['hydrogen', 'jupyter-notebook', 'jupyterlab', 'jupyterlab-extension', 'knitr', 'markdown', 'notebooks', 'rmarkdown', 'rstudio', 'version-control']",2024-01-13,"[('jupyter/nbformat', 0.6959807276725769, 'jupyter', 0), ('jupyter/notebook', 0.6791135668754578, 'jupyter', 1), ('voila-dashboards/voila', 0.6650576591491699, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.6579967737197876, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.6525211930274963, 'jupyter', 2), ('jupyter-lsp/jupyterlab-lsp', 0.6457168459892273, 'jupyter', 3), ('aws/graph-notebook', 0.6454940438270569, 'jupyter', 1), ('jupyter/nbdime', 0.6408078074455261, 'jupyter', 3), ('cohere-ai/notebooks', 0.6391822695732117, 'llm', 1), ('nteract/papermill', 0.632023811340332, 'jupyter', 1), ('jupyter/nbconvert', 0.6044052839279175, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5986511707305908, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.5835863947868347, 'jupyter', 1), ('jupyter/nbgrader', 0.5783310532569885, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5749877095222473, 'study', 0), ('nbqa-dev/nbqa', 0.5719586610794067, 'jupyter', 1), ('quantopian/qgrid', 0.5685391426086426, 'jupyter', 0), ('ipython/ipykernel', 0.5664380788803101, 'util', 1), ('computationalmodelling/nbval', 0.5556824803352356, 'jupyter', 1), ('jupyterlite/jupyterlite', 0.5492159128189087, 'jupyter', 2), ('python-markdown/markdown', 0.5461102724075317, 'util', 1), ('jakevdp/pythondatasciencehandbook', 0.5404434204101562, 'study', 1), ('mamba-org/gator', 0.5371402502059937, 'jupyter', 2), ('tkrabel/bamboolib', 0.5347379446029663, 'pandas', 2), ('maartenbreddels/ipyvolume', 0.520946741104126, 'jupyter', 1), ('xiaohk/stickyland', 0.5160862803459167, 'jupyter', 2), ('bloomberg/ipydatagrid', 0.5105863809585571, 'jupyter', 1)]",85,6.0,,3.15,95,66,68,0,13,26,13,95.0,244.0,90.0,2.6,61
141,nlp,https://github.com/neuml/txtai,[],,[],[],,,,neuml/txtai,txtai,5982,443,77,Python,https://neuml.github.io/txtai,"💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows",neuml,2024-01-14,2020-08-09,181,32.997635933806144,https://avatars.githubusercontent.com/u/59890304?v=4,"💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows","['embeddings', 'information-retrieval', 'language-model', 'large-language-models', 'llm', 'machine-learning', 'neural-search', 'nlp', 'rag', 'retrieval-augmented-generation', 'search', 'search-engine', 'semantic-search', 'sentence-embeddings', 'transformers', 'txtai', 'vector-database', 'vector-search', 'vector-search-engine']","['embeddings', 'information-retrieval', 'language-model', 'large-language-models', 'llm', 'machine-learning', 'neural-search', 'nlp', 'rag', 'retrieval-augmented-generation', 'search', 'search-engine', 'semantic-search', 'sentence-embeddings', 'transformers', 'txtai', 'vector-database', 'vector-search', 'vector-search-engine']",2024-01-12,"[('paddlepaddle/paddlenlp', 0.7093960642814636, 'llm', 5), ('llmware-ai/llmware', 0.7031641602516174, 'llm', 9), ('chroma-core/chroma', 0.6690793633460999, 'data', 1), ('qdrant/fastembed', 0.6601556539535522, 'ml', 4), ('jina-ai/vectordb', 0.6555339097976685, 'data', 4), ('muennighoff/sgpt', 0.6413739323616028, 'llm', 6), ('deepset-ai/haystack', 0.635899007320404, 'llm', 7), ('intellabs/fastrag', 0.6202690005302429, 'nlp', 5), ('milvus-io/bootcamp', 0.6126426458358765, 'data', 3), ('ddangelov/top2vec', 0.608359694480896, 'nlp', 1), ('nomic-ai/semantic-search-app-template', 0.6002048254013062, 'study', 1), ('jina-ai/clip-as-service', 0.5824137330055237, 'nlp', 1), ('eleutherai/the-pile', 0.5822166800498962, 'data', 1), ('docarray/docarray', 0.5759537816047668, 'data', 3), ('activeloopai/deeplake', 0.575560450553894, 'ml-ops', 5), ('lancedb/lancedb', 0.574266254901886, 'data', 3), ('jina-ai/finetuner', 0.56916743516922, 'ml', 1), ('explosion/spacy-llm', 0.5675748586654663, 'llm', 4), ('qdrant/qdrant', 0.5641199350357056, 'data', 7), ('awslabs/dgl-ke', 0.5560591220855713, 'ml', 1), ('zilliztech/gptcache', 0.5468195080757141, 'llm', 3), ('infinitylogesh/mutate', 0.5397577285766602, 'nlp', 1), ('koaning/embetter', 0.538155198097229, 'data', 0), ('amansrivastava17/embedding-as-service', 0.5340142250061035, 'nlp', 2), ('sebischair/lbl2vec', 0.5315554738044739, 'nlp', 2), ('kagisearch/vectordb', 0.529229998588562, 'data', 2), ('hegelai/prompttools', 0.5288627743721008, 'llm', 4), ('plasticityai/magnitude', 0.526243269443512, 'nlp', 3), ('alibaba/easynlp', 0.5239185690879822, 'nlp', 3), ('ukplab/sentence-transformers', 0.5237442255020142, 'nlp', 3), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5210995674133301, 'data', 1), ('koaning/whatlies', 0.5187655687332153, 'nlp', 2), ('marqo-ai/marqo', 0.5169413089752197, 'ml', 7), ('paddlepaddle/rocketqa', 0.5149143934249878, 'nlp', 2), ('huggingface/transformers', 0.5143444538116455, 'nlp', 3), ('salesforce/xgen', 0.5134238600730896, 'llm', 4), ('lm-sys/fastchat', 0.5114730000495911, 'llm', 1), ('deeppavlov/deeppavlov', 0.5076900720596313, 'nlp', 2), ('mooler0410/llmspracticalguide', 0.5057186484336853, 'study', 2), ('allenai/allennlp', 0.50567227602005, 'nlp', 1), ('dylanhogg/llmgraph', 0.5045521259307861, 'ml', 1), ('microsoft/vert-papers', 0.5036661028862, 'nlp', 1), ('princeton-nlp/alce', 0.5001633763313293, 'llm', 0), ('lianjiatech/belle', 0.5000015497207642, 'llm', 0)]",12,7.0,,5.67,74,59,42,0,8,10,8,74.0,152.0,90.0,2.1,61
82,util,https://github.com/sphinx-doc/sphinx,[],,[],[],,,,sphinx-doc/sphinx,sphinx,5855,2037,148,Python,https://www.sphinx-doc.org/,The Sphinx documentation generator,sphinx-doc,2024-01-14,2015-01-02,473,12.363499245852188,https://avatars.githubusercontent.com/u/9928167?v=4,The Sphinx documentation generator,"['docs', 'documentation', 'documentation-tool', 'markdown', 'restructuredtext', 'sphinx']","['docs', 'documentation', 'documentation-tool', 'markdown', 'restructuredtext', 'sphinx']",2024-01-14,"[('executablebooks/jupyter-book', 0.753897488117218, 'jupyter', 0), ('mitmproxy/pdoc', 0.7031545042991638, 'util', 3), ('pdoc3/pdoc', 0.6652024388313293, 'util', 3), ('squidfunk/mkdocs-material', 0.6544510126113892, 'util', 1), ('mkdocs/mkdocs', 0.6297897100448608, 'util', 2), ('mkdocstrings/mkdocstrings', 0.5597922801971436, 'util', 0), ('getpelican/pelican', 0.5320377349853516, 'web', 0)]",796,7.0,,14.88,299,202,110,0,15,21,15,299.0,447.0,90.0,1.5,61
342,web,https://github.com/vitalik/django-ninja,[],,[],[],,,,vitalik/django-ninja,django-ninja,5676,346,75,Python,https://django-ninja.dev,"💨  Fast, Async-ready, Openapi, type hints based framework for building APIs",vitalik,2024-01-14,2020-05-19,193,29.409326424870468,,"💨  Fast, Async-ready, Openapi, type hints based framework for building APIs","['django', 'django-ninja', 'openapi', 'pydantic', 'rest-api', 'swagger', 'swagger-ui']","['django', 'django-ninja', 'openapi', 'pydantic', 'rest-api', 'swagger', 'swagger-ui']",2024-01-08,"[('tiangolo/fastapi', 0.8338611721992493, 'web', 4), ('python-restx/flask-restx', 0.7110880613327026, 'web', 1), ('starlite-api/starlite', 0.7026381492614746, 'web', 3), ('awtkns/fastapi-crudrouter', 0.6941371560096741, 'web', 2), ('hugapi/hug', 0.6697272658348083, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.6605289578437805, 'template', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.6353817582130432, 'template', 2), ('willmcgugan/textual', 0.6247105002403259, 'term', 0), ('falconry/falcon', 0.6103278994560242, 'web', 0), ('rawheel/fastapi-boilerplate', 0.6011227369308472, 'web', 1), ('shishirpatil/gorilla', 0.5843652486801147, 'llm', 0), ('fastai/ghapi', 0.58127361536026, 'util', 1), ('mitmproxy/pdoc', 0.5793086886405945, 'util', 0), ('s3rius/fastapi-template', 0.5742310285568237, 'web', 0), ('fastai/fastcore', 0.5651664137840271, 'util', 0), ('pyeve/eve', 0.563342809677124, 'web', 0), ('alirn76/panther', 0.5587133169174194, 'web', 0), ('openai/openai-python', 0.5567331910133362, 'util', 0), ('kivy/kivy', 0.5482510328292847, 'util', 0), ('huge-success/sanic', 0.5478051900863647, 'web', 0), ('pallets/flask', 0.5399484038352966, 'web', 0), ('prefecthq/server', 0.5366072654724121, 'util', 0), ('plotly/dash', 0.5318993926048279, 'viz', 0), ('lucidrains/toolformer-pytorch', 0.528582751750946, 'llm', 0), ('fastapi-admin/fastapi-admin', 0.5257643461227417, 'web', 0), ('bottlepy/bottle', 0.5206802487373352, 'web', 0), ('flet-dev/flet', 0.5206228494644165, 'web', 0), ('ml-tooling/opyrator', 0.5184093713760376, 'viz', 1), ('langchain-ai/opengpts', 0.5144355297088623, 'llm', 0), ('reflex-dev/reflex', 0.512046754360199, 'web', 0), ('simple-salesforce/simple-salesforce', 0.508848249912262, 'data', 0), ('oobabooga/text-generation-webui', 0.5028524398803711, 'llm', 0), ('alphasecio/langchain-examples', 0.5026804804801941, 'llm', 0), ('eternnoir/pytelegrambotapi', 0.5017293095588684, 'util', 0)]",125,3.0,,4.52,208,110,44,0,13,11,13,208.0,362.0,90.0,1.7,61
754,sim,https://github.com/qiskit/qiskit,[],,[],[],,,,qiskit/qiskit,qiskit,4208,2164,216,Python,https://www.ibm.com/quantum/qiskit,"Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.",qiskit,2024-01-14,2017-03-03,360,11.670364500792394,https://avatars.githubusercontent.com/u/30696987?v=4,"Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.","['qiskit', 'quantum', 'quantum-circuit', 'quantum-computing', 'quantum-programming-language', 'sdk']","['qiskit', 'quantum', 'quantum-circuit', 'quantum-computing', 'quantum-programming-language', 'sdk']",2024-01-12,"[('jackhidary/quantumcomputingbook', 0.5992211699485779, 'study', 3), ('cqcl/tket', 0.5771373510360718, 'util', 1), ('quantumlib/cirq', 0.5546154975891113, 'sim', 1), ('cqcl/lambeq', 0.5245597958564758, 'nlp', 0), ('netket/netket', 0.5100851058959961, 'sim', 1)]",534,5.0,,18.23,804,580,84,0,20,16,20,805.0,1767.0,90.0,2.2,61
1313,diffusion,https://github.com/idea-research/groundingdino,['awesome'],,[],[],,,,idea-research/groundingdino,GroundingDINO,4067,415,30,Python,https://arxiv.org/abs/2303.05499,"Official implementation of the paper ""Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection""",idea-research,2024-01-14,2023-03-09,46,87.06116207951071,https://avatars.githubusercontent.com/u/113572103?v=4,"Official implementation of the paper ""Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection""","['object-detection', 'open-world', 'open-world-detection', 'vision-language', 'vision-language-transformer']","['awesome', 'object-detection', 'open-world', 'open-world-detection', 'vision-language', 'vision-language-transformer']",2023-12-31,"[('idea-research/grounded-segment-anything', 0.6477982997894287, 'llm', 0), ('roboflow/notebooks', 0.5870379209518433, 'study', 1), ('facebookresearch/dinov2', 0.5680932402610779, 'diffusion', 0), ('nvlabs/gcvit', 0.5644093751907349, 'diffusion', 1), ('open-mmlab/mmdetection', 0.5333415269851685, 'ml', 1), ('deci-ai/super-gradients', 0.5182757377624512, 'ml-dl', 1)]",23,6.0,,1.25,67,12,10,0,2,2,2,67.0,67.0,90.0,1.0,61
515,util,https://github.com/spack/spack,[],,[],[],,,,spack/spack,spack,3783,2097,100,Python,https://spack.io,"A flexible package manager that supports multiple versions, configurations, platforms, and compilers.",spack,2024-01-14,2014-01-08,524,7.207675557974959,https://avatars.githubusercontent.com/u/25539161?v=4,"A flexible package manager that supports multiple versions, configurations, platforms, and compilers.","['build-tools', 'hpc', 'linux', 'macos', 'package-manager', 'radiuss', 'scientific-computing', 'spack']","['build-tools', 'hpc', 'linux', 'macos', 'package-manager', 'radiuss', 'scientific-computing', 'spack']",2024-01-13,"[('conda/conda', 0.7269142270088196, 'util', 1), ('mamba-org/mamba', 0.6774295568466187, 'util', 1), ('pomponchik/instld', 0.6380254030227661, 'util', 1), ('pypa/hatch', 0.592124879360199, 'util', 1), ('pypa/setuptools_scm', 0.5813215970993042, 'util', 0), ('mitsuhiko/rye', 0.5651411414146423, 'util', 1), ('pdm-project/pdm', 0.5586650371551514, 'util', 1), ('tiiuae/sbomnix', 0.5559228658676147, 'util', 0), ('indygreg/pyoxidizer', 0.5544406771659851, 'util', 1), ('mtkennerly/dunamai', 0.5440518856048584, 'util', 0), ('python-poetry/poetry', 0.5117734670639038, 'util', 1), ('scikit-build/scikit-build', 0.5056064128875732, 'ml', 0), ('mamba-org/boa', 0.5025382041931152, 'util', 0), ('polyaxon/polyaxon', 0.5023648738861084, 'ml-ops', 0)]",1567,4.0,,109.62,2017,1508,122,0,8,10,8,2016.0,3672.0,90.0,1.8,61
1436,jupyter,https://github.com/jupyterlab/jupyter-ai,[],,[],[],,,,jupyterlab/jupyter-ai,jupyter-ai,2475,225,31,Python,https://jupyter-ai.readthedocs.io/,A generative AI extension for JupyterLab,jupyterlab,2024-01-14,2023-02-09,50,48.80281690140845,https://avatars.githubusercontent.com/u/22800682?v=4,A generative AI extension for JupyterLab,"['generative-ai', 'jupyter', 'jupyterlab', 'jupyterlab-extension']","['generative-ai', 'jupyter', 'jupyterlab', 'jupyterlab-extension']",2024-01-10,"[('chaoleili/jupyterlab_tensorboard', 0.5836908221244812, 'jupyter', 2), ('jupyterlab/jupyterlab', 0.5743774771690369, 'jupyter', 2), ('jupyter-lsp/jupyterlab-lsp', 0.5275683403015137, 'jupyter', 3), ('ipython/ipykernel', 0.5044593811035156, 'util', 1), ('jupyter-widgets/ipywidgets', 0.5035024285316467, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5025471448898315, 'study', 0)]",19,3.0,,5.04,212,134,11,0,42,156,42,212.0,354.0,90.0,1.7,61
1821,llm,https://github.com/predibase/lorax,"['fine-tuned', 'scale', 'gpu']",,[],[],,,,predibase/lorax,lorax,671,52,19,Python,https://predibase.github.io/lorax/,Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs,predibase,2024-01-14,2023-10-20,14,46.049019607843135,https://avatars.githubusercontent.com/u/75280641?v=4,Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs,"['fine-tuning', 'gpt', 'llama', 'llm', 'llm-inference', 'llm-serving', 'llmops', 'lora', 'model-serving', 'pytorch', 'transformers']","['fine-tuned', 'fine-tuning', 'gpt', 'gpu', 'llama', 'llm', 'llm-inference', 'llm-serving', 'llmops', 'lora', 'model-serving', 'pytorch', 'scale', 'transformers']",2024-01-11,"[('vllm-project/vllm', 0.7764987945556641, 'llm', 7), ('bentoml/openllm', 0.7207032442092896, 'ml-ops', 6), ('bigscience-workshop/petals', 0.6941356062889099, 'data', 3), ('ray-project/ray-llm', 0.6269615888595581, 'llm', 5), ('intel/intel-extension-for-transformers', 0.5878379940986633, 'perf', 2), ('tairov/llama2.mojo', 0.568372368812561, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5675815939903259, 'llm', 6), ('hiyouga/llama-factory', 0.5675815343856812, 'llm', 6), ('microsoft/jarvis', 0.5660221576690674, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5625096559524536, 'llm', 4), ('sjtu-ipads/powerinfer', 0.5567286014556885, 'llm', 3), ('eugeneyan/open-llms', 0.5560584664344788, 'study', 1), ('bobazooba/xllm', 0.5401611924171448, 'llm', 4), ('run-llama/llama-hub', 0.5340386629104614, 'data', 1), ('jerryjliu/llama_index', 0.5294747352600098, 'llm', 3), ('young-geng/easylm', 0.5218080878257751, 'llm', 1), ('salesforce/xgen', 0.5184742212295532, 'llm', 1), ('jzhang38/tinyllama', 0.5169039964675903, 'llm', 1), ('skypilot-org/skypilot', 0.5144446492195129, 'llm', 2), ('opengenerativeai/genossgpt', 0.5129587650299072, 'llm', 3), ('tloen/alpaca-lora', 0.507797122001648, 'llm', 1), ('titanml/takeoff', 0.50351482629776, 'llm', 2), ('lightning-ai/lit-gpt', 0.5018265247344971, 'llm', 1)]",40,5.0,,9.62,181,145,3,0,12,53,12,181.0,318.0,90.0,1.8,61
91,web,https://github.com/tornadoweb/tornado,[],,[],['tornado'],1.0,,,tornadoweb/tornado,tornado,21397,5574,994,Python,http://www.tornadoweb.org/,"Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.",tornadoweb,2024-01-13,2009-09-09,750,28.496765601217657,https://avatars.githubusercontent.com/u/7468980?v=4,"Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.",['asynchronous'],['asynchronous'],2024-01-12,"[('encode/starlette', 0.57993084192276, 'web', 0), ('pallets/quart', 0.5757850408554077, 'web', 0), ('python-trio/trio', 0.5695123076438904, 'perf', 0), ('agronholm/anyio', 0.5639466643333435, 'perf', 0), ('timofurrer/awesome-asyncio', 0.5469016432762146, 'study', 0), ('alirn76/panther', 0.5357459187507629, 'web', 0), ('aio-libs/aiohttp', 0.5350281596183777, 'web', 0), ('sumerc/yappi', 0.5336647629737854, 'profiling', 1), ('masoniteframework/masonite', 0.5226184725761414, 'web', 0), ('neoteroi/blacksheep', 0.5203468203544617, 'web', 0), ('klen/muffin', 0.5200356245040894, 'web', 0), ('airtai/faststream', 0.5190186500549316, 'perf', 0), ('bottlepy/bottle', 0.5174039006233215, 'web', 0), ('encode/httpx', 0.5143762230873108, 'web', 0), ('praw-dev/asyncpraw', 0.5063261389732361, 'ml-dl', 0)]",438,6.0,,2.12,32,20,175,0,0,5,5,32.0,29.0,90.0,0.9,60
176,util,https://github.com/delgan/loguru,[],,[],[],1.0,,,delgan/loguru,loguru,17102,690,133,Python,,Python logging made (stupidly) simple,delgan,2024-01-14,2017-08-15,337,50.74777448071217,,Python logging made (stupidly) simple,"['log', 'logger', 'logging']","['log', 'logger', 'logging']",2024-01-10,"[('metachris/logzero', 0.7877286672592163, 'util', 1), ('alexmojaki/snoop', 0.5699717998504639, 'debug', 1)]",52,1.0,,2.1,88,43,78,0,3,3,3,88.0,183.0,90.0,2.1,60
1292,llm,https://github.com/fauxpilot/fauxpilot,[],,[],[],,,,fauxpilot/fauxpilot,fauxpilot,13782,602,123,Python,,FauxPilot - an open-source alternative to GitHub Copilot server,fauxpilot,2024-01-14,2022-08-03,77,177.01651376146788,https://avatars.githubusercontent.com/u/120729571?v=4,FauxPilot - an open-source alternative to GitHub Copilot server,[],[],2023-05-29,"[('fastai/ghapi', 0.5876653790473938, 'util', 0), ('mozillazg/pypy', 0.5119403004646301, 'util', 0)]",14,6.0,,0.27,11,2,18,8,0,0,0,11.0,17.0,90.0,1.5,60
52,graph,https://github.com/networkx/networkx,[],,[],[],,,,networkx/networkx,networkx,13757,3154,274,Python,https://networkx.org,Network Analysis in Python,networkx,2024-01-14,2010-09-06,699,19.676951369023293,https://avatars.githubusercontent.com/u/388785?v=4,Network Analysis in Python,"['complex-networks', 'graph-algorithms', 'graph-analysis', 'graph-generation', 'graph-theory', 'graph-visualization', 'spec-0', 'spec-1', 'spec-4']","['complex-networks', 'graph-algorithms', 'graph-analysis', 'graph-generation', 'graph-theory', 'graph-visualization', 'spec-0', 'spec-1', 'spec-4']",2024-01-13,"[('pygraphviz/pygraphviz', 0.6983128786087036, 'viz', 3), ('graphistry/pygraphistry', 0.6410859823226929, 'data', 1), ('westhealth/pyvis', 0.6360735297203064, 'graph', 0), ('scikit-mobility/scikit-mobility', 0.614628255367279, 'gis', 0), ('artelys/geonetworkx', 0.5718642473220825, 'gis', 0), ('a-r-j/graphein', 0.5483938455581665, 'sim', 0), ('scikit-image/scikit-image', 0.5438209772109985, 'util', 3), ('keon/algorithms', 0.543645977973938, 'util', 0), ('stellargraph/stellargraph', 0.5425050258636475, 'graph', 1), ('kuanb/peartree', 0.532261312007904, 'gis', 0), ('ranaroussi/quantstats', 0.5321804881095886, 'finance', 0), ('h4kor/graph-force', 0.5262413620948792, 'graph', 1), ('plotly/plotly.py', 0.5152801275253296, 'viz', 0), ('thealgorithms/python', 0.5088640451431274, 'study', 0), ('secdev/scapy', 0.5021397471427917, 'util', 0), ('scipy/scipy', 0.5018213391304016, 'math', 0)]",703,2.0,,7.6,303,230,163,0,4,7,4,303.0,572.0,90.0,1.9,60
319,gui,https://github.com/pysimplegui/pysimplegui,[],,[],[],1.0,,,pysimplegui/pysimplegui,PySimpleGUI,12925,1788,231,Python,https://www.PySimpleGUI.com,"Launched in 2018. It's 2023 and PySimpleGUI is actively developed & supported. Create complex windows simply. Supports tkinter, Qt, WxPython, Remi (in browser). Create GUI applications trivially with a full set of widgets. Multi-Window applications are also simple. 3.4 to 3.11 supported. 325+ Demo programs & Cookbook for rapid start. Extensive docs",pysimplegui,2024-01-14,2018-07-11,289,44.590931493346474,,"Launched in 2018. It's 2023 and PySimpleGUI is actively developed & supported. Create complex windows simply. Supports tkinter, Qt, WxPython, Remi (in browser). Create GUI applications trivially with a full set of widgets. Multi-Window applications are also simple. 3.4 to 3.11 supported. 325+ Demo programs & Cookbook for rapid start. Extensive docs","['beginner-friendly', 'datavisualization', 'games', 'gui', 'gui-framework', 'gui-programming', 'gui-window', 'pyside2', 'pysimplegui', 'python-gui', 'qt', 'qt-gui', 'remi', 'systemtray', 'tkinter', 'tkinter-gui', 'tkinter-python', 'user-interface', 'wxpython']","['beginner-friendly', 'datavisualization', 'games', 'gui', 'gui-framework', 'gui-programming', 'gui-window', 'pyside2', 'pysimplegui', 'python-gui', 'qt', 'qt-gui', 'remi', 'systemtray', 'tkinter', 'tkinter-gui', 'tkinter-python', 'user-interface', 'wxpython']",2023-11-26,"[('parthjadhav/tkinter-designer', 0.7242632508277893, 'gui', 4), ('wxwidgets/phoenix', 0.703073263168335, 'gui', 3), ('hoffstadt/dearpygui', 0.6711195111274719, 'gui', 2), ('r0x0r/pywebview', 0.6416908502578735, 'gui', 2), ('pyglet/pyglet', 0.624721884727478, 'gamedev', 0), ('beeware/toga', 0.6206801533699036, 'gui', 1), ('kivy/kivy', 0.600288450717926, 'util', 0), ('willmcgugan/textual', 0.589139997959137, 'term', 0), ('holoviz/panel', 0.5573893189430237, 'viz', 1), ('pypy/pypy', 0.5493513345718384, 'util', 0), ('jupyter-widgets/ipywidgets', 0.5297706127166748, 'jupyter', 0), ('urwid/urwid', 0.5266430974006653, 'term', 0), ('bokeh/bokeh', 0.5222162008285522, 'viz', 0), ('masoniteframework/masonite', 0.5136278867721558, 'web', 0), ('matplotlib/matplotlib', 0.5119383931159973, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5105063319206238, 'jupyter', 0), ('voila-dashboards/voila', 0.5095065236091614, 'jupyter', 0), ('plotly/dash', 0.5075579285621643, 'viz', 1)]",18,4.0,,2.46,134,89,67,2,2,1,2,134.0,455.0,90.0,3.4,60
174,term,https://github.com/tiangolo/typer,[],,[],[],1.0,,,tiangolo/typer,typer,12834,521,69,Python,https://typer.tiangolo.com/,"Typer, build great CLIs. Easy to code. Based on Python type hints.",tiangolo,2024-01-14,2019-12-24,214,59.97196261682243,,"Typer, build great CLIs. Easy to code. Based on Python type hints.","['cli', 'click', 'shell', 'terminal', 'typehints', 'typer']","['cli', 'click', 'shell', 'terminal', 'typehints', 'typer']",2023-12-10,"[('pyscript/pyscript-cli', 0.6438218355178833, 'web', 0), ('kellyjonbrazil/jc', 0.6338556408882141, 'util', 1), ('xonsh/xonsh', 0.614140510559082, 'util', 3), ('google/python-fire', 0.6071035265922546, 'term', 1), ('jquast/blessed', 0.5800941586494446, 'term', 2), ('python-poetry/cleo', 0.5780348777770996, 'term', 1), ('facebook/pyre-check', 0.5768271088600159, 'typing', 0), ('textualize/trogon', 0.5765345692634583, 'term', 3), ('tmbo/questionary', 0.5755541324615479, 'term', 1), ('pytoolz/toolz', 0.5614187121391296, 'util', 0), ('landscapeio/prospector', 0.558975100517273, 'util', 0), ('willmcgugan/rich', 0.5575374364852905, 'term', 1), ('python/cpython', 0.5462526082992554, 'util', 0), ('google/pytype', 0.5461122393608093, 'typing', 0), ('python/mypy', 0.5458303093910217, 'typing', 0), ('urwid/urwid', 0.5432376861572266, 'term', 0), ('astral-sh/ruff', 0.5375232100486755, 'util', 0), ('methexis-inc/terminal-copilot', 0.5327745079994202, 'util', 0), ('pexpect/pexpect', 0.5308032035827637, 'util', 0), ('pypy/pypy', 0.5274914503097534, 'util', 0), ('typesense/typesense-python', 0.5246941447257996, 'data', 0), ('agronholm/typeguard', 0.5242935419082642, 'typing', 0), ('hhatto/autopep8', 0.5092093348503113, 'util', 0), ('evhub/coconut', 0.5052512884140015, 'util', 0)]",37,4.0,,0.62,58,15,49,1,2,6,2,58.0,108.0,90.0,1.9,60
1106,data,https://github.com/redis/redis-py,[],,[],[],,,,redis/redis-py,redis-py,11993,2483,324,Python,,Redis Python Client,redis,2024-01-13,2009-11-06,742,16.150634859561368,https://avatars.githubusercontent.com/u/1529926?v=4,Redis Python Client,"['redis', 'redis-client', 'redis-cluster', 'redis-py']","['redis', 'redis-client', 'redis-cluster', 'redis-py']",2024-01-11,[],429,7.0,,3.37,174,101,173,0,21,7,21,174.0,183.0,90.0,1.1,60
520,diffusion,https://github.com/lucidrains/dalle2-pytorch,[],,[],[],,,,lucidrains/dalle2-pytorch,DALLE2-pytorch,10534,1032,127,Python,,"Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network,  in Pytorch",lucidrains,2024-01-14,2022-04-07,94,111.21870286576168,,"Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network,  in Pytorch","['artificial-intelligence', 'deep-learning', 'text-to-image']","['artificial-intelligence', 'deep-learning', 'text-to-image']",2023-10-19,"[('lucidrains/imagen-pytorch', 0.7950653433799744, 'ml-dl', 3), ('lucidrains/deep-daze', 0.631841242313385, 'ml', 3), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.6031858325004578, 'web', 1), ('saharmor/dalle-playground', 0.5927163362503052, 'diffusion', 2), ('openai/glide-text2im', 0.5790383219718933, 'diffusion', 0), ('sharonzhou/long_stable_diffusion', 0.5584306120872498, 'diffusion', 0), ('salesforce/blip', 0.5417680740356445, 'diffusion', 0), ('pytorch/ignite', 0.5412147641181946, 'ml-dl', 1), ('minimaxir/gpt-2-simple', 0.5398170948028564, 'llm', 0), ('minimaxir/textgenrnn', 0.5387126207351685, 'nlp', 1), ('pytorch-labs/gpt-fast', 0.5372596383094788, 'llm', 0), ('skorch-dev/skorch', 0.5362039804458618, 'ml-dl', 0), ('allenai/allennlp', 0.5331498384475708, 'nlp', 1), ('huggingface/diffusers', 0.532724142074585, 'diffusion', 1), ('hysts/pytorch_image_classification', 0.519826352596283, 'ml-dl', 0), ('alibaba/easynlp', 0.5197792649269104, 'nlp', 1), ('yoadtew/zero-shot-image-to-text', 0.5140002965927124, 'nlp', 0), ('laion-ai/dalle2-laion', 0.5131522417068481, 'diffusion', 1), ('open-mmlab/mmediting', 0.5127325654029846, 'ml', 1), ('denys88/rl_games', 0.5112625956535339, 'ml-rl', 1), ('google-research/electra', 0.5081148743629456, 'ml-dl', 1), ('intel/intel-extension-for-pytorch', 0.5061784982681274, 'perf', 1), ('nvlabs/gcvit', 0.5059126019477844, 'diffusion', 1), ('infinitylogesh/mutate', 0.5058514475822449, 'nlp', 0), ('karpathy/micrograd', 0.5032515525817871, 'study', 0)]",17,2.0,,0.29,12,1,22,3,13,195,13,12.0,25.0,90.0,2.1,60
700,sim,https://github.com/isl-org/open3d,[],,[],[],,,,isl-org/open3d,Open3D,9977,2140,197,C++,http://www.open3d.org,Open3D: A Modern Library for 3D Data Processing,isl-org,2024-01-13,2016-12-02,373,26.707074569789675,https://avatars.githubusercontent.com/u/23507030?v=4,Open3D: A Modern Library for 3D Data Processing,"['3d', '3d-perception', 'arm', 'computer-graphics', 'cpp', 'cuda', 'gpu', 'gui', 'machine-learning', 'mesh-processing', 'odometry', 'opengl', 'pointcloud', 'pytorch', 'reconstruction', 'registration', 'rendering', 'tensorflow', 'visualization']","['3d', '3d-perception', 'arm', 'computer-graphics', 'cpp', 'cuda', 'gpu', 'gui', 'machine-learning', 'mesh-processing', 'odometry', 'opengl', 'pointcloud', 'pytorch', 'reconstruction', 'registration', 'rendering', 'tensorflow', 'visualization']",2024-01-05,"[('facebookresearch/pytorch3d', 0.634926438331604, 'ml-dl', 0), ('kornia/kornia', 0.6128343939781189, 'ml-dl', 2), ('marcomusy/vedo', 0.6037029027938843, 'viz', 2), ('pyvista/pyvista', 0.5594555735588074, 'viz', 3), ('pokepetter/ursina', 0.5535932183265686, 'gamedev', 0), ('earthlab/earthpy', 0.5324108004570007, 'gis', 0), ('panda3d/panda3d', 0.5318998694419861, 'gamedev', 1), ('google/tf-quant-finance', 0.5206282138824463, 'finance', 2), ('dfki-ric/pytransform3d', 0.5204105377197266, 'math', 1), ('tensorlayer/tensorlayer', 0.5200856328010559, 'ml-rl', 1), ('polyaxon/datatile', 0.5179375410079956, 'pandas', 2), ('roboflow/supervision', 0.5120977759361267, 'ml', 3), ('raphaelquast/eomaps', 0.5115019679069519, 'gis', 1), ('geomstats/geomstats', 0.5070082545280457, 'math', 1), ('blackhc/toma', 0.5056933164596558, 'ml-dl', 3), ('domlysz/blendergis', 0.5055702924728394, 'gis', 1), ('cvxgrp/pymde', 0.5052924156188965, 'ml', 5), ('cupy/cupy', 0.5012453198432922, 'math', 2)]",217,6.0,,2.29,280,120,87,0,2,3,2,280.0,370.0,90.0,1.3,60
1446,testing,https://github.com/microsoft/playwright-python,['automation'],,[],[],,,,microsoft/playwright-python,playwright-python,9951,845,127,Python,https://playwright.dev/python/,Python version of the Playwright testing and automation library.,microsoft,2024-01-14,2020-07-01,186,53.2545871559633,https://avatars.githubusercontent.com/u/6154722?v=4,Python version of the Playwright testing and automation library.,"['chromium', 'firefox', 'playwright', 'webkit']","['automation', 'chromium', 'firefox', 'playwright', 'webkit']",2024-01-10,"[('seleniumbase/seleniumbase', 0.6941218972206116, 'testing', 2), ('cobrateam/splinter', 0.6916419267654419, 'testing', 1), ('masoniteframework/masonite', 0.549170970916748, 'web', 0), ('pexpect/pexpect', 0.5341052412986755, 'util', 1), ('r0x0r/pywebview', 0.5271952748298645, 'gui', 1), ('pyscript/pyscript-cli', 0.5237164497375488, 'web', 0), ('pytoolz/toolz', 0.5224789977073669, 'util', 0), ('eleutherai/pyfra', 0.5212807059288025, 'ml', 0), ('amaargiru/pyroad', 0.5205329060554504, 'study', 0), ('pytest-dev/pytest-bdd', 0.5180904865264893, 'testing', 0), ('pyscript/pyscript', 0.5149538516998291, 'web', 0), ('pyodide/pyodide', 0.5146863460540771, 'util', 0), ('nedbat/coveragepy', 0.5143460631370544, 'testing', 0), ('bokeh/bokeh', 0.510855495929718, 'viz', 0), ('urwid/urwid', 0.5090146660804749, 'term', 0), ('willmcgugan/textual', 0.5054807662963867, 'term', 0), ('webpy/webpy', 0.505174994468689, 'web', 0), ('hoffstadt/dearpygui', 0.5034270882606506, 'gui', 0)]",35,1.0,,1.88,140,122,43,0,13,23,13,139.0,190.0,90.0,1.4,60
53,perf,https://github.com/numba/numba,[],,[],[],,,,numba/numba,numba,9159,1130,200,Python,http://numba.pydata.org/,NumPy aware dynamic Python compiler using LLVM,numba,2024-01-13,2012-03-08,620,14.755581127733027,https://avatars.githubusercontent.com/u/1628082?v=4,NumPy aware dynamic Python compiler using LLVM,"['compiler', 'cuda', 'llvm', 'numpy', 'parallel']","['compiler', 'cuda', 'llvm', 'numpy', 'parallel']",2023-12-14,"[('exaloop/codon', 0.7364824414253235, 'perf', 2), ('google/jax', 0.6459792256355286, 'ml', 1), ('lcompilers/lpython', 0.603155255317688, 'util', 1), ('numba/llvmlite', 0.5931792855262756, 'util', 0), ('nvidia/tensorrt-llm', 0.5688282251358032, 'viz', 0), ('ipython/ipyparallel', 0.5564903020858765, 'perf', 1), ('cupy/cupy', 0.555233895778656, 'math', 2), ('nvidia/cuda-python', 0.5387571454048157, 'ml', 0), ('micropython/micropython', 0.5256375670433044, 'util', 0), ('pypy/pypy', 0.5218566656112671, 'util', 1), ('numpy/numpy', 0.5208587050437927, 'math', 1), ('hips/autograd', 0.5151181817054749, 'ml', 0), ('cython/cython', 0.504206120967865, 'util', 0)]",364,4.0,,30.42,265,127,144,1,4,17,4,265.0,598.0,90.0,2.3,60
10,util,https://github.com/cython/cython,[],,[],[],,,,cython/cython,cython,8636,1516,240,Python,https://cython.org,The most widely used Python to C compiler,cython,2024-01-14,2010-11-21,688,12.54711498547115,https://avatars.githubusercontent.com/u/486082?v=4,The most widely used Python to C compiler,"['big-data', 'c', 'cpp', 'cpython', 'cpython-extensions', 'cython', 'performance']","['big-data', 'c', 'cpp', 'cpython', 'cpython-extensions', 'cython', 'performance']",2024-01-12,"[('pypy/pypy', 0.8010214567184448, 'util', 1), ('exaloop/codon', 0.7081640958786011, 'perf', 0), ('python/cpython', 0.6992247700691223, 'util', 1), ('pyston/pyston', 0.6975716352462769, 'util', 0), ('lcompilers/lpython', 0.6913489699363708, 'util', 0), ('scikit-build/scikit-build', 0.6388826966285706, 'ml', 3), ('fastai/fastcore', 0.6373258829116821, 'util', 0), ('faster-cpython/tools', 0.6359994411468506, 'perf', 1), ('pytoolz/toolz', 0.5866791605949402, 'util', 0), ('micropython/micropython', 0.5850077271461487, 'util', 0), ('klen/py-frameworks-bench', 0.5801135301589966, 'perf', 0), ('faster-cpython/ideas', 0.5725173354148865, 'perf', 1), ('libtcod/python-tcod', 0.5713533759117126, 'gamedev', 0), ('joblib/joblib', 0.5678399801254272, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5606357455253601, 'perf', 0), ('pandas-dev/pandas', 0.5577985048294067, 'pandas', 0), ('dylanhogg/awesome-python', 0.5576968193054199, 'study', 0), ('plasma-umass/scalene', 0.5566608905792236, 'profiling', 0), ('pyinfra-dev/pyinfra', 0.5551347732543945, 'util', 0), ('eleutherai/pyfra', 0.5548232793807983, 'ml', 0), ('spotify/annoy', 0.5542510151863098, 'ml', 0), ('ibis-project/ibis', 0.553461492061615, 'data', 0), ('krzjoa/awesome-python-data-science', 0.5530298352241516, 'study', 0), ('facebookincubator/cinder', 0.5495518445968628, 'perf', 1), ('pytables/pytables', 0.549433171749115, 'data', 0), ('hoffstadt/dearpygui', 0.5458163619041443, 'gui', 1), ('ipython/ipyparallel', 0.5451430678367615, 'perf', 0), ('pympler/pympler', 0.5429266691207886, 'perf', 0), ('pypa/hatch', 0.5406709313392639, 'util', 0), ('panda3d/panda3d', 0.5402704477310181, 'gamedev', 0), ('rustpython/rustpython', 0.5352054238319397, 'util', 0), ('google/jax', 0.5345306992530823, 'ml', 0), ('ultrajson/ultrajson', 0.5291088223457336, 'perf', 1), ('markshannon/faster-cpython', 0.5285276770591736, 'perf', 0), ('willmcgugan/textual', 0.52788907289505, 'term', 0), ('fredrik-johansson/mpmath', 0.527232825756073, 'math', 0), ('wesm/pydata-book', 0.5262758135795593, 'study', 0), ('pytoolz/cytoolz', 0.5252465009689331, 'util', 0), ('vaexio/vaex', 0.5234464406967163, 'perf', 0), ('scikit-hep/uproot5', 0.5226044058799744, 'data', 1), ('google/tf-quant-finance', 0.5225537419319153, 'finance', 0), ('numba/llvmlite', 0.5224786996841431, 'util', 0), ('adafruit/circuitpython', 0.5219005346298218, 'util', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5218517780303955, 'study', 0), ('1200wd/bitcoinlib', 0.5212488770484924, 'crypto', 0), ('rubik/radon', 0.5208148956298828, 'util', 0), ('pytorch/glow', 0.5203021168708801, 'ml', 0), ('backtick-se/cowait', 0.5182483792304993, 'util', 0), ('timofurrer/awesome-asyncio', 0.5178491473197937, 'study', 0), ('pyo3/maturin', 0.5176377892494202, 'util', 1), ('samuelcolvin/python-devtools', 0.5175613164901733, 'debug', 0), ('dosisod/refurb', 0.5158920288085938, 'util', 0), ('thealgorithms/python', 0.5155187249183655, 'study', 0), ('p403n1x87/austin', 0.5145069360733032, 'profiling', 1), ('numpy/numpy', 0.5139519572257996, 'math', 0), ('eventual-inc/daft', 0.5138062238693237, 'pandas', 0), ('crunch-io/lazycsv', 0.5136982202529907, 'perf', 0), ('ipython/ipython', 0.5134088397026062, 'util', 0), ('pythonspeed/filprofiler', 0.5108945369720459, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.5089790225028992, 'profiling', 0), ('sumerc/yappi', 0.5085721611976624, 'profiling', 1), ('lukaszahradnik/pyneuralogic', 0.5075069665908813, 'math', 0), ('connorferster/handcalcs', 0.5067312121391296, 'jupyter', 0), ('wxwidgets/phoenix', 0.5055117011070251, 'gui', 0), ('grantjenks/blue', 0.5051524043083191, 'util', 0), ('xonsh/xonsh', 0.5044435262680054, 'util', 0), ('numba/numba', 0.504206120967865, 'perf', 0), ('goldmansachs/gs-quant', 0.5038447380065918, 'finance', 0), ('openai/triton', 0.502590537071228, 'util', 0), ('tobymao/sqlglot', 0.5022580623626709, 'data', 0), ('facebook/pyre-check', 0.5014179944992065, 'typing', 0), ('imageio/imageio', 0.5013166666030884, 'util', 0), ('urwid/urwid', 0.5010424852371216, 'term', 0)]",516,4.0,,14.48,262,160,160,0,19,15,19,262.0,591.0,90.0,2.3,60
7,util,https://github.com/boto/boto3,[],,[],[],,,,boto/boto3,boto3,8518,1865,241,Python,https://aws.amazon.com/sdk-for-python/,AWS SDK for Python,boto,2024-01-13,2014-10-03,486,17.506165590135055,https://avatars.githubusercontent.com/u/327752?v=4,AWS SDK for Python,"['aws', 'aws-sdk', 'cloud', 'cloud-management']","['aws', 'aws-sdk', 'cloud', 'cloud-management']",2024-01-14,"[('samuelcolvin/aioaws', 0.6758776307106018, 'data', 1), ('aws/chalice', 0.66681307554245, 'web', 2), ('nficano/python-lambda', 0.6395252346992493, 'util', 1), ('aws/aws-lambda-python-runtime-interface-client', 0.6017659902572632, 'util', 0), ('jordaneremieff/mangum', 0.589402973651886, 'web', 1), ('awslabs/python-deequ', 0.5848604440689087, 'ml', 1), ('geeogi/async-python-lambda-template', 0.5832446813583374, 'template', 0), ('pynamodb/pynamodb', 0.5776386857032776, 'data', 1), ('drivendataorg/cloudpathlib', 0.5708515644073486, 'data', 0), ('backtick-se/cowait', 0.5623748302459717, 'util', 0), ('localstack/localstack', 0.5544808506965637, 'util', 2), ('aws-samples/sagemaker-ssh-helper', 0.5350908637046814, 'util', 1), ('aws/aws-sdk-pandas', 0.5303450226783752, 'pandas', 1), ('sentinel-hub/sentinelhub-py', 0.5275383591651917, 'gis', 1), ('prefecthq/prefect-aws', 0.5230756402015686, 'data', 1), ('amzn/ion-python', 0.5133451223373413, 'data', 0)]",153,4.0,,10.52,155,119,113,0,0,158,158,155.0,286.0,90.0,1.8,60
71,ml,https://github.com/pymc-devs/pymc3,[],,[],[],,,,pymc-devs/pymc3,pymc,7970,1914,227,Python,https://docs.pymc.io/,Bayesian Modeling and Probabilistic Programming in Python,pymc-devs,2024-01-13,2009-05-05,769,10.364109232769831,https://avatars.githubusercontent.com/u/81121?v=4,Bayesian Modeling and Probabilistic Programming in Python,"['bayesian-inference', 'mcmc', 'probabilistic-programming', 'pytensor', 'statistical-analysis', 'variational-inference']","['bayesian-inference', 'mcmc', 'probabilistic-programming', 'pytensor', 'statistical-analysis', 'variational-inference']",2024-01-12,"[('pyro-ppl/pyro', 0.6964523792266846, 'ml-dl', 3), ('probml/pyprobml', 0.6565049886703491, 'ml', 1), ('crflynn/stochastic', 0.6376107335090637, 'sim', 0), ('uber/orbit', 0.6276130080223083, 'time-series', 1), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.6229053735733032, 'study', 0), ('scikit-learn/scikit-learn', 0.6052919030189514, 'ml', 0), ('awslabs/gluonts', 0.593370795249939, 'time-series', 0), ('infer-actively/pymdp', 0.5866931080818176, 'ml', 0), ('scikit-optimize/scikit-optimize', 0.5821582674980164, 'ml', 0), ('guyallard/markov_clustering', 0.5610766410827637, 'graph', 0), ('stan-dev/pystan', 0.5608965158462524, 'ml', 0), ('statsmodels/statsmodels', 0.5598034858703613, 'ml', 0), ('quantopian/pyfolio', 0.5452370047569275, 'finance', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5400987863540649, 'study', 0), ('pytorch/botorch', 0.5349946618080139, 'ml-dl', 0), ('artemyk/dynpy', 0.5243752002716064, 'sim', 0), ('eleutherai/pyfra', 0.5232195854187012, 'ml', 0), ('sympy/sympy', 0.5158290863037109, 'math', 0), ('goldmansachs/gs-quant', 0.5136345028877258, 'finance', 0), ('selfexplainml/piml-toolbox', 0.5050045847892761, 'ml-interpretability', 0), ('gerdm/prml', 0.5029430985450745, 'study', 0), ('pyomo/pyomo', 0.5023778080940247, 'math', 0)]",474,6.0,,6.98,191,99,179,0,25,6,25,191.0,573.0,90.0,3.0,60
1317,ml-dl,https://github.com/facebookresearch/imagebind,"['pytorch', 'multimodal', 'embeddings']",,[],[],,,,facebookresearch/imagebind,ImageBind,7541,666,100,Python,,ImageBind One Embedding Space to Bind Them All,facebookresearch,2024-01-13,2023-03-23,44,168.6485623003195,https://avatars.githubusercontent.com/u/16943930?v=4,ImageBind One Embedding Space to Bind Them All,[],"['embeddings', 'multimodal', 'pytorch']",2023-11-29,[],15,6.0,,0.37,20,5,10,2,0,0,0,20.0,16.0,90.0,0.8,60
1070,llm,https://github.com/nvidia/megatron-lm,[],,[],[],,,,nvidia/megatron-lm,Megatron-LM,7504,1701,143,Python,,Ongoing research training transformer models at scale,nvidia,2024-01-13,2019-03-21,253,29.576576576576578,https://avatars.githubusercontent.com/u/1728152?v=4,Ongoing research training transformer models at scale,[],[],2024-01-12,"[('bigscience-workshop/megatron-deepspeed', 0.6671424508094788, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6671424508094788, 'llm', 0), ('alignmentresearch/tuned-lens', 0.6293410658836365, 'ml-interpretability', 0), ('eleutherai/knowledge-neurons', 0.5722100138664246, 'ml-interpretability', 0), ('karpathy/mingpt', 0.5354270935058594, 'llm', 0), ('lvwerra/trl', 0.5321753025054932, 'llm', 0), ('opengeos/earthformer', 0.507581353187561, 'gis', 0), ('huggingface/optimum', 0.501192569732666, 'ml', 0)]",118,2.0,,21.87,185,45,59,0,3,4,3,184.0,288.0,90.0,1.6,60
1554,util,https://github.com/kellyjonbrazil/jc,"['serialization', 'jq']",,[],[],,,,kellyjonbrazil/jc,jc,7298,180,29,Python,,"CLI tool and python library that converts the output of popular command-line tools, file-types, and common strings to JSON, YAML, or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts.",kellyjonbrazil,2024-01-14,2019-10-15,224,32.580357142857146,,"CLI tool and python library that converts the output of popular command-line tools, file-types, and common strings to JSON, YAML, or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts.","['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'convert', 'json', 'linux', 'parsers', 'scripting', 'serialize', 'shell-scripting', 'yaml']","['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'convert', 'jq', 'json', 'linux', 'parsers', 'scripting', 'serialization', 'serialize', 'shell-scripting', 'yaml']",2023-12-21,"[('kellyjonbrazil/jello', 0.7774760127067566, 'util', 10), ('google/python-fire', 0.6432965397834778, 'term', 1), ('tiangolo/typer', 0.6338556408882141, 'term', 1), ('python-poetry/cleo', 0.6012207269668579, 'term', 2), ('pyscript/pyscript-cli', 0.5959556102752686, 'web', 0), ('xonsh/xonsh', 0.5756747722625732, 'util', 3), ('thoth-station/micropipenv', 0.5520144701004028, 'util', 0), ('deeplook/sparklines', 0.529207170009613, 'term', 1), ('wandb/client', 0.5254802703857422, 'ml', 0), ('samuelcolvin/python-devtools', 0.5224640369415283, 'debug', 0), ('python-odin/odin', 0.5191496014595032, 'util', 3), ('hadialqattan/pycln', 0.5115851163864136, 'util', 0), ('urwid/urwid', 0.5103952884674072, 'term', 0), ('jquast/blessed', 0.5097572207450867, 'term', 1), ('nteract/papermill', 0.5040388703346252, 'jupyter', 0), ('pytoolz/toolz', 0.503423810005188, 'util', 0)]",41,2.0,,4.48,76,51,52,1,8,25,8,76.0,287.0,90.0,3.8,60
279,nlp,https://github.com/maartengr/bertopic,[],,[],[],,,,maartengr/bertopic,BERTopic,5143,647,48,Python,https://maartengr.github.io/BERTopic/,Leveraging BERT and c-TF-IDF to create easily interpretable topics. ,maartengr,2024-01-13,2020-09-22,175,29.388571428571428,,Leveraging BERT and c-TF-IDF to create easily interpretable topics. ,"['bert', 'ldavis', 'machine-learning', 'nlp', 'sentence-embeddings', 'topic', 'topic-modeling', 'topic-modelling', 'topic-models', 'transformers']","['bert', 'ldavis', 'machine-learning', 'nlp', 'sentence-embeddings', 'topic', 'topic-modeling', 'topic-modelling', 'topic-models', 'transformers']",2024-01-10,"[('ddangelov/top2vec', 0.6046782732009888, 'nlp', 3), ('paddlepaddle/paddlenlp', 0.6038503050804138, 'llm', 3), ('jonasgeiping/cramming', 0.5925445556640625, 'nlp', 1), ('rare-technologies/gensim', 0.5905485153198242, 'nlp', 3), ('sebischair/lbl2vec', 0.5893922448158264, 'nlp', 2), ('alibaba/easynlp', 0.5850814580917358, 'nlp', 4), ('llmware-ai/llmware', 0.5808184742927551, 'llm', 4), ('deepset-ai/farm', 0.5806211233139038, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5704385042190552, 'llm', 3), ('graykode/nlp-tutorial', 0.5367704629898071, 'study', 2), ('huggingface/text-generation-inference', 0.5277509093284607, 'llm', 1), ('koaning/whatlies', 0.5192699432373047, 'nlp', 1), ('jalammar/ecco', 0.5192667841911316, 'ml-interpretability', 1), ('norskregnesentral/skweak', 0.5178706645965576, 'nlp', 0), ('flairnlp/flair', 0.5175113677978516, 'nlp', 2), ('qanastek/drbert', 0.51610267162323, 'llm', 3), ('huggingface/transformers', 0.5144845843315125, 'nlp', 3), ('maartengr/keybert', 0.5103285908699036, 'nlp', 1), ('jina-ai/clip-as-service', 0.5079023838043213, 'nlp', 1), ('bigscience-workshop/biomedical', 0.5077448487281799, 'data', 0), ('explosion/spacy-llm', 0.507010817527771, 'llm', 2), ('jina-ai/finetuner', 0.5031505227088928, 'ml', 1)]",50,4.0,,1.29,185,94,40,0,4,9,4,185.0,603.0,90.0,3.3,60
722,ml-dl,https://github.com/mosaicml/composer,[],,[],[],,,,mosaicml/composer,composer,4781,384,48,Python,http://docs.mosaicml.com,Supercharge Your Model Training,mosaicml,2024-01-13,2021-10-12,120,39.84166666666667,https://avatars.githubusercontent.com/u/75143706?v=4,Supercharge Your Model Training,"['deep-learning', 'machine-learning', 'ml-efficiency', 'ml-systems', 'ml-training', 'neural-network', 'neural-networks', 'pytorch']","['deep-learning', 'machine-learning', 'ml-efficiency', 'ml-systems', 'ml-training', 'neural-network', 'neural-networks', 'pytorch']",2024-01-14,"[('ddbourgin/numpy-ml', 0.6701366305351257, 'ml', 2), ('huggingface/datasets', 0.6692463159561157, 'nlp', 3), ('keras-team/keras', 0.6608446836471558, 'ml-dl', 4), ('onnx/onnx', 0.6314774751663208, 'ml', 4), ('explosion/thinc', 0.6281270980834961, 'ml-dl', 3), ('neuralmagic/sparseml', 0.6215312480926514, 'ml-dl', 1), ('tensorflow/tensorflow', 0.6187223792076111, 'ml-dl', 3), ('huggingface/transformers', 0.6106983423233032, 'nlp', 3), ('lutzroeder/netron', 0.6044427156448364, 'ml', 4), ('hpcaitech/colossalai', 0.5992001891136169, 'llm', 1), ('xplainable/xplainable', 0.5985530614852905, 'ml-interpretability', 1), ('tensorflow/tensor2tensor', 0.5954499244689941, 'ml', 2), ('microsoft/nni', 0.5946601629257202, 'ml', 4), ('alpa-projects/alpa', 0.5876685976982117, 'ml-dl', 2), ('huggingface/autotrain-advanced', 0.5858451724052429, 'ml', 2), ('rwightman/pytorch-image-models', 0.5846765041351318, 'ml-dl', 1), ('google/trax', 0.5817326307296753, 'ml-dl', 2), ('interpretml/interpret', 0.5779016613960266, 'ml-interpretability', 1), ('nccr-itmo/fedot', 0.5770966410636902, 'ml-ops', 1), ('superduperdb/superduperdb', 0.5755331516265869, 'data', 1), ('pytorch/ignite', 0.5748881101608276, 'ml-dl', 4), ('bentoml/bentoml', 0.574347198009491, 'ml-ops', 2), ('ludwig-ai/ludwig', 0.5734363794326782, 'ml-ops', 4), ('amanchadha/coursera-deep-learning-specialization', 0.5713127255439758, 'study', 3), ('neuralmagic/deepsparse', 0.5704326033592224, 'nlp', 0), ('aiqc/aiqc', 0.5669575333595276, 'ml-ops', 0), ('microsoft/onnxruntime', 0.5664753317832947, 'ml', 4), ('nvidia/deeplearningexamples', 0.5654811859130859, 'ml-dl', 2), ('keras-rl/keras-rl', 0.5626094341278076, 'ml-rl', 2), ('mlflow/mlflow', 0.5622864365577698, 'ml-ops', 1), ('awslabs/autogluon', 0.554071307182312, 'ml', 3), ('determined-ai/determined', 0.5523366332054138, 'ml-ops', 3), ('microsoft/deepspeed', 0.5493502616882324, 'ml-dl', 3), ('rasbt/deeplearning-models', 0.5479599833488464, 'ml-dl', 0), ('keras-team/autokeras', 0.5466687083244324, 'ml-dl', 2), ('rafiqhasan/auto-tensorflow', 0.5446411967277527, 'ml-dl', 2), ('kevinmusgrave/pytorch-metric-learning', 0.5446270108222961, 'ml', 3), ('slundberg/shap', 0.5438055396080017, 'ml-interpretability', 2), ('shankarpandala/lazypredict', 0.5417070984840393, 'ml', 1), ('ashleve/lightning-hydra-template', 0.5401220917701721, 'util', 2), ('deepchecks/deepchecks', 0.5395089387893677, 'data', 3), ('blackhc/toma', 0.5379732251167297, 'ml-dl', 2), ('christoschristofidis/awesome-deep-learning', 0.5376695990562439, 'study', 3), ('feast-dev/feast', 0.535942554473877, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5328403115272522, 'ml-ops', 3), ('xl0/lovely-tensors', 0.5325333476066589, 'ml-dl', 2), ('fepegar/torchio', 0.5322839617729187, 'ml-dl', 3), ('ourownstory/neural_prophet', 0.5314452052116394, 'ml', 4), ('pytorchlightning/pytorch-lightning', 0.5314188599586487, 'ml-dl', 3), ('automl/auto-sklearn', 0.5304707288742065, 'ml', 0), ('apple/coremltools', 0.5304659605026245, 'ml', 2), ('winedarksea/autots', 0.5297396183013916, 'time-series', 2), ('roboflow/supervision', 0.529486894607544, 'ml', 3), ('cdpierse/transformers-interpret', 0.5288192629814148, 'ml-interpretability', 3), ('ml-tooling/opyrator', 0.5281829833984375, 'viz', 1), ('opentensor/bittensor', 0.5280351042747498, 'ml', 4), ('csinva/imodels', 0.5268099904060364, 'ml', 1), ('open-mmlab/mmediting', 0.5261213183403015, 'ml', 2), ('googlecloudplatform/vertex-ai-samples', 0.525791585445404, 'ml', 0), ('oegedijk/explainerdashboard', 0.5244570374488831, 'ml-interpretability', 0), ('denys88/rl_games', 0.5201303958892822, 'ml-rl', 2), ('intel/intel-extension-for-pytorch', 0.5193430185317993, 'perf', 4), ('pytorch/captum', 0.5189915299415588, 'ml-interpretability', 0), ('bigscience-workshop/petals', 0.5181496143341064, 'data', 4), ('aleju/imgaug', 0.517113983631134, 'ml', 2), ('karpathy/micrograd', 0.517072856426239, 'study', 0), ('tensorly/tensorly', 0.5158124566078186, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.5126325488090515, 'ml-dl', 1), ('lucidrains/toolformer-pytorch', 0.5117185115814209, 'llm', 1), ('horovod/horovod', 0.5103276371955872, 'ml-ops', 3), ('roboflow/notebooks', 0.5099112391471863, 'study', 3), ('districtdatalabs/yellowbrick', 0.5096165537834167, 'ml', 1), ('deepfakes/faceswap', 0.5090040564537048, 'ml-dl', 3), ('ray-project/ray', 0.5085017085075378, 'ml-ops', 3), ('towhee-io/towhee', 0.5076751112937927, 'ml-ops', 1), ('mrdbourke/pytorch-deep-learning', 0.5073535442352295, 'study', 3), ('huggingface/exporters', 0.5061023235321045, 'ml', 3), ('mlc-ai/mlc-llm', 0.5053991675376892, 'llm', 0), ('uber/petastorm', 0.5039240121841431, 'data', 3), ('deci-ai/super-gradients', 0.5038636326789856, 'ml-dl', 3), ('activeloopai/deeplake', 0.5032862424850464, 'ml-ops', 3), ('milvus-io/bootcamp', 0.5024623870849609, 'data', 1), ('karpathy/nn-zero-to-hero', 0.5013008713722229, 'study', 0), ('huggingface/optimum', 0.5008224248886108, 'ml', 1)]",94,2.0,,11.62,243,215,27,0,19,22,19,243.0,173.0,90.0,0.7,60
709,ml-ops,https://github.com/flyteorg/flyte,[],,[],[],,,,flyteorg/flyte,flyte,4341,460,261,Go,https://flyte.org,"Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.",flyteorg,2024-01-14,2019-10-21,223,19.4539052496799,https://avatars.githubusercontent.com/u/35380635?v=4,"Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.","['data', 'data-analysis', 'data-science', 'dataops', 'declarative', 'fine-tuning', 'flyte', 'golang', 'grpc', 'kubernetes', 'kubernetes-operator', 'llm', 'machine-learning', 'mlops', 'orchestration-engine', 'production', 'production-grade', 'scale', 'workflow']","['data', 'data-analysis', 'data-science', 'dataops', 'declarative', 'fine-tuning', 'flyte', 'golang', 'grpc', 'kubernetes', 'kubernetes-operator', 'llm', 'machine-learning', 'mlops', 'orchestration-engine', 'production', 'production-grade', 'scale', 'workflow']",2024-01-12,"[('kestra-io/kestra', 0.7973306775093079, 'ml-ops', 2), ('dagster-io/dagster', 0.7850085496902466, 'ml-ops', 3), ('bodywork-ml/bodywork-core', 0.6820202469825745, 'ml-ops', 4), ('polyaxon/polyaxon', 0.6784146428108215, 'ml-ops', 5), ('mage-ai/mage-ai', 0.6692841053009033, 'ml-ops', 3), ('apache/airflow', 0.6581413745880127, 'ml-ops', 4), ('orchest/orchest', 0.6491807699203491, 'ml-ops', 3), ('prefecthq/prefect', 0.6408464312553406, 'ml-ops', 3), ('getindata/kedro-kubeflow', 0.6279534697532654, 'ml-ops', 1), ('kubeflow/pipelines', 0.6268972754478455, 'ml-ops', 4), ('airbytehq/airbyte', 0.6267026662826538, 'data', 2), ('backtick-se/cowait', 0.6116994023323059, 'util', 2), ('netflix/metaflow', 0.608452320098877, 'ml-ops', 4), ('prefecthq/server', 0.6038605570793152, 'util', 1), ('avaiga/taipy', 0.6026946902275085, 'data', 2), ('meltano/meltano', 0.5828467607498169, 'ml-ops', 2), ('lithops-cloud/lithops', 0.5794029235839844, 'ml-ops', 1), ('ploomber/ploomber', 0.5782303214073181, 'ml-ops', 4), ('zenml-io/zenml', 0.5718936324119568, 'ml-ops', 5), ('astronomer/astro-sdk', 0.5711103081703186, 'ml-ops', 2), ('kubeflow-kale/kale', 0.5685352683067322, 'ml-ops', 1), ('skypilot-org/skypilot', 0.5598949790000916, 'llm', 2), ('chaostoolkit/chaostoolkit', 0.5509274005889893, 'util', 0), ('fugue-project/fugue', 0.5496802926063538, 'pandas', 1), ('dagworks-inc/hamilton', 0.549081027507782, 'ml-ops', 4), ('allegroai/clearml', 0.5446012616157532, 'ml-ops', 2), ('merantix-momentum/squirrel-core', 0.5294408798217773, 'ml', 3), ('gefyrahq/gefyra', 0.5291821956634521, 'util', 1), ('spotify/luigi', 0.5278602242469788, 'ml-ops', 0), ('modin-project/modin', 0.5223826169967651, 'perf', 1), ('apache/spark', 0.5193337202072144, 'data', 0), ('unionai-oss/unionml', 0.5188993215560913, 'ml-ops', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.5061784386634827, 'template', 0), ('pathwaycom/pathway', 0.503392219543457, 'data', 0), ('featureform/embeddinghub', 0.5021466612815857, 'nlp', 3), ('polyaxon/datatile', 0.5018032789230347, 'pandas', 3), ('bentoml/bentoml', 0.5007025599479675, 'ml-ops', 3)]",198,4.0,,11.79,1217,503,52,0,21,106,21,1213.0,1310.0,90.0,1.1,60
440,gis,https://github.com/osgeo/gdal,[],,[],[],,,,osgeo/gdal,gdal,4275,2286,167,C++,https://gdal.org,GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.,osgeo,2024-01-13,2012-10-09,590,7.245762711864407,https://avatars.githubusercontent.com/u/1058467?v=4,GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.,"['geospatial-data', 'raster', 'remote-sensing', 'vector']","['geospatial-data', 'raster', 'remote-sensing', 'vector']",2024-01-13,"[('remotesensinglab/raster4ml', 0.6307851076126099, 'gis', 2), ('earthlab/earthpy', 0.5730497241020203, 'gis', 2), ('perrygeo/python-rasterstats', 0.5707691311836243, 'gis', 0), ('microsoft/torchgeo', 0.5599908828735352, 'gis', 1), ('osgeo/grass', 0.5594460368156433, 'gis', 3), ('corteva/rioxarray', 0.5278557538986206, 'gis', 1), ('cogeotiff/rio-tiler', 0.5156446099281311, 'gis', 1), ('residentmario/geoplot', 0.5153622627258301, 'gis', 1)]",562,6.0,,70.27,578,506,137,0,11,15,11,578.0,1053.0,90.0,1.8,60
978,sim,https://github.com/astropy/astropy,[],,[],[],,,,astropy/astropy,astropy,4079,1687,139,Python,https://www.astropy.org,Astronomy and astrophysics core library,astropy,2024-01-14,2011-07-21,653,6.239729020979021,https://avatars.githubusercontent.com/u/847984?v=4,Astronomy and astrophysics core library,"['astronomy', 'astrophysics', 'astropy', 'science']","['astronomy', 'astrophysics', 'astropy', 'science']",2024-01-13,"[('roban/cosmolopy', 0.5209611654281616, 'sim', 1)]",514,7.0,,37.42,561,395,152,0,9,12,9,561.0,2172.0,90.0,3.9,60
1820,ml,https://github.com/google-deepmind/graphcast,['forecasting'],GraphCast: Learning skillful medium-range global weather forecasting,[],[],,,,google-deepmind/graphcast,graphcast,3312,358,56,Python,,,google-deepmind,2024-01-14,2023-07-14,28,115.92,https://avatars.githubusercontent.com/u/8596759?v=4,GraphCast: Learning skillful medium-range global weather forecasting,"['weather', 'weather-forecast']","['forecasting', 'weather', 'weather-forecast']",2024-01-05,[],8,2.0,,0.29,45,17,6,0,1,2,1,45.0,125.0,90.0,2.8,60
1879,data,https://github.com/avaiga/taipy,[],,[],[],,,,avaiga/taipy,taipy,2952,237,30,Python,https://www.taipy.io,Turns Data and AI algorithms into production-ready web applications in no time.,avaiga,2024-01-14,2022-02-18,101,29.063291139240505,https://avatars.githubusercontent.com/u/86434771?v=4,Turns Data and AI algorithms into production-ready web applications in no time.,"['automation', 'data-engineering', 'data-ops', 'data-visualization', 'datascience', 'developer-tools', 'hacktoberfest2023', 'mlops', 'orchestration', 'pipeline', 'pipelines', 'taipy-core', 'taipy-gui', 'workflow']","['automation', 'data-engineering', 'data-ops', 'data-visualization', 'datascience', 'developer-tools', 'hacktoberfest2023', 'mlops', 'orchestration', 'pipeline', 'pipelines', 'taipy-core', 'taipy-gui', 'workflow']",2024-01-14,"[('bentoml/bentoml', 0.6463702321052551, 'ml-ops', 1), ('netflix/metaflow', 0.6367209553718567, 'ml-ops', 2), ('ploomber/ploomber', 0.6359885334968567, 'ml-ops', 4), ('mage-ai/mage-ai', 0.6288012266159058, 'ml-ops', 4), ('dagster-io/dagster', 0.6245914101600647, 'ml-ops', 4), ('orchest/orchest', 0.6196687817573547, 'ml-ops', 1), ('ml-tooling/opyrator', 0.617566704750061, 'viz', 0), ('kestra-io/kestra', 0.6105530858039856, 'ml-ops', 4), ('lastmile-ai/aiconfig', 0.6097587943077087, 'util', 1), ('meltano/meltano', 0.608718752861023, 'ml-ops', 2), ('polyaxon/polyaxon', 0.6045488119125366, 'ml-ops', 3), ('sweepai/sweep', 0.6026955842971802, 'llm', 1), ('flyteorg/flyte', 0.6026946902275085, 'ml-ops', 2), ('zenml-io/zenml', 0.5993794202804565, 'ml-ops', 3), ('pythagora-io/gpt-pilot', 0.5966109037399292, 'llm', 1), ('cheshire-cat-ai/core', 0.5955442786216736, 'llm', 0), ('prefecthq/server', 0.5884959697723389, 'util', 3), ('dagworks-inc/hamilton', 0.5867727994918823, 'ml-ops', 3), ('polyaxon/datatile', 0.5837622880935669, 'pandas', 2), ('superduperdb/superduperdb', 0.5622673034667969, 'data', 1), ('pathwaycom/llm-app', 0.5616130232810974, 'llm', 0), ('fmind/mlops-python-package', 0.5603728890419006, 'template', 1), ('pydoit/doit', 0.5572507977485657, 'util', 1), ('allegroai/clearml', 0.5540127158164978, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5504463315010071, 'data', 0), ('airbytehq/airbyte', 0.5423561334609985, 'data', 2), ('prefecthq/marvin', 0.5397293567657471, 'nlp', 0), ('apache/airflow', 0.5394763946533203, 'ml-ops', 5), ('mlc-ai/mlc-llm', 0.5353958010673523, 'llm', 0), ('microsoft/promptflow', 0.5334009528160095, 'llm', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5292978286743164, 'template', 0), ('merantix-momentum/squirrel-core', 0.5268411636352539, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5267717242240906, 'study', 0), ('microsoft/lmops', 0.5267688632011414, 'llm', 0), ('googlecloudplatform/vertex-ai-samples', 0.5246182680130005, 'ml', 1), ('huggingface/datasets', 0.5236226916313171, 'nlp', 0), ('hi-primus/optimus', 0.5233602523803711, 'ml-ops', 0), ('iterative/dvc', 0.5210314989089966, 'ml-ops', 1), ('featureform/embeddinghub', 0.5199906229972839, 'nlp', 1), ('drivendata/cookiecutter-data-science', 0.519536018371582, 'template', 0), ('chaostoolkit/chaostoolkit', 0.5186023712158203, 'util', 1), ('feast-dev/feast', 0.5180974006652832, 'ml-ops', 2), ('wandb/client', 0.5176960825920105, 'ml', 1), ('bodywork-ml/bodywork-core', 0.5173805952072144, 'ml-ops', 3), ('reloadware/reloadium', 0.5162569284439087, 'profiling', 0), ('streamlit/streamlit', 0.5149668455123901, 'viz', 2), ('whylabs/whylogs', 0.5133393406867981, 'util', 1), ('fugue-project/fugue', 0.5130361318588257, 'pandas', 0), ('antonosika/gpt-engineer', 0.5112882852554321, 'llm', 0), ('hpcaitech/colossalai', 0.5108974575996399, 'llm', 0), ('mlflow/mlflow', 0.5100282430648804, 'ml-ops', 0), ('plotly/dash', 0.5096752047538757, 'viz', 1), ('tox-dev/tox', 0.5081842541694641, 'testing', 1), ('activeloopai/deeplake', 0.5062883496284485, 'ml-ops', 1), ('explosion/thinc', 0.5057910680770874, 'ml-dl', 0), ('backtick-se/cowait', 0.5056573152542114, 'util', 1), ('willmcgugan/textual', 0.5015890002250671, 'term', 0)]",46,3.0,,37.62,382,214,23,0,12,8,12,382.0,366.0,90.0,1.0,60
434,nlp,https://github.com/argilla-io/argilla,[],,[],[],,,,argilla-io/argilla,argilla,2823,284,24,Python,https://docs.argilla.io,✨Argilla: the open-source feedback platform for LLMs,argilla-io,2024-01-14,2021-04-28,143,19.623634558093347,https://avatars.githubusercontent.com/u/18415507?v=4,✨Argilla: the open-source feedback platform for LLMs,"['active-learning', 'ai', 'annotation-tool', 'developer-tools', 'gpt-4', 'human-in-the-loop', 'langchain', 'llm', 'machine-learning', 'mlops', 'natural-language-processing', 'nlp', 'rlhf', 'text-annotation', 'text-labeling', 'weak-supervision', 'weakly-supervised-learning']","['active-learning', 'ai', 'annotation-tool', 'developer-tools', 'gpt-4', 'human-in-the-loop', 'langchain', 'llm', 'machine-learning', 'mlops', 'natural-language-processing', 'nlp', 'rlhf', 'text-annotation', 'text-labeling', 'weak-supervision', 'weakly-supervised-learning']",2024-01-12,"[('explosion/spacy-llm', 0.6727258563041687, 'llm', 5), ('tigerlab-ai/tiger', 0.6661051511764526, 'llm', 1), ('doccano/doccano', 0.6546259522438049, 'nlp', 4), ('hegelai/prompttools', 0.644296407699585, 'llm', 2), ('mooler0410/llmspracticalguide', 0.6409274935722351, 'study', 2), ('rasahq/rasa', 0.6305766105651855, 'llm', 3), ('norskregnesentral/skweak', 0.6235673427581787, 'nlp', 2), ('agenta-ai/agenta', 0.6207625269889832, 'llm', 2), ('llmware-ai/llmware', 0.6205747723579407, 'llm', 3), ('night-chen/toolqa', 0.6075721979141235, 'llm', 0), ('paddlepaddle/paddlenlp', 0.6045259833335876, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.6045132875442505, 'llm', 0), ('nebuly-ai/nebullvm', 0.6039616465568542, 'perf', 2), ('aiwaves-cn/agents', 0.5958381295204163, 'nlp', 1), ('lm-sys/fastchat', 0.5902546644210815, 'llm', 0), ('nomic-ai/gpt4all', 0.5886470079421997, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5834850668907166, 'llm', 2), ('embedchain/embedchain', 0.5823776721954346, 'llm', 2), ('young-geng/easylm', 0.5817759037017822, 'llm', 1), ('pathwaycom/llm-app', 0.5815805792808533, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5786020159721375, 'llm', 2), ('openbmb/toolbench', 0.5743700265884399, 'llm', 0), ('confident-ai/deepeval', 0.5710536241531372, 'testing', 1), ('infinitylogesh/mutate', 0.5702595114707947, 'nlp', 0), ('deepset-ai/haystack', 0.5678386688232422, 'llm', 3), ('microsoft/lmops', 0.5673712491989136, 'llm', 2), ('openlmlab/moss', 0.5668735504150391, 'llm', 1), ('nltk/nltk', 0.5657547116279602, 'nlp', 3), ('salesforce/codet5', 0.5648234486579895, 'nlp', 0), ('microsoft/promptflow', 0.555797815322876, 'llm', 2), ('databrickslabs/dolly', 0.5544888973236084, 'llm', 0), ('bigscience-workshop/petals', 0.5500335097312927, 'data', 2), ('bobazooba/xllm', 0.5486025214195251, 'llm', 2), ('ai4finance-foundation/fingpt', 0.5461266040802002, 'finance', 3), ('salesforce/xgen', 0.544439971446991, 'llm', 2), ('bentoml/openllm', 0.5431055426597595, 'ml-ops', 3), ('mlflow/mlflow', 0.5428063869476318, 'ml-ops', 2), ('conceptofmind/toolformer', 0.5423489809036255, 'llm', 0), ('eleutherai/the-pile', 0.542231023311615, 'data', 1), ('google-research/language', 0.5392791628837585, 'nlp', 2), ('cheshire-cat-ai/core', 0.5388045907020569, 'llm', 2), ('allenai/allennlp', 0.5366323590278625, 'nlp', 2), ('microsoft/autogen', 0.5329903364181519, 'llm', 1), ('rcgai/simplyretrieve', 0.5302114486694336, 'llm', 3), ('microsoft/generative-ai-for-beginners', 0.5287173390388489, 'study', 1), ('microsoft/jarvis', 0.5283769369125366, 'llm', 0), ('ludwig-ai/ludwig', 0.5272180438041687, 'ml-ops', 3), ('chancefocus/pixiu', 0.5267559289932251, 'finance', 5), ('hiyouga/llama-factory', 0.5245572328567505, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5245571732521057, 'llm', 2), ('run-llama/rags', 0.5243651866912842, 'llm', 1), ('microsoft/torchscale', 0.5222489237785339, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5221193432807922, 'llm', 0), ('vllm-project/vllm', 0.5203900337219238, 'llm', 2), ('truera/trulens', 0.5192816853523254, 'llm', 2), ('bigscience-workshop/promptsource', 0.5188122987747192, 'nlp', 3), ('hwchase17/langchain', 0.5174627304077148, 'llm', 1), ('microsoft/semantic-kernel', 0.5170361399650574, 'llm', 2), ('deeppavlov/deeppavlov', 0.5165128111839294, 'nlp', 3), ('aimhubio/aim', 0.5164137482643127, 'ml-ops', 3), ('cleanlab/cleanlab', 0.5151708722114563, 'ml', 2), ('lianjiatech/belle', 0.5147097706794739, 'llm', 0), ('dylanhogg/llmgraph', 0.5142863988876343, 'ml', 1), ('mlc-ai/mlc-llm', 0.51301109790802, 'llm', 1), ('alibaba/easynlp', 0.5120981335639954, 'nlp', 2), ('microsoft/unilm', 0.5100960731506348, 'nlp', 2), ('determined-ai/determined', 0.5085978507995605, 'ml-ops', 2), ('lupantech/chameleon-llm', 0.5085508823394775, 'llm', 3), ('flairnlp/flair', 0.5028916597366333, 'nlp', 3), ('titanml/takeoff', 0.5014408230781555, 'llm', 1), ('eugeneyan/open-llms', 0.5009057521820068, 'study', 1), ('intel/intel-extension-for-transformers', 0.5005181431770325, 'perf', 0)]",75,4.0,,25.37,802,596,33,0,31,44,31,801.0,1068.0,90.0,1.3,60
1469,llm,https://github.com/alpha-vllm/llama2-accessory,"['pre-training', 'fine-tuning', 'deployment']",,[],[],,,,alpha-vllm/llama2-accessory,LLaMA2-Accessory,2054,129,30,Python,https://llama2-accessory.readthedocs.io/,An Open-source Toolkit for LLM Development,alpha-vllm,2024-01-13,2023-07-21,27,74.49740932642487,https://avatars.githubusercontent.com/u/140153551?v=4,An Open-source Toolkit for LLM Development,[],"['deployment', 'fine-tuning', 'pre-training']",2024-01-11,"[('tigerlab-ai/tiger', 0.7141748666763306, 'llm', 1), ('h2oai/h2o-llmstudio', 0.6861495971679688, 'llm', 1), ('bentoml/openllm', 0.6482536792755127, 'ml-ops', 1), ('iryna-kondr/scikit-llm', 0.6441587209701538, 'llm', 0), ('hegelai/prompttools', 0.6420565247535706, 'llm', 0), ('agenta-ai/agenta', 0.6384699940681458, 'llm', 0), ('ray-project/llm-applications', 0.6254207491874695, 'llm', 1), ('microsoft/promptflow', 0.6248592734336853, 'llm', 0), ('salesforce/codet5', 0.6213086843490601, 'nlp', 0), ('microsoft/semantic-kernel', 0.6138186454772949, 'llm', 0), ('lightning-ai/lit-gpt', 0.6060934066772461, 'llm', 1), ('argilla-io/argilla', 0.6045132875442505, 'nlp', 0), ('microsoft/torchscale', 0.602177619934082, 'llm', 0), ('eugeneyan/open-llms', 0.6008363962173462, 'study', 0), ('salesforce/xgen', 0.5997952222824097, 'llm', 0), ('citadel-ai/langcheck', 0.5963909029960632, 'llm', 0), ('pathwaycom/llm-app', 0.5948898196220398, 'llm', 0), ('young-geng/easylm', 0.5873928070068359, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5765708088874817, 'study', 0), ('bigscience-workshop/petals', 0.5728069543838501, 'data', 0), ('nat/openplayground', 0.5689014792442322, 'llm', 0), ('vllm-project/vllm', 0.5688649415969849, 'llm', 0), ('deepset-ai/haystack', 0.5652121305465698, 'llm', 0), ('run-llama/llama-hub', 0.561208963394165, 'data', 0), ('hiyouga/llama-factory', 0.5589560270309448, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5589559078216553, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5544970035552979, 'perf', 0), ('microsoft/jarvis', 0.5535730719566345, 'llm', 0), ('ajndkr/lanarky', 0.552966833114624, 'llm', 0), ('nomic-ai/gpt4all', 0.5511795878410339, 'llm', 0), ('conceptofmind/toolformer', 0.5475521087646484, 'llm', 0), ('night-chen/toolqa', 0.542137086391449, 'llm', 0), ('numba/llvmlite', 0.539250910282135, 'util', 0), ('hwchase17/langchain', 0.5383664965629578, 'llm', 0), ('confident-ai/deepeval', 0.5373701453208923, 'testing', 0), ('ludwig-ai/ludwig', 0.5365805625915527, 'ml-ops', 1), ('langchain-ai/langsmith-cookbook', 0.5349544286727905, 'llm', 0), ('shishirpatil/gorilla', 0.5339228510856628, 'llm', 0), ('nebuly-ai/nebullvm', 0.5258677005767822, 'perf', 0), ('openbmb/toolbench', 0.5172504186630249, 'llm', 0), ('alphasecio/langchain-examples', 0.5159311890602112, 'llm', 0), ('berriai/litellm', 0.5151165127754211, 'llm', 0), ('truera/trulens', 0.5145223140716553, 'llm', 0), ('deep-diver/pingpong', 0.5120090842247009, 'llm', 0), ('openai/evals', 0.5116733312606812, 'llm', 0), ('run-llama/llama-lab', 0.5041949152946472, 'llm', 0), ('salesforce/jaxformer', 0.5040596723556519, 'llm', 0), ('cg123/mergekit', 0.5024479627609253, 'llm', 0), ('zenml-io/zenml', 0.5014763474464417, 'ml-ops', 0), ('mmabrouk/chatgpt-wrapper', 0.5011169910430908, 'llm', 0)]",19,5.0,,9.48,73,61,6,0,0,0,0,73.0,107.0,90.0,1.5,60
1709,llm,https://github.com/huggingface/text-embeddings-inference,[],,[],[],,,,huggingface/text-embeddings-inference,text-embeddings-inference,1520,65,20,Rust,https://huggingface.co/docs/text-embeddings-inference/quick_tour,A blazing fast inference solution for text embeddings models,huggingface,2024-01-14,2023-10-13,15,97.61467889908256,https://avatars.githubusercontent.com/u/25720743?v=4,A blazing fast inference solution for text embeddings models,"['ai', 'embeddings', 'huggingface', 'llm', 'ml']","['ai', 'embeddings', 'huggingface', 'llm', 'ml']",2024-01-04,"[('chroma-core/chroma', 0.5832590460777283, 'data', 1), ('plasticityai/magnitude', 0.5618109703063965, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5573744177818298, 'nlp', 2), ('koaning/whatlies', 0.5444438457489014, 'nlp', 1), ('infinitylogesh/mutate', 0.531061053276062, 'nlp', 0), ('sebischair/lbl2vec', 0.5274394154548645, 'nlp', 0), ('facebookresearch/pytorch-biggraph', 0.5272315144538879, 'ml-dl', 0), ('hpcaitech/energonai', 0.5272306799888611, 'ml', 0), ('google-research/electra', 0.5215802788734436, 'ml-dl', 0), ('llmware-ai/llmware', 0.517711341381073, 'llm', 2), ('huggingface/text-generation-inference', 0.5171723365783691, 'llm', 0), ('koaning/embetter', 0.5125843286514282, 'data', 0), ('jina-ai/clip-as-service', 0.5006672143936157, 'nlp', 0)]",7,2.0,,1.25,110,87,3,0,8,32,8,110.0,172.0,90.0,1.6,60
1591,testing,https://github.com/confident-ai/deepeval,"['language-model', 'unit-testing']",,[],[],,,,confident-ai/deepeval,deepeval,1014,60,9,Python,https://docs.confident-ai.com/,The Evaluation Framework for LLMs,confident-ai,2024-01-13,2023-08-10,24,41.028901734104046,https://avatars.githubusercontent.com/u/130858411?v=4,The Evaluation Framework for LLMs,"['chatgpt', 'evaluate-models', 'evaluate-news-article-with-nlp', 'evaluation', 'evaluation-framework', 'evaluation-metrics', 'large', 'llm', 'llm-evaluation', 'llm-evaluation-framework', 'llmops']","['chatgpt', 'evaluate-models', 'evaluate-news-article-with-nlp', 'evaluation', 'evaluation-framework', 'evaluation-metrics', 'language-model', 'large', 'llm', 'llm-evaluation', 'llm-evaluation-framework', 'llmops', 'unit-testing']",2024-01-12,"[('agenta-ai/agenta', 0.690017819404602, 'llm', 3), ('openai/evals', 0.6733431816101074, 'llm', 3), ('citadel-ai/langcheck', 0.6523064374923706, 'llm', 2), ('hiyouga/llama-factory', 0.6391016840934753, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.6391015648841858, 'llm', 2), ('mooler0410/llmspracticalguide', 0.6305698156356812, 'study', 0), ('ai21labs/lm-evaluation', 0.622775137424469, 'llm', 2), ('eugeneyan/open-llms', 0.6072402596473694, 'study', 1), ('young-geng/easylm', 0.6033726334571838, 'llm', 1), ('deepset-ai/haystack', 0.5944290161132812, 'llm', 2), ('explosion/spacy-llm', 0.5939695239067078, 'llm', 1), ('dylanhogg/llmgraph', 0.581521213054657, 'ml', 2), ('microsoft/torchscale', 0.581218957901001, 'llm', 0), ('llmware-ai/llmware', 0.5799471735954285, 'llm', 0), ('bentoml/openllm', 0.575341522693634, 'ml-ops', 2), ('h2oai/h2o-llmstudio', 0.5734099745750427, 'llm', 2), ('lianjiatech/belle', 0.5731056332588196, 'llm', 0), ('argilla-io/argilla', 0.5710536241531372, 'nlp', 1), ('giskard-ai/giskard', 0.5690423250198364, 'data', 1), ('microsoft/promptflow', 0.5659092664718628, 'llm', 2), ('lm-sys/fastchat', 0.5658591985702515, 'llm', 2), ('hegelai/prompttools', 0.5625259876251221, 'llm', 0), ('paddlepaddle/paddlenlp', 0.556614100933075, 'llm', 1), ('fasteval/fasteval', 0.5521769523620605, 'llm', 2), ('hwchase17/langchain', 0.5516130328178406, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5511168241500854, 'perf', 0), ('pathwaycom/llm-app', 0.549860417842865, 'llm', 2), ('jerryjliu/llama_index', 0.5483352541923523, 'llm', 2), ('microsoft/autogen', 0.5460978746414185, 'llm', 2), ('arize-ai/phoenix', 0.5458160042762756, 'ml-interpretability', 1), ('nomic-ai/gpt4all', 0.5438401699066162, 'llm', 1), ('salesforce/xgen', 0.5417569875717163, 'llm', 2), ('next-gpt/next-gpt', 0.5388375520706177, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.5373701453208923, 'llm', 0), ('thudm/chatglm2-6b', 0.5323322415351868, 'llm', 1), ('microsoft/promptcraft-robotics', 0.5318647623062134, 'sim', 2), ('deep-diver/pingpong', 0.5305920243263245, 'llm', 0), ('night-chen/toolqa', 0.529060959815979, 'llm', 0), ('tigerlab-ai/tiger', 0.5287721157073975, 'llm', 1), ('nebuly-ai/nebullvm', 0.5270206928253174, 'perf', 1), ('ibm/dromedary', 0.5251967906951904, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5237811803817749, 'llm', 2), ('anthropics/evals', 0.5211471915245056, 'llm', 0), ('salesforce/codet5', 0.5176749229431152, 'nlp', 1), ('bobazooba/xllm', 0.5172545909881592, 'llm', 2), ('truera/trulens', 0.5170626640319824, 'llm', 3), ('rlancemartin/auto-evaluator', 0.5153782963752747, 'llm', 1), ('shishirpatil/gorilla', 0.5115599036216736, 'llm', 2), ('zilliztech/gptcache', 0.5101267099380493, 'llm', 2), ('juncongmoo/pyllama', 0.5091282725334167, 'llm', 0), ('openbmb/toolbench', 0.5041009783744812, 'llm', 1), ('guidance-ai/guidance', 0.5033418536186218, 'llm', 2), ('eth-sri/lmql', 0.5033408403396606, 'llm', 2), ('bigscience-workshop/petals', 0.5002102255821228, 'data', 0)]",14,3.0,,26.6,202,182,5,0,24,124,24,202.0,335.0,90.0,1.7,60
1344,util,https://github.com/faif/python-patterns,[],,[],[],,,,faif/python-patterns,python-patterns,38820,6988,1661,Python,,A collection of design patterns/idioms in Python,faif,2024-01-14,2012-06-06,607,63.86368977673325,,A collection of design patterns/idioms in Python,"['design-patterns', 'idioms']","['design-patterns', 'idioms']",2023-01-27,"[('brandon-rhodes/python-patterns', 0.6763890385627747, 'util', 0), ('cosmicpython/book', 0.532017707824707, 'study', 0), ('grahamdumpleton/wrapt', 0.5319310426712036, 'util', 0), ('xrudelis/pytrait', 0.516368567943573, 'util', 0), ('pytoolz/toolz', 0.5127255320549011, 'util', 0)]",128,4.0,,0.0,2,2,141,12,0,0,0,2.0,11.0,90.0,5.5,59
108,sim,https://github.com/atsushisakai/pythonrobotics,[],,[],[],,,,atsushisakai/pythonrobotics,PythonRobotics,20781,6275,511,Python,https://atsushisakai.github.io/PythonRobotics/,Python sample codes for robotics algorithms.,atsushisakai,2024-01-14,2016-03-21,410,50.66771159874608,,Python sample codes for robotics algorithms.,"['algorithm', 'animation', 'autonomous-driving', 'autonomous-navigation', 'autonomous-vehicles', 'control', 'cvxpy', 'ekf', 'localization', 'mapping', 'path-planning', 'robot', 'robotics', 'slam']","['algorithm', 'animation', 'autonomous-driving', 'autonomous-navigation', 'autonomous-vehicles', 'control', 'cvxpy', 'ekf', 'localization', 'mapping', 'path-planning', 'robot', 'robotics', 'slam']",2024-01-09,"[('thealgorithms/python', 0.58967125415802, 'study', 1), ('scikit-mobility/scikit-mobility', 0.5399863719940186, 'gis', 0), ('keon/algorithms', 0.5353425741195679, 'util', 1), ('python-odin/odin', 0.5244603753089905, 'util', 0), ('pandas-dev/pandas', 0.5196974277496338, 'pandas', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5075343251228333, 'study', 0)]",121,4.0,,2.56,59,56,95,0,0,0,0,59.0,27.0,90.0,0.5,59
2,ml-dl,https://github.com/apache/incubator-mxnet,[],,[],[],,,,apache/incubator-mxnet,mxnet,20667,6894,1074,C++,https://mxnet.apache.org,"Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more",apache,2024-01-14,2015-04-30,456,45.25148576790741,https://avatars.githubusercontent.com/u/47359?v=4,"Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more",['mxnet'],['mxnet'],2023-01-26,"[('microsoft/deepspeed', 0.6390171051025391, 'ml-dl', 0), ('horovod/horovod', 0.6248153448104858, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5984542965888977, 'ml-dl', 1), ('paddlepaddle/paddle', 0.5943779349327087, 'ml-dl', 0), ('alpa-projects/alpa', 0.5928085446357727, 'ml-dl', 0), ('google/trax', 0.583592414855957, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5671101212501526, 'ml', 0), ('aiqc/aiqc', 0.560641884803772, 'ml-ops', 0), ('tensorflow/tensorflow', 0.5543745160102844, 'ml-dl', 0), ('determined-ai/determined', 0.5505608320236206, 'ml-ops', 0), ('neuralmagic/deepsparse', 0.53566974401474, 'nlp', 0), ('tensorflow/tensor2tensor', 0.5354474186897278, 'ml', 0), ('ray-project/ray', 0.5308846235275269, 'ml-ops', 0), ('bigscience-workshop/petals', 0.5274662971496582, 'data', 0), ('salesforce/warp-drive', 0.5272499322891235, 'ml-rl', 0), ('uber/petastorm', 0.5250005722045898, 'data', 0), ('deepmind/dm-haiku', 0.522907018661499, 'ml-dl', 0), ('denys88/rl_games', 0.5211288332939148, 'ml-rl', 0), ('keras-team/keras', 0.5203127861022949, 'ml-dl', 0), ('deepmind/dm_control', 0.5190567374229431, 'ml-rl', 0), ('adap/flower', 0.5179499387741089, 'ml-ops', 0), ('tensorlayer/tensorlayer', 0.5166865587234497, 'ml-rl', 0), ('huggingface/transformers', 0.5165125131607056, 'nlp', 0), ('ashleve/lightning-hydra-template', 0.5147703886032104, 'util', 0), ('explosion/thinc', 0.5136386156082153, 'ml-dl', 1), ('tlkh/tf-metal-experiments', 0.5093386769294739, 'perf', 0), ('microsoft/jarvis', 0.5016826391220093, 'llm', 0), ('merantix-momentum/squirrel-core', 0.500947892665863, 'ml', 0)]",983,8.0,,0.02,6,0,106,12,0,5,5,6.0,10.0,90.0,1.7,59
19,time-series,https://github.com/facebook/prophet,['time-series'],,[],[],1.0,,,facebook/prophet,prophet,17356,4467,423,Python,https://facebook.github.io/prophet,Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.,facebook,2024-01-13,2016-11-16,375,46.17711896617256,https://avatars.githubusercontent.com/u/69631?v=4,Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.,"['forecasting', 'r']","['forecasting', 'r', 'time-series']",2023-10-18,"[('nixtla/statsforecast', 0.586733341217041, 'time-series', 2), ('alkaline-ml/pmdarima', 0.581493079662323, 'time-series', 2), ('linkedin/greykite', 0.5375127196311951, 'ml', 0), ('winedarksea/autots', 0.5207078456878662, 'time-series', 2), ('firmai/atspy', 0.5104213356971741, 'time-series', 2)]",173,4.0,,0.65,58,16,87,3,4,2,4,58.0,72.0,90.0,1.2,59
177,debug,https://github.com/cool-rr/pysnooper,[],,[],[],1.0,,,cool-rr/pysnooper,PySnooper,16144,954,232,Python,,Never use print for debugging again,cool-rr,2024-01-14,2019-04-18,249,64.64988558352402,,Never use print for debugging again,"['debug', 'debugger', 'introspection', 'logging']","['debug', 'debugger', 'introspection', 'logging']",2024-01-13,"[('gruns/icecream', 0.8130260705947876, 'debug', 1), ('samuelcolvin/python-devtools', 0.5116415619850159, 'debug', 1)]",27,5.0,,0.19,4,1,58,0,1,4,1,4.0,8.0,90.0,2.0,59
56,term,https://github.com/pallets/click,['terminal'],,[],[],1.0,,,pallets/click,click,14685,1418,183,Python,https://click.palletsprojects.com,Python composable command line interface toolkit,pallets,2024-01-14,2014-04-24,509,28.81025784753363,https://avatars.githubusercontent.com/u/16748505?v=4,Python composable command line interface toolkit,"['cli', 'click', 'pallets']","['cli', 'click', 'pallets', 'terminal']",2023-12-29,"[('google/python-fire', 0.6196329593658447, 'term', 1), ('pexpect/pexpect', 0.5701817870140076, 'util', 0), ('jquast/blessed', 0.5604668855667114, 'term', 2), ('hoffstadt/dearpygui', 0.5539004802703857, 'gui', 0), ('textualize/trogon', 0.5391209125518799, 'term', 3), ('beeware/toga', 0.5282005667686462, 'gui', 0), ('urwid/urwid', 0.5238144993782043, 'term', 0), ('python-poetry/cleo', 0.5077859163284302, 'term', 1)]",366,3.0,,1.87,43,27,118,0,4,5,4,43.0,53.0,90.0,1.2,59
1215,gamedev,https://github.com/kitao/pyxel,[],,[],[],,,,kitao/pyxel,pyxel,12839,842,228,Python,,A retro game engine for Python,kitao,2024-01-14,2018-06-10,294,43.62766990291262,,A retro game engine for Python,"['8bit', 'fantasy-console', 'game', 'game-development', 'game-engine', 'gamedev', 'gameengine', 'pico-8', 'pyxel', 'rust', 'tic-80']","['8bit', 'fantasy-console', 'game', 'game-development', 'game-engine', 'gamedev', 'gameengine', 'pico-8', 'pyxel', 'rust', 'tic-80']",2024-01-13,"[('pokepetter/ursina', 0.6735712885856628, 'gamedev', 2), ('panda3d/panda3d', 0.6634293794631958, 'gamedev', 3), ('lordmauve/pgzero', 0.6230867505073547, 'gamedev', 0), ('renpy/renpy', 0.5945414304733276, 'viz', 1), ('pygame/pygame', 0.5883877873420715, 'gamedev', 2), ('pythonarcade/arcade', 0.5633159875869751, 'gamedev', 0), ('pygamelib/pygamelib', 0.5561051964759827, 'gamedev', 2), ('prefecthq/marvin', 0.520916759967804, 'nlp', 0), ('quantconnect/lean', 0.5179747343063354, 'finance', 0), ('willmcgugan/textual', 0.5071513056755066, 'term', 0)]",60,0.0,,9.88,36,29,68,0,14,13,14,36.0,90.0,90.0,2.5,59
61,pandas,https://github.com/ydataai/ydata-profiling,[],,[],[],1.0,,,ydataai/ydata-profiling,ydata-profiling,11667,1607,150,Python,https://docs.profiling.ydata.ai,1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames. ,ydataai,2024-01-14,2016-01-09,420,27.75025484199796,https://avatars.githubusercontent.com/u/57689451?v=4,1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames. ,"['big-data-analytics', 'data-analysis', 'data-exploration', 'data-profiling', 'data-quality', 'data-science', 'deep-learning', 'eda', 'exploration', 'exploratory-data-analysis', 'html-report', 'jupyter', 'jupyter-notebook', 'machine-learning', 'pandas', 'pandas-dataframe', 'pandas-profiling', 'statistics']","['big-data-analytics', 'data-analysis', 'data-exploration', 'data-profiling', 'data-quality', 'data-science', 'deep-learning', 'eda', 'exploration', 'exploratory-data-analysis', 'html-report', 'jupyter', 'jupyter-notebook', 'machine-learning', 'pandas', 'pandas-dataframe', 'pandas-profiling', 'statistics']",2024-01-08,"[('ydataai/ydata-quality', 0.6654260754585266, 'data', 2), ('hi-primus/optimus', 0.6320505142211914, 'ml-ops', 5), ('polyaxon/datatile', 0.6102992296218872, 'pandas', 6), ('great-expectations/great_expectations', 0.606023907661438, 'ml-ops', 5), ('unionai-oss/pandera', 0.5934801697731018, 'pandas', 2), ('pandas-dev/pandas', 0.5782955884933472, 'pandas', 3), ('apache/spark', 0.546349048614502, 'data', 0), ('lux-org/lux', 0.542782723903656, 'viz', 4), ('pola-rs/polars', 0.5423642992973328, 'pandas', 0), ('dagworks-inc/hamilton', 0.5421671867370605, 'ml-ops', 4), ('man-group/dtale', 0.5416145324707031, 'viz', 4), ('modin-project/modin', 0.5375041365623474, 'perf', 2), ('rapidsai/cudf', 0.5349627733230591, 'pandas', 3), ('krzjoa/awesome-python-data-science', 0.5310283303260803, 'study', 5), ('gventuri/pandas-ai', 0.5286967158317566, 'pandas', 3), ('plotly/dash', 0.526740312576294, 'viz', 2), ('ranaroussi/quantstats', 0.5242244005203247, 'finance', 0), ('rubik/radon', 0.5186936259269714, 'util', 0), ('eventual-inc/daft', 0.5120884776115417, 'pandas', 3), ('mementum/bta-lib', 0.5118176937103271, 'finance', 0)]",117,3.0,,2.83,80,30,98,0,17,38,17,80.0,92.0,90.0,1.1,59
50,viz,https://github.com/mwaskom/seaborn,[],,[],[],,,,mwaskom/seaborn,seaborn,11575,1870,259,Python,https://seaborn.pydata.org,Statistical data visualization in Python,mwaskom,2024-01-14,2012-06-18,606,19.096158378505773,,Statistical data visualization in Python,"['data-science', 'data-visualization', 'matplotlib', 'pandas']","['data-science', 'data-visualization', 'matplotlib', 'pandas']",2024-01-13,"[('altair-viz/altair', 0.8215170502662659, 'viz', 0), ('enthought/mayavi', 0.7422676086425781, 'viz', 0), ('man-group/dtale', 0.73142409324646, 'viz', 3), ('residentmario/geoplot', 0.7261803150177002, 'gis', 1), ('matplotlib/matplotlib', 0.6802253127098083, 'viz', 3), ('kanaries/pygwalker', 0.6736312508583069, 'pandas', 2), ('lux-org/lux', 0.6733652949333191, 'viz', 2), ('scitools/iris', 0.6527572870254517, 'gis', 0), ('jakevdp/pythondatasciencehandbook', 0.6520794630050659, 'study', 2), ('holoviz/panel', 0.6487950086593628, 'viz', 1), ('pyqtgraph/pyqtgraph', 0.6456829905509949, 'viz', 0), ('holoviz/holoviz', 0.6366784572601318, 'viz', 0), ('holoviz/hvplot', 0.636111855506897, 'pandas', 0), ('bokeh/bokeh', 0.6307612061500549, 'viz', 0), ('contextlab/hypertools', 0.6260726451873779, 'ml', 1), ('cuemacro/chartpy', 0.6233909130096436, 'viz', 1), ('has2k1/plotnine', 0.6059504747390747, 'viz', 0), ('matplotlib/mplfinance', 0.6046485900878906, 'finance', 1), ('pandas-dev/pandas', 0.6021502017974854, 'pandas', 2), ('holoviz/geoviews', 0.5874853134155273, 'gis', 0), ('scitools/cartopy', 0.5874788761138916, 'gis', 1), ('dfki-ric/pytransform3d', 0.5858793258666992, 'math', 1), ('datapane/datapane', 0.5832452774047852, 'viz', 1), ('adamerose/pandasgui', 0.5795894861221313, 'pandas', 1), ('plotly/plotly.py', 0.5750998854637146, 'viz', 0), ('gregorhd/mapcompare', 0.573523998260498, 'gis', 0), ('blaze/blaze', 0.5715226531028748, 'pandas', 0), ('tkrabel/bamboolib', 0.5646217465400696, 'pandas', 1), ('wesm/pydata-book', 0.5627793073654175, 'study', 0), ('csurfer/pyheat', 0.5626926422119141, 'profiling', 1), ('geopandas/geopandas', 0.5595967769622803, 'gis', 1), ('alexmojaki/heartrate', 0.5591291785240173, 'debug', 0), ('eleutherai/pyfra', 0.5522692799568176, 'ml', 0), ('westhealth/pyvis', 0.5389178395271301, 'graph', 0), ('mckinsey/vizro', 0.535768449306488, 'viz', 1), ('marcomusy/vedo', 0.5347036123275757, 'viz', 0), ('plotly/dash', 0.5341764092445374, 'viz', 2), ('vizzuhq/ipyvizzu', 0.5341234803199768, 'jupyter', 1), ('raphaelquast/eomaps', 0.531478226184845, 'gis', 1), ('federicoceratto/dashing', 0.528854489326477, 'term', 0), ('artelys/geonetworkx', 0.5267704129219055, 'gis', 0), ('netflix/flamescope', 0.5240253806114197, 'viz', 1), ('rjt1990/pyflux', 0.522447407245636, 'time-series', 0), ('graphistry/pygraphistry', 0.5153336524963379, 'data', 1), ('scikit-learn/scikit-learn', 0.5135470628738403, 'ml', 1), ('holoviz/holoviews', 0.513229489326477, 'viz', 0), ('stan-dev/pystan', 0.5127715468406677, 'ml', 0), ('mito-ds/monorepo', 0.5123574733734131, 'jupyter', 3), ('rapidsai/cudf', 0.5099605917930603, 'pandas', 2), ('holoviz/spatialpandas', 0.5058495402336121, 'pandas', 1), ('earthlab/earthpy', 0.5044101476669312, 'gis', 0), ('pysal/pysal', 0.5034690499305725, 'gis', 0), ('hazyresearch/meerkat', 0.5017697215080261, 'viz', 2), ('giswqs/geemap', 0.5008442401885986, 'gis', 1), ('matplotlib/basemap', 0.500274658203125, 'gis', 0), ('polyaxon/datatile', 0.500043511390686, 'pandas', 4)]",210,5.0,,2.29,127,92,141,0,3,3,3,127.0,285.0,90.0,2.2,59
1375,ml,https://github.com/deepmind/alphafold,"['protein', 'biology']",Implementation of the inference pipeline of AlphaFold v2,[],[],,,,deepmind/alphafold,alphafold,11231,2018,215,Python,,Open source code for AlphaFold.,deepmind,2024-01-14,2021-06-17,136,82.14942528735632,https://avatars.githubusercontent.com/u/8596759?v=4,Open source code for AlphaFold.,[],"['biology', 'protein']",2023-11-01,[],19,3.0,,0.54,82,30,31,2,1,5,1,82.0,154.0,90.0,1.9,59
1782,util,https://github.com/caronc/apprise,[],,[],[],,,,caronc/apprise,apprise,9247,335,61,Python,https://hub.docker.com/r/caronc/apprise,Apprise - Push Notifications that work with just about every platform!,caronc,2024-01-14,2017-11-25,322,28.679220203810367,,Apprise - Push Notifications that work with just about every platform!,"['alerts', 'apprise', 'framework', 'notification-api', 'notification-hub', 'notification-service', 'notifications', 'notifier', 'notify', 'push-notifications']","['alerts', 'apprise', 'framework', 'notification-api', 'notification-hub', 'notification-service', 'notifications', 'notifier', 'notify', 'push-notifications']",2024-01-06,"[('liiight/notifiers', 0.6810635924339294, 'util', 3)]",57,3.0,,2.21,81,55,75,0,7,8,7,81.0,179.0,90.0,2.2,59
1391,finance,https://github.com/ai4finance-foundation/finrl,['reinforcement-learning'],,[],[],,,,ai4finance-foundation/finrl,FinRL,8598,2131,194,Jupyter Notebook,https://discord.gg/trsr8SXpW5,FinRL:  Financial Reinforcement Learning. 🔥,ai4finance-foundation,2024-01-13,2020-07-26,183,46.9103663289166,https://avatars.githubusercontent.com/u/68813910?v=4,FinRL:  Financial Reinforcement Learning. 🔥,"['algorithmic-trading', 'deep-reinforcement-learning', 'drl-algorithms', 'drl-framework', 'drl-trading-agents', 'finance', 'fintech', 'multi-agent-learning', 'openai-gym', 'pythorch', 'stock-markets', 'stock-trading', 'tensorflow2', 'trading-tasks']","['algorithmic-trading', 'deep-reinforcement-learning', 'drl-algorithms', 'drl-framework', 'drl-trading-agents', 'finance', 'fintech', 'multi-agent-learning', 'openai-gym', 'pythorch', 'reinforcement-learning', 'stock-markets', 'stock-trading', 'tensorflow2', 'trading-tasks']",2024-01-14,"[('ai4finance-foundation/fingpt', 0.6304659247398376, 'finance', 3), ('polakowo/vectorbt', 0.6179881691932678, 'finance', 2), ('keras-rl/keras-rl', 0.5993512272834778, 'ml-rl', 1), ('pytorch/rl', 0.5981391072273254, 'ml-rl', 1), ('openbb-finance/openbbterminal', 0.5771132111549377, 'finance', 1), ('thu-ml/tianshou', 0.5749337673187256, 'ml-rl', 0), ('chancefocus/pixiu', 0.5721709132194519, 'finance', 1), ('tensorlayer/tensorlayer', 0.5661175847053528, 'ml-rl', 1), ('google/dopamine', 0.5640981793403625, 'ml-rl', 0), ('google/trax', 0.5627601146697998, 'ml-dl', 2), ('denys88/rl_games', 0.5618516206741333, 'ml-rl', 1), ('microsoft/qlib', 0.5603201389312744, 'finance', 3), ('quantconnect/lean', 0.5530506372451782, 'finance', 1), ('adap/flower', 0.551112949848175, 'ml-ops', 0), ('explosion/thinc', 0.5454742312431335, 'ml-dl', 0), ('google/tf-quant-finance', 0.54345703125, 'finance', 1), ('nccr-itmo/fedot', 0.5424655675888062, 'ml-ops', 0), ('opentensor/bittensor', 0.5417373776435852, 'ml', 0), ('zvtvz/zvt', 0.5343793034553528, 'finance', 2), ('kernc/backtesting.py', 0.5252265334129333, 'finance', 2), ('salesforce/warp-drive', 0.5230139493942261, 'ml-rl', 1), ('facebookresearch/reagent', 0.5226213932037354, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5189048647880554, 'ml-rl', 2), ('pettingzoo-team/pettingzoo', 0.5186222791671753, 'ml-rl', 1), ('freqtrade/freqtrade', 0.5126928091049194, 'crypto', 1), ('ddbourgin/numpy-ml', 0.511791467666626, 'ml', 1), ('xplainable/xplainable', 0.5115347504615784, 'ml-interpretability', 0), ('idanya/algo-trader', 0.5114476084709167, 'finance', 1), ('online-ml/river', 0.5111426711082458, 'ml', 0), ('nevronai/metisfl', 0.5089988112449646, 'ml', 0), ('ranaroussi/quantstats', 0.5057669878005981, 'finance', 2), ('keras-team/keras', 0.5041005611419678, 'ml-dl', 0), ('deepmind/dm_control', 0.5040066838264465, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5031803250312805, 'ml-rl', 1), ('tensorly/tensorly', 0.5015732645988464, 'ml-dl', 0)]",108,4.0,,4.33,51,17,42,0,1,1,1,51.0,53.0,90.0,1.0,59
151,ml,https://github.com/catboost/catboost,[],,[],[],,,,catboost/catboost,catboost,7539,1164,197,Python,https://catboost.ai,"A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.",catboost,2024-01-14,2017-07-18,341,22.10850439882698,https://avatars.githubusercontent.com/u/29043415?v=4,"A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.","['big-data', 'catboost', 'categorical-features', 'coreml', 'cuda', 'data-mining', 'data-science', 'decision-trees', 'gbdt', 'gbm', 'gpu', 'gpu-computing', 'gradient-boosting', 'kaggle', 'machine-learning', 'r', 'tutorial']","['big-data', 'catboost', 'categorical-features', 'coreml', 'cuda', 'data-mining', 'data-science', 'decision-trees', 'gbdt', 'gbm', 'gpu', 'gpu-computing', 'gradient-boosting', 'kaggle', 'machine-learning', 'r', 'tutorial']",2024-01-13,"[('microsoft/lightgbm', 0.8396483659744263, 'ml', 8), ('dmlc/xgboost', 0.7875171899795532, 'ml', 3), ('google/tf-quant-finance', 0.5715440511703491, 'finance', 2), ('gradio-app/gradio', 0.553999125957489, 'viz', 2), ('pycaret/pycaret', 0.5486772656440735, 'ml', 3), ('dask/dask-ml', 0.5368636250495911, 'ml', 0), ('oml-team/open-metric-learning', 0.5297994017601013, 'ml', 1), ('rasbt/mlxtend', 0.5251424908638, 'ml', 3), ('determined-ai/determined', 0.5235607028007507, 'ml-ops', 2), ('intel/intel-extension-for-pytorch', 0.5203407406806946, 'perf', 1), ('linkedin/fasttreeshap', 0.5170351266860962, 'ml', 1), ('epistasislab/tpot', 0.515169084072113, 'ml', 3), ('microsoft/flaml', 0.5135185122489929, 'ml', 2), ('rasbt/machine-learning-book', 0.5105630159378052, 'study', 1), ('tensorflow/tensorflow', 0.5088467597961426, 'ml-dl', 1), ('tensorflow/data-validation', 0.5083582997322083, 'ml-ops', 0), ('teamhg-memex/eli5', 0.5058757066726685, 'ml', 2), ('pytorch/torchrec', 0.5050163269042969, 'ml-dl', 2), ('scikit-learn-contrib/lightning', 0.5035675764083862, 'ml', 1), ('uber/petastorm', 0.5008516311645508, 'data', 1)]",1193,2.0,,28.46,217,46,79,0,3,14,3,217.0,167.0,90.0,0.8,59
783,diffusion,https://github.com/ashawkey/stable-dreamfusion,[],,[],[],,,,ashawkey/stable-dreamfusion,stable-dreamfusion,7424,672,125,Python,,Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion.,ashawkey,2024-01-14,2022-10-06,68,108.04158004158005,,Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion.,"['dreamfusion', 'gui', 'image-to-3d', 'nerf', 'stable-diffusion', 'text-to-3d']","['dreamfusion', 'gui', 'image-to-3d', 'nerf', 'stable-diffusion', 'text-to-3d']",2023-08-02,"[('carson-katri/dream-textures', 0.5740616917610168, 'diffusion', 1), ('xavierxiao/dreambooth-stable-diffusion', 0.5586757659912109, 'diffusion', 1), ('sharonzhou/long_stable_diffusion', 0.5333707332611084, 'diffusion', 0), ('openai/point-e', 0.5210638642311096, 'util', 0), ('automatic1111/stable-diffusion-webui', 0.5163194537162781, 'diffusion', 1), ('huggingface/exporters', 0.503178060054779, 'ml', 0), ('thereforegames/unprompted', 0.5019211769104004, 'diffusion', 1)]",20,7.0,,1.75,14,5,16,6,2,2,2,13.0,11.0,90.0,0.8,59
355,ml-ops,https://github.com/netflix/metaflow,[],,[],[],,,,netflix/metaflow,metaflow,7269,703,275,Python,https://metaflow.org,:rocket: Build and manage real-life data science projects with ease!,netflix,2024-01-14,2019-09-17,228,31.88157894736842,https://avatars.githubusercontent.com/u/913567?v=4,🚀 Build and manage real-life data science projects with ease!,"['ai', 'aws', 'azure', 'data-science', 'datascience', 'gcp', 'high-performance-computing', 'kubernetes', 'machine-learning', 'ml', 'ml-infrastructure', 'ml-platform', 'mlops', 'model-management', 'productivity', 'r', 'r-package', 'reproducible-research', 'rstats']","['ai', 'aws', 'azure', 'data-science', 'datascience', 'gcp', 'high-performance-computing', 'kubernetes', 'machine-learning', 'ml', 'ml-infrastructure', 'ml-platform', 'mlops', 'model-management', 'productivity', 'r', 'r-package', 'reproducible-research', 'rstats']",2024-01-11,"[('polyaxon/polyaxon', 0.6915358901023865, 'ml-ops', 5), ('iterative/dvc', 0.6912345886230469, 'ml-ops', 3), ('bentoml/bentoml', 0.6758896708488464, 'ml-ops', 6), ('orchest/orchest', 0.6532567739486694, 'ml-ops', 3), ('feast-dev/feast', 0.6503940224647522, 'ml-ops', 4), ('mlflow/mlflow', 0.6375054717063904, 'ml-ops', 4), ('avaiga/taipy', 0.6367209553718567, 'data', 2), ('ploomber/ploomber', 0.6208223700523376, 'ml-ops', 3), ('googlecloudplatform/vertex-ai-samples', 0.6179714798927307, 'ml', 5), ('skypilot-org/skypilot', 0.6102747321128845, 'llm', 4), ('flyteorg/flyte', 0.608452320098877, 'ml-ops', 4), ('dagster-io/dagster', 0.6058613657951355, 'ml-ops', 2), ('kubeflow/pipelines', 0.5999767184257507, 'ml-ops', 4), ('mage-ai/mage-ai', 0.5949897766113281, 'ml-ops', 2), ('aimhubio/aim', 0.5948770046234131, 'ml-ops', 5), ('mindsdb/mindsdb', 0.594740092754364, 'data', 3), ('polyaxon/datatile', 0.5876936316490173, 'pandas', 2), ('allegroai/clearml', 0.5861303210258484, 'ml-ops', 3), ('bodywork-ml/bodywork-core', 0.5834348201751709, 'ml-ops', 4), ('airbytehq/airbyte', 0.5793136954307556, 'data', 0), ('zenml-io/mlstacks', 0.5789435505867004, 'ml-ops', 2), ('zenml-io/zenml', 0.5758110880851746, 'ml-ops', 5), ('jina-ai/jina', 0.5668376088142395, 'ml', 3), ('whylabs/whylogs', 0.5658851861953735, 'util', 3), ('fmind/mlops-python-package', 0.5633413195610046, 'template', 3), ('meltano/meltano', 0.5626580119132996, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.560415506362915, 'ml', 4), ('activeloopai/deeplake', 0.5537254214286804, 'ml-ops', 5), ('cleanlab/cleanlab', 0.5494846105575562, 'ml', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5488802194595337, 'study', 2), ('xplainable/xplainable', 0.5485888123512268, 'ml-interpretability', 2), ('featureform/embeddinghub', 0.5473335981369019, 'nlp', 4), ('hpcaitech/colossalai', 0.5460145473480225, 'llm', 1), ('drivendata/cookiecutter-data-science', 0.5439575910568237, 'template', 3), ('superduperdb/superduperdb', 0.542826235294342, 'data', 3), ('pythagora-io/gpt-pilot', 0.5420463681221008, 'llm', 1), ('kubeflow-kale/kale', 0.5416892170906067, 'ml-ops', 1), ('lastmile-ai/aiconfig', 0.5414328575134277, 'util', 1), ('eventual-inc/daft', 0.5369682312011719, 'pandas', 2), ('huggingface/datasets', 0.5358194708824158, 'nlp', 1), ('sweepai/sweep', 0.5298407077789307, 'llm', 1), ('hi-primus/optimus', 0.5270899534225464, 'ml-ops', 2), ('kestra-io/kestra', 0.5250420570373535, 'ml-ops', 0), ('streamlit/streamlit', 0.5236718654632568, 'viz', 2), ('dagworks-inc/hamilton', 0.519896924495697, 'ml-ops', 3), ('lithops-cloud/lithops', 0.5198798179626465, 'ml-ops', 1), ('antonosika/gpt-engineer', 0.5169954299926758, 'llm', 1), ('zenodo/zenodo', 0.513927161693573, 'util', 0), ('google/ml-metadata', 0.5132206082344055, 'ml-ops', 0), ('transformeroptimus/superagi', 0.5128297209739685, 'llm', 1), ('pydoit/doit', 0.5121597051620483, 'util', 1), ('wandb/client', 0.5120764374732971, 'ml', 4), ('airbnb/knowledge-repo', 0.5116251707077026, 'data', 1), ('salesforce/logai', 0.5108799934387207, 'util', 2), ('unionai-oss/unionml', 0.5082710981369019, 'ml-ops', 2), ('google-research/language', 0.5073051452636719, 'nlp', 1), ('firmai/industry-machine-learning', 0.5058495402336121, 'study', 3), ('backtick-se/cowait', 0.5032603144645691, 'util', 2)]",78,3.0,,4.12,136,79,53,0,36,27,36,136.0,131.0,90.0,1.0,59
1605,term,https://github.com/saulpw/visidata,[],,[],[],,,,saulpw/visidata,visidata,7159,271,70,Python,http://visidata.org,A terminal spreadsheet multitool for discovering and arranging data,saulpw,2024-01-14,2016-10-27,378,18.903432666918143,,A terminal spreadsheet multitool for discovering and arranging data,"['cli', 'csv', 'datajournalism', 'datawrangling', 'devops-tools', 'eda', 'hdf5', 'json', 'opendata', 'pandas', 'reconciliation', 'spreadsheet', 'sqlite', 'tabular-data', 'tsv', 'tui', 'unix-toolkit']","['cli', 'csv', 'datajournalism', 'datawrangling', 'devops-tools', 'eda', 'hdf5', 'json', 'opendata', 'pandas', 'reconciliation', 'spreadsheet', 'sqlite', 'tabular-data', 'tsv', 'tui', 'unix-toolkit']",2024-01-14,"[('gristlabs/grist-core', 0.6072668433189392, 'data', 1), ('simonw/datasette', 0.6051476001739502, 'data', 3), ('hi-primus/optimus', 0.5910075902938843, 'ml-ops', 0), ('hyperqueryhq/whale', 0.5835681557655334, 'data', 0), ('wireservice/csvkit', 0.5774484276771545, 'util', 0), ('mito-ds/monorepo', 0.5757395625114441, 'jupyter', 1), ('jazzband/tablib', 0.5593103170394897, 'data', 0), ('holoviz/panel', 0.5565743446350098, 'viz', 0), ('python-odin/odin', 0.5557106137275696, 'util', 2), ('airbnb/omniduct', 0.5526487231254578, 'data', 0), ('pytables/pytables', 0.5493369102478027, 'data', 0), ('pandas-dev/pandas', 0.5462385416030884, 'pandas', 1), ('airbytehq/airbyte', 0.5434727668762207, 'data', 0), ('plotly/dash', 0.5414975881576538, 'viz', 0), ('airbnb/knowledge-repo', 0.5398018956184387, 'data', 0), ('unstructured-io/unstructured-api', 0.536859393119812, 'data', 0), ('koaning/clumper', 0.5350844264030457, 'util', 0), ('linealabs/lineapy', 0.5347519516944885, 'jupyter', 0), ('krzjoa/awesome-python-data-science', 0.5329089760780334, 'study', 0), ('tconbeer/harlequin', 0.5237422585487366, 'term', 0), ('dbt-labs/dbt-core', 0.5234029293060303, 'ml-ops', 0), ('intake/intake', 0.5221759080886841, 'data', 0), ('ibis-project/ibis', 0.5209618210792542, 'data', 2), ('zenodo/zenodo', 0.5197573900222778, 'util', 0), ('mckinsey/vizro', 0.517253577709198, 'viz', 0), ('man-group/dtale', 0.5157226324081421, 'viz', 1), ('dagster-io/dagster', 0.515415370464325, 'ml-ops', 0), ('tiangolo/sqlmodel', 0.513899564743042, 'data', 1), ('astanin/python-tabulate', 0.5104148387908936, 'util', 0), ('polyaxon/datatile', 0.5101152658462524, 'pandas', 1), ('ploomber/ploomber', 0.5074170827865601, 'ml-ops', 0), ('quantopian/qgrid', 0.5062557458877563, 'jupyter', 0), ('vaexio/vaex', 0.5060981512069702, 'perf', 2), ('malloydata/malloy-py', 0.5046626925468445, 'data', 0), ('unionai-oss/pandera', 0.5012566447257996, 'pandas', 1), ('apache/spark', 0.5010607838630676, 'data', 0), ('dagworks-inc/hamilton', 0.5007401704788208, 'ml-ops', 1), ('dlt-hub/dlt', 0.5002819299697876, 'data', 0)]",96,3.0,,21.27,269,213,88,0,4,8,4,269.0,516.0,90.0,1.9,59
287,testing,https://github.com/hypothesisworks/hypothesis,[],,[],[],1.0,,,hypothesisworks/hypothesis,hypothesis,7097,590,72,Python,https://hypothesis.works,"Hypothesis is a powerful, flexible, and easy to use library for property-based testing.",hypothesisworks,2024-01-13,2013-03-10,568,12.488436400201106,https://avatars.githubusercontent.com/u/18481919?v=4,"Hypothesis is a powerful, flexible, and easy to use library for property-based testing.","['fuzzing', 'property-based-testing', 'testing']","['fuzzing', 'property-based-testing', 'testing']",2024-01-13,"[('unionai-oss/pandera', 0.5685426592826843, 'pandas', 1), ('nedbat/coveragepy', 0.5107763409614563, 'testing', 0), ('pytest-dev/pytest', 0.505795955657959, 'testing', 1)]",322,4.0,,18.79,83,69,132,0,26,131,26,84.0,177.0,90.0,2.1,59
1197,diffusion,https://github.com/facebookresearch/dinov2,[],,[],[],,,,facebookresearch/dinov2,dinov2,7024,545,93,Jupyter Notebook,,PyTorch code and models for the DINOv2 self-supervised learning method.,facebookresearch,2024-01-14,2023-03-29,43,160.15635179153094,https://avatars.githubusercontent.com/u/16943930?v=4,PyTorch code and models for the DINOv2 self-supervised learning method.,[],[],2023-12-01,"[('lightly-ai/lightly', 0.5861289501190186, 'ml', 0), ('idea-research/groundingdino', 0.5680932402610779, 'diffusion', 0), ('skorch-dev/skorch', 0.5518595576286316, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5447684526443481, 'study', 0), ('pytorch/ignite', 0.5208505988121033, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5111134052276611, 'perf', 0)]",6,1.0,,0.63,140,62,10,1,0,0,0,140.0,246.0,90.0,1.8,59
872,time-series,https://github.com/unit8co/darts,[],,[],[],,,,unit8co/darts,darts,6872,766,60,Python,https://unit8co.github.io/darts/,A python library for user-friendly forecasting and anomaly detection on time series.,unit8co,2024-01-13,2018-09-13,280,24.480407124681935,https://avatars.githubusercontent.com/u/39619745?v=4,A python library for user-friendly forecasting and anomaly detection on time series.,"['anomaly-detection', 'data-science', 'deep-learning', 'forecasting', 'machine-learning', 'time-series']","['anomaly-detection', 'data-science', 'deep-learning', 'forecasting', 'machine-learning', 'time-series']",2024-01-13,"[('aistream-peelout/flow-forecast', 0.7890238165855408, 'time-series', 4), ('yzhao062/pyod', 0.7557379603385925, 'data', 4), ('tdameritrade/stumpy', 0.7482045888900757, 'time-series', 2), ('salesforce/merlion', 0.7470134496688843, 'time-series', 4), ('pycaret/pycaret', 0.7233750820159912, 'ml', 4), ('alkaline-ml/pmdarima', 0.7097618579864502, 'time-series', 3), ('awslabs/gluonts', 0.6495864987373352, 'time-series', 5), ('firmai/atspy', 0.6426480412483215, 'time-series', 2), ('rjt1990/pyflux', 0.5929312109947205, 'time-series', 1), ('linkedin/greykite', 0.5859647989273071, 'ml', 0), ('sktime/sktime', 0.5841982960700989, 'time-series', 4), ('salesforce/deeptime', 0.5719814300537109, 'time-series', 3), ('google/temporian', 0.5616124272346497, 'time-series', 1), ('opengeos/earthformer', 0.5380537509918213, 'gis', 2), ('rasbt/mlxtend', 0.5370567440986633, 'ml', 2), ('winedarksea/autots', 0.5341631770133972, 'time-series', 4), ('scikit-learn-contrib/imbalanced-learn', 0.5324269533157349, 'ml', 2), ('blue-yonder/tsfresh', 0.5309851765632629, 'time-series', 2), ('uber/orbit', 0.5234378576278687, 'time-series', 3), ('wilsonrljr/sysidentpy', 0.5214325785636902, 'time-series', 3), ('microprediction/microprediction', 0.5138264298439026, 'time-series', 1), ('salesforce/logai', 0.5133403539657593, 'util', 2)]",110,4.0,,3.79,228,127,65,0,5,7,5,228.0,353.0,90.0,1.5,59
1090,llm,https://github.com/eleutherai/gpt-neox,[],,[],[],,,,eleutherai/gpt-neox,gpt-neox,6319,921,121,Python,,"An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.",eleutherai,2024-01-13,2020-12-22,162,39.00617283950617,https://avatars.githubusercontent.com/u/68924597?v=4,"An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.","['deepspeed-library', 'gpt-3', 'language-model', 'transformers']","['deepspeed-library', 'gpt-3', 'language-model', 'transformers']",2024-01-13,"[('eleutherai/gpt-neo', 0.6265007257461548, 'llm', 3), ('microsoft/deepspeed', 0.5843392610549927, 'ml-dl', 0), ('marella/ctransformers', 0.5529630184173584, 'nlp', 1), ('karpathy/mingpt', 0.5511136651039124, 'llm', 0), ('huggingface/optimum', 0.5265071988105774, 'ml', 1), ('pytorch/pytorch', 0.5058263540267944, 'ml-dl', 0), ('nvidia/tensorrt-llm', 0.5021648406982422, 'viz', 1)]",111,4.0,,3.4,83,56,37,0,2,1,2,83.0,114.0,90.0,1.4,59
763,data,https://github.com/gristlabs/grist-core,[],,[],[],,,,gristlabs/grist-core,grist-core,5846,240,50,TypeScript,https://www.getgrist.com/,Grist is the evolution of spreadsheets.,gristlabs,2024-01-14,2020-05-22,192,30.357566765578635,https://avatars.githubusercontent.com/u/19978005?v=4,Grist is the evolution of spreadsheets.,"['awesome', 'database', 'spreadsheet']","['awesome', 'database', 'spreadsheet']",2024-01-11,"[('saulpw/visidata', 0.6072668433189392, 'term', 1)]",73,1.0,,15.33,194,96,44,0,14,7,14,194.0,316.0,90.0,1.6,59
432,util,https://github.com/scikit-image/scikit-image,[],,[],[],,,,scikit-image/scikit-image,scikit-image,5735,2241,186,Python,https://scikit-image.org,Image processing in Python,scikit-image,2024-01-13,2011-07-07,655,8.746187363834423,https://avatars.githubusercontent.com/u/897180?v=4,Image processing in Python,"['computer-vision', 'image-processing', 'spec-0', 'spec-1', 'spec-4']","['computer-vision', 'image-processing', 'spec-0', 'spec-1', 'spec-4']",2024-01-11,"[('luispedro/mahotas', 0.6524010300636292, 'viz', 1), ('python-pillow/pillow', 0.6156206130981445, 'util', 1), ('zulko/moviepy', 0.5875822305679321, 'util', 0), ('imageio/imageio', 0.5782924890518188, 'util', 0), ('networkx/networkx', 0.5438209772109985, 'graph', 3), ('lightly-ai/lightly', 0.5242587924003601, 'ml', 1), ('numpy/numpy', 0.5151585936546326, 'math', 0), ('roboflow/supervision', 0.5051987767219543, 'ml', 2)]",644,8.0,,10.75,209,99,152,0,11,9,11,208.0,481.0,90.0,2.3,59
1258,llm,https://github.com/lightning-ai/lit-llama,"['llama', 'language-model', 'nanogpt']",,[],[],,,,lightning-ai/lit-llama,lit-llama,5520,473,67,Python,,"Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.",lightning-ai,2024-01-14,2023-03-22,44,123.05732484076434,https://avatars.githubusercontent.com/u/58386951?v=4,"Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.",[],"['language-model', 'llama', 'nanogpt']",2023-07-19,"[('lightning-ai/lit-gpt', 0.721184253692627, 'llm', 1), ('jzhang38/tinyllama', 0.6785302758216858, 'llm', 2), ('microsoft/llama-2-onnx', 0.643981397151947, 'llm', 2), ('tloen/alpaca-lora', 0.6250495314598083, 'llm', 2), ('bobazooba/xllm', 0.6132877469062805, 'llm', 1), ('hiyouga/llama-factory', 0.5843560099601746, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5843559503555298, 'llm', 2), ('facebookresearch/llama', 0.5731508731842041, 'llm', 2), ('zrrskywalker/llama-adapter', 0.5725643038749695, 'llm', 2), ('bigscience-workshop/petals', 0.5701621770858765, 'data', 1), ('next-gpt/next-gpt', 0.5547515153884888, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5519506335258484, 'perf', 0), ('run-llama/llama-lab', 0.550618588924408, 'llm', 2), ('facebookresearch/llama-recipes', 0.5484828352928162, 'llm', 2), ('young-geng/easylm', 0.5471640825271606, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.5464016199111938, 'llm', 0), ('facebookresearch/codellama', 0.5444297790527344, 'llm', 2), ('sjtu-ipads/powerinfer', 0.5379477143287659, 'llm', 1), ('oobabooga/text-generation-webui', 0.5357398986816406, 'llm', 1), ('jerryjliu/llama_index', 0.5328296422958374, 'llm', 2), ('opengvlab/omniquant', 0.5302090048789978, 'llm', 0), ('salesforce/xgen', 0.5283926129341125, 'llm', 1), ('cstankonrad/long_llama', 0.5269233584403992, 'llm', 2), ('haotian-liu/llava', 0.5264182686805725, 'llm', 1), ('lianjiatech/belle', 0.5208169221878052, 'llm', 1), ('openlm-research/open_llama', 0.5196517109870911, 'llm', 2), ('ggerganov/llama.cpp', 0.5175377726554871, 'llm', 2), ('vllm-project/vllm', 0.5155295729637146, 'llm', 1), ('artidoro/qlora', 0.5125903487205505, 'llm', 1), ('microsoft/lora', 0.5121870040893555, 'llm', 1), ('mlc-ai/web-llm', 0.508145809173584, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5055845975875854, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5034503936767578, 'llm', 1), ('tairov/llama2.mojo', 0.501312792301178, 'llm', 1)]",32,6.0,,4.31,46,26,10,6,0,0,0,46.0,24.0,90.0,0.5,59
185,ml-dl,https://github.com/google/flax,['neural-network'],,[],[],,,,google/flax,flax,5105,582,82,Python,https://flax.readthedocs.io,Flax is a neural network library for JAX that is designed for flexibility.,google,2024-01-14,2020-01-10,211,24.12896691424713,https://avatars.githubusercontent.com/u/1342004?v=4,Flax is a neural network library for JAX that is designed for flexibility.,['jax'],"['jax', 'neural-network']",2024-01-12,"[('deepmind/dm-haiku', 0.6950955986976624, 'ml-dl', 1), ('deepmind/synjax', 0.6082916259765625, 'math', 1), ('deepmind/chex', 0.5962191820144653, 'ml-dl', 1), ('young-geng/easylm', 0.5341013073921204, 'llm', 1), ('google/trax', 0.5265333652496338, 'ml-dl', 1)]",215,2.0,,8.6,213,169,49,0,14,9,14,212.0,281.0,90.0,1.3,59
1819,study,https://github.com/udlbook/udlbook,"['book', 'deep-learning']",,[],[],,,,udlbook/udlbook,udlbook,4087,847,76,Jupyter Notebook,,Understanding Deep Learning - Simon J.D. Prince,udlbook,2024-01-14,2022-08-01,78,52.30164533820841,,Understanding Deep Learning - Simon J.D. Prince,[],"['book', 'deep-learning']",2024-01-10,"[('mrdbourke/pytorch-deep-learning', 0.6033921837806702, 'study', 1), ('rasbt/stat453-deep-learning-ss20', 0.5738182663917542, 'study', 0), ('d2l-ai/d2l-en', 0.573790431022644, 'study', 2), ('tensorflow/tensor2tensor', 0.5561156868934631, 'ml', 1), ('rasbt/deeplearning-models', 0.5459153056144714, 'ml-dl', 0), ('ageron/handson-ml2', 0.539566159248352, 'ml', 0), ('udacity/deep-learning-v2-pytorch', 0.5336586236953735, 'study', 1), ('openai/spinningup', 0.5261555314064026, 'study', 0), ('atcold/nyu-dlsp21', 0.5171167850494385, 'study', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5133402347564697, 'study', 1), ('intellabs/bayesian-torch', 0.5086169838905334, 'ml', 1), ('patchy631/machine-learning', 0.5037120580673218, 'ml', 0), ('graykode/nlp-tutorial', 0.5029069185256958, 'study', 0), ('keras-team/keras', 0.5011566877365112, 'ml-dl', 1)]",8,3.0,,7.65,43,40,18,0,35,28,35,43.0,57.0,90.0,1.3,59
1442,llm,https://github.com/kyegomez/tree-of-thoughts,['prompt-engineering'],,[],[],,,,kyegomez/tree-of-thoughts,tree-of-thoughts,3761,361,48,Python,https://discord.gg/qUtxnK2NMf,Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70% ,kyegomez,2024-01-14,2023-05-21,36,103.64960629921259,,Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70% ,"['artificial-intelligence', 'chatgpt', 'deep-learning', 'gpt4', 'multimodal', 'prompt', 'prompt-engineering', 'prompt-learning', 'prompt-tuning']","['artificial-intelligence', 'chatgpt', 'deep-learning', 'gpt4', 'multimodal', 'prompt', 'prompt-engineering', 'prompt-learning', 'prompt-tuning']",2023-12-24,"[('spcl/graph-of-thoughts', 0.6711254715919495, 'llm', 1), ('lupantech/chameleon-llm', 0.6684974431991577, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.6476341485977173, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.6086257100105286, 'llm', 0), ('stanfordnlp/dspy', 0.5961645245552063, 'llm', 0), ('guidance-ai/guidance', 0.5812641382217407, 'llm', 2), ('lupantech/scienceqa', 0.5537173748016357, 'llm', 0), ('srush/minichain', 0.5509519577026367, 'llm', 1), ('thudm/p-tuning-v2', 0.5489909052848816, 'nlp', 1), ('microsoft/autogen', 0.5404730439186096, 'llm', 1), ('reasoning-machines/pal', 0.5376133918762207, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5277297496795654, 'study', 2), ('databrickslabs/dolly', 0.5184698104858398, 'llm', 0), ('openai/finetune-transformer-lm', 0.5154716968536377, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5084075927734375, 'study', 6), ('jina-ai/thinkgpt', 0.5059940814971924, 'llm', 0), ('hazyresearch/ama_prompting', 0.5045536160469055, 'llm', 1)]",7,3.0,,4.6,7,2,8,1,30,46,30,7.0,7.0,90.0,1.0,59
4,pandas,https://github.com/aws/aws-sdk-pandas,"['apache-arrow', 'parquet', 'awswrangler', 'emr']",,[],[],1.0,,,aws/aws-sdk-pandas,aws-sdk-pandas,3713,668,61,Python,https://aws-sdk-pandas.readthedocs.io,"pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).",aws,2024-01-13,2019-02-26,257,14.447470817120623,https://avatars.githubusercontent.com/u/2232217?v=4,"pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).","['amazon-athena', 'amazon-sagemaker-notebook', 'apache-arrow', 'apache-parquet', 'athena', 'aws', 'aws-glue', 'aws-lambda', 'data-engineering', 'data-science', 'emr', 'etl', 'glue-catalog', 'lambda', 'modin', 'mysql', 'pandas', 'ray', 'redshift']","['amazon-athena', 'amazon-sagemaker-notebook', 'apache-arrow', 'apache-parquet', 'athena', 'aws', 'aws-glue', 'aws-lambda', 'awswrangler', 'data-engineering', 'data-science', 'emr', 'etl', 'glue-catalog', 'lambda', 'modin', 'mysql', 'pandas', 'parquet', 'ray', 'redshift']",2024-01-12,"[('airbytehq/airbyte', 0.6343478560447693, 'data', 4), ('pynamodb/pynamodb', 0.6329684257507324, 'data', 1), ('prefecthq/prefect-aws', 0.6139408946037292, 'data', 1), ('airbnb/omniduct', 0.5810672044754028, 'data', 0), ('tobymao/sqlglot', 0.5701399445533752, 'data', 2), ('ibis-project/ibis', 0.563861608505249, 'data', 2), ('fugue-project/fugue', 0.5624109506607056, 'pandas', 1), ('awslabs/python-deequ', 0.5529321432113647, 'ml', 1), ('hi-primus/optimus', 0.533242404460907, 'ml-ops', 1), ('jordaneremieff/mangum', 0.5317604541778564, 'web', 3), ('zenodo/zenodo', 0.5311623811721802, 'util', 0), ('boto/boto3', 0.5303450226783752, 'util', 1), ('tiangolo/sqlmodel', 0.5277955532073975, 'data', 0), ('geeogi/async-python-lambda-template', 0.5274588465690613, 'template', 0), ('nficano/python-lambda', 0.5259411334991455, 'util', 2), ('astronomer/astro-sdk', 0.525879442691803, 'ml-ops', 3), ('simonw/datasette', 0.525142252445221, 'data', 0), ('apache/spark', 0.5226028561592102, 'data', 0), ('aws/chalice', 0.5215028524398804, 'web', 3), ('eventual-inc/daft', 0.5070556998252869, 'pandas', 2), ('amzn/ion-python', 0.5067648887634277, 'data', 0), ('pola-rs/polars', 0.50155109167099, 'pandas', 0), ('aws/graph-notebook', 0.5009594559669495, 'jupyter', 0)]",143,4.0,,7.15,140,127,59,0,14,24,14,140.0,498.0,90.0,3.6,59
1496,ml-ops,https://github.com/zenml-io/zenml,[],,[],[],,,,zenml-io/zenml,zenml,3428,370,40,Python,https://zenml.io,"ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.",zenml-io,2024-01-14,2020-11-19,166,20.562125107112255,https://avatars.githubusercontent.com/u/88676955?v=4,"ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.","['ai', 'automl', 'data-science', 'deep-learning', 'devops-tools', 'llm', 'llmops', 'machine-learning', 'metadata-tracking', 'ml', 'mlops', 'pipelines', 'production-ready', 'pytorch', 'tensorflow', 'workflow', 'zenml']","['ai', 'automl', 'data-science', 'deep-learning', 'devops-tools', 'llm', 'llmops', 'machine-learning', 'metadata-tracking', 'ml', 'mlops', 'pipelines', 'production-ready', 'pytorch', 'tensorflow', 'workflow', 'zenml']",2024-01-13,"[('allegroai/clearml', 0.66759192943573, 'ml-ops', 4), ('polyaxon/polyaxon', 0.6621537804603577, 'ml-ops', 9), ('orchest/orchest', 0.6482174396514893, 'ml-ops', 3), ('ploomber/ploomber', 0.6197214722633362, 'ml-ops', 5), ('fmind/mlops-python-package', 0.6134677529335022, 'template', 3), ('avaiga/taipy', 0.5993794202804565, 'data', 3), ('bodywork-ml/bodywork-core', 0.5911761522293091, 'ml-ops', 3), ('getindata/kedro-kubeflow', 0.5848532915115356, 'ml-ops', 1), ('microsoft/nni', 0.5792359113693237, 'ml', 7), ('netflix/metaflow', 0.5758110880851746, 'ml-ops', 5), ('flyteorg/flyte', 0.5718936324119568, 'ml-ops', 5), ('bentoml/bentoml', 0.570486843585968, 'ml-ops', 5), ('apache/airflow', 0.5523534417152405, 'ml-ops', 4), ('zenml-io/mlstacks', 0.5486688613891602, 'ml-ops', 2), ('prefecthq/server', 0.5477404594421387, 'util', 1), ('lastmile-ai/aiconfig', 0.5441376566886902, 'util', 2), ('cheshire-cat-ai/core', 0.542861819267273, 'llm', 2), ('unionai-oss/unionml', 0.5419542193412781, 'ml-ops', 2), ('kestra-io/kestra', 0.5403246879577637, 'ml-ops', 1), ('jina-ai/jina', 0.5385584831237793, 'ml', 4), ('mage-ai/mage-ai', 0.5322885513305664, 'ml-ops', 3), ('tox-dev/tox', 0.5314726233482361, 'testing', 0), ('kubeflow/pipelines', 0.5281010866165161, 'ml-ops', 3), ('wandb/client', 0.5277370810508728, 'ml', 6), ('keras-team/autokeras', 0.5269325375556946, 'ml-dl', 4), ('selfexplainml/piml-toolbox', 0.5244457125663757, 'ml-interpretability', 0), ('mlflow/mlflow', 0.5241439938545227, 'ml-ops', 3), ('chaostoolkit/chaostoolkit', 0.5238291621208191, 'util', 1), ('ashleve/lightning-hydra-template', 0.5233268141746521, 'util', 3), ('meltano/meltano', 0.5218309760093689, 'ml-ops', 1), ('pythagora-io/gpt-pilot', 0.5131421089172363, 'llm', 1), ('titanml/takeoff', 0.5115346908569336, 'llm', 1), ('pathwaycom/llm-app', 0.504523515701294, 'llm', 3), ('whylabs/whylogs', 0.5043908357620239, 'util', 3), ('kedro-org/kedro', 0.5034431219100952, 'ml-ops', 2), ('determined-ai/determined', 0.5032854676246643, 'ml-ops', 6), ('alpha-vllm/llama2-accessory', 0.5014763474464417, 'llm', 0), ('ml-tooling/opyrator', 0.5011926293373108, 'viz', 1)]",84,3.0,,16.25,345,300,38,0,41,39,41,345.0,579.0,90.0,1.7,59
1610,llm,https://github.com/li-plus/chatglm.cpp,[],,[],[],,,,li-plus/chatglm.cpp,chatglm.cpp,2154,330,32,C++,,C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & more LLMs,li-plus,2024-01-14,2023-05-23,36,59.833333333333336,,C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & more LLMs,"['baichuan', 'baichuan2', 'chatglm', 'chatglm2', 'chatglm3', 'codegeex2-6b', 'internlm', 'large-language-models', 'nlp']","['baichuan', 'baichuan2', 'chatglm', 'chatglm2', 'chatglm3', 'codegeex2-6b', 'internlm', 'large-language-models', 'nlp']",2023-11-25,"[('thudm/chatglm2-6b', 0.7250033617019653, 'llm', 2), ('intel/intel-extension-for-transformers', 0.6037748456001282, 'perf', 0), ('microsoft/autogen', 0.5944321751594543, 'llm', 0), ('nomic-ai/gpt4all', 0.5896359086036682, 'llm', 0), ('next-gpt/next-gpt', 0.5851370096206665, 'llm', 1), ('hiyouga/llama-factory', 0.5838776230812073, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5838775634765625, 'llm', 3), ('hwchase17/langchain', 0.5806834697723389, 'llm', 0), ('bobazooba/xllm', 0.5759797692298889, 'llm', 1), ('salesforce/xgen', 0.5746297240257263, 'llm', 2), ('fasteval/fasteval', 0.5682762265205383, 'llm', 0), ('eth-sri/lmql', 0.5538818836212158, 'llm', 0), ('embedchain/embedchain', 0.5471507906913757, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5446141362190247, 'llm', 1), ('mlc-ai/web-llm', 0.542007565498352, 'llm', 0), ('run-llama/rags', 0.5372152328491211, 'llm', 0), ('zilliztech/gptcache', 0.5297065377235413, 'llm', 0), ('dylanhogg/llmgraph', 0.524939239025116, 'ml', 0), ('artidoro/qlora', 0.5086432099342346, 'llm', 0), ('salesforce/codet5', 0.5017570853233337, 'nlp', 1)]",11,6.0,,1.37,122,48,8,2,12,18,12,122.0,223.0,90.0,1.8,59
1423,llm,https://github.com/hegelai/prompttools,"['prompt-engineering', 'testing']",,[],[],,,,hegelai/prompttools,prompttools,2100,158,24,Python,http://prompttools.readthedocs.io,"Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).",hegelai,2024-01-14,2023-06-25,31,67.12328767123287,https://avatars.githubusercontent.com/u/136523567?v=4,"Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).","['deep-learning', 'developer-tools', 'embeddings', 'large-language-models', 'llms', 'machine-learning', 'prompt-engineering', 'vector-search']","['deep-learning', 'developer-tools', 'embeddings', 'large-language-models', 'llms', 'machine-learning', 'prompt-engineering', 'testing', 'vector-search']",2024-01-03,"[('agenta-ai/agenta', 0.6764405965805054, 'llm', 3), ('tigerlab-ai/tiger', 0.6615456342697144, 'llm', 1), ('argilla-io/argilla', 0.644296407699585, 'nlp', 2), ('alpha-vllm/llama2-accessory', 0.6420565247535706, 'llm', 0), ('doccano/doccano', 0.6014738082885742, 'nlp', 1), ('llmware-ai/llmware', 0.6007319092750549, 'llm', 3), ('eugeneyan/open-llms', 0.5875352621078491, 'study', 2), ('nomic-ai/gpt4all', 0.5853099226951599, 'llm', 0), ('lancedb/lancedb', 0.5837177634239197, 'data', 0), ('salesforce/codet5', 0.580586850643158, 'nlp', 1), ('night-chen/toolqa', 0.5785123109817505, 'llm', 1), ('nebuly-ai/nebullvm', 0.5767601132392883, 'perf', 1), ('promptslab/awesome-prompt-engineering', 0.5741844773292542, 'study', 3), ('microsoft/promptflow', 0.5713818669319153, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5667575001716614, 'study', 1), ('bentoml/openllm', 0.565089225769043, 'ml-ops', 0), ('young-geng/easylm', 0.5647485256195068, 'llm', 2), ('cheshire-cat-ai/core', 0.5646258592605591, 'llm', 1), ('confident-ai/deepeval', 0.5625259876251221, 'testing', 0), ('alphasecio/langchain-examples', 0.5608381628990173, 'llm', 0), ('conceptofmind/toolformer', 0.5603080987930298, 'llm', 0), ('bigscience-workshop/petals', 0.5545216202735901, 'data', 3), ('iryna-kondr/scikit-llm', 0.5520104765892029, 'llm', 2), ('deepset-ai/haystack', 0.5512160658836365, 'llm', 2), ('microsoft/lmops', 0.5492219924926758, 'llm', 0), ('salesforce/xgen', 0.5444273352622986, 'llm', 1), ('openbmb/toolbench', 0.5396706461906433, 'llm', 0), ('lm-sys/fastchat', 0.534296989440918, 'llm', 0), ('dylanhogg/llmgraph', 0.5341759324073792, 'ml', 0), ('pathwaycom/llm-app', 0.5340642333030701, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5304824709892273, 'llm', 0), ('neuml/txtai', 0.5288627743721008, 'nlp', 4), ('hiyouga/llama-efficient-tuning', 0.5267438292503357, 'llm', 2), ('hiyouga/llama-factory', 0.5267438292503357, 'llm', 2), ('bigscience-workshop/promptsource', 0.526086688041687, 'nlp', 1), ('microsoft/semantic-kernel', 0.5153470039367676, 'llm', 0), ('paddlepaddle/paddlenlp', 0.514409065246582, 'llm', 0), ('openai/evals', 0.513576090335846, 'llm', 0), ('embedchain/embedchain', 0.5133116245269775, 'llm', 0), ('wandb/client', 0.5127788186073303, 'ml', 2), ('microsoft/generative-ai-for-beginners', 0.5114457607269287, 'study', 2), ('ludwig-ai/ludwig', 0.5103862881660461, 'ml-ops', 2), ('microsoft/nni', 0.5096408128738403, 'ml', 2), ('citadel-ai/langcheck', 0.5087067484855652, 'llm', 0), ('activeloopai/deeplake', 0.5075566172599792, 'ml-ops', 4), ('shishirpatil/gorilla', 0.5024935603141785, 'llm', 0), ('bobazooba/xllm', 0.5022645592689514, 'llm', 2), ('mlflow/mlflow', 0.5002617239952087, 'ml-ops', 1)]",10,3.0,,11.04,28,15,7,0,5,10,5,28.0,41.0,90.0,1.5,59
1741,llm,https://github.com/cheshire-cat-ai/core,[],,[],[],,,,cheshire-cat-ai/core,core,1490,186,19,Python,https://cheshirecat.ai,Production ready AI assistant framework,cheshire-cat-ai,2024-01-13,2023-02-08,50,29.297752808988765,https://avatars.githubusercontent.com/u/135242343?v=4,Production ready AI assistant framework,"['ai', 'assistant', 'chatbot', 'docker', 'llm', 'plugin', 'vector-search']","['ai', 'assistant', 'chatbot', 'docker', 'llm', 'plugin', 'vector-search']",2024-01-04,"[('prefecthq/marvin', 0.7232251167297363, 'nlp', 2), ('embedchain/embedchain', 0.6973840594291687, 'llm', 2), ('pathwaycom/llm-app', 0.6957066059112549, 'llm', 2), ('deepset-ai/haystack', 0.6507551074028015, 'llm', 1), ('rcgai/simplyretrieve', 0.6481109261512756, 'llm', 0), ('lm-sys/fastchat', 0.6361826062202454, 'llm', 1), ('mindsdb/mindsdb', 0.6340122222900391, 'data', 3), ('microsoft/promptflow', 0.6264965534210205, 'llm', 2), ('rasahq/rasa', 0.6253566741943359, 'llm', 1), ('jina-ai/jina', 0.6241676807403564, 'ml', 1), ('microsoft/lmops', 0.6224048137664795, 'llm', 1), ('lastmile-ai/aiconfig', 0.6183017492294312, 'util', 2), ('sweepai/sweep', 0.6141369342803955, 'llm', 2), ('minimaxir/simpleaichat', 0.613688588142395, 'llm', 1), ('mlc-ai/mlc-llm', 0.6107067465782166, 'llm', 1), ('marqo-ai/marqo', 0.6071776151657104, 'ml', 1), ('llmware-ai/llmware', 0.6055431962013245, 'llm', 1), ('larsbaunwall/bricky', 0.598059892654419, 'llm', 1), ('bentoml/bentoml', 0.597477376461029, 'ml-ops', 1), ('nomic-ai/gpt4all', 0.5960567593574524, 'llm', 1), ('avaiga/taipy', 0.5955442786216736, 'data', 0), ('deeppavlov/deeppavlov', 0.5946456789970398, 'nlp', 2), ('microsoft/generative-ai-for-beginners', 0.594606339931488, 'study', 1), ('activeloopai/deeplake', 0.586743950843811, 'ml-ops', 3), ('togethercomputer/openchatkit', 0.5846368074417114, 'nlp', 1), ('chatarena/chatarena', 0.5810584425926208, 'llm', 1), ('antonosika/gpt-engineer', 0.5780220031738281, 'llm', 1), ('operand/agency', 0.5766038298606873, 'llm', 2), ('googlecloudplatform/vertex-ai-samples', 0.5718985199928284, 'ml', 1), ('run-llama/rags', 0.5686349868774414, 'llm', 2), ('nvidia/nemo', 0.5682287216186523, 'nlp', 0), ('hegelai/prompttools', 0.5646258592605591, 'llm', 1), ('krohling/bondai', 0.5621045827865601, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5618926882743835, 'llm', 0), ('microsoft/autogen', 0.5565327405929565, 'llm', 1), ('oegedijk/explainerdashboard', 0.5511989593505859, 'ml-interpretability', 0), ('thilinarajapakse/simpletransformers', 0.5490924715995789, 'nlp', 0), ('pytorchlightning/pytorch-lightning', 0.5489669442176819, 'ml-dl', 1), ('superduperdb/superduperdb', 0.547654926776886, 'data', 3), ('smol-ai/developer', 0.5464283227920532, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5447829365730286, 'llm', 3), ('intel/intel-extension-for-transformers', 0.5444093346595764, 'perf', 1), ('microsoft/semantic-kernel', 0.5438644886016846, 'llm', 2), ('zenml-io/zenml', 0.542861819267273, 'ml-ops', 2), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5411517024040222, 'llm', 0), ('hwchase17/langchain', 0.5408321619033813, 'llm', 1), ('fasteval/fasteval', 0.5404486060142517, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.539746105670929, 'study', 1), ('argilla-io/argilla', 0.5388045907020569, 'nlp', 2), ('bigscience-workshop/petals', 0.5387936234474182, 'data', 1), ('transformeroptimus/superagi', 0.5372999310493469, 'llm', 2), ('lancedb/lancedb', 0.5356892347335815, 'data', 0), ('gunthercox/chatterbot', 0.53544682264328, 'nlp', 1), ('qdrant/qdrant', 0.5352751016616821, 'data', 1), ('nebuly-ai/nebullvm', 0.5341480374336243, 'perf', 2), ('ludwig-ai/ludwig', 0.5316358804702759, 'ml-ops', 1), ('tigerlab-ai/tiger', 0.5307193398475647, 'llm', 1), ('reloadware/reloadium', 0.5294227004051208, 'profiling', 1), ('kalliope-project/kalliope', 0.5292161107063293, 'util', 0), ('paddlepaddle/paddlenlp', 0.5240945219993591, 'llm', 1), ('killianlucas/open-interpreter', 0.5222368240356445, 'llm', 0), ('databrickslabs/dolly', 0.519527792930603, 'llm', 1), ('weaviate/verba', 0.5184066295623779, 'llm', 0), ('young-geng/easylm', 0.5178155899047852, 'llm', 1), ('fmind/mlops-python-package', 0.5158709287643433, 'template', 1), ('prefecthq/server', 0.5153402090072632, 'util', 0), ('polyaxon/datatile', 0.5147360563278198, 'pandas', 0), ('aimhubio/aim', 0.5146742463111877, 'ml-ops', 1), ('microsoft/promptcraft-robotics', 0.513721764087677, 'sim', 1), ('polyaxon/polyaxon', 0.512060284614563, 'ml-ops', 0), ('skypilot-org/skypilot', 0.510378360748291, 'llm', 0), ('microsoft/onnxruntime', 0.5098522901535034, 'ml', 0), ('facebookresearch/parlai', 0.5091173648834229, 'nlp', 0), ('huggingface/datasets', 0.508074939250946, 'nlp', 0), ('arize-ai/phoenix', 0.5057440400123596, 'ml-interpretability', 0), ('ml-tooling/opyrator', 0.5056010484695435, 'viz', 0), ('explosion/thinc', 0.5046352744102478, 'ml-dl', 1), ('mlflow/mlflow', 0.5041938424110413, 'ml-ops', 1), ('alphasecio/langchain-examples', 0.5036561489105225, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5033305287361145, 'llm', 2), ('pythagora-io/gpt-pilot', 0.5015438199043274, 'llm', 1)]",59,2.0,,23.79,206,186,11,0,0,20,20,205.0,370.0,90.0,1.8,59
1851,llm,https://github.com/epfllm/meditron,"['medical', 'language-model']",,[],[],,,,epfllm/meditron,meditron,1291,131,20,Python,https://huggingface.co/epfl-llm,Meditron is a suite of open-source medical Large Language Models (LLMs).,epfllm,2024-01-13,2023-11-23,9,132.89705882352942,https://avatars.githubusercontent.com/u/129088087?v=4,Meditron is a suite of open-source medical Large Language Models (LLMs).,[],"['language-model', 'medical']",2024-01-12,"[('bigscience-workshop/biomedical', 0.5808714032173157, 'data', 0), ('qanastek/drbert', 0.5632989406585693, 'llm', 1), ('salesforce/xgen', 0.5559400320053101, 'llm', 1), ('tsinghuadatabasegroup/db-gpt', 0.5455986261367798, 'llm', 1), ('hannibal046/awesome-llm', 0.5448260307312012, 'study', 1), ('lianjiatech/belle', 0.5387333035469055, 'llm', 0), ('bobazooba/xllm', 0.5308298468589783, 'llm', 0), ('explosion/spacy-llm', 0.5306103825569153, 'llm', 0), ('kbressem/medalpaca', 0.5290087461471558, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5282127261161804, 'study', 0), ('eleutherai/the-pile', 0.5269107818603516, 'data', 0), ('young-geng/easylm', 0.5253199934959412, 'llm', 1), ('lm-sys/fastchat', 0.5228744149208069, 'llm', 1), ('eugeneyan/open-llms', 0.5213027596473694, 'study', 0), ('dylanhogg/llmgraph', 0.5147185921669006, 'ml', 0), ('juncongmoo/pyllama', 0.5138061046600342, 'llm', 0), ('agenta-ai/agenta', 0.5137256979942322, 'llm', 0), ('cg123/mergekit', 0.5135393738746643, 'llm', 0), ('next-gpt/next-gpt', 0.5075371265411377, 'llm', 0), ('ibm/dromedary', 0.5021689534187317, 'llm', 1), ('lucidrains/medical-chatgpt', 0.5020337104797363, 'llm', 0), ('tigerlab-ai/tiger', 0.501189649105072, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5010691285133362, 'llm', 1)]",9,6.0,,1.15,25,17,2,0,0,0,0,25.0,33.0,90.0,1.3,59
164,term,https://github.com/tqdm/tqdm,[],,[],[],1.0,,,tqdm/tqdm,tqdm,26830,1377,207,Python,https://tqdm.github.io,":zap: A Fast, Extensible Progress Bar for Python and CLI",tqdm,2024-01-14,2015-06-03,451,59.37717356939614,https://avatars.githubusercontent.com/u/12731565?v=4,":zap: A Fast, Extensible Progress Bar for Python and CLI","['cli', 'console', 'discord', 'gui', 'jupyter', 'keras', 'meter', 'pandas', 'parallel', 'progress', 'progress-bar', 'progressbar', 'progressmeter', 'rate', 'telegram', 'terminal', 'time', 'utilities']","['cli', 'console', 'discord', 'gui', 'jupyter', 'keras', 'meter', 'pandas', 'parallel', 'progress', 'progress-bar', 'progressbar', 'progressmeter', 'rate', 'telegram', 'terminal', 'time', 'utilities']",2023-08-10,"[('wolph/python-progressbar', 0.7864949107170105, 'util', 9), ('rockhopper-technologies/enlighten', 0.7503482699394226, 'term', 0), ('rsalmei/alive-progress', 0.6888198256492615, 'util', 5), ('erotemic/ubelt', 0.5793629884719849, 'util', 1), ('sumerc/yappi', 0.549595832824707, 'profiling', 0), ('pypy/pypy', 0.5426979064941406, 'util', 0), ('hoffstadt/dearpygui', 0.5399507284164429, 'gui', 1), ('faster-cpython/ideas', 0.5260686874389648, 'perf', 0), ('pyqtgraph/pyqtgraph', 0.5137172341346741, 'viz', 0), ('faster-cpython/tools', 0.5104278922080994, 'perf', 0), ('ipython/ipyparallel', 0.5037369728088379, 'perf', 2), ('wxwidgets/phoenix', 0.5025215744972229, 'gui', 1)]",112,3.0,,0.6,51,6,105,5,5,18,5,51.0,46.0,90.0,0.9,58
31,data,https://github.com/jaidedai/easyocr,[],,[],[],,,,jaidedai/easyocr,EasyOCR,20722,2893,299,Python,https://www.jaided.ai,"Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.",jaidedai,2024-01-14,2020-03-14,202,102.36697247706422,https://avatars.githubusercontent.com/u/61448029?v=4,"Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.","['cnn', 'crnn', 'data-mining', 'deep-learning', 'easyocr', 'image-processing', 'information-retrieval', 'lstm', 'machine-learning', 'ocr', 'optical-character-recognition', 'pytorch', 'scene-text', 'scene-text-recognition']","['cnn', 'crnn', 'data-mining', 'deep-learning', 'easyocr', 'image-processing', 'information-retrieval', 'lstm', 'machine-learning', 'ocr', 'optical-character-recognition', 'pytorch', 'scene-text', 'scene-text-recognition']",2023-09-04,"[('rapidai/rapidocr', 0.6983810663223267, 'data', 3), ('alibaba/easynlp', 0.5166882872581482, 'nlp', 3), ('lucidrains/imagen-pytorch', 0.5161765217781067, 'ml-dl', 1), ('madmaze/pytesseract', 0.5150726437568665, 'data', 1), ('hrnet/hrnet-semantic-segmentation', 0.5142496824264526, 'ml', 0), ('imageio/imageio', 0.5057637095451355, 'util', 0), ('huggingface/datasets', 0.5004381537437439, 'nlp', 3)]",128,1.0,,0.38,73,17,47,4,1,6,1,73.0,79.0,90.0,1.1,58
1777,ml-dl,https://github.com/openai/clip,['clip'],,[],[],,,,openai/clip,CLIP,20129,2785,301,Jupyter Notebook,,"CLIP (Contrastive Language-Image Pretraining),  Predict the most relevant text snippet given an image",openai,2024-01-14,2020-12-16,162,123.59912280701755,https://avatars.githubusercontent.com/u/14957082?v=4,"CLIP (Contrastive Language-Image Pretraining),  Predict the most relevant text snippet given an image","['deep-learning', 'machine-learning']","['clip', 'deep-learning', 'machine-learning']",2023-07-08,"[('jina-ai/clip-as-service', 0.6114118695259094, 'nlp', 1), ('rom1504/clip-retrieval', 0.5981614589691162, 'ml', 2), ('alibaba/easynlp', 0.5683876872062683, 'nlp', 2), ('google-research/electra', 0.5659348964691162, 'ml-dl', 1), ('microsoft/unilm', 0.5614645481109619, 'nlp', 0), ('salesforce/blip', 0.5517219305038452, 'diffusion', 0), ('yueyu1030/attrprompt', 0.5419546961784363, 'llm', 0), ('lucidrains/imagen-pytorch', 0.5373484492301941, 'ml-dl', 1), ('ofa-sys/ofa', 0.5293337106704712, 'llm', 0), ('saharmor/dalle-playground', 0.5254179239273071, 'diffusion', 1), ('compvis/stable-diffusion', 0.5251467227935791, 'diffusion', 0), ('openai/finetune-transformer-lm', 0.5225598812103271, 'llm', 0), ('lucidrains/deep-daze', 0.5211201906204224, 'ml', 1), ('nomic-ai/nomic', 0.5130395889282227, 'nlp', 0), ('nvlabs/prismer', 0.5122661590576172, 'diffusion', 0), ('jina-ai/finetuner', 0.505680501461029, 'ml', 0), ('extreme-bert/extreme-bert', 0.5033907890319824, 'llm', 2)]",20,4.0,,0.04,38,7,37,6,0,0,0,38.0,47.0,90.0,1.2,58
103,ml-ops,https://github.com/spotify/luigi,[],,[],[],,,,spotify/luigi,luigi,17022,2388,477,Python,,"Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. ",spotify,2024-01-13,2012-09-20,592,28.718727404193782,https://avatars.githubusercontent.com/u/251374?v=4,"Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. ","['hadoop', 'luigi', 'orchestration-framework', 'scheduling']","['hadoop', 'luigi', 'orchestration-framework', 'scheduling']",2024-01-08,"[('backtick-se/cowait', 0.5577235221862793, 'util', 0), ('dagster-io/dagster', 0.5423729419708252, 'ml-ops', 0), ('apache/spark', 0.537491500377655, 'data', 0), ('flyteorg/flyte', 0.5278602242469788, 'ml-ops', 0), ('ploomber/ploomber', 0.5267884135246277, 'ml-ops', 0), ('kestra-io/kestra', 0.5256256461143494, 'ml-ops', 0), ('fastai/fastcore', 0.5237264037132263, 'util', 0), ('dagworks-inc/hamilton', 0.5168716907501221, 'ml-ops', 0), ('prefecthq/prefect', 0.5162726640701294, 'ml-ops', 0), ('fugue-project/fugue', 0.5144204497337341, 'pandas', 0), ('malloydata/malloy-py', 0.5109559893608093, 'data', 0), ('orchest/orchest', 0.5029619336128235, 'ml-ops', 0)]",613,4.0,,0.44,22,16,138,0,4,6,4,22.0,19.0,90.0,0.9,58
668,diffusion,https://github.com/borisdayma/dalle-mini,[],,[],[],,,,borisdayma/dalle-mini,dalle-mini,14484,1186,110,Python,https://www.craiyon.com,DALL·E Mini - Generate images from a text prompt,borisdayma,2024-01-13,2021-07-03,134,107.74495217853348,,DALL·E Mini - Generate images from a text prompt,[],[],2023-08-22,"[('saharmor/dalle-playground', 0.7689334750175476, 'diffusion', 0), ('lucidrains/deep-daze', 0.5611160397529602, 'ml', 0), ('laion-ai/dalle2-laion', 0.5235878229141235, 'diffusion', 0), ('1j01/textual-paint', 0.510983943939209, 'term', 0), ('thudm/cogvideo', 0.5106388926506042, 'ml', 0), ('openai/image-gpt', 0.5032017827033997, 'llm', 0)]",28,7.0,,0.27,3,0,31,5,0,2,2,3.0,3.0,90.0,1.0,58
1473,web,https://github.com/getpelican/pelican,[],,[],[],,,,getpelican/pelican,pelican,11956,1832,339,Python,https://getpelican.com,Static site generator that supports Markdown and reST syntax. Powered by Python.,getpelican,2024-01-13,2010-10-16,693,17.241862381540997,https://avatars.githubusercontent.com/u/2043492?v=4,Static site generator that supports Markdown and reST syntax. Powered by Python.,"['pelican', 'static-site-generator']","['pelican', 'static-site-generator']",2024-01-12,"[('python-markdown/markdown', 0.6716626882553101, 'util', 0), ('mkdocs/mkdocs', 0.5778642296791077, 'util', 1), ('sphinx-doc/sphinx', 0.5320377349853516, 'util', 0), ('google/latexify_py', 0.5140236020088196, 'util', 0), ('instagram/monkeytype', 0.5085812211036682, 'typing', 0)]",454,6.0,,2.4,141,110,161,0,2,5,2,141.0,258.0,90.0,1.8,58
1176,llm,https://github.com/databrickslabs/dolly,[],,[],[],,,,databrickslabs/dolly,dolly,10687,1161,132,Python,https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html,"Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform",databrickslabs,2024-01-13,2023-03-24,44,239.77243589743588,https://avatars.githubusercontent.com/u/49501376?v=4,"Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform","['chatbot', 'databricks', 'dolly', 'gpt']","['chatbot', 'databricks', 'dolly', 'gpt']",2023-06-30,"[('lm-sys/fastchat', 0.6816812753677368, 'llm', 1), ('rasahq/rasa', 0.6492189168930054, 'llm', 1), ('microsoft/autogen', 0.6176787614822388, 'llm', 2), ('deeppavlov/deeppavlov', 0.5981650352478027, 'nlp', 1), ('huggingface/text-generation-inference', 0.5939000248908997, 'llm', 1), ('infinitylogesh/mutate', 0.5938010811805725, 'nlp', 0), ('eleutherai/the-pile', 0.5931678414344788, 'data', 0), ('blinkdl/chatrwkv', 0.592911422252655, 'llm', 1), ('hannibal046/awesome-llm', 0.5911993384361267, 'study', 1), ('togethercomputer/redpajama-data', 0.5899375677108765, 'llm', 0), ('run-llama/rags', 0.5840879678726196, 'llm', 1), ('huggingface/transformers', 0.5820251703262329, 'nlp', 0), ('nvidia/nemo', 0.5758463144302368, 'nlp', 0), ('ravenscroftj/turbopilot', 0.5751809477806091, 'llm', 0), ('jonasgeiping/cramming', 0.5738639831542969, 'nlp', 0), ('embedchain/embedchain', 0.5724859237670898, 'llm', 0), ('fasteval/fasteval', 0.5695880055427551, 'llm', 0), ('nomic-ai/gpt4all', 0.568418562412262, 'llm', 1), ('freedomintelligence/llmzoo', 0.5654436945915222, 'llm', 0), ('lianjiatech/belle', 0.5646651387214661, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5645289421081543, 'llm', 0), ('huggingface/datasets', 0.5632453560829163, 'nlp', 0), ('facebookresearch/parlai', 0.5629587769508362, 'nlp', 0), ('yueyu1030/attrprompt', 0.5601263642311096, 'llm', 0), ('killianlucas/open-interpreter', 0.559283971786499, 'llm', 0), ('argilla-io/argilla', 0.5544888973236084, 'nlp', 0), ('openlmlab/moss', 0.5508648157119751, 'llm', 0), ('explosion/spacy-llm', 0.5508483648300171, 'llm', 1), ('nvidia/deeplearningexamples', 0.548334538936615, 'ml-dl', 0), ('minimaxir/aitextgen', 0.5475108623504639, 'llm', 0), ('allenai/allennlp', 0.5456701517105103, 'nlp', 0), ('xtekky/gpt4free', 0.5452688932418823, 'llm', 2), ('bigscience-workshop/petals', 0.5440896153450012, 'data', 2), ('deepset-ai/haystack', 0.5437489151954651, 'llm', 0), ('bytedance/lightseq', 0.5437467098236084, 'nlp', 1), ('extreme-bert/extreme-bert', 0.5428717136383057, 'llm', 0), ('llmware-ai/llmware', 0.5424628257751465, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5416833758354187, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.541381299495697, 'nlp', 0), ('gunthercox/chatterbot', 0.5377427339553833, 'nlp', 1), ('deepset-ai/farm', 0.5375163555145264, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5367728471755981, 'llm', 0), ('young-geng/easylm', 0.5357629656791687, 'llm', 1), ('keras-team/keras-nlp', 0.5354406237602234, 'nlp', 0), ('juncongmoo/pyllama', 0.5352316498756409, 'llm', 0), ('lupantech/chameleon-llm', 0.5342248678207397, 'llm', 0), ('optimalscale/lmflow', 0.5335168242454529, 'llm', 0), ('openbmb/toolbench', 0.5324987173080444, 'llm', 0), ('nltk/nltk', 0.5305935144424438, 'nlp', 0), ('reasoning-machines/pal', 0.5263434052467346, 'llm', 0), ('next-gpt/next-gpt', 0.5201497077941895, 'llm', 0), ('nebuly-ai/nebullvm', 0.5198688507080078, 'perf', 0), ('rcgai/simplyretrieve', 0.5198461413383484, 'llm', 0), ('cheshire-cat-ai/core', 0.519527792930603, 'llm', 1), ('microsoft/lora', 0.5188591480255127, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5184698104858398, 'llm', 0), ('guidance-ai/guidance', 0.5170300602912903, 'llm', 0), ('salesforce/blip', 0.516700029373169, 'diffusion', 0), ('microsoft/unilm', 0.5165247917175293, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5159105062484741, 'llm', 0), ('mindsdb/mindsdb', 0.5149961113929749, 'data', 2), ('stanfordnlp/dspy', 0.5139095783233643, 'llm', 0), ('srush/minichain', 0.5138243436813354, 'llm', 0), ('aiwaves-cn/agents', 0.5108225345611572, 'nlp', 0), ('cg123/mergekit', 0.5106679797172546, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5099220275878906, 'llm', 0), ('explosion/spacy', 0.5090692043304443, 'nlp', 0), ('microsoft/generative-ai-for-beginners', 0.5089173316955566, 'study', 1), ('thilinarajapakse/simpletransformers', 0.507858157157898, 'nlp', 0), ('norskregnesentral/skweak', 0.5073420405387878, 'nlp', 0), ('merantix-momentum/squirrel-core', 0.5068414807319641, 'ml', 0), ('thudm/chatglm2-6b', 0.506598949432373, 'llm', 0), ('bobazooba/xllm', 0.5060972571372986, 'llm', 1), ('google-research/language', 0.5051336288452148, 'nlp', 0), ('bigscience-workshop/biomedical', 0.5042515397071838, 'data', 0), ('titanml/takeoff', 0.5038862228393555, 'llm', 0), ('mlc-ai/web-llm', 0.5030844807624817, 'llm', 0), ('prefecthq/marvin', 0.5005607008934021, 'nlp', 1), ('iryna-kondr/scikit-llm', 0.5001913905143738, 'llm', 0), ('night-chen/toolqa', 0.5000602006912231, 'llm', 0)]",14,3.0,,1.31,3,1,10,7,0,0,0,3.0,2.0,90.0,0.7,58
1798,web,https://github.com/aws/chalice,[],,[],[],1.0,,,aws/chalice,chalice,10151,1087,239,Python,,Python Serverless Microframework for AWS,aws,2024-01-13,2016-05-27,400,25.341298145506418,https://avatars.githubusercontent.com/u/2232217?v=4,Python Serverless Microframework for AWS,"['aws', 'aws-apigateway', 'aws-lambda', 'cloud', 'lambda', 'python27', 'serverless', 'serverless-framework']","['aws', 'aws-apigateway', 'aws-lambda', 'cloud', 'lambda', 'python27', 'serverless', 'serverless-framework']",2023-12-14,"[('nficano/python-lambda', 0.9045315980911255, 'util', 3), ('jordaneremieff/mangum', 0.677313506603241, 'web', 4), ('aws/aws-lambda-python-runtime-interface-client', 0.672527015209198, 'util', 0), ('boto/boto3', 0.66681307554245, 'util', 2), ('geeogi/async-python-lambda-template', 0.6311405301094055, 'template', 0), ('rpgreen/apilogs', 0.6253986954689026, 'util', 4), ('localstack/localstack', 0.5926650762557983, 'util', 2), ('pallets/quart', 0.5842033624649048, 'web', 0), ('falconry/falcon', 0.5833958387374878, 'web', 0), ('samuelcolvin/aioaws', 0.5785471200942993, 'data', 1), ('backtick-se/cowait', 0.5667223334312439, 'util', 0), ('pynamodb/pynamodb', 0.5429194569587708, 'data', 1), ('eventual-inc/daft', 0.5330685973167419, 'pandas', 0), ('awslabs/python-deequ', 0.5238412618637085, 'ml', 1), ('aws/serverless-application-model', 0.5237606167793274, 'util', 3), ('lithops-cloud/lithops', 0.5222293138504028, 'ml-ops', 1), ('aws/aws-sdk-pandas', 0.5215028524398804, 'pandas', 3), ('micropython/micropython', 0.5198830366134644, 'util', 0), ('pallets/flask', 0.5189753770828247, 'web', 0), ('neoteroi/blacksheep', 0.5133174657821655, 'web', 0), ('pyinfra-dev/pyinfra', 0.5128588080406189, 'util', 0), ('pylons/waitress', 0.5070593953132629, 'web', 0), ('developmentseed/geolambda', 0.5061563849449158, 'gis', 0), ('amzn/ion-python', 0.5045038461685181, 'data', 0), ('aws-samples/serverless-pdf-chat', 0.5006961226463318, 'llm', 1)]",200,5.0,,0.88,51,17,93,1,0,12,12,51.0,64.0,90.0,1.3,58
1125,util,https://github.com/secdev/scapy,[],,[],[],,,,secdev/scapy,scapy,9682,2001,228,Python,https://scapy.net,Scapy: the Python-based interactive packet manipulation program & library. Supports Python 2 & Python 3.,secdev,2024-01-13,2015-10-01,434,22.27209990141308,https://avatars.githubusercontent.com/u/14927208?v=4,Scapy: the Python-based interactive packet manipulation program & library. Supports Python 2 & Python 3.,"['network', 'network-analysis', 'network-discovery', 'network-security', 'network-visualization', 'packet-analyser', 'packet-capture', 'packet-crafting', 'packet-sniffer', 'pcap', 'scapy', 'security', 'security-tools']","['network', 'network-analysis', 'network-discovery', 'network-security', 'network-visualization', 'packet-analyser', 'packet-capture', 'packet-crafting', 'packet-sniffer', 'pcap', 'scapy', 'security', 'security-tools']",2024-01-01,"[('hoffstadt/dearpygui', 0.5505430698394775, 'gui', 0), ('paramiko/paramiko', 0.5442431569099426, 'util', 0), ('pyca/pynacl', 0.5417189002037048, 'util', 0), ('alexmojaki/snoop', 0.5415452122688293, 'debug', 0), ('requests/toolbelt', 0.5415241122245789, 'util', 0), ('pyston/pyston', 0.5411231517791748, 'util', 0), ('pypy/pypy', 0.5363399982452393, 'util', 0), ('westhealth/pyvis', 0.5347074866294861, 'graph', 1), ('ethereum/web3.py', 0.5224668383598328, 'crypto', 0), ('py4j/py4j', 0.5051878690719604, 'util', 0), ('networkx/networkx', 0.5021397471427917, 'graph', 0)]",446,4.0,,3.81,267,229,101,1,0,64,64,267.0,140.0,90.0,0.5,58
285,data,https://github.com/simonw/datasette,[],,[],[],1.0,,,simonw/datasette,datasette,8614,623,102,Python,https://datasette.io,An open source multi-tool for exploring and publishing data,simonw,2024-01-13,2017-10-23,327,26.331004366812227,,An open source multi-tool for exploring and publishing data,"['asgi', 'automatic-api', 'csv', 'datasets', 'datasette', 'datasette-io', 'docker', 'json', 'sql', 'sqlite']","['asgi', 'automatic-api', 'csv', 'datasets', 'datasette', 'datasette-io', 'docker', 'json', 'sql', 'sqlite']",2024-01-12,"[('airbytehq/airbyte', 0.663290798664093, 'data', 0), ('meltano/meltano', 0.6087267398834229, 'ml-ops', 0), ('saulpw/visidata', 0.6051476001739502, 'term', 3), ('airbnb/omniduct', 0.6034160256385803, 'data', 0), ('zenodo/zenodo', 0.6021542549133301, 'util', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5789318084716797, 'template', 2), ('orchest/orchest', 0.5611031651496887, 'ml-ops', 1), ('unstructured-io/unstructured-api', 0.5543832778930664, 'data', 0), ('dagster-io/dagster', 0.5505277514457703, 'ml-ops', 0), ('tiangolo/sqlmodel', 0.5402882695198059, 'data', 2), ('ploomber/ploomber', 0.5322346091270447, 'ml-ops', 0), ('starlite-api/starlite', 0.526665985584259, 'web', 1), ('polyaxon/datatile', 0.525797963142395, 'pandas', 0), ('aws/aws-sdk-pandas', 0.525142252445221, 'pandas', 0), ('intake/intake', 0.5237489938735962, 'data', 0), ('falconry/falcon', 0.5216467380523682, 'web', 1), ('piccolo-orm/piccolo_admin', 0.5212988257408142, 'data', 2), ('streamlit/streamlit', 0.5206021666526794, 'viz', 0), ('plotly/dash', 0.5204837918281555, 'viz', 0), ('coleifer/peewee', 0.5180539488792419, 'data', 1), ('mattbierbaum/arxiv-public-datasets', 0.5164351463317871, 'data', 0), ('google/ml-metadata', 0.5161576271057129, 'ml-ops', 0), ('holoviz/panel', 0.5158277153968811, 'viz', 0), ('mage-ai/mage-ai', 0.5153981447219849, 'ml-ops', 1), ('airbnb/knowledge-repo', 0.5094085335731506, 'data', 0), ('darribas/gds_env', 0.5070315599441528, 'gis', 1), ('hyperqueryhq/whale', 0.5066918134689331, 'data', 0), ('mito-ds/monorepo', 0.5057903528213501, 'jupyter', 0), ('whylabs/whylogs', 0.5017004609107971, 'util', 0), ('python-odin/odin', 0.5009411573410034, 'util', 2)]",75,4.0,,2.62,67,20,76,0,10,23,10,67.0,95.0,90.0,1.4,58
1308,study,https://github.com/mooler0410/llmspracticalguide,['awesome'],,[],[],,,,mooler0410/llmspracticalguide,LLMsPracticalGuide,7737,583,168,,https://arxiv.org/abs/2304.13712v2,"A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)",mooler0410,2024-01-14,2023-04-23,40,192.0531914893617,,"A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)","['large-language-models', 'natural-language-processing', 'nlp', 'survey']","['awesome', 'large-language-models', 'natural-language-processing', 'nlp', 'survey']",2023-08-06,"[('salesforce/xgen', 0.6607375741004944, 'llm', 2), ('eugeneyan/open-llms', 0.6558331847190857, 'study', 2), ('explosion/spacy-llm', 0.6550344228744507, 'llm', 3), ('dylanhogg/llmgraph', 0.6462394595146179, 'ml', 0), ('young-geng/easylm', 0.6447182893753052, 'llm', 2), ('argilla-io/argilla', 0.6409274935722351, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.632652759552002, 'llm', 1), ('confident-ai/deepeval', 0.6305698156356812, 'testing', 0), ('hannibal046/awesome-llm', 0.6038196682929993, 'study', 1), ('llmware-ai/llmware', 0.6028851270675659, 'llm', 2), ('night-chen/toolqa', 0.6017274260520935, 'llm', 1), ('nomic-ai/gpt4all', 0.6006626486778259, 'llm', 0), ('ibm/dromedary', 0.5917999148368835, 'llm', 0), ('eleutherai/the-pile', 0.5788910984992981, 'data', 0), ('salesforce/codet5', 0.5782813429832458, 'nlp', 1), ('alpha-vllm/llama2-accessory', 0.5765708088874817, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5758503079414368, 'llm', 1), ('hiyouga/llama-factory', 0.575850248336792, 'llm', 1), ('microsoft/torchscale', 0.5727461576461792, 'llm', 1), ('lianjiatech/belle', 0.5725797414779663, 'llm', 0), ('deepset-ai/haystack', 0.5716323256492615, 'llm', 2), ('hegelai/prompttools', 0.5667575001716614, 'llm', 1), ('tigerlab-ai/tiger', 0.5661339163780212, 'llm', 1), ('juncongmoo/pyllama', 0.5613843202590942, 'llm', 0), ('ray-project/ray-llm', 0.5559900999069214, 'llm', 1), ('agenta-ai/agenta', 0.5545148849487305, 'llm', 1), ('bobazooba/xllm', 0.5522361397743225, 'llm', 1), ('infinitylogesh/mutate', 0.5493302345275879, 'nlp', 0), ('nebuly-ai/nebullvm', 0.5461376905441284, 'perf', 1), ('microsoft/generative-ai-for-beginners', 0.5448139309883118, 'study', 0), ('tatsu-lab/stanford_alpaca', 0.5418017506599426, 'llm', 0), ('explosion/spacy-models', 0.5413009524345398, 'nlp', 2), ('cg123/mergekit', 0.5390522480010986, 'llm', 0), ('microsoft/jarvis', 0.5358785390853882, 'llm', 0), ('vllm-project/vllm', 0.5348982810974121, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5321641564369202, 'llm', 0), ('jina-ai/thinkgpt', 0.5296755433082581, 'llm', 0), ('aiwaves-cn/agents', 0.5291845202445984, 'nlp', 0), ('epfllm/meditron', 0.5282127261161804, 'llm', 0), ('whitead/paper-qa', 0.5275523066520691, 'llm', 1), ('lexpredict/lexpredict-lexnlp', 0.525945782661438, 'nlp', 1), ('citadel-ai/langcheck', 0.5259177088737488, 'llm', 0), ('spcl/graph-of-thoughts', 0.5230525732040405, 'llm', 1), ('predibase/llm_distillation_playbook', 0.5229454040527344, 'llm', 0), ('nltk/nltk', 0.5214924812316895, 'nlp', 2), ('shishirpatil/gorilla', 0.5199465751647949, 'llm', 0), ('nat/openplayground', 0.5199169516563416, 'llm', 0), ('next-gpt/next-gpt', 0.5197857022285461, 'llm', 1), ('bentoml/openllm', 0.516433596611023, 'ml-ops', 0), ('bigscience-workshop/petals', 0.511151909828186, 'data', 2), ('microsoft/autogen', 0.5106921792030334, 'llm', 0), ('bigscience-workshop/biomedical', 0.5104791522026062, 'data', 0), ('graykode/nlp-tutorial', 0.510050356388092, 'study', 2), ('deep-diver/pingpong', 0.5093730688095093, 'llm', 0), ('artidoro/qlora', 0.5064951181411743, 'llm', 0), ('lm-sys/fastchat', 0.5060816407203674, 'llm', 0), ('neuml/txtai', 0.5057186484336853, 'nlp', 2), ('intel/intel-extension-for-transformers', 0.5048488974571228, 'perf', 0), ('allenai/allennlp', 0.5047245025634766, 'nlp', 2), ('zilliztech/gptcache', 0.5039639472961426, 'llm', 0), ('guidance-ai/guidance', 0.5038110613822937, 'llm', 0), ('hwchase17/langchain', 0.5019939541816711, 'llm', 0), ('freedomintelligence/llmzoo', 0.5019903182983398, 'llm', 0), ('microsoft/unilm', 0.5018056631088257, 'nlp', 1)]",13,6.0,,1.67,7,1,9,5,0,0,0,7.0,2.0,90.0,0.3,58
710,util,https://github.com/jazzband/pip-tools,[],,[],[],,,,jazzband/pip-tools,pip-tools,7269,607,104,Python,https://pip-tools.rtfd.io,A set of tools to keep your pinned Python dependencies fresh.,jazzband,2024-01-13,2012-09-10,594,12.234431353690791,https://avatars.githubusercontent.com/u/15129049?v=4,A set of tools to keep your pinned Python dependencies fresh.,"['hashes', 'lockfile', 'packaging', 'pip', 'pip-compile', 'pip-tools', 'requirements', 'setuptools']","['hashes', 'lockfile', 'packaging', 'pip', 'pip-compile', 'pip-tools', 'requirements', 'setuptools']",2024-01-05,"[('pdm-project/pdm', 0.6316167116165161, 'util', 1), ('thoth-station/micropipenv', 0.6279821991920471, 'util', 2), ('python-poetry/poetry', 0.5958384871482849, 'util', 1), ('pypa/hatch', 0.5882735848426819, 'util', 1), ('indygreg/pyoxidizer', 0.5879077315330505, 'util', 1), ('pypi/warehouse', 0.5816731452941895, 'util', 0), ('pyupio/safety', 0.5792787671089172, 'security', 0), ('mitsuhiko/rye', 0.5770611763000488, 'util', 1), ('tezromach/python-package-template', 0.5716038346290588, 'template', 0), ('pypa/pipenv', 0.5539312958717346, 'util', 2), ('pomponchik/instld', 0.5494071245193481, 'util', 1), ('pypa/flit', 0.5470367670059204, 'util', 1), ('omry/omegaconf', 0.527951180934906, 'util', 0), ('mkdocstrings/griffe', 0.5269049406051636, 'util', 0), ('dosisod/refurb', 0.5137325525283813, 'util', 0), ('trailofbits/pip-audit', 0.5135653614997864, 'security', 1), ('ofek/pyapp', 0.5037299394607544, 'util', 1)]",203,6.0,,3.46,73,41,138,0,8,11,8,73.0,204.0,90.0,2.8,58
1216,util,https://github.com/googlecloudplatform/python-docs-samples,[],,[],[],,,,googlecloudplatform/python-docs-samples,python-docs-samples,6777,6352,388,Jupyter Notebook,,Code samples used on cloud.google.com,googlecloudplatform,2024-01-13,2015-05-04,456,14.857187597870341,https://avatars.githubusercontent.com/u/2810941?v=4,Code samples used on cloud.google.com,['samples'],['samples'],2024-01-12,"[('googlecloudplatform/vertex-ai-samples', 0.5574522018432617, 'ml', 1)]",591,4.0,,32.98,467,365,106,0,0,0,0,466.0,602.0,90.0,1.3,58
1345,llm,https://github.com/bhaskatripathi/pdfgpt,[],,[],[],,,,bhaskatripathi/pdfgpt,pdfGPT,6422,805,48,Python,https://bhaskartripathi-pdfgpt-turbo.hf.space,PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!,bhaskatripathi,2024-01-13,2023-03-07,47,136.63829787234042,,PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!,"['chatpdf', 'chatwithpdf', 'pdfgpt']","['chatpdf', 'chatwithpdf', 'pdfgpt']",2023-09-04,"[('mayooear/gpt4-pdf-chatbot-langchain', 0.6926714777946472, 'llm', 0), ('h2oai/h2ogpt', 0.5438480377197266, 'llm', 0), ('minimaxir/simpleaichat', 0.5324260592460632, 'llm', 0), ('run-llama/rags', 0.5228466987609863, 'llm', 0), ('jorisschellekens/borb', 0.5003618597984314, 'util', 0)]",11,4.0,,2.06,11,6,10,4,0,0,0,11.0,14.0,90.0,1.3,58
545,ml,https://github.com/open-mmlab/mmediting,[],,[],[],,,,open-mmlab/mmediting,mmagic,6233,1032,98,Jupyter Notebook,https://mmagic.readthedocs.io/en/latest/,"OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.",open-mmlab,2024-01-14,2019-08-23,231,26.91610117211598,https://avatars.githubusercontent.com/u/10245193?v=4,"OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.","['aigc', 'computer-vision', 'deep-learning', 'diffusion', 'diffusion-models', 'generative-adversarial-network', 'generative-ai', 'image-editing', 'image-generation', 'image-processing', 'image-synthesis', 'inpainting', 'matting', 'pytorch', 'super-resolution', 'text2image', 'video-frame-interpolation', 'video-interpolation', 'video-super-resolution']","['aigc', 'computer-vision', 'deep-learning', 'diffusion', 'diffusion-models', 'generative-adversarial-network', 'generative-ai', 'image-editing', 'image-generation', 'image-processing', 'image-synthesis', 'inpainting', 'matting', 'pytorch', 'super-resolution', 'text2image', 'video-frame-interpolation', 'video-interpolation', 'video-super-resolution']",2024-01-10,"[('roboflow/supervision', 0.6228029727935791, 'ml', 4), ('albumentations-team/albumentations', 0.5985205769538879, 'ml-dl', 2), ('invoke-ai/invokeai', 0.5879208445549011, 'diffusion', 2), ('lucidrains/imagen-pytorch', 0.5784053206443787, 'ml-dl', 1), ('automatic1111/stable-diffusion-webui', 0.5722965002059937, 'diffusion', 5), ('sanster/lama-cleaner', 0.5644564032554626, 'ml-dl', 2), ('bentoml/bentoml', 0.5629596710205078, 'ml-ops', 2), ('huggingface/datasets', 0.5629530549049377, 'nlp', 3), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5617620944976807, 'web', 0), ('open-mmlab/mmsegmentation', 0.5589426755905151, 'ml', 1), ('rwightman/pytorch-image-models', 0.5570010542869568, 'ml-dl', 1), ('xpixelgroup/basicsr', 0.5552972555160522, 'ml-dl', 2), ('lucidrains/deep-daze', 0.5528916716575623, 'ml', 1), ('nvlabs/gcvit', 0.5518020987510681, 'diffusion', 1), ('saharmor/dalle-playground', 0.5508294105529785, 'diffusion', 0), ('lutzroeder/netron', 0.5503832101821899, 'ml', 2), ('aleju/imgaug', 0.5496745109558105, 'ml', 1), ('nateraw/stable-diffusion-videos', 0.5489683747291565, 'diffusion', 0), ('awslabs/autogluon', 0.5436397194862366, 'ml', 3), ('activeloopai/deeplake', 0.5429714918136597, 'ml-ops', 4), ('openai/image-gpt', 0.5370244383811951, 'llm', 0), ('luodian/otter', 0.53682941198349, 'llm', 1), ('davidadsp/generative_deep_learning_2nd_edition', 0.5358924269676208, 'study', 3), ('idea-research/grounded-segment-anything', 0.5329544544219971, 'llm', 1), ('microsoft/generative-ai-for-beginners', 0.532807469367981, 'study', 1), ('iperov/deepfacelab', 0.5308938026428223, 'ml-dl', 1), ('mosaicml/composer', 0.5261213183403015, 'ml-dl', 2), ('ddbourgin/numpy-ml', 0.5251216292381287, 'ml', 0), ('deci-ai/super-gradients', 0.5221906900405884, 'ml-dl', 3), ('fepegar/torchio', 0.5203762650489807, 'ml-dl', 2), ('carson-katri/dream-textures', 0.5198704600334167, 'diffusion', 1), ('thudm/cogvideo', 0.5184540748596191, 'ml', 0), ('roboflow/notebooks', 0.5162927508354187, 'study', 3), ('chenyangqiqi/fatezero', 0.5141303539276123, 'diffusion', 1), ('alibaba/easynlp', 0.5134133696556091, 'nlp', 2), ('tensorflow/tensorflow', 0.5129084587097168, 'ml-dl', 1), ('lucidrains/dalle2-pytorch', 0.5127325654029846, 'diffusion', 1), ('espnet/espnet', 0.5127143859863281, 'nlp', 2), ('blakeblackshear/frigate', 0.5040640234947205, 'util', 0), ('promptslab/awesome-prompt-engineering', 0.5029655694961548, 'study', 1), ('zulko/moviepy', 0.5014966726303101, 'util', 0), ('huggingface/autotrain-advanced', 0.5010733008384705, 'ml', 1)]",126,4.0,,5.38,71,44,54,0,8,6,8,71.0,57.0,90.0,0.8,58
1295,ml-ops,https://github.com/bentoml/bentoml,[],,[],[],,,,bentoml/bentoml,BentoML,6093,692,72,Python,https://bentoml.com,Build Production-Grade AI Applications,bentoml,2024-01-13,2019-04-02,252,24.178571428571427,https://avatars.githubusercontent.com/u/49176046?v=4,Build Production-Grade AI Applications,"['ai', 'ai-infra', 'bentoml', 'deep-learning', 'generative-ai', 'inference-api', 'kubernetes', 'llmops', 'lmops', 'machine-learning', 'microservices', 'ml-platform', 'mlops', 'model-deployment', 'model-inference', 'model-management', 'model-serving', 'multimodal-deep-learning']","['ai', 'ai-infra', 'bentoml', 'deep-learning', 'generative-ai', 'inference-api', 'kubernetes', 'llmops', 'lmops', 'machine-learning', 'microservices', 'ml-platform', 'mlops', 'model-deployment', 'model-inference', 'model-management', 'model-serving', 'multimodal-deep-learning']",2024-01-11,"[('hpcaitech/colossalai', 0.719754159450531, 'llm', 2), ('polyaxon/polyaxon', 0.70576012134552, 'ml-ops', 4), ('jina-ai/jina', 0.692993700504303, 'ml', 6), ('netflix/metaflow', 0.6758896708488464, 'ml-ops', 6), ('lastmile-ai/aiconfig', 0.6600256562232971, 'util', 2), ('alirezadir/machine-learning-interview-enlightener', 0.657772958278656, 'study', 3), ('avaiga/taipy', 0.6463702321052551, 'data', 1), ('mlc-ai/mlc-llm', 0.6454080939292908, 'llm', 0), ('microsoft/promptflow', 0.6441292762756348, 'llm', 1), ('googlecloudplatform/vertex-ai-samples', 0.6409500241279602, 'ml', 2), ('mlflow/mlflow', 0.6369062662124634, 'ml-ops', 3), ('microsoft/lmops', 0.6321725249290466, 'llm', 1), ('onnx/onnx', 0.6247395277023315, 'ml', 2), ('kubeflow/pipelines', 0.6110785603523254, 'ml-ops', 3), ('pytorchlightning/pytorch-lightning', 0.6110719442367554, 'ml-dl', 3), ('ludwig-ai/ludwig', 0.6104065775871277, 'ml-ops', 2), ('antonosika/gpt-engineer', 0.6053545475006104, 'llm', 1), ('nvidia/deeplearningexamples', 0.6029508113861084, 'ml-dl', 1), ('feast-dev/feast', 0.6025741696357727, 'ml-ops', 2), ('cheshire-cat-ai/core', 0.597477376461029, 'llm', 1), ('huggingface/datasets', 0.596264123916626, 'nlp', 2), ('bodywork-ml/bodywork-core', 0.5959450602531433, 'ml-ops', 3), ('pathwaycom/llm-app', 0.5931383371353149, 'llm', 2), ('activeloopai/deeplake', 0.5921602249145508, 'ml-ops', 4), ('prefecthq/marvin', 0.591640830039978, 'nlp', 1), ('explosion/thinc', 0.5913470387458801, 'ml-dl', 3), ('ml-tooling/opyrator', 0.5898632407188416, 'viz', 2), ('mindsdb/mindsdb', 0.5877701044082642, 'data', 2), ('xplainable/xplainable', 0.583275318145752, 'ml-interpretability', 1), ('microsoft/nni', 0.5809151530265808, 'ml', 3), ('microsoft/onnxruntime', 0.5801241993904114, 'ml', 2), ('alpa-projects/alpa', 0.5799961686134338, 'ml-dl', 2), ('thilinarajapakse/simpletransformers', 0.5780894160270691, 'nlp', 0), ('mosaicml/composer', 0.574347198009491, 'ml-dl', 2), ('operand/agency', 0.5735385417938232, 'llm', 3), ('unity-technologies/ml-agents', 0.5712394714355469, 'ml-rl', 2), ('zenml-io/zenml', 0.570486843585968, 'ml-ops', 5), ('sweepai/sweep', 0.5681687593460083, 'llm', 1), ('nccr-itmo/fedot', 0.5645349621772766, 'ml-ops', 1), ('microsoft/semantic-kernel', 0.5644956827163696, 'llm', 1), ('tensorflow/tensorflow', 0.5644592642784119, 'ml-dl', 2), ('open-mmlab/mmediting', 0.5629596710205078, 'ml', 2), ('allegroai/clearml', 0.5609636902809143, 'ml-ops', 4), ('lutzroeder/netron', 0.5586127042770386, 'ml', 3), ('ddbourgin/numpy-ml', 0.5563209652900696, 'ml', 1), ('oegedijk/explainerdashboard', 0.553854763507843, 'ml-interpretability', 0), ('fmind/mlops-python-package', 0.5531289577484131, 'template', 2), ('bentoml/openllm', 0.5529214143753052, 'ml-ops', 5), ('google-research/google-research', 0.5528449416160583, 'ml', 2), ('pythagora-io/gpt-pilot', 0.5526854395866394, 'llm', 1), ('winedarksea/autots', 0.5495694875717163, 'time-series', 2), ('tensorflow/tensor2tensor', 0.5486508011817932, 'ml', 2), ('superduperdb/superduperdb', 0.5483355522155762, 'data', 3), ('uber/fiber', 0.5481660962104797, 'data', 1), ('arize-ai/phoenix', 0.547683835029602, 'ml-interpretability', 2), ('merantix-momentum/squirrel-core', 0.5453324913978577, 'ml', 3), ('llmware-ai/llmware', 0.5451672673225403, 'llm', 3), ('aimhubio/aim', 0.5450233817100525, 'ml-ops', 3), ('microsoft/generative-ai-for-beginners', 0.5438612699508667, 'study', 2), ('deepmind/dm_control', 0.5436348915100098, 'ml-rl', 2), ('iterative/dvc', 0.5435565710067749, 'ml-ops', 2), ('skypilot-org/skypilot', 0.5428783297538757, 'llm', 3), ('ray-project/ray', 0.5420705080032349, 'ml-ops', 2), ('amanchadha/coursera-deep-learning-specialization', 0.540047824382782, 'study', 1), ('determined-ai/determined', 0.5386102795600891, 'ml-ops', 5), ('keras-team/keras', 0.5380653738975525, 'ml-dl', 2), ('polyaxon/datatile', 0.5369633436203003, 'pandas', 1), ('tensorlayer/tensorlayer', 0.5359321236610413, 'ml-rl', 1), ('kubeflow/fairing', 0.5353989005088806, 'ml-ops', 0), ('lucidrains/toolformer-pytorch', 0.5346093773841858, 'llm', 1), ('qdrant/qdrant', 0.5326546430587769, 'data', 2), ('bigscience-workshop/petals', 0.5308198928833008, 'data', 2), ('orchest/orchest', 0.52862948179245, 'ml-ops', 2), ('huggingface/transformers', 0.5276902914047241, 'nlp', 2), ('stability-ai/stability-sdk', 0.5263645052909851, 'diffusion', 0), ('google-research/language', 0.5239987969398499, 'nlp', 1), ('google/mediapipe', 0.5233632922172546, 'ml', 2), ('transformeroptimus/superagi', 0.5222785472869873, 'llm', 2), ('whylabs/whylogs', 0.5217410922050476, 'util', 2), ('giskard-ai/giskard', 0.5216226577758789, 'data', 3), ('roboflow/supervision', 0.5209812521934509, 'ml', 2), ('interpretml/interpret', 0.5196956992149353, 'ml-interpretability', 2), ('adap/flower', 0.519675612449646, 'ml-ops', 3), ('titanml/takeoff', 0.5187811851501465, 'llm', 0), ('deepchecks/deepchecks', 0.5182715058326721, 'data', 3), ('csinva/imodels', 0.5169817209243774, 'ml', 2), ('keras-rl/keras-rl', 0.5166836977005005, 'ml-rl', 1), ('cleanlab/cleanlab', 0.5148969292640686, 'ml', 0), ('gradio-app/gradio', 0.5144384503364563, 'viz', 2), ('google/trax', 0.5128212571144104, 'ml-dl', 2), ('nvidia/nemo', 0.5116295218467712, 'nlp', 1), ('makcedward/nlpaug', 0.5093832612037659, 'nlp', 2), ('invoke-ai/invokeai', 0.5075830221176147, 'diffusion', 0), ('unionai-oss/unionml', 0.5069453716278076, 'ml-ops', 2), ('wandb/client', 0.5063665509223938, 'ml', 4), ('keras-team/keras-nlp', 0.5048489570617676, 'nlp', 2), ('flyteorg/flyte', 0.5007025599479675, 'ml-ops', 3)]",192,2.0,,9.81,185,134,58,0,21,24,21,185.0,153.0,90.0,0.8,58
626,util,https://github.com/pyca/cryptography,[],,[],[],,,,pyca/cryptography,cryptography,6009,1735,125,Python,https://cryptography.io,cryptography is a package designed to expose cryptographic primitives and recipes to Python developers.,pyca,2024-01-14,2013-08-07,546,10.988244514106583,https://avatars.githubusercontent.com/u/5615737?v=4,cryptography is a package designed to expose cryptographic primitives and recipes to Python developers.,['cryptography'],['cryptography'],2024-01-14,"[('legrandin/pycryptodome', 0.8198734521865845, 'util', 1), ('pyca/pynacl', 0.659361720085144, 'util', 1), ('1200wd/bitcoinlib', 0.6551878452301025, 'crypto', 0), ('primal100/pybitcointools', 0.6024011373519897, 'crypto', 0), ('pypy/pypy', 0.5772532224655151, 'util', 0), ('man-c/pycoingecko', 0.5643881559371948, 'crypto', 0), ('pyupio/safety', 0.5316013097763062, 'security', 0), ('pyston/pyston', 0.5291821360588074, 'util', 0), ('pytoolz/toolz', 0.5245878100395203, 'util', 0), ('aswinnnn/pyscan', 0.5230307579040527, 'security', 0), ('sympy/sympy', 0.518089234828949, 'math', 0), ('paramiko/paramiko', 0.5152232646942139, 'util', 0), ('ta-lib/ta-lib-python', 0.5043237209320068, 'finance', 0), ('mkdocstrings/griffe', 0.5040481090545654, 'util', 0)]",303,4.0,,32.37,542,518,127,0,0,12,12,542.0,303.0,90.0,0.6,58
1534,ml-interpretability,https://github.com/interpretml/interpret,['interpretability'],,[],[],,,,interpretml/interpret,interpret,5868,704,141,C++,https://interpret.ml/docs,Fit interpretable models. Explain blackbox machine learning. ,interpretml,2024-01-13,2019-05-03,247,23.702250432775532,https://avatars.githubusercontent.com/u/27173223?v=4,Fit interpretable models. Explain blackbox machine learning. ,"['ai', 'artificial-intelligence', 'bias', 'blackbox', 'differential-privacy', 'explainability', 'explainable-ai', 'explainable-ml', 'gradient-boosting', 'iml', 'interpretability', 'interpretable-ai', 'interpretable-machine-learning', 'interpretable-ml', 'interpretml', 'machine-learning', 'scikit-learn', 'transparency', 'xai']","['ai', 'artificial-intelligence', 'bias', 'blackbox', 'differential-privacy', 'explainability', 'explainable-ai', 'explainable-ml', 'gradient-boosting', 'iml', 'interpretability', 'interpretable-ai', 'interpretable-machine-learning', 'interpretable-ml', 'interpretml', 'machine-learning', 'scikit-learn', 'transparency', 'xai']",2024-01-14,"[('csinva/imodels', 0.7508851289749146, 'ml', 7), ('oegedijk/explainerdashboard', 0.6743864417076111, 'ml-interpretability', 1), ('xplainable/xplainable', 0.6733729839324951, 'ml-interpretability', 4), ('seldonio/alibi', 0.6674531102180481, 'ml-interpretability', 3), ('maif/shapash', 0.6510236859321594, 'ml', 5), ('marcotcr/lime', 0.634106457233429, 'ml-interpretability', 1), ('tensorflow/lucid', 0.626980721950531, 'ml-interpretability', 2), ('pair-code/lit', 0.6197747588157654, 'ml-interpretability', 1), ('pytorch/captum', 0.6127338409423828, 'ml-interpretability', 3), ('cdpierse/transformers-interpret', 0.6050029993057251, 'ml-interpretability', 3), ('slundberg/shap', 0.5977214574813843, 'ml-interpretability', 4), ('mosaicml/composer', 0.5779016613960266, 'ml-dl', 1), ('rafiqhasan/auto-tensorflow', 0.5614568591117859, 'ml-dl', 1), ('teamhg-memex/eli5', 0.5581210851669312, 'ml', 2), ('eleutherai/pythia', 0.5574303269386292, 'ml-interpretability', 2), ('giskard-ai/giskard', 0.5559372305870056, 'data', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.542955756187439, 'study', 3), ('explosion/thinc', 0.5354365706443787, 'ml-dl', 3), ('linkedin/fasttreeshap', 0.5312417149543762, 'ml', 3), ('hpcaitech/colossalai', 0.5297350883483887, 'llm', 1), ('arize-ai/phoenix', 0.5245933532714844, 'ml-interpretability', 0), ('selfexplainml/piml-toolbox', 0.5234452486038208, 'ml-interpretability', 1), ('polyaxon/datatile', 0.5212517976760864, 'pandas', 1), ('ddbourgin/numpy-ml', 0.5201536417007446, 'ml', 2), ('bentoml/bentoml', 0.5196956992149353, 'ml-ops', 2), ('onnx/onnx', 0.5163048505783081, 'ml', 2), ('tensorflow/tensorflow', 0.5142463445663452, 'ml-dl', 1), ('nccr-itmo/fedot', 0.5118159651756287, 'ml-ops', 1), ('makcedward/nlpaug', 0.5114248394966125, 'nlp', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5096079707145691, 'study', 2), ('carla-recourse/carla', 0.5091050863265991, 'ml', 5), ('lutzroeder/netron', 0.5063915848731995, 'ml', 2), ('whylabs/whylogs', 0.5045889019966125, 'util', 1), ('tensorflow/tensor2tensor', 0.5023159980773926, 'ml', 1), ('huggingface/datasets', 0.5013808608055115, 'nlp', 1), ('huggingface/autotrain-advanced', 0.500921368598938, 'ml', 1)]",40,3.0,,14.46,30,11,57,0,8,9,8,30.0,67.0,90.0,2.2,58
403,perf,https://github.com/python-trio/trio,[],,[],[],,,,python-trio/trio,trio,5713,316,85,Python,https://trio.readthedocs.io,Trio – a friendly Python library for async concurrency and I/O,python-trio,2024-01-13,2017-01-16,367,15.560700389105058,https://avatars.githubusercontent.com/u/26335827?v=4,Trio – a friendly Python library for async concurrency and I/O,"['async', 'async-await', 'io', 'networking', 'structured-concurrency', 'trio']","['async', 'async-await', 'io', 'networking', 'structured-concurrency', 'trio']",2024-01-10,"[('agronholm/anyio', 0.8090512156486511, 'perf', 2), ('magicstack/uvloop', 0.6619237065315247, 'util', 3), ('alirn76/panther', 0.6534282565116882, 'web', 0), ('samuelcolvin/arq', 0.6487094759941101, 'data', 1), ('pallets/quart', 0.6473005414009094, 'web', 0), ('eventlet/eventlet', 0.6357116103172302, 'perf', 0), ('aio-libs/aiohttp', 0.6344983577728271, 'web', 1), ('sumerc/yappi', 0.6272547245025635, 'profiling', 0), ('tiangolo/asyncer', 0.6240719556808472, 'perf', 2), ('airtai/faststream', 0.6224059462547302, 'perf', 0), ('encode/httpx', 0.5947321057319641, 'web', 1), ('noxdafox/pebble', 0.5942329168319702, 'perf', 0), ('collerek/ormar', 0.5920240879058838, 'data', 0), ('python-greenlet/greenlet', 0.5789716839790344, 'perf', 0), ('joblib/joblib', 0.5737351179122925, 'util', 0), ('tox-dev/py-filelock', 0.5723570585250854, 'util', 0), ('tornadoweb/tornado', 0.5695123076438904, 'web', 0), ('geeogi/async-python-lambda-template', 0.5684411525726318, 'template', 0), ('timofurrer/awesome-asyncio', 0.5602687001228333, 'study', 0), ('miguelgrinberg/python-socketio', 0.5562794208526611, 'util', 0), ('klen/muffin', 0.5527318716049194, 'web', 1), ('neoteroi/blacksheep', 0.5497913956642151, 'web', 0), ('ipython/ipyparallel', 0.549788773059845, 'perf', 0), ('pytoolz/toolz', 0.5491801500320435, 'util', 0), ('fastai/fastcore', 0.5491400361061096, 'util', 0), ('pyston/pyston', 0.5414426922798157, 'util', 0), ('pypy/pypy', 0.5405836701393127, 'util', 0), ('fluentpython/example-code-2e', 0.5376577377319336, 'study', 0), ('encode/uvicorn', 0.5339199304580688, 'web', 0), ('bogdanp/dramatiq', 0.5322513580322266, 'util', 0), ('python-cachier/cachier', 0.528437077999115, 'perf', 0), ('joblib/loky', 0.5253786444664001, 'perf', 0), ('klen/py-frameworks-bench', 0.5250015258789062, 'perf', 0), ('micropython/micropython', 0.5247344970703125, 'util', 0), ('encode/starlette', 0.5227438807487488, 'web', 1), ('dgilland/cacheout', 0.5188122987747192, 'perf', 0), ('agronholm/apscheduler', 0.5111809968948364, 'util', 0), ('hyperopt/hyperopt', 0.510098934173584, 'ml', 0), ('dask/dask', 0.5067204236984253, 'perf', 0), ('samuelcolvin/watchfiles', 0.5042653679847717, 'util', 0), ('backtick-se/cowait', 0.5035473704338074, 'util', 0), ('samuelcolvin/aioaws', 0.5013687610626221, 'data', 0)]",154,3.0,,7.71,153,107,85,0,5,4,5,153.0,482.0,90.0,3.2,58
944,security,https://github.com/stamparm/maltrail,[],,[],[],,,,stamparm/maltrail,maltrail,5548,1017,229,Python,,Malicious traffic detection system,stamparm,2024-01-14,2014-12-04,477,11.613636363636363,,Malicious traffic detection system,"['attack-detection', 'intrusion-detection', 'malware', 'network-monitoring', 'security', 'sensor']","['attack-detection', 'intrusion-detection', 'malware', 'network-monitoring', 'security', 'sensor']",2024-01-13,[],47,4.0,,305.44,35,30,111,0,12,6,12,35.0,48.0,90.0,1.4,58
547,ml,https://github.com/open-mmlab/mmcv,['computer-vision'],,[],[],,,,open-mmlab/mmcv,mmcv,5409,1575,86,Python,https://mmcv.readthedocs.io/en/latest/,OpenMMLab Computer Vision Foundation,open-mmlab,2024-01-14,2018-08-22,283,19.055359838953194,https://avatars.githubusercontent.com/u/10245193?v=4,OpenMMLab Computer Vision Foundation,[],['computer-vision'],2024-01-07,"[('open-mmlab/mmdetection', 0.6718772649765015, 'ml', 0), ('open-mmlab/mmsegmentation', 0.6504673361778259, 'ml', 0), ('deci-ai/super-gradients', 0.5693687796592712, 'ml-dl', 1), ('luispedro/mahotas', 0.5330637693405151, 'viz', 1), ('roboflow/supervision', 0.524324893951416, 'ml', 1)]",260,7.0,,1.87,91,52,66,0,5,20,5,91.0,146.0,90.0,1.6,58
1666,ml-ops,https://github.com/kestra-io/kestra,[],,[],[],1.0,,,kestra-io/kestra,kestra,5200,285,54,Java,https://kestra.io,"Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.",kestra-io,2024-01-14,2019-08-24,231,22.469135802469136,https://avatars.githubusercontent.com/u/59033362?v=4,"Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.","['data', 'data-engineering', 'data-integration', 'data-orchestration', 'data-orchestrator', 'data-pipeline', 'data-quality', 'elt', 'etl', 'low-code', 'orchestration', 'pipeline', 'reverse-etl', 'scheduler', 'workflow', 'workflow-engine']","['data', 'data-engineering', 'data-integration', 'data-orchestration', 'data-orchestrator', 'data-pipeline', 'data-quality', 'elt', 'etl', 'low-code', 'orchestration', 'pipeline', 'reverse-etl', 'scheduler', 'workflow', 'workflow-engine']",2024-01-12,"[('flyteorg/flyte', 0.7973306775093079, 'ml-ops', 2), ('dagster-io/dagster', 0.7322535514831543, 'ml-ops', 7), ('mage-ai/mage-ai', 0.6831650137901306, 'ml-ops', 8), ('prefecthq/server', 0.6529881954193115, 'util', 3), ('apache/airflow', 0.6468701362609863, 'ml-ops', 9), ('prefecthq/prefect', 0.6422749757766724, 'ml-ops', 6), ('airbytehq/airbyte', 0.6355409622192383, 'data', 7), ('orchest/orchest', 0.6342953443527222, 'ml-ops', 1), ('ploomber/ploomber', 0.6272913217544556, 'ml-ops', 2), ('astronomer/astro-sdk', 0.6130484342575073, 'ml-ops', 2), ('avaiga/taipy', 0.6105530858039856, 'data', 4), ('backtick-se/cowait', 0.5885079503059387, 'util', 2), ('meltano/meltano', 0.5790954232215881, 'ml-ops', 3), ('dagworks-inc/hamilton', 0.5751848816871643, 'ml-ops', 3), ('polyaxon/polyaxon', 0.561455249786377, 'ml-ops', 1), ('fugue-project/fugue', 0.5582082271575928, 'pandas', 0), ('zenml-io/zenml', 0.5403246879577637, 'ml-ops', 1), ('allegroai/clearml', 0.5374925136566162, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5349816083908081, 'ml-ops', 2), ('apache/spark', 0.5336428284645081, 'data', 0), ('getindata/kedro-kubeflow', 0.5321413278579712, 'ml-ops', 0), ('modin-project/modin', 0.5311633348464966, 'perf', 0), ('merantix-momentum/squirrel-core', 0.5266260504722595, 'ml', 0), ('spotify/luigi', 0.5256256461143494, 'ml-ops', 0), ('netflix/metaflow', 0.5250420570373535, 'ml-ops', 0), ('pathwaycom/pathway', 0.5241698026657104, 'data', 0), ('fastai/fastcore', 0.5094014406204224, 'util', 0), ('pytables/pytables', 0.5085448026657104, 'data', 0), ('linealabs/lineapy', 0.5073179602622986, 'jupyter', 0), ('pydoit/doit', 0.5050574541091919, 'util', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.501535952091217, 'template', 0)]",41,2.0,,20.23,915,469,53,0,37,23,37,915.0,611.0,90.0,0.7,58
67,util,https://github.com/pycqa/pylint,['code-quality'],,[],['pylint'],,,,pycqa/pylint,pylint,4989,1067,77,Python,https://pylint.readthedocs.io/en/latest/,It's not just a linter that annoys you!,pycqa,2024-01-13,2015-12-09,424,11.742770679219905,https://avatars.githubusercontent.com/u/121692054?v=4,It's not just a linter that annoys you!,"['code-quality', 'linter', 'pep8', 'static-analysis', 'static-code-analysis']","['code-quality', 'linter', 'pep8', 'static-analysis', 'static-code-analysis']",2024-01-10,"[('astral-sh/ruff', 0.569158136844635, 'util', 5), ('google/pytype', 0.5689358115196228, 'typing', 4), ('klen/pylama', 0.5042141675949097, 'util', 1), ('python/mypy', 0.5039985179901123, 'typing', 2)]",553,3.0,,15.12,288,145,99,0,21,23,21,288.0,524.0,90.0,1.8,58
815,ml,https://github.com/project-monai/monai,[],,[],[],,,,project-monai/monai,MONAI,4983,920,93,Python,https://monai.io/,AI Toolkit for Healthcare Imaging,project-monai,2024-01-14,2019-10-11,224,22.188931297709924,https://avatars.githubusercontent.com/u/56449156?v=4,AI Toolkit for Healthcare Imaging,"['deep-learning', 'healthcare-imaging', 'medical-image-computing', 'medical-image-processing', 'monai', 'pytorch']","['deep-learning', 'healthcare-imaging', 'medical-image-computing', 'medical-image-processing', 'monai', 'pytorch']",2024-01-12,"[('fepegar/torchio', 0.8480987548828125, 'ml-dl', 4), ('albumentations-team/albumentations', 0.5541568994522095, 'ml-dl', 1), ('tensorflow/tensorflow', 0.539412260055542, 'ml-dl', 1), ('microsoft/onnxruntime', 0.5315036773681641, 'ml', 2), ('lucidrains/medical-chatgpt', 0.5313103795051575, 'llm', 1), ('keras-team/keras', 0.5263016819953918, 'ml-dl', 2), ('oneil512/insight', 0.5232803225517273, 'ml', 0), ('open-mmlab/mmsegmentation', 0.5131828188896179, 'ml', 1), ('nvidia/deeplearningexamples', 0.5109444260597229, 'ml-dl', 2), ('huggingface/datasets', 0.5077080130577087, 'nlp', 2), ('tensorflow/tensor2tensor', 0.5071702599525452, 'ml', 1)]",176,3.0,,8.88,358,233,52,0,2,20,2,358.0,519.0,90.0,1.4,58
750,util,https://github.com/pypa/hatch,"['package-manager', 'packaging']",,[],[],,,,pypa/hatch,hatch,4939,281,49,Python,https://hatch.pypa.io/latest/,"Modern, extensible Python project management",pypa,2024-01-14,2017-05-31,347,14.19835728952772,https://avatars.githubusercontent.com/u/647025?v=4,"Modern, extensible Python project management","['build', 'cli', 'packaging', 'plugin', 'versioning', 'virtualenv']","['build', 'cli', 'package-manager', 'packaging', 'plugin', 'versioning', 'virtualenv']",2024-01-13,"[('python-poetry/poetry', 0.7199224233627319, 'util', 2), ('pypa/pipenv', 0.7155768275260925, 'util', 2), ('mitsuhiko/rye', 0.7138620615005493, 'util', 2), ('indygreg/pyoxidizer', 0.7048346400260925, 'util', 2), ('pomponchik/instld', 0.6995571255683899, 'util', 1), ('pdm-project/pdm', 0.6953819394111633, 'util', 2), ('dosisod/refurb', 0.6656979322433472, 'util', 1), ('pypa/flit', 0.6373385787010193, 'util', 2), ('pypa/virtualenv', 0.6300379633903503, 'util', 1), ('pyenv/pyenv', 0.6283936500549316, 'util', 0), ('ofek/pyapp', 0.6210417747497559, 'util', 3), ('tezromach/python-package-template', 0.6175110936164856, 'template', 0), ('pypi/warehouse', 0.6159988641738892, 'util', 0), ('eugeneyan/python-collab-template', 0.613045871257782, 'template', 0), ('beeware/briefcase', 0.6087289452552795, 'util', 0), ('conda/conda-build', 0.5962553024291992, 'util', 0), ('pypa/build', 0.5941884517669678, 'util', 1), ('pyodide/micropip', 0.5937002897262573, 'util', 0), ('omry/omegaconf', 0.5926797986030579, 'util', 0), ('spack/spack', 0.592124879360199, 'util', 1), ('jazzband/pip-tools', 0.5882735848426819, 'util', 1), ('martinheinz/python-project-blueprint', 0.5873016119003296, 'template', 0), ('willmcgugan/textual', 0.5866153836250305, 'term', 1), ('tedivm/robs_awesome_python_template', 0.582638144493103, 'template', 0), ('eleutherai/pyfra', 0.5818159580230713, 'ml', 0), ('thoth-station/micropipenv', 0.5751578211784363, 'util', 0), ('pyscaffold/pyscaffold', 0.5699118971824646, 'template', 0), ('amaargiru/pyroad', 0.5697453618049622, 'study', 0), ('pypy/pypy', 0.5679408311843872, 'util', 0), ('regebro/pyroma', 0.5634012818336487, 'util', 1), ('mtkennerly/dunamai', 0.56247878074646, 'util', 2), ('pytables/pytables', 0.5594925880432129, 'data', 0), ('mamba-org/mamba', 0.5550651550292969, 'util', 2), ('hoffstadt/dearpygui', 0.5478973388671875, 'gui', 0), ('malloydata/malloy-py', 0.547518789768219, 'data', 0), ('conda/conda', 0.5469714999198914, 'util', 2), ('pyinfra-dev/pyinfra', 0.5454100966453552, 'util', 0), ('urwid/urwid', 0.5425727963447571, 'term', 0), ('pallets/flask', 0.5425235033035278, 'web', 0), ('python/cpython', 0.5416925549507141, 'util', 0), ('cython/cython', 0.5406709313392639, 'util', 0), ('pympler/pympler', 0.5399362444877625, 'perf', 0), ('samuelcolvin/python-devtools', 0.5391005277633667, 'debug', 0), ('mamba-org/gator', 0.5378934741020203, 'jupyter', 0), ('backtick-se/cowait', 0.5353484153747559, 'util', 0), ('landscapeio/prospector', 0.5343112945556641, 'util', 0), ('exaloop/codon', 0.5342748761177063, 'perf', 0), ('rubik/radon', 0.5330091714859009, 'util', 1), ('pypa/gh-action-pypi-publish', 0.5319401025772095, 'util', 0), ('libtcod/python-tcod', 0.5316311120986938, 'gamedev', 0), ('google/gin-config', 0.5304906368255615, 'util', 0), ('erotemic/ubelt', 0.5291941165924072, 'util', 0), ('pytoolz/toolz', 0.5271018743515015, 'util', 0), ('pyo3/maturin', 0.5255593061447144, 'util', 1), ('mkdocstrings/griffe', 0.5250239968299866, 'util', 0), ('bottlepy/bottle', 0.5236046314239502, 'web', 0), ('kubeflow/fairing', 0.5211179256439209, 'ml-ops', 0), ('pypa/setuptools_scm', 0.5210332274436951, 'util', 2), ('mitmproxy/pdoc', 0.520322322845459, 'util', 0), ('citadel-ai/langcheck', 0.5197140574455261, 'llm', 0), ('google/python-fire', 0.5173959732055664, 'term', 1), ('trailofbits/pip-audit', 0.5167754292488098, 'security', 0), ('tox-dev/pipdeptree', 0.5165544152259827, 'util', 1), ('fastai/fastcore', 0.5164702534675598, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5143115520477295, 'util', 1), ('orchest/orchest', 0.510188639163971, 'ml-ops', 0), ('psf/black', 0.5100483894348145, 'util', 0), ('scikit-build/scikit-build', 0.509085476398468, 'ml', 1), ('pypa/pipx', 0.5080375671386719, 'util', 1), ('prompt-toolkit/ptpython', 0.5076357126235962, 'util', 1), ('cookiecutter/cookiecutter', 0.5061737895011902, 'template', 0), ('grahamdumpleton/wrapt', 0.5048109292984009, 'util', 0), ('sqlalchemy/mako', 0.5044662356376648, 'template', 0), ('pyston/pyston', 0.5036124587059021, 'util', 0), ('ethereum/py-evm', 0.5026881694793701, 'crypto', 0), ('sourcery-ai/sourcery', 0.5025754570960999, 'util', 0), ('python-cachier/cachier', 0.502075731754303, 'perf', 0), ('jquast/blessed', 0.5016807913780212, 'term', 1)]",54,6.0,,3.71,238,173,81,0,18,16,18,238.0,511.0,90.0,2.1,58
1818,gui,https://github.com/beeware/toga,"['toolkit', 'gui']",,[],[],,,,beeware/toga,toga,3998,673,85,Python,https://toga.readthedocs.io/en/latest/,"A Python native, OS native GUI toolkit.",beeware,2024-01-13,2014-08-01,495,8.06745459786682,https://avatars.githubusercontent.com/u/19795701?v=4,"A Python native, OS native GUI toolkit.",[],"['gui', 'toolkit']",2024-01-11,"[('hoffstadt/dearpygui', 0.7974780201911926, 'gui', 2), ('kivy/kivy', 0.692936360836029, 'util', 0), ('parthjadhav/tkinter-designer', 0.6891065239906311, 'gui', 1), ('r0x0r/pywebview', 0.6624377965927124, 'gui', 1), ('urwid/urwid', 0.6368706822395325, 'term', 0), ('wxwidgets/phoenix', 0.6360959410667419, 'gui', 1), ('dddomodossola/remi', 0.6270981431007385, 'gui', 1), ('pysimplegui/pysimplegui', 0.6206801533699036, 'gui', 1), ('willmcgugan/textual', 0.5924068689346313, 'term', 0), ('adamerose/pandasgui', 0.590601921081543, 'pandas', 1), ('alexmojaki/snoop', 0.5817451477050781, 'debug', 0), ('pyglet/pyglet', 0.572860598564148, 'gamedev', 0), ('tkrabel/bamboolib', 0.5691761374473572, 'pandas', 0), ('pyston/pyston', 0.5686622262001038, 'util', 0), ('beeware/briefcase', 0.5662544965744019, 'util', 0), ('holoviz/panel', 0.5590908527374268, 'viz', 1), ('jquast/blessed', 0.5573833584785461, 'term', 0), ('pypy/pypy', 0.5504774451255798, 'util', 0), ('holoviz/holoviz', 0.5480042099952698, 'viz', 0), ('python/cpython', 0.5390973091125488, 'util', 0), ('gradio-app/gradio', 0.5377219915390015, 'viz', 0), ('eleutherai/pyfra', 0.5376284718513489, 'ml', 0), ('pytoolz/toolz', 0.5362921953201294, 'util', 0), ('google/gin-config', 0.5335937738418579, 'util', 0), ('samuelcolvin/python-devtools', 0.5332743525505066, 'debug', 0), ('kubeflow/fairing', 0.5289453268051147, 'ml-ops', 0), ('pallets/click', 0.5282005667686462, 'term', 0), ('pyqtgraph/pyqtgraph', 0.5267707109451294, 'viz', 0), ('fastai/fastcore', 0.5267270803451538, 'util', 0), ('goldmansachs/gs-quant', 0.5231086611747742, 'finance', 0), ('erotemic/ubelt', 0.5228415727615356, 'util', 0), ('huggingface/huggingface_hub', 0.5219206213951111, 'ml', 0), ('indygreg/pyoxidizer', 0.5212326645851135, 'util', 0), ('klen/py-frameworks-bench', 0.5207023024559021, 'perf', 0), ('landscapeio/prospector', 0.517236590385437, 'util', 0), ('google/python-fire', 0.5118656754493713, 'term', 0), ('pympler/pympler', 0.5115267634391785, 'perf', 0), ('python-rope/rope', 0.5071592330932617, 'util', 0), ('micropython/micropython', 0.5063884258270264, 'util', 0), ('libtcod/python-tcod', 0.5042990446090698, 'gamedev', 0), ('weaviate/weaviate-python-client', 0.5031599998474121, 'util', 0)]",256,7.0,,35.79,200,127,115,0,4,7,4,200.0,486.0,90.0,2.4,58
748,ml,https://github.com/marqo-ai/marqo,[],,[],[],,,,marqo-ai/marqo,marqo,3856,162,35,Python,https://www.marqo.ai/,Vector search for humans. Also available on cloud - cloud.marqo.ai,marqo-ai,2024-01-13,2022-08-01,78,49.345521023766,https://avatars.githubusercontent.com/u/103185353?v=4,Vector search for humans. Also available on cloud - cloud.marqo.ai,"['chatgpt', 'clip', 'deep-learning', 'gpt', 'hnsw', 'information-retrieval', 'knn', 'large-language-models', 'machine-learning', 'machinelearning', 'multi-modal', 'natural-language-processing', 'search-engine', 'semantic-search', 'tensor-search', 'transformers', 'vector-search', 'vision-language', 'visual-search']","['chatgpt', 'clip', 'deep-learning', 'gpt', 'hnsw', 'information-retrieval', 'knn', 'large-language-models', 'machine-learning', 'machinelearning', 'multi-modal', 'natural-language-processing', 'search-engine', 'semantic-search', 'tensor-search', 'transformers', 'vector-search', 'vision-language', 'visual-search']",2024-01-11,"[('qdrant/qdrant', 0.7367421984672546, 'data', 4), ('cheshire-cat-ai/core', 0.6071776151657104, 'llm', 1), ('activeloopai/deeplake', 0.6008663177490234, 'ml-ops', 4), ('milvus-io/bootcamp', 0.5712332725524902, 'data', 1), ('googlecloudplatform/vertex-ai-samples', 0.5693247318267822, 'ml', 0), ('docarray/docarray', 0.5685033798217773, 'data', 4), ('weaviate/demo-text2vec-openai', 0.5455718040466309, 'util', 1), ('jina-ai/jina', 0.5435925126075745, 'ml', 2), ('mindsdb/mindsdb', 0.5285826325416565, 'data', 3), ('lancedb/lancedb', 0.5243335366249084, 'data', 2), ('rcgai/simplyretrieve', 0.5212767124176025, 'llm', 3), ('neuml/txtai', 0.5169413089752197, 'nlp', 7), ('tensorflow/tensorflow', 0.5045498013496399, 'ml-dl', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5009713768959045, 'study', 2)]",30,2.0,,7.23,115,93,18,0,18,17,18,115.0,33.0,90.0,0.3,58
429,viz,https://github.com/holoviz/panel,[],,[],[],,,,holoviz/panel,panel,3647,420,53,Python,https://panel.holoviz.org,Panel: The powerful data exploration & web app framework for Python,holoviz,2024-01-14,2018-08-23,283,12.85448136958711,https://avatars.githubusercontent.com/u/51678735?v=4,Panel: The powerful data exploration & web app framework for Python,"['bokeh', 'control-panels', 'dashboards', 'dataapp', 'datascience', 'dataviz', 'gui', 'holoviews', 'holoviz', 'hvplot', 'jupyter', 'matplotlib', 'panel', 'plotly']","['bokeh', 'control-panels', 'dashboards', 'dataapp', 'datascience', 'dataviz', 'gui', 'holoviews', 'holoviz', 'hvplot', 'jupyter', 'matplotlib', 'panel', 'plotly']",2024-01-13,"[('plotly/dash', 0.7759690284729004, 'viz', 2), ('bokeh/bokeh', 0.7603949308395386, 'viz', 2), ('holoviz/holoviz', 0.7308956384658813, 'viz', 4), ('man-group/dtale', 0.7240487337112427, 'viz', 0), ('plotly/plotly.py', 0.7203231453895569, 'viz', 1), ('kanaries/pygwalker', 0.6920881271362305, 'pandas', 2), ('holoviz/hvplot', 0.6510308980941772, 'pandas', 2), ('mwaskom/seaborn', 0.6487950086593628, 'viz', 1), ('residentmario/geoplot', 0.6484428644180298, 'gis', 1), ('eleutherai/pyfra', 0.6469884514808655, 'ml', 0), ('giswqs/geemap', 0.6401718258857727, 'gis', 2), ('federicoceratto/dashing', 0.6392130851745605, 'term', 0), ('matplotlib/matplotlib', 0.6378490328788757, 'viz', 1), ('vizzuhq/ipyvizzu', 0.6363608837127686, 'jupyter', 2), ('pyqtgraph/pyqtgraph', 0.6308204531669617, 'viz', 0), ('polyaxon/datatile', 0.6254085302352905, 'pandas', 2), ('adamerose/pandasgui', 0.6248031854629517, 'pandas', 1), ('gradio-app/gradio', 0.6235347390174866, 'viz', 0), ('cuemacro/chartpy', 0.6215455532073975, 'viz', 3), ('jakevdp/pythondatasciencehandbook', 0.6207575798034668, 'study', 1), ('tkrabel/bamboolib', 0.620628833770752, 'pandas', 0), ('opengeos/leafmap', 0.6154477596282959, 'gis', 3), ('pandas-dev/pandas', 0.6123045682907104, 'pandas', 0), ('willmcgugan/textual', 0.6071124076843262, 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221,jupyter,https://github.com/jupyterlite/jupyterlite,[],,[],[],,,,jupyterlite/jupyterlite,jupyterlite,3470,258,40,TypeScript,https://jupyterlite.rtfd.io/en/stable/try/lab,Wasm powered Jupyter running in the browser 💡,jupyterlite,2024-01-10,2021-03-27,148,23.37824831568816,https://avatars.githubusercontent.com/u/81094398?v=4,Wasm powered Jupyter running in the browser 💡,"['jupyter', 'jupyterlab', 'jupyterlab-extension', 'lite', 'pyodide', 'wasm', 'webassembly']","['jupyter', 'jupyterlab', 'jupyterlab-extension', 'lite', 'pyodide', 'wasm', 'webassembly']",2024-01-10,"[('voila-dashboards/voila', 0.6913954615592957, 'jupyter', 2), ('jupyterlab/jupyterlab', 0.6288254261016846, 'jupyter', 2), ('jupyterlab/jupyterlab-desktop', 0.6264117956161499, 'jupyter', 2), ('jupyter/notebook', 0.6035483479499817, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.5854482650756836, 'jupyter', 1), ('pyodide/pyodide', 0.5671476721763611, 'util', 2), ('maartenbreddels/ipyvolume', 0.5511241555213928, 'jupyter', 1), ('mwouts/jupytext', 0.5492159128189087, 'jupyter', 2), ('mamba-org/gator', 0.5473883152008057, 'jupyter', 1), ('cherrypy/cherrypy', 0.5396081805229187, 'web', 0), ('jupyter-widgets/ipyleaflet', 0.539129376411438, 'gis', 2), ('ipython/ipykernel', 0.5333779454231262, 'util', 1), ('computationalmodelling/nbval', 0.5270730257034302, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5260722041130066, 'jupyter', 1), ('jupyter/nbviewer', 0.5257112979888916, 'jupyter', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5251054763793945, 'jupyter', 3), ('jupyter/nbformat', 0.5237820744514465, 'jupyter', 0), ('webpy/webpy', 0.5002906918525696, 'web', 0)]",56,5.0,,3.04,84,52,34,0,22,493,22,84.0,156.0,90.0,1.9,58
1013,llm,https://github.com/whitead/paper-qa,[],,[],[],1.0,,,whitead/paper-qa,paper-qa,3383,321,43,Python,,LLM Chain for answering questions from documents with citations,whitead,2024-01-13,2023-02-05,51,65.96378830083566,,LLM Chain for answering questions from documents with citations,"['chatgpt', 'nlp', 'question-answering']","['chatgpt', 'nlp', 'question-answering']",2023-12-07,"[('rlancemartin/auto-evaluator', 0.5889334678649902, 'llm', 1), ('princeton-nlp/alce', 0.5843133330345154, 'llm', 0), ('night-chen/toolqa', 0.54909348487854, 'llm', 1), ('explosion/spacy-llm', 0.5373473763465881, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5275523066520691, 'study', 1), ('deepset-ai/haystack', 0.5140941143035889, 'llm', 3)]",12,4.0,,3.31,33,17,11,1,75,83,75,33.0,22.0,90.0,0.7,58
1528,llm,https://github.com/minimaxir/simpleaichat,[],,[],[],,,,minimaxir/simpleaichat,simpleaichat,3227,210,34,Python,,"Python package for easily interfacing with chat apps, with robust features and minimal code complexity.",minimaxir,2024-01-12,2023-05-06,38,83.97397769516729,,"Python package for easily interfacing with chat apps, with robust features and minimal code complexity.","['ai', 'chatgpt']","['ai', 'chatgpt']",2024-01-08,"[('embedchain/embedchain', 0.7047023773193359, 'llm', 2), ('run-llama/rags', 0.6775078177452087, 'llm', 1), ('togethercomputer/openchatkit', 0.6666284203529358, 'nlp', 0), ('killianlucas/open-interpreter', 0.6259564757347107, 'llm', 1), ('rcgai/simplyretrieve', 0.6199681162834167, 'llm', 0), ('cheshire-cat-ai/core', 0.613688588142395, 'llm', 1), ('prefecthq/marvin', 0.6048039197921753, 'nlp', 1), ('blinkdl/chatrwkv', 0.5946717262268066, 'llm', 1), ('rasahq/rasa', 0.5821955800056458, 'llm', 0), ('krohling/bondai', 0.5760908722877502, 'llm', 0), ('chatarena/chatarena', 0.5708586573600769, 'llm', 2), ('nomic-ai/gpt4all', 0.5681280493736267, 'llm', 0), ('chainlit/chainlit', 0.5655502080917358, 'llm', 1), ('willmcgugan/textual', 0.5615967512130737, 'term', 0), ('deeppavlov/deeppavlov', 0.559428870677948, 'nlp', 1), ('fasteval/fasteval', 0.559170663356781, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5525240302085876, 'llm', 0), ('microsoft/autogen', 0.5520246028900146, 'llm', 1), ('openai/gpt-discord-bot', 0.552017331123352, 'llm', 0), ('larsbaunwall/bricky', 0.5494028925895691, 'llm', 1), ('openai/openai-cookbook', 0.5488078594207764, 'ml', 1), ('hoffstadt/dearpygui', 0.5460814237594604, 'gui', 0), ('nvidia/nemo', 0.5450947284698486, 'nlp', 0), ('xtekky/gpt4free', 0.5435710549354553, 'llm', 1), ('pathwaycom/llm-app', 0.5408278703689575, 'llm', 0), ('gunthercox/chatterbot', 0.5406122207641602, 'nlp', 0), ('uberi/speech_recognition', 0.5369465351104736, 'ml', 0), ('langchain-ai/chat-langchain', 0.5350149273872375, 'llm', 0), ('bhaskatripathi/pdfgpt', 0.5324260592460632, 'llm', 0), ('gventuri/pandas-ai', 0.5319541692733765, 'pandas', 1), ('openlmlab/moss', 0.5309569835662842, 'llm', 1), ('hwchase17/langchain', 0.5275580883026123, 'llm', 0), ('pndurette/gtts', 0.526296854019165, 'util', 0), ('lm-sys/fastchat', 0.526286244392395, 'llm', 0), ('masoniteframework/masonite', 0.5257116556167603, 'web', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5190370678901672, 'llm', 0), ('reloadware/reloadium', 0.509559154510498, 'profiling', 2), ('kalliope-project/kalliope', 0.5094994902610779, 'util', 0), ('minimaxir/aitextgen', 0.507713258266449, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5073516964912415, 'nlp', 0), ('eternnoir/pytelegrambotapi', 0.5022081732749939, 'util', 0), ('mnotgod96/appagent', 0.501400351524353, 'llm', 1)]",12,2.0,,2.29,28,10,8,0,6,9,6,28.0,29.0,90.0,1.0,58
1286,data,https://github.com/docarray/docarray,[],,[],[],,,,docarray/docarray,docarray,2620,216,45,Python,https://docs.docarray.org/,"Represent, send, store and search multimodal data",docarray,2024-01-14,2021-12-14,111,23.603603603603602,https://avatars.githubusercontent.com/u/117445116?v=4,"Represent, send, store and search multimodal data","['cross-modal', 'data-structures', 'dataclass', 'deep-learning', 'docarray', 'elasticsearch', 'fastapi', 'machine-learning', 'multi-modal', 'multimodal', 'nearest-neighbor-search', 'nested-data', 'neural-search', 'protobuf', 'pydantic', 'pytorch', 'qdrant', 'semantic-search', 'weaviate']","['cross-modal', 'data-structures', 'dataclass', 'deep-learning', 'docarray', 'elasticsearch', 'fastapi', 'machine-learning', 'multi-modal', 'multimodal', 'nearest-neighbor-search', 'nested-data', 'neural-search', 'protobuf', 'pydantic', 'pytorch', 'qdrant', 'semantic-search', 'weaviate']",2024-01-02,"[('milvus-io/bootcamp', 0.677416205406189, 'data', 1), ('next-gpt/next-gpt', 0.5986325144767761, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5803972482681274, 'nlp', 0), ('nomic-ai/nomic', 0.5800225734710693, 'nlp', 0), ('activeloopai/deeplake', 0.5793482661247253, 'ml-ops', 3), ('neuml/txtai', 0.5759537816047668, 'nlp', 3), ('marqo-ai/marqo', 0.5685033798217773, 'ml', 4), ('jina-ai/clip-as-service', 0.5395568609237671, 'nlp', 3), ('qdrant/qdrant', 0.5395107865333557, 'data', 3), ('freedmand/semantra', 0.5385406017303467, 'ml', 2), ('explosion/thinc', 0.5351216793060303, 'ml-dl', 3), ('jina-ai/jina', 0.5284995436668396, 'ml', 5), ('jina-ai/finetuner', 0.5270631313323975, 'ml', 1), ('lutzroeder/netron', 0.51971834897995, 'ml', 3), ('paddlepaddle/paddlenlp', 0.517856240272522, 'llm', 1), ('facebookresearch/mmf', 0.516020655632019, 'ml-dl', 3), ('gradio-app/gradio', 0.5143248438835144, 'viz', 2), ('huggingface/datasets', 0.511342465877533, 'nlp', 3), ('huggingface/autotrain-advanced', 0.5089057087898254, 'ml', 2), ('awslabs/autogluon', 0.5075685381889343, 'ml', 3), ('jina-ai/vectordb', 0.5065739154815674, 'data', 1), ('huggingface/transformers', 0.5064188241958618, 'nlp', 3), ('ddbourgin/numpy-ml', 0.5052067637443542, 'ml', 1), ('a-r-j/graphein', 0.5024240612983704, 'sim', 2), ('tensorlayer/tensorlayer', 0.5022443532943726, 'ml-rl', 1), ('intellabs/fastrag', 0.501563549041748, 'nlp', 2)]",72,2.0,,8.4,35,21,25,0,17,81,17,35.0,124.0,90.0,3.5,58
1358,gis,https://github.com/opengeos/segment-geospatial,[],,[],[],,,,opengeos/segment-geospatial,segment-geospatial,2478,247,52,Python,https://samgeo.gishub.org,A Python package for segmenting geospatial data with the Segment Anything Model (SAM),opengeos,2024-01-13,2023-04-19,40,60.65034965034965,https://avatars.githubusercontent.com/u/129896036?v=4,A Python package for segmenting geospatial data with the Segment Anything Model (SAM),"['artificial-intelligence', 'deep-learning', 'geopython', 'geospatial', 'machine-learning', 'segment-anything', 'segmentation']","['artificial-intelligence', 'deep-learning', 'geopython', 'geospatial', 'machine-learning', 'segment-anything', 'segmentation']",2023-12-07,"[('earthlab/earthpy', 0.5494171977043152, 'gis', 0), ('sentinel-hub/eo-learn', 0.5391361117362976, 'gis', 1), ('geopandas/geopandas', 0.5388274788856506, 'gis', 1), ('microsoft/torchgeo', 0.5342783331871033, 'gis', 2), ('osgeo/grass', 0.5276463627815247, 'gis', 2), ('residentmario/geoplot', 0.5210736989974976, 'gis', 0), ('fatiando/verde', 0.5204988121986389, 'gis', 2), ('remotesensinglab/raster4ml', 0.5023316144943237, 'gis', 1)]",11,4.0,,2.94,22,12,9,1,22,30,22,22.0,36.0,90.0,1.6,58
859,util,https://github.com/dosisod/refurb,[],,[],[],1.0,,,dosisod/refurb,refurb,2425,55,16,Python,,A tool for refurbishing and modernizing Python codebases,dosisod,2024-01-10,2022-07-27,78,30.7518115942029,,A tool for refurbishing and modernizing Python codebases,"['cli', 'gplv3', 'mypy', 'python310', 'testing']","['cli', 'gplv3', 'mypy', 'python310', 'testing']",2024-01-13,"[('pypa/hatch', 0.6656979322433472, 'util', 1), ('facebookincubator/bowler', 0.5965598225593567, 'util', 0), ('pypa/pipenv', 0.5785287618637085, 'util', 0), ('rubik/radon', 0.5743918418884277, 'util', 1), ('prompt-toolkit/ptpython', 0.573533296585083, 'util', 1), ('python-rope/rope', 0.5674479007720947, 'util', 0), ('pypy/pypy', 0.5643258094787598, 'util', 0), ('pdm-project/pdm', 0.5603682398796082, 'util', 0), ('jendrikseipp/vulture', 0.5567197203636169, 'util', 0), ('google/jax', 0.5519415736198425, 'ml', 0), ('nedbat/coveragepy', 0.5513451099395752, 'testing', 0), ('pympler/pympler', 0.5501025915145874, 'perf', 0), ('indygreg/pyoxidizer', 0.5480595827102661, 'util', 0), ('amaargiru/pyroad', 0.5455231070518494, 'study', 0), ('sourcery-ai/sourcery', 0.5449427366256714, 'util', 0), ('pyston/pyston', 0.5432273149490356, 'util', 0), ('python/cpython', 0.5401459336280823, 'util', 0), ('microsoft/pycodegpt', 0.5347919464111328, 'llm', 0), ('hhatto/autopep8', 0.5332099795341492, 'util', 0), ('eleutherai/pyfra', 0.5314339399337769, 'ml', 0), ('google/gin-config', 0.5311492085456848, 'util', 0), ('exaloop/codon', 0.5304907560348511, 'perf', 0), ('pytoolz/toolz', 0.5235017538070679, 'util', 0), ('hadialqattan/pycln', 0.5226925611495972, 'util', 0), ('cython/cython', 0.5158920288085938, 'util', 0), ('psf/black', 0.514424204826355, 'util', 0), ('jazzband/pip-tools', 0.5137325525283813, 'util', 0), ('erotemic/ubelt', 0.513725996017456, 'util', 0), ('asottile/reorder-python-imports', 0.5132452249526978, 'util', 0), ('libtcod/python-tcod', 0.5105434060096741, 'gamedev', 0), ('google/yapf', 0.5096585154533386, 'util', 0), ('beeware/briefcase', 0.5095663666725159, 'util', 0), ('landscapeio/prospector', 0.5087876915931702, 'util', 0), ('eugeneyan/python-collab-template', 0.5078116059303284, 'template', 0), ('mkdocstrings/griffe', 0.5076294541358948, 'util', 0), ('samuelcolvin/python-devtools', 0.5065819621086121, 'debug', 0), ('pypa/virtualenv', 0.5063945055007935, 'util', 0), ('willmcgugan/textual', 0.5046628713607788, 'term', 1), ('dgilland/cacheout', 0.5029307007789612, 'perf', 0), ('pypi/warehouse', 0.5017030239105225, 'util', 0)]",16,7.0,,2.67,33,28,18,0,20,25,20,33.0,61.0,90.0,1.8,58
1809,data,https://github.com/lancedb/lancedb,['vectordb'],,[],[],,,,lancedb/lancedb,lancedb,1903,113,19,Python,https://lancedb.github.io/lancedb/,"Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!",lancedb,2024-01-14,2023-02-28,48,39.645833333333336,https://avatars.githubusercontent.com/u/108903835?v=4,"Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!","['approximate-nearest-neighbor-search', 'image-search', 'nearest-neighbor-search', 'recommender-system', 'search-engine', 'semantic-search', 'similarity-search', 'vector-database']","['approximate-nearest-neighbor-search', 'image-search', 'nearest-neighbor-search', 'recommender-system', 'search-engine', 'semantic-search', 'similarity-search', 'vector-database', 'vectordb']",2024-01-14,"[('activeloopai/deeplake', 0.7212356925010681, 'ml-ops', 1), ('qdrant/qdrant', 0.6593559384346008, 'data', 7), ('pathwaycom/llm-app', 0.6573936343193054, 'llm', 1), ('chroma-core/chroma', 0.6137559413909912, 'data', 1), ('jina-ai/vectordb', 0.612372100353241, 'data', 2), ('featureform/embeddinghub', 0.6071080565452576, 'nlp', 1), ('microsoft/semantic-kernel', 0.6036065220832825, 'llm', 0), ('alphasecio/langchain-examples', 0.601751446723938, 'llm', 1), ('superduperdb/superduperdb', 0.6009349226951599, 'data', 1), ('hegelai/prompttools', 0.5837177634239197, 'llm', 0), ('ludwig-ai/ludwig', 0.5785049796104431, 'ml-ops', 0), ('neuml/txtai', 0.574266254901886, 'nlp', 3), ('milvus-io/bootcamp', 0.566419780254364, 'data', 2), ('nebuly-ai/nebullvm', 0.5628674030303955, 'perf', 0), ('dgarnitz/vectorflow', 0.5619788765907288, 'data', 0), ('jerryjliu/llama_index', 0.5558105707168579, 'llm', 1), ('deepset-ai/haystack', 0.5541740655899048, 'llm', 1), ('qdrant/vector-db-benchmark', 0.5436363816261292, 'perf', 1), ('bigscience-workshop/petals', 0.5394517183303833, 'data', 0), ('microsoft/promptflow', 0.5394440293312073, 'llm', 0), ('mindsdb/mindsdb', 0.5393930077552795, 'data', 1), ('cheshire-cat-ai/core', 0.5356892347335815, 'llm', 0), ('tigerlab-ai/tiger', 0.531322181224823, 'llm', 0), ('zilliztech/gptcache', 0.5283774137496948, 'llm', 2), ('feast-dev/feast', 0.5253369808197021, 'ml-ops', 0), ('marqo-ai/marqo', 0.5243335366249084, 'ml', 2), ('llmware-ai/llmware', 0.5239478945732117, 'llm', 1), ('kagisearch/vectordb', 0.5210687518119812, 'data', 1), ('intel/intel-extension-for-transformers', 0.5155736207962036, 'perf', 0), ('paddlepaddle/paddlenlp', 0.5152085423469543, 'llm', 1), ('microsoft/torchscale', 0.5128360390663147, 'llm', 0), ('coleifer/peewee', 0.5083345770835876, 'data', 0), ('qdrant/fastembed', 0.507724940776825, 'ml', 1), ('vllm-project/vllm', 0.5034992694854736, 'llm', 0), ('run-llama/llama-hub', 0.5028201937675476, 'data', 0), ('ml-tooling/opyrator', 0.501854658126831, 'viz', 0)]",39,2.0,,11.44,290,201,11,0,68,107,68,289.0,239.0,90.0,0.8,58
1513,llm,https://github.com/neulab/prompt2model,"['language-model', 'deployment']",,[],[],,,,neulab/prompt2model,prompt2model,1768,152,23,Python,,prompt2model - Generate Deployable Models from Natural Language Instructions,neulab,2024-01-13,2023-03-27,44,40.05177993527508,https://avatars.githubusercontent.com/u/22324665?v=4,prompt2model - Generate Deployable Models from Natural Language Instructions,[],"['deployment', 'language-model']",2024-01-12,"[('hazyresearch/ama_prompting', 0.687862753868103, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.6848369836807251, 'llm', 1), ('1rgs/jsonformer', 0.6683309674263, 'llm', 0), ('guidance-ai/guidance', 0.6568854451179504, 'llm', 1), ('ctlllll/llm-toolmaker', 0.623613178730011, 'llm', 1), ('promptslab/promptify', 0.5863036513328552, 'nlp', 0), ('srush/minichain', 0.5800988674163818, 'llm', 0), ('conceptofmind/toolformer', 0.5687054395675659, 'llm', 1), ('yizhongw/self-instruct', 0.5685259699821472, 'llm', 1), ('agenta-ai/agenta', 0.5488535761833191, 'llm', 0), ('ai21labs/lm-evaluation', 0.5453250408172607, 'llm', 1), ('juncongmoo/pyllama', 0.5409372448921204, 'llm', 0), ('reasoning-machines/pal', 0.5284048318862915, 'llm', 1), ('cg123/mergekit', 0.5275200009346008, 'llm', 0), ('thudm/codegeex', 0.525926411151886, 'llm', 0), ('hannibal046/awesome-llm', 0.525016188621521, 'study', 1), ('bigscience-workshop/promptsource', 0.5239977240562439, 'nlp', 0), ('facebookresearch/shepherd', 0.52313631772995, 'llm', 1), ('lianjiatech/belle', 0.5222576260566711, 'llm', 0), ('lm-sys/fastchat', 0.5152702331542969, 'llm', 1), ('microsoft/autogen', 0.5132074356079102, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5068913102149963, 'llm', 1), ('defog-ai/sqlcoder', 0.5062905550003052, 'llm', 1)]",13,6.0,,3.19,33,18,10,0,9,11,9,33.0,56.0,90.0,1.7,58
1873,llm,https://github.com/llmware-ai/llmware,[],,[],[],,,,llmware-ai/llmware,llmware,1744,141,29,Python,https://pypi.org/project/llmware/,"Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.",llmware-ai,2024-01-14,2023-09-29,17,99.2520325203252,https://avatars.githubusercontent.com/u/145479774?v=4,"Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.","['ai', 'bert', 'embedding-vectors', 'embeddings', 'faiss', 'generative-ai', 'information-retrieval', 'large-language-models', 'machine-learning', 'milvus', 'nlp', 'parsing', 'pytorch', 'question-answering', 'rag', 'retrieval-augmented-generation', 'semantic-search', 'transformers']","['ai', 'bert', 'embedding-vectors', 'embeddings', 'faiss', 'generative-ai', 'information-retrieval', 'large-language-models', 'machine-learning', 'milvus', 'nlp', 'parsing', 'pytorch', 'question-answering', 'rag', 'retrieval-augmented-generation', 'semantic-search', 'transformers']",2024-01-10,"[('paddlepaddle/paddlenlp', 0.7560280561447144, 'llm', 4), ('neuml/txtai', 0.7031641602516174, 'nlp', 9), ('deepset-ai/haystack', 0.6901717185974121, 'llm', 11), ('intellabs/fastrag', 0.669150710105896, 'nlp', 6), ('explosion/spacy-llm', 0.6662053465843201, 'llm', 3), ('jina-ai/finetuner', 0.6378600597381592, 'ml', 1), ('night-chen/toolqa', 0.6234237551689148, 'llm', 2), ('argilla-io/argilla', 0.6205747723579407, 'nlp', 3), ('thilinarajapakse/simpletransformers', 0.6119190454483032, 'nlp', 2), ('deepset-ai/farm', 0.6115893721580505, 'nlp', 4), ('cheshire-cat-ai/core', 0.6055431962013245, 'llm', 1), ('mooler0410/llmspracticalguide', 0.6028851270675659, 'study', 2), ('hegelai/prompttools', 0.6007319092750549, 'llm', 3), ('nvidia/deeplearningexamples', 0.5966246128082275, 'ml-dl', 3), ('young-geng/easylm', 0.593721330165863, 'llm', 1), ('alibaba/easynlp', 0.592271625995636, 'nlp', 5), ('lm-sys/fastchat', 0.5916407704353333, 'llm', 0), ('ddangelov/top2vec', 0.5913721323013306, 'nlp', 2), ('microsoft/generative-ai-for-beginners', 0.5911704301834106, 'study', 4), ('rcgai/simplyretrieve', 0.5904434323310852, 'llm', 5), ('huggingface/transformers', 0.5898783206939697, 'nlp', 4), ('jonasgeiping/cramming', 0.5896017551422119, 'nlp', 1), ('eugeneyan/obsidian-copilot', 0.5871189832687378, 'llm', 3), ('nebuly-ai/nebullvm', 0.5862234830856323, 'perf', 2), ('jina-ai/clip-as-service', 0.5855153203010559, 'nlp', 2), ('extreme-bert/extreme-bert', 0.581847071647644, 'llm', 4), ('maartengr/bertopic', 0.5808184742927551, 'nlp', 4), ('confident-ai/deepeval', 0.5799471735954285, 'testing', 0), ('tigerlab-ai/tiger', 0.5777447819709778, 'llm', 2), ('pathwaycom/llm-app', 0.5754697322845459, 'llm', 3), ('lianjiatech/belle', 0.5731709003448486, 'llm', 0), ('chroma-core/chroma', 0.5633347034454346, 'data', 1), ('openbmb/toolbench', 0.5629839301109314, 'llm', 0), ('infinitylogesh/mutate', 0.5625592470169067, 'nlp', 0), ('arize-ai/phoenix', 0.5607779026031494, 'ml-interpretability', 0), ('graykode/nlp-tutorial', 0.559558093547821, 'study', 3), ('rasahq/rasa', 0.5576108694076538, 'llm', 2), ('microsoft/lmops', 0.5573378205299377, 'llm', 1), ('mindsdb/mindsdb', 0.55570387840271, 'data', 3), ('eleutherai/the-pile', 0.5548039078712463, 'data', 0), ('explosion/spacy', 0.5547406077384949, 'nlp', 3), ('explosion/thinc', 0.5531507730484009, 'ml-dl', 4), ('explosion/spacy-models', 0.5522136092185974, 'nlp', 2), ('ludwig-ai/ludwig', 0.5516101717948914, 'ml-ops', 2), ('lucidrains/toolformer-pytorch', 0.5493955016136169, 'llm', 1), ('plasticityai/magnitude', 0.5475419759750366, 'nlp', 3), ('salesforce/xgen', 0.5470395684242249, 'llm', 2), ('deeppavlov/deeppavlov', 0.5467641353607178, 'nlp', 4), ('bentoml/bentoml', 0.5451672673225403, 'ml-ops', 3), ('databrickslabs/dolly', 0.5424628257751465, 'llm', 0), ('flairnlp/flair', 0.5408936738967896, 'nlp', 3), ('huggingface/text-generation-inference', 0.5404112339019775, 'llm', 2), ('mlc-ai/mlc-llm', 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1267,perf,https://github.com/intel/intel-extension-for-transformers,[],,[],[],,,,intel/intel-extension-for-transformers,intel-extension-for-transformers,1672,166,25,C++,,⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡,intel,2024-01-14,2022-11-11,63,26.30112359550562,https://avatars.githubusercontent.com/u/17888862?v=4,⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡,"['4-bits', 'attention-sink', 'chatbot', 'chatpdf', 'cpu', 'gaudi2', 'gpu', 'habana', 'intel-optimized-llamacpp', 'large-language-model', 'llm-cpu', 'llm-inference', 'neural-chat', 'neural-chat-7b', 'neurips2023', 'pc', 'speculative-decoding', 'streamingllm', 'xeon']","['4-bits', 'attention-sink', 'chatbot', 'chatpdf', 'cpu', 'gaudi2', 'gpu', 'habana', 'intel-optimized-llamacpp', 'large-language-model', 'llm-cpu', 'llm-inference', 'neural-chat', 'neural-chat-7b', 'neurips2023', 'pc', 'speculative-decoding', 'streamingllm', 'xeon']",2024-01-13,"[('bigscience-workshop/petals', 0.7305524945259094, 'data', 1), ('nomic-ai/gpt4all', 0.7130550742149353, 'llm', 2), ('h2oai/h2o-llmstudio', 0.6861023902893066, 'llm', 1), ('pathwaycom/llm-app', 0.6731693148612976, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.6634297966957092, 'llm', 1), ('hwchase17/langchain', 0.662899911403656, 'llm', 1), ('microsoft/promptflow', 0.6592389345169067, 'llm', 0), ('bobazooba/xllm', 0.6433131694793701, 'llm', 0), ('deepset-ai/haystack', 0.6430325508117676, 'llm', 0), ('zilliztech/gptcache', 0.6376039981842041, 'llm', 1), ('microsoft/semantic-kernel', 0.6336042284965515, 'llm', 0), ('run-llama/rags', 0.6312287449836731, 'llm', 1), ('microsoft/autogen', 0.6164318323135376, 'llm', 2), ('microsoft/llmlingua', 0.6135388016700745, 'llm', 0), ('embedchain/embedchain', 0.6132449507713318, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.608906626701355, 'llm', 0), ('hiyouga/llama-factory', 0.6089065670967102, 'llm', 0), ('li-plus/chatglm.cpp', 0.6037748456001282, 'llm', 0), ('thudm/chatglm2-6b', 0.6007229685783386, 'llm', 0), ('vllm-project/vllm', 0.5956281423568726, 'llm', 0), ('fasteval/fasteval', 0.5938782095909119, 'llm', 0), ('bentoml/openllm', 0.5912082195281982, 'ml-ops', 1), ('predibase/lorax', 0.5878379940986633, 'llm', 2), ('young-geng/easylm', 0.5872876048088074, 'llm', 1), ('mlc-ai/web-llm', 0.5826047658920288, 'llm', 0), ('chainlit/chainlit', 0.5824191570281982, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5800312757492065, 'llm', 0), ('salesforce/xgen', 0.5792416334152222, 'llm', 0), ('shishirpatil/gorilla', 0.5774163603782654, 'llm', 0), ('mmabrouk/chatgpt-wrapper', 0.5764015913009644, 'llm', 1), ('tigerlab-ai/tiger', 0.5748347640037537, 'llm', 0), ('lightning-ai/lit-gpt', 0.5745608806610107, 'llm', 0), ('nebuly-ai/nebullvm', 0.5715976357460022, 'perf', 0), ('rcgai/simplyretrieve', 0.5712392330169678, 'llm', 0), ('microsoft/torchscale', 0.570101261138916, 'llm', 0), ('microsoft/promptcraft-robotics', 0.5677699446678162, 'sim', 0), ('paddlepaddle/paddlenlp', 0.5665012001991272, 'llm', 0), ('run-llama/llama-hub', 0.5623946189880371, 'data', 0), ('alpha-vllm/llama2-accessory', 0.5544970035552979, 'llm', 0), ('dylanhogg/llmgraph', 0.5522487163543701, 'ml', 1), ('lightning-ai/lit-llama', 0.5519506335258484, 'llm', 0), ('confident-ai/deepeval', 0.5511168241500854, 'testing', 0), ('chatarena/chatarena', 0.5510158538818359, 'llm', 0), ('eugeneyan/open-llms', 0.5479944944381714, 'study', 0), ('ludwig-ai/ludwig', 0.5449299216270447, 'ml-ops', 0), ('cheshire-cat-ai/core', 0.5444093346595764, 'llm', 1), ('next-gpt/next-gpt', 0.5425037145614624, 'llm', 0), ('agenta-ai/agenta', 0.5398895144462585, 'llm', 0), ('nat/openplayground', 0.5346694588661194, 'llm', 0), ('mlc-ai/mlc-llm', 0.5288311243057251, 'llm', 0), ('salesforce/codet5', 0.5260124802589417, 'nlp', 0), ('jzhang38/tinyllama', 0.5206746459007263, 'llm', 0), ('titanml/takeoff', 0.5163436532020569, 'llm', 0), ('ray-project/ray-llm', 0.5162841081619263, 'llm', 1), ('haotian-liu/llava', 0.5162800550460815, 'llm', 1), ('lancedb/lancedb', 0.5155736207962036, 'data', 0), ('microsoft/jarvis', 0.5154780149459839, 'llm', 0), ('prefecthq/marvin', 0.5106049180030823, 'nlp', 0), ('lm-sys/fastchat', 0.5099033117294312, 'llm', 1), ('xtekky/gpt4free', 0.5091184973716736, 'llm', 1), ('mnotgod96/appagent', 0.5081307888031006, 'llm', 0), ('artidoro/qlora', 0.5075502395629883, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5048488974571228, 'study', 0), ('microsoft/lmops', 0.5034509301185608, 'llm', 0), ('tloen/alpaca-lora', 0.5027761459350586, 'llm', 0), ('explosion/spacy-llm', 0.5016040205955505, 'llm', 0), ('argilla-io/argilla', 0.5005181431770325, 'nlp', 0), ('aws-samples/serverless-pdf-chat', 0.5002910494804382, 'llm', 0)]",91,2.0,,24.88,702,650,14,0,8,15,8,702.0,760.0,90.0,1.1,58
1760,term,https://github.com/tconbeer/harlequin,"['tool', 'sql', 'data']",,[],[],1.0,,,tconbeer/harlequin,harlequin,1637,30,12,Python,https://harlequin.sh,The SQL IDE for Your Terminal.,tconbeer,2024-01-14,2023-05-02,39,41.97435897435897,,The SQL IDE for Your Terminal.,[],"['data', 'sql', 'tool']",2024-01-12,"[('tconbeer/sqlfmt', 0.569164514541626, 'data', 1), ('tiangolo/sqlmodel', 0.569147527217865, 'data', 1), ('sqlalchemy/sqlalchemy', 0.5578516721725464, 'data', 1), ('simonw/sqlite-utils', 0.5377789735794067, 'data', 0), ('saulpw/visidata', 0.5237422585487366, 'term', 0), ('methexis-inc/terminal-copilot', 0.5151594877243042, 'util', 0), ('ibis-project/ibis', 0.5111363530158997, 'data', 1)]",8,6.0,,4.44,131,112,9,0,50,68,50,131.0,88.0,90.0,0.7,58
1123,ml-rl,https://github.com/pytorch/rl,['reinforcement-learning'],,[],[],,,,pytorch/rl,rl,1621,212,40,Python,https://pytorch.org/rl,"A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.",pytorch,2024-01-13,2022-02-01,104,15.586538461538462,https://avatars.githubusercontent.com/u/21003710?v=4,"A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.","['ai', 'control', 'decision-making', 'distributed-computing', 'machine-learning', 'marl', 'model-based-reinforcement-learning', 'multi-agent-reinforcement-learning', 'pytorch', 'reinforcement-learning', 'rl', 'robotics', 'torch']","['ai', 'control', 'decision-making', 'distributed-computing', 'machine-learning', 'marl', 'model-based-reinforcement-learning', 'multi-agent-reinforcement-learning', 'pytorch', 'reinforcement-learning', 'rl', 'robotics', 'torch']",2024-01-14,"[('thu-ml/tianshou', 0.7527033090591431, 'ml-rl', 2), ('denys88/rl_games', 0.7474822402000427, 'ml-rl', 2), ('tensorlayer/tensorlayer', 0.7300659418106079, 'ml-rl', 1), ('humancompatibleai/imitation', 0.6811489462852478, 'ml-rl', 0), ('deepmind/acme', 0.6701556444168091, 'ml-rl', 1), ('pytorch/ignite', 0.6654600501060486, 'ml-dl', 2), ('google/dopamine', 0.6595585942268372, 'ml-rl', 2), ('shangtongzhang/reinforcement-learning-an-introduction', 0.6356885433197021, 'study', 1), ('skorch-dev/skorch', 0.6245636940002441, 'ml-dl', 2), ('facebookresearch/habitat-lab', 0.6235378384590149, 'sim', 3), ('unity-technologies/ml-agents', 0.6213619709014893, 'ml-rl', 2), ('keras-rl/keras-rl', 0.6202392578125, 'ml-rl', 2), ('pettingzoo-team/pettingzoo', 0.6150112748146057, 'ml-rl', 2), ('mrdbourke/pytorch-deep-learning', 0.6149892210960388, 'study', 2), ('arise-initiative/robosuite', 0.6132838129997253, 'ml-rl', 2), ('farama-foundation/gymnasium', 0.6115036010742188, 'ml-rl', 1), ('salesforce/warp-drive', 0.6080675721168518, 'ml-rl', 2), ('intel/intel-extension-for-pytorch', 0.6046189069747925, 'perf', 2), ('ai4finance-foundation/finrl', 0.5981391072273254, 'finance', 1), ('google/trax', 0.5977087020874023, 'ml-dl', 2), ('karpathy/micrograd', 0.5971662402153015, 'study', 0), ('openai/gym', 0.5818626880645752, 'ml-rl', 1), ('huggingface/transformers', 0.5793347954750061, 'nlp', 2), ('pyg-team/pytorch_geometric', 0.5780683755874634, 'ml-dl', 1), ('rasbt/machine-learning-book', 0.5708683729171753, 'study', 2), ('explosion/thinc', 0.5706343650817871, 'ml-dl', 3), ('allenai/allennlp', 0.5637892484664917, 'nlp', 1), ('ray-project/ray', 0.5598548054695129, 'ml-ops', 3), ('nvidia/apex', 0.552091121673584, 'ml-dl', 0), ('lightly-ai/lightly', 0.5465163588523865, 'ml', 2), ('openai/baselines', 0.5460628867149353, 'ml-rl', 0), ('pyro-ppl/pyro', 0.5450016856193542, 'ml-dl', 2), ('nvidia-omniverse/omniisaacgymenvs', 0.5446157455444336, 'sim', 0), ('kornia/kornia', 0.5435752272605896, 'ml-dl', 3), ('kzl/decision-transformer', 0.5421603322029114, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.539577305316925, 'ml', 2), ('determined-ai/determined', 0.5359745025634766, 'ml-ops', 2), ('projectmesa/mesa', 0.5338829159736633, 'sim', 0), ('facebookresearch/theseus', 0.5316617488861084, 'math', 2), ('nvidia-omniverse/orbit', 0.5298061966896057, 'sim', 1), ('merantix-momentum/squirrel-core', 0.527829647064209, 'ml', 3), ('pytorch/data', 0.5270489454269409, 'data', 0), ('ddbourgin/numpy-ml', 0.5264372229576111, 'ml', 2), ('keras-team/keras', 0.5254106521606445, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.5253444314002991, 'util', 1), ('aws/sagemaker-python-sdk', 0.5228525996208191, 'ml', 2), ('inspirai/timechamber', 0.5206745862960815, 'sim', 1), ('lukaszahradnik/pyneuralogic', 0.5153552889823914, 'math', 2), ('probml/pyprobml', 0.514581561088562, 'ml', 2), ('pytorch/captum', 0.5139393210411072, 'ml-interpretability', 0), ('xl0/lovely-tensors', 0.5124273896217346, 'ml-dl', 1), ('intellabs/bayesian-torch', 0.5093467831611633, 'ml', 1), ('tensorflow/tensorflow', 0.508491039276123, 'ml-dl', 1), ('deepmind/dm_control', 0.507379412651062, 'ml-rl', 2), ('operand/agency', 0.5073025822639465, 'llm', 2), ('mlflow/mlflow', 0.5072483420372009, 'ml-ops', 2), ('bulletphysics/bullet3', 0.505810022354126, 'sim', 2), ('huggingface/huggingface_hub', 0.5057786703109741, 'ml', 2), ('google/tf-quant-finance', 0.5055859088897705, 'finance', 0), ('d2l-ai/d2l-en', 0.5053571462631226, 'study', 3), ('facebookresearch/pytorch3d', 0.5044132471084595, 'ml-dl', 0), ('huggingface/accelerate', 0.5024619102478027, 'ml', 0), ('facebookresearch/reagent', 0.5019720196723938, 'ml-rl', 0), ('ggerganov/ggml', 0.5019291043281555, 'ml', 1), ('horovod/horovod', 0.5009804368019104, 'ml-ops', 2)]",130,4.0,,11.87,212,172,24,0,8,7,8,212.0,568.0,90.0,2.7,58
193,template,https://github.com/tiangolo/full-stack-fastapi-postgresql,[],,[],[],,,,tiangolo/full-stack-fastapi-postgresql,full-stack-fastapi-postgresql,14174,2531,249,TypeScript,,"Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.",tiangolo,2024-01-14,2019-02-23,257,55.0599334073252,,"Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.","['backend', 'celery', 'cookiecutter', 'docker', 'fastapi', 'frontend', 'json', 'json-schema', 'jwt', 'letsencrypt', 'openapi', 'openapi3', 'pgadmin', 'postgresql', 'swagger', 'traefik', 'vue', 'vuex']","['backend', 'celery', 'cookiecutter', 'docker', 'fastapi', 'frontend', 'json', 'json-schema', 'jwt', 'letsencrypt', 'openapi', 'openapi3', 'pgadmin', 'postgresql', 'swagger', 'traefik', 'vue', 'vuex']",2023-12-27,"[('tiangolo/fastapi', 0.6879447102546692, 'web', 6), ('piccolo-orm/piccolo_admin', 0.6423637866973877, 'data', 2), ('vitalik/django-ninja', 0.6353817582130432, 'web', 2), ('rawheel/fastapi-boilerplate', 0.6298112869262695, 'web', 3), ('aeternalis-ingenium/fastapi-backend-template', 0.6168069839477539, 'web', 4), ('hugapi/hug', 0.5920613408088684, 'util', 0), ('starlite-api/starlite', 0.5915384292602539, 'web', 2), ('python-restx/flask-restx', 0.5876633524894714, 'web', 2), ('simonw/datasette', 0.5789318084716797, 'data', 2), ('huge-success/sanic', 0.5729467868804932, 'web', 0), ('airbytehq/airbyte', 0.5710554718971252, 'data', 1), ('zenodo/zenodo', 0.5640177726745605, 'util', 1), ('awtkns/fastapi-crudrouter', 0.5604096055030823, 'web', 2), ('prefecthq/server', 0.5580776333808899, 'util', 0), ('orchest/orchest', 0.5576450228691101, 'ml-ops', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5566263198852539, 'template', 1), ('s3rius/fastapi-template', 0.5562103986740112, 'web', 2), ('coleifer/peewee', 0.5561289191246033, 'data', 0), ('alphasecio/langchain-examples', 0.5519778728485107, 'llm', 0), ('willmcgugan/textual', 0.5517593026161194, 'term', 0), ('falconry/falcon', 0.5487000942230225, 'web', 0), ('dmontagu/fastapi_client', 0.5413647890090942, 'web', 0), ('buuntu/fastapi-react', 0.5327474474906921, 'template', 4), ('avaiga/taipy', 0.5292978286743164, 'data', 0), ('tiangolo/sqlmodel', 0.5256924629211426, 'data', 3), ('shishirpatil/gorilla', 0.5247654914855957, 'llm', 0), ('ajndkr/lanarky', 0.5205399394035339, 'llm', 1), ('pallets/werkzeug', 0.520187497138977, 'web', 0), ('gefyrahq/gefyra', 0.5177972912788391, 'util', 1), ('pallets/flask', 0.5168726444244385, 'web', 0), ('flet-dev/flet', 0.5064703822135925, 'web', 0), ('flyteorg/flyte', 0.5061784386634827, 'ml-ops', 0), ('pyeve/eve', 0.5053060054779053, 'web', 0), ('kestra-io/kestra', 0.501535952091217, 'ml-ops', 0), ('plotly/dash', 0.5003242492675781, 'viz', 0)]",21,4.0,,0.52,63,36,60,1,0,1,1,63.0,79.0,90.0,1.3,57
1716,util,https://github.com/google/yapf,['code-quality'],,[],[],,,,google/yapf,yapf,13543,958,214,Python,,A formatter for Python files,google,2024-01-14,2015-03-18,462,29.25956790123457,https://avatars.githubusercontent.com/u/1342004?v=4,A formatter for Python files,"['formatter', 'google']","['code-quality', 'formatter', 'google']",2023-11-08,"[('grantjenks/blue', 0.749129056930542, 'util', 2), ('hhatto/autopep8', 0.7038267850875854, 'util', 1), ('psf/black', 0.6890390515327454, 'util', 2), ('danielnoord/pydocstringformatter', 0.6070597171783447, 'util', 1), ('astral-sh/ruff', 0.6007269024848938, 'util', 1), ('pycqa/isort', 0.5961623191833496, 'util', 2), ('google/latexify_py', 0.5899499654769897, 'util', 0), ('landscapeio/prospector', 0.5676681995391846, 'util', 0), ('google/pytype', 0.5599178075790405, 'typing', 1), ('pygments/pygments', 0.5552298426628113, 'util', 0), ('python-markdown/markdown', 0.5518056154251099, 'util', 0), ('pycqa/flake8', 0.5436343550682068, 'util', 1), ('rubik/radon', 0.5423753261566162, 'util', 0), ('jendrikseipp/vulture', 0.5393766164779663, 'util', 1), ('pycqa/pyflakes', 0.5379751920700073, 'util', 0), ('nedbat/coveragepy', 0.535642147064209, 'testing', 0), ('imageio/imageio', 0.5354976654052734, 'util', 0), ('pytoolz/toolz', 0.5353810787200928, 'util', 0), ('agronholm/typeguard', 0.5313608050346375, 'typing', 1), ('microsoft/pyright', 0.5311009287834167, 'typing', 1), ('hadialqattan/pycln', 0.5293893218040466, 'util', 0), ('connorferster/handcalcs', 0.5293661952018738, 'jupyter', 0), ('willmcgugan/rich', 0.5237478017807007, 'term', 0), ('dask/fastparquet', 0.5155162811279297, 'data', 0), ('fsspec/filesystem_spec', 0.5129982233047485, 'util', 0), ('instagram/monkeytype', 0.5119272470474243, 'typing', 1), ('dosisod/refurb', 0.5096585154533386, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5080813765525818, 'study', 0), ('pyutils/line_profiler', 0.5072764158248901, 'profiling', 0), ('pyfpdf/fpdf2', 0.5062951445579529, 'util', 0), ('pyston/pyston', 0.5001851320266724, 'util', 0)]",151,4.0,,2.19,39,14,107,2,0,8,8,39.0,46.0,90.0,1.2,57
1045,nlp,https://github.com/jina-ai/clip-as-service,[],,[],[],,,,jina-ai/clip-as-service,clip-as-service,12043,2056,217,Python,https://clip-as-service.jina.ai,"🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP",jina-ai,2024-01-13,2018-11-12,272,44.25249343832021,https://avatars.githubusercontent.com/u/60539444?v=4,"🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP","['bert', 'bert-as-service', 'clip-as-service', 'clip-model', 'cross-modal-retrieval', 'cross-modality', 'deep-learning', 'image2vec', 'multi-modality', 'neural-search', 'onnx', 'openai', 'pytorch', 'sentence-encoding', 'sentence2vec']","['bert', 'bert-as-service', 'clip-as-service', 'clip-model', 'cross-modal-retrieval', 'cross-modality', 'deep-learning', 'image2vec', 'multi-modality', 'neural-search', 'onnx', 'openai', 'pytorch', 'sentence-encoding', 'sentence2vec']",2023-12-20,"[('jina-ai/finetuner', 0.7554095387458801, 'ml', 2), ('ukplab/sentence-transformers', 0.7321420311927795, 'nlp', 0), ('rom1504/clip-retrieval', 0.6420944929122925, 'ml', 1), ('amansrivastava17/embedding-as-service', 0.6331066489219666, 'nlp', 4), ('openai/clip', 0.6114118695259094, 'ml-dl', 1), ('alibaba/easynlp', 0.6006197333335876, 'nlp', 3), ('paddlepaddle/paddlenlp', 0.5931671261787415, 'llm', 2), ('ddangelov/top2vec', 0.5902553796768188, 'nlp', 1), ('llmware-ai/llmware', 0.5855153203010559, 'llm', 2), ('qdrant/fastembed', 0.584290087223053, 'ml', 1), ('neuml/txtai', 0.5824137330055237, 'nlp', 1), ('extreme-bert/extreme-bert', 0.5639864802360535, 'llm', 3), ('plasticityai/magnitude', 0.5636166334152222, 'nlp', 0), ('intellabs/fastrag', 0.561098039150238, 'nlp', 0), ('nomic-ai/nomic', 0.5556612610816956, 'nlp', 0), ('koaning/whatlies', 0.553788423538208, 'nlp', 0), ('graykode/nlp-tutorial', 0.5440134406089783, 'study', 2), ('chroma-core/chroma', 0.5438365340232849, 'data', 0), ('deepset-ai/farm', 0.5426168441772461, 'nlp', 3), ('muennighoff/sgpt', 0.5414046049118042, 'llm', 1), ('huggingface/transformers', 0.540093183517456, 'nlp', 3), ('docarray/docarray', 0.5395568609237671, 'data', 3), ('lucidrains/imagen-pytorch', 0.5368449091911316, 'ml-dl', 1), ('koaning/embetter', 0.5349407196044922, 'data', 0), ('nvidia/deeplearningexamples', 0.5337973833084106, 'ml-dl', 2), ('jina-ai/vectordb', 0.5312561988830566, 'data', 1), ('facebookresearch/mmf', 0.52168208360672, 'ml-dl', 2), ('explosion/thinc', 0.5208788514137268, 'ml-dl', 2), ('nvlabs/prismer', 0.5206122994422913, 'diffusion', 0), ('milvus-io/bootcamp', 0.5121859312057495, 'data', 1), ('maartengr/bertopic', 0.5079023838043213, 'nlp', 1), ('paddlepaddle/rocketqa', 0.506847620010376, 'nlp', 0), ('deeppavlov/deeppavlov', 0.5053911805152893, 'nlp', 1), ('luodian/otter', 0.5049297213554382, 'llm', 2), ('ofa-sys/ofa', 0.5020403265953064, 'llm', 0), ('huggingface/text-embeddings-inference', 0.5006672143936157, 'llm', 0)]",66,4.0,,0.31,16,7,63,1,2,24,2,16.0,32.0,90.0,2.0,57
598,ml,https://github.com/cleanlab/cleanlab,[],,[],[],,,,cleanlab/cleanlab,cleanlab,7697,619,79,Python,https://cleanlab.ai,"The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.",cleanlab,2024-01-13,2018-05-11,298,25.779425837320574,https://avatars.githubusercontent.com/u/90712480?v=4,"The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.","['active-learning', 'annotation', 'data-analysis', 'data-centric-ai', 'data-cleaning', 'data-curation', 'data-labeling', 'data-profiling', 'data-quality', 'data-science', 'data-validation', 'dataops', 'dataquality', 'datasets', 'labeling', 'llms', 'noisy-labels', 'out-of-distribution-detection', 'outlier-detection', 'weak-supervision']","['active-learning', 'annotation', 'data-analysis', 'data-centric-ai', 'data-cleaning', 'data-curation', 'data-labeling', 'data-profiling', 'data-quality', 'data-science', 'data-validation', 'dataops', 'dataquality', 'datasets', 'labeling', 'llms', 'noisy-labels', 'out-of-distribution-detection', 'outlier-detection', 'weak-supervision']",2024-01-12,"[('ydataai/ydata-quality', 0.583878219127655, 'data', 0), ('whylabs/whylogs', 0.5660983324050903, 'util', 3), ('doccano/doccano', 0.5571958422660828, 'nlp', 2), ('csinva/imodels', 0.5562312602996826, 'ml', 1), ('netflix/metaflow', 0.5494846105575562, 'ml-ops', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5334048867225647, 'study', 0), ('koaning/embetter', 0.5257184505462646, 'data', 1), ('argilla-io/argilla', 0.5151708722114563, 'nlp', 2), ('bentoml/bentoml', 0.5148969292640686, 'ml-ops', 0), ('koaning/bulk', 0.5123386383056641, 'data', 1), ('polyaxon/datatile', 0.5088824033737183, 'pandas', 4), ('makcedward/nlpaug', 0.505994439125061, 'nlp', 1), ('mlflow/mlflow', 0.5033455491065979, 'ml-ops', 0)]",44,3.0,,5.56,135,69,69,0,4,2,4,135.0,157.0,90.0,1.2,57
552,ml-dl,https://github.com/arogozhnikov/einops,[],,[],[],,,,arogozhnikov/einops,einops,7548,328,69,Python,https://einops.rocks,"Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)",arogozhnikov,2024-01-13,2018-09-22,279,27.012269938650306,,"Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)","['chainer', 'cupy', 'deep-learning', 'einops', 'jax', 'keras', 'numpy', 'pytorch', 'tensor', 'tensorflow']","['chainer', 'cupy', 'deep-learning', 'einops', 'jax', 'keras', 'numpy', 'pytorch', 'tensor', 'tensorflow']",2024-01-11,"[('tensorly/tensorly', 0.7486700415611267, 'ml-dl', 6), ('ggerganov/ggml', 0.674818754196167, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.6702998876571655, 'perf', 2), ('rafiqhasan/auto-tensorflow', 0.6536889672279358, 'ml-dl', 1), ('huggingface/transformers', 0.6441165804862976, 'nlp', 4), ('patrick-kidger/torchtyping', 0.6377165913581848, 'typing', 1), ('tensorflow/addons', 0.6348954439163208, 'ml', 2), ('pytorch/ignite', 0.6346755027770996, 'ml-dl', 2), ('xl0/lovely-tensors', 0.6286166310310364, 'ml-dl', 2), ('pytorch/pytorch', 0.624183177947998, 'ml-dl', 3), ('nvidia/apex', 0.6182481646537781, 'ml-dl', 0), ('horovod/horovod', 0.6111495494842529, 'ml-ops', 4), ('google/tf-quant-finance', 0.6102033853530884, 'finance', 1), ('tlkh/tf-metal-experiments', 0.6099873781204224, 'perf', 2), ('karpathy/micrograd', 0.600235104560852, 'study', 0), ('skorch-dev/skorch', 0.599236786365509, 'ml-dl', 1), ('tensorflow/similarity', 0.5858622193336487, 'ml-dl', 2), ('rentruewang/koila', 0.5856212973594666, 'ml', 2), ('explosion/thinc', 0.5852126479148865, 'ml-dl', 4), ('google/gin-config', 0.5846500396728516, 'util', 1), ('keras-team/keras', 0.580649197101593, 'ml-dl', 4), ('tensorflow/mesh', 0.5747230052947998, 'ml-dl', 0), ('nvidia/tensorrt-llm', 0.5731545686721802, 'viz', 0), ('nvidia/deeplearningexamples', 0.5647768378257751, 'ml-dl', 3), ('keras-team/keras-nlp', 0.5635877251625061, 'nlp', 3), ('huggingface/accelerate', 0.5633874535560608, 'ml', 0), ('rasbt/machine-learning-book', 0.5617372989654541, 'study', 2), ('mrdbourke/m1-machine-learning-test', 0.556743323802948, 'ml', 1), ('nyandwi/modernconvnets', 0.5543664693832397, 'ml-dl', 2), ('huggingface/exporters', 0.5518941283226013, 'ml', 3), ('google/jax', 0.5517240762710571, 'ml', 2), ('ashleve/lightning-hydra-template', 0.5508671998977661, 'util', 2), ('cupy/cupy', 0.5499424934387207, 'math', 3), ('blackhc/toma', 0.5450114011764526, 'ml-dl', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5417112112045288, 'study', 0), ('neuralmagic/sparseml', 0.5392698645591736, 'ml-dl', 3), ('ageron/handson-ml2', 0.5353853702545166, 'ml', 0), ('facebookresearch/pytorch3d', 0.5348989963531494, 'ml-dl', 0), ('pytorch/data', 0.5333592891693115, 'data', 0), ('google/trax', 0.5327727198600769, 'ml-dl', 3), ('mrdbourke/pytorch-deep-learning', 0.528740644454956, 'study', 2), ('pypy/pypy', 0.5274959206581116, 'util', 0), ('pytorch/captum', 0.524075984954834, 'ml-interpretability', 0), ('pytoolz/toolz', 0.5189169645309448, 'util', 0), ('tensorflow/tensorflow', 0.515922486782074, 'ml-dl', 2), ('ddbourgin/numpy-ml', 0.5156410336494446, 'ml', 0), ('deepmind/dm-haiku', 0.5126287341117859, 'ml-dl', 2), ('microsoft/onnxruntime', 0.5109891295433044, 'ml', 3), ('pyg-team/pytorch_geometric', 0.5090392231941223, 'ml-dl', 2), ('onnx/onnx', 0.5081114172935486, 'ml', 4), ('denys88/rl_games', 0.5077354311943054, 'ml-rl', 2), ('uber/petastorm', 0.5071843862533569, 'data', 3), ('graykode/nlp-tutorial', 0.5046815276145935, 'study', 2), ('tensorflow/lucid', 0.5045328140258789, 'ml-interpretability', 1), ('tensorlayer/tensorlayer', 0.5044419765472412, 'ml-rl', 2), ('nvidia/cuda-python', 0.5039113759994507, 'ml', 0), ('ml-explore/mlx', 0.5031680464744568, 'ml', 2), ('aistream-peelout/flow-forecast', 0.5023390650749207, 'time-series', 2), ('salesforce/deeptime', 0.5012999773025513, 'time-series', 1), ('huggingface/huggingface_hub', 0.5012728571891785, 'ml', 2), ('timdettmers/bitsandbytes', 0.5005117654800415, 'util', 0)]",26,8.0,,1.92,16,10,65,0,5,2,5,16.0,22.0,90.0,1.4,57
1142,util,https://github.com/eternnoir/pytelegrambotapi,[],,[],[],,,,eternnoir/pytelegrambotapi,pyTelegramBotAPI,7429,1975,225,Python,,Python Telegram bot api.,eternnoir,2024-01-14,2015-06-26,448,16.561464968152865,,Python Telegram bot api.,"['bot-api', 'python-api', 'telegram', 'telegram-bot', 'telegram-bot-api']","['bot-api', 'python-api', 'telegram', 'telegram-bot', 'telegram-bot-api']",2024-01-12,"[('mitmproxy/pdoc', 0.5450152158737183, 'util', 0), ('openai/gpt-discord-bot', 0.5340373516082764, 'llm', 0), ('pdoc3/pdoc', 0.5075708031654358, 'util', 0), ('hugapi/hug', 0.507483184337616, 'util', 1), ('freqtrade/freqtrade', 0.5027558207511902, 'crypto', 1), ('minimaxir/simpleaichat', 0.5022081732749939, 'llm', 0), ('vitalik/django-ninja', 0.5017293095588684, 'web', 0), ('togethercomputer/openchatkit', 0.5013675093650818, 'nlp', 0)]",228,2.0,,4.0,66,63,104,0,7,8,7,67.0,200.0,90.0,3.0,57
409,web,https://github.com/encode/uvicorn,[],,[],[],,,,encode/uvicorn,uvicorn,7420,685,91,Python,https://www.uvicorn.org/,"An ASGI web server, for Python. 🦄",encode,2024-01-14,2017-05-31,347,21.3305954825462,https://avatars.githubusercontent.com/u/19159390?v=4,"An ASGI web server, for Python. 🦄","['asgi', 'asyncio', 'http', 'http-server']","['asgi', 'asyncio', 'http', 'http-server']",2024-01-03,"[('neoteroi/blacksheep', 0.8586666584014893, 'web', 4), ('encode/httpx', 0.8501601815223694, 'web', 2), ('pallets/quart', 0.8250173926353455, 'web', 3), ('aio-libs/aiohttp', 0.7939640879631042, 'web', 3), ('encode/starlette', 0.6668508052825928, 'web', 1), ('falconry/falcon', 0.6588360667228699, 'web', 2), ('klen/muffin', 0.6518058180809021, 'web', 2), ('cherrypy/cherrypy', 0.6493438482284546, 'web', 2), ('pylons/waitress', 0.6356403827667236, 'web', 1), ('huge-success/sanic', 0.633416473865509, 'web', 2), ('timofurrer/awesome-asyncio', 0.618058979511261, 'study', 1), ('psf/requests', 0.6152986884117126, 'web', 1), ('starlite-api/starlite', 0.6137245893478394, 'web', 2), ('alirn76/panther', 0.6067794561386108, 'web', 0), ('miguelgrinberg/python-socketio', 0.5958155989646912, 'util', 1), ('pallets/flask', 0.5909908413887024, 'web', 0), ('pallets/werkzeug', 0.5832026600837708, 'web', 1), ('jordaneremieff/mangum', 0.5822369456291199, 'web', 2), ('requests/toolbelt', 0.5721771717071533, 'util', 1), ('webpy/webpy', 0.5704232454299927, 'web', 0), ('reflex-dev/reflex', 0.5666500926017761, 'web', 0), ('masoniteframework/masonite', 0.5623204708099365, 'web', 0), ('pylons/pyramid', 0.5620464086532593, 'web', 0), ('benoitc/gunicorn', 0.5551705360412598, 'web', 2), ('python-trio/trio', 0.5339199304580688, 'perf', 0), ('bottlepy/bottle', 0.5337467193603516, 'web', 0), ('samuelcolvin/aioaws', 0.5331629514694214, 'data', 1), ('simple-salesforce/simple-salesforce', 0.5294420123100281, 'data', 0), ('emmett-framework/emmett', 0.514312207698822, 'web', 2), ('hugapi/hug', 0.5106011629104614, 'util', 2), ('sumerc/yappi', 0.5030171871185303, 'profiling', 2), ('websocket-client/websocket-client', 0.5029712319374084, 'web', 0), ('ets-labs/python-dependency-injector', 0.5008002519607544, 'util', 1)]",174,4.0,,2.46,70,45,81,0,9,23,9,70.0,69.0,90.0,1.0,57
770,util,https://github.com/google/latexify_py,[],,[],[],,,,google/latexify_py,latexify_py,6714,366,56,Python,,A library to generate LaTeX expression from Python code.,google,2024-01-13,2020-07-25,183,36.60280373831776,https://avatars.githubusercontent.com/u/1342004?v=4,A library to generate LaTeX expression from Python code.,[],[],2023-12-08,"[('connorferster/handcalcs', 0.7623890042304993, 'jupyter', 0), ('pytoolz/toolz', 0.6760282516479492, 'util', 0), ('julienpalard/pipe', 0.5925378799438477, 'util', 0), ('google/yapf', 0.5899499654769897, 'util', 0), ('pyston/pyston', 0.5858403444290161, 'util', 0), ('python/cpython', 0.5829751491546631, 'util', 0), ('pypy/pypy', 0.5807206630706787, 'util', 0), ('hhatto/autopep8', 0.5800272822380066, 'util', 0), ('pyparsing/pyparsing', 0.5657850503921509, 'util', 0), ('sympy/sympy', 0.5628668069839478, 'math', 0), ('pygments/pygments', 0.5412126183509827, 'util', 0), ('pyfpdf/fpdf2', 0.5399512052536011, 'util', 0), ('instagram/libcst', 0.5354775786399841, 'util', 0), ('grantjenks/blue', 0.5342947244644165, 'util', 0), ('python-markdown/markdown', 0.5334113240242004, 'util', 0), ('instagram/monkeytype', 0.5287166237831116, 'typing', 0), ('mnooner256/pyqrcode', 0.527988612651825, 'util', 0), ('python-rope/rope', 0.5269980430603027, 'util', 0), ('msaelices/py2mojo', 0.5220133662223816, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.518977165222168, 'study', 0), ('pyscf/pyscf', 0.5187242031097412, 'sim', 0), ('brandon-rhodes/python-patterns', 0.5185614824295044, 'util', 0), ('getpelican/pelican', 0.5140236020088196, 'web', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5139066576957703, 'study', 0), ('sqlalchemy/mako', 0.5123310685157776, 'template', 0), ('psf/black', 0.5119499564170837, 'util', 0), ('microsoft/pycodegpt', 0.5082912445068359, 'llm', 0), ('numba/llvmlite', 0.5081674456596375, 'util', 0), ('eleutherai/pyfra', 0.5066225528717041, 'ml', 0), ('pmorissette/ffn', 0.5060634016990662, 'finance', 0), ('gbeced/pyalgotrade', 0.5051184296607971, 'finance', 0), ('has2k1/plotnine', 0.5012533068656921, 'viz', 0), ('nedbat/coveragepy', 0.5010238885879517, 'testing', 0)]",29,5.0,,0.27,24,21,42,1,6,4,6,24.0,55.0,90.0,2.3,57
1451,util,https://github.com/conda/conda,"['package-manager', 'packaging']",,[],[],,,,conda/conda,conda,5923,1467,197,Python,https://docs.conda.io/projects/conda/,"A system-level, binary package and environment manager running on all major operating systems and platforms.",conda,2024-01-14,2012-10-15,589,10.053588748787584,https://avatars.githubusercontent.com/u/6392739?v=4,"A system-level, binary package and environment manager running on all major operating systems and platforms.","['conda', 'package-management']","['conda', 'package-management', 'package-manager', 'packaging']",2024-01-12,"[('mamba-org/mamba', 0.752036988735199, 'util', 3), ('spack/spack', 0.7269142270088196, 'util', 1), ('pomponchik/instld', 0.6547331809997559, 'util', 1), ('indygreg/pyoxidizer', 0.5986080169677734, 'util', 2), ('conda/conda-build', 0.5913727283477783, 'util', 2), ('mamba-org/quetz', 0.5807616710662842, 'util', 1), ('mitsuhiko/rye', 0.5606615543365479, 'util', 2), ('mamba-org/boa', 0.5487179756164551, 'util', 1), ('pdm-project/pdm', 0.5474826097488403, 'util', 2), ('pypa/hatch', 0.5469714999198914, 'util', 2), ('tiiuae/sbomnix', 0.5382207632064819, 'util', 0), ('pypa/setuptools_scm', 0.532427966594696, 'util', 1), ('conda/conda-pack', 0.5275230407714844, 'util', 1), ('python-poetry/poetry', 0.527414858341217, 'util', 2), ('ofek/pyapp', 0.5070095658302307, 'util', 1)]",448,3.0,,14.12,767,573,137,0,14,26,14,767.0,832.0,90.0,1.1,57
1889,ml,https://github.com/kevinmusgrave/pytorch-metric-learning,"['pytorch', 'embeddings']",,[],[],,,,kevinmusgrave/pytorch-metric-learning,pytorch-metric-learning,5618,646,65,Python,https://kevinmusgrave.github.io/pytorch-metric-learning/,"The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.",kevinmusgrave,2024-01-14,2019-10-23,222,25.20897435897436,,"The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.","['computer-vision', 'contrastive-learning', 'deep-learning', 'deep-metric-learning', 'embeddings', 'image-retrieval', 'machine-learning', 'metric-learning', 'pytorch', 'self-supervised-learning']","['computer-vision', 'contrastive-learning', 'deep-learning', 'deep-metric-learning', 'embeddings', 'image-retrieval', 'machine-learning', 'metric-learning', 'pytorch', 'self-supervised-learning']",2023-12-16,"[('oml-team/open-metric-learning', 0.7173192501068115, 'ml', 5), ('roboflow/supervision', 0.6149064302444458, 'ml', 4), ('scikit-learn-contrib/metric-learn', 0.5882241129875183, 'ml', 2), ('qdrant/quaterion', 0.584888219833374, 'ml', 5), ('lightly-ai/lightly', 0.581474244594574, 'ml', 7), ('tensorflow/tensorflow', 0.5597424507141113, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5495540499687195, 'ml-dl', 3), ('huggingface/datasets', 0.5495465993881226, 'nlp', 4), ('pytorch/ignite', 0.5479029417037964, 'ml-dl', 3), ('gradio-app/gradio', 0.5473379492759705, 'viz', 2), ('mosaicml/composer', 0.5446270108222961, 'ml-dl', 3), ('keras-team/keras', 0.5415274500846863, 'ml-dl', 3), ('azavea/raster-vision', 0.5324239134788513, 'gis', 4), ('ddbourgin/numpy-ml', 0.5313518643379211, 'ml', 1), ('tensorflow/similarity', 0.5301912426948547, 'ml-dl', 4), ('onnx/onnx', 0.5226200222969055, 'ml', 3), ('datasystemslab/geotorch', 0.5219647884368896, 'gis', 1), ('huggingface/transformers', 0.5175970792770386, 'nlp', 3), ('kornia/kornia', 0.5158526301383972, 'ml-dl', 4), ('aiqc/aiqc', 0.5146724581718445, 'ml-ops', 0), ('awslabs/autogluon', 0.5119850635528564, 'ml', 4), ('nvlabs/gcvit', 0.5083329081535339, 'diffusion', 1), ('uber/petastorm', 0.5072482824325562, 'data', 3), ('albumentations-team/albumentations', 0.5062373876571655, 'ml-dl', 2), ('aleju/imgaug', 0.506173312664032, 'ml', 2), ('determined-ai/determined', 0.5060969591140747, 'ml-ops', 3), ('microsoft/nni', 0.5050148367881775, 'ml', 3), ('pytorch/torchrec', 0.5045345425605774, 'ml-dl', 2), ('ml-tooling/opyrator', 0.502253532409668, 'viz', 1), ('lutzroeder/netron', 0.5019212365150452, 'ml', 3), ('pyg-team/pytorch_geometric', 0.501497745513916, 'ml-dl', 2)]",40,6.0,,2.56,24,18,51,1,13,12,13,24.0,54.0,90.0,2.2,57
354,ml-ops,https://github.com/feast-dev/feast,[],,[],[],,,,feast-dev/feast,feast,5029,896,69,Python,https://feast.dev,Feature Store for Machine Learning,feast-dev,2024-01-14,2018-12-10,268,18.754928076718166,https://avatars.githubusercontent.com/u/57027613?v=4,Feature Store for Machine Learning,"['big-data', 'data-engineering', 'data-quality', 'data-science', 'feature-store', 'features', 'machine-learning', 'ml', 'mlops']","['big-data', 'data-engineering', 'data-quality', 'data-science', 'feature-store', 'features', 'machine-learning', 'ml', 'mlops']",2024-01-13,"[('featureform/embeddinghub', 0.7385993599891663, 'nlp', 6), ('polyaxon/polyaxon', 0.6851301193237305, 'ml-ops', 4), ('netflix/metaflow', 0.6503940224647522, 'ml-ops', 4), ('firmai/industry-machine-learning', 0.6406149864196777, 'study', 2), ('kubeflow/pipelines', 0.6390533447265625, 'ml-ops', 3), ('onnx/onnx', 0.6326718330383301, 'ml', 2), ('mlflow/mlflow', 0.6303765773773193, 'ml-ops', 2), ('bentoml/bentoml', 0.6025741696357727, 'ml-ops', 2), ('huggingface/datasets', 0.5980408191680908, 'nlp', 1), ('googlecloudplatform/vertex-ai-samples', 0.595111072063446, 'ml', 3), ('hpcaitech/colossalai', 0.585966944694519, 'llm', 0), ('xplainable/xplainable', 0.5823587775230408, 'ml-interpretability', 2), ('activeloopai/deeplake', 0.5807369947433472, 'ml-ops', 4), ('polyaxon/datatile', 0.570514976978302, 'pandas', 3), ('tensorflow/tensorflow', 0.5688017010688782, 'ml-dl', 2), ('microsoft/nni', 0.5683546662330627, 'ml', 3), ('mage-ai/mage-ai', 0.5513089299201965, 'ml-ops', 3), ('alirezadir/machine-learning-interview-enlightener', 0.5510007739067078, 'study', 1), ('whylabs/whylogs', 0.5498430132865906, 'util', 4), ('fatiando/verde', 0.5436317324638367, 'gis', 1), ('sktime/sktime', 0.5417338609695435, 'time-series', 2), ('winedarksea/autots', 0.5365046262741089, 'time-series', 1), ('mosaicml/composer', 0.535942554473877, 'ml-dl', 1), ('online-ml/river', 0.5350134372711182, 'ml', 2), ('ploomber/ploomber', 0.5336986780166626, 'ml-ops', 4), ('superduperdb/superduperdb', 0.530910313129425, 'data', 2), ('bodywork-ml/bodywork-core', 0.5267590284347534, 'ml-ops', 3), ('lancedb/lancedb', 0.5253369808197021, 'data', 0), ('great-expectations/great_expectations', 0.5238198041915894, 'ml-ops', 4), ('milvus-io/bootcamp', 0.5231591463088989, 'data', 0), ('keras-team/keras', 0.5223841071128845, 'ml-dl', 2), ('scikit-learn/scikit-learn', 0.5212818384170532, 'ml', 2), ('avaiga/taipy', 0.5180974006652832, 'data', 2), ('google/mediapipe', 0.5142419338226318, 'ml', 1), ('qdrant/qdrant', 0.5136101245880127, 'data', 2), ('ml-tooling/opyrator', 0.5118728876113892, 'viz', 1), ('iterative/dvc', 0.5111587047576904, 'ml-ops', 2), ('giskard-ai/giskard', 0.507591962814331, 'data', 2), ('gradio-app/gradio', 0.5052893757820129, 'viz', 2), ('google-research/google-research', 0.5038433074951172, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.5033879280090332, 'study', 2), ('explosion/thinc', 0.5021685361862183, 'ml-dl', 1), ('nccr-itmo/fedot', 0.5015140175819397, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5014446973800659, 'data', 2)]",222,4.0,,3.38,118,52,62,0,13,27,13,117.0,126.0,90.0,1.1,57
227,ml,https://github.com/online-ml/river,[],,[],[],1.0,,,online-ml/river,river,4605,551,85,Python,https://riverml.xyz,🌊 Online machine learning in Python,online-ml,2024-01-13,2019-01-24,261,17.59552401746725,https://avatars.githubusercontent.com/u/47002673?v=4,🌊 Online machine learning in Python,"['concept-drift', 'data-science', 'incremental-learning', 'machine-learning', 'online-learning', 'online-machine-learning', 'online-statistics', 'real-time-processing', 'stream-processing', 'streaming', 'streaming-data']","['concept-drift', 'data-science', 'incremental-learning', 'machine-learning', 'online-learning', 'online-machine-learning', 'online-statistics', 'real-time-processing', 'stream-processing', 'streaming', 'streaming-data']",2024-01-01,"[('scikit-learn/scikit-learn', 0.6860164403915405, 'ml', 2), ('jeshraghian/snntorch', 0.615203320980072, 'ml-dl', 1), ('gradio-app/gradio', 0.6122671961784363, 'viz', 2), ('ddbourgin/numpy-ml', 0.6021139025688171, 'ml', 1), ('xplainable/xplainable', 0.584074079990387, 'ml-interpretability', 2), ('pycaret/pycaret', 0.5788997411727905, 'ml', 2), ('ml-tooling/opyrator', 0.574630618095398, 'viz', 1), ('rasbt/mlxtend', 0.5713738799095154, 'ml', 2), ('polyaxon/datatile', 0.5639887452125549, 'pandas', 1), ('awslabs/gluonts', 0.5579661130905151, 'time-series', 2), ('tensorly/tensorly', 0.5535548329353333, 'ml-dl', 1), ('jovianml/opendatasets', 0.5532159209251404, 'data', 2), ('merantix-momentum/squirrel-core', 0.5505257248878479, 'ml', 2), ('google/mediapipe', 0.541152834892273, 'ml', 2), ('firmai/atspy', 0.5391773581504822, 'time-series', 0), ('mlflow/mlflow', 0.5388703346252441, 'ml-ops', 1), ('pathwaycom/pathway', 0.5360531210899353, 'data', 1), ('tensorflow/tensorflow', 0.5353706479072571, 'ml-dl', 1), ('feast-dev/feast', 0.5350134372711182, 'ml-ops', 2), ('clips/pattern', 0.5316668152809143, 'nlp', 1), ('featurelabs/featuretools', 0.5314729809761047, 'ml', 2), ('fatiando/verde', 0.531051754951477, 'gis', 1), ('sktime/sktime', 0.5300047993659973, 'time-series', 2), ('statsmodels/statsmodels', 0.5298870205879211, 'ml', 1), ('firmai/industry-machine-learning', 0.5276996493339539, 'study', 2), ('thealgorithms/python', 0.5223714709281921, 'study', 0), ('automl/auto-sklearn', 0.5197332501411438, 'ml', 0), ('scikit-mobility/scikit-mobility', 0.5196621417999268, 'gis', 1), ('nccr-itmo/fedot', 0.5161853432655334, 'ml-ops', 1), ('dylanhogg/awesome-python', 0.5161080360412598, 'study', 2), ('reloadware/reloadium', 0.5157492160797119, 'profiling', 0), ('sentinel-hub/eo-learn', 0.5141457915306091, 'gis', 1), ('probml/pyprobml', 0.5124793648719788, 'ml', 1), ('scikit-learn-contrib/imbalanced-learn', 0.5116428732872009, 'ml', 2), ('quantconnect/lean', 0.511631429195404, 'finance', 0), ('ai4finance-foundation/finrl', 0.5111426711082458, 'finance', 0), ('epistasislab/tpot', 0.5093849897384644, 'ml', 2), ('crflynn/stochastic', 0.507440447807312, 'sim', 0), ('google/trax', 0.5071595311164856, 'ml-dl', 1), ('eventual-inc/daft', 0.506720244884491, 'pandas', 2), ('googlecloudplatform/vertex-ai-samples', 0.505128800868988, 'ml', 1), ('google/temporian', 0.5042147040367126, 'time-series', 0), ('ranaroussi/quantstats', 0.5004301071166992, 'finance', 0)]",108,6.0,,5.67,137,31,61,0,7,7,7,137.0,161.0,90.0,1.2,57
887,time-series,https://github.com/awslabs/gluonts,[],,[],[],,,,awslabs/gluonts,gluonts,4008,758,74,Python,https://ts.gluon.ai,Probabilistic time series modeling in Python,awslabs,2024-01-12,2019-05-15,245,16.302149912841372,https://avatars.githubusercontent.com/u/3299148?v=4,Probabilistic time series modeling in Python,"['artificial-intelligence', 'aws', 'data-science', 'deep-learning', 'forecasting', 'machine-learning', 'mxnet', 'neural-networks', 'pytorch', 'sagemaker', 'time-series', 'time-series-forecasting', 'time-series-prediction', 'timeseries', 'torch']","['artificial-intelligence', 'aws', 'data-science', 'deep-learning', 'forecasting', 'machine-learning', 'mxnet', 'neural-networks', 'pytorch', 'sagemaker', 'time-series', 'time-series-forecasting', 'time-series-prediction', 'timeseries', 'torch']",2024-01-10,"[('firmai/atspy', 0.695907711982727, 'time-series', 2), ('alkaline-ml/pmdarima', 0.6824057102203369, 'time-series', 3), ('unit8co/darts', 0.6495864987373352, 'time-series', 5), ('uber/orbit', 0.6446792483329773, 'time-series', 4), ('rjt1990/pyflux', 0.623365581035614, 'time-series', 1), ('scikit-learn/scikit-learn', 0.6101686358451843, 'ml', 2), ('crflynn/stochastic', 0.5971387624740601, 'sim', 0), ('pymc-devs/pymc3', 0.593370795249939, 'ml', 0), ('probml/pyprobml', 0.5823290348052979, 'ml', 2), ('aistream-peelout/flow-forecast', 0.5800959467887878, 'time-series', 5), ('salesforce/deeptime', 0.5721127390861511, 'time-series', 4), ('pycaret/pycaret', 0.5713929533958435, 'ml', 3), ('statsmodels/statsmodels', 0.5710023641586304, 'ml', 2), ('ddbourgin/numpy-ml', 0.5705474615097046, 'ml', 2), ('google/temporian', 0.5661502480506897, 'time-series', 1), ('ourownstory/neural_prophet', 0.5649511814117432, 'ml', 7), ('winedarksea/autots', 0.5618434548377991, 'time-series', 4), ('sktime/sktime', 0.5607674717903137, 'time-series', 4), ('online-ml/river', 0.5579661130905151, 'ml', 2), ('tdameritrade/stumpy', 0.5509535074234009, 'time-series', 1), ('pyro-ppl/pyro', 0.5475439429283142, 'ml-dl', 3), ('salesforce/merlion', 0.5388320684432983, 'time-series', 3), ('microprediction/microprediction', 0.5331199765205383, 'time-series', 2), ('bashtage/arch', 0.5267676115036011, 'time-series', 2), ('jeshraghian/snntorch', 0.5149070620536804, 'ml-dl', 3), ('nixtla/statsforecast', 0.5115534067153931, 'time-series', 4), ('gradio-app/gradio', 0.5049344301223755, 'viz', 3), ('opengeos/earthformer', 0.502416729927063, 'gis', 2)]",110,5.0,,5.29,96,63,57,0,34,23,34,96.0,130.0,90.0,1.4,57
1608,llm,https://github.com/openbmb/toolbench,"['instruction-tuning', 'evaluation']",,[],[],,,,openbmb/toolbench,ToolBench,3959,336,49,Python,https://openbmb.github.io/ToolBench/,"An open platform for training, serving, and evaluating large language model for tool learning.",openbmb,2024-01-14,2023-05-28,35,112.19838056680162,https://avatars.githubusercontent.com/u/89920203?v=4,"An open platform for training, serving, and evaluating large language model for tool learning.",[],"['evaluation', 'instruction-tuning']",2023-11-22,"[('lm-sys/fastchat', 0.7016268968582153, 'llm', 1), ('ai21labs/lm-evaluation', 0.6778345704078674, 'llm', 0), ('conceptofmind/toolformer', 0.6644108891487122, 'llm', 0), ('ctlllll/llm-toolmaker', 0.6306527853012085, 'llm', 0), ('night-chen/toolqa', 0.6098852753639221, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5833466649055481, 'llm', 0), ('bigscience-workshop/biomedical', 0.5767196416854858, 'data', 0), ('argilla-io/argilla', 0.5743700265884399, 'nlp', 0), ('lianjiatech/belle', 0.5728527903556824, 'llm', 0), ('openlmlab/leval', 0.5642274022102356, 'llm', 1), ('llmware-ai/llmware', 0.5629839301109314, 'llm', 0), ('optimalscale/lmflow', 0.5591222643852234, 'llm', 0), ('juncongmoo/pyllama', 0.5528221130371094, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5517364740371704, 'llm', 1), ('young-geng/easylm', 0.5505062341690063, 'llm', 0), ('hannibal046/awesome-llm', 0.5423779487609863, 'study', 0), ('yizhongw/self-instruct', 0.542241096496582, 'llm', 1), ('hegelai/prompttools', 0.5396706461906433, 'llm', 0), ('hiyouga/llama-factory', 0.5351808667182922, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5351806879043579, 'llm', 1), ('databrickslabs/dolly', 0.5324987173080444, 'llm', 0), ('freedomintelligence/llmzoo', 0.5291275978088379, 'llm', 0), ('openlmlab/moss', 0.5276908278465271, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5260429382324219, 'llm', 0), ('huggingface/evaluate', 0.5237149000167847, 'ml', 1), ('agenta-ai/agenta', 0.5236167907714844, 'llm', 0), ('jonasgeiping/cramming', 0.521796703338623, 'nlp', 0), ('luohongyin/sail', 0.5190768241882324, 'llm', 0), ('airi-institute/probing_framework', 0.5175856351852417, 'nlp', 0), ('alpha-vllm/llama2-accessory', 0.5172504186630249, 'llm', 0), ('cg123/mergekit', 0.5107592344284058, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5084372758865356, 'llm', 0), ('salesforce/codet5', 0.5070496201515198, 'nlp', 0), ('confident-ai/deepeval', 0.5041009783744812, 'testing', 1), ('next-gpt/next-gpt', 0.5017238259315491, 'llm', 1), ('guidance-ai/guidance', 0.5015900135040283, 'llm', 0), ('tigerlab-ai/tiger', 0.5014435052871704, 'llm', 0), ('huggingface/text-generation-inference', 0.5014093518257141, 'llm', 0), ('microsoft/unilm', 0.5009614825248718, 'nlp', 0)]",16,3.0,,2.83,63,24,8,2,0,0,0,63.0,63.0,90.0,1.0,57
1770,typing,https://github.com/python/typeshed,['code-quality'],,[],[],,,,python/typeshed,typeshed,3908,1680,78,Python,,"Collection of library stubs for Python, with static types",python,2024-01-13,2015-03-05,464,8.409468183215493,https://avatars.githubusercontent.com/u/1525981?v=4,"Collection of library stubs for Python, with static types","['stub', 'types', 'typing']","['code-quality', 'stub', 'types', 'typing']",2024-01-12,"[('instagram/monkeytype', 0.723702609539032, 'typing', 1), ('google/pytype', 0.7091848254203796, 'typing', 3), ('python/mypy', 0.6911789178848267, 'typing', 3), ('microsoft/pyright', 0.6651108264923096, 'typing', 1), ('pytoolz/toolz', 0.5580030083656311, 'util', 0), ('facebook/pyre-check', 0.5521007180213928, 'typing', 1), ('agronholm/typeguard', 0.5519527792930603, 'typing', 1), ('astral-sh/ruff', 0.5103850364685059, 'util', 1), ('landscapeio/prospector', 0.509270191192627, 'util', 0)]",1367,5.0,,22.35,463,365,108,0,0,0,0,463.0,1257.0,90.0,2.7,57
269,web,https://github.com/strawberry-graphql/strawberry,[],,[],[],,,,strawberry-graphql/strawberry,strawberry,3613,484,44,Python,https://strawberry.rocks,A GraphQL library for Python that leverages type annotations 🍓,strawberry-graphql,2024-01-13,2018-12-21,266,13.553590568060022,https://avatars.githubusercontent.com/u/48071860?v=4,A GraphQL library for Python that leverages type annotations 🍓,"['asgi', 'asyncio', 'django', 'graphql', 'graphql-library', 'graphql-schema', 'graphql-server', 'mypy', 'starlette', 'strawberry']","['asgi', 'asyncio', 'django', 'graphql', 'graphql-library', 'graphql-schema', 'graphql-server', 'mypy', 'starlette', 'strawberry']",2024-01-07,"[('instagram/monkeytype', 0.6149357557296753, 'typing', 0), ('patrick-kidger/torchtyping', 0.5875641703605652, 'typing', 0), ('facebook/pyre-check', 0.5584018230438232, 'typing', 0), ('accenture/ampligraph', 0.5520169734954834, 'data', 0), ('tiangolo/sqlmodel', 0.5469264984130859, 'data', 0), ('jsonpickle/jsonpickle', 0.5452963709831238, 'data', 0), ('sqlalchemy/sqlalchemy', 0.5367457866668701, 'data', 0), ('pytoolz/toolz', 0.5352164506912231, 'util', 0), ('plotly/plotly.py', 0.5247325897216797, 'viz', 0), ('tobymao/sqlglot', 0.5215062499046326, 'data', 0), ('marshmallow-code/marshmallow', 0.5134101510047913, 'util', 0), ('mcfunley/pugsql', 0.5126366019248962, 'data', 0), ('typesense/typesense-python', 0.5120099782943726, 'data', 0), ('ibis-project/ibis', 0.5083762407302856, 'data', 0), ('aws/graph-notebook', 0.5056154131889343, 'jupyter', 0), ('s3rius/fastapi-template', 0.5046817064285278, 'web', 2), ('pydantic/pydantic', 0.5045525431632996, 'util', 0), ('nicolas-hbt/pygraft', 0.5039038062095642, 'ml', 0)]",237,5.0,,9.63,643,186,62,0,164,131,164,643.0,664.0,90.0,1.0,57
1092,llm,https://github.com/eleutherai/lm-evaluation-harness,"['benchmark', 'evaluation', 'language-model']",,[],[],,,,eleutherai/lm-evaluation-harness,lm-evaluation-harness,3589,921,34,Python,https://www.eleuther.ai,A framework for few-shot evaluation of language models.,eleutherai,2024-01-14,2020-08-28,178,20.0984,https://avatars.githubusercontent.com/u/68924597?v=4,A framework for few-shot evaluation of language models.,"['evaluation-framework', 'language-model', 'transformer']","['benchmark', 'evaluation', 'evaluation-framework', 'language-model', 'transformer']",2024-01-12,"[('ai21labs/lm-evaluation', 0.7471644282341003, 'llm', 2), ('huggingface/setfit', 0.6814461350440979, 'nlp', 0), ('freedomintelligence/llmzoo', 0.6675116419792175, 'llm', 1), ('openlmlab/leval', 0.6121481657028198, 'llm', 2), ('juncongmoo/pyllama', 0.6021994948387146, 'llm', 0), ('lm-sys/fastchat', 0.6016319394111633, 'llm', 2), ('reasoning-machines/pal', 0.5877846479415894, 'llm', 1), ('cg123/mergekit', 0.5827073454856873, 'llm', 0), ('hannibal046/awesome-llm', 0.5770836472511292, 'study', 1), ('jonasgeiping/cramming', 0.5666804909706116, 'nlp', 1), ('nvlabs/prismer', 0.5629435777664185, 'diffusion', 1), ('anthropics/evals', 0.5547993183135986, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5522134304046631, 'llm', 1), ('openbmb/toolbench', 0.5517364740371704, 'llm', 1), ('bigscience-workshop/biomedical', 0.5494688153266907, 'data', 0), ('lianjiatech/belle', 0.5487149953842163, 'llm', 0), ('srush/minichain', 0.542423665523529, 'llm', 0), ('huggingface/evaluate', 0.5417475700378418, 'ml', 1), ('yueyu1030/attrprompt', 0.5361191034317017, 'llm', 0), ('fasteval/fasteval', 0.5358782410621643, 'llm', 2), ('mit-han-lab/streaming-llm', 0.5352895259857178, 'llm', 0), ('ofa-sys/ofa', 0.5323944091796875, 'llm', 0), ('salesforce/blip', 0.5254507064819336, 'diffusion', 0), ('microsoft/lora', 0.5240524411201477, 'llm', 1), ('ai21labs/in-context-ralm', 0.5203155875205994, 'llm', 1), ('alibaba/easynlp', 0.520174503326416, 'nlp', 0), ('explosion/spacy-models', 0.5173574686050415, 'nlp', 0), ('yizhongw/self-instruct', 0.513164758682251, 'llm', 1), ('young-geng/easylm', 0.5126963257789612, 'llm', 2), ('jina-ai/finetuner', 0.5112559199333191, 'ml', 0), ('conceptofmind/toolformer', 0.5054224729537964, 'llm', 1), ('next-gpt/next-gpt', 0.5040009617805481, 'llm', 0)]",103,2.0,,29.67,484,372,41,0,1,1,1,484.0,1016.0,90.0,2.1,57
486,util,https://github.com/pydata/xarray,[],,[],[],,,,pydata/xarray,xarray,3318,996,109,Python,https://xarray.dev,N-D labeled arrays and datasets in Python,pydata,2024-01-13,2013-09-30,539,6.154213036565978,https://avatars.githubusercontent.com/u/1284191?v=4,N-D labeled arrays and datasets in Python,"['dask', 'netcdf', 'numpy', 'pandas', 'xarray']","['dask', 'netcdf', 'numpy', 'pandas', 'xarray']",2024-01-08,"[('holoviz/hvplot', 0.5328260064125061, 'pandas', 0), ('zarr-developers/zarr-python', 0.5044090151786804, 'data', 0)]",465,6.0,,9.1,425,283,125,0,15,9,15,425.0,1167.0,90.0,2.7,57
1298,ml-ops,https://github.com/determined-ai/determined,[],,[],[],,,,determined-ai/determined,determined,2696,338,75,Go,https://determined.ai,"Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.",determined-ai,2024-01-13,2020-04-07,199,13.547738693467336,https://avatars.githubusercontent.com/u/26636771?v=4,"Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.","['data-science', 'deep-learning', 'distributed-training', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'keras', 'kubernetes', 'machine-learning', 'ml-infrastructure', 'ml-platform', 'mlops', 'pytorch', 'tensorflow']","['data-science', 'deep-learning', 'distributed-training', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'keras', 'kubernetes', 'machine-learning', 'ml-infrastructure', 'ml-platform', 'mlops', 'pytorch', 'tensorflow']",2024-01-12,"[('tensorflow/tensorflow', 0.7361361384391785, 'ml-dl', 3), ('horovod/horovod', 0.6985735297203064, 'ml-ops', 5), ('microsoft/deepspeed', 0.6881573796272278, 'ml-dl', 3), ('mlflow/mlflow', 0.6798075437545776, 'ml-ops', 1), ('wandb/client', 0.6777682900428772, 'ml', 11), ('polyaxon/polyaxon', 0.6687636971473694, 'ml-ops', 9), ('microsoft/nni', 0.6512665748596191, 'ml', 8), ('microsoft/onnxruntime', 0.643320620059967, 'ml', 4), ('rasbt/machine-learning-book', 0.6383908987045288, 'study', 3), ('ray-project/ray', 0.6333746314048767, 'ml-ops', 7), ('intel/intel-extension-for-pytorch', 0.6318769454956055, 'perf', 3), ('google/vizier', 0.6237358450889587, 'ml', 4), ('optuna/optuna', 0.6228476166725159, 'ml', 2), ('ashleve/lightning-hydra-template', 0.6221935153007507, 'util', 3), ('aimhubio/aim', 0.614514172077179, 'ml-ops', 5), ('pytorch/ignite', 0.6104621291160583, 'ml-dl', 3), ('uber/petastorm', 0.6062763929367065, 'data', 4), ('paddlepaddle/paddle', 0.6040452122688293, 'ml-dl', 3), ('automl/auto-sklearn', 0.6021798253059387, 'ml', 3), ('huggingface/datasets', 0.599459171295166, 'nlp', 4), ('merantix-momentum/squirrel-core', 0.5961888432502747, 'ml', 5), ('aws/sagemaker-python-sdk', 0.5937497019767761, 'ml', 3), ('kubeflow-kale/kale', 0.5921743512153625, 'ml-ops', 1), ('onnx/onnx', 0.5901457667350769, 'ml', 5), ('tensorflow/tensor2tensor', 0.5887267589569092, 'ml', 2), ('kubeflow/katib', 0.5883219242095947, 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106,nlp,https://github.com/nltk/nltk,[],,[],[],,,,nltk/nltk,nltk,12688,2824,468,Python,https://www.nltk.org,NLTK Source,nltk,2024-01-13,2009-09-07,751,16.89159376188665,https://avatars.githubusercontent.com/u/124114?v=4,NLTK Source,"['machine-learning', 'natural-language-processing', 'nlp', 'nltk']","['machine-learning', 'natural-language-processing', 'nlp', 'nltk']",2023-12-24,"[('allenai/allennlp', 0.6935926675796509, 'nlp', 2), ('flairnlp/flair', 0.6725092530250549, 'nlp', 3), ('explosion/spacy-models', 0.6720556020736694, 'nlp', 3), ('explosion/spacy', 0.6628869771957397, 'nlp', 3), ('sloria/textblob', 0.6578431129455566, 'nlp', 3), ('lexpredict/lexpredict-lexnlp', 0.6562715172767639, 'nlp', 1), ('rasahq/rasa', 0.6307575106620789, 'llm', 3), ('keras-team/keras-nlp', 0.6287647485733032, 'nlp', 3), ('alibaba/easynlp', 0.6162857413291931, 'nlp', 2), ('explosion/spacy-llm', 0.6157956123352051, 'llm', 3), ('norskregnesentral/skweak', 0.608248770236969, 'nlp', 1), ('graykode/nlp-tutorial', 0.5764945149421692, 'study', 2), ('makcedward/nlpaug', 0.5745749473571777, 'nlp', 3), ('bigscience-workshop/promptsource', 0.5687624216079712, 'nlp', 3), ('argilla-io/argilla', 0.5657547116279602, 'nlp', 3), ('paddlepaddle/paddlenlp', 0.5588723421096802, 'llm', 1), ('jbesomi/texthero', 0.5517333149909973, 'nlp', 2), ('huggingface/transformers', 0.5491923689842224, 'nlp', 3), ('vi3k6i5/flashtext', 0.5405317544937134, 'data', 1), ('infinitylogesh/mutate', 0.5343964695930481, 'nlp', 0), ('explosion/spacy-streamlit', 0.5315085053443909, 'nlp', 3), ('databrickslabs/dolly', 0.5305935144424438, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5303866267204285, 'nlp', 0), ('thilinarajapakse/simpletransformers', 0.5254354476928711, 'nlp', 0), ('yueyu1030/attrprompt', 0.5240321755409241, 'llm', 1), ('doccano/doccano', 0.5226835608482361, 'nlp', 2), ('mooler0410/llmspracticalguide', 0.5214924812316895, 'study', 2), ('nvidia/nemo', 0.5199841260910034, 'nlp', 1), ('google-research/language', 0.5192822217941284, 'nlp', 2), ('killianlucas/open-interpreter', 0.5141381621360779, 'llm', 0), ('llmware-ai/llmware', 0.5111628770828247, 'llm', 2), ('huggingface/text-generation-inference', 0.5107032060623169, 'llm', 1), ('franck-dernoncourt/neuroner', 0.5021610856056213, 'nlp', 2), ('rasbt/machine-learning-book', 0.5020496249198914, 'study', 1), ('deepset-ai/farm', 0.5018087029457092, 'nlp', 1), ('explosion/thinc', 0.5009826421737671, 'ml-dl', 3)]",452,6.0,,1.58,82,46,175,1,0,3,3,82.0,125.0,90.0,1.5,56
1336,llm,https://github.com/blinkdl/rwkv-lm,[],,[],[],,,,blinkdl/rwkv-lm,RWKV-LM,10652,753,129,Python,,"RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, ""infinite"" ctx_len, and free sentence embedding.",blinkdl,2024-01-14,2021-08-08,129,82.39116022099448,,"RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, ""infinite"" ctx_len, and free sentence embedding.","['attention-mechanism', 'chatgpt', 'deep-learning', 'gpt', 'gpt-2', 'gpt-3', 'language-model', 'linear-attention', 'lstm', 'pytorch', 'rnn', 'rwkv', 'transformer', 'transformers']","['attention-mechanism', 'chatgpt', 'deep-learning', 'gpt', 'gpt-2', 'gpt-3', 'language-model', 'linear-attention', 'lstm', 'pytorch', 'rnn', 'rwkv', 'transformer', 'transformers']",2023-12-28,"[('blinkdl/chatrwkv', 0.6400032043457031, 'llm', 5), ('bytedance/lightseq', 0.5059091448783875, 'nlp', 2)]",5,1.0,,3.77,34,13,30,1,1,2,1,34.0,48.0,90.0,1.4,56
366,ml,https://github.com/megvii-basedetection/yolox,[],,[],[],,,,megvii-basedetection/yolox,YOLOX,8778,2096,74,Python,,"YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/",megvii-basedetection,2024-01-12,2021-07-17,132,66.28478964401295,https://avatars.githubusercontent.com/u/67775453?v=4,"YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/","['deep-learning', 'megengine', 'ncnn', 'object-detection', 'onnx', 'openvino', 'pytorch', 'tensorrt', 'yolo', 'yolov3', 'yolox']","['deep-learning', 'megengine', 'ncnn', 'object-detection', 'onnx', 'openvino', 'pytorch', 'tensorrt', 'yolo', 'yolov3', 'yolox']",2023-05-23,"[('microsoft/onnxruntime', 0.5817757844924927, 'ml', 3), ('deci-ai/super-gradients', 0.5785552859306335, 'ml-dl', 3), ('open-mmlab/mmdetection', 0.545394778251648, 'ml', 3), ('horovod/horovod', 0.5106202363967896, 'ml-ops', 2), ('neuralmagic/deepsparse', 0.5051378607749939, 'nlp', 2), ('roboflow/supervision', 0.5012127161026001, 'ml', 4)]",74,5.0,,0.15,49,13,30,8,0,2,2,49.0,46.0,90.0,0.9,56
456,perf,https://github.com/nebuly-ai/nebullvm,[],,[],[],,,,nebuly-ai/nebullvm,nebuly,8331,662,96,Python,https://www.nebuly.com/,The user analytics platform for LLMs,nebuly-ai,2024-01-14,2022-02-12,102,81.3347280334728,https://avatars.githubusercontent.com/u/83510798?v=4,The user analytics platform for LLMs,"['ai', 'analytics', 'artificial-intelligence', 'deeplearning', 'large-language-models', 'llm']","['ai', 'analytics', 'artificial-intelligence', 'deeplearning', 'large-language-models', 'llm']",2023-10-28,"[('pathwaycom/llm-app', 0.6677808165550232, 'llm', 1), ('microsoft/semantic-kernel', 0.6294217705726624, 'llm', 3), ('deepset-ai/haystack', 0.6285997033119202, 'llm', 2), ('tigerlab-ai/tiger', 0.6134838461875916, 'llm', 2), ('mlc-ai/mlc-llm', 0.6126720309257507, 'llm', 1), ('nomic-ai/gpt4all', 0.6061845421791077, 'llm', 0), ('argilla-io/argilla', 0.6039616465568542, 'nlp', 2), ('microsoft/promptflow', 0.5947597622871399, 'llm', 2), ('llmware-ai/llmware', 0.5862234830856323, 'llm', 2), ('microsoft/lmops', 0.5789636373519897, 'llm', 1), ('hegelai/prompttools', 0.5767601132392883, 'llm', 1), ('bigscience-workshop/petals', 0.5763605237007141, 'data', 1), ('night-chen/toolqa', 0.5734456181526184, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5715976357460022, 'perf', 0), ('activeloopai/deeplake', 0.5655378103256226, 'ml-ops', 3), ('iryna-kondr/scikit-llm', 0.5629363059997559, 'llm', 1), ('lancedb/lancedb', 0.5628674030303955, 'data', 0), ('young-geng/easylm', 0.5627244114875793, 'llm', 1), ('microsoft/jarvis', 0.5589395761489868, 'llm', 0), ('paddlepaddle/paddlenlp', 0.556330144405365, 'llm', 1), ('vllm-project/vllm', 0.5560014247894287, 'llm', 1), ('microsoft/autogen', 0.554498553276062, 'llm', 0), ('embedchain/embedchain', 0.5530699491500854, 'llm', 2), ('aimhubio/aim', 0.5485852360725403, 'ml-ops', 1), ('aiwaves-cn/agents', 0.5476635098457336, 'nlp', 1), ('mooler0410/llmspracticalguide', 0.5461376905441284, 'study', 1), ('salesforce/codet5', 0.5400938987731934, 'nlp', 1), ('microsoft/torchscale', 0.5394699573516846, 'llm', 0), ('salesforce/xgen', 0.5392612814903259, 'llm', 2), ('ray-project/ray-llm', 0.5362703800201416, 'llm', 2), ('alphasecio/langchain-examples', 0.5353783369064331, 'llm', 1), ('cheshire-cat-ai/core', 0.5341480374336243, 'llm', 2), ('bobazooba/xllm', 0.5337145328521729, 'llm', 2), ('agenta-ai/agenta', 0.5309828519821167, 'llm', 2), ('explosion/spacy-llm', 0.5306288003921509, 'llm', 2), ('bentoml/openllm', 0.5305408835411072, 'ml-ops', 2), ('mindsdb/mindsdb', 0.529395341873169, 'data', 3), ('titanml/takeoff', 0.5286065936088562, 'llm', 1), ('chatarena/chatarena', 0.5283238887786865, 'llm', 3), ('eleutherai/the-pile', 0.528251588344574, 'data', 1), ('dylanhogg/llmgraph', 0.527693510055542, 'ml', 1), ('ludwig-ai/ludwig', 0.5271685123443604, 'ml-ops', 2), ('confident-ai/deepeval', 0.5270206928253174, 'testing', 1), ('lm-sys/fastchat', 0.5269201993942261, 'llm', 0), ('jina-ai/thinkgpt', 0.5268137454986572, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5265906453132629, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.5258677005767822, 'llm', 0), ('sweepai/sweep', 0.5257266163825989, 'llm', 2), ('eugeneyan/open-llms', 0.5213199257850647, 'study', 2), ('databrickslabs/dolly', 0.5198688507080078, 'llm', 0), ('mlflow/mlflow', 0.5198503136634827, 'ml-ops', 1), ('jerryjliu/llama_index', 0.518968403339386, 'llm', 1), ('determined-ai/determined', 0.5143813490867615, 'ml-ops', 0), ('truera/trulens', 0.5128412842750549, 'llm', 1), ('shishirpatil/gorilla', 0.512731671333313, 'llm', 1), ('salesforce/logai', 0.5123262405395508, 'util', 1), ('googlecloudplatform/vertex-ai-samples', 0.5115224719047546, 'ml', 1), ('superduperdb/superduperdb', 0.5111740827560425, 'data', 1), ('chancefocus/pixiu', 0.5107274651527405, 'finance', 2), ('operand/agency', 0.5101591348648071, 'llm', 3), ('rasahq/rasa', 0.5099035501480103, 'llm', 0), ('arize-ai/phoenix', 0.5097155570983887, 'ml-interpretability', 0), ('nvidia/deeplearningexamples', 0.5064698457717896, 'ml-dl', 1), ('rcgai/simplyretrieve', 0.5062447786331177, 'llm', 2), ('infinitylogesh/mutate', 0.5052860379219055, 'nlp', 0), ('huggingface/datasets', 0.5037619471549988, 'nlp', 0), ('modularml/mojo', 0.5025712847709656, 'util', 1), ('deep-diver/llm-as-chatbot', 0.5001731514930725, 'llm', 0)]",40,5.0,,8.1,0,0,23,3,5,13,5,0.0,0.0,90.0,0.0,56
1034,finance,https://github.com/quantconnect/lean,[],,[],[],,,,quantconnect/lean,Lean,8317,3085,415,C#,https://lean.io,"Lean Algorithmic Trading Engine by QuantConnect (Python, C#)",quantconnect,2024-01-14,2014-11-28,478,17.378805970149255,https://avatars.githubusercontent.com/u/3912814?v=4,"Lean Algorithmic Trading Engine by QuantConnect (Python, C#)","['algorithm', 'algorithmic-trading-engine', 'c-sharp', 'finance', 'forex', 'lean-engine', 'options', 'quantconnect', 'stock-indicators', 'trading', 'trading-algorithms', 'trading-bot', 'trading-platform', 'trading-strategies']","['algorithm', 'algorithmic-trading-engine', 'c-sharp', 'finance', 'forex', 'lean-engine', 'options', 'quantconnect', 'stock-indicators', 'trading', 'trading-algorithms', 'trading-bot', 'trading-platform', 'trading-strategies']",2024-01-11,"[('gbeced/pyalgotrade', 0.7084618806838989, 'finance', 0), ('quantopian/zipline', 0.6676159501075745, 'finance', 0), ('ranaroussi/quantstats', 0.6603469848632812, 'finance', 1), ('polakowo/vectorbt', 0.6572080254554749, 'finance', 3), ('goldmansachs/gs-quant', 0.6400971412658691, 'finance', 1), ('zvtvz/zvt', 0.6376045942306519, 'finance', 3), ('gbeced/basana', 0.6268382668495178, 'finance', 1), ('robcarver17/pysystemtrade', 0.6064723134040833, 'finance', 0), ('kernc/backtesting.py', 0.5955772995948792, 'finance', 5), ('idanya/algo-trader', 0.5937037467956543, 'finance', 2), ('freqtrade/freqtrade', 0.5934526324272156, 'crypto', 1), ('polyaxon/datatile', 0.590923011302948, 'pandas', 0), ('cuemacro/finmarketpy', 0.588824987411499, 'finance', 1), ('willmcgugan/textual', 0.5675188899040222, 'term', 0), ('ccxt/ccxt', 0.5551174879074097, 'crypto', 1), ('ai4finance-foundation/finrl', 0.5530506372451782, 'finance', 1), ('ta-lib/ta-lib-python', 0.5490601062774658, 'finance', 1), ('hydrosquall/tiingo-python', 0.5375146865844727, 'finance', 1), ('blankly-finance/blankly', 0.5336184501647949, 'finance', 3), ('plotly/dash', 0.529013454914093, 'viz', 1), ('thealgorithms/python', 0.5260835886001587, 'study', 1), ('google/tf-quant-finance', 0.5243059992790222, 'finance', 1), ('microsoft/qlib', 0.5189212560653687, 'finance', 1), ('kitao/pyxel', 0.5179747343063354, 'gamedev', 0), ('gradio-app/gradio', 0.5162667632102966, 'viz', 0), ('keon/algorithms', 0.512477457523346, 'util', 1), ('online-ml/river', 0.511631429195404, 'ml', 0), ('clips/pattern', 0.5098506808280945, 'nlp', 0), ('1200wd/bitcoinlib', 0.5076516270637512, 'crypto', 0), ('numerai/example-scripts', 0.5045038461685181, 'finance', 0), ('panda3d/panda3d', 0.5038788318634033, 'gamedev', 0), ('explosion/spacy', 0.5002435445785522, 'nlp', 0)]",198,2.0,,10.94,236,170,111,0,0,331,331,236.0,127.0,90.0,0.5,56
420,ml-dl,https://github.com/pyro-ppl/pyro,[],,[],[],,,,pyro-ppl/pyro,pyro,8243,985,204,Python,http://pyro.ai,Deep universal probabilistic programming with Python and PyTorch,pyro-ppl,2024-01-13,2017-06-16,345,23.853245142620917,https://avatars.githubusercontent.com/u/46794900?v=4,Deep universal probabilistic programming with Python and PyTorch,"['bayesian', 'bayesian-inference', 'deep-learning', 'machine-learning', 'probabilistic-modeling', 'probabilistic-programming', 'pytorch', 'variational-inference']","['bayesian', 'bayesian-inference', 'deep-learning', 'machine-learning', 'probabilistic-modeling', 'probabilistic-programming', 'pytorch', 'variational-inference']",2024-01-14,"[('pymc-devs/pymc3', 0.6964523792266846, 'ml', 3), ('intellabs/bayesian-torch', 0.6956607699394226, 'ml', 3), ('probml/pyprobml', 0.6461431980133057, 'ml', 3), ('pytorch/botorch', 0.6212801933288574, 'ml-dl', 0), ('thu-ml/tianshou', 0.5777061581611633, 'ml-rl', 1), ('huggingface/transformers', 0.5731987953186035, 'nlp', 3), ('rasbt/machine-learning-book', 0.5722088813781738, 'study', 3), ('mrdbourke/pytorch-deep-learning', 0.5717006325721741, 'study', 3), ('denys88/rl_games', 0.558512806892395, 'ml-rl', 2), ('pytorch/ignite', 0.5561047196388245, 'ml-dl', 3), ('keras-team/keras', 0.5552471876144409, 'ml-dl', 3), ('awslabs/gluonts', 0.5475439429283142, 'time-series', 3), ('pytorch/rl', 0.5450016856193542, 'ml-rl', 2), ('ddbourgin/numpy-ml', 0.5446068644523621, 'ml', 2), ('google/trax', 0.539913535118103, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.538422167301178, 'perf', 3), ('tensorlayer/tensorlayer', 0.5288735628128052, 'ml-rl', 1), ('ageron/handson-ml2', 0.522969663143158, 'ml', 0), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5214691162109375, 'study', 0), ('nvidia/deeplearningexamples', 0.5212848782539368, 'ml-dl', 2), ('lukaszahradnik/pyneuralogic', 0.5193347930908203, 'math', 3), ('keras-rl/keras-rl', 0.5166857242584229, 'ml-rl', 1), ('scikit-optimize/scikit-optimize', 0.5141356587409973, 'ml', 1), ('microsoft/deepspeed', 0.5087716579437256, 'ml-dl', 3), ('karpathy/micrograd', 0.5077245831489563, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5035637617111206, 'study', 0), ('pyg-team/pytorch_geometric', 0.5015919208526611, 'ml-dl', 2), ('uber/orbit', 0.5006424188613892, 'time-series', 4)]",148,5.0,,1.27,39,26,80,0,2,5,2,39.0,51.0,90.0,1.3,56
1260,llm,https://github.com/microsoft/lora,[],,[],[],,,,microsoft/lora,LoRA,7851,476,58,Python,https://arxiv.org/abs/2106.09685,"Code for loralib, an implementation of ""LoRA: Low-Rank Adaptation of Large Language Models""",microsoft,2024-01-14,2021-06-18,136,57.48640167364017,https://avatars.githubusercontent.com/u/6154722?v=4,"Code for loralib, an implementation of ""LoRA: Low-Rank Adaptation of Large Language Models""","['adaptation', 'deberta', 'deep-learning', 'gpt-2', 'gpt-3', 'language-model', 'lora', 'low-rank', 'pytorch', 'roberta']","['adaptation', 'deberta', 'deep-learning', 'gpt-2', 'gpt-3', 'language-model', 'lora', 'low-rank', 'pytorch', 'roberta']",2024-01-09,"[('hannibal046/awesome-llm', 0.6244948506355286, 'study', 1), ('next-gpt/next-gpt', 0.6170973181724548, 'llm', 0), ('lianjiatech/belle', 0.5939985513687134, 'llm', 1), ('bobazooba/xllm', 0.5789094567298889, 'llm', 2), ('microsoft/autogen', 0.5736963152885437, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5733075141906738, 'llm', 2), ('hiyouga/llama-factory', 0.5733075141906738, 'llm', 2), ('yueyu1030/attrprompt', 0.5668091177940369, 'llm', 0), ('togethercomputer/redpajama-data', 0.5614542365074158, 'llm', 0), ('cg123/mergekit', 0.5608856678009033, 'llm', 0), ('ai21labs/lm-evaluation', 0.55837482213974, 'llm', 1), ('infinitylogesh/mutate', 0.5570184588432312, 'nlp', 1), ('freedomintelligence/llmzoo', 0.5567244291305542, 'llm', 1), ('lm-sys/fastchat', 0.5482615828514099, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5445288419723511, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5398515462875366, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5398515462875366, 'llm', 0), ('huggingface/text-generation-inference', 0.5352991223335266, 'llm', 2), ('extreme-bert/extreme-bert', 0.5327245593070984, 'llm', 3), ('salesforce/blip', 0.52529376745224, 'diffusion', 0), ('eleutherai/lm-evaluation-harness', 0.5240524411201477, 'llm', 1), ('fasteval/fasteval', 0.5221768021583557, 'llm', 0), ('lupantech/chameleon-llm', 0.5212894082069397, 'llm', 1), ('openai/finetune-transformer-lm', 0.520248532295227, 'llm', 0), ('young-geng/easylm', 0.519779622554779, 'llm', 2), ('databrickslabs/dolly', 0.5188591480255127, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5186458230018616, 'llm', 0), ('juncongmoo/pyllama', 0.5170261263847351, 'llm', 0), ('nvlabs/prismer', 0.5161336064338684, 'diffusion', 1), ('oobabooga/text-generation-webui', 0.5137597918510437, 'llm', 1), ('lightning-ai/lit-llama', 0.5121870040893555, 'llm', 1), ('huggingface/transformers', 0.5106838941574097, 'nlp', 3), ('xtekky/gpt4free', 0.5105166435241699, 'llm', 2), ('thudm/chatglm2-6b', 0.5071834921836853, 'llm', 0), ('ggerganov/ggml', 0.5070888996124268, 'ml', 0), ('bytedance/lightseq', 0.5001585483551025, 'nlp', 0)]",12,3.0,,0.37,26,7,31,0,0,2,2,26.0,31.0,90.0,1.2,56
469,gui,https://github.com/parthjadhav/tkinter-designer,[],,[],[],,,,parthjadhav/tkinter-designer,Tkinter-Designer,7773,742,78,Python,,An easy and fast way to create a Python GUI 🐍,parthjadhav,2024-01-14,2021-05-18,141,55.12765957446808,,An easy and fast way to create a Python GUI 🐍,"['automatic', 'collaborate', 'drag-and-drop', 'easy', 'easy-to-use', 'fast', 'figma', 'gui', 'gui-application', 'learn', 'python-script', 'tkinter', 'tkinter-designer', 'tkinter-graphic-interface', 'tkinter-gui', 'tkinter-python', 'tkinter-widgets']","['automatic', 'collaborate', 'drag-and-drop', 'easy', 'easy-to-use', 'fast', 'figma', 'gui', 'gui-application', 'learn', 'python-script', 'tkinter', 'tkinter-designer', 'tkinter-graphic-interface', 'tkinter-gui', 'tkinter-python', 'tkinter-widgets']",2024-01-04,"[('pysimplegui/pysimplegui', 0.7242632508277893, 'gui', 4), ('hoffstadt/dearpygui', 0.6940200924873352, 'gui', 1), ('r0x0r/pywebview', 0.6899272799491882, 'gui', 1), ('beeware/toga', 0.6891065239906311, 'gui', 1), ('willmcgugan/textual', 0.5848771333694458, 'term', 0), ('wxwidgets/phoenix', 0.58327716588974, 'gui', 1), ('holoviz/panel', 0.5603848099708557, 'viz', 1), ('kivy/kivy', 0.554401695728302, 'util', 0), ('urwid/urwid', 0.5480352640151978, 'term', 0), ('adamerose/pandasgui', 0.5451450943946838, 'pandas', 1), ('holoviz/holoviz', 0.5346206426620483, 'viz', 0), ('pyglet/pyglet', 0.5310909748077393, 'gamedev', 0), ('jquast/blessed', 0.5266092419624329, 'term', 0), ('tkrabel/bamboolib', 0.5206194519996643, 'pandas', 0), ('matplotlib/matplotlib', 0.5160230398178101, 'viz', 0), ('bokeh/bokeh', 0.5127165913581848, 'viz', 0), ('pypy/pypy', 0.5007215738296509, 'util', 0)]",45,2.0,,0.23,39,10,32,0,1,3,1,39.0,59.0,90.0,1.5,56
923,ml-rl,https://github.com/lucidrains/palm-rlhf-pytorch,[],,[],[],,,,lucidrains/palm-rlhf-pytorch,PaLM-rlhf-pytorch,7494,649,139,Python,,Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM,lucidrains,2024-01-12,2022-12-09,59,125.79856115107914,,Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM,"['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'human-feedback', 'reinforcement-learning', 'transformers']","['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'human-feedback', 'reinforcement-learning', 'transformers']",2023-04-05,"[('denys88/rl_games', 0.5653679370880127, 'ml-rl', 2), ('deepmind/android_env', 0.5164604187011719, 'ml-dl', 1)]",5,3.0,,0.58,4,2,13,9,15,65,15,4.0,4.0,90.0,1.0,56
960,ml-rl,https://github.com/thu-ml/tianshou,[],,[],[],,,,thu-ml/tianshou,tianshou,7086,1071,90,Python,https://tianshou.readthedocs.io,An elegant PyTorch deep reinforcement learning library.,thu-ml,2024-01-12,2018-04-16,302,23.452482269503545,https://avatars.githubusercontent.com/u/19198992?v=4,An elegant PyTorch deep reinforcement learning library.,"['a2c', 'atari', 'bcq', 'benchmark', 'cql', 'ddpg', 'double-dqn', 'dqn', 'drl', 'imitation-learning', 'mujoco', 'npg', 'policy-gradient', 'ppo', 'pytorch', 'rl', 'sac', 'td3', 'trpo']","['a2c', 'atari', 'bcq', 'benchmark', 'cql', 'ddpg', 'double-dqn', 'dqn', 'drl', 'imitation-learning', 'mujoco', 'npg', 'policy-gradient', 'ppo', 'pytorch', 'rl', 'sac', 'td3', 'trpo']",2024-01-12,"[('denys88/rl_games', 0.7549825310707092, 'ml-rl', 1), ('pytorch/rl', 0.7527033090591431, 'ml-rl', 2), ('humancompatibleai/imitation', 0.7333576679229736, 'ml-rl', 1), ('openai/baselines', 0.6823237538337708, 'ml-rl', 0), ('salesforce/warp-drive', 0.6808977723121643, 'ml-rl', 1), ('tensorlayer/tensorlayer', 0.6603164076805115, 'ml-rl', 1), ('keras-rl/keras-rl', 0.6519415378570557, 'ml-rl', 0), ('kzl/decision-transformer', 0.6393781900405884, 'ml-rl', 1), ('google/trax', 0.625861406326294, 'ml-dl', 0), ('google/dopamine', 0.6199951171875, 'ml-rl', 1), ('pytorch/ignite', 0.6126653552055359, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5997803211212158, 'study', 1), ('unity-technologies/ml-agents', 0.598300576210022, 'ml-rl', 0), ('openai/spinningup', 0.5972070097923279, 'study', 0), ('karpathy/micrograd', 0.5894965529441833, 'study', 0), ('deepmind/acme', 0.5823108553886414, 'ml-rl', 0), ('pyro-ppl/pyro', 0.5777061581611633, 'ml-dl', 1), ('openai/gym', 0.5749742984771729, 'ml-rl', 0), ('ai4finance-foundation/finrl', 0.5749337673187256, 'finance', 0), ('inspirai/timechamber', 0.5735207796096802, 'sim', 0), ('farama-foundation/gymnasium', 0.57296222448349, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.5683255791664124, 'ml', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5635957717895508, 'study', 0), ('facebookresearch/habitat-lab', 0.5616220831871033, 'sim', 0), ('pettingzoo-team/pettingzoo', 0.556010365486145, 'ml-rl', 0), ('intel/intel-extension-for-pytorch', 0.5456005930900574, 'perf', 1), ('skorch-dev/skorch', 0.5379747152328491, 'ml-dl', 1), ('nvidia-omniverse/isaacgymenvs', 0.5374286770820618, 'sim', 0), ('facebookresearch/reagent', 0.5355173945426941, 'ml-rl', 0), ('intellabs/bayesian-torch', 0.5311893820762634, 'ml', 1), ('deepmind/dm_control', 0.5307285189628601, 'ml-rl', 1), ('nvidia-omniverse/omniisaacgymenvs', 0.5271867513656616, 'sim', 0), ('arise-initiative/robosuite', 0.5250179171562195, 'ml-rl', 0), ('facebookresearch/pytorch3d', 0.5248837471008301, 'ml-dl', 0), ('nvidia/apex', 0.5245383381843567, 'ml-dl', 0), ('facebookresearch/theseus', 0.5228813886642456, 'math', 1), ('rasbt/machine-learning-book', 0.5220516920089722, 'study', 1), ('pyg-team/pytorch_geometric', 0.5205722451210022, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.5155929923057556, 'study', 1), ('huggingface/transformers', 0.5113345980644226, 'nlp', 1), ('allenai/allennlp', 0.5055130124092102, 'nlp', 1), ('huggingface/deep-rl-class', 0.5006495714187622, 'study', 0)]",65,4.0,,4.12,77,37,70,0,1,5,1,77.0,155.0,90.0,2.0,56
717,ml,https://github.com/py-why/dowhy,[],,[],[],,,,py-why/dowhy,dowhy,6454,883,137,Python,https://www.pywhy.org/dowhy,"DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. ",py-why,2024-01-13,2018-05-31,295,21.82512077294686,https://avatars.githubusercontent.com/u/101266056?v=4,"DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. ","['bayesian-networks', 'causal-inference', 'causal-machine-learning', 'causal-models', 'causality', 'data-science', 'do-calculus', 'graphical-models', 'machine-learning', 'treatment-effects']","['bayesian-networks', 'causal-inference', 'causal-machine-learning', 'causal-models', 'causality', 'data-science', 'do-calculus', 'graphical-models', 'machine-learning', 'treatment-effects']",2024-01-08,"[('mckinsey/causalnex', 0.7358757853507996, 'math', 5), ('willianfuks/tfcausalimpact', 0.6020914912223816, 'math', 1), ('py-why/econml', 0.5789477229118347, 'ml', 4), ('eleutherai/pyfra', 0.5379133820533752, 'ml', 0), ('quantecon/quantecon.py', 0.513481080532074, 'sim', 0)]",82,5.0,,3.46,119,107,68,0,4,3,4,119.0,102.0,90.0,0.9,56
278,jupyter,https://github.com/nteract/papermill,[],,[],[],,,,nteract/papermill,papermill,5497,409,93,Python,http://papermill.readthedocs.io/en/latest/,"📚 Parameterize, execute, and analyze notebooks",nteract,2024-01-14,2017-07-06,342,16.03959983326386,https://avatars.githubusercontent.com/u/12401040?v=4,"📚 Parameterize, execute, and analyze notebooks","['julia', 'jupyter', 'notebook', 'notebook-generator', 'notebooks', 'nteract', 'pipeline', 'publishing', 'r', 'scala']","['julia', 'jupyter', 'notebook', 'notebook-generator', 'notebooks', 'nteract', 'pipeline', 'publishing', 'r', 'scala']",2024-01-01,"[('mwouts/jupytext', 0.632023811340332, 'jupyter', 1), ('jupyter/nbformat', 0.618989109992981, 'jupyter', 0), ('cohere-ai/notebooks', 0.5747708082199097, 'llm', 1), ('jupyter/notebook', 0.5524148344993591, 'jupyter', 2), ('aws/graph-notebook', 0.5402319431304932, 'jupyter', 1), ('linealabs/lineapy', 0.5371261835098267, 'jupyter', 0), ('ploomber/ploomber', 0.5353273153305054, 'ml-ops', 2), ('jupyter/nbgrader', 0.532752513885498, 'jupyter', 1), ('jupyter/nbconvert', 0.5243469476699829, 'jupyter', 0), ('quantopian/qgrid', 0.516223669052124, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5121607780456543, 'study', 0), ('malloydata/malloy-py', 0.5121511220932007, 'data', 0), ('jupyter/nbdime', 0.5114229917526245, 'jupyter', 1), ('pytoolz/toolz', 0.5096691250801086, 'util', 0), ('jupyter-widgets/ipywidgets', 0.5082428455352783, 'jupyter', 0), ('fluentpython/example-code-2e', 0.5064553022384644, 'study', 0), ('kellyjonbrazil/jc', 0.5040388703346252, 'util', 0), ('fastai/fastcore', 0.5034418106079102, 'util', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5029893517494202, 'jupyter', 3)]",114,7.0,,0.69,51,40,79,0,0,12,12,51.0,86.0,90.0,1.7,56
361,ml-ops,https://github.com/allegroai/clearml,[],,[],[],,,,allegroai/clearml,clearml,4979,626,91,Python,https://clear.ml/docs,"ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management",allegroai,2024-01-14,2019-06-10,242,20.562241887905603,https://avatars.githubusercontent.com/u/38647316?v=4,"ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management","['ai', 'clearml', 'control', 'deep-learning', 'deeplearning', 'devops', 'experiment', 'experiment-manager', 'k8s', 'machine-learning', 'machinelearning', 'mlops', 'trains', 'trainsai', 'version', 'version-control']","['ai', 'clearml', 'control', 'deep-learning', 'deeplearning', 'devops', 'experiment', 'experiment-manager', 'k8s', 'machine-learning', 'machinelearning', 'mlops', 'trains', 'trainsai', 'version', 'version-control']",2024-01-12,"[('zenml-io/zenml', 0.66759192943573, 'ml-ops', 4), ('polyaxon/polyaxon', 0.6574744582176208, 'ml-ops', 4), ('iterative/dvc', 0.618588924407959, 'ml-ops', 2), ('bodywork-ml/bodywork-core', 0.6049355268478394, 'ml-ops', 3), ('netflix/metaflow', 0.5861303210258484, 'ml-ops', 3), ('fmind/mlops-python-package', 0.5833768844604492, 'template', 2), ('getindata/kedro-kubeflow', 0.5791431069374084, 'ml-ops', 2), ('bentoml/bentoml', 0.5609636902809143, 'ml-ops', 4), ('kubeflow/pipelines', 0.5604597330093384, 'ml-ops', 2), ('orchest/orchest', 0.5581900477409363, 'ml-ops', 1), ('ploomber/ploomber', 0.5556386113166809, 'ml-ops', 2), ('avaiga/taipy', 0.5540127158164978, 'data', 1), ('flyteorg/flyte', 0.5446012616157532, 'ml-ops', 2), ('tox-dev/tox', 0.5439307689666748, 'testing', 0), ('mage-ai/mage-ai', 0.5418257713317871, 'ml-ops', 1), ('kestra-io/kestra', 0.5374925136566162, 'ml-ops', 0), ('unionai-oss/unionml', 0.536503255367279, 'ml-ops', 2), ('microsoft/nni', 0.5334336757659912, 'ml', 3), ('lastmile-ai/aiconfig', 0.5247846841812134, 'util', 1), ('wandb/client', 0.5239328742027283, 'ml', 3), ('prefecthq/prefect', 0.5174835324287415, 'ml-ops', 0)]",88,3.0,,2.83,74,21,56,0,18,34,18,74.0,141.0,90.0,1.9,56
1371,llm,https://github.com/minedojo/voyager,[],,[],[],,,,minedojo/voyager,Voyager,4708,445,60,JavaScript,https://voyager.minedojo.org/,An Open-Ended Embodied Agent with Large Language Models,minedojo,2024-01-14,2023-05-25,35,131.824,https://avatars.githubusercontent.com/u/98871221?v=4,An Open-Ended Embodied Agent with Large Language Models,"['embodied-learning', 'large-language-models', 'minecraft', 'open-ended-learning']","['embodied-learning', 'large-language-models', 'minecraft', 'open-ended-learning']",2023-07-27,"[('facebookresearch/droidlet', 0.6723781228065491, 'sim', 0), ('facebookresearch/habitat-lab', 0.6467283964157104, 'sim', 0), ('aiwaves-cn/agents', 0.5751522183418274, 'nlp', 0), ('jina-ai/thinkgpt', 0.5710037350654602, 'llm', 0), ('humanoidagents/humanoidagents', 0.56615149974823, 'sim', 0), ('lm-sys/fastchat', 0.5574104189872742, 'llm', 0), ('luodian/otter', 0.5513647794723511, 'llm', 0), ('inspirai/timechamber', 0.5301423072814941, 'sim', 0), ('lupantech/chameleon-llm', 0.5282621383666992, 'llm', 0), ('operand/agency', 0.516743540763855, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5137326121330261, 'llm', 0), ('next-gpt/next-gpt', 0.509900689125061, 'llm', 1)]",13,4.0,,0.42,23,16,8,6,0,0,0,23.0,25.0,90.0,1.1,56
375,util,https://github.com/spotify/pedalboard,[],,[],[],,,,spotify/pedalboard,pedalboard,4677,219,56,C++,https://spotify.github.io/pedalboard/,🎛 🔊 A Python library for working with audio.,spotify,2024-01-13,2021-07-06,134,34.90298507462686,https://avatars.githubusercontent.com/u/251374?v=4,🎛 🔊 A Python library for working with audio.,"['audio', 'audio-processing', 'audio-production', 'audio-research', 'audio-unit', 'juce', 'pybind11', 'tensorflow', 'vst3', 'vst3-host']","['audio', 'audio-processing', 'audio-production', 'audio-research', 'audio-unit', 'juce', 'pybind11', 'tensorflow', 'vst3', 'vst3-host']",2023-12-14,"[('bastibe/python-soundfile', 0.7314440608024597, 'util', 0), ('irmen/pyminiaudio', 0.7280553579330444, 'util', 0), ('uberi/speech_recognition', 0.6759282946586609, 'ml', 1), ('taylorsmarks/playsound', 0.6269357204437256, 'util', 0), ('libaudioflux/audioflux', 0.5974409580230713, 'util', 2), ('speechbrain/speechbrain', 0.5878923535346985, 'nlp', 2), ('quodlibet/mutagen', 0.5746901035308838, 'util', 0), ('nateshmbhat/pyttsx3', 0.5451530814170837, 'util', 0), ('pndurette/gtts', 0.5376996994018555, 'util', 0), ('pytoolz/toolz', 0.5321218371391296, 'util', 0), ('jamesturk/jellyfish', 0.5257704854011536, 'nlp', 0), ('facebookresearch/audiocraft', 0.5256170034408569, 'util', 1), ('espnet/espnet', 0.5202059745788574, 'nlp', 0), ('pypy/pypy', 0.5107285976409912, 'util', 0), ('googleapis/python-speech', 0.5077892541885376, 'ml', 0)]",27,5.0,,2.19,35,13,31,1,20,22,20,35.0,42.0,90.0,1.2,56
1452,util,https://github.com/conda-forge/miniforge,[],,[],[],,,,conda-forge/miniforge,miniforge,4654,266,50,Shell,https://conda-forge.org/miniforge,A conda-forge distribution.,conda-forge,2024-01-14,2019-11-14,219,21.182054616384914,https://avatars.githubusercontent.com/u/11897326?v=4,A conda-forge distribution.,[],[],2023-12-21,"[('conda/conda-pack', 0.5824256539344788, 'util', 0), ('mamba-org/quetz', 0.5642527341842651, 'util', 0), ('conda-forge/feedstocks', 0.5309390425682068, 'util', 0), ('mamba-org/boa', 0.5230752825737, 'util', 0)]",37,5.0,,1.42,59,30,51,1,15,19,15,59.0,143.0,90.0,2.4,56
347,ml-ops,https://github.com/evidentlyai/evidently,[],,[],[],,,,evidentlyai/evidently,evidently,4312,477,43,Jupyter Notebook,,Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b,evidentlyai,2024-01-12,2020-11-25,165,25.998277347114556,https://avatars.githubusercontent.com/u/75031056?v=4,Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b,"['data-drift', 'data-science', 'html-report', 'jupyter-notebook', 'machine-learning', 'machine-learning-operations', 'mlops', 'model-monitoring', 'pandas-dataframe', 'production-machine-learning']","['data-drift', 'data-science', 'html-report', 'jupyter-notebook', 'machine-learning', 'machine-learning-operations', 'mlops', 'model-monitoring', 'pandas-dataframe', 'production-machine-learning']",2024-01-12,"[('deepchecks/deepchecks', 0.6157994866371155, 'data', 8), ('fmind/mlops-python-package', 0.5556315779685974, 'template', 1), ('selfexplainml/piml-toolbox', 0.5544941425323486, 'ml-interpretability', 0), ('huggingface/evaluate', 0.5467391014099121, 'ml', 1), ('kubeflow/fairing', 0.531782329082489, 'ml-ops', 0), ('districtdatalabs/yellowbrick', 0.531039834022522, 'ml', 1), ('polyaxon/polyaxon', 0.5295282006263733, 'ml-ops', 3), ('arize-ai/phoenix', 0.5009012818336487, 'ml-interpretability', 2)]",57,3.0,,6.9,148,121,38,0,25,21,25,148.0,76.0,90.0,0.5,56
826,util,https://github.com/adafruit/circuitpython,[],,[],[],,,,adafruit/circuitpython,circuitpython,3787,1073,128,C,https://circuitpython.org,CircuitPython - a Python implementation for teaching coding with microcontrollers,adafruit,2024-01-13,2016-08-20,388,9.749540272158882,https://avatars.githubusercontent.com/u/181069?v=4,CircuitPython - a Python implementation for teaching coding with microcontrollers,"['beginner', 'circuitpython', 'cpython', 'education', 'embedded', 'microcontroller', 'micropython']","['beginner', 'circuitpython', 'cpython', 'education', 'embedded', 'microcontroller', 'micropython']",2024-01-13,"[('micropython/micropython', 0.7091054916381836, 'util', 3), ('python/cpython', 0.6647933125495911, 'util', 1), ('fchollet/deep-learning-with-python-notebooks', 0.6453861594200134, 'study', 0), ('pypy/pypy', 0.6234596371650696, 'util', 1), ('pyston/pyston', 0.5873665809631348, 'util', 0), ('norvig/pytudes', 0.5722380876541138, 'util', 0), ('ipython/ipyparallel', 0.5501038432121277, 'perf', 0), ('sympy/sympy', 0.5332902669906616, 'math', 0), ('cohere-ai/notebooks', 0.5329226851463318, 'llm', 0), ('jeshraghian/snntorch', 0.5297858119010925, 'ml-dl', 0), ('masoniteframework/masonite', 0.5271365642547607, 'web', 0), ('ageron/handson-ml2', 0.5219725966453552, 'ml', 0), ('cython/cython', 0.5219005346298218, 'util', 1), ('1200wd/bitcoinlib', 0.519145131111145, 'crypto', 0), ('primal100/pybitcointools', 0.518639862537384, 'crypto', 0), ('intel/intel-extension-for-pytorch', 0.5163028240203857, 'perf', 0), ('r0x0r/pywebview', 0.5162983536720276, 'gui', 0), ('brandtbucher/specialist', 0.5155614018440247, 'perf', 1), ('eleutherai/pyfra', 0.5133049488067627, 'ml', 0), ('hoffstadt/dearpygui', 0.511806309223175, 'gui', 0), ('imageio/imageio', 0.506452739238739, 'util', 0), ('faster-cpython/tools', 0.5050845742225647, 'perf', 1), ('rasbt/machine-learning-book', 0.5035032033920288, 'study', 0), ('fastai/fastcore', 0.5033023953437805, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5031493902206421, 'study', 0), ('joblib/joblib', 0.5005473494529724, 'util', 0)]",1121,4.0,,0.0,538,356,90,0,30,37,30,537.0,1228.0,90.0,2.3,56
918,study,https://github.com/roboflow/notebooks,[],,[],[],,,,roboflow/notebooks,notebooks,3584,553,54,Jupyter Notebook,https://roboflow.com/models,"Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.",roboflow,2024-01-13,2022-11-18,62,57.278538812785385,https://avatars.githubusercontent.com/u/53104118?v=4,"Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.","['amazon-sagemaker-lab', 'automatic-labeling-system', 'computer-vision', 'deep-learning', 'deep-neural-networks', 'google-colab', 'image-classification', 'image-segmentation', 'machine-learning', 'object-detection', 'open-vocabulary-detection', 'open-vocabulary-segmentation', 'pytorch', 'tutorial', 'yolov5', 'yolov6', 'yolov7', 'yolov8', 'zero-shot-classification', 'zero-shot-detection']","['amazon-sagemaker-lab', 'automatic-labeling-system', 'computer-vision', 'deep-learning', 'deep-neural-networks', 'google-colab', 'image-classification', 'image-segmentation', 'machine-learning', 'object-detection', 'open-vocabulary-detection', 'open-vocabulary-segmentation', 'pytorch', 'tutorial', 'yolov5', 'yolov6', 'yolov7', 'yolov8', 'zero-shot-classification', 'zero-shot-detection']",2024-01-10,"[('deci-ai/super-gradients', 0.8082399368286133, 'ml-dl', 5), ('roboflow/supervision', 0.6496574878692627, 'ml', 5), ('lucidrains/vit-pytorch', 0.6355553865432739, 'ml-dl', 2), ('idea-research/grounded-segment-anything', 0.6104524731636047, 'llm', 3), ('google-research/maxvit', 0.5931783318519592, 'ml', 2), ('facebookresearch/vissl', 0.5875295996665955, 'ml', 0), ('idea-research/groundingdino', 0.5870379209518433, 'diffusion', 1), ('nvlabs/gcvit', 0.5799825191497803, 'diffusion', 2), ('blakeblackshear/frigate', 0.5778411626815796, 'util', 1), ('rwightman/pytorch-image-models', 0.5683378577232361, 'ml-dl', 1), ('open-mmlab/mmdetection', 0.5606690049171448, 'ml', 2), ('matterport/mask_rcnn', 0.5560668706893921, 'ml-dl', 1), ('microsoft/torchgeo', 0.5472760200500488, 'gis', 3), ('ludwig-ai/ludwig', 0.5439817309379578, 'ml-ops', 4), ('christoschristofidis/awesome-deep-learning', 0.5378063917160034, 'study', 2), ('kornia/kornia', 0.5266382098197937, 'ml-dl', 4), ('salesforce/blip', 0.523171603679657, 'diffusion', 0), ('nyandwi/modernconvnets', 0.5212321877479553, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5195624828338623, 'ml-dl', 3), ('open-mmlab/mmediting', 0.5162927508354187, 'ml', 3), ('datasystemslab/geotorch', 0.5124086141586304, 'gis', 2), ('towhee-io/towhee', 0.511427640914917, 'ml-ops', 2), ('facebookresearch/segment-anything', 0.51060950756073, 'ml-dl', 1), ('sanster/lama-cleaner', 0.5101523995399475, 'ml-dl', 1), ('mosaicml/composer', 0.5099112391471863, 'ml-dl', 3)]",21,3.0,,2.79,33,16,14,0,0,1,1,32.0,33.0,90.0,1.0,56
878,study,https://github.com/huggingface/deep-rl-class,[],,[],[],,,,huggingface/deep-rl-class,deep-rl-class,3426,510,86,MDX,,This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.,huggingface,2024-01-13,2022-04-21,92,36.952234206471495,https://avatars.githubusercontent.com/u/25720743?v=4,This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.,"['deep-learning', 'deep-reinforcement-learning', 'reinforcement-learning', 'reinforcement-learning-excercises']","['deep-learning', 'deep-reinforcement-learning', 'reinforcement-learning', 'reinforcement-learning-excercises']",2024-01-02,"[('openai/spinningup', 0.5750541090965271, 'study', 0), ('huggingface/huggingface_hub', 0.5541204810142517, 'ml', 1), ('huggingface/diffusion-models-class', 0.551882803440094, 'study', 0), ('tensorlayer/tensorlayer', 0.5484325885772705, 'ml-rl', 2), ('farama-foundation/gymnasium', 0.5383160710334778, 'ml-rl', 1), ('nvidia-omniverse/isaacgymenvs', 0.534843921661377, 'sim', 0), ('keras-rl/keras-rl', 0.5257301926612854, 'ml-rl', 1), ('pettingzoo-team/pettingzoo', 0.5236347317695618, 'ml-rl', 1), ('facebookresearch/habitat-lab', 0.5081996917724609, 'sim', 3), ('thu-ml/tianshou', 0.5006495714187622, 'ml-rl', 0)]",86,3.0,,6.08,64,37,21,0,0,0,0,64.0,122.0,90.0,1.9,56
362,ml-ops,https://github.com/kubeflow/pipelines,[],,[],[],,,,kubeflow/pipelines,pipelines,3364,1513,104,Python,https://www.kubeflow.org/docs/components/pipelines/,Machine Learning Pipelines for Kubeflow,kubeflow,2024-01-13,2018-05-12,298,11.272379128769746,https://avatars.githubusercontent.com/u/33164907?v=4,Machine Learning Pipelines for Kubeflow,"['data-science', 'kubeflow', 'kubeflow-pipelines', 'kubernetes', 'machine-learning', 'mlops', 'pipeline']","['data-science', 'kubeflow', 'kubeflow-pipelines', 'kubernetes', 'machine-learning', 'mlops', 'pipeline']",2024-01-12,"[('bodywork-ml/bodywork-core', 0.8104010820388794, 'ml-ops', 5), ('getindata/kedro-kubeflow', 0.7283107042312622, 'ml-ops', 3), ('polyaxon/polyaxon', 0.7241019010543823, 'ml-ops', 4), ('kubeflow-kale/kale', 0.692866325378418, 'ml-ops', 3), ('orchest/orchest', 0.6418040990829468, 'ml-ops', 3), ('feast-dev/feast', 0.6390533447265625, 'ml-ops', 3), ('flyteorg/flyte', 0.6268972754478455, 'ml-ops', 4), ('mage-ai/mage-ai', 0.622243344783783, 'ml-ops', 3), ('bentoml/bentoml', 0.6110785603523254, 'ml-ops', 3), ('unionai-oss/unionml', 0.6105091571807861, 'ml-ops', 2), ('netflix/metaflow', 0.5999767184257507, 'ml-ops', 4), ('mlflow/mlflow', 0.5984587669372559, 'ml-ops', 1), ('onnx/onnx', 0.5777002573013306, 'ml', 1), ('ploomber/ploomber', 0.568588137626648, 'ml-ops', 3), ('allegroai/clearml', 0.5604597330093384, 'ml-ops', 2), ('determined-ai/determined', 0.5401206016540527, 'ml-ops', 4), ('koaning/scikit-lego', 0.539598822593689, 'ml', 1), ('microsoft/nni', 0.5366452932357788, 'ml', 3), ('dgarnitz/vectorflow', 0.5347921252250671, 'data', 1), ('zenml-io/zenml', 0.5281010866165161, 'ml-ops', 3), ('tensorflow/tensorflow', 0.5275462865829468, 'ml-dl', 1), ('keras-team/keras-nlp', 0.5210731625556946, 'nlp', 1), ('firmai/industry-machine-learning', 0.5197041034698486, 'study', 2), ('automl/auto-sklearn', 0.5186381936073303, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5168536901473999, 'ml', 2), ('apache/airflow', 0.5132193565368652, 'ml-ops', 3), ('huggingface/datasets', 0.511340856552124, 'nlp', 1), ('whylabs/whylogs', 0.5075204372406006, 'util', 3), ('gefyrahq/gefyra', 0.5056382417678833, 'util', 1), ('nccr-itmo/fedot', 0.5040023922920227, 'ml-ops', 1), ('xplainable/xplainable', 0.5017673969268799, 'ml-interpretability', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5008544921875, 'study', 1)]",386,1.0,,18.08,597,336,69,0,31,28,31,596.0,1212.0,90.0,2.0,56
728,data,https://github.com/ibis-project/ibis,[],,[],[],1.0,,,ibis-project/ibis,ibis,3364,466,80,Python,https://ibis-project.org,The flexibility of Python with the scale and performance of modern SQL.,ibis-project,2024-01-14,2015-04-17,458,7.33582554517134,https://avatars.githubusercontent.com/u/27442526?v=4,The flexibility of Python with the scale and performance of modern SQL.,"['bigquery', 'clickhouse', 'dask', 'database', 'datafusion', 'duckdb', 'impala', 'mssql', 'mysql', 'pandas', 'polars', 'postgresql', 'pyarrow', 'pyspark', 'snowflake', 'sql', 'sqlalchemy', 'sqlite', 'trino']","['bigquery', 'clickhouse', 'dask', 'database', 'datafusion', 'duckdb', 'impala', 'mssql', 'mysql', 'pandas', 'polars', 'postgresql', 'pyarrow', 'pyspark', 'snowflake', 'sql', 'sqlalchemy', 'sqlite', 'trino']",2024-01-13,"[('tiangolo/sqlmodel', 0.8095237612724304, 'data', 2), ('tobymao/sqlglot', 0.7856696248054504, 'data', 8), ('sqlalchemy/sqlalchemy', 0.741746723651886, 'data', 2), ('kayak/pypika', 0.6313249468803406, 'data', 1), ('machow/siuba', 0.6308576464653015, 'pandas', 2), ('mcfunley/pugsql', 0.6264197826385498, 'data', 1), ('macbre/sql-metadata', 0.6164320707321167, 'data', 2), ('vaexio/vaex', 0.6082955598831177, 'perf', 1), ('andialbrecht/sqlparse', 0.6058615446090698, 'data', 0), ('fugue-project/fugue', 0.5997548699378967, 'pandas', 4), ('malloydata/malloy-py', 0.5992289185523987, 'data', 1), ('datafold/data-diff', 0.5967792272567749, 'data', 6), ('coleifer/peewee', 0.5938148498535156, 'data', 1), ('pytables/pytables', 0.5871632695198059, 'data', 0), ('sfu-db/connector-x', 0.5806707143783569, 'data', 2), ('krzjoa/awesome-python-data-science', 0.5760530233383179, 'study', 0), ('pandas-dev/pandas', 0.5663455724716187, 'pandas', 1), ('fastai/fastcore', 0.566156804561615, 'util', 0), ('aws/aws-sdk-pandas', 0.563861608505249, 'pandas', 2), ('plotly/dash', 0.5618459582328796, 'viz', 0), ('cython/cython', 0.553461492061615, 'util', 0), ('eleutherai/pyfra', 0.5528563857078552, 'ml', 0), ('sqlalchemy/alembic', 0.5510525107383728, 'data', 2), ('pola-rs/polars', 0.5494480729103088, 'pandas', 1), ('airbytehq/airbyte', 0.5454192161560059, 'data', 5), ('collerek/ormar', 0.5441567301750183, 'data', 1), ('simonw/sqlite-utils', 0.540537416934967, 'data', 1), ('dylanhogg/awesome-python', 0.5329792499542236, 'study', 1), ('holoviz/panel', 0.5315485596656799, 'viz', 0), ('apache/spark', 0.5312047004699707, 'data', 1), ('falconry/falcon', 0.5278680324554443, 'web', 0), ('unionai-oss/pandera', 0.525162398815155, 'pandas', 1), ('pyston/pyston', 0.5222876667976379, 'util', 0), ('jina-ai/vectordb', 0.5219907760620117, 'data', 0), ('saulpw/visidata', 0.5209618210792542, 'term', 2), ('hi-primus/optimus', 0.5208921432495117, 'ml-ops', 2), ('googleapis/python-bigquery', 0.5185703635215759, 'data', 0), ('aminalaee/sqladmin', 0.512384831905365, 'data', 1), ('pytoolz/toolz', 0.5118052363395691, 'util', 0), ('python-cachier/cachier', 0.5115165710449219, 'perf', 0), ('aio-libs/aiopg', 0.5114596486091614, 'data', 2), ('aio-libs/aiomysql', 0.5113950967788696, 'data', 2), ('tconbeer/harlequin', 0.5111363530158997, 'term', 1), ('mause/duckdb_engine', 0.5086135864257812, 'data', 3), ('strawberry-graphql/strawberry', 0.5083762407302856, 'web', 0), ('dagworks-inc/hamilton', 0.5063190460205078, 'ml-ops', 1), ('rawheel/fastapi-boilerplate', 0.502396821975708, 'web', 2), ('geopandas/geopandas', 0.5019082427024841, 'gis', 1), ('pyparsing/pyparsing', 0.5017038583755493, 'util', 0), ('klen/muffin', 0.5011864900588989, 'web', 0), ('pypy/pypy', 0.5000938177108765, 'util', 0)]",165,4.0,,55.63,674,586,106,0,9,5,9,673.0,1004.0,90.0,1.5,56
870,time-series,https://github.com/nixtla/statsforecast,[],,[],[],,,,nixtla/statsforecast,statsforecast,3316,223,31,Python,https://nixtlaverse.nixtla.io/statsforecast,Lightning ⚡️ fast forecasting with statistical and econometric models.,nixtla,2024-01-14,2021-11-24,113,29.124215809284816,https://avatars.githubusercontent.com/u/79945230?v=4,Lightning ⚡️ fast forecasting with statistical and econometric models.,"['arima', 'automl', 'baselines', 'data-science', 'econometrics', 'ets', 'exponential-smoothing', 'fbprophet', 'forecasting', 'machine-learning', 'mstl', 'naive', 'neuralprophet', 'predictions', 'prophet', 'seasonal-naive', 'statistics', 'theta', 'time-series']","['arima', 'automl', 'baselines', 'data-science', 'econometrics', 'ets', 'exponential-smoothing', 'fbprophet', 'forecasting', 'machine-learning', 'mstl', 'naive', 'neuralprophet', 'predictions', 'prophet', 'seasonal-naive', 'statistics', 'theta', 'time-series']",2024-01-12,"[('ourownstory/neural_prophet', 0.6677179336547852, 'ml', 6), ('winedarksea/autots', 0.6360719799995422, 'time-series', 4), ('linkedin/greykite', 0.5983828902244568, 'ml', 0), ('facebook/prophet', 0.586733341217041, 'time-series', 2), ('alkaline-ml/pmdarima', 0.5763822197914124, 'time-series', 5), ('firmai/atspy', 0.5731773972511292, 'time-series', 2), ('autoviml/auto_ts', 0.5572653412818909, 'time-series', 4), ('sktime/sktime', 0.5418822169303894, 'time-series', 4), ('awslabs/autogluon', 0.5410817265510559, 'ml', 5), ('salesforce/merlion', 0.5316644906997681, 'time-series', 4), ('uber/orbit', 0.5311532020568848, 'time-series', 5), ('salesforce/deeptime', 0.5292312502861023, 'time-series', 2), ('microsoft/flaml', 0.5162665843963623, 'ml', 3), ('awslabs/gluonts', 0.5115534067153931, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.506076991558075, 'time-series', 2)]",35,3.0,,3.21,126,102,26,0,4,14,4,126.0,183.0,90.0,1.5,56
595,gis,https://github.com/giswqs/geemap,[],,[],[],,,,giswqs/geemap,geemap,3049,1042,116,Python,https://geemap.org,A Python package for interactive geospaital analysis and visualization with Google Earth Engine.,giswqs,2024-01-14,2020-03-08,203,14.998594518622628,https://avatars.githubusercontent.com/u/26841718?v=4,A Python package for interactive geospaital analysis and visualization with Google Earth Engine.,"['colab', 'data-science', 'dataviz', 'earth-engine', 'earthengine', 'folium', 'geospatial', 'gis', 'google-earth-engine', 'image-processing', 'ipyleaflet', 'ipywidgets', 'jupyter', 'jupyter-notebook', 'landsat', 'mapping', 'remote-sensing', 'streamlit', 'streamlit-webapp']","['colab', 'data-science', 'dataviz', 'earth-engine', 'earthengine', 'folium', 'geospatial', 'gis', 'google-earth-engine', 'image-processing', 'ipyleaflet', 'ipywidgets', 'jupyter', 'jupyter-notebook', 'landsat', 'mapping', 'remote-sensing', 'streamlit', 'streamlit-webapp']",2024-01-12,"[('opengeos/leafmap', 0.7121515274047852, 'gis', 11), ('scitools/iris', 0.6783716082572937, 'gis', 0), ('residentmario/geoplot', 0.6778126358985901, 'gis', 0), ('raphaelquast/eomaps', 0.6554696559906006, 'gis', 3), ('holoviz/holoviz', 0.6438645124435425, 'viz', 0), ('bokeh/bokeh', 0.6420087218284607, 'viz', 1), ('holoviz/panel', 0.6401718258857727, 'viz', 2), ('gregorhd/mapcompare', 0.6100561022758484, 'gis', 0), ('visgl/deck.gl', 0.6095970869064331, 'viz', 0), ('plotly/dash', 0.6076993346214294, 'viz', 2), ('plotly/plotly.py', 0.5846720933914185, 'viz', 1), ('holoviz/geoviews', 0.5793654918670654, 'gis', 0), ('altair-viz/altair', 0.5686102509498596, 'viz', 0), ('geopandas/geopandas', 0.5684166550636292, 'gis', 2), ('sentinel-hub/eo-learn', 0.5640432834625244, 'gis', 0), ('maartenbreddels/ipyvolume', 0.5499334931373596, 'jupyter', 3), ('google/earthengine-api', 0.5498977899551392, 'gis', 0), ('osgeo/grass', 0.5491253137588501, 'gis', 6), ('earthlab/earthpy', 0.5480675101280212, 'gis', 0), ('vispy/vispy', 0.5475439429283142, 'viz', 0), ('python-visualization/folium', 0.5445337295532227, 'gis', 1), ('man-group/dtale', 0.5396121144294739, 'viz', 2), ('googleapis/google-api-python-client', 0.5369963645935059, 'util', 0), ('gradio-app/gradio', 0.534578800201416, 'viz', 1), ('kanaries/pygwalker', 0.5327471494674683, 'pandas', 0), ('has2k1/plotnine', 0.5272516012191772, 'viz', 0), ('vizzuhq/ipyvizzu', 0.5243290066719055, 'jupyter', 3), ('polyaxon/datatile', 0.5178652405738831, 'pandas', 1), ('imageio/imageio', 0.505904495716095, 'util', 0), ('radiantearth/radiant-mlhub', 0.5028256773948669, 'gis', 0), ('pytroll/satpy', 0.5017055869102478, 'gis', 0), ('mwaskom/seaborn', 0.5008442401885986, 'viz', 1)]",52,5.0,,5.27,83,79,47,0,45,46,45,83.0,168.0,90.0,2.0,56
549,ml-dl,https://github.com/pytorch/botorch,[],,[],[],,,,pytorch/botorch,botorch,2871,359,53,Jupyter Notebook,https://botorch.org/,Bayesian optimization in PyTorch,pytorch,2024-01-14,2018-07-30,287,9.998507462686566,https://avatars.githubusercontent.com/u/21003710?v=4,Bayesian optimization in PyTorch,[],[],2024-01-12,"[('pyro-ppl/pyro', 0.6212801933288574, 'ml-dl', 0), ('intellabs/bayesian-torch', 0.6108170747756958, 'ml', 0), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5796522498130798, 'study', 0), ('pytorch/ignite', 0.5717622637748718, 'ml-dl', 0), ('nvidia/apex', 0.5640817880630493, 'ml-dl', 0), ('laekov/fastmoe', 0.5626605749130249, 'ml', 0), ('deepmind/kfac-jax', 0.55311119556427, 'math', 0), ('scikit-optimize/scikit-optimize', 0.5391286611557007, 'ml', 0), ('davidmrau/mixture-of-experts', 0.5366146564483643, 'ml', 0), ('pymc-devs/pymc3', 0.5349946618080139, 'ml', 0), ('tanelp/tiny-diffusion', 0.5327487587928772, 'diffusion', 0), ('mrdbourke/pytorch-deep-learning', 0.5209388136863708, 'study', 0), ('pytorch/captum', 0.509192168712616, 'ml-interpretability', 0), ('kshitij12345/torchnnprofiler', 0.5020992159843445, 'profiling', 0), ('skorch-dev/skorch', 0.5020517110824585, 'ml-dl', 0)]",108,4.0,,6.67,131,111,66,0,10,8,10,131.0,512.0,90.0,3.9,56
436,gis,https://github.com/opengeos/leafmap,[],,[],[],,,,opengeos/leafmap,leafmap,2809,326,52,Python,https://leafmap.org,A Python package for interactive mapping and geospatial analysis  with minimal coding in a Jupyter environment,opengeos,2024-01-13,2021-03-10,150,18.620265151515152,https://avatars.githubusercontent.com/u/129896036?v=4,A Python package for interactive mapping and geospatial analysis  with minimal coding in a Jupyter environment,"['data-science', 'dataviz', 'folium', 'geoparquet', 'geopython', 'geospatial', 'geospatial-analysis', 'gis', 'ipyleaflet', 'jupyter', 'jupyter-notebook', 'leafmap', 'mapping', 'plotly', 'streamlit', 'streamlit-webapp', 'whiteboxtools']","['data-science', 'dataviz', 'folium', 'geoparquet', 'geopython', 'geospatial', 'geospatial-analysis', 'gis', 'ipyleaflet', 'jupyter', 'jupyter-notebook', 'leafmap', 'mapping', 'plotly', 'streamlit', 'streamlit-webapp', 'whiteboxtools']",2024-01-11,"[('giswqs/geemap', 0.7121515274047852, 'gis', 11), ('residentmario/geoplot', 0.6929628252983093, 'gis', 0), ('raphaelquast/eomaps', 0.6778762936592102, 'gis', 3), ('geopandas/geopandas', 0.671466052532196, 'gis', 3), ('vizzuhq/ipyvizzu', 0.6331526637077332, 'jupyter', 3), ('holoviz/panel', 0.6154477596282959, 'viz', 3), ('artelys/geonetworkx', 0.6138547658920288, 'gis', 0), ('holoviz/geoviews', 0.6069517135620117, 'gis', 0), ('holoviz/holoviz', 0.6019681692123413, 'viz', 0), ('gregorhd/mapcompare', 0.6006041169166565, 'gis', 0), ('plotly/plotly.py', 0.5990200638771057, 'viz', 2), ('bokeh/bokeh', 0.5971899032592773, 'viz', 1), ('giswqs/mapwidget', 0.5904461741447449, 'gis', 4), ('wesm/pydata-book', 0.5878331065177917, 'study', 0), ('scitools/iris', 0.5820508003234863, 'gis', 0), ('ipython/ipyparallel', 0.580319344997406, 'perf', 1), ('quantopian/qgrid', 0.5799870491027832, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5771999359130859, 'jupyter', 3), ('geopandas/contextily', 0.5741623044013977, 'gis', 1), ('earthlab/earthpy', 0.572256863117218, 'gis', 0), ('jakevdp/pythondatasciencehandbook', 0.5667238235473633, 'study', 1), ('pysal/pysal', 0.5648621320724487, 'gis', 0), ('darribas/gds_env', 0.563289999961853, 'gis', 0), ('plotly/dash', 0.5570528507232666, 'viz', 3), ('scitools/cartopy', 0.5553449988365173, 'gis', 0), ('has2k1/plotnine', 0.5541620254516602, 'viz', 0), ('man-group/dtale', 0.5516546368598938, 'viz', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5463659763336182, 'study', 0), ('cohere-ai/notebooks', 0.546214759349823, 'llm', 0), ('altair-viz/altair', 0.5457186698913574, 'viz', 0), ('marceloprates/prettymaps', 0.5452340841293335, 'viz', 1), ('pyston/pyston', 0.5413333773612976, 'util', 0), ('toblerity/rtree', 0.5405087471008301, 'gis', 0), ('pypy/pypy', 0.5401880741119385, 'util', 0), ('python-visualization/folium', 0.5398542284965515, 'gis', 1), ('aws/graph-notebook', 0.5389112830162048, 'jupyter', 2), ('jupyter-widgets/ipyleaflet', 0.5388780832290649, 'gis', 1), ('kanaries/pygwalker', 0.5377007722854614, 'pandas', 1), ('pyproj4/pyproj', 0.5364670157432556, 'gis', 1), ('pytoolz/toolz', 0.5356908440589905, 'util', 0), ('python/cpython', 0.5353587865829468, 'util', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5345747470855713, 'jupyter', 2), ('makepath/xarray-spatial', 0.5318371057510376, 'gis', 0), ('openeventdata/mordecai', 0.5265222191810608, 'gis', 0), ('eleutherai/pyfra', 0.525818407535553, 'ml', 0), ('jalammar/ecco', 0.5254456996917725, 'ml-interpretability', 0), ('voila-dashboards/voila', 0.5204005241394043, 'jupyter', 2), ('pandas-dev/pandas', 0.5185487866401672, 'pandas', 1), ('pyglet/pyglet', 0.5158253312110901, 'gamedev', 0), ('osgeo/grass', 0.5151031017303467, 'gis', 5), ('uber/h3-py', 0.5105132460594177, 'gis', 2), ('cloudsen12/easystac', 0.5067197680473328, 'gis', 1), ('bitcraft/pytmx', 0.5061635375022888, 'gamedev', 0), ('scikit-mobility/scikit-mobility', 0.5058870911598206, 'gis', 1), ('amaargiru/pyroad', 0.503073513507843, 'study', 0)]",29,6.0,,5.35,77,74,35,0,58,43,58,77.0,104.0,90.0,1.4,56
806,data,https://github.com/datafold/data-diff,[],,[],[],,,,datafold/data-diff,data-diff,2686,189,21,Python,https://docs.datafold.com,Compare tables within or across databases,datafold,2024-01-14,2022-03-07,99,27.092219020172912,https://avatars.githubusercontent.com/u/63129412?v=4,Compare tables within or across databases,"['data', 'data-diffing', 'data-engineering', 'data-quality', 'data-quality-monitoring', 'data-science', 'database', 'databricks-sql', 'dataengineering', 'dataquality', 'dbt', 'mysql', 'oracle-database', 'postgres', 'postgresql', 'rdbms', 'snowflake', 'sql', 'trino']","['data', 'data-diffing', 'data-engineering', 'data-quality', 'data-quality-monitoring', 'data-science', 'database', 'databricks-sql', 'dataengineering', 'dataquality', 'dbt', 'mysql', 'oracle-database', 'postgres', 'postgresql', 'rdbms', 'snowflake', 'sql', 'trino']",2024-01-12,"[('ibis-project/ibis', 0.5967792272567749, 'data', 6), ('tobymao/sqlglot', 0.5574495196342468, 'data', 5), ('tiangolo/sqlmodel', 0.5563005805015564, 'data', 1), ('dbt-labs/dbt-core', 0.550411581993103, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5429127216339111, 'ml-ops', 4), ('unionai-oss/pandera', 0.5363107919692993, 'pandas', 0)]",53,2.0,,11.88,133,98,23,0,48,32,48,133.0,120.0,90.0,0.9,56
1628,llm,https://github.com/next-gpt/next-gpt,[],,[],[],,,,next-gpt/next-gpt,NExT-GPT,2579,266,57,Python,https://next-gpt.github.io/,Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model,next-gpt,2024-01-13,2023-08-30,21,117.99346405228758,,Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model,"['chatgpt', 'foundation-models', 'gpt-4', 'instruction-tuning', 'large-language-models', 'llm', 'multi-modal-chatgpt', 'multimodal', 'visual-language-learning']","['chatgpt', 'foundation-models', 'gpt-4', 'instruction-tuning', 'large-language-models', 'llm', 'multi-modal-chatgpt', 'multimodal', 'visual-language-learning']",2024-01-09,"[('microsoft/autogen', 0.7022780776023865, 'llm', 2), ('hannibal046/awesome-llm', 0.6860809922218323, 'study', 0), ('lianjiatech/belle', 0.6763371229171753, 'llm', 0), ('mlc-ai/web-llm', 0.6524577140808105, 'llm', 2), ('xtekky/gpt4free', 0.6495850086212158, 'llm', 2), ('guidance-ai/guidance', 0.6470729112625122, 'llm', 1), ('hiyouga/llama-factory', 0.6337036490440369, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.6337035894393921, 'llm', 3), ('thudm/chatglm2-6b', 0.6282567381858826, 'llm', 2), ('lm-sys/fastchat', 0.6228191256523132, 'llm', 0), ('openlmlab/moss', 0.6183462738990784, 'llm', 2), ('microsoft/lora', 0.6170973181724548, 'llm', 0), ('haotian-liu/llava', 0.6144143342971802, 'llm', 6), ('bobazooba/xllm', 0.61397784948349, 'llm', 4), ('baichuan-inc/baichuan-13b', 0.6081295013427734, 'llm', 3), ('lupantech/chameleon-llm', 0.6010711193084717, 'llm', 3), ('docarray/docarray', 0.5986325144767761, 'data', 1), ('optimalscale/lmflow', 0.5973320603370667, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5953162908554077, 'llm', 0), ('freedomintelligence/llmzoo', 0.5918599963188171, 'llm', 0), ('ai21labs/lm-evaluation', 0.5911809802055359, 'llm', 0), ('li-plus/chatglm.cpp', 0.5851370096206665, 'llm', 1), ('microsoft/torchscale', 0.582415759563446, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5799466967582703, 'llm', 2), ('blinkdl/chatrwkv', 0.5798339247703552, 'llm', 1), ('fasteval/fasteval', 0.57789546251297, 'llm', 1), ('young-geng/easylm', 0.5767945647239685, 'llm', 1), ('nomic-ai/gpt4all', 0.5750716328620911, 'llm', 0), ('nvlabs/prismer', 0.5736103057861328, 'diffusion', 0), ('eth-sri/lmql', 0.5689839124679565, 'llm', 1), ('oobabooga/text-generation-webui', 0.5688201785087585, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5671409964561462, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5671409964561462, 'llm', 0), ('salesforce/xgen', 0.5664035081863403, 'llm', 2), ('cg123/mergekit', 0.5641329884529114, 'llm', 1), ('lightning-ai/lit-llama', 0.5547515153884888, 'llm', 0), ('mnotgod96/appagent', 0.5528967976570129, 'llm', 2), ('run-llama/rags', 0.5521267652511597, 'llm', 2), ('huggingface/text-generation-inference', 0.5511967539787292, 'llm', 0), ('togethercomputer/redpajama-data', 0.5498401522636414, 'llm', 0), ('salesforce/blip', 0.5469942092895508, 'diffusion', 0), ('infinitylogesh/mutate', 0.5463948845863342, 'nlp', 0), ('juncongmoo/pyllama', 0.5461319088935852, 'llm', 0), ('killianlucas/open-interpreter', 0.5455114841461182, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5425037145614624, 'perf', 0), ('dylanhogg/llmgraph', 0.5404556393623352, 'ml', 2), ('bigscience-workshop/petals', 0.5393829941749573, 'data', 1), ('confident-ai/deepeval', 0.5388375520706177, 'testing', 2), ('ofa-sys/ofa', 0.538471519947052, 'llm', 1), ('hwchase17/langchain', 0.5355835556983948, 'llm', 0), ('nvidia/tensorrt-llm', 0.5343412160873413, 'viz', 0), ('facebookresearch/seamless_communication', 0.5332794785499573, 'nlp', 0), ('guardrails-ai/guardrails', 0.5325504541397095, 'llm', 1), ('reasoning-machines/pal', 0.532139778137207, 'llm', 1), ('yueyu1030/attrprompt', 0.5283357501029968, 'llm', 1), ('salesforce/codet5', 0.5271520614624023, 'nlp', 1), ('microsoft/unilm', 0.5269091129302979, 'nlp', 3), ('embedchain/embedchain', 0.5254507064819336, 'llm', 2), ('keirp/automatic_prompt_engineer', 0.525351881980896, 'llm', 0), ('openai/gpt-2', 0.5251544713973999, 'llm', 0), ('cstankonrad/long_llama', 0.5235233306884766, 'llm', 0), ('zhudotexe/kani', 0.5206196904182434, 'llm', 3), ('databrickslabs/dolly', 0.5201497077941895, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5197857022285461, 'study', 1), ('whu-zqh/chatgpt-vs.-bert', 0.515221357345581, 'llm', 1), ('salesforce/codegen', 0.5129567384719849, 'nlp', 1), ('ray-project/ray-llm', 0.5125154256820679, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5125038027763367, 'llm', 0), ('ludwig-ai/ludwig', 0.5110254287719727, 'ml-ops', 1), ('explosion/spacy-llm', 0.5109996199607849, 'llm', 3), ('eleutherai/gpt-neo', 0.5107851624488831, 'llm', 0), ('langchain-ai/langgraph', 0.5100759863853455, 'llm', 0), ('minedojo/voyager', 0.509900689125061, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5096499919891357, 'nlp', 0), ('srush/minichain', 0.5092249512672424, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5090202689170837, 'llm', 2), ('conceptofmind/toolformer', 0.5089335441589355, 'llm', 0), ('lingjzhu/charsiug2p', 0.5078827738761902, 'nlp', 0), ('epfllm/meditron', 0.5075371265411377, 'llm', 0), ('deepset-ai/haystack', 0.5068576335906982, 'llm', 2), ('eleutherai/lm-evaluation-harness', 0.5040009617805481, 'llm', 0), ('agenta-ai/agenta', 0.503362774848938, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5027459859848022, 'study', 1), ('moymix/taskmatrix', 0.5018583536148071, 'llm', 1), ('openbmb/toolbench', 0.5017238259315491, 'llm', 1), ('luodian/otter', 0.501610517501831, 'llm', 5), ('thilinarajapakse/simpletransformers', 0.5003290772438049, 'nlp', 0)]",4,2.0,,4.06,58,21,5,0,0,0,0,58.0,30.0,90.0,0.5,56
835,ml,https://github.com/aws/sagemaker-python-sdk,[],,[],[],,,,aws/sagemaker-python-sdk,sagemaker-python-sdk,1995,1104,132,Python,https://sagemaker.readthedocs.io/,A library for training and deploying machine learning models on Amazon SageMaker,aws,2024-01-12,2017-11-14,324,6.157407407407407,https://avatars.githubusercontent.com/u/2232217?v=4,A library for training and deploying machine learning models on Amazon SageMaker,"['aws', 'huggingface', 'machine-learning', 'mxnet', 'pytorch', 'sagemaker', 'tensorflow']","['aws', 'huggingface', 'machine-learning', 'mxnet', 'pytorch', 'sagemaker', 'tensorflow']",2024-01-11,"[('aws-samples/sagemaker-ssh-helper', 0.671806812286377, 'util', 3), ('huggingface/huggingface_hub', 0.6623826026916504, 'ml', 2), ('mlflow/mlflow', 0.6176590919494629, 'ml-ops', 1), ('ashleve/lightning-hydra-template', 0.6082078814506531, 'util', 1), ('kubeflow/fairing', 0.5987374186515808, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.5961750149726868, 'ml', 3), ('horovod/horovod', 0.5958705544471741, 'ml-ops', 4), ('determined-ai/determined', 0.5937497019767761, 'ml-ops', 3), ('rasbt/machine-learning-book', 0.586579442024231, 'study', 2), ('huggingface/exporters', 0.5837609171867371, 'ml', 3), ('huggingface/datasets', 0.5822443962097168, 'nlp', 3), ('tensorflow/tensorflow', 0.578117311000824, 'ml-dl', 2), ('skorch-dev/skorch', 0.5779036283493042, 'ml-dl', 3), ('uber/petastorm', 0.5683594346046448, 'data', 3), ('huggingface/transformers', 0.5665184855461121, 'nlp', 3), ('pytorch/ignite', 0.5633373856544495, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5589292645454407, 'ml-rl', 1), ('gradio-app/gradio', 0.5586919188499451, 'viz', 1), ('ggerganov/ggml', 0.5583137273788452, 'ml', 1), ('titanml/takeoff', 0.5581379532814026, 'llm', 0), ('intel/intel-extension-for-pytorch', 0.5577678680419922, 'perf', 2), ('microsoft/nni', 0.5550652146339417, 'ml', 3), ('google/tf-quant-finance', 0.5465176105499268, 'finance', 1), ('radiantearth/radiant-mlhub', 0.5445782542228699, 'gis', 1), ('ml-tooling/opyrator', 0.5434074401855469, 'viz', 1), ('firmai/industry-machine-learning', 0.5424572229385376, 'study', 1), ('wandb/client', 0.5404186248779297, 'ml', 3), ('polyaxon/polyaxon', 0.5382318496704102, 'ml-ops', 4), ('ageron/handson-ml2', 0.5365365743637085, 'ml', 0), ('karpathy/micrograd', 0.5261572003364563, 'study', 0), ('keras-team/autokeras', 0.5244501233100891, 'ml-dl', 2), ('eventual-inc/daft', 0.5235341787338257, 'pandas', 1), ('pytorch/rl', 0.5228525996208191, 'ml-rl', 2), ('activeloopai/deeplake', 0.5221602320671082, 'ml-ops', 3), ('tlkh/tf-metal-experiments', 0.5217053890228271, 'perf', 1), ('microsoft/flaml', 0.5215294361114502, 'ml', 1), ('oml-team/open-metric-learning', 0.5199127197265625, 'ml', 1), ('dylanhogg/awesome-python', 0.5184698104858398, 'study', 1), ('nvidia/deeplearningexamples', 0.5177861452102661, 'ml-dl', 3), ('tensorflow/tensor2tensor', 0.5145456790924072, 'ml', 1), ('microsoft/onnxruntime', 0.513953447341919, 'ml', 3), ('rafiqhasan/auto-tensorflow', 0.512946367263794, 'ml-dl', 2), ('googlecloudplatform/vertex-ai-samples', 0.5073953866958618, 'ml', 0), ('pycaret/pycaret', 0.5068821310997009, 'ml', 1), ('adap/flower', 0.506871223449707, 'ml-ops', 3), ('ray-project/ray', 0.5026758909225464, 'ml-ops', 3), ('lightly-ai/lightly', 0.5022170543670654, 'ml', 2), ('dmlc/xgboost', 0.5017920732498169, 'ml', 1)]",417,2.0,,12.08,465,368,75,0,88,91,88,465.0,2810.0,90.0,6.0,56
1242,ml-interpretability,https://github.com/arize-ai/phoenix,[],,[],[],,,,arize-ai/phoenix,phoenix,1906,128,23,Jupyter Notebook,https://docs.arize.com/phoenix,"AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook.",arize-ai,2024-01-13,2022-11-09,63,29.847874720357943,https://avatars.githubusercontent.com/u/59858760?v=4,"AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook.","['ai-monitoring', 'ai-observability', 'ai-roi', 'clustering', 'llm-eval', 'llmops', 'ml-monitoring', 'ml-observability', 'mlops', 'model-monitoring', 'model-observability', 'umap']","['ai-monitoring', 'ai-observability', 'ai-roi', 'clustering', 'llm-eval', 'llmops', 'ml-monitoring', 'ml-observability', 'mlops', 'model-monitoring', 'model-observability', 'umap']",2024-01-12,"[('giskard-ai/giskard', 0.6145030856132507, 'data', 2), ('microsoft/lmops', 0.5890821814537048, 'llm', 0), ('llmware-ai/llmware', 0.5607779026031494, 'llm', 0), ('bentoml/bentoml', 0.547683835029602, 'ml-ops', 2), ('microsoft/promptflow', 0.5460477471351624, 'llm', 0), ('tigerlab-ai/tiger', 0.5459373593330383, 'llm', 0), ('confident-ai/deepeval', 0.5458160042762756, 'testing', 1), ('truera/trulens', 0.532617449760437, 'llm', 1), ('interpretml/interpret', 0.5245933532714844, 'ml-interpretability', 0), ('lastmile-ai/aiconfig', 0.5230908393859863, 'util', 0), ('mlc-ai/mlc-llm', 0.5153992772102356, 'llm', 0), ('nebuly-ai/nebullvm', 0.5097155570983887, 'perf', 0), ('googlecloudplatform/vertex-ai-samples', 0.5083956122398376, 'ml', 1), ('cheshire-cat-ai/core', 0.5057440400123596, 'llm', 0), ('csinva/imodels', 0.5046104192733765, 'ml', 0), ('evidentlyai/evidently', 0.5009012818336487, 'ml-ops', 2), ('openai/evals', 0.500573456287384, 'llm', 0)]",30,1.0,,26.98,518,437,14,0,75,90,75,519.0,394.0,90.0,0.8,56
1743,llm,https://github.com/microsoft/llmlingua,"['inference', 'performance']",,[],[],,,,microsoft/llmlingua,LLMLingua,1887,93,17,Python,https://llmlingua.com/,"To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. ",microsoft,2024-01-14,2023-07-07,29,63.81159420289855,https://avatars.githubusercontent.com/u/6154722?v=4,"To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. ",[],"['inference', 'performance']",2024-01-13,"[('vllm-project/vllm', 0.6239213347434998, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6135388016700745, 'perf', 0), ('lightning-ai/lit-gpt', 0.5566024780273438, 'llm', 0), ('bentoml/openllm', 0.5037171244621277, 'ml-ops', 0)]",7,3.0,,0.67,49,26,6,0,4,8,4,49.0,70.0,90.0,1.4,56
463,ml-dl,https://github.com/pytorch/torchrec,[],,[],[],,,,pytorch/torchrec,torchrec,1625,328,29,Python,,Pytorch domain library for recommendation systems,pytorch,2024-01-14,2021-07-12,133,12.204935622317597,https://avatars.githubusercontent.com/u/21003710?v=4,Pytorch domain library for recommendation systems,"['cuda', 'deep-learning', 'gpu', 'pytorch', 'recommendation-system', 'recommender-system', 'sharding']","['cuda', 'deep-learning', 'gpu', 'pytorch', 'recommendation-system', 'recommender-system', 'sharding']",2024-01-13,"[('rucaibox/recbole', 0.7371825575828552, 'ml', 3), ('nicolashug/surprise', 0.5874725580215454, 'ml', 0), ('pytorch/data', 0.5872920751571655, 'data', 0), ('pytorch/ignite', 0.5860687494277954, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.570334255695343, 'ml-dl', 2), ('cvxgrp/pymde', 0.5692107677459717, 'ml', 3), ('blackhc/toma', 0.5616428256034851, 'ml-dl', 2), ('microsoft/recommenders', 0.5602710247039795, 'study', 2), ('mrdbourke/pytorch-deep-learning', 0.5445482730865479, 'study', 2), ('rasbt/machine-learning-book', 0.5437749624252319, 'study', 2), ('oml-team/open-metric-learning', 0.5388403534889221, 'ml', 2), ('cupy/cupy', 0.5291244387626648, 'math', 2), ('intel/intel-extension-for-pytorch', 0.5260089635848999, 'perf', 2), ('google/tf-quant-finance', 0.5250528454780579, 'finance', 1), ('skorch-dev/skorch', 0.5207085609436035, 'ml-dl', 1), ('a-r-j/graphein', 0.5193598866462708, 'sim', 2), ('rentruewang/koila', 0.5190505385398865, 'ml', 2), ('xl0/lovely-tensors', 0.5139912366867065, 'ml-dl', 2), ('uber/petastorm', 0.5117344260215759, 'data', 2), ('qdrant/fastembed', 0.5069307088851929, 'ml', 0), ('catboost/catboost', 0.5050163269042969, 'ml', 2), ('facebookresearch/pytorch3d', 0.5049778819084167, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5045345425605774, 'ml', 2), ('rapidsai/cudf', 0.5002225637435913, 'pandas', 2)]",198,5.0,,9.6,194,142,30,0,5,4,5,194.0,572.0,90.0,2.9,56
1541,llm,https://github.com/weaviate/verba,['retrieval-augmentation'],,[],[],,,,weaviate/verba,Verba,1585,157,31,Python,,Retrieval Augmented Generation (RAG) chatbot powered by Weaviate,weaviate,2024-01-14,2023-07-28,26,59.6505376344086,https://avatars.githubusercontent.com/u/37794290?v=4,Retrieval Augmented Generation (RAG) chatbot powered by Weaviate,[],['retrieval-augmentation'],2024-01-02,"[('rcgai/simplyretrieve', 0.6520878076553345, 'llm', 0), ('embedchain/embedchain', 0.5736026167869568, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5466259121894836, 'llm', 0), ('lm-sys/fastchat', 0.5395397543907166, 'llm', 0), ('openlmlab/moss', 0.5387402772903442, 'llm', 0), ('langchain-ai/chat-langchain', 0.5274889469146729, 'llm', 0), ('togethercomputer/openchatkit', 0.5258392691612244, 'nlp', 0), ('cheshire-cat-ai/core', 0.5184066295623779, 'llm', 0)]",8,1.0,,2.92,91,51,6,0,3,6,3,91.0,196.0,90.0,2.2,56
290,ml-ops,https://github.com/dagworks-inc/hamilton,['mlops'],,[],[],,,,dagworks-inc/hamilton,hamilton,1120,63,12,Jupyter Notebook,https://hamilton.dagworks.io/en/latest/,"Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.",dagworks-inc,2024-01-13,2023-02-23,48,22.991202346041057,https://avatars.githubusercontent.com/u/116846391?v=4,"Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.","['dag', 'data-analysis', 'data-engineering', 'data-science', 'dataframe', 'etl', 'etl-framework', 'etl-pipeline', 'feature-engineering', 'featurization', 'lineage', 'llmops', 'machine-learning', 'mlops', 'numpy', 'orchestration', 'pandas', 'software-engineering']","['dag', 'data-analysis', 'data-engineering', 'data-science', 'dataframe', 'etl', 'etl-framework', 'etl-pipeline', 'feature-engineering', 'featurization', 'lineage', 'llmops', 'machine-learning', 'mlops', 'numpy', 'orchestration', 'pandas', 'software-engineering']",2024-01-13,"[('python-odin/odin', 0.6443514227867126, 'util', 0), ('polyaxon/datatile', 0.6286333203315735, 'pandas', 3), ('orchest/orchest', 0.6139141917228699, 'ml-ops', 5), ('mage-ai/mage-ai', 0.6080176830291748, 'ml-ops', 5), ('ploomber/ploomber', 0.6052381992340088, 'ml-ops', 4), ('fastai/fastcore', 0.603600263595581, 'util', 0), ('krzjoa/awesome-python-data-science', 0.5971183776855469, 'study', 3), ('airbytehq/airbyte', 0.5934673547744751, 'data', 3), ('dagster-io/dagster', 0.5917893052101135, 'ml-ops', 5), ('merantix-momentum/squirrel-core', 0.5907508730888367, 'ml', 2), ('fugue-project/fugue', 0.5906447768211365, 'pandas', 2), ('pandas-dev/pandas', 0.590579092502594, 'pandas', 4), ('plotly/dash', 0.5894344449043274, 'viz', 1), ('backtick-se/cowait', 0.5880764722824097, 'util', 2), ('avaiga/taipy', 0.5867727994918823, 'data', 3), ('meltano/meltano', 0.5787143111228943, 'ml-ops', 1), ('wandb/client', 0.5770619511604309, 'ml', 3), ('hi-primus/optimus', 0.5767509341239929, 'ml-ops', 3), ('kestra-io/kestra', 0.5751848816871643, 'ml-ops', 3), ('eventual-inc/daft', 0.5710514783859253, 'pandas', 4), ('gradio-app/gradio', 0.5623276233673096, 'viz', 3), ('dylanhogg/awesome-python', 0.5567782521247864, 'study', 3), ('eleutherai/pyfra', 0.5552157163619995, 'ml', 0), ('polyaxon/polyaxon', 0.5525692701339722, 'ml-ops', 3), ('flyteorg/flyte', 0.549081027507782, 'ml-ops', 4), ('kubeflow-kale/kale', 0.5480688214302063, 'ml-ops', 1), ('ydataai/ydata-profiling', 0.5421671867370605, 'pandas', 4), ('featurelabs/featuretools', 0.5415907502174377, 'ml', 3), ('unionai-oss/pandera', 0.5392426252365112, 'pandas', 1), ('goldmansachs/gs-quant', 0.5357715487480164, 'finance', 0), ('huggingface/datasets', 0.5354235172271729, 'nlp', 3), ('google/pyglove', 0.5348206162452698, 'util', 1), ('thealgorithms/python', 0.5340306162834167, 'study', 0), ('pytoolz/toolz', 0.5323624014854431, 'util', 0), ('malloydata/malloy-py', 0.532253086566925, 'data', 0), ('pathwaycom/pathway', 0.5315225720405579, 'data', 1), ('rasbt/mlxtend', 0.5286049842834473, 'ml', 2), ('google/ml-metadata', 0.5270819664001465, 'ml-ops', 0), ('ranaroussi/quantstats', 0.5268656015396118, 'finance', 0), ('kubeflow/fairing', 0.5266019105911255, 'ml-ops', 0), ('man-group/dtale', 0.525833010673523, 'viz', 3), ('selfexplainml/piml-toolbox', 0.5237611532211304, 'ml-interpretability', 0), ('firmai/industry-machine-learning', 0.5236888527870178, 'study', 2), ('thoth-station/micropipenv', 0.522192656993866, 'util', 0), ('netflix/metaflow', 0.519896924495697, 'ml-ops', 3), ('epistasislab/tpot', 0.5198063850402832, 'ml', 3), ('holoviz/panel', 0.5194111466407776, 'viz', 0), ('great-expectations/great_expectations', 0.5179693102836609, 'ml-ops', 3), ('spotify/luigi', 0.5168716907501221, 'ml-ops', 0), ('scikit-mobility/scikit-mobility', 0.5164218544960022, 'gis', 2), ('tobymao/sqlglot', 0.5160204768180847, 'data', 0), ('scikit-learn/scikit-learn', 0.514525830745697, 'ml', 3), ('mlflow/mlflow', 0.5135458111763, 'ml-ops', 1), ('lk-geimfari/mimesis', 0.5131853222846985, 'data', 2), ('whylabs/whylogs', 0.5131041407585144, 'util', 3), ('keon/algorithms', 0.5108981728553772, 'util', 0), ('pythagora-io/gpt-pilot', 0.510085940361023, 'llm', 0), ('apache/airflow', 0.5085076093673706, 'ml-ops', 7), ('googlecloudplatform/vertex-ai-samples', 0.5069316625595093, 'ml', 2), ('ibis-project/ibis', 0.5063190460205078, 'data', 1), ('pypa/pipenv', 0.5052401423454285, 'util', 0), ('linealabs/lineapy', 0.5050464868545532, 'jupyter', 0), ('dlt-hub/dlt', 0.5049058198928833, 'data', 1), ('saulpw/visidata', 0.5007401704788208, 'term', 1)]",40,3.0,,12.56,185,148,11,0,55,84,55,186.0,240.0,90.0,1.3,56
1875,llm,https://github.com/agenta-ai/agenta,['llmops'],,[],[],,,,agenta-ai/agenta,agenta,623,126,13,Python,http://www.agenta.ai,"The all-in-one LLMOps platform: prompt management, evaluation, human feedback, and deployment all in one place.",agenta-ai,2024-01-14,2023-04-26,39,15.630824372759857,https://avatars.githubusercontent.com/u/127993667?v=4,"The all-in-one LLMOps platform: prompt management, evaluation, human feedback, and deployment all in one place.","['langchain', 'large-language-models', 'llama-index', 'llm', 'llm-evaluation', 'llm-framework', 'llm-tools', 'llmops', 'llms', 'prompt-engineering', 'prompt-management', 'prompt-toolkit', 'rag', 'rag-evaluation']","['langchain', 'large-language-models', 'llama-index', 'llm', 'llm-evaluation', 'llm-framework', 'llm-tools', 'llmops', 'llms', 'prompt-engineering', 'prompt-management', 'prompt-toolkit', 'rag', 'rag-evaluation']",2024-01-12,"[('confident-ai/deepeval', 0.690017819404602, 'testing', 3), ('hegelai/prompttools', 0.6764405965805054, 'llm', 3), ('eugeneyan/open-llms', 0.6486678719520569, 'study', 3), ('hwchase17/langchain', 0.6438751816749573, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.6384699940681458, 'llm', 0), ('bentoml/openllm', 0.6264759302139282, 'ml-ops', 2), ('argilla-io/argilla', 0.6207625269889832, 'nlp', 2), ('microsoft/promptflow', 0.6172305941581726, 'llm', 2), ('citadel-ai/langcheck', 0.613926351070404, 'llm', 0), ('young-geng/easylm', 0.6034758687019348, 'llm', 1), ('h2oai/h2o-llmstudio', 0.6011561751365662, 'llm', 1), ('promptslab/promptify', 0.5992403030395508, 'nlp', 3), ('deepset-ai/haystack', 0.5929967761039734, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5762568116188049, 'llm', 1), ('guidance-ai/guidance', 0.5751789808273315, 'llm', 1), ('nomic-ai/gpt4all', 0.5681662559509277, 'llm', 0), ('salesforce/xgen', 0.5658103227615356, 'llm', 2), ('hiyouga/llama-factory', 0.5645887851715088, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5645886659622192, 'llm', 3), ('pathwaycom/llm-app', 0.5643110275268555, 'llm', 3), ('tigerlab-ai/tiger', 0.5620597004890442, 'llm', 3), ('bigscience-workshop/petals', 0.5601279735565186, 'data', 1), ('lm-sys/fastchat', 0.5597670674324036, 'llm', 0), ('deep-diver/pingpong', 0.557935893535614, 'llm', 0), ('microsoft/lmops', 0.5574962496757507, 'llm', 1), ('microsoft/autogen', 0.557157039642334, 'llm', 2), ('openai/evals', 0.5553388595581055, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5545148849487305, 'study', 1), ('neulab/prompt2model', 0.5488535761833191, 'llm', 0), ('nat/openplayground', 0.5450541973114014, 'llm', 0), ('salesforce/codet5', 0.5426530241966248, 'nlp', 1), ('intel/intel-extension-for-transformers', 0.5398895144462585, 'perf', 0), ('night-chen/toolqa', 0.539746880531311, 'llm', 1), ('run-llama/llama-lab', 0.5381309986114502, 'llm', 1), ('conceptofmind/toolformer', 0.5377708673477173, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.53215092420578, 'study', 2), ('nebuly-ai/nebullvm', 0.5309828519821167, 'perf', 2), ('microsoft/promptcraft-robotics', 0.5304033756256104, 'sim', 2), ('langchain-ai/langsmith-cookbook', 0.5301238894462585, 'llm', 0), ('explosion/spacy-llm', 0.5294641256332397, 'llm', 3), ('lianjiatech/belle', 0.5287581086158752, 'llm', 0), ('microsoft/torchscale', 0.5262821912765503, 'llm', 0), ('bigscience-workshop/promptsource', 0.52383953332901, 'nlp', 0), ('openbmb/toolbench', 0.5236167907714844, 'llm', 0), ('ibm/dromedary', 0.5226309895515442, 'llm', 0), ('ai21labs/lm-evaluation', 0.5219810009002686, 'llm', 0), ('alphasecio/langchain-examples', 0.5152485370635986, 'llm', 2), ('langchain-ai/langgraph', 0.5138852000236511, 'llm', 1), ('epfllm/meditron', 0.5137256979942322, 'llm', 0), ('ray-project/ray-llm', 0.5125582218170166, 'llm', 3), ('ajndkr/lanarky', 0.5057575702667236, 'llm', 1), ('iryna-kondr/scikit-llm', 0.5052707195281982, 'llm', 1), ('next-gpt/next-gpt', 0.503362774848938, 'llm', 2), ('hazyresearch/ama_prompting', 0.5027536153793335, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.5022589564323425, 'llm', 0)]",55,5.0,,73.85,505,427,9,0,55,74,55,507.0,448.0,90.0,0.9,56
943,ml,https://github.com/lutzroeder/netron,[],,[],[],,,,lutzroeder/netron,netron,25153,2629,296,JavaScript,https://netron.app,"Visualizer for neural network, deep learning and machine learning models",lutzroeder,2024-01-14,2010-12-26,683,36.81183357725277,,"Visualizer for neural network, deep learning and machine learning models","['ai', 'caffe', 'caffe2', 'coreml', 'darknet', 'deep-learning', 'deeplearning', 'keras', 'machine-learning', 'machinelearning', 'ml', 'mxnet', 'neural-network', 'onnx', 'paddle', 'pytorch', 'tensorflow', 'tensorflow-lite', 'torch', 'visualizer']","['ai', 'caffe', 'caffe2', 'coreml', 'darknet', 'deep-learning', 'deeplearning', 'keras', 'machine-learning', 'machinelearning', 'ml', 'mxnet', 'neural-network', 'onnx', 'paddle', 'pytorch', 'tensorflow', 'tensorflow-lite', 'torch', 'visualizer']",2024-01-14,"[('neuralmagic/sparseml', 0.6401857137680054, 'ml-dl', 4), ('roboflow/supervision', 0.6290085911750793, 'ml', 4), ('onnx/onnx', 0.616361141204834, 'ml', 9), 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0.5644053816795349, 'ml', 5), ('deci-ai/super-gradients', 0.5625087022781372, 'ml-dl', 3), ('bentoml/bentoml', 0.5586127042770386, 'ml-ops', 3), ('tensorflow/tensor2tensor', 0.5569348335266113, 'ml', 2), ('keras-team/keras', 0.5549449920654297, 'ml-dl', 4), ('man-group/dtale', 0.5549408793449402, 'viz', 0), ('determined-ai/determined', 0.5507928729057312, 'ml-ops', 5), ('open-mmlab/mmediting', 0.5503832101821899, 'ml', 2), ('xl0/lovely-tensors', 0.549081027507782, 'ml-dl', 2), ('christoschristofidis/awesome-deep-learning', 0.5479288101196289, 'study', 3), ('keras-team/autokeras', 0.5447315573692322, 'ml-dl', 4), ('pyg-team/pytorch_geometric', 0.5430858731269836, 'ml-dl', 2), ('towhee-io/towhee', 0.5428910851478577, 'ml-ops', 1), ('harisiqbal88/plotneuralnet', 0.542097270488739, 'ml', 0), ('oegedijk/explainerdashboard', 0.54157954454422, 'ml-interpretability', 0), ('tensorlayer/tensorlayer', 0.5410547256469727, 'ml-rl', 3), ('nvlabs/gcvit', 0.5401598811149597, 'diffusion', 1), ('ludwig-ai/ludwig', 0.5391374230384827, 'ml-ops', 7), ('danielegrattarola/spektral', 0.5387458205223083, 'ml-dl', 3), ('keras-team/keras-cv', 0.5374981760978699, 'ml-dl', 1), ('ashleve/lightning-hydra-template', 0.5372906923294067, 'util', 2), ('hysts/pytorch_image_classification', 0.5342903137207031, 'ml-dl', 1), ('tensorflow/addons', 0.5334285497665405, 'ml', 4), ('awslabs/autogluon', 0.5327269434928894, 'ml', 3), ('fepegar/torchio', 0.531164288520813, 'ml-dl', 3), ('microsoft/nni', 0.5304214954376221, 'ml', 5), ('horovod/horovod', 0.5276727080345154, 'ml-ops', 8), ('intel/intel-extension-for-pytorch', 0.5267831683158875, 'perf', 4), ('opentensor/bittensor', 0.5261327028274536, 'ml', 5), ('huggingface/transformers', 0.5258775949478149, 'nlp', 4), ('rafiqhasan/auto-tensorflow', 0.5258046388626099, 'ml-dl', 4), ('skorch-dev/skorch', 0.5238518714904785, 'ml-dl', 2), ('lucidrains/imagen-pytorch', 0.52129065990448, 'ml-dl', 1), ('gradio-app/gradio', 0.520858645439148, 'viz', 2), 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0.5053067803382874, 'ml', 4), ('hpcaitech/colossalai', 0.5051214098930359, 'llm', 2), ('tensorly/tensorly', 0.5027545094490051, 'ml-dl', 4), ('mrdbourke/pytorch-deep-learning', 0.5020248889923096, 'study', 3), ('kevinmusgrave/pytorch-metric-learning', 0.5019212365150452, 'ml', 3), ('googlecloudplatform/vertex-ai-samples', 0.5002110004425049, 'ml', 2)]",1,1.0,,21.67,76,65,159,0,2,0,2,76.0,87.0,90.0,1.1,55
423,ml-dl,https://github.com/albumentations-team/albumentations,[],,[],[],,,,albumentations-team/albumentations,albumentations,13001,1564,130,Python,https://albumentations.ai,Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation:  https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125,albumentations-team,2024-01-13,2018-06-06,294,44.09253875968992,https://avatars.githubusercontent.com/u/57894582?v=4,Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation:  https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125,"['augmentation', 'deep-learning', 'detection', 'fast-augmentations', 'image-augmentation', 'image-classification', 'image-processing', 'image-segmentation', 'machine-learning', 'object-detection', 'segmentation']","['augmentation', 'deep-learning', 'detection', 'fast-augmentations', 'image-augmentation', 'image-classification', 'image-processing', 'image-segmentation', 'machine-learning', 'object-detection', 'segmentation']",2023-12-07,"[('mdbloice/augmentor', 0.6705105900764465, 'ml', 3), ('facebookresearch/augly', 0.6632611751556396, 'data', 0), ('aleju/imgaug', 0.6503161787986755, 'ml', 4), ('open-mmlab/mmediting', 0.5985205769538879, 'ml', 2), ('fepegar/torchio', 0.5927706956863403, 'ml-dl', 3), ('deci-ai/super-gradients', 0.5558651685714722, 'ml-dl', 3), ('project-monai/monai', 0.5541568994522095, 'ml', 1), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5520427823066711, 'web', 0), ('visual-layer/fastdup', 0.5445998311042786, 'ml', 5), ('neuralmagic/sparseml', 0.5420981645584106, 'ml-dl', 2), ('lightly-ai/lightly', 0.5409029126167297, 'ml', 2), ('rom1504/clip-retrieval', 0.5307724475860596, 'ml', 1), ('lutzroeder/netron', 0.5197222828865051, 'ml', 2), ('nvlabs/gcvit', 0.5185796618461609, 'diffusion', 2), ('sanster/lama-cleaner', 0.515965461730957, 'ml-dl', 0), ('roboflow/supervision', 0.5144950151443481, 'ml', 4), ('open-mmlab/mmsegmentation', 0.5108982920646667, 'ml', 1), ('google-research/deeplab2', 0.5106143355369568, 'ml', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5062373876571655, 'ml', 2), ('azavea/raster-vision', 0.50620436668396, 'gis', 3), ('keras-team/autokeras', 0.5026241540908813, 'ml-dl', 2), ('kornia/kornia', 0.5021526217460632, 'ml-dl', 3)]",133,3.0,,0.35,41,15,68,1,1,3,1,41.0,31.0,90.0,0.8,55
647,profiling,https://github.com/benfred/py-spy,[],,[],[],,,,benfred/py-spy,py-spy,11366,429,112,Rust,,Sampling profiler for Python programs,benfred,2024-01-13,2018-08-01,286,39.62250996015936,,Sampling profiler for Python programs,"['performance-analysis', 'profiler', 'profiling']","['performance-analysis', 'profiler', 'profiling']",2023-12-16,"[('pythonspeed/filprofiler', 0.7144114971160889, 'profiling', 0), ('pyutils/line_profiler', 0.6891393065452576, 'profiling', 0), ('sumerc/yappi', 0.6047118902206421, 'profiling', 0), ('p403n1x87/austin', 0.5970548987388611, 'profiling', 1), ('joerick/pyinstrument', 0.5834751129150391, 'profiling', 1), ('pympler/pympler', 0.5802413821220398, 'perf', 0), ('klen/py-frameworks-bench', 0.5661771297454834, 'perf', 0), ('jiffyclub/snakeviz', 0.5489193797111511, 'profiling', 0), ('plasma-umass/scalene', 0.533981204032898, 'profiling', 3), ('pythonprofilers/memory_profiler', 0.5219977498054504, 'profiling', 0), ('csurfer/pyheat', 0.5209768414497375, 'profiling', 1), ('lcompilers/lpython', 0.5141026377677917, 'util', 0), ('google/pytype', 0.5136662721633911, 'typing', 0), ('nedbat/coveragepy', 0.5097211599349976, 'testing', 0), ('ionelmc/pytest-benchmark', 0.5037754774093628, 'testing', 0)]",37,3.0,,0.46,48,16,66,1,0,6,6,48.0,41.0,90.0,0.9,55
1119,data,https://github.com/coleifer/peewee,[],,[],[],,,,coleifer/peewee,peewee,10573,1373,198,Python,http://docs.peewee-orm.com/,"a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb",coleifer,2024-01-13,2010-10-11,694,15.231734924881662,,"a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb","['dank', 'gametight', 'peewee', 'sqlite']","['dank', 'gametight', 'peewee', 'sqlite']",2024-01-05,"[('mcfunley/pugsql', 0.6096048951148987, 'data', 0), ('ibis-project/ibis', 0.5938148498535156, 'data', 1), ('tiangolo/sqlmodel', 0.5841237306594849, 'data', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5561289191246033, 'template', 0), ('piccolo-orm/piccolo_admin', 0.5553250908851624, 'data', 1), ('aio-libs/aiopg', 0.5507586002349854, 'data', 0), ('tobymao/sqlglot', 0.5414004325866699, 'data', 1), ('airbytehq/airbyte', 0.53973788022995, 'data', 0), ('simonw/datasette', 0.5180539488792419, 'data', 1), ('zenodo/zenodo', 0.5084249377250671, 'util', 0), ('lancedb/lancedb', 0.5083345770835876, 'data', 0)]",153,3.0,,1.83,42,42,161,0,5,14,5,42.0,84.0,90.0,2.0,55
5,web,https://github.com/benoitc/gunicorn,[],,[],[],,,,benoitc/gunicorn,gunicorn,9324,1706,225,Python,http://www.gunicorn.org,"gunicorn 'Green Unicorn' is a WSGI HTTP Server for UNIX, fast clients and sleepy applications.",benoitc,2024-01-14,2009-11-30,739,12.614611519134133,,"gunicorn 'Green Unicorn' is a WSGI HTTP Server for UNIX, fast clients and sleepy applications.","['http', 'http-server', 'wsgi', 'wsgi-server']","['http', 'http-server', 'wsgi', 'wsgi-server']",2024-01-05,"[('pallets/werkzeug', 0.6659462451934814, 'web', 2), ('pylons/waitress', 0.6345184445381165, 'web', 2), ('bottlepy/bottle', 0.6153336763381958, 'web', 1), ('cherrypy/cherrypy', 0.5961728096008301, 'web', 2), ('pallets/flask', 0.5726903676986694, 'web', 1), ('pylons/pyramid', 0.5611777305603027, 'web', 1), ('encode/uvicorn', 0.5551705360412598, 'web', 2), ('neoteroi/blacksheep', 0.542283296585083, 'web', 2), ('encode/httpx', 0.5388432145118713, 'web', 1), ('falconry/falcon', 0.5321671366691589, 'web', 2), ('pylons/webob', 0.5041623711585999, 'web', 1), ('klen/muffin', 0.5010045766830444, 'web', 0)]",417,6.0,,1.42,162,80,172,0,3,7,3,162.0,190.0,90.0,1.2,55
1332,nlp,https://github.com/google/sentencepiece,"['word-segmentation', 'tokeniser']",,[],[],1.0,,,google/sentencepiece,sentencepiece,8799,1078,125,C++,,Unsupervised text tokenizer for Neural Network-based text generation.,google,2024-01-14,2017-03-07,360,24.441666666666666,https://avatars.githubusercontent.com/u/1342004?v=4,Unsupervised text tokenizer for Neural Network-based text generation.,"['natural-language-processing', 'neural-machine-translation', 'word-segmentation']","['natural-language-processing', 'neural-machine-translation', 'tokeniser', 'word-segmentation']",2024-01-14,"[('minimaxir/textgenrnn', 0.6446799635887146, 'nlp', 0), ('huggingface/text-generation-inference', 0.607265830039978, 'llm', 0), ('google-research/electra', 0.5957339406013489, 'ml-dl', 0), ('lucidrains/deep-daze', 0.5594016909599304, 'ml', 0), ('sharonzhou/long_stable_diffusion', 0.5528421401977539, 'diffusion', 0), ('minimaxir/aitextgen', 0.5358452796936035, 'llm', 0), ('infinitylogesh/mutate', 0.5126572847366333, 'nlp', 0)]",81,4.0,,1.31,65,46,83,0,3,4,3,65.0,68.0,90.0,1.0,55
1195,llm,https://github.com/thudm/codegeex,[],,[],[],,,,thudm/codegeex,CodeGeeX,7468,525,78,Python,https://codegeex.cn,CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023),thudm,2024-01-13,2022-09-17,71,104.552,https://avatars.githubusercontent.com/u/48590610?v=4,CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023),"['code-generation', 'pretrained-models', 'tools']","['code-generation', 'pretrained-models', 'tools']",2023-08-04,"[('salesforce/codet5', 0.6897627115249634, 'nlp', 1), ('salesforce/codegen', 0.6133092045783997, 'nlp', 0), ('bigcode-project/starcoder', 0.5817055106163025, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5676085948944092, 'llm', 0), ('conceptofmind/toolformer', 0.5580187439918518, 'llm', 0), ('asottile/pyupgrade', 0.5523767471313477, 'util', 0), ('neulab/prompt2model', 0.525926411151886, 'llm', 0), ('microsoft/pycodegpt', 0.5184867978096008, 'llm', 1), ('yueyu1030/attrprompt', 0.510209858417511, 'llm', 0), ('guidance-ai/guidance', 0.5090668201446533, 'llm', 0), ('pre-commit/pre-commit', 0.5082143545150757, 'util', 0), ('lianjiatech/belle', 0.5078340172767639, 'llm', 0), ('ravenscroftj/turbopilot', 0.5072124600410461, 'llm', 0), ('lupantech/chameleon-llm', 0.5048568844795227, 'llm', 0), ('juncongmoo/pyllama', 0.503732442855835, 'llm', 0), ('salesforce/xgen', 0.5032574534416199, 'llm', 0), ('thudm/glm-130b', 0.5032495856285095, 'llm', 0), ('openai/finetune-transformer-lm', 0.5009891390800476, 'llm', 0), ('yizhongw/self-instruct', 0.5008969902992249, 'llm', 0)]",13,6.0,,0.9,25,2,16,5,0,0,0,25.0,15.0,90.0,0.6,55
1372,web,https://github.com/reactive-python/reactpy,[],ReactPy is a library for building user interfaces in Python without Javascript,[],[],,,,reactive-python/reactpy,reactpy,7438,363,58,Python,https://reactpy.dev,"It's React, but in Python",reactive-python,2024-01-13,2019-02-19,258,28.829457364341085,https://avatars.githubusercontent.com/u/106191177?v=4,"It's React, but in Python","['javascript', 'react', 'reactpy']","['javascript', 'react', 'reactpy']",2023-12-28,"[('r0x0r/pywebview', 0.5532059073448181, 'gui', 1), ('webpy/webpy', 0.5520175695419312, 'web', 0), ('pyodide/pyodide', 0.5306482911109924, 'util', 0), ('urwid/urwid', 0.5233248472213745, 'term', 0)]",21,4.0,,2.15,35,12,60,1,12,23,12,35.0,50.0,90.0,1.4,55
680,util,https://github.com/py-pdf/pypdf2,[],,[],[],,,,py-pdf/pypdf2,pypdf,6900,1301,148,Python,https://pypdf.readthedocs.io/en/latest/,"A pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files",py-pdf,2024-01-14,2012-01-06,629,10.959836623553437,https://avatars.githubusercontent.com/u/102914013?v=4,"A pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files","['help-wanted', 'pdf', 'pdf-documents', 'pdf-manipulation', 'pdf-parser', 'pdf-parsing', 'pypdf2']","['help-wanted', 'pdf', 'pdf-documents', 'pdf-manipulation', 'pdf-parser', 'pdf-parsing', 'pypdf2']",2024-01-11,"[('pyfpdf/fpdf2', 0.6898808479309082, 'util', 1), ('jorisschellekens/borb', 0.6551130414009094, 'util', 1), ('camelot-dev/camelot', 0.6539286971092224, 'util', 0), ('pypdfium2-team/pypdfium2', 0.6358337998390198, 'util', 2), ('pdfminer/pdfminer.six', 0.5491688847541809, 'util', 1), ('unstructured-io/pipeline-paddleocr', 0.5316691398620605, 'data', 1)]",216,1.0,,8.63,184,124,146,0,37,9,37,184.0,483.0,90.0,2.6,55
1492,llm,https://github.com/bigcode-project/starcoder,['code-generation'],,[],[],,,,bigcode-project/starcoder,starcoder,6776,476,65,Python,,Home of StarCoder: fine-tuning & inference!,bigcode-project,2024-01-13,2023-04-24,40,168.79715302491104,https://avatars.githubusercontent.com/u/110470554?v=4,Home of StarCoder: fine-tuning & inference!,[],['code-generation'],2023-06-29,"[('salesforce/codegen', 0.601254940032959, 'nlp', 0), ('huggingface/text-generation-inference', 0.5950606465339661, 'llm', 0), ('salesforce/codet5', 0.5859589576721191, 'nlp', 1), ('openai/image-gpt', 0.5846189260482788, 'llm', 0), ('thudm/codegeex', 0.5817055106163025, 'llm', 1), ('bytedance/lightseq', 0.5453761219978333, 'nlp', 0), ('microsoft/pycodegpt', 0.5438132882118225, 'llm', 1), ('deepmind/deepmind-research', 0.5220165252685547, 'ml', 0), ('facebookresearch/codellama', 0.5031982064247131, 'llm', 0)]",8,3.0,,1.31,16,1,9,7,0,0,0,16.0,10.0,90.0,0.6,55
20,typing,https://github.com/facebook/pyre-check,['code-quality'],,[],[],,,,facebook/pyre-check,pyre-check,6597,477,110,Python,https://pyre-check.org/,Performant type-checking for python.,facebook,2024-01-12,2017-11-10,324,20.325264084507044,https://avatars.githubusercontent.com/u/69631?v=4,Performant type-checking for python.,"['abstract-interpretation', 'code-quality', 'control-flow-analysis', 'ocaml', 'program-analysis', 'security', 'static-analysis', 'taint-analysis', 'type-check', 'typechecker']","['abstract-interpretation', 'code-quality', 'control-flow-analysis', 'ocaml', 'program-analysis', 'security', 'static-analysis', 'taint-analysis', 'type-check', 'typechecker']",2024-01-12,"[('agronholm/typeguard', 0.8064729571342468, 'typing', 2), ('google/pytype', 0.7848848104476929, 'typing', 3), ('microsoft/pyright', 0.7650810480117798, 'typing', 2), ('instagram/monkeytype', 0.6643034815788269, 'typing', 1), ('python/mypy', 0.628227949142456, 'typing', 2), ('pydantic/pydantic', 0.6196001768112183, 'util', 0), ('patrick-kidger/torchtyping', 0.6189740300178528, 'typing', 0), ('rubik/radon', 0.6015112400054932, 'util', 1), ('landscapeio/prospector', 0.5935577154159546, 'util', 0), ('pycqa/mccabe', 0.5869566202163696, 'util', 0), ('pytoolz/toolz', 0.5858352184295654, 'util', 0), ('tiangolo/typer', 0.5768271088600159, 'term', 0), ('nedbat/coveragepy', 0.5666431784629822, 'testing', 0), ('pyupio/safety', 0.5607929229736328, 'security', 1), ('xrudelis/pytrait', 0.5590521693229675, 'util', 0), ('strawberry-graphql/strawberry', 0.5584018230438232, 'web', 0), ('python/typeshed', 0.5521007180213928, 'typing', 1), ('eugeneyan/python-collab-template', 0.5504177212715149, 'template', 0), ('aswinnnn/pyscan', 0.5478062033653259, 'security', 2), ('marshmallow-code/marshmallow', 0.5461035966873169, 'util', 0), ('pympler/pympler', 0.5445180535316467, 'perf', 0), ('pyston/pyston', 0.5409777760505676, 'util', 0), ('python-odin/odin', 0.5255442261695862, 'util', 0), ('pypy/pypy', 0.5224389433860779, 'util', 0), ('psf/black', 0.5206736922264099, 'util', 1), ('python-rope/rope', 0.5157984495162964, 'util', 0), ('python/cpython', 0.5143840312957764, 'util', 0), ('grantjenks/blue', 0.5125738382339478, 'util', 1), ('gaogaotiantian/viztracer', 0.5102528929710388, 'profiling', 0), ('pyeve/cerberus', 0.5100794434547424, 'data', 0), ('scikit-mobility/scikit-mobility', 0.5081936120986938, 'gis', 0), ('astral-sh/ruff', 0.5057628154754639, 'util', 2), ('pycqa/flake8', 0.5048815608024597, 'util', 2), ('facebookincubator/bowler', 0.5039924383163452, 'util', 0), ('cython/cython', 0.5014179944992065, 'util', 0)]",254,2.0,,29.19,13,4,75,0,1,14,1,13.0,21.0,90.0,1.6,55
70,util,https://github.com/pygithub/pygithub,[],,[],[],,,,pygithub/pygithub,PyGithub,6469,1713,111,Python,https://pygithub.readthedocs.io/,Typed interactions with the GitHub API v3,pygithub,2024-01-13,2012-02-25,622,10.393160431489557,https://avatars.githubusercontent.com/u/11288996?v=4,Typed interactions with the GitHub API v3,"['github', 'github-api', 'pygithub']","['github', 'github-api', 'pygithub']",2024-01-01,"[('fastai/ghapi', 0.5488420724868774, 'util', 2)]",346,4.0,,4.0,103,47,145,0,10,9,10,103.0,179.0,90.0,1.7,55
1814,study,https://github.com/mrdbourke/pytorch-deep-learning,[],,[],[],,,,mrdbourke/pytorch-deep-learning,pytorch-deep-learning,6384,2082,88,Jupyter Notebook,https://learnpytorch.io,Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.,mrdbourke,2024-01-14,2021-10-19,119,53.64705882352941,,Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.,"['deep-learning', 'machine-learning', 'pytorch']","['deep-learning', 'machine-learning', 'pytorch']",2024-01-11,"[('pytorch/ignite', 0.7811650037765503, 'ml-dl', 3), ('mrdbourke/tensorflow-deep-learning', 0.7342724800109863, 'study', 1), ('skorch-dev/skorch', 0.6955669522285461, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.6894313097000122, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.6849406957626343, 'study', 3), ('mrdbourke/zero-to-mastery-ml', 0.676668107509613, 'study', 2), ('nvidia/apex', 0.6514618396759033, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.6339874267578125, 'perf', 3), ('ageron/handson-ml2', 0.633056104183197, 'ml', 0), ('denys88/rl_games', 0.6325936913490295, 'ml-rl', 2), ('udacity/deep-learning-v2-pytorch', 0.6218847632408142, 'study', 2), ('d2l-ai/d2l-en', 0.6161856055259705, 'study', 3), ('pytorch/rl', 0.6149892210960388, 'ml-rl', 2), ('xl0/lovely-tensors', 0.613914966583252, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.605148196220398, 'util', 2), ('udlbook/udlbook', 0.6033921837806702, 'study', 1), ('karpathy/micrograd', 0.602993369102478, 'study', 0), ('allenai/allennlp', 0.6028923988342285, 'nlp', 2), ('graykode/nlp-tutorial', 0.6003298163414001, 'study', 1), ('thu-ml/tianshou', 0.5997803211212158, 'ml-rl', 1), ('tensorlayer/tensorlayer', 0.5996901392936707, 'ml-rl', 1), ('intellabs/bayesian-torch', 0.5984524488449097, 'ml', 2), ('nicolas-chaulet/torch-points3d', 0.5965690016746521, 'ml', 0), ('facebookresearch/pytorch3d', 0.5950154066085815, 'ml-dl', 0), ('keras-team/keras', 0.5936383008956909, 'ml-dl', 3), ('pytorch/captum', 0.5934852361679077, 'ml-interpretability', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5927961468696594, 'study', 0), ('rentruewang/koila', 0.5876379609107971, 'ml', 3), ('davidadsp/generative_deep_learning_2nd_edition', 0.5829065442085266, 'study', 2), ('openai/spinningup', 0.580998420715332, 'study', 0), ('christoschristofidis/awesome-deep-learning', 0.5786244869232178, 'study', 2), ('huggingface/accelerate', 0.5736592411994934, 'ml', 0), ('pyro-ppl/pyro', 0.5717006325721741, 'ml-dl', 3), ('pytorch/data', 0.5634875893592834, 'data', 0), ('rasbt/deeplearning-models', 0.5607303977012634, 'ml-dl', 0), ('lucidrains/imagen-pytorch', 0.5554819703102112, 'ml-dl', 1), ('keras-rl/keras-rl', 0.554979681968689, 'ml-rl', 1), ('huggingface/transformers', 0.5548473596572876, 'nlp', 3), ('lightly-ai/lightly', 0.5538014769554138, 'ml', 3), ('tensorflow/tensor2tensor', 0.5526059865951538, 'ml', 2), ('horovod/horovod', 0.548136830329895, 'ml-ops', 3), ('ggerganov/ggml', 0.5461394190788269, 'ml', 1), ('pytorch/torchrec', 0.5445482730865479, 'ml-dl', 2), ('blackhc/toma', 0.5438900589942932, 'ml-dl', 2), ('salesforce/blip', 0.5360457897186279, 'diffusion', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5357545614242554, 'study', 1), ('nvidia/deeplearningexamples', 0.5341781973838806, 'ml-dl', 2), ('humancompatibleai/imitation', 0.5296970009803772, 'ml-rl', 0), ('arogozhnikov/einops', 0.528740644454956, 'ml-dl', 2), ('laekov/fastmoe', 0.5278257131576538, 'ml', 0), ('hazyresearch/hgcn', 0.5278249979019165, 'ml', 0), ('huggingface/huggingface_hub', 0.5265445113182068, 'ml', 3), ('kshitij12345/torchnnprofiler', 0.5249190926551819, 'profiling', 0), ('tensorflow/tensorflow', 0.5236486196517944, 'ml-dl', 2), ('aistream-peelout/flow-forecast', 0.5234374403953552, 'time-series', 2), ('uber/petastorm', 0.5223989486694336, 'data', 3), ('karpathy/mingpt', 0.5222852230072021, 'llm', 0), ('microsoft/jarvis', 0.5219099521636963, 'llm', 2), ('nvlabs/gcvit', 0.520950198173523, 'diffusion', 1), ('pytorch/botorch', 0.5209388136863708, 'ml-dl', 0), ('rasbt/stat453-deep-learning-ss20', 0.5198850631713867, 'study', 0), ('whitead/dmol-book', 0.5192140340805054, 'ml-dl', 1), ('patchy631/machine-learning', 0.5156992673873901, 'ml', 0), ('neuralmagic/sparseml', 0.5144563317298889, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5134690999984741, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5127461552619934, 'ml-dl', 1), ('dmlc/dgl', 0.5118030905723572, 'ml-dl', 1), ('determined-ai/determined', 0.5115607380867004, 'ml-ops', 3), ('mosaicml/composer', 0.5073535442352295, 'ml-dl', 3), ('rafiqhasan/auto-tensorflow', 0.5060444474220276, 'ml-dl', 1), ('cvxgrp/pymde', 0.5034160017967224, 'ml', 2), ('optimalscale/lmflow', 0.503092348575592, 'llm', 2), ('lutzroeder/netron', 0.5020248889923096, 'ml', 3), ('microsoft/deepspeed', 0.5005961656570435, 'ml-dl', 3), ('google-research/deeplab2', 0.5000344514846802, 'ml', 0)]",42,3.0,,2.42,41,9,27,0,0,0,0,41.0,27.0,90.0,0.7,55
1177,diffusion,https://github.com/openai/consistency_models,[],,[],[],,,,openai/consistency_models,consistency_models,5787,379,60,Python,,Official repo for consistency models.,openai,2024-01-13,2023-02-26,48,119.8491124260355,https://avatars.githubusercontent.com/u/14957082?v=4,Official repo for consistency models.,[],[],2023-08-12,[],9,7.0,,0.23,18,1,11,5,0,0,0,18.0,16.0,90.0,0.9,55
1081,util,https://github.com/buildbot/buildbot,[],,[],[],,,,buildbot/buildbot,buildbot,5127,1655,199,Python,https://www.buildbot.net,Python-based continuous integration testing framework; your pull requests are more than welcome!,buildbot,2024-01-14,2010-07-06,708,7.241525423728813,https://avatars.githubusercontent.com/u/324515?v=4,Python-based continuous integration testing framework; your pull requests are more than welcome!,"['ci', 'ci-framework', 'continuous-integration']","['ci', 'ci-framework', 'continuous-integration']",2024-01-09,"[('eleutherai/pyfra', 0.6259655952453613, 'ml', 0), ('nedbat/coveragepy', 0.57981938123703, 'testing', 0), ('wolever/parameterized', 0.5751279592514038, 'testing', 0), ('willmcgugan/textual', 0.5555017590522766, 'term', 0), ('masoniteframework/masonite', 0.549967885017395, 'web', 0), ('cobrateam/splinter', 0.5403817296028137, 'testing', 0), ('tox-dev/tox', 0.5279530882835388, 'testing', 1), ('taverntesting/tavern', 0.5253430604934692, 'testing', 0), ('getsentry/responses', 0.5227794647216797, 'testing', 0), ('ethereum/web3.py', 0.5160036087036133, 'crypto', 0), ('google/gin-config', 0.5084891319274902, 'util', 0), ('pytest-dev/pytest-xdist', 0.5035778880119324, 'testing', 0)]",856,5.0,,22.69,255,203,165,0,6,13,6,255.0,235.0,90.0,0.9,55
98,jupyter,https://github.com/voila-dashboards/voila,[],,[],[],,,,voila-dashboards/voila,voila,5051,487,77,Python,https://voila.readthedocs.io,Voilà turns Jupyter notebooks into standalone web applications,voila-dashboards,2024-01-14,2018-08-21,284,17.785211267605632,https://avatars.githubusercontent.com/u/55792893?v=4,Voilà turns Jupyter notebooks into standalone web applications,"['dashboarding', 'jupyter', 'jupyter-notebook', 'jupyterlab-extension']","['dashboarding', 'jupyter', 'jupyter-notebook', 'jupyterlab-extension']",2024-01-11,"[('jupyterlab/jupyterlab-desktop', 0.7262636423110962, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.7114137411117554, 'jupyter', 1), ('jupyter/notebook', 0.6948908567428589, 'jupyter', 2), ('jupyterlite/jupyterlite', 0.6913954615592957, 'jupyter', 2), ('aws/graph-notebook', 0.6807038187980652, 'jupyter', 2), ('mwouts/jupytext', 0.6650576591491699, 'jupyter', 2), ('jupyter/nbviewer', 0.6448253989219666, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.6425187587738037, 'jupyter', 2), ('maartenbreddels/ipyvolume', 0.6421133279800415, 'jupyter', 2), ('cohere-ai/notebooks', 0.60094153881073, 'llm', 0), ('jupyter/nbformat', 0.5947333574295044, 'jupyter', 0), ('holoviz/panel', 0.5938263535499573, 'viz', 1), ('jupyter/nbconvert', 0.5891522169113159, 'jupyter', 0), ('xiaohk/stickyland', 0.5852877497673035, 'jupyter', 2), ('jupyter/nbdime', 0.5848777890205383, 'jupyter', 3), ('jupyter-widgets/ipyleaflet', 0.5810590386390686, 'gis', 2), ('plotly/dash', 0.580794095993042, 'viz', 1), ('pallets/flask', 0.5802770256996155, 'web', 0), ('jupyterlab/jupyterlab', 0.5699175596237183, 'jupyter', 1), ('webpy/webpy', 0.5608404278755188, 'web', 0), ('ipython/ipyparallel', 0.5572918057441711, 'perf', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5473379492759705, 'jupyter', 3), ('plotly/plotly.py', 0.5468341112136841, 'viz', 1), ('mamba-org/gator', 0.5456347465515137, 'jupyter', 2), ('reflex-dev/reflex', 0.5454056859016418, 'web', 0), ('bloomberg/ipydatagrid', 0.5380343794822693, 'jupyter', 1), ('tkrabel/bamboolib', 0.5368069410324097, 'pandas', 1), ('bokeh/bokeh', 0.5361472368240356, 'viz', 1), ('ipython/ipykernel', 0.5356465578079224, 'util', 2), ('quantopian/qgrid', 0.5343106985092163, 'jupyter', 0), ('willmcgugan/textual', 0.5331439971923828, 'term', 0), ('giswqs/mapwidget', 0.5279869437217712, 'gis', 1), ('rapidsai/jupyterlab-nvdashboard', 0.5271745920181274, 'jupyter', 0), ('masoniteframework/masonite', 0.5220005512237549, 'web', 0), ('klen/muffin', 0.5211663246154785, 'web', 0), ('r0x0r/pywebview', 0.5208461880683899, 'gui', 0), ('opengeos/leafmap', 0.5204005241394043, 'gis', 2), ('computationalmodelling/nbval', 0.5194684267044067, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5183944702148438, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.5165765881538391, 'study', 1), ('bottlepy/bottle', 0.5106417536735535, 'web', 0), ('pysimplegui/pysimplegui', 0.5095065236091614, 'gui', 0), ('cherrypy/cherrypy', 0.5070238709449768, 'web', 0), ('seleniumbase/seleniumbase', 0.5014607906341553, 'testing', 0)]",68,4.0,,1.71,41,22,66,0,18,32,18,41.0,66.0,90.0,1.6,55
257,crypto,https://github.com/ethereum/web3.py,[],,[],[],1.0,,,ethereum/web3.py,web3.py,4591,1654,119,Python,http://web3py.readthedocs.io,A python interface for interacting with the Ethereum blockchain and ecosystem.,ethereum,2024-01-14,2016-04-14,406,11.2880224798033,https://avatars.githubusercontent.com/u/6250754?v=4,A python interface for interacting with the Ethereum blockchain and ecosystem.,[],[],2024-01-10,"[('primal100/pybitcointools', 0.6811222434043884, 'crypto', 0), ('ethereum/py-evm', 0.6437891721725464, 'crypto', 0), ('gbeced/basana', 0.6058024168014526, 'finance', 0), ('1200wd/bitcoinlib', 0.6057431101799011, 'crypto', 0), ('willmcgugan/textual', 0.5721923112869263, 'term', 0), ('gbeced/pyalgotrade', 0.570094883441925, 'finance', 0), ('pyston/pyston', 0.5668970942497253, 'util', 0), ('masoniteframework/masonite', 0.5657337307929993, 'web', 0), ('eleutherai/pyfra', 0.5655627250671387, 'ml', 0), ('bottlepy/bottle', 0.5624367594718933, 'web', 0), ('man-c/pycoingecko', 0.5624127388000488, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5618115067481995, 'finance', 0), ('simple-salesforce/simple-salesforce', 0.5512309670448303, 'data', 0), ('hoffstadt/dearpygui', 0.5474371314048767, 'gui', 0), ('replicate/replicate-python', 0.5461574792861938, 'ml', 0), ('urwid/urwid', 0.5449756979942322, 'term', 0), ('robcarver17/pysystemtrade', 0.5425991415977478, 'finance', 0), ('falconry/falcon', 0.540778636932373, 'web', 0), ('requests/toolbelt', 0.5374890565872192, 'util', 0), ('pynamodb/pynamodb', 0.536690890789032, 'data', 0), ('pmaji/crypto-whale-watching-app', 0.5342921614646912, 'crypto', 0), ('secdev/scapy', 0.5224668383598328, 'util', 0), ('buildbot/buildbot', 0.5160036087036133, 'util', 0), ('pallets/flask', 0.5149424076080322, 'web', 0), ('amzn/ion-python', 0.5136985778808594, 'data', 0), ('scrapy/scrapy', 0.5123814344406128, 'data', 0), ('pytoolz/toolz', 0.5079506039619446, 'util', 0), ('webpy/webpy', 0.5061323046684265, 'web', 0), ('trailofbits/pip-audit', 0.5048879981040955, 'security', 0), ('nasdaq/data-link-python', 0.5034674406051636, 'finance', 0), ('cherrypy/cherrypy', 0.5029838681221008, 'web', 0), ('encode/httpx', 0.5025968551635742, 'web', 0), ('snyk-labs/pysnyk', 0.5023728013038635, 'security', 0)]",249,4.0,,9.17,106,76,94,0,0,27,27,106.0,104.0,90.0,1.0,55
1842,llm,https://github.com/langchain-ai/chat-langchain,"['rag', 'question-answering', 'docs']",Locally hosted chatbot specifically focused on question answering over the LangChain documentation,[],[],,,,langchain-ai/chat-langchain,chat-langchain,4229,1008,46,Python,https://chat.langchain.com,,langchain-ai,2024-01-13,2023-01-16,54,78.10817941952507,https://avatars.githubusercontent.com/u/126733545?v=4,Locally hosted chatbot specifically focused on question answering over the LangChain documentation,[],"['docs', 'question-answering', 'rag']",2024-01-11,"[('lm-sys/fastchat', 0.628669023513794, 'llm', 0), ('togethercomputer/openchatkit', 0.6002198457717896, 'nlp', 0), ('embedchain/embedchain', 0.5953378677368164, 'llm', 0), ('nomic-ai/gpt4all', 0.594020426273346, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5839036107063293, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5771211981773376, 'nlp', 0), ('hwchase17/langchain', 0.5708911418914795, 'llm', 0), ('openai/chatgpt-retrieval-plugin', 0.5608171820640564, 'llm', 0), ('rcgai/simplyretrieve', 0.5566864013671875, 'llm', 0), ('gkamradt/langchain-tutorials', 0.5390220284461975, 'study', 0), ('larsbaunwall/bricky', 0.5356094837188721, 'llm', 0), ('minimaxir/simpleaichat', 0.5350149273872375, 'llm', 0), ('fasteval/fasteval', 0.534351110458374, 'llm', 0), ('weaviate/verba', 0.5274889469146729, 'llm', 0), ('openlmlab/moss', 0.5243722200393677, 'llm', 0), ('deeppavlov/deeppavlov', 0.5239380598068237, 'nlp', 1), ('blinkdl/chatrwkv', 0.521385908126831, 'llm', 0), ('mlc-ai/web-llm', 0.5200137495994568, 'llm', 0), ('run-llama/rags', 0.5135616064071655, 'llm', 1), ('rasahq/rasa', 0.5066604614257812, 'llm', 0), ('thudm/chatglm2-6b', 0.5037407875061035, 'llm', 0), ('killianlucas/open-interpreter', 0.5030243396759033, 'llm', 0)]",16,1.0,,2.63,51,35,12,0,0,0,0,51.0,56.0,90.0,1.1,55
1239,llm,https://github.com/togethercomputer/redpajama-data,[],,[],[],,,,togethercomputer/redpajama-data,RedPajama-Data,4058,321,78,Python,,The RedPajama-Data repository contains code for preparing large datasets for training large language models.,togethercomputer,2024-01-13,2023-04-14,41,97.61512027491409,https://avatars.githubusercontent.com/u/109101822?v=4,The RedPajama-Data repository contains code for preparing large datasets for training large language models.,[],[],2023-12-27,"[('hannibal046/awesome-llm', 0.6528944969177246, 'study', 0), ('yueyu1030/attrprompt', 0.6486657857894897, 'llm', 0), ('freedomintelligence/llmzoo', 0.6302767992019653, 'llm', 0), ('bigscience-workshop/biomedical', 0.6301681995391846, 'data', 0), ('eleutherai/the-pile', 0.6279685497283936, 'data', 0), ('cg123/mergekit', 0.6147141456604004, 'llm', 0), ('infinitylogesh/mutate', 0.6091659069061279, 'nlp', 0), ('ai21labs/lm-evaluation', 0.60612553358078, 'llm', 0), ('databrickslabs/dolly', 0.5899375677108765, 'llm', 0), ('lm-sys/fastchat', 0.5893017053604126, 'llm', 0), ('huggingface/text-generation-inference', 0.5783564448356628, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5778451561927795, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5702430605888367, 'llm', 0), ('lianjiatech/belle', 0.5700726509094238, 'llm', 0), ('openlm-research/open_llama', 0.5621833801269531, 'llm', 0), ('microsoft/lora', 0.5614542365074158, 'llm', 0), ('next-gpt/next-gpt', 0.5498401522636414, 'llm', 0), ('openai/finetune-transformer-lm', 0.5497167706489563, 'llm', 0), ('salesforce/xgen', 0.5379053950309753, 'llm', 0), ('prefecthq/langchain-prefect', 0.5340298414230347, 'llm', 0), ('bytedance/lightseq', 0.5332623720169067, 'nlp', 0), ('huawei-noah/pretrained-language-model', 0.5328022837638855, 'nlp', 0), ('juncongmoo/pyllama', 0.5303771495819092, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5299484729766846, 'llm', 0), ('srush/minichain', 0.5224243998527527, 'llm', 0), ('lupantech/chameleon-llm', 0.5191816687583923, 'llm', 0), ('princeton-nlp/alce', 0.5159098505973816, 'llm', 0), ('microsoft/autogen', 0.5106208920478821, 'llm', 0), ('microsoft/unilm', 0.5096539855003357, 'nlp', 0), ('ravenscroftj/turbopilot', 0.5085808038711548, 'llm', 0), ('oobabooga/text-generation-webui', 0.5050533413887024, 'llm', 0), ('optimalscale/lmflow', 0.5022752285003662, 'llm', 0)]",8,3.0,,0.54,28,18,9,1,0,0,0,28.0,39.0,90.0,1.4,55
504,ml-ops,https://github.com/adap/flower,[],,[],[],,,,adap/flower,flower,3479,686,33,Python,https://flower.dev,Flower: A Friendly Federated Learning Framework,adap,2024-01-14,2020-02-17,206,16.876645876645878,https://avatars.githubusercontent.com/u/57905187?v=4,Flower: A Friendly Federated Learning Framework,"['ai', 'android', 'artificial-intelligence', 'cpp', 'deep-learning', 'federated-analytics', 'federated-learning', 'federated-learning-framework', 'fleet-intelligence', 'fleet-learning', 'flower', 'framework', 'grpc', 'ios', 'machine-learning', 'pytorch', 'raspberry-pi', 'scikit-learn', 'tensorflow']","['ai', 'android', 'artificial-intelligence', 'cpp', 'deep-learning', 'federated-analytics', 'federated-learning', 'federated-learning-framework', 'fleet-intelligence', 'fleet-learning', 'flower', 'framework', 'grpc', 'ios', 'machine-learning', 'pytorch', 'raspberry-pi', 'scikit-learn', 'tensorflow']",2024-01-08,"[('nevronai/metisfl', 0.8411728739738464, 'ml', 6), ('jonasgeiping/breaching', 0.6508305668830872, 'ml', 3), ('horovod/horovod', 0.6284599304199219, 'ml-ops', 4), ('nccr-itmo/fedot', 0.618629515171051, 'ml-ops', 1), ('tensorflow/tensorflow', 0.6108560562133789, 'ml-dl', 3), ('mlflow/mlflow', 0.5906698107719421, 'ml-ops', 2), ('determined-ai/determined', 0.5839700698852539, 'ml-ops', 4), ('explosion/thinc', 0.5687860250473022, 'ml-dl', 6), ('onnx/onnx', 0.5646280646324158, 'ml', 5), ('polyaxon/polyaxon', 0.5568473935127258, 'ml-ops', 5), ('ml-tooling/opyrator', 0.5547651648521423, 'viz', 1), ('ai4finance-foundation/finrl', 0.551112949848175, 'finance', 0), ('gradio-app/gradio', 0.5504258871078491, 'viz', 2), ('merantix-momentum/squirrel-core', 0.5503032803535461, 'ml', 5), ('microsoft/onnxruntime', 0.5461448431015015, 'ml', 5), ('ludwig-ai/ludwig', 0.533837080001831, 'ml-ops', 3), ('alpa-projects/alpa', 0.5309455990791321, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5287955403327942, 'ml-rl', 3), ('uber/petastorm', 0.5249413847923279, 'data', 4), ('nvidia/deeplearningexamples', 0.5243560671806335, 'ml-dl', 3), ('eventual-inc/daft', 0.5243489146232605, 'pandas', 2), ('bentoml/bentoml', 0.519675612449646, 'ml-ops', 3), ('microsoft/deepspeed', 0.5188344120979309, 'ml-dl', 3), ('apache/incubator-mxnet', 0.5179499387741089, 'ml-dl', 0), ('aiqc/aiqc', 0.5167785882949829, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.5161627531051636, 'ml', 1), ('aimhubio/aim', 0.5154430270195007, 'ml-ops', 4), ('tensorly/tensorly', 0.5153002142906189, 'ml-dl', 3), ('operand/agency', 0.5125753283500671, 'llm', 4), ('dylanhogg/awesome-python', 0.510422945022583, 'study', 2), ('opentensor/bittensor', 0.5103121399879456, 'ml', 4), ('pytorchlightning/pytorch-lightning', 0.5078296065330505, 'ml-dl', 5), ('koaning/human-learn', 0.5073186755180359, 'data', 2), ('aws/sagemaker-python-sdk', 0.506871223449707, 'ml', 3), ('d2l-ai/d2l-en', 0.505675733089447, 'study', 4), ('uber/fiber', 0.505595326423645, 'data', 1), ('firmai/industry-machine-learning', 0.5053890347480774, 'study', 1), ('jina-ai/jina', 0.5048282146453857, 'ml', 4), ('deepmind/dm-haiku', 0.5038338303565979, 'ml-dl', 2), ('huggingface/huggingface_hub', 0.5031947493553162, 'ml', 3), ('deepmind/dm_control', 0.502716064453125, 'ml-rl', 3)]",97,3.0,,14.02,360,260,48,0,5,4,5,359.0,210.0,90.0,0.6,55
1710,perf,https://github.com/facebookincubator/cinder,['cpython'],,[],[],,,,facebookincubator/cinder,cinder,3301,121,60,Python,https://trycinder.com,Cinder is Meta's internal performance-oriented production version of CPython.,facebookincubator,2024-01-14,2021-03-16,150,22.006666666666668,https://avatars.githubusercontent.com/u/19538647?v=4,Cinder is Meta's internal performance-oriented production version of CPython.,"['compiler', 'interpreter', 'jit', 'runtime']","['compiler', 'cpython', 'interpreter', 'jit', 'runtime']",2024-01-13,"[('rustpython/rustpython', 0.5831960439682007, 'util', 3), ('python/cpython', 0.5806695222854614, 'util', 1), ('faster-cpython/ideas', 0.5721518397331238, 'perf', 1), ('faster-cpython/tools', 0.5622638463973999, 'perf', 1), ('pypy/pypy', 0.5570579767227173, 'util', 2), ('brandtbucher/specialist', 0.5538285374641418, 'perf', 1), ('cython/cython', 0.5495518445968628, 'util', 1), ('scikit-build/scikit-build', 0.5411034226417542, 'ml', 1), ('fastai/fastcore', 0.5330604910850525, 'util', 0), ('sumerc/yappi', 0.5309968590736389, 'profiling', 0), ('astral-sh/ruff', 0.5143499374389648, 'util', 0), ('p403n1x87/austin', 0.5041605830192566, 'profiling', 0)]",1760,6.0,,7.19,10,8,34,0,0,0,0,12.0,14.0,90.0,1.2,55
1293,llm,https://github.com/microsoft/lmops,[],,[],[],,,,microsoft/lmops,LMOps,2828,192,55,Python,https://aka.ms/GeneralAI,General technology for enabling AI capabilities w/ LLMs and MLLMs,microsoft,2024-01-13,2022-12-13,59,47.932203389830505,https://avatars.githubusercontent.com/u/6154722?v=4,General technology for enabling AI capabilities w/ LLMs and MLLMs,"['agi', 'gpt', 'language-model', 'llm', 'lm', 'lmops', 'nlp', 'pretraining', 'prompt', 'promptist', 'x-prompt']","['agi', 'gpt', 'language-model', 'llm', 'lm', 'lmops', 'nlp', 'pretraining', 'prompt', 'promptist', 'x-prompt']",2024-01-02,"[('mlc-ai/mlc-llm', 0.7063540816307068, 'llm', 2), ('microsoft/promptflow', 0.6579537987709045, 'llm', 3), ('prefecthq/marvin', 0.6434080600738525, 'nlp', 2), ('lastmile-ai/aiconfig', 0.6332518458366394, 'util', 1), ('bentoml/bentoml', 0.6321725249290466, 'ml-ops', 1), ('cheshire-cat-ai/core', 0.6224048137664795, 'llm', 1), ('operand/agency', 0.6131894588470459, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.6037994623184204, 'study', 2), ('pathwaycom/llm-app', 0.6022082567214966, 'llm', 1), ('microsoft/semantic-kernel', 0.6015781760215759, 'llm', 1), ('arize-ai/phoenix', 0.5890821814537048, 'ml-interpretability', 0), ('pytorchlightning/pytorch-lightning', 0.5851262211799622, 'ml-dl', 0), ('antonosika/gpt-engineer', 0.5837531089782715, 'llm', 0), ('microsoft/torchscale', 0.5832264423370361, 'llm', 0), ('bentoml/openllm', 0.581097424030304, 'ml-ops', 1), ('nebuly-ai/nebullvm', 0.5789636373519897, 'perf', 1), ('mindsdb/mindsdb', 0.5788654088973999, 'data', 2), ('lucidrains/toolformer-pytorch', 0.5752981901168823, 'llm', 1), ('torantulino/auto-gpt', 0.5733479261398315, 'llm', 0), ('ludwig-ai/ludwig', 0.5721259713172913, 'ml-ops', 1), ('argilla-io/argilla', 0.5673712491989136, 'nlp', 2), ('oneil512/insight', 0.5636782050132751, 'ml', 2), ('transformeroptimus/superagi', 0.5605264902114868, 'llm', 2), ('deepset-ai/haystack', 0.5590223073959351, 'llm', 2), ('agenta-ai/agenta', 0.5574962496757507, 'llm', 1), ('llmware-ai/llmware', 0.5573378205299377, 'llm', 1), ('microsoft/autogen', 0.555975079536438, 'llm', 1), ('sweepai/sweep', 0.5525568127632141, 'llm', 1), ('tigerlab-ai/tiger', 0.5503789186477661, 'llm', 1), ('microsoft/promptcraft-robotics', 0.5502687096595764, 'sim', 1), ('giskard-ai/giskard', 0.5501245260238647, 'data', 0), ('hegelai/prompttools', 0.5492219924926758, 'llm', 0), ('explosion/spacy-llm', 0.5478001832962036, 'llm', 2), ('promptslab/awesome-prompt-engineering', 0.538474440574646, 'study', 2), ('nccr-itmo/fedot', 0.5380227565765381, 'ml-ops', 0), ('hpcaitech/colossalai', 0.5340853929519653, 'llm', 0), ('mlflow/mlflow', 0.5320460200309753, 'ml-ops', 0), ('microsoft/jarvis', 0.5317671895027161, 'llm', 0), ('rasahq/rasa', 0.5303319096565247, 'llm', 1), ('chatarena/chatarena', 0.529494047164917, 'llm', 0), ('avaiga/taipy', 0.5267688632011414, 'data', 0), ('thilinarajapakse/simpletransformers', 0.5261474251747131, 'nlp', 0), ('young-geng/easylm', 0.5255038738250732, 'llm', 1), ('embedchain/embedchain', 0.5240100622177124, 'llm', 1), ('bigscience-workshop/petals', 0.5237755179405212, 'data', 2), ('googlecloudplatform/vertex-ai-samples', 0.5236150026321411, 'ml', 0), ('rcgai/simplyretrieve', 0.5208574533462524, 'llm', 1), ('nomic-ai/gpt4all', 0.5191216468811035, 'llm', 1), ('google/dopamine', 0.5175570249557495, 'ml-rl', 0), ('polyaxon/polyaxon', 0.5171562433242798, 'ml-ops', 0), ('guardrails-ai/guardrails', 0.5142502784729004, 'llm', 1), ('onnx/onnx', 0.5136048793792725, 'ml', 0), ('eugeneyan/obsidian-copilot', 0.5132716298103333, 'llm', 1), ('unity-technologies/ml-agents', 0.5131751298904419, 'ml-rl', 0), ('vllm-project/vllm', 0.5128405094146729, 'llm', 2), ('huggingface/datasets', 0.5126212239265442, 'nlp', 1), ('nvidia/nemo', 0.5125614404678345, 'nlp', 2), ('h2oai/h2o-llmstudio', 0.5096468329429626, 'llm', 2), ('jina-ai/thinkgpt', 0.5094317197799683, 'llm', 1), ('ml-tooling/opyrator', 0.5080411434173584, 'viz', 0), ('microsoft/unilm', 0.5072470903396606, 'nlp', 2), ('activeloopai/deeplake', 0.504906177520752, 'ml-ops', 1), ('pan-ml/panml', 0.5039084553718567, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5034509301185608, 'perf', 0), ('titanml/takeoff', 0.5029642581939697, 'llm', 2), ('ray-project/ray', 0.5007473826408386, 'ml-ops', 0)]",22,4.0,,1.54,68,54,13,0,0,0,0,68.0,95.0,90.0,1.4,55
51,testing,https://github.com/nedbat/coveragepy,[],,[],[],,,,nedbat/coveragepy,coveragepy,2742,392,32,Python,https://coverage.readthedocs.io,The code coverage tool for Python,nedbat,2024-01-12,2018-06-23,292,9.376648754274548,,The code coverage tool for Python,[],[],2024-01-13,"[('eugeneyan/python-collab-template', 0.6858402490615845, 'template', 0), ('wolever/parameterized', 0.6841293573379517, 'testing', 0), ('pytest-dev/pytest-bdd', 0.6206257939338684, 'testing', 0), ('ionelmc/pytest-benchmark', 0.6183704733848572, 'testing', 0), ('eleutherai/pyfra', 0.6178824305534363, 'ml', 0), ('pmorissette/bt', 0.6093915700912476, 'finance', 0), ('landscapeio/prospector', 0.6040084362030029, 'util', 0), ('rubik/radon', 0.6033869981765747, 'util', 0), ('alexmojaki/snoop', 0.6032272577285767, 'debug', 0), ('pytest-dev/pytest-cov', 0.5966586470603943, 'testing', 0), ('pympler/pympler', 0.5953695178031921, 'perf', 0), ('sourcery-ai/sourcery', 0.5949026942253113, 'util', 0), ('getsentry/responses', 0.5841493606567383, 'testing', 0), ('pyutils/line_profiler', 0.5808612108230591, 'profiling', 0), ('buildbot/buildbot', 0.57981938123703, 'util', 0), ('google/pytype', 0.5753951072692871, 'typing', 0), ('klen/pylama', 0.5741574764251709, 'util', 0), ('pycqa/pyflakes', 0.5710163116455078, 'util', 0), ('gaogaotiantian/viztracer', 0.5709172487258911, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.5676537156105042, 'profiling', 0), ('facebook/pyre-check', 0.5666431784629822, 'typing', 0), ('pytoolz/toolz', 0.5574312210083008, 'util', 0), ('samuelcolvin/python-devtools', 0.5552131533622742, 'debug', 0), ('hhatto/autopep8', 0.5550077557563782, 'util', 0), ('pypy/pypy', 0.5531808733940125, 'util', 0), ('grantjenks/blue', 0.5528746843338013, 'util', 0), ('dosisod/refurb', 0.5513451099395752, 'util', 0), ('reloadware/reloadium', 0.5490651726722717, 'profiling', 0), ('psf/black', 0.5479041337966919, 'util', 0), ('python/cpython', 0.5459318161010742, 'util', 0), ('taverntesting/tavern', 0.5402073264122009, 'testing', 0), ('snyk/faker-security', 0.5369202494621277, 'security', 0), ('samuelcolvin/dirty-equals', 0.5362508296966553, 'util', 0), ('google/yapf', 0.535642147064209, 'util', 0), ('spulec/freezegun', 0.5298979878425598, 'testing', 0), ('lk-geimfari/mimesis', 0.5268024206161499, 'data', 0), ('aswinnnn/pyscan', 0.525583028793335, 'security', 0), ('jendrikseipp/vulture', 0.5239750146865845, 'util', 0), ('mgedmin/check-manifest', 0.5218181610107422, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.521513819694519, 'study', 0), ('locustio/locust', 0.5198348760604858, 'testing', 0), ('astral-sh/ruff', 0.5197017788887024, 'util', 0), ('agronholm/typeguard', 0.5188262462615967, 'typing', 0), ('amaargiru/pyroad', 0.5170186161994934, 'study', 0), ('requests/toolbelt', 0.5164726376533508, 'util', 0), ('klen/py-frameworks-bench', 0.514492928981781, 'perf', 0), ('microsoft/playwright-python', 0.5143460631370544, 'testing', 0), ('pycqa/bandit', 0.5142317414283752, 'security', 0), ('pyston/pyston', 0.5141503810882568, 'util', 0), ('jiffyclub/snakeviz', 0.5139472484588623, 'profiling', 0), ('cuemacro/finmarketpy', 0.5129390954971313, 'finance', 0), ('cobrateam/splinter', 0.512545645236969, 'testing', 0), ('hypothesisworks/hypothesis', 0.5107763409614563, 'testing', 0), ('pytest-dev/pytest-xdist', 0.5098601579666138, 'testing', 0), ('benfred/py-spy', 0.5097211599349976, 'profiling', 0), ('featurelabs/featuretools', 0.5096982717514038, 'ml', 0), ('brandon-rhodes/python-patterns', 0.5084773302078247, 'util', 0), ('microsoft/pycodegpt', 0.506847083568573, 'llm', 0), ('samuelcolvin/pytest-pretty', 0.506611704826355, 'testing', 0), ('hadialqattan/pycln', 0.5058234930038452, 'util', 0), ('pycaret/pycaret', 0.5046213269233704, 'ml', 0), ('google/python-fire', 0.5045384764671326, 'term', 0), ('pycqa/flake8', 0.5013235807418823, 'util', 0), ('google/latexify_py', 0.5010238885879517, 'util', 0)]",168,6.0,,8.23,55,30,68,0,15,23,15,55.0,138.0,90.0,2.5,55
1281,viz,https://github.com/pyvista/pyvista,[],,[],[],,,,pyvista/pyvista,pyvista,2144,407,34,Python,https://docs.pyvista.org,3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK),pyvista,2024-01-14,2017-05-31,347,6.1634496919917865,https://avatars.githubusercontent.com/u/50384771?v=4,3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK),"['3d', 'mesh', 'mesh-processing', 'meshviewer', 'open-science', 'plotting', 'scientific-research', 'scientific-visualization', 'visualization', 'vtk']","['3d', 'mesh', 'mesh-processing', 'meshviewer', 'open-science', 'plotting', 'scientific-research', 'scientific-visualization', 'visualization', 'vtk']",2024-01-13,"[('marcomusy/vedo', 0.7296451330184937, 'viz', 6), ('pyqtgraph/pyqtgraph', 0.6187593936920166, 'viz', 2), ('contextlab/hypertools', 0.5857540369033813, 'ml', 1), ('enthought/mayavi', 0.5794352293014526, 'viz', 2), ('districtdatalabs/yellowbrick', 0.578895092010498, 'ml', 1), ('holoviz/hvplot', 0.5786033868789673, 'pandas', 1), ('holoviz/holoviz', 0.5743590593338013, 'viz', 0), ('mckinsey/vizro', 0.5597613453865051, 'viz', 1), ('bokeh/bokeh', 0.559531569480896, 'viz', 2), ('isl-org/open3d', 0.5594555735588074, 'sim', 3), ('matplotlib/matplotlib', 0.5509993433952332, 'viz', 1), ('man-group/dtale', 0.5372913479804993, 'viz', 1), ('plotly/plotly.py', 0.5286508798599243, 'viz', 1), ('visgl/deck.gl', 0.5255587100982666, 'viz', 1), ('residentmario/geoplot', 0.5253320336341858, 'gis', 0), ('holoviz/panel', 0.5228663682937622, 'viz', 0), ('gaogaotiantian/viztracer', 0.5193122029304504, 'profiling', 1), ('polyaxon/datatile', 0.5150445699691772, 'pandas', 0), ('maartenbreddels/ipyvolume', 0.5043342709541321, 'jupyter', 2), ('pygraphviz/pygraphviz', 0.5015963315963745, 'viz', 0)]",153,4.0,,15.13,391,278,81,0,12,19,12,390.0,1158.0,90.0,3.0,55
371,gis,https://github.com/microsoft/torchgeo,[],,[],[],,,,microsoft/torchgeo,torchgeo,2046,247,45,Python,https://torchgeo.rtfd.io,"TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data",microsoft,2024-01-12,2021-05-21,140,14.554878048780488,https://avatars.githubusercontent.com/u/6154722?v=4,"TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data","['computer-vision', 'datasets', 'deep-learning', 'earth-observation', 'geospatial', 'models', 'pytorch', 'remote-sensing', 'satellite-imagery', 'torchvision', 'transforms']","['computer-vision', 'datasets', 'deep-learning', 'earth-observation', 'geospatial', 'models', 'pytorch', 'remote-sensing', 'satellite-imagery', 'torchvision', 'transforms']",2024-01-12,"[('datasystemslab/geotorch', 0.6509654521942139, 'gis', 1), ('developmentseed/label-maker', 0.629837691783905, 'gis', 4), ('remotesensinglab/raster4ml', 0.6221429705619812, 'gis', 1), ('azavea/raster-vision', 0.6176372766494751, 'gis', 5), ('osgeo/grass', 0.6095272302627563, 'gis', 3), ('huggingface/datasets', 0.570610761642456, 'nlp', 4), ('nvlabs/gcvit', 0.5610058307647705, 'diffusion', 1), ('osgeo/gdal', 0.5599908828735352, 'gis', 1), ('fatiando/verde', 0.5598282217979431, 'gis', 1), ('opengeos/earthformer', 0.5563389658927917, 'gis', 2), ('aleju/imgaug', 0.5551947355270386, 'ml', 1), ('plant99/felicette', 0.554535448551178, 'gis', 3), ('kornia/kornia', 0.5526466369628906, 'ml-dl', 3), ('roboflow/notebooks', 0.5472760200500488, 'study', 3), ('awslabs/autogluon', 0.5401058197021484, 'ml', 3), ('roboflow/supervision', 0.5378150939941406, 'ml', 3), ('opengeos/segment-geospatial', 0.5342783331871033, 'gis', 2), ('deci-ai/super-gradients', 0.5279808640480042, 'ml-dl', 3), ('rwightman/pytorch-image-models', 0.5265195965766907, 'ml-dl', 1), ('lutzroeder/netron', 0.5072435140609741, 'ml', 2)]",53,7.0,,10.5,171,132,32,0,4,4,4,171.0,261.0,90.0,1.5,55
765,nlp,https://github.com/huggingface/setfit,[],,[],[],,,,huggingface/setfit,setfit,1804,185,21,Jupyter Notebook,https://hf.co/docs/setfit,Efficient few-shot learning with Sentence Transformers,huggingface,2024-01-13,2022-06-30,82,21.810017271157168,https://avatars.githubusercontent.com/u/25720743?v=4,Efficient few-shot learning with Sentence Transformers,"['few-shot-learning', 'nlp', 'sentence-transformers']","['few-shot-learning', 'nlp', 'sentence-transformers']",2024-01-11,"[('eleutherai/lm-evaluation-harness', 0.6814461350440979, 'llm', 0), ('alibaba/easynlp', 0.5513603091239929, 'nlp', 1), ('ofa-sys/ofa', 0.5320513844490051, 'llm', 0), ('bigscience-workshop/t-zero', 0.5152558088302612, 'llm', 0), ('google-research/electra', 0.5080073475837708, 'ml-dl', 1)]",48,4.0,,4.63,110,86,19,0,5,9,5,110.0,182.0,90.0,1.7,55
1572,llm,https://github.com/pathwaycom/llm-app,[],,[],[],,,,pathwaycom/llm-app,llm-app,1568,101,21,Python,https://pathway.com/developers/showcases/llm-app-pathway/,LLM App is a production framework for building and serving AI applications and LLM-enabled real-time data pipelines.,pathwaycom,2024-01-13,2023-07-19,27,56.287179487179486,https://avatars.githubusercontent.com/u/25750857?v=4,LLM App is a production framework for building and serving AI applications and LLM-enabled real-time data pipelines.,"['chatbot', 'hugging-face', 'llm', 'llm-local', 'llm-prompting', 'llm-security', 'llmops', 'machine-learning', 'open-ai', 'pathway', 'rag', 'real-time', 'retrieval-augmented-generation', 'vector-database', 'vector-index']","['chatbot', 'hugging-face', 'llm', 'llm-local', 'llm-prompting', 'llm-security', 'llmops', 'machine-learning', 'open-ai', 'pathway', 'rag', 'real-time', 'retrieval-augmented-generation', 'vector-database', 'vector-index']",2023-12-27,"[('microsoft/semantic-kernel', 0.7631767988204956, 'llm', 1), ('microsoft/promptflow', 0.7594974040985107, 'llm', 1), ('deepset-ai/haystack', 0.7468881607055664, 'llm', 1), ('cheshire-cat-ai/core', 0.6957066059112549, 'llm', 2), ('deep-diver/llm-as-chatbot', 0.6957004070281982, 'llm', 1), ('embedchain/embedchain', 0.6783232688903809, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6731693148612976, 'perf', 1), ('hwchase17/langchain', 0.6680853366851807, 'llm', 1), ('nebuly-ai/nebullvm', 0.6677808165550232, 'perf', 1), ('nomic-ai/gpt4all', 0.6673745512962341, 'llm', 1), ('lancedb/lancedb', 0.6573936343193054, 'data', 1), ('tigerlab-ai/tiger', 0.6506632566452026, 'llm', 2), ('h2oai/h2o-llmstudio', 0.6417025327682495, 'llm', 2), ('alphasecio/langchain-examples', 0.6350022554397583, 'llm', 1), ('bigscience-workshop/petals', 0.6320452690124512, 'data', 2), ('mnotgod96/appagent', 0.6296766400337219, 'llm', 1), ('shishirpatil/gorilla', 0.6179982423782349, 'llm', 1), ('mlc-ai/mlc-llm', 0.6101855039596558, 'llm', 1), 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1767,ml-ops,https://github.com/meltano/meltano,[],,[],[],,,,meltano/meltano,meltano,1447,139,13,Python,https://meltano.com/,"Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.",meltano,2024-01-14,2021-06-21,136,10.628541448058762,https://avatars.githubusercontent.com/u/43816713?v=4,"Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.","['connectors', 'data', 'data-engineering', 'data-pipelines', 'dataops', 'dataops-platform', 'elt', 'extract-data', 'integration', 'loaders', 'meltano', 'meltano-sdk', 'open-source', 'opensource', 'pipelines', 'singer', 'tap', 'taps', 'target', 'targets']","['connectors', 'data', 'data-engineering', 'data-pipelines', 'dataops', 'dataops-platform', 'elt', 'extract-data', 'integration', 'loaders', 'meltano', 'meltano-sdk', 'open-source', 'opensource', 'pipelines', 'singer', 'tap', 'taps', 'target', 'targets']",2024-01-12,"[('mage-ai/mage-ai', 0.6557142734527588, 'ml-ops', 5), ('airbytehq/airbyte', 0.6305922865867615, 'data', 3), ('ploomber/ploomber', 0.6284797787666321, 'ml-ops', 2), ('orchest/orchest', 0.6131107211112976, 'ml-ops', 2), ('simonw/datasette', 0.6087267398834229, 'data', 0), ('avaiga/taipy', 0.608718752861023, 'data', 2), ('dagster-io/dagster', 0.60458904504776, 'ml-ops', 2), ('linealabs/lineapy', 0.5844327211380005, 'jupyter', 0), ('flyteorg/flyte', 0.5828467607498169, 'ml-ops', 2), ('kestra-io/kestra', 0.5790954232215881, 'ml-ops', 3), ('dagworks-inc/hamilton', 0.5787143111228943, 'ml-ops', 1), ('streamlit/streamlit', 0.5685817003250122, 'viz', 0), ('polyaxon/datatile', 0.5661336183547974, 'pandas', 1), ('netflix/metaflow', 0.5626580119132996, 'ml-ops', 0), ('airbnb/omniduct', 0.5620505213737488, 'data', 0), ('whylabs/whylogs', 0.5477058291435242, 'util', 1), ('hi-primus/optimus', 0.5476229190826416, 'ml-ops', 0), ('kedro-org/kedro', 0.5286129117012024, 'ml-ops', 0), ('fugue-project/fugue', 0.5231815576553345, 'pandas', 0), ('zenml-io/zenml', 0.5218309760093689, 'ml-ops', 1), ('featureform/embeddinghub', 0.5155656337738037, 'nlp', 0), ('polyaxon/polyaxon', 0.5144167542457581, 'ml-ops', 1), ('ml-tooling/opyrator', 0.5139985084533691, 'viz', 0), ('tiangolo/fastapi', 0.5132185220718384, 'web', 0), ('huggingface/datasets', 0.5098901391029358, 'nlp', 0), ('astronomer/astro-sdk', 0.5098263621330261, 'ml-ops', 1), ('drivendata/cookiecutter-data-science', 0.5092251300811768, 'template', 0), ('pythagora-io/gpt-pilot', 0.5066982507705688, 'llm', 0), ('merantix-momentum/squirrel-core', 0.506161093711853, 'ml', 1), ('great-expectations/great_expectations', 0.5034080147743225, 'ml-ops', 1), ('kubeflow/fairing', 0.5027660131454468, 'ml-ops', 0)]",157,4.0,,20.71,143,110,31,0,19,106,19,143.0,296.0,90.0,2.1,55
1466,util,https://github.com/lcompilers/lpython,[],,[],[],,,,lcompilers/lpython,lpython,1175,122,28,C++,https://lpython.org/,Python compiler,lcompilers,2024-01-12,2021-12-29,108,10.793963254593177,https://avatars.githubusercontent.com/u/96538276?v=4,Python compiler,"['compiler', 'high-performance']","['compiler', 'high-performance']",2024-01-11,"[('exaloop/codon', 0.7257847189903259, 'perf', 2), ('cython/cython', 0.6913489699363708, 'util', 0), ('pypy/pypy', 0.6041545271873474, 'util', 1), ('numba/numba', 0.603155255317688, 'perf', 1), ('pyston/pyston', 0.6024731397628784, 'util', 0), ('klen/py-frameworks-bench', 0.587522566318512, 'perf', 0), ('markshannon/faster-cpython', 0.5289682149887085, 'perf', 0), ('faster-cpython/tools', 0.5253562927246094, 'perf', 0), ('numba/llvmlite', 0.5191760063171387, 'util', 0), ('fastai/fastcore', 0.5177335739135742, 'util', 0), ('pympler/pympler', 0.5168886184692383, 'perf', 0), ('p403n1x87/austin', 0.5144882798194885, 'profiling', 0), ('benfred/py-spy', 0.5141026377677917, 'profiling', 0), ('pyutils/line_profiler', 0.5114628076553345, 'profiling', 0), ('joblib/joblib', 0.5082236528396606, 'util', 0), ('google/jax', 0.5010378956794739, 'ml', 0)]",65,6.0,,30.87,97,58,25,0,11,12,11,97.0,217.0,90.0,2.2,55
1835,llm,https://github.com/hao-ai-lab/lookaheaddecoding,"['decoding', 'lookahead']",Break the Sequential Dependency of LLM Inference Using Lookahead Decoding,[],[],,,,hao-ai-lab/lookaheaddecoding,LookaheadDecoding,802,49,9,Python,,,hao-ai-lab,2024-01-14,2023-11-21,10,80.2,https://avatars.githubusercontent.com/u/149045815?v=4,Break the Sequential Dependency of LLM Inference Using Lookahead Decoding,[],"['decoding', 'lookahead']",2024-01-09,"[('karpathy/llama2.c', 0.5412218570709229, 'llm', 0), ('facebookresearch/llama', 0.5299458503723145, 'llm', 0), ('artidoro/qlora', 0.5174603462219238, 'llm', 0), ('facebookresearch/codellama', 0.5007719397544861, 'llm', 0)]",5,2.0,,0.31,44,24,2,0,0,0,0,44.0,175.0,90.0,4.0,55
499,ml,https://github.com/ageron/handson-ml2,[],,[],[],,,,ageron/handson-ml2,handson-ml2,26281,12333,648,Jupyter Notebook,,"A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.",ageron,2024-01-14,2019-01-08,264,99.54924242424242,,"A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.",[],[],2023-02-04,"[('fchollet/deep-learning-with-python-notebooks', 0.8291416168212891, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.6813152432441711, 'study', 0), ('gradio-app/gradio', 0.6595058441162109, 'viz', 0), ('rasbt/machine-learning-book', 0.6461301445960999, 'study', 0), ('firmai/industry-machine-learning', 0.6424956321716309, 'study', 0), ('mrdbourke/pytorch-deep-learning', 0.633056104183197, 'study', 0), ('wesm/pydata-book', 0.6252664923667908, 'study', 0), 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671,ml-dl,https://github.com/facebookresearch/detectron,[],,[],[],,,,facebookresearch/detectron,Detectron,26066,5568,944,Python,,"FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.",facebookresearch,2024-01-14,2017-10-05,329,79.05632582322357,https://avatars.githubusercontent.com/u/16943930?v=4,"FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.",[],[],2023-10-19,"[('matterport/mask_rcnn', 0.5304756760597229, 'ml-dl', 0), ('open-mmlab/mmdetection', 0.5015984177589417, 'ml', 0)]",43,3.0,,0.12,2,0,76,3,0,0,0,2.0,2.0,90.0,1.0,54
1243,ml,https://github.com/jindongwang/transferlearning,[],,[],[],,,,jindongwang/transferlearning,transferlearning,12474,3731,336,Python,http://transferlearning.xyz/,"Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习",jindongwang,2024-01-14,2017-04-30,352,35.40875912408759,,"Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习","['deep-learning', 'domain-adaptation', 'domain-adaption', 'domain-generalization', 'few-shot', 'few-shot-learning', 'generalization', 'machine-learning', 'meta-learning', 'paper', 'papers', 'representation-learning', 'self-supervised-learning', 'style-transfer', 'survey', 'theory', 'transfer-learning', 'transferlearning', 'tutorial-code', 'unsupervised-learning']","['deep-learning', 'domain-adaptation', 'domain-adaption', 'domain-generalization', 'few-shot', 'few-shot-learning', 'generalization', 'machine-learning', 'meta-learning', 'paper', 'papers', 'representation-learning', 'self-supervised-learning', 'style-transfer', 'survey', 'theory', 'transfer-learning', 'transferlearning', 'tutorial-code', 'unsupervised-learning']",2024-01-08,"[('amanchadha/coursera-deep-learning-specialization', 0.5513812899589539, 'study', 1), ('huggingface/autotrain-advanced', 0.5214440822601318, 'ml', 2), ('patchy631/machine-learning', 0.5060864090919495, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5019001364707947, 'study', 2), ('udacity/deep-learning-v2-pytorch', 0.501124918460846, 'study', 2), ('awslabs/autogluon', 0.5002632141113281, 'ml', 3)]",40,4.0,,0.96,14,7,82,0,0,0,0,14.0,22.0,90.0,1.6,54
425,ml-dl,https://github.com/facebookresearch/detr,[],,[],[],,,,facebookresearch/detr,detr,12338,2222,149,Python,,End-to-End Object Detection with Transformers,facebookresearch,2024-01-14,2020-05-26,192,64.26041666666667,https://avatars.githubusercontent.com/u/16943930?v=4,End-to-End Object Detection with Transformers,[],[],2023-02-07,"[('cvg/lightglue', 0.5226452350616455, 'ml-dl', 0), ('nvlabs/gcvit', 0.5166937112808228, 'diffusion', 0), ('matterport/mask_rcnn', 0.5123329758644104, 'ml-dl', 0)]",26,7.0,,0.02,36,7,44,11,0,0,0,36.0,47.0,90.0,1.3,54
1380,ml,https://github.com/microsoft/swin-transformer,[],,[],[],,,,microsoft/swin-transformer,Swin-Transformer,12319,1937,125,Python,https://arxiv.org/abs/2103.14030,"This is an official implementation for ""Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"".",microsoft,2024-01-14,2021-03-25,148,82.83669548511047,https://avatars.githubusercontent.com/u/6154722?v=4,"This is an official implementation for ""Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"".","['ade20k', 'image-classification', 'imagenet', 'mask-rcnn', 'mscoco', 'object-detection', 'semantic-segmentation', 'swin-transformer']","['ade20k', 'image-classification', 'imagenet', 'mask-rcnn', 'mscoco', 'object-detection', 'semantic-segmentation', 'swin-transformer']",2023-08-16,"[('nvlabs/gcvit', 0.6548908352851868, 'diffusion', 4), ('google-research/maxvit', 0.5897934436798096, 'ml', 1), ('lucidrains/vit-pytorch', 0.5670905113220215, 'ml-dl', 1), ('open-mmlab/mmdetection', 0.5538708567619324, 'ml', 3), ('deci-ai/super-gradients', 0.5506076812744141, 'ml-dl', 4), ('open-mmlab/mmsegmentation', 0.5363519191741943, 'ml', 2), ('hrnet/hrnet-semantic-segmentation', 0.5201693177223206, 'ml', 1), ('roboflow/supervision', 0.5062756538391113, 'ml', 1)]",13,8.0,,0.02,20,3,34,5,0,0,0,20.0,11.0,90.0,0.6,54
85,ml,https://github.com/statsmodels/statsmodels,[],,[],[],,,,statsmodels/statsmodels,statsmodels,9210,2836,279,Python,http://www.statsmodels.org/devel/,Statsmodels: statistical modeling and econometrics in Python,statsmodels,2024-01-13,2011-06-12,659,13.969664138678223,https://avatars.githubusercontent.com/u/717666?v=4,Statsmodels: statistical modeling and econometrics in Python,"['count-model', 'data-analysis', 'data-science', 'econometrics', 'forecasting', 'generalized-linear-models', 'hypothesis-testing', 'prediction', 'regression-models', 'robust-estimation', 'statistics', 'timeseries-analysis']","['count-model', 'data-analysis', 'data-science', 'econometrics', 'forecasting', 'generalized-linear-models', 'hypothesis-testing', 'prediction', 'regression-models', 'robust-estimation', 'statistics', 'timeseries-analysis']",2024-01-04,"[('firmai/atspy', 0.6599208116531372, 'time-series', 1), ('ranaroussi/quantstats', 0.6285594701766968, 'finance', 0), ('alkaline-ml/pmdarima', 0.6218242645263672, 'time-series', 2), ('scikit-learn/scikit-learn', 0.6116586327552795, 'ml', 3), ('scikit-mobility/scikit-mobility', 0.5975031852722168, 'gis', 3), ('bashtage/arch', 0.5926198363304138, 'time-series', 1), ('plotly/dash', 0.5885524749755859, 'viz', 1), ('awslabs/gluonts', 0.5710023641586304, 'time-series', 2), ('goldmansachs/gs-quant', 0.5688977241516113, 'finance', 0), ('pymc-devs/pymc3', 0.5598034858703613, 'ml', 0), ('uber/orbit', 0.5589537024497986, 'time-series', 2), ('stan-dev/pystan', 0.5519436001777649, 'ml', 0), ('quantecon/quantecon.py', 0.5498467087745667, 'sim', 0), ('pandas-dev/pandas', 0.5481253266334534, 'pandas', 2), ('rjt1990/pyflux', 0.5408421158790588, 'time-series', 1), ('crflynn/stochastic', 0.5304479002952576, 'sim', 0), ('online-ml/river', 0.5298870205879211, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.5274232625961304, 'study', 3), ('rasbt/mlxtend', 0.5218582153320312, 'ml', 1), ('eleutherai/pyfra', 0.5143864154815674, 'ml', 0), ('polyaxon/datatile', 0.5121821165084839, 'pandas', 2), ('wesm/pydata-book', 0.5118904709815979, 'study', 0), ('cuemacro/finmarketpy', 0.5101310610771179, 'finance', 0)]",421,2.0,,6.69,232,140,153,0,3,4,3,232.0,184.0,90.0,0.8,54
528,util,https://github.com/facebookresearch/hydra,[],,[],[],,,,facebookresearch/hydra,hydra,7864,616,124,Python,https://hydra.cc,Hydra is a framework for elegantly configuring complex applications,facebookresearch,2024-01-14,2019-06-12,241,32.515062020082695,https://avatars.githubusercontent.com/u/16943930?v=4,Hydra is a framework for elegantly configuring complex applications,[],[],2023-11-30,"[('ashleve/lightning-hydra-template', 0.5850319266319275, 'util', 0), ('google/gin-config', 0.5556942224502563, 'util', 0), ('willmcgugan/textual', 0.5213847160339355, 'term', 0), ('alphasecio/langchain-examples', 0.5024363994598389, 'llm', 0)]",114,3.0,,0.63,69,20,56,2,1,5,1,69.0,142.0,90.0,2.1,54
1192,util,https://github.com/xonsh/xonsh,['shell'],,[],[],,,,xonsh/xonsh,xonsh,7471,633,105,Python,http://xon.sh,":shell: Python-powered, cross-platform, Unix-gazing shell.",xonsh,2024-01-14,2015-01-21,470,15.866808252427184,https://avatars.githubusercontent.com/u/17418188?v=4,":shell: Python-powered, cross-platform, Unix-gazing shell.","['bash', 'cli', 'command-line', 'console', 'devops', 'fish', 'iterm2', 'prompt', 'python-shell', 'script', 'shell', 'terminal', 'windows-terminal', 'xonsh', 'zsh']","['bash', 'cli', 'command-line', 'console', 'devops', 'fish', 'iterm2', 'prompt', 'python-shell', 'script', 'shell', 'terminal', 'windows-terminal', 'xonsh', 'zsh']",2023-12-31,"[('tiangolo/typer', 0.614140510559082, 'term', 3), ('kellyjonbrazil/jc', 0.5756747722625732, 'util', 3), ('jquast/blessed', 0.569438099861145, 'term', 2), ('pygamelib/pygamelib', 0.5624502301216125, 'gamedev', 0), ('urwid/urwid', 0.5422582030296326, 'term', 0), ('pypy/pypy', 0.5222761631011963, 'util', 0), ('tmbo/questionary', 0.5194598436355591, 'term', 1), ('python/cpython', 0.5193564891815186, 'util', 0), ('federicoceratto/dashing', 0.5140331387519836, 'term', 1), ('google/python-fire', 0.5076464414596558, 'term', 1), ('evhub/coconut', 0.5075111985206604, 'util', 1), ('hoffstadt/dearpygui', 0.504555881023407, 'gui', 0), ('cython/cython', 0.5044435262680054, 'util', 0)]",320,2.0,,1.9,64,34,109,0,4,14,4,64.0,118.0,90.0,1.8,54
1274,util,https://github.com/googleapis/google-api-python-client,[],,[],[],,,,googleapis/google-api-python-client,google-api-python-client,7135,2452,284,Python,https://googleapis.github.io/google-api-python-client/docs/,🐍 The official Python client library for Google's discovery based APIs.,googleapis,2024-01-13,2014-01-08,524,13.594175285792053,https://avatars.githubusercontent.com/u/16785467?v=4,🐍 The official Python client library for Google's discovery based APIs.,[],[],2024-01-09,"[('nv7-github/googlesearch', 0.6702570915222168, 'util', 0), ('dsdanielpark/bard-api', 0.6036682724952698, 'llm', 0), ('openai/openai-python', 0.5968145728111267, 'util', 0), ('dialogflow/dialogflow-python-client-v2', 0.5611771941184998, 'nlp', 0), ('radiantearth/radiant-mlhub', 0.5603718757629395, 'gis', 0), ('typesense/typesense-python', 0.5588173866271973, 'data', 0), ('snyk-labs/pysnyk', 0.5522992610931396, 'security', 0), ('psf/requests', 0.552095353603363, 'web', 0), ('jovianml/opendatasets', 0.551531970500946, 'data', 0), ('meilisearch/meilisearch-python', 0.5505169034004211, 'data', 0), ('googleapis/python-speech', 0.5471388101577759, 'ml', 0), ('pytoolz/toolz', 0.5453507304191589, 'util', 0), ('urwid/urwid', 0.54488605260849, 'term', 0), ('simple-salesforce/simple-salesforce', 0.541388213634491, 'data', 0), ('qdrant/qdrant-client', 0.5371494293212891, 'util', 0), ('giswqs/geemap', 0.5369963645935059, 'gis', 0), ('scholarly-python-package/scholarly', 0.5367330312728882, 'data', 0), ('requests/toolbelt', 0.5348877906799316, 'util', 0), ('clips/pattern', 0.5339103937149048, 'nlp', 0), ('fastai/ghapi', 0.5308494567871094, 'util', 0), ('huggingface/huggingface_hub', 0.5284603238105774, 'ml', 0), ('shishirpatil/gorilla', 0.524009108543396, 'llm', 0), ('goldsmith/wikipedia', 0.5227052569389343, 'data', 0), ('mitmproxy/pdoc', 0.5224719047546387, 'util', 0), ('hydrosquall/tiingo-python', 0.5202198624610901, 'finance', 0), ('pndurette/gtts', 0.5190337300300598, 'util', 0), ('landscapeio/prospector', 0.5145336389541626, 'util', 0), ('googleapis/python-bigquery', 0.5121608376502991, 'data', 0), ('amaargiru/pyroad', 0.5120947957038879, 'study', 0), ('hugapi/hug', 0.5113233327865601, 'util', 0), ('serpapi/google-search-results-python', 0.5091744065284729, 'util', 0), ('scrapy/scrapy', 0.5074482560157776, 'data', 0), ('cohere-ai/cohere-python', 0.5053079128265381, 'util', 0), ('1200wd/bitcoinlib', 0.5004116892814636, 'crypto', 0)]",190,3.0,,2.69,79,55,122,0,41,18,41,77.0,84.0,90.0,1.1,54
44,ml,https://github.com/lmcinnes/umap,[],,[],[],,,,lmcinnes/umap,umap,6678,754,128,Python,,Uniform Manifold Approximation and Projection,lmcinnes,2024-01-14,2017-07-02,343,19.45318352059925,,Uniform Manifold Approximation and Projection,"['dimensionality-reduction', 'machine-learning', 'topological-data-analysis', 'umap', 'visualization']","['dimensionality-reduction', 'machine-learning', 'topological-data-analysis', 'umap', 'visualization']",2024-01-08,"[('geomstats/geomstats', 0.5977250933647156, 'math', 1)]",128,7.0,,1.29,30,8,80,0,2,4,2,30.0,36.0,90.0,1.2,54
194,util,https://github.com/pycqa/isort,['code-quality'],,[],[],,,,pycqa/isort,isort,6190,604,48,Python,https://pycqa.github.io/isort/,A Python utility / library to sort imports.,pycqa,2024-01-14,2013-09-02,543,11.396633350867964,https://avatars.githubusercontent.com/u/8749848?v=4,A Python utility / library to sort imports.,"['auto-formatter', 'cleaner', 'cli', 'formatter', 'isort', 'linter', 'python-utility', 'sorting-imports']","['auto-formatter', 'cleaner', 'cli', 'code-quality', 'formatter', 'isort', 'linter', 'python-utility', 'sorting-imports']",2024-01-12,"[('hadialqattan/pycln', 0.6549221277236938, 'util', 0), ('google/yapf', 0.5961623191833496, 'util', 2), ('asottile/reorder-python-imports', 0.5951371192932129, 'util', 2), ('landscapeio/prospector', 0.574863851070404, 'util', 0), ('sethmmorton/natsort', 0.5276709794998169, 'util', 0), ('google/pytype', 0.5101639032363892, 'typing', 2), ('hhatto/autopep8', 0.5075655579566956, 'util', 1), ('grantjenks/blue', 0.5017447471618652, 'util', 2)]",294,7.0,,1.38,53,33,126,0,5,14,5,53.0,75.0,90.0,1.4,54
741,study,https://github.com/zhanymkanov/fastapi-best-practices,[],,[],[],,,,zhanymkanov/fastapi-best-practices,fastapi-best-practices,5917,449,91,,,FastAPI Best Practices and Conventions we used at our startup,zhanymkanov,2024-01-14,2022-08-09,77,76.84415584415585,,FastAPI Best Practices and Conventions we used at our startup,"['best-practices', 'fastapi']","['best-practices', 'fastapi']",2023-10-22,"[('fastapi-users/fastapi-users', 0.6014936566352844, 'web', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5217467546463013, 'template', 1), ('dmontagu/fastapi_client', 0.5196253061294556, 'web', 0), ('tiangolo/fastapi', 0.5182605981826782, 'web', 1)]",10,5.0,,0.21,5,2,17,3,0,0,0,5.0,11.0,90.0,2.2,54
369,time-series,https://github.com/facebookresearch/kats,['time-series'],,[],[],,,,facebookresearch/kats,Kats,4647,508,77,Python,,"Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends. ",facebookresearch,2024-01-14,2021-02-25,152,30.429373246024323,https://avatars.githubusercontent.com/u/16943930?v=4,"Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends. ",[],['time-series'],2024-01-10,"[('sktime/sktime', 0.5305997729301453, 'time-series', 1), ('alkaline-ml/pmdarima', 0.5154160261154175, 'time-series', 1), ('salesforce/merlion', 0.5117724537849426, 'time-series', 1)]",136,4.0,,1.75,8,4,35,0,0,1,1,8.0,12.0,90.0,1.5,54
380,ml-ops,https://github.com/aimhubio/aim,[],,[],[],,,,aimhubio/aim,aim,4468,274,45,Python,https://aimstack.io,Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.,aimhubio,2024-01-13,2019-05-31,243,18.343695014662757,https://avatars.githubusercontent.com/u/51399196?v=4,Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.,"['ai', 'data-science', 'data-visualization', 'experiment-tracking', 'machine-learning', 'metadata', 'metadata-tracking', 'ml', 'mlflow', 'mlops', 'prompt-engineering', 'pytorch', 'tensorboard', 'tensorflow', 'visualization']","['ai', 'data-science', 'data-visualization', 'experiment-tracking', 'machine-learning', 'metadata', 'metadata-tracking', 'ml', 'mlflow', 'mlops', 'prompt-engineering', 'pytorch', 'tensorboard', 'tensorflow', 'visualization']",2024-01-12,"[('wandb/client', 0.696733832359314, 'ml', 5), ('polyaxon/datatile', 0.6386370062828064, 'pandas', 5), ('determined-ai/determined', 0.614514172077179, 'ml-ops', 5), ('netflix/metaflow', 0.5948770046234131, 'ml-ops', 5), ('mlflow/mlflow', 0.5842969417572021, 'ml-ops', 4), ('iterative/dvc', 0.5769718885421753, 'ml-ops', 3), ('whylabs/whylogs', 0.5764767527580261, 'util', 3), ('salesforce/logai', 0.5716543197631836, 'util', 2), ('microsoft/onnxruntime', 0.5700400471687317, 'ml', 3), ('transformeroptimus/superagi', 0.5632416605949402, 'llm', 1), ('polyaxon/polyaxon', 0.5590616464614868, 'ml-ops', 6), ('activeloopai/deeplake', 0.5577347874641418, 'ml-ops', 7), ('nebuly-ai/nebullvm', 0.5485852360725403, 'perf', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5477184653282166, 'study', 2), ('huggingface/datasets', 0.5470335483551025, 'nlp', 3), ('bentoml/bentoml', 0.5450233817100525, 'ml-ops', 3), ('tensorflow/tensorflow', 0.5438287258148193, 'ml-dl', 3), ('doccano/doccano', 0.5435435771942139, 'nlp', 1), ('prefecthq/marvin', 0.541201114654541, 'nlp', 1), ('sweepai/sweep', 0.5406528115272522, 'llm', 1), ('googlecloudplatform/vertex-ai-samples', 0.5386273264884949, 'ml', 4), ('oegedijk/explainerdashboard', 0.5350830554962158, 'ml-interpretability', 0), ('roboflow/supervision', 0.5213971138000488, 'ml', 3), ('argilla-io/argilla', 0.5164137482643127, 'nlp', 3), ('adap/flower', 0.5154430270195007, 'ml-ops', 4), ('merantix-momentum/squirrel-core', 0.5153231024742126, 'ml', 6), ('csinva/imodels', 0.5152265429496765, 'ml', 4), ('cheshire-cat-ai/core', 0.5146742463111877, 'llm', 1), ('tensorlayer/tensorlayer', 0.5126657485961914, 'ml-rl', 1), ('gradio-app/gradio', 0.5126237869262695, 'viz', 3), ('kedro-org/kedro-viz', 0.5116142630577087, 'ml-ops', 2), ('mlc-ai/mlc-llm', 0.509802520275116, 'llm', 0), ('bulletphysics/bullet3', 0.5082801580429077, 'sim', 0), ('pathwaycom/llm-app', 0.506819486618042, 'llm', 1), ('google-research/language', 0.5032878518104553, 'nlp', 1), ('fmind/mlops-python-package', 0.5028169751167297, 'template', 3), ('laion-ai/open-assistant', 0.5019581913948059, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5019053220748901, 'ml-dl', 4), ('tlkh/tf-metal-experiments', 0.501725971698761, 'perf', 1)]",58,4.0,,2.5,79,29,56,0,9,36,9,79.0,91.0,90.0,1.2,54
1552,study,https://github.com/neetcode-gh/leetcode,"['interview-questions', 'data-structures', 'leetcode']",Leetcode solutions for NeetCode.io,[],[],,,,neetcode-gh/leetcode,leetcode,4459,2046,40,JavaScript,,Leetcode solutions,neetcode-gh,2024-01-14,2021-01-20,157,28.24705882352941,,Leetcode solutions,[],"['data-structures', 'interview-questions', 'leetcode']",2024-01-13,"[('mdmzfzl/neetcode-solutions', 0.6274089217185974, 'study', 3)]",612,1.0,,34.92,182,100,36,0,0,0,0,181.0,51.0,90.0,0.3,54
353,ml-interpretability,https://github.com/pytorch/captum,[],,[],[],,,,pytorch/captum,captum,4372,469,225,Python,https://captum.ai,Model interpretability and understanding for PyTorch,pytorch,2024-01-14,2019-08-27,231,18.926406926406926,https://avatars.githubusercontent.com/u/21003710?v=4,Model interpretability and understanding for PyTorch,"['feature-attribution', 'feature-importance', 'interpretability', 'interpretable-ai', 'interpretable-ml']","['feature-attribution', 'feature-importance', 'interpretability', 'interpretable-ai', 'interpretable-ml']",2024-01-08,"[('pytorch/ignite', 0.6821672916412354, 'ml-dl', 0), ('tensorflow/lucid', 0.6784272193908691, 'ml-interpretability', 1), ('csinva/imodels', 0.6309248208999634, 'ml', 1), ('skorch-dev/skorch', 0.6131489276885986, 'ml-dl', 0), ('interpretml/interpret', 0.6127338409423828, 'ml-interpretability', 3), ('mrdbourke/pytorch-deep-learning', 0.5934852361679077, 'study', 0), ('allenai/allennlp', 0.5909707546234131, 'nlp', 0), ('marcotcr/lime', 0.5743399262428284, 'ml-interpretability', 1), ('intel/intel-extension-for-pytorch', 0.5694814324378967, 'perf', 0), ('pair-code/lit', 0.5618991851806641, 'ml-interpretability', 0), ('nvidia/apex', 0.5534337759017944, 'ml-dl', 0), ('eleutherai/pythia', 0.5508965253829956, 'ml-interpretability', 2), ('xl0/lovely-tensors', 0.5507166385650635, 'ml-dl', 0), ('huggingface/transformers', 0.5457330942153931, 'nlp', 0), ('huggingface/accelerate', 0.5386924743652344, 'ml', 0), ('pytorch/data', 0.5359891057014465, 'data', 0), ('selfexplainml/piml-toolbox', 0.5324576497077942, 'ml-interpretability', 0), ('rasbt/machine-learning-book', 0.5297553539276123, 'study', 0), ('arogozhnikov/einops', 0.524075984954834, 'ml-dl', 0), ('ibm/transition-amr-parser', 0.5239638090133667, 'nlp', 0), ('hysts/pytorch_image_classification', 0.5230705142021179, 'ml-dl', 0), ('mosaicml/composer', 0.5189915299415588, 'ml-dl', 0), ('speechbrain/speechbrain', 0.5150958895683289, 'nlp', 0), ('pytorch/rl', 0.5139393210411072, 'ml-rl', 0), ('rafiqhasan/auto-tensorflow', 0.5135285258293152, 'ml-dl', 0), ('rentruewang/koila', 0.5131102800369263, 'ml', 0), ('salesforce/blip', 0.5107000470161438, 'diffusion', 0), ('ashleve/lightning-hydra-template', 0.5099735260009766, 'util', 0), ('pytorch/botorch', 0.509192168712616, 'ml-dl', 0), ('blackhc/toma', 0.5078623294830322, 'ml-dl', 0), ('seldonio/alibi', 0.5067712664604187, 'ml-interpretability', 1), ('cvxgrp/pymde', 0.5022547245025635, 'ml', 0)]",104,3.0,,1.0,61,40,53,0,1,2,1,61.0,181.0,90.0,3.0,54
604,testing,https://github.com/seleniumbase/seleniumbase,[],,[],[],,,,seleniumbase/seleniumbase,SeleniumBase,3859,871,125,Python,https://seleniumbase.io,"Browser automation framework for testing with Selenium, Python, and pytest. Includes a Dashboard, a Recorder for generating tests, Undetected Mode, and more.",seleniumbase,2024-01-13,2014-03-04,517,7.4642166344294,https://avatars.githubusercontent.com/u/17287301?v=4,"Browser automation framework for testing with Selenium, Python, and pytest. Includes a Dashboard, a Recorder for generating tests, Undetected Mode, and more.","['behave', 'chrome', 'chromedriver', 'e2e-testing', 'firefox', 'pytest', 'pytest-plugin', 'selenium', 'selenium-python', 'seleniumbase', 'test', 'unittests', 'web-automation', 'webdriver', 'webkit']","['behave', 'chrome', 'chromedriver', 'e2e-testing', 'firefox', 'pytest', 'pytest-plugin', 'selenium', 'selenium-python', 'seleniumbase', 'test', 'unittests', 'web-automation', 'webdriver', 'webkit']",2024-01-04,"[('cobrateam/splinter', 0.7703961730003357, 'testing', 2), ('microsoft/playwright-python', 0.6941218972206116, 'testing', 2), ('webpy/webpy', 0.5600611567497253, 'web', 0), ('taverntesting/tavern', 0.5573855042457581, 'testing', 1), ('bokeh/bokeh', 0.5535537004470825, 'viz', 0), ('masoniteframework/masonite', 0.5500764846801758, 'web', 0), ('alirezamika/autoscraper', 0.5415842533111572, 'data', 0), ('pyodide/pyodide', 0.540057897567749, 'util', 0), ('wolever/parameterized', 0.5371958613395691, 'testing', 0), ('clips/pattern', 0.5308951735496521, 'nlp', 0), ('plotly/dash', 0.5277555584907532, 'viz', 0), ('r0x0r/pywebview', 0.5268258452415466, 'gui', 1), ('jiffyclub/snakeviz', 0.5255619287490845, 'profiling', 0), ('robotframework/robotframework', 0.5185773968696594, 'testing', 0), ('pytest-dev/pytest-testinfra', 0.5065727233886719, 'testing', 1), ('roniemartinez/dude', 0.5045518279075623, 'util', 1), ('scrapy/scrapy', 0.5043920278549194, 'data', 0), ('pallets/flask', 0.5030795931816101, 'web', 0), ('voila-dashboards/voila', 0.5014607906341553, 'jupyter', 0)]",37,5.0,,16.27,167,160,120,0,130,91,130,167.0,348.0,90.0,2.1,54
1212,ml,https://github.com/sanchit-gandhi/whisper-jax,[],,[],[],,,,sanchit-gandhi/whisper-jax,whisper-jax,3813,322,39,Jupyter Notebook,,JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.,sanchit-gandhi,2024-01-13,2023-03-02,47,79.91317365269461,,JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.,"['deep-learning', 'jax', 'speech-recognition', 'speech-to-text', 'whisper']","['deep-learning', 'jax', 'speech-recognition', 'speech-to-text', 'whisper']",2023-12-15,"[('ggerganov/whisper.cpp', 0.6906029582023621, 'util', 3), ('deepmind/dm-haiku', 0.6133875846862793, 'ml-dl', 2), ('m-bain/whisperx', 0.5212621688842773, 'nlp', 3)]",4,2.0,,2.44,44,17,11,1,0,0,0,44.0,62.0,90.0,1.4,54
284,crypto,https://github.com/ethereum/consensus-specs,[],,[],[],,,,ethereum/consensus-specs,consensus-specs,3329,977,246,Python,,Ethereum Proof-of-Stake Consensus Specifications,ethereum,2024-01-12,2018-09-20,279,11.901430030643514,https://avatars.githubusercontent.com/u/6250754?v=4,Ethereum Proof-of-Stake Consensus Specifications,[],[],2024-01-11,[],148,3.0,,10.83,251,107,65,0,16,16,16,251.0,225.0,90.0,0.9,54
1759,data,https://github.com/rom1504/img2dataset,[],,[],[],,,,rom1504/img2dataset,img2dataset,2953,288,29,Python,,"Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.",rom1504,2024-01-13,2021-08-11,128,22.916851441241686,,"Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.","['big-data', 'dataset', 'deep-learning', 'download-images', 'image', 'image-dataset', 'multimodal']","['big-data', 'dataset', 'deep-learning', 'download-images', 'image', 'image-dataset', 'multimodal']",2024-01-13,"[('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5232915878295898, 'web', 0), ('aiqc/aiqc', 0.5084776282310486, 'ml-ops', 0), ('microsoft/deepspeed', 0.5075109601020813, 'ml-dl', 1)]",32,5.0,,0.54,48,29,30,0,4,35,4,48.0,95.0,90.0,2.0,54
1359,llm,https://github.com/iryna-kondr/scikit-llm,[],,[],[],,,,iryna-kondr/scikit-llm,scikit-llm,2820,226,36,Python,https://beastbyte.ai/,Seamlessly integrate LLMs into scikit-learn.,iryna-kondr,2024-01-12,2023-05-12,37,75.05703422053232,,Seamlessly integrate LLMs into scikit-learn.,"['chatgpt', 'deep-learning', 'llm', 'machine-learning', 'scikit-learn', 'transformers']","['chatgpt', 'deep-learning', 'llm', 'machine-learning', 'scikit-learn', 'transformers']",2023-12-25,"[('microsoft/jarvis', 0.6588683128356934, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.6441587209701538, 'llm', 0), ('tigerlab-ai/tiger', 0.6261765956878662, 'llm', 1), ('koaning/scikit-lego', 0.614821195602417, 'ml', 2), ('vllm-project/vllm', 0.6003293395042419, 'llm', 1), ('microsoft/semantic-kernel', 0.5926992893218994, 'llm', 1), ('bigscience-workshop/petals', 0.5923831462860107, 'data', 2), ('pathwaycom/llm-app', 0.5918958187103271, 'llm', 2), ('microsoft/torchscale', 0.5895041823387146, 'llm', 1), ('intel/scikit-learn-intelex', 0.5876615047454834, 'perf', 2), ('h2oai/h2o-llmstudio', 0.5840808749198914, 'llm', 2), ('argilla-io/argilla', 0.5834850668907166, 'nlp', 2), ('rasbt/machine-learning-book', 0.5804186463356018, 'study', 3), ('intel/intel-extension-for-transformers', 0.5800312757492065, 'perf', 0), ('nomic-ai/gpt4all', 0.5748793482780457, 'llm', 0), ('skops-dev/skops', 0.5726227760314941, 'ml-ops', 2), ('nebuly-ai/nebullvm', 0.5629363059997559, 'perf', 1), ('deepset-ai/haystack', 0.5621213912963867, 'llm', 3), ('bobazooba/xllm', 0.5620294213294983, 'llm', 3), ('ray-project/ray-llm', 0.5598890781402588, 'llm', 2), ('automl/auto-sklearn', 0.5549347996711731, 'ml', 1), ('microsoft/onnxruntime', 0.5525389313697815, 'ml', 3), ('hegelai/prompttools', 0.5520104765892029, 'llm', 2), ('explosion/spacy-llm', 0.550988495349884, 'llm', 2), ('microsoft/promptcraft-robotics', 0.5381367206573486, 'sim', 2), ('bentoml/openllm', 0.5351253151893616, 'ml-ops', 1), ('ludwig-ai/ludwig', 0.5349180698394775, 'ml-ops', 3), ('huggingface/transformers', 0.5332709550857544, 'nlp', 2), ('mooler0410/llmspracticalguide', 0.5321641564369202, 'study', 0), ('microsoft/promptflow', 0.5263214707374573, 'llm', 2), ('young-geng/easylm', 0.5262447595596313, 'llm', 1), ('koaning/human-learn', 0.5255882143974304, 'data', 2), ('night-chen/toolqa', 0.522339940071106, 'llm', 0), ('truera/trulens', 0.5199382901191711, 'llm', 2), ('horovod/horovod', 0.5187541842460632, 'ml-ops', 2), ('skorch-dev/skorch', 0.5175820589065552, 'ml-dl', 2), ('optimalscale/lmflow', 0.5123019814491272, 'llm', 2), ('onnx/onnx', 0.5105788111686707, 'ml', 3), ('llmware-ai/llmware', 0.5102970600128174, 'llm', 2), ('embedchain/embedchain', 0.5067012310028076, 'llm', 2), ('salesforce/xgen', 0.5054061412811279, 'llm', 1), ('agenta-ai/agenta', 0.5052707195281982, 'llm', 1), ('lightning-ai/lit-gpt', 0.5048611164093018, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5023376941680908, 'llm', 2), ('ageron/handson-ml2', 0.5018590688705444, 'ml', 0), ('determined-ai/determined', 0.5017955303192139, 'ml-ops', 2), ('databrickslabs/dolly', 0.5001913905143738, 'llm', 0)]",9,1.0,,1.77,10,6,8,1,14,24,14,10.0,12.0,90.0,1.2,54
1429,ml-dl,https://github.com/cvg/lightglue,[],,[],[],,,,cvg/lightglue,LightGlue,2664,259,46,Python,,LightGlue: Local Feature Matching at Light Speed (ICCV 2023),cvg,2024-01-13,2023-06-25,31,85.15068493150685,https://avatars.githubusercontent.com/u/840224?v=4,LightGlue: Local Feature Matching at Light Speed (ICCV 2023),"['deep-learning', 'image-matching', 'pose-estimation', 'transformers']","['deep-learning', 'image-matching', 'pose-estimation', 'transformers']",2023-11-21,"[('facebookresearch/detr', 0.5226452350616455, 'ml-dl', 0)]",6,2.0,,0.5,35,7,7,2,1,2,1,35.0,65.0,90.0,1.9,54
1792,perf,https://github.com/airtai/faststream,[],,[],[],,,,airtai/faststream,faststream,1435,53,12,Python,https://faststream.airt.ai/latest/,"FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.",airtai,2024-01-13,2022-12-01,60,23.63529411764706,https://avatars.githubusercontent.com/u/84014356?v=4,"FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.","['asyncapi', 'asyncio', 'distributed-systems', 'fastkafka', 'faststream', 'kafka', 'nats', 'propan', 'rabbitmq', 'redis', 'stream-processing']","['asyncapi', 'asyncio', 'distributed-systems', 'fastkafka', 'faststream', 'kafka', 'nats', 'propan', 'rabbitmq', 'redis', 'stream-processing']",2024-01-13,"[('pathwaycom/pathway', 0.6327610611915588, 'data', 1), ('samuelcolvin/arq', 0.6273122429847717, 'data', 2), ('python-trio/trio', 0.6224059462547302, 'perf', 0), ('magicstack/uvloop', 0.6053869128227234, 'util', 1), ('agronholm/anyio', 0.5833213329315186, 'perf', 1), ('miguelgrinberg/python-socketio', 0.5774848461151123, 'util', 1), ('pallets/quart', 0.5664022564888, 'web', 1), ('bogdanp/dramatiq', 0.5551525354385376, 'util', 1), ('aio-libs/aiohttp', 0.5545108914375305, 'web', 1), ('encode/httpx', 0.5473020672798157, 'web', 1), ('alirn76/panther', 0.5416358709335327, 'web', 0), ('sumerc/yappi', 0.5390495657920837, 'profiling', 1), ('backtick-se/cowait', 0.5363417267799377, 'util', 0), ('samuelcolvin/watchfiles', 0.5354728698730469, 'util', 1), ('encode/starlette', 0.5320852398872375, 'web', 0), ('fastai/fastcore', 0.5284400582313538, 'util', 0), ('neoteroi/blacksheep', 0.5211659073829651, 'web', 1), ('geeogi/async-python-lambda-template', 0.5201819539070129, 'template', 0), ('tornadoweb/tornado', 0.5190186500549316, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5075085759162903, 'template', 0), ('fugue-project/fugue', 0.5063053369522095, 'pandas', 1), ('mher/flower', 0.5014379620552063, 'perf', 2)]",23,2.0,,12.35,309,283,14,0,37,33,37,307.0,249.0,90.0,0.8,54
1675,study,https://github.com/realpython/python-guide,[],,[],[],,,,realpython/python-guide,python-guide,27160,5988,1384,Batchfile,https://docs.python-guide.org,"Python best practices guidebook, written for humans. ",realpython,2024-01-13,2011-03-15,672,40.416666666666664,https://avatars.githubusercontent.com/u/5448020?v=4,"Python best practices guidebook, written for humans. ","['book', 'guide', 'kennethreitz']","['book', 'guide', 'kennethreitz']",2023-06-13,"[('amaargiru/pyroad', 0.6154986023902893, 'study', 0), ('wesm/pydata-book', 0.5613322854042053, 'study', 0), ('eleutherai/pyfra', 0.5539833307266235, 'ml', 0), ('brandon-rhodes/python-patterns', 0.5476312637329102, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5322774648666382, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5209663510322571, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5066047310829163, 'study', 0), ('python/cpython', 0.5037524104118347, 'util', 0), ('pytoolz/toolz', 0.5026092529296875, 'util', 0), ('ageron/handson-ml2', 0.5025880336761475, 'ml', 0)]",474,6.0,,0.0,5,1,156,7,0,0,0,5.0,5.0,90.0,1.0,53
683,ml-dl,https://github.com/matterport/mask_rcnn,[],,[],[],,,,matterport/mask_rcnn,Mask_RCNN,23803,11620,587,Python,,Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow,matterport,2024-01-14,2017-10-19,327,72.6333914559721,https://avatars.githubusercontent.com/u/4206481?v=4,Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow,"['instance-segmentation', 'keras', 'mask-rcnn', 'object-detection', 'tensorflow']","['instance-segmentation', 'keras', 'mask-rcnn', 'object-detection', 'tensorflow']",2019-03-31,"[('open-mmlab/mmdetection', 0.5989094376564026, 'ml', 3), ('roboflow/notebooks', 0.5560668706893921, 'study', 1), ('nyandwi/modernconvnets', 0.5529733300209045, 'ml-dl', 2), ('blakeblackshear/frigate', 0.5429755449295044, 'util', 2), ('facebookresearch/segment-anything', 0.5424768328666687, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5377986431121826, 'ml-dl', 1), ('facebookresearch/detectron', 0.5304756760597229, 'ml-dl', 0), ('roboflow/supervision', 0.528638482093811, 'ml', 3), ('nvlabs/gcvit', 0.5284178256988525, 'diffusion', 1), ('facebookresearch/detr', 0.5123329758644104, 'ml-dl', 0)]",47,6.0,,0.0,49,12,76,59,0,0,0,49.0,51.0,90.0,1.0,53
643,util,https://github.com/keon/algorithms,[],,[],[],,,,keon/algorithms,algorithms,23270,4629,635,Python,,Minimal examples of data structures and algorithms in Python,keon,2024-01-13,2016-11-17,375,61.93536121673004,,Minimal examples of data structures and algorithms in Python,"['algorithm', 'algorithms', 'competitive-programming', 'data-structure', 'graph', 'search', 'sort', 'tree']","['algorithm', 'algorithms', 'competitive-programming', 'data-structure', 'graph', 'search', 'sort', 'tree']",2023-04-04,"[('thealgorithms/python', 0.707886815071106, 'study', 1), ('joowani/binarytree', 0.6219033002853394, 'util', 2), ('python-odin/odin', 0.6064568161964417, 'util', 0), ('pandas-dev/pandas', 0.6043705940246582, 'pandas', 0), ('pyomo/pyomo', 0.5572477579116821, 'math', 0), ('krzjoa/awesome-python-data-science', 0.5512800812721252, 'study', 0), ('gbeced/pyalgotrade', 0.5470435619354248, 'finance', 0), ('quantopian/zipline', 0.5438115000724792, 'finance', 0), ('networkx/networkx', 0.543645977973938, 'graph', 0), ('atsushisakai/pythonrobotics', 0.5353425741195679, 'sim', 1), ('sympy/sympy', 0.5286232233047485, 'math', 0), ('pytoolz/toolz', 0.520076334476471, 'util', 0), ('quantconnect/lean', 0.512477457523346, 'finance', 1), ('tiangolo/sqlmodel', 0.5121207237243652, 'data', 0), ('ranaroussi/quantstats', 0.5114973187446594, 'finance', 0), ('dagworks-inc/hamilton', 0.5108981728553772, 'ml-ops', 0), ('plotly/dash', 0.5095182657241821, 'viz', 0), ('scikit-learn/scikit-learn', 0.5060738921165466, 'ml', 0), ('rasbt/mlxtend', 0.5041128993034363, 'ml', 0), ('python/cpython', 0.5038732290267944, 'util', 0), ('scikit-mobility/scikit-mobility', 0.5026717782020569, 'gis', 0)]",198,4.0,,0.12,10,1,87,10,0,0,0,10.0,6.0,90.0,0.6,53
105,nlp,https://github.com/rare-technologies/gensim,[],,[],[],,,,rare-technologies/gensim,gensim,14914,4381,433,Python,https://radimrehurek.com/gensim,Topic Modelling for Humans,rare-technologies,2024-01-14,2011-02-10,676,22.03884314967279,,Topic Modelling for Humans,"['data-mining', 'data-science', 'document-similarity', 'fasttext', 'gensim', 'information-retrieval', 'machine-learning', 'natural-language-processing', 'neural-network', 'nlp', 'topic-modeling', 'word-embeddings', 'word-similarity', 'word2vec']","['data-mining', 'data-science', 'document-similarity', 'fasttext', 'gensim', 'information-retrieval', 'machine-learning', 'natural-language-processing', 'neural-network', 'nlp', 'topic-modeling', 'word-embeddings', 'word-similarity', 'word2vec']",2023-10-01,"[('ddangelov/top2vec', 0.59908527135849, 'nlp', 2), ('maartengr/bertopic', 0.5905485153198242, 'nlp', 3), ('brettkromkamp/topic-db', 0.539949893951416, 'data', 0), ('ddbourgin/numpy-ml', 0.5375690460205078, 'ml', 3), ('sebischair/lbl2vec', 0.5351875424385071, 'nlp', 4), ('sloria/textblob', 0.5176156759262085, 'nlp', 2), ('milvus-io/bootcamp', 0.5120472311973572, 'data', 1)]",449,6.0,,1.42,20,3,157,4,1,6,1,20.0,25.0,90.0,1.2,53
1884,util,https://github.com/ninja-build/ninja,['build'],Ninja is a small build system with a focus on speed.,[],[],,,,ninja-build/ninja,ninja,10184,1544,264,C++,https://ninja-build.org/,a small build system with a focus on speed,ninja-build,2024-01-14,2011-02-06,677,15.036490191942628,https://avatars.githubusercontent.com/u/11653218?v=4,a small build system with a focus on speed,[],['build'],2024-01-02,"[('scikit-build/scikit-build', 0.562438428401947, 'ml', 0)]",285,4.0,,0.73,76,42,157,0,0,2,2,76.0,107.0,90.0,1.4,53
133,ml,https://github.com/epistasislab/tpot,[],,[],[],,,,epistasislab/tpot,tpot,9381,1552,290,Python,http://epistasislab.github.io/tpot/,A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.,epistasislab,2024-01-13,2015-11-03,430,21.816279069767443,https://avatars.githubusercontent.com/u/20861190?v=4,A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.,"['adsp', 'ag066833', 'aiml', 'alzheimer', 'alzheimers', 'automated-machine-learning', 'automation', 'automl', 'data-science', 'feature-engineering', 'gradient-boosting', 'hyperparameter-optimization', 'machine-learning', 'model-selection', 'nia', 'parameter-tuning', 'random-forest', 'scikit-learn', 'u01ag066833']","['adsp', 'ag066833', 'aiml', 'alzheimer', 'alzheimers', 'automated-machine-learning', 'automation', 'automl', 'data-science', 'feature-engineering', 'gradient-boosting', 'hyperparameter-optimization', 'machine-learning', 'model-selection', 'nia', 'parameter-tuning', 'random-forest', 'scikit-learn', 'u01ag066833']",2023-12-08,"[('automl/auto-sklearn', 0.6574358940124512, 'ml', 4), ('featurelabs/featuretools', 0.6507097482681274, 'ml', 6), ('microsoft/nni', 0.6485167741775513, 'ml', 6), ('google/pyglove', 0.6232022643089294, 'util', 2), ('scikit-learn/scikit-learn', 0.6184797286987305, 'ml', 2), ('mljar/mljar-supervised', 0.6145864129066467, 'ml', 8), ('google/vizier', 0.6137571930885315, 'ml', 2), ('microsoft/flaml', 0.6104899048805237, 'ml', 7), ('nccr-itmo/fedot', 0.6070546507835388, 'ml-ops', 6), ('gradio-app/gradio', 0.5918328166007996, 'viz', 2), ('rasbt/mlxtend', 0.5866554379463196, 'ml', 2), ('determined-ai/determined', 0.5670955777168274, 'ml-ops', 3), ('districtdatalabs/yellowbrick', 0.5539801120758057, 'ml', 3), ('ray-project/ray', 0.5462473034858704, 'ml-ops', 5), ('wandb/client', 0.5462185144424438, 'ml', 3), ('scikit-learn-contrib/imbalanced-learn', 0.545037567615509, 'ml', 2), ('merantix-momentum/squirrel-core', 0.5355274081230164, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5343623161315918, 'ml-interpretability', 0), ('pycaret/pycaret', 0.5342784523963928, 'ml', 2), ('awslabs/autogluon', 0.5319231748580933, 'ml', 6), ('scikit-optimize/scikit-optimize', 0.5264012217521667, 'ml', 3), ('kubeflow/fairing', 0.5261349081993103, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5252538323402405, 'viz', 1), ('ageron/handson-ml2', 0.5202245712280273, 'ml', 0), ('dagworks-inc/hamilton', 0.5198063850402832, 'ml-ops', 3), ('catboost/catboost', 0.515169084072113, 'ml', 3), ('polyaxon/polyaxon', 0.5143507122993469, 'ml-ops', 3), ('keras-team/autokeras', 0.5138368010520935, 'ml-dl', 3), ('koaning/scikit-lego', 0.5125021934509277, 'ml', 2), ('rasbt/machine-learning-book', 0.5111925601959229, 'study', 2), ('online-ml/river', 0.5093849897384644, 'ml', 2), ('karpathy/micrograd', 0.5062547922134399, 'study', 0), ('huggingface/datasets', 0.50594562292099, 'nlp', 1), ('rafiqhasan/auto-tensorflow', 0.5045480132102966, 'ml-dl', 2), ('firmai/atspy', 0.5008826851844788, 'time-series', 0)]",118,8.0,,0.4,14,6,100,1,2,4,2,14.0,16.0,90.0,1.1,53
397,web,https://github.com/falconry/falcon,[],,[],[],,,,falconry/falcon,falcon,9306,926,262,Python,https://falcon.readthedocs.io/en/stable/,"The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.",falconry,2024-01-12,2012-12-06,581,15.9975442043222,https://avatars.githubusercontent.com/u/11353642?v=4,"The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.","['api', 'api-rest', 'asgi', 'framework', 'http', 'microservices', 'rest', 'web', 'wsgi']","['api', 'api-rest', 'asgi', 'framework', 'http', 'microservices', 'rest', 'web', 'wsgi']",2023-12-26,"[('pallets/flask', 0.7083405256271362, 'web', 1), ('neoteroi/blacksheep', 0.6875892877578735, 'web', 4), ('pallets/quart', 0.6842796206474304, 'web', 1), ('starlite-api/starlite', 0.6774393916130066, 'web', 3), ('bottlepy/bottle', 0.677134096622467, 'web', 2), ('encode/uvicorn', 0.6588360667228699, 'web', 2), ('klen/muffin', 0.6537138819694519, 'web', 1), ('python-restx/flask-restx', 0.6462195515632629, 'web', 2), ('masoniteframework/masonite', 0.6416914463043213, 'web', 2), ('pallets/werkzeug', 0.630772590637207, 'web', 2), ('simple-salesforce/simple-salesforce', 0.6300815939903259, 'data', 1), ('hugapi/hug', 0.6300604343414307, 'util', 1), ('requests/toolbelt', 0.6299983263015747, 'util', 1), ('pylons/pyramid', 0.6222757697105408, 'web', 1), ('webpy/webpy', 0.6171799302101135, 'web', 0), ('encode/httpx', 0.6106772422790527, 'web', 1), ('vitalik/django-ninja', 0.6103278994560242, 'web', 0), ('tiangolo/fastapi', 0.608084499835968, 'web', 4), ('cherrypy/cherrypy', 0.6051623821258545, 'web', 1), ('reflex-dev/reflex', 0.5998751521110535, 'web', 1), ('scrapy/scrapy', 0.5992222428321838, 'data', 1), ('tiangolo/sqlmodel', 0.5907256007194519, 'data', 0), ('eleutherai/pyfra', 0.5904573202133179, 'ml', 0), ('nficano/python-lambda', 0.5897996425628662, 'util', 1), ('jordaneremieff/mangum', 0.5848598480224609, 'web', 1), ('aws/chalice', 0.5833958387374878, 'web', 0), ('alirn76/panther', 0.5779109001159668, 'web', 1), ('pyeve/eve', 0.5698334574699402, 'web', 1), ('ml-tooling/opyrator', 0.5693379044532776, 'viz', 1), ('willmcgugan/textual', 0.562833845615387, 'term', 1), ('psf/requests', 0.5613847970962524, 'web', 1), ('pylons/waitress', 0.5612348318099976, 'web', 0), ('timofurrer/awesome-asyncio', 0.5593904852867126, 'study', 0), ('backtick-se/cowait', 0.5538949370384216, 'util', 0), ('taverntesting/tavern', 0.5523936152458191, 'testing', 1), ('plotly/dash', 0.5521128177642822, 'viz', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5512214303016663, 'template', 0), ('flet-dev/flet', 0.5503961443901062, 'web', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5487000942230225, 'template', 0), ('nasdaq/data-link-python', 0.5472946763038635, 'finance', 0), ('kubeflow/fairing', 0.5438461303710938, 'ml-ops', 0), ('fastai/fastcore', 0.5430771708488464, 'util', 0), ('holoviz/panel', 0.5421604514122009, 'viz', 0), ('ethereum/web3.py', 0.540778636932373, 'crypto', 0), ('clips/pattern', 0.5375832915306091, 'nlp', 0), ('ets-labs/python-dependency-injector', 0.5372539758682251, 'util', 0), ('amzn/ion-python', 0.5367324352264404, 'data', 0), ('roniemartinez/dude', 0.5347031354904175, 'util', 1), ('huge-success/sanic', 0.5344210267066956, 'web', 3), ('benoitc/gunicorn', 0.5321671366691589, 'web', 2), ('python-odin/odin', 0.5320534706115723, 'util', 0), ('merantix-momentum/squirrel-core', 0.5313084721565247, 'ml', 0), ('ibis-project/ibis', 0.5278680324554443, 'data', 0), ('locustio/locust', 0.5264105200767517, 'testing', 1), ('pyinfra-dev/pyinfra', 0.5231207609176636, 'util', 0), ('openai/openai-python', 0.5228433609008789, 'util', 0), ('ajndkr/lanarky', 0.5218005180358887, 'llm', 1), ('dylanhogg/awesome-python', 0.5216991901397705, 'study', 0), ('simonw/datasette', 0.5216467380523682, 'data', 1), ('pytoolz/toolz', 0.5214901566505432, 'util', 0), ('lk-geimfari/mimesis', 0.5205872654914856, 'data', 0), ('geopandas/geopandas', 0.5166769027709961, 'gis', 0), ('alirezamika/autoscraper', 0.513289749622345, 'data', 0), ('aio-libs/aiohttp', 0.512401819229126, 'web', 1), ('pytables/pytables', 0.5114437937736511, 'data', 0), ('pynamodb/pynamodb', 0.5108199119567871, 'data', 0), ('sqlalchemy/sqlalchemy', 0.509283185005188, 'data', 0), ('radiantearth/radiant-mlhub', 0.5079211592674255, 'gis', 0), ('snyk-labs/pysnyk', 0.5076401829719543, 'security', 1), ('1200wd/bitcoinlib', 0.5075222849845886, 'crypto', 0), ('amaargiru/pyroad', 0.5072835087776184, 'study', 0), ('micropython/micropython', 0.507040798664093, 'util', 0), ('eventual-inc/daft', 0.5012557506561279, 'pandas', 0), ('shishirpatil/gorilla', 0.500187873840332, 'llm', 1)]",201,4.0,,0.42,43,24,135,1,6,7,6,43.0,59.0,90.0,1.4,53
293,util,https://github.com/paramiko/paramiko,[],,[],[],,,,paramiko/paramiko,paramiko,8659,2010,316,Python,http://paramiko.org,The leading native Python SSHv2 protocol library.,paramiko,2024-01-14,2009-02-02,782,11.070867579908676,https://avatars.githubusercontent.com/u/1108455?v=4,The leading native Python SSHv2 protocol library.,[],[],2023-12-18,"[('pypy/pypy', 0.6160950064659119, 'util', 0), ('pyston/pyston', 0.5798661708831787, 'util', 0), ('secdev/scapy', 0.5442431569099426, 'util', 0), ('legrandin/pycryptodome', 0.5410947203636169, 'util', 0), ('pytoolz/toolz', 0.5407304763793945, 'util', 0), ('1200wd/bitcoinlib', 0.5394938588142395, 'crypto', 0), ('ethereum/py-evm', 0.5358531475067139, 'crypto', 0), ('urwid/urwid', 0.5337045192718506, 'term', 0), ('cherrypy/cherrypy', 0.5295555591583252, 'web', 0), ('oracle/graalpython', 0.5263757109642029, 'util', 0), ('encode/httpx', 0.5251051187515259, 'web', 0), ('websocket-client/websocket-client', 0.5153641700744629, 'web', 0), ('pyca/cryptography', 0.5152232646942139, 'util', 0), ('python/cpython', 0.5128664374351501, 'util', 0), ('pyca/pynacl', 0.5029951930046082, 'util', 0), ('libtcod/python-tcod', 0.502030611038208, 'gamedev', 0), ('primal100/pybitcointools', 0.5015236139297485, 'crypto', 0)]",187,5.0,,2.58,65,15,182,1,0,12,12,65.0,111.0,90.0,1.7,53
1433,ml-dl,https://github.com/nvidia/apex,[],,[],[],,,,nvidia/apex,apex,7797,1306,102,Python,,A PyTorch Extension:  Tools for easy mixed precision and distributed training in Pytorch,nvidia,2024-01-14,2018-04-23,301,25.891366223908918,https://avatars.githubusercontent.com/u/1728152?v=4,A PyTorch Extension:  Tools for easy mixed precision and distributed training in Pytorch,[],[],2024-01-12,"[('pytorch/ignite', 0.76711106300354, 'ml-dl', 0), ('huggingface/accelerate', 0.7648141980171204, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.7110769152641296, 'perf', 0), ('skorch-dev/skorch', 0.6882312893867493, 'ml-dl', 0), ('pytorch/data', 0.6665452122688293, 'data', 0), ('laekov/fastmoe', 0.6603500247001648, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.6514618396759033, 'study', 0), ('karpathy/micrograd', 0.6441670060157776, 'study', 0), ('rasbt/machine-learning-book', 0.6338503956794739, 'study', 0), ('arogozhnikov/einops', 0.6182481646537781, 'ml-dl', 0), ('denys88/rl_games', 0.6122349500656128, 'ml-rl', 0), ('karpathy/mingpt', 0.6042015552520752, 'llm', 0), ('rentruewang/koila', 0.6033901572227478, 'ml', 0), ('nicolas-chaulet/torch-points3d', 0.5915487408638, 'ml', 0), ('horovod/horovod', 0.5866835117340088, 'ml-ops', 0), ('allenai/allennlp', 0.5706870555877686, 'nlp', 0), ('pyg-team/pytorch_geometric', 0.5672152042388916, 'ml-dl', 0), ('pytorch/botorch', 0.5640817880630493, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5612041354179382, 'ml-dl', 0), ('determined-ai/determined', 0.5580594539642334, 'ml-ops', 0), ('pytorch/captum', 0.5534337759017944, 'ml-interpretability', 0), ('ashleve/lightning-hydra-template', 0.553119957447052, 'util', 0), ('davidmrau/mixture-of-experts', 0.5525276064872742, 'ml', 0), ('pytorch/rl', 0.552091121673584, 'ml-rl', 0), ('huggingface/transformers', 0.5461992621421814, 'nlp', 0), ('kshitij12345/torchnnprofiler', 0.5381832718849182, 'profiling', 0), ('blackhc/toma', 0.5342817902565002, 'ml-dl', 0), ('pytorch-labs/gpt-fast', 0.5303529500961304, 'llm', 0), ('xl0/lovely-tensors', 0.5290429592132568, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.5290184020996094, 'ml-dl', 0), ('intellabs/bayesian-torch', 0.5274893045425415, 'ml', 0), ('thu-ml/tianshou', 0.5245383381843567, 'ml-rl', 0), ('timdettmers/bitsandbytes', 0.5227283239364624, 'util', 0), ('faster-cpython/tools', 0.5224118828773499, 'perf', 0), ('salesforce/blip', 0.5196071267127991, 'diffusion', 0), ('mcahny/deep-video-inpainting', 0.514928936958313, 'ml-dl', 0), ('huggingface/optimum', 0.5144795179367065, 'ml', 0), ('huggingface/peft', 0.5134326219558716, 'llm', 0), ('uber/petastorm', 0.5052030086517334, 'data', 0), ('dask/dask-ml', 0.5042513608932495, 'ml', 0), ('paddlepaddle/paddle', 0.5022732019424438, 'ml-dl', 0), ('hazyresearch/hgcn', 0.502190113067627, 'ml', 0)]",125,2.0,,1.65,70,35,70,0,0,1,1,70.0,87.0,90.0,1.2,53
388,data,https://github.com/yzhao062/pyod,[],,[],[],,,,yzhao062/pyod,pyod,7738,1307,148,Python,http://pyod.readthedocs.io,A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection),yzhao062,2024-01-13,2017-10-03,330,23.44848484848485,,A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection),"['anomaly', 'anomaly-detection', 'autoencoder', 'data-analysis', 'data-mining', 'data-science', 'deep-learning', 'fraud-detection', 'machine-learning', 'neural-networks', 'novelty-detection', 'out-of-distribution-detection', 'outlier-detection', 'outlier-ensembles', 'outliers', 'unsupervised-learning']","['anomaly', 'anomaly-detection', 'autoencoder', 'data-analysis', 'data-mining', 'data-science', 'deep-learning', 'fraud-detection', 'machine-learning', 'neural-networks', 'novelty-detection', 'out-of-distribution-detection', 'outlier-detection', 'outlier-ensembles', 'outliers', 'unsupervised-learning']",2023-12-16,"[('pycaret/pycaret', 0.7633078694343567, 'ml', 3), ('unit8co/darts', 0.7557379603385925, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.6631090044975281, 'time-series', 2), ('tdameritrade/stumpy', 0.6203436255455017, 'time-series', 2), ('rasbt/mlxtend', 0.6038527488708496, 'ml', 4), ('scikit-learn-contrib/imbalanced-learn', 0.589371383190155, 'ml', 3), ('salesforce/merlion', 0.5439088940620422, 'time-series', 2), ('featurelabs/featuretools', 0.5403005480766296, 'ml', 2), ('salesforce/logai', 0.5268727540969849, 'util', 2), ('mdbloice/augmentor', 0.5144423842430115, 'ml', 3), ('scikit-learn/scikit-learn', 0.5111293196678162, 'ml', 3), ('alkaline-ml/pmdarima', 0.5062800049781799, 'time-series', 1), ('jeshraghian/snntorch', 0.5056750178337097, 'ml-dl', 2)]",50,5.0,,0.83,21,9,76,1,4,6,4,21.0,25.0,90.0,1.2,53
1257,llm,https://github.com/openlm-research/open_llama,"['llama', 'language-model']",OpenLLaMA: An Open Reproduction of LLaMA,['2302.13971'],[],,,,openlm-research/open_llama,open_llama,7006,362,115,,,"OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset",openlm-research,2024-01-13,2023-04-28,39,177.04693140794222,https://avatars.githubusercontent.com/u/132110378?v=4,"OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset",[],"['language-model', 'llama']",2023-07-16,"[('microsoft/llama-2-onnx', 0.6107590794563293, 'llm', 2), ('jzhang38/tinyllama', 0.5819482207298279, 'llm', 2), ('facebookresearch/llama', 0.5816770195960999, 'llm', 2), ('togethercomputer/redpajama-data', 0.5621833801269531, 'llm', 0), ('lm-sys/fastchat', 0.5599814653396606, 'llm', 1), ('karpathy/llama2.c', 0.5405749678611755, 'llm', 2), ('cg123/mergekit', 0.537260890007019, 'llm', 1), ('facebookresearch/codellama', 0.5370882153511047, 'llm', 2), ('facebookresearch/llama-recipes', 0.536719799041748, 'llm', 2), ('yueyu1030/attrprompt', 0.5280580520629883, 'llm', 0), ('bobazooba/xllm', 0.5272755026817322, 'llm', 1), ('lightning-ai/lit-llama', 0.5196517109870911, 'llm', 2), ('bigscience-workshop/petals', 0.5177329778671265, 'data', 1), ('lupantech/chameleon-llm', 0.5159003138542175, 'llm', 1), ('mshumer/gpt-llm-trainer', 0.5152719616889954, 'llm', 0), ('eleutherai/the-pile', 0.5138996839523315, 'data', 0), ('juncongmoo/pyllama', 0.5135765671730042, 'llm', 0), ('google-research/language', 0.5121092796325684, 'nlp', 0), ('tairov/llama2.mojo', 0.5117022395133972, 'llm', 1), ('tigerlab-ai/tiger', 0.5050448179244995, 'llm', 0), ('run-llama/llama-lab', 0.5049968957901001, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5018919110298157, 'llm', 1), ('aiwaves-cn/agents', 0.5005034804344177, 'nlp', 1)]",3,3.0,,0.35,8,3,9,6,0,0,0,8.0,3.0,90.0,0.4,53
69,gamedev,https://github.com/pygame/pygame,[],,[],[],1.0,,,pygame/pygame,pygame,6667,2977,160,C,https://www.pygame.org,"🐍🎮 pygame (the library) is a Free and Open Source python programming language library for making multimedia applications like games built on top of the excellent SDL library. C, Python, Native, OpenGL.",pygame,2024-01-14,2017-03-26,357,18.66013594562175,https://avatars.githubusercontent.com/u/20628127?v=4,"🐍🎮 pygame (the library) is a Free and Open Source python programming language library for making multimedia applications like games built on top of the excellent SDL library. C, Python, Native, OpenGL.","['game-dev', 'game-development', 'gamedev', 'pygame', 'sdl', 'sdl2']","['game-dev', 'game-development', 'gamedev', 'pygame', 'sdl', 'sdl2']",2023-12-30,"[('pyglet/pyglet', 0.717469334602356, 'gamedev', 1), ('renpy/pygame_sdl2', 0.7130681872367859, 'gamedev', 2), ('lordmauve/pgzero', 0.6985493302345276, 'gamedev', 1), ('pygamelib/pygamelib', 0.6512402892112732, 'gamedev', 2), ('pythonarcade/arcade', 0.6259638071060181, 'gamedev', 0), ('panda3d/panda3d', 0.609826922416687, 'gamedev', 2), ('kitao/pyxel', 0.5883877873420715, 'gamedev', 2), ('pokepetter/ursina', 0.5800055861473083, 'gamedev', 1), ('viblo/pymunk', 0.5696349740028381, 'sim', 1), ('renpy/renpy', 0.5524816513061523, 'viz', 0), ('pypy/pypy', 0.5523542165756226, 'util', 0), ('pytoolz/toolz', 0.5287861824035645, 'util', 0), ('hoffstadt/dearpygui', 0.5225834846496582, 'gui', 0), ('urwid/urwid', 0.5163580775260925, 'term', 0), ('jquast/blessed', 0.504092812538147, 'term', 0)]",315,0.0,,9.33,125,37,83,0,11,13,11,125.0,134.0,90.0,1.1,53
74,gis,https://github.com/python-visualization/folium,[],,[],[],,,,python-visualization/folium,folium,6539,2245,167,Python,https://python-visualization.github.io/folium/,Python Data. Leaflet.js Maps. ,python-visualization,2024-01-13,2013-05-09,559,11.68274629913221,https://avatars.githubusercontent.com/u/9969242?v=4,Python Data. Leaflet.js Maps. ,"['data-science', 'data-visualization', 'javascript', 'maps']","['data-science', 'data-visualization', 'javascript', 'maps']",2024-01-02,"[('jupyter-widgets/ipyleaflet', 0.6334434151649475, 'gis', 0), ('bokeh/bokeh', 0.5900196433067322, 'viz', 1), ('plotly/dash', 0.5898042321205139, 'viz', 2), ('giswqs/mapwidget', 0.5697619915008545, 'gis', 0), ('giswqs/geemap', 0.5445337295532227, 'gis', 1), ('raphaelquast/eomaps', 0.5424359440803528, 'gis', 0), ('opengeos/leafmap', 0.5398542284965515, 'gis', 1), ('holoviz/panel', 0.5396947860717773, 'viz', 0), ('plotly/plotly.py', 0.5229291319847107, 'viz', 0), ('man-group/dtale', 0.5144950747489929, 'viz', 2)]",159,5.0,,2.0,57,41,130,0,2,2,2,57.0,114.0,90.0,2.0,53
793,web,https://github.com/pallets/werkzeug,[],,[],[],,,,pallets/werkzeug,werkzeug,6480,1729,221,Python,https://werkzeug.palletsprojects.com,The comprehensive WSGI web application library.,pallets,2024-01-13,2010-10-18,693,9.348722176422093,https://avatars.githubusercontent.com/u/16748505?v=4,The comprehensive WSGI web application library.,"['http', 'pallets', 'werkzeug', 'wsgi']","['http', 'pallets', 'werkzeug', 'wsgi']",2024-01-01,"[('pallets/flask', 0.7842201590538025, 'web', 3), ('pylons/pyramid', 0.7471210360527039, 'web', 1), ('bottlepy/bottle', 0.6876417994499207, 'web', 1), ('benoitc/gunicorn', 0.6659462451934814, 'web', 2), ('masoniteframework/masonite', 0.6412118673324585, 'web', 0), ('falconry/falcon', 0.630772590637207, 'web', 2), ('pylons/waitress', 0.6237661838531494, 'web', 0), ('pylons/webob', 0.6051927804946899, 'web', 1), ('neoteroi/blacksheep', 0.5996673703193665, 'web', 1), ('webpy/webpy', 0.5957249999046326, 'web', 0), ('cherrypy/cherrypy', 0.5869470834732056, 'web', 1), ('encode/uvicorn', 0.5832026600837708, 'web', 1), ('klen/muffin', 0.5703849196434021, 'web', 0), ('encode/httpx', 0.5651618838310242, 'web', 1), ('scrapy/scrapy', 0.5650268197059631, 'data', 0), ('reflex-dev/reflex', 0.5643238425254822, 'web', 0), ('requests/toolbelt', 0.5638414025306702, 'util', 1), ('psf/requests', 0.559667706489563, 'web', 1), ('pallets/quart', 0.5403130054473877, 'web', 0), ('python-restx/flask-restx', 0.5365567803382874, 'web', 0), ('willmcgugan/textual', 0.5303859114646912, 'term', 0), ('hugapi/hug', 0.5272501111030579, 'util', 1), ('eleutherai/pyfra', 0.523544430732727, 'ml', 0), ('emmett-framework/emmett', 0.5205287337303162, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.520187497138977, 'template', 0), ('mlc-ai/web-llm', 0.5196402668952942, 'llm', 0), ('roniemartinez/dude', 0.5181063413619995, 'util', 0), ('flet-dev/flet', 0.5143693089485168, 'web', 0), ('starlite-api/starlite', 0.5068367123603821, 'web', 0), ('aminalaee/sqladmin', 0.5044029355049133, 'data', 1), ('encode/starlette', 0.5027870535850525, 'web', 1), ('clips/pattern', 0.5018793344497681, 'nlp', 0)]",486,5.0,,4.12,47,33,161,0,12,7,12,47.0,41.0,90.0,0.9,53
1640,llm,https://github.com/nat/openplayground,"['language-model', 'local']",,[],[],,,,nat/openplayground,openplayground,5904,441,58,TypeScript,,An LLM playground you can run on your laptop,nat,2024-01-14,2023-02-26,48,122.27218934911242,,An LLM playground you can run on your laptop,[],"['language-model', 'local']",2023-06-05,"[('eugeneyan/open-llms', 0.6209505796432495, 'study', 0), ('alphasecio/langchain-examples', 0.6207661628723145, 'llm', 0), ('hwchase17/langchain', 0.607382595539093, 'llm', 1), ('young-geng/easylm', 0.593249499797821, 'llm', 1), ('thudm/chatglm2-6b', 0.5894226431846619, 'llm', 0), ('mlc-ai/web-llm', 0.589094340801239, 'llm', 1), ('nomic-ai/gpt4all', 0.5870195627212524, 'llm', 1), ('langchain-ai/langgraph', 0.5708506107330322, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5689014792442322, 'llm', 0), ('tigerlab-ai/tiger', 0.5651535987854004, 'llm', 0), ('salesforce/xgen', 0.5570892095565796, 'llm', 1), ('hiyouga/llama-factory', 0.5530926585197449, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5530924797058105, 'llm', 1), ('agenta-ai/agenta', 0.5450541973114014, 'llm', 0), ('salesforce/codet5', 0.5432443022727966, 'nlp', 1), ('microsoft/llama-2-onnx', 0.5403831005096436, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5346694588661194, 'perf', 0), ('microsoft/torchscale', 0.5345559120178223, 'llm', 0), ('conceptofmind/toolformer', 0.5319477915763855, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5300737619400024, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5199169516563416, 'study', 0), ('nvidia/nemo-guardrails', 0.5180691480636597, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5115349292755127, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5090302228927612, 'llm', 0), ('hannibal046/awesome-llm', 0.5065643787384033, 'study', 1), ('deep-diver/pingpong', 0.5059070587158203, 'llm', 0), ('prefecthq/langchain-prefect', 0.5041869282722473, 'llm', 0), ('mlc-ai/mlc-llm', 0.5039643049240112, 'llm', 1), ('cg123/mergekit', 0.5010144710540771, 'llm', 0)]",16,3.0,,0.67,11,2,11,7,0,0,0,11.0,5.0,90.0,0.5,53
182,security,https://github.com/pycqa/bandit,['code-quality'],,[],[],,,,pycqa/bandit,bandit,5722,569,66,Python,https://bandit.readthedocs.io,Bandit is a tool designed to find common security issues in Python code.,pycqa,2024-01-13,2018-04-26,300,19.028028503562947,https://avatars.githubusercontent.com/u/8749848?v=4,Bandit is a tool designed to find common security issues in Python code.,"['bandit', 'linter', 'security', 'security-scanner', 'security-tools', 'static-code-analysis']","['bandit', 'code-quality', 'linter', 'security', 'security-scanner', 'security-tools', 'static-code-analysis']",2024-01-13,"[('aswinnnn/pyscan', 0.5267046093940735, 'security', 3), ('nedbat/coveragepy', 0.5142317414283752, 'testing', 0)]",175,5.0,,0.87,48,29,70,0,2,7,2,48.0,49.0,90.0,1.0,53
1354,util,https://github.com/icloud-photos-downloader/icloud_photos_downloader,"['photos-export', 'library-photos']",,[],[],,,,icloud-photos-downloader/icloud_photos_downloader,icloud_photos_downloader,5476,506,100,Python,,A command-line tool to download photos from iCloud,icloud-photos-downloader,2024-01-14,2016-05-13,402,13.602555003548616,https://avatars.githubusercontent.com/u/73247967?v=4,A command-line tool to download photos from iCloud,[],"['library-photos', 'photos-export']",2024-01-05,[],36,2.0,,2.23,97,57,93,0,28,4,28,96.0,244.0,90.0,2.5,53
510,util,https://github.com/agronholm/apscheduler,[],,[],[],,,,agronholm/apscheduler,apscheduler,5463,698,128,Python,,Task scheduling library for Python,agronholm,2024-01-14,2016-03-27,409,13.347643979057592,,Task scheduling library for Python,[],[],2024-01-11,"[('dbader/schedule', 0.7123571634292603, 'util', 0), ('dask/dask', 0.6700900197029114, 'perf', 0), ('pyinvoke/invoke', 0.6340285539627075, 'util', 0), ('pypy/pypy', 0.6140989065170288, 'util', 0), ('pytoolz/toolz', 0.6112861037254333, 'util', 0), ('bogdanp/dramatiq', 0.6053295135498047, 'util', 0), ('dask/distributed', 0.5948215126991272, 'perf', 0), ('python/cpython', 0.5778864622116089, 'util', 0), ('joblib/loky', 0.5760906934738159, 'perf', 0), ('eleutherai/pyfra', 0.5697214603424072, 'ml', 0), ('faster-cpython/ideas', 0.5676429867744446, 'perf', 0), ('erotemic/ubelt', 0.5611792802810669, 'util', 0), ('pyston/pyston', 0.5583381652832031, 'util', 0), ('pympler/pympler', 0.5432737469673157, 'perf', 0), ('joblib/joblib', 0.5400955080986023, 'util', 0), ('micropython/micropython', 0.5366904139518738, 'util', 0), ('sumerc/yappi', 0.5361764430999756, 'profiling', 0), ('ipython/ipyparallel', 0.529596745967865, 'perf', 0), ('fastai/fastcore', 0.5230339765548706, 'util', 0), ('kubeflow/fairing', 0.5141321420669556, 'ml-ops', 0), ('requests/toolbelt', 0.5136269330978394, 'util', 0), ('samuelcolvin/arq', 0.5115315914154053, 'data', 0), ('python-trio/trio', 0.5111809968948364, 'perf', 0), ('firmai/atspy', 0.5056720972061157, 'time-series', 0), ('faster-cpython/tools', 0.5040667057037354, 'perf', 0), ('artemyk/dynpy', 0.5037860870361328, 'sim', 0), ('merantix-momentum/squirrel-core', 0.5034119486808777, 'ml', 0), ('backtick-se/cowait', 0.502835750579834, 'util', 0), ('urwid/urwid', 0.501419186592102, 'term', 0)]",44,3.0,,2.81,48,27,95,0,2,8,2,48.0,184.0,90.0,3.8,53
561,gis,https://github.com/gboeing/osmnx,[],,[],[],,,,gboeing/osmnx,osmnx,4514,805,116,Python,https://osmnx.readthedocs.io,"OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.",gboeing,2024-01-13,2016-07-24,392,11.506919155134742,,"OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.","['geography', 'geospatial', 'gis', 'mapping', 'networks', 'networkx', 'openstreetmap', 'osm', 'osmnx', 'overpass-api', 'routing', 'spatial', 'spatial-analysis', 'spatial-data', 'street-networks', 'transport', 'transportation', 'urban', 'urban-planning']","['geography', 'geospatial', 'gis', 'mapping', 'networks', 'networkx', 'openstreetmap', 'osm', 'osmnx', 'overpass-api', 'routing', 'spatial', 'spatial-analysis', 'spatial-data', 'street-networks', 'transport', 'transportation', 'urban', 'urban-planning']",2024-01-12,"[('gboeing/osmnx-examples', 0.7930247187614441, 'gis', 5), ('marceloprates/prettymaps', 0.6797459125518799, 'viz', 1), ('gboeing/street-network-models', 0.5225948691368103, 'sim', 0), ('westhealth/pyvis', 0.5170513987541199, 'graph', 1)]",83,3.0,,11.06,42,38,91,0,0,8,8,42.0,68.0,90.0,1.6,53
727,ml-dl,https://github.com/pytorch/ignite,[],,[],[],1.0,,,pytorch/ignite,ignite,4411,611,60,Python,https://pytorch-ignite.ai,High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.,pytorch,2024-01-13,2017-11-23,322,13.668437361664454,https://avatars.githubusercontent.com/u/21003710?v=4,High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.,"['deep-learning', 'machine-learning', 'metrics', 'neural-network', 'pytorch']","['deep-learning', 'machine-learning', 'metrics', 'neural-network', 'pytorch']",2024-01-11,"[('skorch-dev/skorch', 0.8268391489982605, 'ml-dl', 2), ('mrdbourke/pytorch-deep-learning', 0.7811650037765503, 'study', 3), ('intel/intel-extension-for-pytorch', 0.7741976380348206, 'perf', 4), ('nvidia/apex', 0.76711106300354, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.7287918925285339, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.7284553050994873, 'study', 3), ('karpathy/micrograd', 0.7160765528678894, 'study', 0), ('pytorch/captum', 0.6821672916412354, 'ml-interpretability', 0), ('denys88/rl_games', 0.6810092926025391, 'ml-rl', 2), ('pytorch/rl', 0.6654600501060486, 'ml-rl', 2), ('allenai/allennlp', 0.6625421047210693, 'nlp', 2), ('huggingface/accelerate', 0.6613282561302185, 'ml', 0), ('intellabs/bayesian-torch', 0.6580493450164795, 'ml', 2), ('pytorch/data', 0.655742347240448, 'data', 0), ('ashleve/lightning-hydra-template', 0.6497684717178345, 'util', 2), ('xl0/lovely-tensors', 0.6465728282928467, 'ml-dl', 2), ('arogozhnikov/einops', 0.6346755027770996, 'ml-dl', 2), ('huggingface/transformers', 0.6338894367218018, 'nlp', 3), ('lightly-ai/lightly', 0.6327634453773499, 'ml', 3), ('laekov/fastmoe', 0.6223480701446533, 'ml', 0), ('rentruewang/koila', 0.6201809644699097, 'ml', 4), ('lucidrains/imagen-pytorch', 0.6169648766517639, 'ml-dl', 1), ('nicolas-chaulet/torch-points3d', 0.6167212128639221, 'ml', 0), ('ageron/handson-ml2', 0.6152977347373962, 'ml', 0), ('hysts/pytorch_image_classification', 0.615060031414032, 'ml-dl', 1), ('facebookresearch/pytorch3d', 0.6142131686210632, 'ml-dl', 0), ('neuralmagic/sparseml', 0.6128144860267639, 'ml-dl', 1), ('thu-ml/tianshou', 0.6126653552055359, 'ml-rl', 1), ('determined-ai/determined', 0.6104621291160583, 'ml-ops', 3), ('horovod/horovod', 0.6100185513496399, 'ml-ops', 3), ('tensorlayer/tensorlayer', 0.6070582866668701, 'ml-rl', 2), ('oml-team/open-metric-learning', 0.6061093807220459, 'ml', 2), ('kshitij12345/torchnnprofiler', 0.6059595346450806, 'profiling', 0), ('ggerganov/ggml', 0.6042031645774841, 'ml', 1), ('keras-team/keras', 0.5964016318321228, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5939213633537292, 'ml-dl', 3), ('tensorflow/lucid', 0.5938147902488708, 'ml-interpretability', 1), ('nvidia/deeplearningexamples', 0.5919349193572998, 'ml-dl', 2), ('mdbloice/augmentor', 0.5899521708488464, 'ml', 2), ('pytorch/torchrec', 0.5860687494277954, 'ml-dl', 2), ('uber/petastorm', 0.5855341553688049, 'data', 3), ('nvlabs/gcvit', 0.5840114951133728, 'diffusion', 1), ('salesforce/blip', 0.5812243223190308, 'diffusion', 0), ('lutzroeder/netron', 0.5811353325843811, 'ml', 4), ('tensorflow/tensor2tensor', 0.5802233219146729, 'ml', 2), ('rasbt/deeplearning-models', 0.5799865126609802, 'ml-dl', 0), ('explosion/thinc', 0.5792794823646545, 'ml-dl', 3), ('mosaicml/composer', 0.5748881101608276, 'ml-dl', 4), ('pytorch/botorch', 0.5717622637748718, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5676509141921997, 'ml', 3), ('tlkh/tf-metal-experiments', 0.5673794746398926, 'perf', 1), ('cvxgrp/pymde', 0.5655942559242249, 'ml', 2), ('aws/sagemaker-python-sdk', 0.5633373856544495, 'ml', 2), ('pytorch/pytorch', 0.5628584623336792, 'ml-dl', 3), ('blackhc/toma', 0.5623881220817566, 'ml-dl', 2), ('microsoft/deepspeed', 0.5605365037918091, 'ml-dl', 3), ('aistream-peelout/flow-forecast', 0.5596166253089905, 'time-series', 2), ('pyro-ppl/pyro', 0.5561047196388245, 'ml-dl', 3), ('nyandwi/modernconvnets', 0.5554874539375305, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.5545974373817444, 'ml-dl', 1), ('hazyresearch/hgcn', 0.551753580570221, 'ml', 0), ('huggingface/huggingface_hub', 0.5494725108146667, 'ml', 3), ('google/tf-quant-finance', 0.5492528676986694, 'finance', 0), ('karpathy/mingpt', 0.548469066619873, 'llm', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5479029417037964, 'ml', 3), ('davidmrau/mixture-of-experts', 0.5471916198730469, 'ml', 1), ('deci-ai/super-gradients', 0.5459545850753784, 'ml-dl', 3), ('huggingface/evaluate', 0.5455392003059387, 'ml', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5449861288070679, 'study', 0), ('lucidrains/dalle2-pytorch', 0.5412147641181946, 'diffusion', 1), ('d2l-ai/d2l-en', 0.54007488489151, 'study', 3), ('tensorly/tensorly', 0.5390286445617676, 'ml-dl', 2), ('deepmind/dm-haiku', 0.5363015532493591, 'ml-dl', 2), ('graykode/nlp-tutorial', 0.5355981588363647, 'study', 1), ('speechbrain/speechbrain', 0.5351871848106384, 'nlp', 2), ('dmlc/dgl', 0.5350419878959656, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5337355136871338, 'study', 3), ('qdrant/quaterion', 0.5309311747550964, 'ml', 3), ('huggingface/optimum', 0.5307228565216064, 'ml', 1), ('huggingface/diffusers', 0.528372049331665, 'diffusion', 2), ('tensorflow/addons', 0.5261369943618774, 'ml', 3), ('ddbourgin/numpy-ml', 0.5257222652435303, 'ml', 1), ('huggingface/datasets', 0.523903489112854, 'nlp', 3), ('optimalscale/lmflow', 0.5235216617584229, 'llm', 2), ('koaning/human-learn', 0.522743284702301, 'data', 1), ('salesforce/deeptime', 0.5224276185035706, 'time-series', 1), ('onnx/onnx', 0.521939218044281, 'ml', 4), ('humancompatibleai/imitation', 0.5217827558517456, 'ml-rl', 0), ('facebookresearch/dinov2', 0.5208505988121033, 'diffusion', 0), ('google-research/deeplab2', 0.5203951597213745, 'ml', 0), ('huggingface/peft', 0.5194254517555237, 'llm', 1), ('timdettmers/bitsandbytes', 0.5188927054405212, 'util', 0), ('tensorflow/data-validation', 0.5164223313331604, 'ml-ops', 0), ('kubeflow/fairing', 0.5159615874290466, 'ml-ops', 0), ('neuralmagic/deepsparse', 0.5143234133720398, 'nlp', 0), ('rwightman/pytorch-image-models', 0.512000322341919, 'ml-dl', 1), ('tensorflow/similarity', 0.5106225609779358, 'ml-dl', 2), ('facebookresearch/theseus', 0.5099302530288696, 'math', 2), ('keras-rl/keras-rl', 0.5081517696380615, 'ml-rl', 1), ('udacity/deep-learning-v2-pytorch', 0.5055270195007324, 'study', 3), ('keras-team/autokeras', 0.5053526759147644, 'ml-dl', 2), ('ray-project/ray', 0.504410982131958, 'ml-ops', 3), ('jeshraghian/snntorch', 0.5029792189598083, 'ml-dl', 2), ('kornia/kornia', 0.5025068521499634, 'ml-dl', 4), ('calculatedcontent/weightwatcher', 0.5007023811340332, 'ml-dl', 0), ('aiqc/aiqc', 0.5004318356513977, 'ml-ops', 0)]",204,7.0,,2.77,115,105,75,0,3,3,3,115.0,53.0,90.0,0.5,53
43,data,https://github.com/lk-geimfari/mimesis,[],,[],[],,,,lk-geimfari/mimesis,mimesis,4144,321,62,Python,https://mimesis.name,Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently. ,lk-geimfari,2024-01-14,2016-09-09,385,10.747684327528715,,Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently. ,"['api-mock', 'data', 'dataframe', 'datascience', 'dummy', 'fake', 'faker', 'fixtures', 'generator', 'json', 'json-generator', 'mimesis', 'mock', 'pandas', 'polars', 'schema', 'syntetic', 'synthetic-data', 'testing']","['api-mock', 'data', 'dataframe', 'datascience', 'dummy', 'fake', 'faker', 'fixtures', 'generator', 'json', 'json-generator', 'mimesis', 'mock', 'pandas', 'polars', 'schema', 'syntetic', 'synthetic-data', 'testing']",2024-01-12,"[('joke2k/faker', 0.6268561482429504, 'data', 3), ('getsentry/responses', 0.5823182463645935, 'testing', 0), ('python-odin/odin', 0.5751269459724426, 'util', 1), ('pytoolz/toolz', 0.5705004334449768, 'util', 0), ('marshmallow-code/marshmallow', 0.5606078505516052, 'util', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5495461225509644, 'template', 0), ('snyk/faker-security', 0.5424483418464661, 'security', 0), ('fastai/fastcore', 0.5298691987991333, 'util', 0), ('pytables/pytables', 0.5296199321746826, 'data', 0), ('eleutherai/pyfra', 0.5290538668632507, 'ml', 0), ('nedbat/coveragepy', 0.5268024206161499, 'testing', 0), ('kubeflow/fairing', 0.5213274955749512, 'ml-ops', 0), ('falconry/falcon', 0.5205872654914856, 'web', 0), ('pyeve/cerberus', 0.5154099464416504, 'data', 0), ('brokenloop/jsontopydantic', 0.5148674845695496, 'util', 0), ('dagworks-inc/hamilton', 0.5131853222846985, 'ml-ops', 2), ('pypy/pypy', 0.5108677744865417, 'util', 0), ('unionai-oss/pandera', 0.509531557559967, 'pandas', 3), ('pandas-dev/pandas', 0.5089730024337769, 'pandas', 2), ('jsonpickle/jsonpickle', 0.5048171281814575, 'data', 1), ('tiangolo/sqlmodel', 0.5006476640701294, 'data', 1)]",117,4.0,,4.63,51,44,89,0,11,9,11,51.0,73.0,90.0,1.4,53
335,ml,https://github.com/apple/coremltools,[],,[],[],,,,apple/coremltools,coremltools,3860,581,116,Python,https://coremltools.readme.io,"Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.",apple,2024-01-14,2017-06-30,343,11.234927234927236,https://avatars.githubusercontent.com/u/10639145?v=4,"Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.","['coreml', 'coremltools', 'machine-learning', 'model-conversion', 'model-converter', 'pytorch', 'tensorflow']","['coreml', 'coremltools', 'machine-learning', 'model-conversion', 'model-converter', 'pytorch', 'tensorflow']",2024-01-10,"[('huggingface/exporters', 0.6746289730072021, 'ml', 6), ('microsoft/nni', 0.5904099345207214, 'ml', 3), ('huggingface/datasets', 0.5799334049224854, 'nlp', 3), ('polyaxon/polyaxon', 0.5726978778839111, 'ml-ops', 3), ('selfexplainml/piml-toolbox', 0.558883786201477, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.5530053377151489, 'ml', 1), ('keras-team/autokeras', 0.546789288520813, 'ml-dl', 2), ('deepchecks/deepchecks', 0.539066731929779, 'data', 2), ('lucidrains/toolformer-pytorch', 0.5380272269248962, 'llm', 0), ('nccr-itmo/fedot', 0.5334883332252502, 'ml-ops', 1), ('mosaicml/composer', 0.5304659605026245, 'ml-dl', 2), ('mlflow/mlflow', 0.5302109122276306, 'ml-ops', 1), ('onnx/onnx', 0.5105490684509277, 'ml', 3), ('wandb/client', 0.510352373123169, 'ml', 3), ('lutzroeder/netron', 0.5053067803382874, 'ml', 4), ('kubeflow/fairing', 0.5047186613082886, 'ml-ops', 0)]",159,3.0,,2.06,127,82,80,0,6,6,6,126.0,286.0,90.0,2.3,53
252,ml,https://github.com/microsoft/flaml,[],,[],[],,,,microsoft/flaml,FLAML,3493,488,56,Jupyter Notebook,https://microsoft.github.io/FLAML/,A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.,microsoft,2024-01-13,2020-08-20,179,19.436406995230524,https://avatars.githubusercontent.com/u/6154722?v=4,A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.,"['automated-machine-learning', 'automl', 'classification', 'data-science', 'deep-learning', 'finetuning', 'hyperparam', 'hyperparameter-optimization', 'jupyter-notebook', 'machine-learning', 'natural-language-generation', 'natural-language-processing', 'random-forest', 'regression', 'scikit-learn', 'tabular-data', 'timeseries-forecasting', 'tuning']","['automated-machine-learning', 'automl', 'classification', 'data-science', 'deep-learning', 'finetuning', 'hyperparam', 'hyperparameter-optimization', 'jupyter-notebook', 'machine-learning', 'natural-language-generation', 'natural-language-processing', 'random-forest', 'regression', 'scikit-learn', 'tabular-data', 'timeseries-forecasting', 'tuning']",2023-11-29,"[('mljar/mljar-supervised', 0.7940219044685364, 'ml', 7), ('microsoft/nni', 0.7865293025970459, 'ml', 6), ('keras-team/autokeras', 0.7519674897193909, 'ml-dl', 4), ('awslabs/autogluon', 0.7324180603027344, 'ml', 9), ('automl/auto-sklearn', 0.7281423807144165, 'ml', 4), ('shankarpandala/lazypredict', 0.6851475834846497, 'ml', 4), ('winedarksea/autots', 0.6591876745223999, 'time-series', 3), ('ray-project/tune-sklearn', 0.6497718095779419, 'ml', 2), ('nccr-itmo/fedot', 0.6372272372245789, 'ml-ops', 4), ('epistasislab/tpot', 0.6104899048805237, 'ml', 7), ('kubeflow/katib', 0.6054434776306152, 'ml', 0), ('featurelabs/featuretools', 0.6044269800186157, 'ml', 5), ('karpathy/micrograd', 0.5962915420532227, 'study', 0), ('determined-ai/determined', 0.5710462927818298, 'ml-ops', 4), ('huggingface/datasets', 0.5613746047019958, 'nlp', 3), ('alpa-projects/alpa', 0.5595440864562988, 'ml-dl', 2), ('ray-project/ray', 0.5589168667793274, 'ml-ops', 5), ('rafiqhasan/auto-tensorflow', 0.5583081841468811, 'ml-dl', 2), ('google/pyglove', 0.5550763607025146, 'util', 2), ('alkaline-ml/pmdarima', 0.5537622570991516, 'time-series', 1), ('huggingface/evaluate', 0.5472498536109924, 'ml', 1), ('ggerganov/ggml', 0.5470587611198425, 'ml', 1), ('salesforce/merlion', 0.5453396439552307, 'time-series', 2), ('google/vizier', 0.5446196794509888, 'ml', 4), ('firmai/atspy', 0.5425217747688293, 'time-series', 0), ('huggingface/autotrain-advanced', 0.5361581444740295, 'ml', 3), ('uber/petastorm', 0.5358170866966248, 'data', 2), ('rasbt/mlxtend', 0.5349463224411011, 'ml', 2), ('scikit-optimize/scikit-optimize', 0.5336490869522095, 'ml', 3), ('xplainable/xplainable', 0.5324146747589111, 'ml-interpretability', 2), ('autoviml/auto_ts', 0.5320088863372803, 'time-series', 1), ('hiyouga/llama-factory', 0.5290379524230957, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5290378928184509, 'llm', 0), ('rasbt/machine-learning-book', 0.5261116623878479, 'study', 3), ('tensorflow/tensor2tensor', 0.5255224704742432, 'ml', 2), ('neuralmagic/sparseml', 0.525197446346283, 'ml-dl', 1), ('ashleve/lightning-hydra-template', 0.5247229337692261, 'util', 1), ('koaning/human-learn', 0.5246617197990417, 'data', 2), ('explosion/thinc', 0.5239095091819763, 'ml-dl', 3), ('tensorflow/data-validation', 0.5238473415374756, 'ml-ops', 0), ('huggingface/transformers', 0.5236698985099792, 'nlp', 3), ('gradio-app/gradio', 0.5217346549034119, 'viz', 3), ('aws/sagemaker-python-sdk', 0.5215294361114502, 'ml', 1), ('wandb/client', 0.5212470293045044, 'ml', 4), ('tigerlab-ai/tiger', 0.5200607180595398, 'llm', 1), ('teamhg-memex/eli5', 0.5194395184516907, 'ml', 3), ('ourownstory/neural_prophet', 0.5170196294784546, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5168565511703491, 'ml', 2), ('patchy631/machine-learning', 0.5163537263870239, 'ml', 0), ('nixtla/statsforecast', 0.5162665843963623, 'time-series', 3), ('ml-tooling/opyrator', 0.5143886804580688, 'viz', 1), ('sktime/sktime', 0.5142588019371033, 'time-series', 3), ('optuna/optuna', 0.5139668583869934, 'ml', 2), ('catboost/catboost', 0.5135185122489929, 'ml', 2), ('oml-team/open-metric-learning', 0.5119624733924866, 'ml', 2), ('google/temporian', 0.511090874671936, 'time-series', 0), ('linkedin/greykite', 0.5093781352043152, 'ml', 0), ('ageron/handson-ml2', 0.5074008107185364, 'ml', 0), ('pycaret/pycaret', 0.5066448450088501, 'ml', 4), ('selfexplainml/piml-toolbox', 0.5052233338356018, 'ml-interpretability', 0), ('huggingface/peft', 0.5017809867858887, 'llm', 0), ('merantix-momentum/squirrel-core', 0.5003235340118408, 'ml', 4), ('polyaxon/polyaxon', 0.5002272129058838, 'ml-ops', 4)]",80,4.0,,2.98,31,13,41,2,18,20,18,31.0,50.0,90.0,1.6,53
472,nlp,https://github.com/neuralmagic/deepsparse,[],,[],[],,,,neuralmagic/deepsparse,deepsparse,2707,160,53,Python,https://neuralmagic.com/deepsparse/,Sparsity-aware deep learning inference runtime for CPUs,neuralmagic,2024-01-13,2020-12-14,163,16.592819614711033,https://avatars.githubusercontent.com/u/68670575?v=4,Sparsity-aware deep learning inference runtime for CPUs,"['computer-vision', 'cpus', 'deepsparse', 'inference', 'llm-inference', 'machinelearning', 'nlp', 'object-detection', 'onnx', 'performance', 'pretrained-models', 'pruning', 'quantization', 'sparsification']","['computer-vision', 'cpus', 'deepsparse', 'inference', 'llm-inference', 'machinelearning', 'nlp', 'object-detection', 'onnx', 'performance', 'pretrained-models', 'pruning', 'quantization', 'sparsification']",2024-01-10,"[('neuralmagic/sparseml', 0.7135436534881592, 'ml-dl', 5), ('microsoft/deepspeed', 0.643064022064209, 'ml-dl', 1), ('microsoft/onnxruntime', 0.6196989417076111, 'ml', 1), ('alpa-projects/alpa', 0.6077420711517334, 'ml-dl', 0), ('lutzroeder/netron', 0.5835353136062622, 'ml', 2), ('huggingface/datasets', 0.5752450823783875, 'nlp', 2), ('tlkh/tf-metal-experiments', 0.5730476379394531, 'perf', 0), ('mosaicml/composer', 0.5704326033592224, 'ml-dl', 0), ('squeezeailab/squeezellm', 0.5691953301429749, 'llm', 1), ('intel/intel-extension-for-pytorch', 0.5653654932975769, 'perf', 1), ('bigscience-workshop/petals', 0.5630180835723877, 'data', 2), ('keras-team/keras', 0.5612209439277649, 'ml-dl', 0), ('onnx/onnx', 0.5601222515106201, 'ml', 1), ('aiqc/aiqc', 0.556576669216156, 'ml-ops', 0), ('nvidia/deeplearningexamples', 0.554892361164093, 'ml-dl', 2), ('determined-ai/determined', 0.5497815608978271, 'ml-ops', 0), ('huggingface/optimum', 0.5494052171707153, 'ml', 3), ('vllm-project/vllm', 0.5437913537025452, 'llm', 1), ('nyandwi/modernconvnets', 0.5420705080032349, 'ml-dl', 1), ('explosion/thinc', 0.5401971936225891, 'ml-dl', 1), ('apache/incubator-mxnet', 0.53566974401474, 'ml-dl', 0), ('facebookresearch/ppuda', 0.5350483059883118, 'ml-dl', 0), ('tensorflow/tensorflow', 0.5337933301925659, 'ml-dl', 0), ('huggingface/transformers', 0.532548189163208, 'nlp', 2), ('tensorflow/tensor2tensor', 0.5306204557418823, 'ml', 0), ('rwightman/pytorch-image-models', 0.5303251147270203, 'ml-dl', 1), ('horovod/horovod', 0.5292316675186157, 'ml-ops', 1), ('roboflow/supervision', 0.5291524529457092, 'ml', 2), ('deepfakes/faceswap', 0.5284426212310791, 'ml-dl', 0), ('ddbourgin/numpy-ml', 0.5217757225036621, 'ml', 0), ('pytorch/glow', 0.514583945274353, 'ml', 0), ('pytorch/ignite', 0.5143234133720398, 'ml-dl', 0), ('calculatedcontent/weightwatcher', 0.5087005496025085, 'ml-dl', 0), ('paddlepaddle/paddle', 0.5083123445510864, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5080384612083435, 'study', 0), ('blackhc/toma', 0.5067216753959656, 'ml-dl', 0), ('cvxgrp/pymde', 0.5062499642372131, 'ml', 0), ('pytorchlightning/pytorch-lightning', 0.5051679015159607, 'ml-dl', 0), ('megvii-basedetection/yolox', 0.5051378607749939, 'ml', 2), ('polyaxon/polyaxon', 0.504664957523346, 'ml-ops', 0), ('ludwig-ai/ludwig', 0.5014819502830505, 'ml-ops', 2), ('facebookresearch/pytorch3d', 0.5011691451072693, 'ml-dl', 0), ('fepegar/torchio', 0.5005324482917786, 'ml-dl', 0)]",41,3.0,,8.13,222,202,38,0,10,12,10,222.0,98.0,90.0,0.4,53
539,data,https://github.com/sqlalchemy/alembic,[],,[],[],,,,sqlalchemy/alembic,alembic,2302,211,19,Python,,A database migrations tool for SQLAlchemy.,sqlalchemy,2024-01-13,2018-11-27,270,8.525925925925925,https://avatars.githubusercontent.com/u/6043126?v=4,A database migrations tool for SQLAlchemy.,"['sql', 'sqlalchemy']","['sql', 'sqlalchemy']",2024-01-13,"[('sqlalchemy/sqlalchemy', 0.8273295164108276, 'data', 2), ('agronholm/sqlacodegen', 0.6634976267814636, 'data', 0), ('mause/duckdb_engine', 0.6483481526374817, 'data', 2), ('tiangolo/sqlmodel', 0.6223205924034119, 'data', 2), ('aminalaee/sqladmin', 0.5552855730056763, 'data', 1), ('ibis-project/ibis', 0.5510525107383728, 'data', 2), ('aeternalis-ingenium/fastapi-backend-template', 0.5487288236618042, 'web', 1), ('mcfunley/pugsql', 0.5098458528518677, 'data', 1)]",181,5.0,,2.63,84,64,62,0,16,24,16,84.0,228.0,90.0,2.7,53
1689,util,https://github.com/pypa/setuptools,"['setuptools', 'build']",,[],[],,,,pypa/setuptools,setuptools,2224,1095,93,Python,https://pypi.org/project/setuptools/,Official project repository for the Setuptools build system,pypa,2024-01-12,2016-03-29,409,5.4376528117359415,https://avatars.githubusercontent.com/u/647025?v=4,Official project repository for the Setuptools build system,[],"['build', 'setuptools']",2024-01-11,"[('pyo3/setuptools-rust', 0.6672810912132263, 'util', 2)]",587,4.0,,15.08,145,71,95,0,28,83,28,146.0,273.0,90.0,1.9,53
1898,pandas,https://github.com/delta-io/delta-rs,"['databricks', 'rust']",,[],[],,,,delta-io/delta-rs,delta-rs,1620,305,38,Rust,https://delta-io.github.io/delta-rs/,"A native Rust library for Delta Lake, with bindings into Python",delta-io,2024-01-16,2020-04-26,196,8.253275109170305,https://avatars.githubusercontent.com/u/49767398?v=4,"A native Rust library for Delta Lake, with bindings into Python","['databricks', 'delta', 'delta-lake', 'pandas', 'pandas-dataframe', 'rust']","['databricks', 'delta', 'delta-lake', 'pandas', 'pandas-dataframe', 'rust']",2024-01-16,"[('eventual-inc/daft', 0.6028104424476624, 'pandas', 1), ('pola-rs/polars', 0.596839427947998, 'pandas', 1), ('sfu-db/connector-x', 0.5911102890968323, 'data', 1), ('pyo3/pyo3', 0.5582752227783203, 'util', 1), ('tkrabel/bamboolib', 0.5383087396621704, 'pandas', 1), ('pyo3/maturin', 0.532139241695404, 'util', 1), ('rustpython/rustpython', 0.5259521007537842, 'util', 1), ('pyo3/rust-numpy', 0.5224049687385559, 'util', 1), ('pandas-dev/pandas', 0.5217164754867554, 'pandas', 1), ('geopandas/geopandas', 0.5198134183883667, 'gis', 1), ('mito-ds/monorepo', 0.503544807434082, 'jupyter', 1), ('pytoolz/toolz', 0.5006144642829895, 'util', 0)]",128,3.0,,9.9,455,310,45,0,24,18,24,455.0,994.0,90.0,2.2,53
630,util,https://github.com/pygments/pygments,[],,[],[],,,,pygments/pygments,pygments,1487,579,33,Python,http://pygments.org/,Pygments is a generic syntax highlighter written in Python,pygments,2024-01-13,2019-08-31,230,6.453192808431494,https://avatars.githubusercontent.com/u/50935516?v=4,Pygments is a generic syntax highlighter written in Python,['syntax-highlighting'],['syntax-highlighting'],2024-01-13,"[('hhatto/autopep8', 0.600104570388794, 'util', 0), ('grantjenks/blue', 0.5901092886924744, 'util', 0), ('pypy/pypy', 0.5847700834274292, 'util', 0), ('python/cpython', 0.5650127530097961, 'util', 0), ('willmcgugan/rich', 0.5573404431343079, 'term', 1), ('google/yapf', 0.5552298426628113, 'util', 0), ('instagram/libcst', 0.5501353144645691, 'util', 0), ('pyglet/pyglet', 0.5452966094017029, 'gamedev', 0), ('pycqa/pylint-django', 0.5438166856765747, 'util', 0), ('google/latexify_py', 0.5412126183509827, 'util', 0), ('hoffstadt/dearpygui', 0.5314857959747314, 'gui', 0), ('python-markdown/markdown', 0.5284628868103027, 'util', 0), ('psf/black', 0.5274278521537781, 'util', 0), ('landscapeio/prospector', 0.5254445672035217, 'util', 0), ('pyston/pyston', 0.5150144696235657, 'util', 0), ('webpy/webpy', 0.5060795545578003, 'web', 0), ('pypi/warehouse', 0.5018252730369568, 'util', 0), ('pytoolz/toolz', 0.5010949969291687, 'util', 0), ('brandtbucher/specialist', 0.5001950263977051, 'perf', 0)]",821,5.0,,7.12,137,107,53,0,7,15,7,137.0,282.0,90.0,2.1,53
1558,ml,https://github.com/huggingface/huggingface_hub,[],,[],[],,,,huggingface/huggingface_hub,huggingface_hub,1449,354,58,Python,https://huggingface.co/docs/huggingface_hub,The official Python client for the Huggingface Hub.,huggingface,2024-01-14,2020-12-22,162,8.944444444444445,https://avatars.githubusercontent.com/u/25720743?v=4,The official Python client for the Huggingface Hub.,"['deep-learning', 'machine-learning', 'model-hub', 'models', 'natural-language-processing', 'pretrained-models', 'pytorch']","['deep-learning', 'machine-learning', 'model-hub', 'models', 'natural-language-processing', 'pretrained-models', 'pytorch']",2024-01-12,"[('skorch-dev/skorch', 0.6804894804954529, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.6623826026916504, 'ml', 2), ('huggingface/exporters', 0.6611513495445251, 'ml', 3), ('kubeflow/fairing', 0.624878466129303, 'ml-ops', 0), ('huggingface/transformers', 0.6154810190200806, 'nlp', 6), ('gradio-app/gradio', 0.6130750775337219, 'viz', 3), ('radiantearth/radiant-mlhub', 0.6118897199630737, 'gis', 1), ('rasbt/machine-learning-book', 0.6020623445510864, 'study', 3), ('huggingface/datasets', 0.5904778242111206, 'nlp', 4), ('huggingface/notebooks', 0.5788795948028564, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.5788046717643738, 'perf', 3), ('hugapi/hug', 0.5746172070503235, 'util', 0), ('skops-dev/skops', 0.5664080381393433, 'ml-ops', 1), ('dylanhogg/awesome-python', 0.5635517835617065, 'study', 3), ('merantix-momentum/squirrel-core', 0.5627287030220032, 'ml', 4), ('uber/petastorm', 0.5579898357391357, 'data', 3), ('ashleve/lightning-hydra-template', 0.5572559237480164, 'util', 2), ('openai/openai-python', 0.5553815960884094, 'util', 0), ('huggingface/deep-rl-class', 0.5541204810142517, 'study', 1), ('hoffstadt/dearpygui', 0.5539893507957458, 'gui', 0), ('ageron/handson-ml2', 0.5522385835647583, 'ml', 0), ('pytorch/ignite', 0.5494725108146667, 'ml-dl', 3), ('deepfakes/faceswap', 0.5489972233772278, 'ml-dl', 2), ('dmlc/dgl', 0.5465887784957886, 'ml-dl', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5410536527633667, 'study', 0), ('ddbourgin/numpy-ml', 0.5406401753425598, 'ml', 1), ('tensorly/tensorly', 0.5400019884109497, 'ml-dl', 2), ('allenai/allennlp', 0.5300591588020325, 'nlp', 3), ('googleapis/google-api-python-client', 0.5284603238105774, 'util', 0), ('iperov/deepfacelab', 0.5282143950462341, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5281078815460205, 'ml-rl', 1), ('fastai/fastcore', 0.5268593430519104, 'util', 0), ('mrdbourke/pytorch-deep-learning', 0.5265445113182068, 'study', 3), ('featurelabs/featuretools', 0.5263670682907104, 'ml', 1), ('ta-lib/ta-lib-python', 0.5248498916625977, 'finance', 0), ('pypy/pypy', 0.5246903896331787, 'util', 0), ('nvidia/deeplearningexamples', 0.5234879851341248, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.5223245620727539, 'ml-dl', 2), ('beeware/toga', 0.5219206213951111, 'gui', 0), ('timofurrer/awesome-asyncio', 0.5210409164428711, 'study', 0), ('alibaba/easynlp', 0.5210217237472534, 'nlp', 4), ('ggerganov/ggml', 0.5206968188285828, 'ml', 1), ('huggingface/autotrain-advanced', 0.5198477506637573, 'ml', 3), ('facebookresearch/pytorch3d', 0.5197669863700867, 'ml-dl', 0), ('selfexplainml/piml-toolbox', 0.5193759202957153, 'ml-interpretability', 0), ('weaviate/weaviate-python-client', 0.5164599418640137, 'util', 0), ('google/temporian', 0.5130770802497864, 'time-series', 0), ('ml-tooling/opyrator', 0.5117499828338623, 'viz', 1), ('eleutherai/pyfra', 0.5113564133644104, 'ml', 0), ('python/cpython', 0.5113198757171631, 'util', 0), ('nielsrogge/transformers-tutorials', 0.5096057057380676, 'study', 1), ('willmcgugan/textual', 0.5090726613998413, 'term', 0), ('lucidrains/toolformer-pytorch', 0.5079225301742554, 'llm', 1), ('nvlabs/gcvit', 0.5078686475753784, 'diffusion', 1), ('xl0/lovely-tensors', 0.5074052214622498, 'ml-dl', 2), ('pytorch/rl', 0.5057786703109741, 'ml-rl', 2), ('lightly-ai/lightly', 0.5056184530258179, 'ml', 3), ('adap/flower', 0.5031947493553162, 'ml-ops', 3), ('numpy/numpy', 0.5030168890953064, 'math', 0), ('speechbrain/speechbrain', 0.5026688575744629, 'nlp', 2), ('pycaret/pycaret', 0.5024893283843994, 'ml', 1), ('microsoft/onnxruntime', 0.5021616816520691, 'ml', 3), ('probml/pyprobml', 0.5018727779388428, 'ml', 2), ('mdbloice/augmentor', 0.5017703175544739, 'ml', 2), ('arogozhnikov/einops', 0.5012728571891785, 'ml-dl', 2), ('wandb/client', 0.5004013180732727, 'ml', 3), ('alphasecio/langchain-examples', 0.5002479553222656, 'llm', 0), ('nevronai/metisfl', 0.5000477433204651, 'ml', 2)]",127,2.0,,7.19,267,213,37,0,25,32,25,265.0,759.0,90.0,2.9,53
1724,llm,https://github.com/ray-project/ray-llm,[],,[],[],,,,ray-project/ray-llm,ray-llm,949,61,21,Python,https://aviary.anyscale.com,RayLLM - LLMs on Ray,ray-project,2024-01-13,2023-05-31,34,27.225409836065573,https://avatars.githubusercontent.com/u/22125274?v=4,RayLLM - LLMs on Ray,"['distributed-systems', 'large-language-models', 'llm', 'llm-inference', 'llm-serving', 'llmops', 'ray', 'serving', 'transformers']","['distributed-systems', 'large-language-models', 'llm', 'llm-inference', 'llm-serving', 'llmops', 'ray', 'serving', 'transformers']",2024-01-08,"[('vllm-project/vllm', 0.7471011877059937, 'llm', 3), ('bentoml/openllm', 0.6621026396751404, 'ml-ops', 4), ('predibase/lorax', 0.6269615888595581, 'llm', 5), ('artidoro/qlora', 0.6143306493759155, 'llm', 0), ('salesforce/xgen', 0.6136285662651062, 'llm', 2), ('ray-project/ray-educational-materials', 0.6102384924888611, 'study', 4), ('bigscience-workshop/petals', 0.6017612218856812, 'data', 2), ('bobazooba/xllm', 0.5996918082237244, 'llm', 2), ('sjtu-ipads/powerinfer', 0.5986047387123108, 'llm', 3), ('young-geng/easylm', 0.5914445519447327, 'llm', 1), ('eugeneyan/open-llms', 0.5834382772445679, 'study', 2), ('explosion/spacy-llm', 0.5664099454879761, 'llm', 2), ('microsoft/torchscale', 0.5647855997085571, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5598890781402588, 'llm', 2), ('ray-project/ray', 0.5598110556602478, 'ml-ops', 3), ('nomic-ai/gpt4all', 0.5573980808258057, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5559900999069214, 'study', 1), ('microsoft/jarvis', 0.5456799268722534, 'llm', 0), ('squeezeailab/squeezellm', 0.5445480942726135, 'llm', 2), ('microsoft/autogen', 0.5439698100090027, 'llm', 2), ('nebuly-ai/nebullvm', 0.5362703800201416, 'perf', 2), ('cg123/mergekit', 0.5349216461181641, 'llm', 1), ('hiyouga/llama-factory', 0.5283878445625305, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.528387725353241, 'llm', 3), ('deepset-ai/haystack', 0.5249264240264893, 'llm', 2), ('citadel-ai/langcheck', 0.524300754070282, 'llm', 0), ('deep-diver/pingpong', 0.5193211436271667, 'llm', 0), ('titanml/takeoff', 0.5190406441688538, 'llm', 1), ('thudm/chatglm2-6b', 0.5179040431976318, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5162841081619263, 'perf', 1), ('agenta-ai/agenta', 0.5125582218170166, 'llm', 3), ('next-gpt/next-gpt', 0.5125154256820679, 'llm', 2), ('lianjiatech/belle', 0.5085573792457581, 'llm', 0), ('opengvlab/omniquant', 0.5080553293228149, 'llm', 2), ('jina-ai/thinkgpt', 0.5078703165054321, 'llm', 0), ('juncongmoo/pyllama', 0.505372941493988, 'llm', 0), ('dylanhogg/llmgraph', 0.5024334192276001, 'ml', 1)]",21,5.0,,3.06,54,20,8,0,10,15,10,54.0,56.0,90.0,1.0,53
688,ml-dl,https://github.com/iperov/deepfacelab,[],,[],[],,,,iperov/deepfacelab,DeepFaceLab,44089,9977,1114,Python,,DeepFaceLab is the leading software for creating deepfakes.,iperov,2024-01-14,2018-06-04,295,149.38189738625363,,DeepFaceLab is the leading software for creating deepfakes.,"['arxiv', 'creating-deepfakes', 'deep-face-swap', 'deep-learning', 'deep-neural-networks', 'deepface', 'deepfacelab', 'deepfakes', 'deeplearning', 'face-swap', 'faceswap', 'fakeapp', 'machine-learning', 'neural-nets', 'neural-networks']","['arxiv', 'creating-deepfakes', 'deep-face-swap', 'deep-learning', 'deep-neural-networks', 'deepface', 'deepfacelab', 'deepfakes', 'deeplearning', 'face-swap', 'faceswap', 'fakeapp', 'machine-learning', 'neural-nets', 'neural-networks']",2023-04-27,"[('deepfakes/faceswap', 0.8627434968948364, 'ml-dl', 12), ('nvidia/deeplearningexamples', 0.5346398949623108, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5308938026428223, 'ml', 1), ('huggingface/huggingface_hub', 0.5282143950462341, 'ml', 2), ('rwightman/pytorch-image-models', 0.5256627798080444, 'ml-dl', 0), ('fepegar/torchio', 0.5164421200752258, 'ml-dl', 2), ('deepchecks/deepchecks', 0.515143871307373, 'data', 2), ('deepmind/deepmind-research', 0.5150367021560669, 'ml', 0), ('huggingface/datasets', 0.5133152008056641, 'nlp', 2), ('microsoft/deepspeed', 0.5126926302909851, 'ml-dl', 2), ('alpa-projects/alpa', 0.511199414730072, 'ml-dl', 2), ('awslabs/autogluon', 0.5111877918243408, 'ml', 2), ('christoschristofidis/awesome-deep-learning', 0.5062230825424194, 'study', 2), ('neuralmagic/sparseml', 0.5047088861465454, 'ml-dl', 0), ('keras-team/autokeras', 0.5044635534286499, 'ml-dl', 2)]",22,0.0,,0.02,11,2,68,9,0,0,0,11.0,5.0,90.0,0.5,52
933,llm,https://github.com/karpathy/mingpt,[],,[],[],,,,karpathy/mingpt,minGPT,17452,2101,249,Python,,A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training,karpathy,2024-01-14,2020-08-17,180,96.87866772402855,,A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training,[],[],2023-01-08,"[('ist-daslab/gptq', 0.7072771787643433, 'llm', 0), ('minimaxir/gpt-2-simple', 0.6132301688194275, 'llm', 0), ('nvidia/apex', 0.6042015552520752, 'ml-dl', 0), ('bigscience-workshop/megatron-deepspeed', 0.6039530634880066, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6039530634880066, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.6038249135017395, 'study', 0), ('huggingface/optimum', 0.5994217395782471, 'ml', 0), ('pytorch-labs/gpt-fast', 0.5887901782989502, 'llm', 0), ('nvlabs/gcvit', 0.5820482969284058, 'diffusion', 0), ('huggingface/transformers', 0.5732393860816956, 'nlp', 0), ('eleutherai/gpt-neo', 0.5706537961959839, 'llm', 0), ('eleutherai/gpt-neox', 0.5511136651039124, 'llm', 0), ('karpathy/nanogpt', 0.5497898459434509, 'llm', 0), ('pytorch/ignite', 0.548469066619873, 'ml-dl', 0), ('explosion/spacy-transformers', 0.5479704141616821, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5471770763397217, 'ml-interpretability', 0), ('huggingface/accelerate', 0.5470962524414062, 'ml', 0), ('eleutherai/knowledge-neurons', 0.5461640954017639, 'ml-interpretability', 0), ('promptslab/awesome-prompt-engineering', 0.5419448018074036, 'study', 0), ('nvidia/megatron-lm', 0.5354270935058594, 'llm', 0), ('mrdbourke/pytorch-deep-learning', 0.5222852230072021, 'study', 0), ('lucidrains/vit-pytorch', 0.5179693102836609, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5158076882362366, 'perf', 0), ('openai/image-gpt', 0.5077084302902222, 'llm', 0), ('apple/ml-ane-transformers', 0.5006250143051147, 'ml', 0)]",15,4.0,,0.0,5,1,42,12,0,0,0,5.0,4.0,90.0,0.8,52
505,ml,https://github.com/tensorflow/tensor2tensor,[],,[],[],,,,tensorflow/tensor2tensor,tensor2tensor,14478,3407,468,Python,,Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.,tensorflow,2024-01-14,2017-06-15,345,41.87851239669421,https://avatars.githubusercontent.com/u/15658638?v=4,Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.,"['deep-learning', 'machine-learning', 'machine-translation', 'reinforcement-learning', 'tpu']","['deep-learning', 'machine-learning', 'machine-translation', 'reinforcement-learning', 'tpu']",2023-04-01,"[('tensorlayer/tensorlayer', 0.6871770024299622, 'ml-rl', 2), ('huggingface/datasets', 0.6564465761184692, 'nlp', 2), ('tensorflow/tensorflow', 0.6497355103492737, 'ml-dl', 2), ('unity-technologies/ml-agents', 0.6379924416542053, 'ml-rl', 3), ('microsoft/deepspeed', 0.6303361058235168, 'ml-dl', 2), ('explosion/thinc', 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1644,util,https://github.com/dbader/schedule,['scheduler'],,[],[],1.0,,,dbader/schedule,schedule,11297,996,216,Python,https://schedule.readthedocs.io/,Python job scheduling for humans.,dbader,2024-01-13,2013-05-19,558,20.23515864892528,,Python job scheduling for humans.,[],['scheduler'],2023-12-10,"[('agronholm/apscheduler', 0.7123571634292603, 'util', 0), ('dask/dask', 0.5425211191177368, 'perf', 0), ('pyinvoke/invoke', 0.514901876449585, 'util', 0)]",59,5.0,,0.27,16,6,130,1,0,2,2,16.0,29.0,90.0,1.8,52
166,nlp,https://github.com/doccano/doccano,[],,[],[],,,,doccano/doccano,doccano,8649,1653,129,Python,https://doccano.herokuapp.com,Open source annotation tool for machine learning practitioners.,doccano,2024-01-14,2018-05-09,298,28.940248565965582,https://avatars.githubusercontent.com/u/58067660?v=4,Open source annotation tool for machine learning practitioners.,"['annotation-tool', 'data-labeling', 'dataset', 'datasets', 'machine-learning', 'natural-language-processing', 'nuxt', 'nuxtjs', 'text-annotation', 'vue', 'vuejs']","['annotation-tool', 'data-labeling', 'dataset', 'datasets', 'machine-learning', 'natural-language-processing', 'nuxt', 'nuxtjs', 'text-annotation', 'vue', 'vuejs']",2023-08-10,"[('argilla-io/argilla', 0.6546259522438049, 'nlp', 4), ('mlflow/mlflow', 0.6210007667541504, 'ml-ops', 1), ('hegelai/prompttools', 0.6014738082885742, 'llm', 1), ('rasahq/rasa', 0.5829582214355469, 'llm', 2), ('tensorflow/tensorflow', 0.5740697383880615, 'ml-dl', 1), ('microsoft/nni', 0.573647141456604, 'ml', 1), ('tigerlab-ai/tiger', 0.5629643797874451, 'llm', 0), ('wandb/client', 0.5617552399635315, 'ml', 1), ('polyaxon/polyaxon', 0.5601885914802551, 'ml-ops', 1), ('cleanlab/cleanlab', 0.5571958422660828, 'ml', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5449758768081665, 'study', 1), ('aimhubio/aim', 0.5435435771942139, 'ml-ops', 1), ('huggingface/datasets', 0.5418587923049927, 'nlp', 3), ('patchy631/machine-learning', 0.5417935848236084, 'ml', 0), ('ai4finance-foundation/fingpt', 0.5407078862190247, 'finance', 1), ('onnx/onnx', 0.5324045419692993, 'ml', 1), ('google-research/language', 0.5283976197242737, 'nlp', 2), ('polyaxon/datatile', 0.5258282423019409, 'pandas', 0), ('nltk/nltk', 0.5226835608482361, 'nlp', 2), ('determined-ai/determined', 0.5204253196716309, 'ml-ops', 1), ('districtdatalabs/yellowbrick', 0.5116096138954163, 'ml', 1), ('featurelabs/featuretools', 0.5110718607902527, 'ml', 1), ('firmai/industry-machine-learning', 0.50450199842453, 'study', 1)]",104,4.0,,1.87,49,9,69,5,1,6,1,49.0,62.0,90.0,1.3,52
28,ml-dl,https://github.com/google/trax,[],,[],[],,,,google/trax,trax,7858,818,148,Python,,Trax — Deep Learning with Clear Code and Speed,google,2024-01-14,2019-10-05,225,34.85804816223067,https://avatars.githubusercontent.com/u/1342004?v=4,Trax — Deep Learning with Clear Code and Speed,"['deep-learning', 'deep-reinforcement-learning', 'jax', 'machine-learning', 'numpy', 'reinforcement-learning', 'transformer']","['deep-learning', 'deep-reinforcement-learning', 'jax', 'machine-learning', 'numpy', 'reinforcement-learning', 'transformer']",2023-11-15,"[('keras-team/keras', 0.7093995809555054, 'ml-dl', 3), ('keras-rl/keras-rl', 0.6790956258773804, 'ml-rl', 2), ('tensorlayer/tensorlayer', 0.6657304167747498, 'ml-rl', 2), ('explosion/thinc', 0.6631956696510315, 'ml-dl', 3), ('huggingface/transformers', 0.659750759601593, 'nlp', 4), ('deepmind/dm-haiku', 0.6491378545761108, 'ml-dl', 3), ('denys88/rl_games', 0.6481664776802063, 'ml-rl', 2), ('ddbourgin/numpy-ml', 0.6432744264602661, 'ml', 2), ('salesforce/warp-drive', 0.640018880367279, 'ml-rl', 2), ('deepmind/dm_control', 0.6313595771789551, 'ml-rl', 3), ('thu-ml/tianshou', 0.625861406326294, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.615790605545044, 'ml', 3), ('tensorflow/tensorflow', 0.6033942103385925, 'ml-dl', 2), ('unity-technologies/ml-agents', 0.5988940596580505, 'ml-rl', 4), ('pytorch/rl', 0.5977087020874023, 'ml-rl', 2), ('microsoft/deepspeed', 0.5964576601982117, 'ml-dl', 2), ('alpa-projects/alpa', 0.5918993949890137, 'ml-dl', 3), ('d2l-ai/d2l-en', 0.587719202041626, 'study', 4), ('apache/incubator-mxnet', 0.583592414855957, 'ml-dl', 0), ('mosaicml/composer', 0.5817326307296753, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5805593132972717, 'ml-dl', 1), ('onnx/onnx', 0.5766066908836365, 'ml', 2), ('kzl/decision-transformer', 0.5694336891174316, 'ml-rl', 0), ('ray-project/ray', 0.568706214427948, 'ml-ops', 3), ('ai4finance-foundation/finrl', 0.5627601146697998, 'finance', 2), ('gradio-app/gradio', 0.5625860095024109, 'viz', 2), ('microsoft/onnxruntime', 0.5622458457946777, 'ml', 2), ('aiqc/aiqc', 0.555395245552063, 'ml-ops', 0), ('pytorchlightning/pytorch-lightning', 0.550957202911377, 'ml-dl', 2), ('determined-ai/determined', 0.5475213527679443, 'ml-ops', 2), ('huggingface/optimum', 0.5470688343048096, 'ml', 0), ('ml-tooling/opyrator', 0.5450164079666138, 'viz', 1), ('pyro-ppl/pyro', 0.539913535118103, 'ml-dl', 2), ('huggingface/datasets', 0.5373858213424683, 'nlp', 3), ('arogozhnikov/einops', 0.5327727198600769, 'ml-dl', 3), ('openai/baselines', 0.5311444401741028, 'ml-rl', 0), ('karpathy/micrograd', 0.5311139822006226, 'study', 0), ('keras-team/autokeras', 0.5302404761314392, 'ml-dl', 2), ('deepmodeling/deepmd-kit', 0.5293905735015869, 'sim', 1), ('google/flax', 0.5265333652496338, 'ml-dl', 1), ('microsoft/nni', 0.5194593071937561, 'ml', 2), ('deepmind/pysc2', 0.5190588235855103, 'ml-rl', 2), ('thilinarajapakse/simpletransformers', 0.5190243721008301, 'nlp', 0), ('bigscience-workshop/petals', 0.5161072611808777, 'data', 3), ('ludwig-ai/ludwig', 0.5144282579421997, 'ml-ops', 2), ('koaning/human-learn', 0.514390230178833, 'data', 1), ('tlkh/tf-metal-experiments', 0.5128339529037476, 'perf', 1), ('bentoml/bentoml', 0.5128212571144104, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.512615442276001, 'study', 2), ('modularml/mojo', 0.5107121467590332, 'util', 1), ('uber/petastorm', 0.5106483101844788, 'data', 2), ('awslabs/autogluon', 0.5100558996200562, 'ml', 2), ('google/dopamine', 0.5098437666893005, 'ml-rl', 0), ('online-ml/river', 0.5071595311164856, 'ml', 1), ('pytorch/pytorch', 0.5070921778678894, 'ml-dl', 3), ('inspirai/timechamber', 0.5059916377067566, 'sim', 2), ('huggingface/autotrain-advanced', 0.5045042037963867, 'ml', 2), ('facebookresearch/habitat-lab', 0.5037897825241089, 'sim', 3), ('young-geng/easylm', 0.5037261843681335, 'llm', 3), ('wandb/client', 0.502128541469574, 'ml', 4), ('rwightman/pytorch-image-models', 0.5016557574272156, 'ml-dl', 0), ('xplainable/xplainable', 0.5001837015151978, 'ml-interpretability', 1)]",79,5.0,,0.1,8,3,52,2,0,4,4,8.0,4.0,90.0,0.5,52
646,profiling,https://github.com/joerick/pyinstrument,[],,[],[],,,,joerick/pyinstrument,pyinstrument,5802,235,53,Python,https://pyinstrument.readthedocs.io/,🚴 Call stack profiler for Python. Shows you why your code is slow!,joerick,2024-01-13,2014-03-13,515,11.250415512465374,,🚴 Call stack profiler for Python. Shows you why your code is slow!,"['async', 'django', 'performance', 'profile', 'profiler']","['async', 'django', 'performance', 'profile', 'profiler']",2024-01-06,"[('sumerc/yappi', 0.6088473200798035, 'profiling', 2), ('benfred/py-spy', 0.5834751129150391, 'profiling', 1), ('jiffyclub/snakeviz', 0.5636839866638184, 'profiling', 0), ('pythonspeed/filprofiler', 0.5218971967697144, 'profiling', 0), ('pyutils/line_profiler', 0.5132960677146912, 'profiling', 0)]",55,7.0,,1.98,18,10,120,0,5,6,5,18.0,34.0,90.0,1.9,52
1167,study,https://github.com/gkamradt/langchain-tutorials,[],,[],[],,,,gkamradt/langchain-tutorials,langchain-tutorials,5691,1717,97,Jupyter Notebook,,Overview and tutorial of the LangChain Library,gkamradt,2024-01-14,2023-02-13,50,113.4957264957265,,Overview and tutorial of the LangChain Library,[],[],2023-11-23,"[('prefecthq/langchain-prefect', 0.7797976732254028, 'llm', 0), ('langchain-ai/langgraph', 0.6401094794273376, 'llm', 0), ('logspace-ai/langflow', 0.5531355142593384, 'llm', 0), ('alphasecio/langchain-examples', 0.5529564023017883, 'llm', 0), ('langchain-ai/chat-langchain', 0.5390220284461975, 'llm', 0), ('langchain-ai/langsmith-sdk', 0.5348765254020691, 'llm', 0), ('hannibal046/awesome-llm', 0.5057912468910217, 'study', 0)]",17,4.0,,1.75,1,0,11,2,0,0,0,1.0,0.0,90.0,0.0,52
15,ml-dl,https://github.com/skorch-dev/skorch,[],,[],[],,,,skorch-dev/skorch,skorch,5518,379,82,Jupyter Notebook,,A scikit-learn compatible neural network library that wraps PyTorch,skorch-dev,2024-01-13,2017-07-18,341,16.181818181818183,https://avatars.githubusercontent.com/u/47992320?v=4,A scikit-learn compatible neural network library that wraps PyTorch,"['huggingface', 'machine-learning', 'pytorch', 'scikit-learn']","['huggingface', 'machine-learning', 'pytorch', 'scikit-learn']",2024-01-08,"[('pytorch/ignite', 0.8268391489982605, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.777802050113678, 'study', 3), ('intel/intel-extension-for-pytorch', 0.7512941360473633, 'perf', 2), ('mrdbourke/pytorch-deep-learning', 0.6955669522285461, 'study', 2), ('nvidia/apex', 0.6882312893867493, 'ml-dl', 0), ('huggingface/huggingface_hub', 0.6804894804954529, 'ml', 2), ('pyg-team/pytorch_geometric', 0.6679598093032837, 'ml-dl', 1), ('karpathy/micrograd', 0.644400417804718, 'study', 0), 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'ml-dl', 1), ('kubeflow/fairing', 0.501205325126648, 'ml-ops', 0), ('dmlc/dgl', 0.5011494755744934, 'ml-dl', 0)]",61,5.0,,0.96,18,13,79,0,3,3,3,18.0,29.0,90.0,1.6,52
823,typing,https://github.com/python-attrs/attrs,[],,[],[],1.0,,,python-attrs/attrs,attrs,4977,388,65,Python,https://www.attrs.org/,Python Classes Without Boilerplate,python-attrs,2024-01-13,2015-01-27,470,10.58936170212766,https://avatars.githubusercontent.com/u/25880274?v=4,Python Classes Without Boilerplate,"['attributes', 'boilerplate', 'classes', 'oop']","['attributes', 'boilerplate', 'classes', 'oop']",2024-01-13,"[('martinheinz/python-project-blueprint', 0.521111011505127, 'template', 1), ('landscapeio/prospector', 0.5136226415634155, 'util', 0), ('xrudelis/pytrait', 0.5021693110466003, 'util', 0)]",154,3.0,,3.29,49,36,109,0,2,3,2,49.0,124.0,90.0,2.5,52
213,data,https://github.com/facebookresearch/augly,[],,[],[],,,,facebookresearch/augly,AugLy,4853,295,67,Python,https://ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models/,"A data augmentations library for audio, image, text, and video.",facebookresearch,2024-01-12,2021-06-09,137,35.20310880829015,https://avatars.githubusercontent.com/u/16943930?v=4,"A data augmentations library for audio, image, text, and video.",[],[],2023-11-08,"[('albumentations-team/albumentations', 0.6632611751556396, 'ml-dl', 0), ('mdbloice/augmentor', 0.6478663086891174, 'ml', 0), ('aleju/imgaug', 0.5716978311538696, 'ml', 0), ('nomic-ai/nomic', 0.528243899345398, 'nlp', 0), ('researchmm/sttn', 0.5215305089950562, 'ml-dl', 0)]",34,3.0,,0.21,6,2,32,2,0,3,3,6.0,14.0,90.0,2.3,52
92,ml,https://github.com/uber/causalml,[],,[],[],,,,uber/causalml,causalml,4514,753,80,Python,,Uplift modeling and causal inference with machine learning algorithms,uber,2024-01-13,2019-07-09,238,18.96638655462185,https://avatars.githubusercontent.com/u/538264?v=4,Uplift modeling and causal inference with machine learning algorithms,"['causal-inference', 'incubation', 'machine-learning', 'uplift-modeling']","['causal-inference', 'incubation', 'machine-learning', 'uplift-modeling']",2024-01-12,"[('py-why/econml', 0.5542822480201721, 'ml', 2)]",59,4.0,,1.6,90,74,55,0,2,3,2,90.0,90.0,90.0,1.0,52
199,viz,https://github.com/man-group/dtale,[],,[],[],,,,man-group/dtale,dtale,4398,371,73,TypeScript,http://alphatechadmin.pythonanywhere.com,Visualizer for pandas data structures,man-group,2024-01-14,2019-07-15,237,18.545783132530122,https://avatars.githubusercontent.com/u/5859004?v=4,Visualizer for pandas data structures,"['data-analysis', 'data-science', 'data-visualization', 'flask', 'ipython', 'jupyter-notebook', 'pandas', 'plotly-dash', 'python27', 'react', 'react-virtualized', 'visualization', 'xarray']","['data-analysis', 'data-science', 'data-visualization', 'flask', 'ipython', 'jupyter-notebook', 'pandas', 'plotly-dash', 'python27', 'react', 'react-virtualized', 'visualization', 'xarray']",2024-01-05,"[('mwaskom/seaborn', 0.73142409324646, 'viz', 3), ('holoviz/panel', 0.7240487337112427, 'viz', 0), ('kanaries/pygwalker', 0.7181293368339539, 'pandas', 3), ('lux-org/lux', 0.7073760032653809, 'viz', 3), ('holoviz/holoviz', 0.7002979516983032, 'viz', 0), ('bokeh/bokeh', 0.6867046356201172, 'viz', 1), ('plotly/plotly.py', 0.6799153089523315, 'viz', 3), ('plotly/dash', 0.6759928464889526, 'viz', 5), ('residentmario/geoplot', 0.6714649796485901, 'gis', 0), ('holoviz/hvplot', 0.6696478724479675, 'pandas', 0), ('pandas-dev/pandas', 0.6628751754760742, 'pandas', 3), ('altair-viz/altair', 0.6516591310501099, 'viz', 1), ('jakevdp/pythondatasciencehandbook', 0.6342079639434814, 'study', 2), ('pyqtgraph/pyqtgraph', 0.6243569850921631, 'viz', 1), ('tkrabel/bamboolib', 0.6187593936920166, 'pandas', 2), ('enthought/mayavi', 0.6186628937721252, 'viz', 1), ('adamerose/pandasgui', 0.6185036897659302, 'pandas', 1), ('vizzuhq/ipyvizzu', 0.6090724468231201, 'jupyter', 3), ('vaexio/vaex', 0.6020064353942871, 'perf', 2), ('wesm/pydata-book', 0.5984211564064026, 'study', 0), ('polyaxon/datatile', 0.5954803824424744, 'pandas', 3), ('scitools/iris', 0.5857634544372559, 'gis', 1), ('mckinsey/vizro', 0.584793746471405, 'viz', 3), ('graphistry/pygraphistry', 0.5765081644058228, 'data', 2), ('has2k1/plotnine', 0.5764876008033752, 'viz', 1), ('federicoceratto/dashing', 0.5763976573944092, 'term', 0), ('matplotlib/matplotlib', 0.5757876634597778, 'viz', 2), ('rapidsai/cudf', 0.5755491256713867, 'pandas', 3), ('maartenbreddels/ipyvolume', 0.5735721588134766, 'jupyter', 1), ('krzjoa/awesome-python-data-science', 0.5704981088638306, 'study', 3), ('contextlab/hypertools', 0.5685189366340637, 'ml', 2), ('quantopian/qgrid', 0.560211718082428, 'jupyter', 0), ('lutzroeder/netron', 0.5549408793449402, 'ml', 0), ('hazyresearch/meerkat', 0.5547817945480347, 'viz', 2), ('ranaroussi/quantstats', 0.55287766456604, 'finance', 1), ('cuemacro/chartpy', 0.5520331263542175, 'viz', 0), ('mito-ds/monorepo', 0.5518519282341003, 'jupyter', 4), ('opengeos/leafmap', 0.5516546368598938, 'gis', 2), ('gregorhd/mapcompare', 0.5462871789932251, 'gis', 0), ('holoviz/datashader', 0.5459538698196411, 'gis', 0), ('dylanhogg/awesome-python', 0.5450599193572998, 'study', 2), ('ydataai/ydata-profiling', 0.5416145324707031, 'pandas', 4), ('scikit-hep/awkward-1.0', 0.5412405729293823, 'data', 2), ('datapane/datapane', 0.5396667718887329, 'viz', 1), ('giswqs/geemap', 0.5396121144294739, 'gis', 2), ('pyvista/pyvista', 0.5372913479804993, 'viz', 1), ('vispy/vispy', 0.5336646437644958, 'viz', 1), ('matplotlib/mplfinance', 0.5290730595588684, 'finance', 0), ('dagworks-inc/hamilton', 0.525833010673523, 'ml-ops', 3), ('python-odin/odin', 0.5247855186462402, 'util', 0), ('holoviz/spatialpandas', 0.5217031240463257, 'pandas', 1), ('holoviz/geoviews', 0.5216120481491089, 'gis', 0), ('geopandas/geopandas', 0.5202478766441345, 'gis', 1), ('pola-rs/polars', 0.5197016596794128, 'pandas', 0), ('scitools/cartopy', 0.519442617893219, 'gis', 0), ('raphaelquast/eomaps', 0.5189169049263, 'gis', 1), ('districtdatalabs/yellowbrick', 0.5179511308670044, 'ml', 1), ('saulpw/visidata', 0.5157226324081421, 'term', 1), ('python-visualization/folium', 0.5144950747489929, 'gis', 2), ('eleutherai/pyfra', 0.5136914253234863, 'ml', 0), ('twopirllc/pandas-ta', 0.5126270651817322, 'finance', 2), ('unionai-oss/pandera', 0.5104021430015564, 'pandas', 1), ('marcomusy/vedo', 0.5097540020942688, 'viz', 1), ('visgl/deck.gl', 0.5085725784301758, 'viz', 2), ('hi-primus/optimus', 0.5049717426300049, 'ml-ops', 2), ('tokern/data-lineage', 0.5029366612434387, 'data', 0), ('imageio/imageio', 0.5025433897972107, 'util', 0), ('koaning/drawdata', 0.5000766515731812, 'jupyter', 0)]",30,2.0,,2.42,30,18,55,0,30,37,30,30.0,42.0,90.0,1.4,52
1779,viz,https://github.com/renpy/renpy,[],,[],[],,,,renpy/renpy,renpy,4311,648,144,Ren'Py,http://www.renpy.org/,The Ren'Py Visual Novel Engine,renpy,2024-01-14,2012-06-28,604,7.128986534372785,https://avatars.githubusercontent.com/u/1900740?v=4,The Ren'Py Visual Novel Engine,"['engine', 'game', 'novel', 'renpy', 'visual', 'visual-novel']","['engine', 'game', 'novel', 'renpy', 'visual', 'visual-novel']",2024-01-14,"[('pokepetter/ursina', 0.6157830357551575, 'gamedev', 0), ('kitao/pyxel', 0.5945414304733276, 'gamedev', 1), ('pygame/pygame', 0.5524816513061523, 'gamedev', 0), ('panda3d/panda3d', 0.5438269972801208, 'gamedev', 0), ('pyscript/pyscript-cli', 0.5380860567092896, 'web', 0), ('fastai/fastcore', 0.5279468894004822, 'util', 0), ('python/cpython', 0.5222944617271423, 'util', 0), ('hoffstadt/dearpygui', 0.5120179057121277, 'gui', 0), ('mynameisfiber/high_performance_python_2e', 0.5067214369773865, 'study', 0), ('zulko/moviepy', 0.5059114098548889, 'util', 0), ('gradio-app/gradio', 0.5017328858375549, 'viz', 0), ('amaargiru/pyroad', 0.500852644443512, 'study', 0)]",194,1.0,,31.38,346,293,141,0,8,47,8,345.0,606.0,90.0,1.8,52
356,data,https://github.com/amundsen-io/amundsen,[],,[],[],,,,amundsen-io/amundsen,amundsen,4179,947,237,Python,https://www.amundsen.io/amundsen/,"Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.",amundsen-io,2024-01-13,2019-05-14,246,16.98780487804878,https://avatars.githubusercontent.com/u/67136999?v=4,"Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.","['amundsen', 'data-catalog', 'data-discovery', 'linuxfoundation', 'metadata']","['amundsen', 'data-catalog', 'data-discovery', 'linuxfoundation', 'metadata']",2024-01-11,[],222,2.0,,1.42,36,21,57,0,7,28,7,36.0,42.0,90.0,1.2,52
755,sim,https://github.com/quantumlib/cirq,[],,[],[],,,,quantumlib/cirq,Cirq,4027,948,192,Python,,"A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.",quantumlib,2024-01-14,2017-12-14,319,12.59562109025916,https://avatars.githubusercontent.com/u/31279789?v=4,"A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.","['cirq', 'nisq', 'quantum-algorithms', 'quantum-circuits', 'quantum-computing']","['cirq', 'nisq', 'quantum-algorithms', 'quantum-circuits', 'quantum-computing']",2024-01-13,"[('cqcl/lambeq', 0.6558890342712402, 'nlp', 0), ('pyscf/pyscf', 0.6541039347648621, 'sim', 0), ('cqcl/tket', 0.6193458437919617, 'util', 1), ('jackhidary/quantumcomputingbook', 0.5691633224487305, 'study', 2), ('qiskit/qiskit', 0.5546154975891113, 'sim', 1), ('netket/netket', 0.5286350250244141, 'sim', 0), ('zeromq/pyzmq', 0.5248025059700012, 'util', 0)]",213,2.0,,4.87,166,98,74,0,2,4,2,166.0,243.0,90.0,1.5,52
802,web,https://github.com/fastapi-users/fastapi-users,[],,[],[],,,,fastapi-users/fastapi-users,fastapi-users,3772,341,38,Python,https://fastapi-users.github.io/fastapi-users/,Ready-to-use and customizable users management for FastAPI,fastapi-users,2024-01-14,2019-10-05,225,16.732572877059567,https://avatars.githubusercontent.com/u/89578248?v=4,Ready-to-use and customizable users management for FastAPI,"['async', 'asyncio', 'fastapi', 'fastapi-users', 'starlette', 'users']","['async', 'asyncio', 'fastapi', 'fastapi-users', 'starlette', 'users']",2023-12-28,"[('dmontagu/fastapi_client', 0.6202594637870789, 'web', 0), ('zhanymkanov/fastapi-best-practices', 0.6014936566352844, 'study', 1), ('tiangolo/fastapi', 0.599251925945282, 'web', 4), ('s3rius/fastapi-template', 0.595072329044342, 'web', 2), ('fastapi-admin/fastapi-admin', 0.5555592775344849, 'web', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5379810333251953, 'template', 1), ('aminalaee/sqladmin', 0.5336757302284241, 'data', 3), ('awtkns/fastapi-crudrouter', 0.5089220404624939, 'web', 3), ('starlite-api/starlite', 0.5006967186927795, 'web', 1)]",62,4.0,,1.1,20,15,52,1,9,23,9,20.0,35.0,90.0,1.8,52
171,ml,https://github.com/ourownstory/neural_prophet,[],,[],[],,,,ourownstory/neural_prophet,neural_prophet,3494,453,53,Python,https://neuralprophet.com,NeuralProphet: A simple forecasting package,ourownstory,2024-01-12,2020-05-04,195,17.904831625183014,,NeuralProphet: A simple forecasting package,"['artificial-intelligence', 'autoregression', 'deep-learning', 'fbprophet', 'forecast', 'forecasting', 'forecasting-algorithm', 'forecasting-model', 'machine-learning', 'neural', 'neural-network', 'neuralprophet', 'prediction', 'prophet', 'pytorch', 'seasonality', 'time-series', 'timeseries', 'trend']","['artificial-intelligence', 'autoregression', 'deep-learning', 'fbprophet', 'forecast', 'forecasting', 'forecasting-algorithm', 'forecasting-model', 'machine-learning', 'neural', 'neural-network', 'neuralprophet', 'prediction', 'prophet', 'pytorch', 'seasonality', 'time-series', 'timeseries', 'trend']",2023-12-23,"[('winedarksea/autots', 0.6846452355384827, 'time-series', 4), ('nixtla/statsforecast', 0.6677179336547852, 'time-series', 6), ('awslabs/autogluon', 0.6234149932861328, 'ml', 5), ('salesforce/deeptime', 0.5901058316230774, 'time-series', 3), ('aistream-peelout/flow-forecast', 0.5880416035652161, 'time-series', 4), ('microprediction/microprediction', 0.5824498534202576, 'time-series', 3), ('uber/orbit', 0.5688930153846741, 'time-series', 5), ('awslabs/gluonts', 0.5649511814117432, 'time-series', 7), ('firmai/atspy', 0.5586233735084534, 'time-series', 2), ('microsoft/nni', 0.5511186718940735, 'ml', 4), ('alkaline-ml/pmdarima', 0.5404297709465027, 'time-series', 3), ('nccr-itmo/fedot', 0.5363118648529053, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5321094989776611, 'ml-dl', 3), ('mosaicml/composer', 0.5314452052116394, 'ml-dl', 4), ('sktime/sktime', 0.529460608959198, 'time-series', 3), ('ddbourgin/numpy-ml', 0.5267665982246399, 'ml', 1), ('activeloopai/deeplake', 0.524020791053772, 'ml-ops', 3), ('salesforce/merlion', 0.5225098133087158, 'time-series', 3), ('mindsdb/mindsdb', 0.5187014937400818, 'data', 4), ('microsoft/flaml', 0.5170196294784546, 'ml', 2), ('xplainable/xplainable', 0.5147477984428406, 'ml-interpretability', 2), ('opengeos/earthformer', 0.5128765106201172, 'gis', 2), ('keras-team/autokeras', 0.5104817748069763, 'ml-dl', 2), ('huggingface/transformers', 0.5077774524688721, 'nlp', 3), ('automl/auto-sklearn', 0.5033456683158875, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.503267228603363, 'study', 2), ('explosion/thinc', 0.501133382320404, 'ml-dl', 4)]",50,2.0,,3.35,65,48,45,1,11,8,11,65.0,107.0,90.0,1.6,52
1635,util,https://github.com/osohq/oso,['authorization'],,[],[],,,,osohq/oso,oso,3335,169,31,Rust,https://docs.osohq.com,Oso is a batteries-included framework for building authorization in your application.,osohq,2024-01-14,2020-05-04,195,17.0900439238653,https://avatars.githubusercontent.com/u/47367300?v=4,Oso is a batteries-included framework for building authorization in your application.,"['abac', 'access-control', 'authorization', 'authorization-framework', 'go', 'java', 'logic-programming', 'nodejs', 'policy-engine', 'rbac', 'rbac-authorization', 'rbac-roles', 'ruby', 'rust', 'security']","['abac', 'access-control', 'authorization', 'authorization-framework', 'go', 'java', 'logic-programming', 'nodejs', 'policy-engine', 'rbac', 'rbac-authorization', 'rbac-roles', 'ruby', 'rust', 'security']",2024-01-13,[],66,5.0,,0.81,17,8,45,0,7,54,7,17.0,27.0,90.0,1.6,52
267,jupyter,https://github.com/jupyterlab/jupyterlab-desktop,[],,[],[],,,,jupyterlab/jupyterlab-desktop,jupyterlab-desktop,3199,297,52,TypeScript,,"JupyterLab desktop application, based on Electron.",jupyterlab,2024-01-12,2017-05-04,351,9.095450852965069,https://avatars.githubusercontent.com/u/22800682?v=4,"JupyterLab desktop application, based on Electron.","['jupyter', 'jupyter-notebook', 'jupyterlab']","['jupyter', 'jupyter-notebook', 'jupyterlab']",2024-01-05,"[('jupyterlab/jupyterlab', 0.7525447607040405, 'jupyter', 2), ('voila-dashboards/voila', 0.7262636423110962, 'jupyter', 2), ('jupyter/notebook', 0.7161470651626587, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.7082852721214294, 'jupyter', 0), ('jupyter/nbformat', 0.6620615720748901, 'jupyter', 0), ('mwouts/jupytext', 0.6525211930274963, 'jupyter', 2), ('aws/graph-notebook', 0.6357448697090149, 'jupyter', 2), ('maartenbreddels/ipyvolume', 0.6347945928573608, 'jupyter', 2), ('jupyter/nbconvert', 0.6312793493270874, 'jupyter', 0), ('jupyterlite/jupyterlite', 0.6264117956161499, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.6113208532333374, 'jupyter', 2), ('ipython/ipykernel', 0.6094779968261719, 'util', 2), ('cohere-ai/notebooks', 0.5881903767585754, 'llm', 0), ('ipython/ipyparallel', 0.5839833617210388, 'perf', 1), ('jupyter-widgets/ipyleaflet', 0.5790235996246338, 'gis', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5716159343719482, 'jupyter', 3), ('computationalmodelling/nbval', 0.5705468654632568, 'jupyter', 1), ('bloomberg/ipydatagrid', 0.5657516717910767, 'jupyter', 0), ('quantopian/qgrid', 0.5566320419311523, 'jupyter', 0), ('mamba-org/gator', 0.5549662709236145, 'jupyter', 1), ('jupyter/nbdime', 0.552423357963562, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.5509034395217896, 'study', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5508671402931213, 'study', 0), ('tkrabel/bamboolib', 0.5493032336235046, 'pandas', 2), ('xiaohk/stickyland', 0.5476192235946655, 'jupyter', 2), ('holoviz/panel', 0.5330305695533752, 'viz', 1), ('nteract/testbook', 0.5236980319023132, 'jupyter', 1), ('r0x0r/pywebview', 0.520444393157959, 'gui', 0), ('giswqs/mapwidget', 0.5143319964408875, 'gis', 1), ('ageron/handson-ml2', 0.510633647441864, 'ml', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5053638815879822, 'jupyter', 0), ('jupyter/nbviewer', 0.5004984736442566, 'jupyter', 2)]",39,5.0,,4.31,52,37,82,0,11,5,11,52.0,113.0,90.0,2.2,52
807,data,https://github.com/deepchecks/deepchecks,[],,[],[],,,,deepchecks/deepchecks,deepchecks,3169,229,16,Python,https://docs.deepchecks.com/stable,"Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.",deepchecks,2024-01-13,2021-10-11,120,26.3769322235434,https://avatars.githubusercontent.com/u/92298186?v=4,"Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.","['data-drift', 'data-science', 'data-validation', 'deep-learning', 'html-report', 'jupyter-notebook', 'machine-learning', 'ml', 'mlops', 'model-monitoring', 'model-validation', 'pandas-dataframe', 'pytorch']","['data-drift', 'data-science', 'data-validation', 'deep-learning', 'html-report', 'jupyter-notebook', 'machine-learning', 'ml', 'mlops', 'model-monitoring', 'model-validation', 'pandas-dataframe', 'pytorch']",2023-12-18,"[('evidentlyai/evidently', 0.6157994866371155, 'ml-ops', 8), ('polyaxon/polyaxon', 0.5667403340339661, 'ml-ops', 6), ('microsoft/deepspeed', 0.5651904940605164, 'ml-dl', 3), ('determined-ai/determined', 0.5619574189186096, 'ml-ops', 5), ('huggingface/datasets', 0.5608824491500854, 'nlp', 3), ('wandb/client', 0.557380735874176, 'ml', 5), ('giskard-ai/giskard', 0.550081193447113, 'data', 3), ('tensorflow/tensor2tensor', 0.5471684336662292, 'ml', 2), ('tensorflow/tensorflow', 0.5415099859237671, 'ml-dl', 3), ('mlflow/mlflow', 0.5400528907775879, 'ml-ops', 2), ('microsoft/nni', 0.5397449731826782, 'ml', 5), ('mosaicml/composer', 0.5395089387893677, 'ml-dl', 3), ('apple/coremltools', 0.539066731929779, 'ml', 2), ('nvidia/deeplearningexamples', 0.537693440914154, 'ml-dl', 2), ('unity-technologies/ml-agents', 0.5319340825080872, 'ml-rl', 2), ('googlecloudplatform/vertex-ai-samples', 0.5285258889198303, 'ml', 3), ('polyaxon/datatile', 0.5274471640586853, 'pandas', 3), ('bentoml/bentoml', 0.5182715058326721, 'ml-ops', 3), ('iperov/deepfacelab', 0.515143871307373, 'ml-dl', 2), ('explosion/thinc', 0.5133498311042786, 'ml-dl', 3), ('uber/petastorm', 0.5133116245269775, 'data', 3), ('tensorflow/data-validation', 0.512187123298645, 'ml-ops', 0), ('aiqc/aiqc', 0.5002310872077942, 'ml-ops', 0)]",52,2.0,,4.63,51,43,28,1,13,27,13,51.0,31.0,90.0,0.6,52
1595,ml-ops,https://github.com/towhee-io/towhee,[],,[],[],,,,towhee-io/towhee,towhee,2902,243,42,Python,https://towhee.io,Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.,towhee-io,2024-01-13,2021-07-13,133,21.81954887218045,https://avatars.githubusercontent.com/u/87362374?v=4,Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.,"['computer-vision', 'convolutional-networks', 'embedding-vectors', 'embeddings', 'feature-extraction', 'feature-vector', 'image-processing', 'image-retrieval', 'llm', 'machine-learning', 'milvus', 'pipeline', 'towhee', 'transformer', 'unstructured-data', 'video-processing', 'vision-transformer', 'vit']","['computer-vision', 'convolutional-networks', 'embedding-vectors', 'embeddings', 'feature-extraction', 'feature-vector', 'image-processing', 'image-retrieval', 'llm', 'machine-learning', 'milvus', 'pipeline', 'towhee', 'transformer', 'unstructured-data', 'video-processing', 'vision-transformer', 'vit']",2023-12-04,"[('huggingface/datasets', 0.5772765278816223, 'nlp', 2), ('awslabs/autogluon', 0.5480068325996399, 'ml', 2), ('roboflow/supervision', 0.544347882270813, 'ml', 4), ('lutzroeder/netron', 0.5428910851478577, 'ml', 1), ('activeloopai/deeplake', 0.5378854870796204, 'ml-ops', 4), ('deci-ai/super-gradients', 0.5340181589126587, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5302903652191162, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5275624394416809, 'ml-dl', 1), ('ludwig-ai/ludwig', 0.5253190398216248, 'ml-ops', 3), ('uber/petastorm', 0.5247065424919128, 'data', 1), ('huggingface/transformers', 0.5219820737838745, 'nlp', 2), ('visual-layer/fastdup', 0.5181317925453186, 'ml', 2), ('roboflow/notebooks', 0.511427640914917, 'study', 2), ('tensorflow/tensorflow', 0.5106989145278931, 'ml-dl', 1), ('neuralmagic/sparseml', 0.5096480250358582, 'ml-dl', 0), ('rwightman/pytorch-image-models', 0.5081286430358887, 'ml-dl', 0), ('mosaicml/composer', 0.5076751112937927, 'ml-dl', 1), ('streamlit/streamlit', 0.5068960785865784, 'viz', 1), ('microsoft/nni', 0.5067926049232483, 'ml', 1), ('polyaxon/polyaxon', 0.5038055181503296, 'ml-ops', 1), ('dgarnitz/vectorflow', 0.5035332441329956, 'data', 2), ('alpa-projects/alpa', 0.5001883506774902, 'ml-dl', 2)]",34,1.0,,3.27,26,24,30,1,5,8,5,26.0,90.0,90.0,3.5,52
1509,llm,https://github.com/defog-ai/sqlcoder,"['language-model', 'sql']",,[],[],1.0,,,defog-ai/sqlcoder,sqlcoder,1962,114,22,Jupyter Notebook,,SoTA LLM for converting natural language questions to SQL queries,defog-ai,2024-01-13,2023-08-17,23,82.73493975903614,https://avatars.githubusercontent.com/u/79135711?v=4,SoTA LLM for converting natural language questions to SQL queries,[],"['language-model', 'sql']",2023-11-15,"[('night-chen/toolqa', 0.5368439555168152, 'llm', 0), ('srush/minichain', 0.512403130531311, 'llm', 0), ('neulab/prompt2model', 0.5062905550003052, 'llm', 1)]",5,3.0,,0.92,29,9,5,2,0,0,0,29.0,45.0,90.0,1.6,52
466,ml,https://github.com/huggingface/optimum,[],,[],[],,,,huggingface/optimum,optimum,1879,316,53,Python,https://huggingface.co/docs/optimum/main/,🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools,huggingface,2024-01-13,2021-07-20,132,14.234848484848484,https://avatars.githubusercontent.com/u/25720743?v=4,🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools,"['graphcore', 'habana', 'inference', 'intel', 'onnx', 'onnxruntime', 'optimization', 'pytorch', 'quantization', 'tflite', 'training', 'transformers']","['graphcore', 'habana', 'inference', 'intel', 'onnx', 'onnxruntime', 'optimization', 'pytorch', 'quantization', 'tflite', 'training', 'transformers']",2024-01-12,"[('huggingface/transformers', 0.682058572769165, 'nlp', 1), ('huggingface/peft', 0.6415036916732788, 'llm', 2), ('ist-daslab/gptq', 0.6178866624832153, 'llm', 0), ('karpathy/mingpt', 0.5994217395782471, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5732378363609314, 'ml-interpretability', 2), ('intel/intel-extension-for-pytorch', 0.560483992099762, 'perf', 3), ('apple/ml-ane-transformers', 0.5597668886184692, 'ml', 0), ('neuralmagic/deepsparse', 0.5494052171707153, 'nlp', 3), ('google/trax', 0.5470688343048096, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5409281849861145, 'ml', 2), ('microsoft/deepspeed', 0.5390816926956177, 'ml-dl', 2), ('vllm-project/vllm', 0.5363191962242126, 'llm', 2), ('nvlabs/gcvit', 0.5319724678993225, 'diffusion', 0), ('karpathy/micrograd', 0.5316488146781921, 'study', 0), ('pytorch/ignite', 0.5307228565216064, 'ml-dl', 1), ('nielsrogge/transformers-tutorials', 0.5274924635887146, 'study', 2), ('huggingface/datasets', 0.5267567038536072, 'nlp', 1), ('eleutherai/gpt-neox', 0.5265071988105774, 'llm', 1), ('huggingface/exporters', 0.5176335573196411, 'ml', 2), ('eleutherai/knowledge-neurons', 0.5159322619438171, 'ml-interpretability', 1), ('pytorch/glow', 0.5153691172599792, 'ml', 0), ('nvidia/apex', 0.5144795179367065, 'ml-dl', 0), ('neuralmagic/sparseml', 0.5119937658309937, 'ml-dl', 2), ('explosion/spacy-transformers', 0.5108177661895752, 'llm', 1), ('tlkh/tf-metal-experiments', 0.5074736475944519, 'perf', 0), ('ray-project/ray', 0.5062342286109924, 'ml-ops', 2), ('nvidia/megatron-lm', 0.501192569732666, 'llm', 0), ('mosaicml/composer', 0.5008224248886108, 'ml-dl', 1)]",89,1.0,,9.33,249,165,30,0,30,21,30,249.0,333.0,90.0,1.3,52
1891,llm,https://github.com/cg123/mergekit,[],,[],[],,,,cg123/mergekit,mergekit,1458,128,23,Python,,Tools for merging pretrained large language models.,cg123,2024-01-14,2023-08-21,23,63.0,,Tools for merging pretrained large language models.,"['llama', 'llm', 'model-merging']","['llama', 'llm', 'model-merging']",2024-01-14,"[('infinitylogesh/mutate', 0.6713061332702637, 'nlp', 0), ('juncongmoo/pyllama', 0.6644551753997803, 'llm', 0), ('ai21labs/lm-evaluation', 0.6523741483688354, 'llm', 0), ('hannibal046/awesome-llm', 0.6510716080665588, 'study', 0), ('ctlllll/llm-toolmaker', 0.6498943567276001, 'llm', 0), ('young-geng/easylm', 0.6466503143310547, 'llm', 1), ('yizhongw/self-instruct', 0.6453080177307129, 'llm', 0), ('freedomintelligence/llmzoo', 0.641423225402832, 'llm', 0), ('salesforce/xgen', 0.6309936046600342, 'llm', 1), ('predibase/llm_distillation_playbook', 0.622988224029541, 'llm', 0), ('bigscience-workshop/biomedical', 0.6185329556465149, 'data', 0), ('togethercomputer/redpajama-data', 0.6147141456604004, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.6082916855812073, 'nlp', 0), ('lianjiatech/belle', 0.6080819368362427, 'llm', 1), ('eleutherai/the-pile', 0.607460081577301, 'data', 1), ('hiyouga/llama-factory', 0.5988380908966064, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5988380312919617, 'llm', 2), ('explosion/spacy-llm', 0.5918619632720947, 'llm', 2), ('thudm/chatglm2-6b', 0.58427494764328, 'llm', 1), ('lm-sys/fastchat', 0.58380526304245, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5827073454856873, 'llm', 0), ('jzhang38/tinyllama', 0.5811179876327515, 'llm', 1), ('microsoft/autogen', 0.5664942264556885, 'llm', 0), ('bobazooba/xllm', 0.5655726194381714, 'llm', 2), ('optimalscale/lmflow', 0.5650473237037659, 'llm', 0), ('next-gpt/next-gpt', 0.5641329884529114, 'llm', 1), ('microsoft/lora', 0.5608856678009033, 'llm', 0), ('prefecthq/langchain-prefect', 0.5573033094406128, 'llm', 0), ('facebookresearch/llama', 0.5536092519760132, 'llm', 1), ('facebookresearch/llama-recipes', 0.5452256202697754, 'llm', 1), ('microsoft/llama-2-onnx', 0.5430543422698975, 'llm', 1), ('thudm/glm-130b', 0.542129635810852, 'llm', 0), ('karpathy/llama2.c', 0.541799783706665, 'llm', 1), ('huggingface/text-generation-inference', 0.541591465473175, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5398347973823547, 'llm', 2), ('mooler0410/llmspracticalguide', 0.5390522480010986, 'study', 0), ('openlm-research/open_llama', 0.537260890007019, 'llm', 1), ('ray-project/ray-llm', 0.5349216461181641, 'llm', 1), ('conceptofmind/toolformer', 0.5336092710494995, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5332975387573242, 'llm', 0), ('artidoro/qlora', 0.5330007076263428, 'llm', 0), ('guidance-ai/guidance', 0.5327853560447693, 'llm', 0), ('dylanhogg/llmgraph', 0.5279366970062256, 'ml', 1), ('neulab/prompt2model', 0.5275200009346008, 'llm', 0), ('openai/finetune-transformer-lm', 0.5270984768867493, 'llm', 0), ('reasoning-machines/pal', 0.5245123505592346, 'llm', 0), ('ofa-sys/ofa', 0.5244383811950684, 'llm', 0), ('aiwaves-cn/agents', 0.5241085886955261, 'nlp', 1), ('oobabooga/text-generation-webui', 0.5220274925231934, 'llm', 0), ('jonasgeiping/cramming', 0.5206159353256226, 'nlp', 0), ('microsoft/unilm', 0.5200158357620239, 'nlp', 1), ('tigerlab-ai/tiger', 0.5194407105445862, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5188269019126892, 'llm', 0), ('facebookresearch/codellama', 0.51674485206604, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5143988132476807, 'llm', 2), ('epfllm/meditron', 0.5135393738746643, 'llm', 0), ('princeton-nlp/alce', 0.5112486481666565, 'llm', 0), ('guardrails-ai/guardrails', 0.511229395866394, 'llm', 1), ('openbmb/toolbench', 0.5107592344284058, 'llm', 0), ('databrickslabs/dolly', 0.5106679797172546, 'llm', 0), ('bigscience-workshop/petals', 0.5101160407066345, 'data', 1), ('nomic-ai/gpt4all', 0.5065050721168518, 'llm', 0), ('deepset-ai/haystack', 0.5054006576538086, 'llm', 0), ('squeezeailab/squeezellm', 0.504544734954834, 'llm', 2), ('srush/minichain', 0.5045192837715149, 'llm', 0), ('cstankonrad/long_llama', 0.5025786757469177, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.5024479627609253, 'llm', 0), ('nat/openplayground', 0.5010144710540771, 'llm', 0), ('night-chen/toolqa', 0.5008574724197388, 'llm', 0)]",4,0.0,,2.29,107,65,5,0,1,5,1,107.0,262.0,90.0,2.4,52
1170,llm,https://github.com/chatarena/chatarena,[],,[],[],,,,chatarena/chatarena,chatarena,1127,113,19,Python,https://www.chatarena.org/,ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.,chatarena,2024-01-12,2023-03-06,47,23.906060606060606,https://avatars.githubusercontent.com/u/62961550?v=4,ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.,"['ai', 'artificial-intelligence', 'chatgpt', 'gpt-4', 'large-language-models', 'multi-agent', 'multi-agent-reinforcement-learning', 'multi-agent-simulation', 'natural-language-processing']","['ai', 'artificial-intelligence', 'chatgpt', 'gpt-4', 'large-language-models', 'multi-agent', 'multi-agent-reinforcement-learning', 'multi-agent-simulation', 'natural-language-processing']",2023-12-21,"[('embedchain/embedchain', 0.6508777141571045, 'llm', 2), ('prefecthq/marvin', 0.6427308917045593, 'nlp', 1), ('rcgai/simplyretrieve', 0.6356537342071533, 'llm', 3), ('nomic-ai/gpt4all', 0.6325280070304871, 'llm', 0), ('microsoft/autogen', 0.6130094528198242, 'llm', 2), ('lm-sys/fastchat', 0.6125940680503845, 'llm', 0), ('run-llama/rags', 0.6069520711898804, 'llm', 1), ('deep-diver/llm-as-chatbot', 0.5871044397354126, 'llm', 0), ('pathwaycom/llm-app', 0.5828151702880859, 'llm', 0), ('hwchase17/langchain', 0.5813690423965454, 'llm', 0), ('cheshire-cat-ai/core', 0.5810584425926208, 'llm', 1), ('microsoft/promptcraft-robotics', 0.5778838992118835, 'sim', 1), ('minimaxir/simpleaichat', 0.5708586573600769, 'llm', 2), ('fasteval/fasteval', 0.5657923221588135, 'llm', 0), ('deepset-ai/haystack', 0.563785970211029, 'llm', 3), ('krohling/bondai', 0.5611550807952881, 'llm', 0), ('langchain-ai/langgraph', 0.5562337636947632, 'llm', 0), ('openlmlab/moss', 0.5541568398475647, 'llm', 3), ('intel/intel-extension-for-transformers', 0.5510158538818359, 'perf', 0), ('mnotgod96/appagent', 0.5439862012863159, 'llm', 1), ('aiwaves-cn/agents', 0.5419332385063171, 'nlp', 0), ('microsoft/promptflow', 0.5370725989341736, 'llm', 2), ('lupantech/chameleon-llm', 0.5320706963539124, 'llm', 3), ('microsoft/lmops', 0.529494047164917, 'llm', 0), ('nebuly-ai/nebullvm', 0.5283238887786865, 'perf', 3), ('larsbaunwall/bricky', 0.5276904106140137, 'llm', 1), ('nvidia/nemo', 0.5267899036407471, 'nlp', 0), ('operand/agency', 0.5225850343704224, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5216999053955078, 'study', 2), ('deeppavlov/deeppavlov', 0.5164510011672974, 'nlp', 2), ('blinkdl/chatrwkv', 0.5140795111656189, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5097920894622803, 'llm', 2), ('gunthercox/chatterbot', 0.5086801052093506, 'nlp', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5086156725883484, 'llm', 0), ('mindsdb/mindsdb', 0.5072094798088074, 'data', 2), ('rasahq/rasa', 0.5054959058761597, 'llm', 1), ('thudm/chatglm2-6b', 0.5024993419647217, 'llm', 1)]",15,6.0,,5.96,68,67,10,1,15,18,15,68.0,28.0,90.0,0.4,52
602,util,https://github.com/norvig/pytudes,[],,[],[],,,,norvig/pytudes,pytudes,22095,2385,768,Jupyter Notebook,,"Python programs, usually short, of considerable difficulty, to perfect particular skills.",norvig,2024-01-13,2017-03-01,360,61.229216152019,,"Python programs, usually short, of considerable difficulty, to perfect particular skills.","['demonstrate-skills', 'practice', 'programming']","['demonstrate-skills', 'practice', 'programming']",2024-01-02,"[('python/cpython', 0.6239404082298279, 'util', 0), ('google/pyglove', 0.5999535322189331, 'util', 0), ('adafruit/circuitpython', 0.5722380876541138, 'util', 0), ('sympy/sympy', 0.5708892941474915, 'math', 0), ('amaargiru/pyroad', 0.5647484064102173, 'study', 0), ('pypy/pypy', 0.5617297887802124, 'util', 0), ('eleutherai/pyfra', 0.5598863363265991, 'ml', 0), ('pyston/pyston', 0.5527113080024719, 'util', 0), ('microsoft/pycodegpt', 0.5215215682983398, 'llm', 0), ('stanfordnlp/dspy', 0.5077176690101624, 'llm', 0), ('evhub/coconut', 0.5068784356117249, 'util', 0), ('xrudelis/pytrait', 0.5027519464492798, 'util', 0), ('sourcery-ai/sourcery', 0.5019119381904602, 'util', 0), ('scikit-learn/scikit-learn', 0.5002601742744446, 'ml', 0)]",44,3.0,,0.65,0,0,84,0,0,0,0,0.0,0.0,90.0,0.0,51
370,viz,https://github.com/marceloprates/prettymaps,[],,[],[],,,,marceloprates/prettymaps,prettymaps,10652,541,83,Jupyter Notebook,,"A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.",marceloprates,2024-01-13,2021-03-05,151,70.27709707822808,,"A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.","['cartography', 'generative-art', 'jupyter-notebook', 'maps', 'matplotlib', 'openstreetmap']","['cartography', 'generative-art', 'jupyter-notebook', 'maps', 'matplotlib', 'openstreetmap']",2023-02-15,"[('gboeing/osmnx', 0.6797459125518799, 'gis', 1), ('raphaelquast/eomaps', 0.6013832688331604, 'gis', 1), ('scitools/cartopy', 0.5919488072395325, 'gis', 2), ('holoviz/geoviews', 0.5674756765365601, 'gis', 0), ('gboeing/osmnx-examples', 0.562412440776825, 'gis', 2), ('gregorhd/mapcompare', 0.557414710521698, 'gis', 0), ('opengeos/leafmap', 0.5452340841293335, 'gis', 1), ('residentmario/geoplot', 0.5294914245605469, 'gis', 1), ('geopandas/contextily', 0.5095080137252808, 'gis', 3)]",15,4.0,,0.31,5,0,35,11,1,6,1,5.0,4.0,90.0,0.8,51
22,nlp,https://github.com/facebookresearch/parlai,[],,[],[],,,,facebookresearch/parlai,ParlAI,10381,2091,287,Python,https://parl.ai,A framework for training and evaluating AI models on a variety of openly available dialogue datasets.,facebookresearch,2024-01-13,2017-04-24,353,29.396035598705502,https://avatars.githubusercontent.com/u/16943930?v=4,A framework for training and evaluating AI models on a variety of openly available dialogue datasets.,[],[],2023-11-03,"[('nvidia/nemo', 0.6826277375221252, 'nlp', 0), ('krohling/bondai', 0.680033266544342, 'llm', 0), ('deeppavlov/deeppavlov', 0.6255822777748108, 'nlp', 0), ('rasahq/rasa', 0.5971487164497375, 'llm', 0), ('lm-sys/fastchat', 0.5788213610649109, 'llm', 0), ('minimaxir/aitextgen', 0.5751350522041321, 'llm', 0), ('databrickslabs/dolly', 0.5629587769508362, 'llm', 0), ('openlmlab/moss', 0.5612495541572571, 'llm', 0), ('rcgai/simplyretrieve', 0.5574244856834412, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5316453576087952, 'nlp', 0), ('cheshire-cat-ai/core', 0.5091173648834229, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5056885480880737, 'study', 0), ('fasteval/fasteval', 0.5017014145851135, 'llm', 0)]",217,3.0,,1.25,5,2,82,2,1,6,1,5.0,5.0,90.0,1.0,51
1084,util,https://github.com/pytube/pytube,[],,[],[],,,,pytube/pytube,pytube,9837,2177,194,Python,https://pytube.io,"A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.",pytube,2024-01-14,2012-03-18,619,15.884429065743944,https://avatars.githubusercontent.com/u/16789089?v=4,"A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.","['api-wrapper', 'pythonic', 'youtube']","['api-wrapper', 'pythonic', 'youtube']",2023-05-20,"[('yt-dlp/yt-dlp', 0.5481389164924622, 'util', 0), ('psycoguana/subredditmediadownloader', 0.5245307087898254, 'data', 0)]",112,5.0,,0.38,88,16,144,8,0,10,10,88.0,140.0,90.0,1.6,51
158,util,https://github.com/pallets/jinja,[],,[],[],,,,pallets/jinja,jinja,9717,1591,251,Python,https://jinja.palletsprojects.com,A very fast and expressive template engine.,pallets,2024-01-13,2010-10-17,693,14.015866474345765,https://avatars.githubusercontent.com/u/16748505?v=4,A very fast and expressive template engine.,"['jinja', 'jinja2', 'pallets', 'template-engine', 'templates']","['jinja', 'jinja2', 'pallets', 'template-engine', 'templates']",2024-01-10,"[('s3rius/fastapi-template', 0.5498924255371094, 'web', 0), ('sqlalchemy/mako', 0.54783034324646, 'template', 0), ('thereforegames/unprompted', 0.5376675128936768, 'diffusion', 1), ('django/django', 0.5016000270843506, 'web', 1), ('pallets/flask', 0.5012500882148743, 'web', 2)]",306,4.0,,0.96,37,21,161,0,1,4,1,37.0,45.0,90.0,1.2,51
142,ml,https://github.com/featurelabs/featuretools,[],,[],[],1.0,,,featurelabs/featuretools,featuretools,6933,856,158,Python,https://www.featuretools.com,An open source python library for automated feature engineering,featurelabs,2024-01-13,2017-09-08,333,20.784154175588863,https://avatars.githubusercontent.com/u/12972388?v=4,An open source python library for automated feature engineering,"['automated-feature-engineering', 'automated-machine-learning', 'automl', 'data-science', 'feature-engineering', 'machine-learning', 'scikit-learn']","['automated-feature-engineering', 'automated-machine-learning', 'automl', 'data-science', 'feature-engineering', 'machine-learning', 'scikit-learn']",2023-12-07,"[('google/temporian', 0.7070109844207764, 'time-series', 1), ('rasbt/mlxtend', 0.6914775371551514, 'ml', 2), ('pycaret/pycaret', 0.6861603856086731, 'ml', 2), ('microsoft/nni', 0.6769810914993286, 'ml', 5), ('automl/auto-sklearn', 0.6524330377578735, 'ml', 3), ('epistasislab/tpot', 0.6507097482681274, 'ml', 6), ('mljar/mljar-supervised', 0.645880401134491, 'ml', 6), ('gradio-app/gradio', 0.6402891278266907, 'viz', 2), ('scikit-learn/scikit-learn', 0.6306010484695435, 'ml', 2), ('microsoft/flaml', 0.6044269800186157, 'ml', 5), ('dylanhogg/awesome-python', 0.6018655300140381, 'study', 2), ('google/pyglove', 0.601290225982666, 'util', 2), ('kubeflow/fairing', 0.5992475152015686, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.5986401438713074, 'ml', 2), ('teamhg-memex/eli5', 0.5933169722557068, 'ml', 3), ('rasbt/machine-learning-book', 0.5927860736846924, 'study', 2), ('lightly-ai/lightly', 0.5864541530609131, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.580768883228302, 'study', 3), ('nccr-itmo/fedot', 0.5784884095191956, 'ml-ops', 3), ('pytoolz/toolz', 0.5733933448791504, 'util', 0), ('districtdatalabs/yellowbrick', 0.5721520185470581, 'ml', 2), ('mdbloice/augmentor', 0.5716681480407715, 'ml', 1), ('skops-dev/skops', 0.5711256265640259, 'ml-ops', 2), ('scikit-learn-contrib/imbalanced-learn', 0.5710511207580566, 'ml', 2), ('scikit-learn-contrib/metric-learn', 0.5696079730987549, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5691878795623779, 'ml-interpretability', 0), ('firmai/atspy', 0.5671241283416748, 'time-series', 0), ('keras-team/autokeras', 0.5649420022964478, 'ml-dl', 3), ('mlflow/mlflow', 0.5563209652900696, 'ml-ops', 1), ('ageron/handson-ml2', 0.5548660159111023, 'ml', 0), ('sourcery-ai/sourcery', 0.554305911064148, 'util', 0), ('pandas-dev/pandas', 0.5537092685699463, 'pandas', 1), ('awslabs/autogluon', 0.5521774291992188, 'ml', 5), ('amaargiru/pyroad', 0.548759937286377, 'study', 0), ('ta-lib/ta-lib-python', 0.5429303646087646, 'finance', 0), ('wandb/client', 0.5428714752197266, 'ml', 2), ('tensorflow/tensorflow', 0.5424914360046387, 'ml-dl', 1), ('tensorflow/data-validation', 0.5417819023132324, 'ml-ops', 0), ('dagworks-inc/hamilton', 0.5415907502174377, 'ml-ops', 3), ('koaning/human-learn', 0.5413556694984436, 'data', 2), ('yzhao062/pyod', 0.5403005480766296, 'data', 2), ('winedarksea/autots', 0.5383171439170837, 'time-series', 3), ('rafiqhasan/auto-tensorflow', 0.5342097878456116, 'ml-dl', 2), ('online-ml/river', 0.5314729809761047, 'ml', 2), ('alkaline-ml/pmdarima', 0.5302847623825073, 'time-series', 1), ('oml-team/open-metric-learning', 0.5298222899436951, 'ml', 1), ('goldmansachs/gs-quant', 0.5276992321014404, 'finance', 0), ('sentinel-hub/eo-learn', 0.5274897813796997, 'gis', 1), ('huggingface/huggingface_hub', 0.5263670682907104, 'ml', 1), ('koaning/scikit-lego', 0.5262479782104492, 'ml', 2), ('jovianml/opendatasets', 0.5258838534355164, 'data', 2), ('eleutherai/pyfra', 0.5247564315795898, 'ml', 0), ('polyaxon/datatile', 0.5225564241409302, 'pandas', 1), ('huggingface/datasets', 0.5206751227378845, 'nlp', 1), ('huggingface/evaluate', 0.5203360915184021, 'ml', 1), ('samuelcolvin/python-devtools', 0.5202803015708923, 'debug', 0), ('pypy/pypy', 0.5145143866539001, 'util', 0), ('fmind/mlops-python-package', 0.5135179758071899, 'template', 0), ('intel/intel-extension-for-pytorch', 0.512016773223877, 'perf', 1), ('doccano/doccano', 0.5110718607902527, 'nlp', 1), ('csinva/imodels', 0.5102759599685669, 'ml', 3), ('nedbat/coveragepy', 0.5096982717514038, 'testing', 0), ('patchy631/machine-learning', 0.5084584355354309, 'ml', 0), ('earthlab/earthpy', 0.5079247355461121, 'gis', 0), ('weecology/deepforest', 0.5066676139831543, 'gis', 0), ('pyeve/cerberus', 0.503200888633728, 'data', 0)]",71,2.0,,1.65,30,21,77,1,8,24,8,30.0,18.0,90.0,0.6,51
771,study,https://github.com/nielsrogge/transformers-tutorials,[],,[],[],,,,nielsrogge/transformers-tutorials,Transformers-Tutorials,6629,1045,111,Jupyter Notebook,,This repository contains demos I made with the Transformers library by HuggingFace.,nielsrogge,2024-01-13,2020-08-31,178,37.21170809943865,,This repository contains demos I made with the Transformers library by HuggingFace.,"['bert', 'gpt-2', 'layoutlm', 'pytorch', 'transformers', 'vision-transformer']","['bert', 'gpt-2', 'layoutlm', 'pytorch', 'transformers', 'vision-transformer']",2024-01-11,"[('karpathy/mingpt', 0.6038249135017395, 'llm', 0), ('nvlabs/gcvit', 0.5923160910606384, 'diffusion', 1), ('huggingface/transformers', 0.5857082605361938, 'nlp', 2), ('bigscience-workshop/megatron-deepspeed', 0.5679675936698914, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5679675936698914, 'llm', 0), ('huggingface/exporters', 0.5573378205299377, 'ml', 1), ('marella/ctransformers', 0.553908109664917, 'nlp', 1), ('alignmentresearch/tuned-lens', 0.5491597652435303, 'ml-interpretability', 2), ('ist-daslab/gptq', 0.5349066257476807, 'llm', 0), ('huggingface/optimum', 0.5274924635887146, 'ml', 2), ('pytorch-labs/gpt-fast', 0.5239831805229187, 'llm', 1), ('opengeos/earthformer', 0.5140464901924133, 'gis', 0), ('huggingface/huggingface_hub', 0.5096057057380676, 'ml', 1), ('promptslab/awesome-prompt-engineering', 0.5090713500976562, 'study', 0)]",5,2.0,,1.48,42,7,41,0,0,0,0,42.0,73.0,90.0,1.7,51
1063,diffusion,https://github.com/timothybrooks/instruct-pix2pix,[],"PyTorch implementation of InstructPix2Pix, an instruction-based image editing model, based on the original CompVis/stable_diffusion repo.",[],[],,,,timothybrooks/instruct-pix2pix,instruct-pix2pix,5565,495,64,Python,,,timothybrooks,2024-01-13,2023-01-09,55,100.91968911917098,,"PyTorch implementation of InstructPix2Pix, an instruction-based image editing model, based on the original CompVis/stable_diffusion repo.",[],[],2023-01-31,"[('carson-katri/dream-textures', 0.5679231286048889, 'diffusion', 0), ('huggingface/diffusers', 0.5238240957260132, 'diffusion', 0), ('sanster/lama-cleaner', 0.5218861699104309, 'ml-dl', 0), ('compvis/latent-diffusion', 0.5207135081291199, 'diffusion', 0), ('stability-ai/stablediffusion', 0.5207132697105408, 'diffusion', 0), ('lkwq007/stablediffusion-infinity', 0.5145807266235352, 'diffusion', 0), ('openai/image-gpt', 0.512509286403656, 'llm', 0), ('automatic1111/stable-diffusion-webui', 0.5089647769927979, 'diffusion', 0), ('mcahny/deep-video-inpainting', 0.500446081161499, 'ml-dl', 0)]",13,3.0,,0.1,10,3,12,12,0,0,0,10.0,10.0,90.0,1.0,51
1907,util,https://github.com/pypa/virtualenv,"['pip', 'venv', 'virtualenv']","A tool to create isolated Python environments. Since Python 3.3, a subset of it has been integrated into the standard lib venv module.",[],[],,,,pypa/virtualenv,virtualenv,4621,1091,169,Python,https://virtualenv.pypa.io,Virtual Python Environment builder,pypa,2024-01-20,2011-03-06,673,6.8633566730320394,https://avatars.githubusercontent.com/u/647025?v=4,Virtual Python Environment builder,"['cython', 'jython', 'pypa', 'pypy', 'pypy3', 'virtualenv']","['cython', 'jython', 'pip', 'pypa', 'pypy', 'pypy3', 'venv', 'virtualenv']",2024-01-16,"[('pypa/pipenv', 0.6951494216918945, 'util', 3), ('pypa/hatch', 0.6300379633903503, 'util', 1), ('pyenv/pyenv', 0.6280038952827454, 'util', 2), ('pypa/pipx', 0.6216922998428345, 'util', 2), ('pypy/pypy', 0.6060330271720886, 'util', 0), ('pantsbuild/pex', 0.5755523443222046, 'util', 1), ('ofek/pyapp', 0.5487356781959534, 'util', 0), ('pyglet/pyglet', 0.541969895362854, 'gamedev', 0), ('jquast/blessed', 0.5412343144416809, 'term', 0), ('pypi/warehouse', 0.5271876454353333, 'util', 0), ('thoth-station/micropipenv', 0.5239235758781433, 'util', 1), ('computationalmodelling/nbval', 0.523438036441803, 'jupyter', 0), ('pyo3/maturin', 0.5136356353759766, 'util', 1), ('hoffstadt/dearpygui', 0.5124236941337585, 'gui', 0), ('ipython/ipyparallel', 0.5087124109268188, 'perf', 0), ('dosisod/refurb', 0.5063945055007935, 'util', 0), ('pyodide/micropip', 0.5000215172767639, 'util', 0)]",113,5.0,,2.17,35,26,157,0,17,17,17,35.0,74.0,90.0,2.1,51
286,gis,https://github.com/geopandas/geopandas,"['geopandas', 'pandas', 'gis']",,[],[],1.0,,,geopandas/geopandas,geopandas,4017,908,106,Python,http://geopandas.org/,Python tools for geographic data,geopandas,2024-01-13,2013-06-27,552,7.267769449470148,https://avatars.githubusercontent.com/u/8130715?v=4,Python tools for geographic data,"['geoparquet', 'geospatial', 'pandas', 'spatial']","['geopandas', 'geoparquet', 'geospatial', 'gis', 'pandas', 'spatial']",2024-01-07,"[('artelys/geonetworkx', 0.7633013725280762, 'gis', 0), ('residentmario/geoplot', 0.7451832890510559, 'gis', 1), ('holoviz/spatialpandas', 0.6860671043395996, 'pandas', 2), ('opengeos/leafmap', 0.671466052532196, 'gis', 3), ('openeventdata/mordecai', 0.6333655714988708, 'gis', 0), ('raphaelquast/eomaps', 0.6084570288658142, 'gis', 2), ('earthlab/earthpy', 0.6019172668457031, 'gis', 0), ('anitagraser/movingpandas', 0.5975525379180908, 'gis', 1), ('pandas-dev/pandas', 0.5796288251876831, 'pandas', 1), ('holoviz/geoviews', 0.5721185803413391, 'gis', 0), ('giswqs/geemap', 0.5684166550636292, 'gis', 2), ('pysal/pysal', 0.5672765374183655, 'gis', 0), ('mwaskom/seaborn', 0.5595967769622803, 'viz', 1), ('gregorhd/mapcompare', 0.5594103932380676, 'gis', 0), ('tkrabel/bamboolib', 0.5592805743217468, 'pandas', 1), ('makepath/xarray-spatial', 0.5509017705917358, 'gis', 0), ('holoviz/panel', 0.550635039806366, 'viz', 0), ('toblerity/rtree', 0.5479511618614197, 'gis', 0), ('pyproj4/pyproj', 0.5460281372070312, 'gis', 1), ('scitools/iris', 0.5416747331619263, 'gis', 0), ('opengeos/segment-geospatial', 0.5388274788856506, 'gis', 1), ('cloudsen12/easystac', 0.534330427646637, 'gis', 1), ('scikit-mobility/scikit-mobility', 0.5326095819473267, 'gis', 0), ('jakevdp/pythondatasciencehandbook', 0.531819760799408, 'study', 1), ('wesm/pydata-book', 0.531495213508606, 'study', 0), ('goldmansachs/gs-quant', 0.5308281183242798, 'finance', 0), ('eleutherai/pyfra', 0.5261996388435364, 'ml', 0), ('sqlalchemy/sqlalchemy', 0.5259225964546204, 'data', 0), ('plotly/dash', 0.525221586227417, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5221031308174133, 'study', 0), ('blaze/blaze', 0.5217344164848328, 'pandas', 0), ('man-group/dtale', 0.5202478766441345, 'viz', 1), ('delta-io/delta-rs', 0.5198134183883667, 'pandas', 1), ('adamerose/pandasgui', 0.51722252368927, 'pandas', 1), ('falconry/falcon', 0.5166769027709961, 'web', 0), ('holoviz/holoviz', 0.515374481678009, 'viz', 0), ('mito-ds/monorepo', 0.5150602459907532, 'jupyter', 1), ('contextlab/hypertools', 0.5144107341766357, 'ml', 0), ('python-odin/odin', 0.5138459801673889, 'util', 0), ('fatiando/verde', 0.5108627080917358, 'gis', 1), ('imageio/imageio', 0.5076414942741394, 'util', 0), ('scitools/cartopy', 0.5073442459106445, 'gis', 1), ('kanaries/pygwalker', 0.5026245713233948, 'pandas', 1), ('ibis-project/ibis', 0.5019082427024841, 'data', 1)]",216,4.0,,3.25,141,85,128,0,6,3,6,141.0,211.0,90.0,1.5,51
157,profiling,https://github.com/gaogaotiantian/viztracer,[],,[],[],,,,gaogaotiantian/viztracer,viztracer,3909,343,48,Python,https://viztracer.readthedocs.io/,VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.,gaogaotiantian,2024-01-14,2020-08-05,181,21.494893951296152,,VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.,"['debugging', 'flamegraph', 'logging', 'profiling', 'tracer', 'visualization']","['debugging', 'flamegraph', 'logging', 'profiling', 'tracer', 'visualization']",2024-01-08,"[('alexmojaki/heartrate', 0.6707364320755005, 'debug', 1), ('alexmojaki/snoop', 0.623710036277771, 'debug', 2), ('pympler/pympler', 0.6162266731262207, 'perf', 0), ('ionelmc/python-hunter', 0.6034563779830933, 'debug', 2), ('landscapeio/prospector', 0.6018545627593994, 'util', 0), ('pyutils/line_profiler', 0.5905494689941406, 'profiling', 0), ('altair-viz/altair', 0.5886167287826538, 'viz', 1), ('wandb/client', 0.5766005516052246, 'ml', 0), ('nedbat/coveragepy', 0.5709172487258911, 'testing', 0), ('pythonprofilers/memory_profiler', 0.5663729906082153, 'profiling', 0), ('holoviz/holoviz', 0.5649738311767578, 'viz', 0), ('samuelcolvin/python-devtools', 0.5530471205711365, 'debug', 0), ('jiffyclub/snakeviz', 0.5514275431632996, 'profiling', 0), ('mckinsey/vizro', 0.5495272874832153, 'viz', 1), ('bokeh/bokeh', 0.5466111302375793, 'viz', 1), ('alexmojaki/birdseye', 0.5461402535438538, 'debug', 1), ('holoviz/panel', 0.5426017045974731, 'viz', 0), ('open-telemetry/opentelemetry-python-contrib', 0.5290429592132568, 'util', 0), ('nschloe/perfplot', 0.528630793094635, 'perf', 0), ('klen/pylama', 0.5285407304763794, 'util', 0), ('rubik/radon', 0.5236186385154724, 'util', 0), ('polyaxon/datatile', 0.5233743786811829, 'pandas', 0), ('pyvista/pyvista', 0.5193122029304504, 'viz', 1), ('eleutherai/pyfra', 0.5164215564727783, 'ml', 0), ('google/pytype', 0.5134180784225464, 'typing', 0), ('willmcgugan/textual', 0.5108093023300171, 'term', 0), ('facebook/pyre-check', 0.5102528929710388, 'typing', 0), ('vispy/vispy', 0.503200888633728, 'viz', 1), ('plotly/plotly.py', 0.5023961067199707, 'viz', 1), ('open-telemetry/opentelemetry-python', 0.5011972784996033, 'util', 1), ('sourcery-ai/sourcery', 0.5000627636909485, 'util', 0), ('hoffstadt/dearpygui', 0.5000486373901367, 'gui', 0)]",25,3.0,,0.79,25,14,42,0,2,23,2,25.0,52.0,90.0,2.1,51
183,testing,https://github.com/tox-dev/tox,[],,[],[],,,,tox-dev/tox,tox,3426,502,42,Python,https://tox.wiki,Command line driven CI frontend and development task automation tool.,tox-dev,2024-01-14,2016-09-17,384,8.911928651059085,https://avatars.githubusercontent.com/u/20345659?v=4,Command line driven CI frontend and development task automation tool.,"['actions', 'appveyor', 'automation', 'azure-pipelines', 'circleci', 'cli', 'continuous-integration', 'gitlab', 'pep-621', 'testing', 'travis', 'venv', 'virtualenv']","['actions', 'appveyor', 'automation', 'azure-pipelines', 'circleci', 'cli', 'continuous-integration', 'gitlab', 'pep-621', 'testing', 'travis', 'venv', 'virtualenv']",2024-01-12,"[('ianmiell/shutit', 0.5560944080352783, 'util', 0), ('allegroai/clearml', 0.5439307689666748, 'ml-ops', 0), ('pydoit/doit', 0.5430145859718323, 'util', 0), ('zenml-io/zenml', 0.5314726233482361, 'ml-ops', 0), ('buildbot/buildbot', 0.5279530882835388, 'util', 1), ('pytest-dev/pytest-testinfra', 0.5243973731994629, 'testing', 1), ('orchest/orchest', 0.523241400718689, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5215947031974792, 'ml-ops', 0), ('flipkart-incubator/astra', 0.5185132026672363, 'web', 0), ('python-poetry/cleo', 0.515224039554596, 'term', 2), ('ploomber/ploomber', 0.5083051323890686, 'ml-ops', 0), ('avaiga/taipy', 0.5081842541694641, 'data', 1)]",68,6.0,,3.0,48,35,89,0,38,29,38,48.0,66.0,90.0,1.4,51
897,util,https://github.com/pypi/warehouse,[],,[],[],,,,pypi/warehouse,warehouse,3422,1041,112,Python,https://pypi.org,The Python Package Index,pypi,2024-01-14,2013-03-30,565,6.052046488125316,https://avatars.githubusercontent.com/u/2964877?v=4,The Python Package Index,"['package-registry', 'package-repository', 'pypi-source']","['package-registry', 'package-repository', 'pypi-source']",2024-01-12,"[('pdm-project/pdm', 0.6904469728469849, 'util', 0), ('indygreg/pyoxidizer', 0.6707281470298767, 'util', 0), ('mitsuhiko/rye', 0.6509472131729126, 'util', 0), ('pyodide/micropip', 0.6480095386505127, 'util', 0), ('pypa/flit', 0.6236358284950256, 'util', 0), ('pypa/hatch', 0.6159988641738892, 'util', 0), ('pypa/gh-action-pypi-publish', 0.6143344044685364, 'util', 0), ('hugovk/pypistats', 0.6038178205490112, 'util', 0), ('regebro/pyroma', 0.6017106771469116, 'util', 0), ('pomponchik/instld', 0.5979329347610474, 'util', 0), ('python-poetry/poetry', 0.5964840054512024, 'util', 0), ('jazzband/pip-tools', 0.5816731452941895, 'util', 0), ('tox-dev/pipdeptree', 0.5815759897232056, 'util', 0), ('mozillazg/pypy', 0.5795431733131409, 'util', 0), ('ofek/pyapp', 0.5665706992149353, 'util', 0), ('mgedmin/check-manifest', 0.5615488886833191, 'util', 0), ('pypy/pypy', 0.546995222568512, 'util', 0), ('landscapeio/prospector', 0.545502245426178, 'util', 0), ('tiangolo/poetry-version-plugin', 0.537349283695221, 'util', 0), ('mkdocstrings/griffe', 0.5345215797424316, 'util', 0), ('urwid/urwid', 0.5293185710906982, 'term', 0), ('prompt-toolkit/ptpython', 0.5278775691986084, 'util', 0), ('pypa/virtualenv', 0.5271876454353333, 'util', 0), ('tezromach/python-package-template', 0.5265879034996033, 'template', 0), ('pypa/installer', 0.5235101580619812, 'util', 0), ('ipython/ipython', 0.5227155685424805, 'util', 0), ('omry/omegaconf', 0.5100882053375244, 'util', 0), ('hadialqattan/pycln', 0.5063982605934143, 'util', 0), ('rubik/radon', 0.5054935216903687, 'util', 0), ('pygments/pygments', 0.5018252730369568, 'util', 0), ('dosisod/refurb', 0.5017030239105225, 'util', 0)]",370,7.0,,13.9,524,430,131,0,0,0,0,523.0,587.0,90.0,1.1,51
804,ml-ops,https://github.com/ploomber/ploomber,[],,[],[],,,,ploomber/ploomber,ploomber,3306,222,29,Python,https://ploomber.io,"The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️",ploomber,2024-01-14,2020-01-20,210,15.732154996600952,https://avatars.githubusercontent.com/u/60114551?v=4,"The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️","['data-engineering', 'data-science', 'jupyter', 'jupyter-notebooks', 'machine-learning', 'mlops', 'notebooks', 'papermill', 'pipelines', 'pycharm', 'vscode', 'workflow']","['data-engineering', 'data-science', 'jupyter', 'jupyter-notebooks', 'machine-learning', 'mlops', 'notebooks', 'papermill', 'pipelines', 'pycharm', 'vscode', 'workflow']",2024-01-03,"[('orchest/orchest', 0.862511932849884, 'ml-ops', 5), ('linealabs/lineapy', 0.739909827709198, 'jupyter', 0), ('mage-ai/mage-ai', 0.7089954614639282, 'ml-ops', 4), ('avaiga/taipy', 0.6359885334968567, 'data', 4), ('meltano/meltano', 0.6284797787666321, 'ml-ops', 2), ('kestra-io/kestra', 0.6272913217544556, 'ml-ops', 2), ('netflix/metaflow', 0.6208223700523376, 'ml-ops', 3), ('zenml-io/zenml', 0.6197214722633362, 'ml-ops', 5), ('airbytehq/airbyte', 0.6143587827682495, 'data', 1), ('dagworks-inc/hamilton', 0.6052381992340088, 'ml-ops', 4), ('dagster-io/dagster', 0.6021184325218201, 'ml-ops', 4), ('hi-primus/optimus', 0.5924459099769592, 'ml-ops', 2), ('fastai/fastcore', 0.5885465741157532, 'util', 0), ('polyaxon/polyaxon', 0.5840852856636047, 'ml-ops', 6), ('flyteorg/flyte', 0.5782303214073181, 'ml-ops', 4), ('kubeflow-kale/kale', 0.5709792971611023, 'ml-ops', 1), ('bodywork-ml/bodywork-core', 0.5688678026199341, 'ml-ops', 3), ('kubeflow/pipelines', 0.568588137626648, 'ml-ops', 3), ('astronomer/astro-sdk', 0.5675156116485596, 'ml-ops', 1), ('pypa/pipenv', 0.5622181296348572, 'util', 0), ('huggingface/datasets', 0.5587560534477234, 'nlp', 1), ('willmcgugan/textual', 0.5585596561431885, 'term', 0), ('allegroai/clearml', 0.5556386113166809, 'ml-ops', 2), ('pydoit/doit', 0.5523402094841003, 'util', 2), ('great-expectations/great_expectations', 0.5521277785301208, 'ml-ops', 3), ('streamlit/streamlit', 0.5482877492904663, 'viz', 2), ('kubeflow/fairing', 0.5463899970054626, 'ml-ops', 0), ('pythagora-io/gpt-pilot', 0.5441665649414062, 'llm', 0), ('polyaxon/datatile', 0.5431307554244995, 'pandas', 2), ('plotly/dash', 0.5395582914352417, 'viz', 2), ('whylabs/whylogs', 0.5393771529197693, 'util', 3), ('nteract/papermill', 0.5353273153305054, 'jupyter', 2), ('merantix-momentum/squirrel-core', 0.5347124338150024, 'ml', 2), ('feast-dev/feast', 0.5336986780166626, 'ml-ops', 4), ('google/ml-metadata', 0.5324745774269104, 'ml-ops', 0), ('simonw/datasette', 0.5322346091270447, 'data', 0), ('featureform/embeddinghub', 0.529184103012085, 'nlp', 3), ('spotify/luigi', 0.5267884135246277, 'ml-ops', 0), ('python-odin/odin', 0.5260776877403259, 'util', 0), ('backtick-se/cowait', 0.5248901844024658, 'util', 2), ('malloydata/malloy-py', 0.5213688015937805, 'data', 0), ('kedro-org/kedro', 0.5186499357223511, 'ml-ops', 2), ('pathwaycom/pathway', 0.5119403004646301, 'data', 0), ('fmind/mlops-python-package', 0.5116260051727295, 'template', 1), ('tobymao/sqlglot', 0.5114932656288147, 'data', 0), ('vaexio/vaex', 0.5101336240768433, 'perf', 2), ('prefecthq/prefect', 0.5095990896224976, 'ml-ops', 3), ('tox-dev/tox', 0.5083051323890686, 'testing', 0), ('saulpw/visidata', 0.5074170827865601, 'term', 0), ('gradio-app/gradio', 0.5065774321556091, 'viz', 2), ('krzjoa/awesome-python-data-science', 0.5054386258125305, 'study', 2), ('iterative/dvc', 0.5037345886230469, 'ml-ops', 2), ('firmai/industry-machine-learning', 0.5007092952728271, 'study', 2), ('prefecthq/server', 0.5003852248191833, 'util', 1)]",80,1.0,,1.37,24,16,48,0,0,29,29,24.0,68.0,90.0,2.8,51
1383,diffusion,https://github.com/mlc-ai/web-stable-diffusion,[],,[],[],,,,mlc-ai/web-stable-diffusion,web-stable-diffusion,3273,196,33,Jupyter Notebook,https://mlc.ai/web-stable-diffusion,Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support. ,mlc-ai,2024-01-13,2023-03-06,47,69.42727272727272,https://avatars.githubusercontent.com/u/106173866?v=4,Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support. ,"['deep-learning', 'stable-diffusion', 'tvm', 'web-assembly', 'webgpu', 'webml']","['deep-learning', 'stable-diffusion', 'tvm', 'web-assembly', 'webgpu', 'webml']",2023-07-18,"[('automatic1111/stable-diffusion-webui', 0.7200703024864197, 'diffusion', 2), ('thereforegames/unprompted', 0.6838393807411194, 'diffusion', 2), ('mlc-ai/web-llm', 0.6759905219078064, 'llm', 4), ('civitai/sd_civitai_extension', 0.6748091578483582, 'llm', 0), ('bentoml/onediffusion', 0.6144503355026245, 'diffusion', 1), ('comfyanonymous/comfyui', 0.6069907546043396, 'diffusion', 1), ('carson-katri/dream-textures', 0.5855890512466431, 'diffusion', 1), ('aiqc/aiqc', 0.5372481346130371, 'ml-ops', 0), ('titanml/takeoff', 0.5177373886108398, 'llm', 0), ('bigscience-workshop/petals', 0.5074443817138672, 'data', 1)]",8,5.0,,0.75,8,1,10,6,0,0,0,8.0,11.0,90.0,1.4,51
140,viz,https://github.com/vispy/vispy,[],,[],[],,,,vispy/vispy,vispy,3170,614,117,Python,http://vispy.org,Main repository for Vispy,vispy,2024-01-12,2013-03-21,566,5.593647592639274,https://avatars.githubusercontent.com/u/3934254?v=4,Main repository for Vispy,"['opengl', 'visualization']","['opengl', 'visualization']",2023-12-28,"[('holoviz/holoviz', 0.6010532379150391, 'viz', 0), ('maartenbreddels/ipyvolume', 0.5861509442329407, 'jupyter', 0), ('altair-viz/altair', 0.5802785158157349, 'viz', 1), ('holoviz/geoviews', 0.571336567401886, 'gis', 0), ('visgl/deck.gl', 0.5518122911453247, 'viz', 1), ('residentmario/geoplot', 0.5477085709571838, 'gis', 0), ('giswqs/geemap', 0.5475439429283142, 'gis', 0), ('graphistry/pygraphistry', 0.5377211570739746, 'data', 1), ('man-group/dtale', 0.5336646437644958, 'viz', 1), ('plotly/plotly.py', 0.523070216178894, 'viz', 1), ('enthought/mayavi', 0.5171870589256287, 'viz', 1), ('pyglet/pyglet', 0.5124850273132324, 'gamedev', 1), ('bokeh/bokeh', 0.5114392638206482, 'viz', 1), ('marcomusy/vedo', 0.5108841061592102, 'viz', 1), ('has2k1/plotnine', 0.5095175504684448, 'viz', 0), ('scitools/cartopy', 0.5065507888793945, 'gis', 0), ('dfki-ric/pytransform3d', 0.5056593418121338, 'math', 1), ('gaogaotiantian/viztracer', 0.503200888633728, 'profiling', 1)]",192,8.0,,2.87,40,23,132,1,4,3,4,40.0,169.0,90.0,4.2,51
1650,nlp,https://github.com/maartengr/keybert,[],,[],[],,,,maartengr/keybert,KeyBERT,3034,317,33,Python,https://MaartenGr.github.io/KeyBERT/,Minimal keyword extraction with BERT,maartengr,2024-01-14,2020-10-22,170,17.772384937238495,,Minimal keyword extraction with BERT,"['bert', 'keyphrase-extraction', 'keyword-extraction', 'mmr']","['bert', 'keyphrase-extraction', 'keyword-extraction', 'mmr']",2024-01-03,"[('vi3k6i5/flashtext', 0.5377181768417358, 'data', 1), ('whu-zqh/chatgpt-vs.-bert', 0.529996395111084, 'llm', 1), ('jonasgeiping/cramming', 0.5119235515594482, 'nlp', 0), ('maartengr/bertopic', 0.5103285908699036, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5080262422561646, 'llm', 1)]",9,8.0,,0.15,19,9,39,0,1,3,1,19.0,60.0,90.0,3.2,51
837,time-series,https://github.com/tdameritrade/stumpy,[],,[],[],,,,tdameritrade/stumpy,stumpy,2896,274,54,Python,https://stumpy.readthedocs.io/en/latest/,STUMPY is a powerful and scalable Python library for modern time series analysis,tdameritrade,2024-01-13,2019-05-03,247,11.697634160415465,https://avatars.githubusercontent.com/u/5022525?v=4,STUMPY is a powerful and scalable Python library for modern time series analysis,"['anomaly-detection', 'dask', 'data-science', 'matrix-profile', 'motif-discovery', 'numba', 'pattern-matching', 'pydata', 'time-series-analysis', 'time-series-data-mining', 'time-series-segmentation']","['anomaly-detection', 'dask', 'data-science', 'matrix-profile', 'motif-discovery', 'numba', 'pattern-matching', 'pydata', 'time-series-analysis', 'time-series-data-mining', 'time-series-segmentation']",2024-01-12,"[('unit8co/darts', 0.7482045888900757, 'time-series', 2), ('alkaline-ml/pmdarima', 0.6753336787223816, 'time-series', 0), ('pycaret/pycaret', 0.6499117016792297, 'ml', 2), ('rjt1990/pyflux', 0.6353744268417358, 'time-series', 0), ('yzhao062/pyod', 0.6203436255455017, 'data', 2), ('firmai/atspy', 0.6093729138374329, 'time-series', 1), ('blue-yonder/tsfresh', 0.5777381062507629, 'time-series', 1), ('aistream-peelout/flow-forecast', 0.567234992980957, 'time-series', 2), ('google/temporian', 0.5609158277511597, 'time-series', 0), ('salesforce/merlion', 0.5562719106674194, 'time-series', 1), ('rasbt/mlxtend', 0.5554875135421753, 'ml', 1), ('awslabs/gluonts', 0.5509535074234009, 'time-series', 1), ('dateutil/dateutil', 0.530729353427887, 'util', 0), ('pandas-dev/pandas', 0.523211658000946, 'pandas', 1), ('contextlab/hypertools', 0.5135495662689209, 'ml', 0), ('ta-lib/ta-lib-python', 0.509051501750946, 'finance', 0), ('makepath/xarray-spatial', 0.5024837851524353, 'gis', 1)]",36,3.0,,1.92,12,5,57,0,1,6,1,12.0,98.0,90.0,8.2,51
1435,llm,https://github.com/baichuan-inc/baichuan-13b,[],,[],[],,,,baichuan-inc/baichuan-13b,Baichuan-13B,2867,218,31,Python,https://huggingface.co/baichuan-inc/Baichuan-13B-Chat,A 13B large language model developed by Baichuan Intelligent Technology,baichuan-inc,2024-01-13,2023-07-10,29,98.37745098039215,https://avatars.githubusercontent.com/u/136167093?v=4,A 13B large language model developed by Baichuan Intelligent Technology,"['artificial-intelligence', 'benchmark', 'ceval', 'chatgpt', 'chinese', 'gpt-4', 'huggingface', 'large-language-models', 'mmlu', 'natural-language-processing']","['artificial-intelligence', 'benchmark', 'ceval', 'chatgpt', 'chinese', 'gpt-4', 'huggingface', 'large-language-models', 'mmlu', 'natural-language-processing']",2023-09-06,"[('lianjiatech/belle', 0.6785591840744019, 'llm', 0), ('hannibal046/awesome-llm', 0.6444519758224487, 'study', 0), ('freedomintelligence/llmzoo', 0.640003502368927, 'llm', 0), ('next-gpt/next-gpt', 0.6081295013427734, 'llm', 3), ('yueyu1030/attrprompt', 0.6001567840576172, 'llm', 2), ('ctlllll/llm-toolmaker', 0.5928089022636414, 'llm', 0), ('lm-sys/fastchat', 0.5909283757209778, 'llm', 0), ('microsoft/autogen', 0.5874255895614624, 'llm', 2), ('thudm/chatglm2-6b', 0.5701199173927307, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5544146299362183, 'nlp', 0), ('openlmlab/moss', 0.5460556149482727, 'llm', 3), ('huggingface/text-generation-inference', 0.546048104763031, 'llm', 0), ('ai21labs/lm-evaluation', 0.5453725457191467, 'llm', 0), ('li-plus/chatglm.cpp', 0.5446141362190247, 'llm', 1), ('microsoft/lora', 0.5445288419723511, 'llm', 0), ('jonasgeiping/cramming', 0.5425389409065247, 'nlp', 0), ('databrickslabs/dolly', 0.5416833758354187, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5372994542121887, 'llm', 1), ('prefecthq/langchain-prefect', 0.5365051627159119, 'llm', 1), ('guidance-ai/guidance', 0.5342095494270325, 'llm', 1), ('thudm/chatglm-6b', 0.5303475856781006, 'llm', 0), ('togethercomputer/redpajama-data', 0.5299484729766846, 'llm', 0), ('hiyouga/llama-factory', 0.5287744402885437, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5287743806838989, 'llm', 1), ('bobazooba/xllm', 0.5246362090110779, 'llm', 3), ('cg123/mergekit', 0.5188269019126892, 'llm', 0), ('timdettmers/bitsandbytes', 0.5144120454788208, 'util', 0), ('salesforce/xgen', 0.5127484798431396, 'llm', 1), ('lupantech/chameleon-llm', 0.5114999413490295, 'llm', 2), ('paddlepaddle/rocketqa', 0.5105500817298889, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5055248141288757, 'llm', 0), ('oobabooga/text-generation-webui', 0.5040669441223145, 'llm', 0), ('explosion/spacy-models', 0.5027161836624146, 'nlp', 1), ('juncongmoo/pyllama', 0.5018168091773987, 'llm', 0), ('mlc-ai/web-llm', 0.5006130933761597, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5003429055213928, 'llm', 0)]",6,3.0,,0.62,23,6,6,4,0,0,0,23.0,19.0,90.0,0.8,51
450,pandas,https://github.com/unionai-oss/pandera,[],,[],[],1.0,,,unionai-oss/pandera,pandera,2779,238,20,Python,https://www.union.ai/pandera,"A light-weight, flexible, and expressive statistical data testing library",unionai-oss,2024-01-12,2018-11-01,273,10.152922755741127,https://avatars.githubusercontent.com/u/94206482?v=4,"A light-weight, flexible, and expressive statistical data testing library","['assertions', 'data-assertions', 'data-check', 'data-cleaning', 'data-processing', 'data-validation', 'data-verification', 'dataframe-schema', 'dataframes', 'hypothesis-testing', 'pandas', 'pandas-dataframe', 'pandas-validation', 'pandas-validator', 'schema', 'testing', 'testing-tools', 'validation']","['assertions', 'data-assertions', 'data-check', 'data-cleaning', 'data-processing', 'data-validation', 'data-verification', 'dataframe-schema', 'dataframes', 'hypothesis-testing', 'pandas', 'pandas-dataframe', 'pandas-validation', 'pandas-validator', 'schema', 'testing', 'testing-tools', 'validation']",2023-12-15,"[('pandas-dev/pandas', 0.6850487589836121, 'pandas', 1), ('pyeve/cerberus', 0.6107540726661682, 'data', 1), ('ydataai/ydata-profiling', 0.5934801697731018, 'pandas', 2), ('hypothesisworks/hypothesis', 0.5685426592826843, 'testing', 1), ('python-odin/odin', 0.5636017322540283, 'util', 1), ('polyaxon/datatile', 0.55992591381073, 'pandas', 2), ('hi-primus/optimus', 0.557157576084137, 'ml-ops', 1), ('krzjoa/awesome-python-data-science', 0.5424999594688416, 'study', 0), ('tensorflow/data-validation', 0.5394289493560791, 'ml-ops', 0), ('dagworks-inc/hamilton', 0.5392426252365112, 'ml-ops', 1), ('datafold/data-diff', 0.5363107919692993, 'data', 0), ('great-expectations/great_expectations', 0.5351150035858154, 'ml-ops', 0), ('ydataai/ydata-quality', 0.5350058078765869, 'data', 1), ('plotly/dash', 0.5268102288246155, 'viz', 0), ('ibis-project/ibis', 0.525162398815155, 'data', 1), ('dylanhogg/awesome-python', 0.5203728079795837, 'study', 1), ('mementum/bta-lib', 0.5138059258460999, 'finance', 0), ('rasbt/mlxtend', 0.5126389265060425, 'ml', 0), ('man-group/dtale', 0.5104021430015564, 'viz', 1), ('lk-geimfari/mimesis', 0.509531557559967, 'data', 3), ('wolever/parameterized', 0.5074113607406616, 'testing', 0), ('tobymao/sqlglot', 0.5052539706230164, 'data', 0), ('saulpw/visidata', 0.5012566447257996, 'term', 1)]",111,4.0,,2.27,111,47,63,1,23,16,23,111.0,196.0,90.0,1.8,51
1310,study,https://github.com/promptslab/awesome-prompt-engineering,['awesome'],,[],[],,,,promptslab/awesome-prompt-engineering,Awesome-Prompt-Engineering,2728,231,49,Python,https://discord.gg/m88xfYMbK6,"This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc ",promptslab,2024-01-14,2023-02-09,50,53.79154929577465,https://avatars.githubusercontent.com/u/120981762?v=4,"This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc ","['chatgpt', 'chatgpt-api', 'deep-learning', 'few-shot-learning', 'gpt', 'gpt-3', 'machine-learning', 'openai', 'prompt', 'prompt-based-learning', 'prompt-engineering', 'prompt-generator', 'prompt-learning', 'prompt-toolkit', 'prompt-tuning', 'promptengineering', 'text-to-image', 'text-to-speech', 'text-to-video']","['awesome', 'chatgpt', 'chatgpt-api', 'deep-learning', 'few-shot-learning', 'gpt', 'gpt-3', 'machine-learning', 'openai', 'prompt', 'prompt-based-learning', 'prompt-engineering', 'prompt-generator', 'prompt-learning', 'prompt-toolkit', 'prompt-tuning', 'promptengineering', 'text-to-image', 'text-to-speech', 'text-to-video']",2024-01-04,"[('promptslab/promptify', 0.7121634483337402, 'nlp', 8), ('microsoft/generative-ai-for-beginners', 0.6337803602218628, 'study', 4), ('bigscience-workshop/promptsource', 0.5903843641281128, 'nlp', 1), ('hegelai/prompttools', 0.5741844773292542, 'llm', 3), ('saharmor/dalle-playground', 0.5731377005577087, 'diffusion', 3), ('thudm/p-tuning-v2', 0.5705534815788269, 'nlp', 1), ('microsoft/promptbase', 0.56931471824646, 'llm', 1), ('karpathy/mingpt', 0.5419448018074036, 'llm', 0), ('microsoft/lmops', 0.538474440574646, 'llm', 2), ('keirp/automatic_prompt_engineer', 0.5347884297370911, 'llm', 1), ('agenta-ai/agenta', 0.53215092420578, 'llm', 2), ('ofa-sys/ofa', 0.5315465927124023, 'llm', 2), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5258796811103821, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5253517031669617, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5253517031669617, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.5090713500976562, 'study', 0), ('kyegomez/tree-of-thoughts', 0.5084075927734375, 'llm', 6), ('openai/image-gpt', 0.5061761140823364, 'llm', 0), ('openlmlab/moss', 0.5036895871162415, 'llm', 2), ('open-mmlab/mmediting', 0.5029655694961548, 'ml', 1), ('thilinarajapakse/simpletransformers', 0.5007800459861755, 'nlp', 0), ('ist-daslab/gptq', 0.5007389187812805, 'llm', 0)]",13,8.0,,2.08,3,3,11,0,0,0,0,3.0,0.0,90.0,0.0,51
1254,ml,https://github.com/huggingface/autotrain-advanced,[],AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models,[],[],,,,huggingface/autotrain-advanced,autotrain-advanced,2629,309,66,Python,https://huggingface.co/autotrain,🤗 AutoTrain Advanced,huggingface,2024-01-13,2020-12-15,163,16.12883435582822,https://avatars.githubusercontent.com/u/25720743?v=4,🤗 AutoTrain Advanced,"['autotrain', 'deep-learning', 'huggingface', 'machine-learning', 'natural-language-processing', 'natural-language-understanding']","['autotrain', 'deep-learning', 'huggingface', 'machine-learning', 'natural-language-processing', 'natural-language-understanding']",2023-12-28,"[('awslabs/autogluon', 0.6629055142402649, 'ml', 3), ('winedarksea/autots', 0.6144011616706848, 'time-series', 2), ('keras-team/autokeras', 0.613167405128479, 'ml-dl', 2), ('nccr-itmo/fedot', 0.5916618704795837, 'ml-ops', 1), ('mosaicml/composer', 0.5858451724052429, 'ml-dl', 2), ('microsoft/nni', 0.5753588080406189, 'ml', 2), ('automl/auto-sklearn', 0.5570406317710876, 'ml', 0), ('huggingface/transformers', 0.5453631281852722, 'nlp', 3), ('alpa-projects/alpa', 0.5402303338050842, 'ml-dl', 2), ('huggingface/datasets', 0.5383889079093933, 'nlp', 3), ('microsoft/flaml', 0.5361581444740295, 'ml', 3), ('explosion/thinc', 0.5315551161766052, 'ml-dl', 3), ('xplainable/xplainable', 0.5284545421600342, 'ml-interpretability', 1), ('makcedward/nlpaug', 0.5261876583099365, 'nlp', 2), ('jindongwang/transferlearning', 0.5214440822601318, 'ml', 2), ('huggingface/huggingface_hub', 0.5198477506637573, 'ml', 3), ('deepfakes/faceswap', 0.5193936228752136, 'ml-dl', 2), ('thilinarajapakse/simpletransformers', 0.517383873462677, 'nlp', 0), ('alibaba/easynlp', 0.5123465657234192, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5114012360572815, 'llm', 3), ('luodian/otter', 0.5102346539497375, 'llm', 2), ('docarray/docarray', 0.5089057087898254, 'data', 2), ('microsoft/unilm', 0.5079038143157959, 'nlp', 0), ('nvidia/deeplearningexamples', 0.5064716339111328, 'ml-dl', 1), ('google/trax', 0.5045042037963867, 'ml-dl', 2), ('torantulino/auto-gpt', 0.5032193660736084, 'llm', 0), ('microsoft/semi-supervised-learning', 0.5021505355834961, 'ml', 3), ('salesforce/blip', 0.5011047124862671, 'diffusion', 0), ('open-mmlab/mmediting', 0.5010733008384705, 'ml', 1), ('interpretml/interpret', 0.500921368598938, 'ml-interpretability', 1), ('christoschristofidis/awesome-deep-learning', 0.5007786750793457, 'study', 2), ('amanchadha/coursera-deep-learning-specialization', 0.500561535358429, 'study', 1)]",19,4.0,,7.81,255,236,37,1,0,0,0,255.0,618.0,90.0,2.4,51
1553,util,https://github.com/beeware/briefcase,"['package', 'python-to-exe', 'bundle']",,[],[],,,,beeware/briefcase,briefcase,2206,310,45,Python,https://briefcase.readthedocs.io/,Tools to support converting a Python project into a standalone native application.,beeware,2024-01-12,2015-07-28,444,4.968468468468468,https://avatars.githubusercontent.com/u/19795701?v=4,Tools to support converting a Python project into a standalone native application.,[],"['bundle', 'package', 'python-to-exe']",2024-01-14,"[('pyinstaller/pyinstaller', 0.6962027549743652, 'util', 3), ('ofek/pyapp', 0.6602802276611328, 'util', 1), ('indygreg/pyoxidizer', 0.6296486258506775, 'util', 0), ('pypa/hatch', 0.6087289452552795, 'util', 0), ('beeware/toga', 0.5662544965744019, 'gui', 0), ('pyodide/micropip', 0.561262845993042, 'util', 0), ('pypa/pipenv', 0.546190619468689, 'util', 0), ('pypy/pypy', 0.5429807901382446, 'util', 0), ('pypa/pipx', 0.5395711064338684, 'util', 0), ('mitsuhiko/rye', 0.5340247750282288, 'util', 0), ('willmcgugan/textual', 0.5323150753974915, 'term', 0), ('python-poetry/poetry', 0.5169381499290466, 'util', 0), ('connorferster/handcalcs', 0.5107748508453369, 'jupyter', 0), ('dosisod/refurb', 0.5095663666725159, 'util', 0), ('hoffstadt/dearpygui', 0.5077422261238098, 'gui', 0), ('r0x0r/pywebview', 0.5074366927146912, 'gui', 0), ('linkedin/shiv', 0.5067877173423767, 'util', 0), ('kubeflow/fairing', 0.5065484046936035, 'ml-ops', 0), ('samuelcolvin/python-devtools', 0.5053153038024902, 'debug', 0), ('pypa/flit', 0.5033259987831116, 'util', 0)]",135,7.0,,18.13,113,90,103,0,5,7,5,113.0,198.0,90.0,1.8,51
1160,jupyter,https://github.com/mito-ds/monorepo,[],,[],[],,,,mito-ds/monorepo,mito,2096,143,22,Python,https://trymito.io,"The mitosheet package, trymito.io, and other public Mito code.",mito-ds,2024-01-12,2022-01-20,105,19.82702702702703,https://avatars.githubusercontent.com/u/55325926?v=4,"The mitosheet package, trymito.io, and other public Mito code.","['data', 'data-analysis', 'data-science', 'data-visualization', 'jupyter', 'pandas', 'streamlit-component']","['data', 'data-analysis', 'data-science', 'data-visualization', 'jupyter', 'pandas', 'streamlit-component']",2024-01-09,"[('holoviz/panel', 0.5779578685760498, 'viz', 1), ('saulpw/visidata', 0.5757395625114441, 'term', 1), ('polyaxon/datatile', 0.5668829083442688, 'pandas', 3), ('ta-lib/ta-lib-python', 0.5616872906684875, 'finance', 0), ('vaexio/vaex', 0.5610413551330566, 'perf', 1), ('rapidsai/cudf', 0.5557620525360107, 'pandas', 3), ('wesm/pydata-book', 0.5535025000572205, 'study', 0), ('man-group/dtale', 0.5518519282341003, 'viz', 4), ('kubeflow-kale/kale', 0.5466421842575073, 'ml-ops', 0), ('plotly/dash', 0.5446365475654602, 'viz', 3), ('jakevdp/pythondatasciencehandbook', 0.5431113243103027, 'study', 1), ('twopirllc/pandas-ta', 0.5410447716712952, 'finance', 1), ('pyqtgraph/pyqtgraph', 0.5288783311843872, 'viz', 0), ('fchollet/deep-learning-with-python-notebooks', 0.525928795337677, 'study', 0), ('gradio-app/gradio', 0.5249914526939392, 'viz', 3), ('geopandas/geopandas', 0.5150602459907532, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.5128975510597229, 'study', 3), ('mwaskom/seaborn', 0.5123574733734131, 'viz', 3), ('alphasecio/langchain-examples', 0.5109399557113647, 'llm', 0), ('astral-sh/ruff', 0.5106766223907471, 'util', 0), ('lux-org/lux', 0.509769856929779, 'viz', 3), ('cohere-ai/notebooks', 0.5074410438537598, 'llm', 0), ('imageio/imageio', 0.5070230960845947, 'util', 0), ('scitools/iris', 0.5061976909637451, 'gis', 1), ('simonw/datasette', 0.5057903528213501, 'data', 0), ('pandas-dev/pandas', 0.5044918656349182, 'pandas', 3), ('delta-io/delta-rs', 0.503544807434082, 'pandas', 1), ('jovianml/opendatasets', 0.5031294822692871, 'data', 1), ('mementum/bta-lib', 0.5002267360687256, 'finance', 0)]",7,2.0,,42.33,272,214,24,0,0,0,0,272.0,366.0,90.0,1.3,51
316,gamedev,https://github.com/pyglet/pyglet,[],,[],[],,,,pyglet/pyglet,pyglet,1675,282,31,Python,http://pyglet.org,"pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications.",pyglet,2024-01-13,2019-06-09,242,6.913325471698113,https://avatars.githubusercontent.com/u/51539834?v=4,"pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications.","['gamedev', 'opengl', 'pyglet', 'scientific-visualization']","['gamedev', 'opengl', 'pyglet', 'scientific-visualization']",2024-01-13,"[('pygame/pygame', 0.717469334602356, 'gamedev', 1), ('hoffstadt/dearpygui', 0.6631710529327393, 'gui', 0), ('pypy/pypy', 0.6459312438964844, 'util', 0), ('pysimplegui/pysimplegui', 0.624721884727478, 'gui', 0), ('python/cpython', 0.5984718799591064, 'util', 0), ('pygamelib/pygamelib', 0.5976749062538147, 'gamedev', 1), ('r0x0r/pywebview', 0.593908965587616, 'gui', 0), ('viblo/pymunk', 0.5925350189208984, 'sim', 1), ('wxwidgets/phoenix', 0.5862021446228027, 'gui', 0), ('urwid/urwid', 0.5836073160171509, 'term', 0), ('webpy/webpy', 0.5836023688316345, 'web', 0), ('maartenbreddels/ipyvolume', 0.580999493598938, 'jupyter', 1), ('holoviz/holoviz', 0.5789063572883606, 'viz', 0), ('beeware/toga', 0.572860598564148, 'gui', 0), ('altair-viz/altair', 0.5653769969940186, 'viz', 0), ('imageio/imageio', 0.5611344575881958, 'util', 0), ('pyston/pyston', 0.5540895462036133, 'util', 0), ('pygments/pygments', 0.5452966094017029, 'util', 0), ('jquast/blessed', 0.5451313853263855, 'term', 0), ('lordmauve/pgzero', 0.544232964515686, 'gamedev', 0), ('graphistry/pygraphistry', 0.5440356135368347, 'data', 0), ('eleutherai/pyfra', 0.5430480241775513, 'ml', 0), ('pympler/pympler', 0.5425298810005188, 'perf', 0), ('pytoolz/toolz', 0.5421815514564514, 'util', 0), ('pypa/virtualenv', 0.541969895362854, 'util', 0), ('inducer/pudb', 0.5417070984840393, 'debug', 0), ('kivy/kivy', 0.5390869975090027, 'util', 0), ('matplotlib/matplotlib', 0.5357295870780945, 'viz', 0), ('pylons/pyramid', 0.5355060696601868, 'web', 0), ('bokeh/bokeh', 0.5342501401901245, 'viz', 0), ('alexmojaki/snoop', 0.5321446061134338, 'debug', 0), ('parthjadhav/tkinter-designer', 0.5310909748077393, 'gui', 0), ('pythonarcade/arcade', 0.5264350175857544, 'gamedev', 1), ('willmcgugan/textual', 0.5208635330200195, 'term', 0), ('connorferster/handcalcs', 0.5195719003677368, 'jupyter', 0), ('gboeing/pynamical', 0.5189253091812134, 'sim', 0), ('alexmojaki/heartrate', 0.5163092613220215, 'debug', 0), ('opengeos/leafmap', 0.5158253312110901, 'gis', 0), ('bottlepy/bottle', 0.5153478384017944, 'web', 0), ('zulko/moviepy', 0.5143889784812927, 'util', 0), ('panda3d/panda3d', 0.5137691497802734, 'gamedev', 2), ('vispy/vispy', 0.5124850273132324, 'viz', 1), ('wesm/pydata-book', 0.5112947225570679, 'study', 0), ('pyodide/pyodide', 0.5096766948699951, 'util', 0), ('scitools/cartopy', 0.5082891583442688, 'gis', 0), ('holoviz/geoviews', 0.5069261789321899, 'gis', 0), ('py4j/py4j', 0.5067098140716553, 'util', 0), ('holoviz/panel', 0.5058193206787109, 'viz', 0), ('python-pillow/pillow', 0.505748450756073, 'util', 0), ('erotemic/ubelt', 0.5050175786018372, 'util', 0), ('jiffyclub/snakeviz', 0.5042890310287476, 'profiling', 0), ('plotly/plotly.py', 0.5040709972381592, 'viz', 0), ('kanaries/pygwalker', 0.5040571689605713, 'pandas', 0), ('landscapeio/prospector', 0.5031312108039856, 'util', 0)]",156,4.0,,5.0,92,69,56,0,8,13,8,92.0,193.0,90.0,2.1,51
1563,llm,https://github.com/ray-project/llm-applications,['rag'],,[],[],,,,ray-project/llm-applications,llm-applications,1207,135,14,Jupyter Notebook,,A comprehensive guide to building RAG-based LLM applications for production.,ray-project,2024-01-13,2023-08-16,23,50.59281437125748,https://avatars.githubusercontent.com/u/22125274?v=4,A comprehensive guide to building RAG-based LLM applications for production.,"['anyscale', 'fine-tuning', 'llama2', 'llms', 'machine-learning', 'openai', 'ray', 'serving']","['anyscale', 'fine-tuning', 'llama2', 'llms', 'machine-learning', 'openai', 'rag', 'ray', 'serving']",2024-01-08,"[('alpha-vllm/llama2-accessory', 0.6254207491874695, 'llm', 1), ('bentoml/openllm', 0.6087220311164856, 'ml-ops', 2), ('tigerlab-ai/tiger', 0.6013271808624268, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5635949373245239, 'llm', 2), ('run-llama/llama-hub', 0.5521401166915894, 'data', 0), ('eugeneyan/open-llms', 0.5464956760406494, 'study', 1), ('hiyouga/llama-factory', 0.5320853590965271, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5320852994918823, 'llm', 2), ('jerryjliu/llama_index', 0.5219264030456543, 'llm', 2), ('langchain-ai/langsmith-cookbook', 0.515418529510498, 'llm', 0), ('tloen/alpaca-lora', 0.5138564109802246, 'llm', 0), ('berriai/litellm', 0.5112382173538208, 'llm', 1), ('deepset-ai/haystack', 0.5103896260261536, 'llm', 1), ('vllm-project/vllm', 0.5022127032279968, 'llm', 0)]",4,3.0,,1.33,28,21,5,0,8,22,8,28.0,5.0,90.0,0.2,51
1763,data,https://github.com/dlt-hub/dlt,"['duckdb', 'data-engineering']",,[],[],,,,dlt-hub/dlt,dlt,1028,71,14,Python,https://dlthub.com/docs,data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ ,dlt-hub,2024-01-13,2022-01-26,104,9.803814713896458,https://avatars.githubusercontent.com/u/89419010?v=4,data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ ,"['data', 'data-engineering', 'data-lake', 'data-loading', 'data-warehouse', 'elt', 'extract', 'load', 'transform']","['data', 'data-engineering', 'data-lake', 'data-loading', 'data-warehouse', 'duckdb', 'elt', 'extract', 'load', 'transform']",2024-01-12,"[('pandas-dev/pandas', 0.554896891117096, 'pandas', 0), ('dbt-labs/dbt-core', 0.5388659834861755, 'ml-ops', 1), ('pytables/pytables', 0.5339342355728149, 'data', 0), ('airbytehq/airbyte', 0.5255663394927979, 'data', 3), ('erotemic/ubelt', 0.5195381045341492, 'util', 0), ('holoviz/panel', 0.5181348323822021, 'viz', 0), ('pytorch/data', 0.5167723298072815, 'data', 0), ('databricks/dbt-databricks', 0.5166671276092529, 'data', 0), ('jovianml/opendatasets', 0.5093324184417725, 'data', 0), ('wesm/pydata-book', 0.5088979005813599, 'study', 0), ('imageio/imageio', 0.5059615969657898, 'util', 0), ('dagworks-inc/hamilton', 0.5049058198928833, 'ml-ops', 1), ('intake/intake', 0.5047716498374939, 'data', 0), ('saulpw/visidata', 0.5002819299697876, 'term', 0)]",39,3.0,,28.83,245,189,24,0,46,34,46,243.0,289.0,90.0,1.2,51
864,pandas,https://github.com/eventual-inc/daft,[],,[],[],,,,eventual-inc/daft,Daft,987,57,11,Rust,https://getdaft.io,"Distributed DataFrames for Python designed for the cloud, powered by Rust",eventual-inc,2024-01-12,2022-04-25,92,10.711627906976744,https://avatars.githubusercontent.com/u/98941975?v=4,"Distributed DataFrames for Python designed for the cloud, powered by Rust","['data-engineering', 'data-science', 'dataframe', 'deep-learning', 'distributed-computing', 'image-processing', 'machine-learning', 'rust']","['data-engineering', 'data-science', 'dataframe', 'deep-learning', 'distributed-computing', 'image-processing', 'machine-learning', 'rust']",2024-01-13,"[('backtick-se/cowait', 0.7438012361526489, 'util', 2), ('pola-rs/polars', 0.7005141377449036, 'pandas', 2), ('merantix-momentum/squirrel-core', 0.6807416081428528, 'ml', 3), ('fugue-project/fugue', 0.6778815388679504, 'pandas', 2), ('horovod/horovod', 0.6405372023582458, 'ml-ops', 2), ('delta-io/delta-rs', 0.6028104424476624, 'pandas', 1), ('fastai/fastcore', 0.572355329990387, 'util', 0), ('vaexio/vaex', 0.5715435743331909, 'perf', 3), ('dagworks-inc/hamilton', 0.5710514783859253, 'ml-ops', 4), ('dylanhogg/awesome-python', 0.5679042935371399, 'study', 3), ('sfu-db/connector-x', 0.5663425326347351, 'data', 2), ('gradio-app/gradio', 0.5626286864280701, 'viz', 3), ('lithops-cloud/lithops', 0.5597764849662781, 'ml-ops', 0), ('uber/petastorm', 0.557521641254425, 'data', 2), ('jmcarpenter2/swifter', 0.55370032787323, 'pandas', 0), ('polyaxon/datatile', 0.5458943843841553, 'pandas', 1), ('tensorflow/tensorflow', 0.5434378385543823, 'ml-dl', 2), ('krzjoa/awesome-python-data-science', 0.5433785915374756, 'study', 3), ('rapidsai/cudf', 0.5403209924697876, 'pandas', 2), ('uber/fiber', 0.5396055579185486, 'data', 2), ('netflix/metaflow', 0.5369682312011719, 'ml-ops', 2), ('hi-primus/optimus', 0.5366455316543579, 'ml-ops', 2), ('orchest/orchest', 0.5366385579109192, 'ml-ops', 2), ('pyo3/maturin', 0.5351252555847168, 'util', 1), ('aws/chalice', 0.5330685973167419, 'web', 0), ('apache/spark', 0.5322152972221375, 'data', 0), ('google/tf-quant-finance', 0.5302774906158447, 'finance', 0), ('pyinfra-dev/pyinfra', 0.5292609333992004, 'util', 0), ('eleutherai/pyfra', 0.5290564894676208, 'ml', 0), ('dask/dask', 0.5266561508178711, 'perf', 0), ('klen/muffin', 0.5256049036979675, 'web', 0), ('adap/flower', 0.5243489146232605, 'ml-ops', 2), ('aws/sagemaker-python-sdk', 0.5235341787338257, 'ml', 1), ('paddlepaddle/paddle', 0.5211067199707031, 'ml-dl', 2), ('pytables/pytables', 0.5193186402320862, 'data', 0), ('mlflow/mlflow', 0.5185021162033081, 'ml-ops', 1), ('nalepae/pandarallel', 0.5181572437286377, 'pandas', 0), ('masoniteframework/masonite', 0.5179644227027893, 'web', 0), ('darribas/gds_env', 0.5175384879112244, 'gis', 0), ('pandas-dev/pandas', 0.5169585347175598, 'pandas', 2), ('nevronai/metisfl', 0.515454113483429, 'ml', 2), ('kubeflow-kale/kale', 0.5150558352470398, 'ml-ops', 1), ('rustpython/rustpython', 0.5145300626754761, 'util', 1), ('determined-ai/determined', 0.5144329071044922, 'ml-ops', 3), ('cython/cython', 0.5138062238693237, 'util', 0), ('ydataai/ydata-profiling', 0.5120884776115417, 'pandas', 3), ('pallets/flask', 0.5096858143806458, 'web', 0), ('aws/aws-sdk-pandas', 0.5070556998252869, 'pandas', 2), ('polyaxon/polyaxon', 0.5069470405578613, 'ml-ops', 3), ('online-ml/river', 0.506720244884491, 'ml', 2), ('panda3d/panda3d', 0.5059236288070679, 'gamedev', 0), ('google/gin-config', 0.5050129890441895, 'util', 0), ('nficano/python-lambda', 0.5019513964653015, 'util', 0), ('jina-ai/jina', 0.5019222497940063, 'ml', 2), ('falconry/falcon', 0.5012557506561279, 'web', 0)]",17,3.0,,17.12,327,270,21,0,30,33,30,325.0,375.0,90.0,1.2,51
624,gis,https://github.com/pytroll/satpy,[],,[],[],,,,pytroll/satpy,satpy,978,277,35,Python,http://satpy.readthedocs.org/en/latest/,Python package for earth-observing satellite data processing,pytroll,2024-01-12,2016-02-09,416,2.3509615384615383,https://avatars.githubusercontent.com/u/13004956?v=4,Python package for earth-observing satellite data processing,"['dask', 'satellite', 'weather', 'xarray']","['dask', 'satellite', 'weather', 'xarray']",2024-01-10,"[('sentinel-hub/eo-learn', 0.6639524102210999, 'gis', 0), ('scitools/iris', 0.6135034561157227, 'gis', 0), ('sentinel-hub/sentinelhub-py', 0.6114635467529297, 'gis', 0), ('radiantearth/radiant-mlhub', 0.5896352529525757, 'gis', 0), ('cloudsen12/easystac', 0.5563086271286011, 'gis', 0), ('opengeos/earthformer', 0.5178881287574768, 'gis', 0), ('autoviml/auto_ts', 0.5144683718681335, 'time-series', 0), ('giswqs/geemap', 0.5017055869102478, 'gis', 0)]",149,7.0,,28.92,152,90,96,0,11,12,11,152.0,631.0,90.0,4.2,51
1737,llm,https://github.com/akariasai/self-rag,"['rag', 'retrieval']",,[],[],,,,akariasai/self-rag,self-rag,946,66,9,Python,https://selfrag.github.io/,"This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.",akariasai,2024-01-13,2023-10-10,16,59.125,,"This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.",[],"['rag', 'retrieval']",2023-12-22,[],7,2.0,,0.54,35,24,3,1,0,0,0,35.0,107.0,90.0,3.1,51
805,nlp,https://github.com/gunthercox/chatterbot,[],,[],[],,,,gunthercox/chatterbot,ChatterBot,13767,4453,553,Python,https://chatterbot.readthedocs.io,"ChatterBot is a machine learning, conversational dialog engine for creating chat bots",gunthercox,2024-01-13,2014-09-28,487,28.25241864555849,,"ChatterBot is a machine learning, conversational dialog engine for creating chat bots","['bot', 'chatbot', 'chatterbot', 'conversation', 'language', 'machine-learning']","['bot', 'chatbot', 'chatterbot', 'conversation', 'language', 'machine-learning']",2021-06-01,"[('togethercomputer/openchatkit', 0.6964467167854309, 'nlp', 1), ('rasahq/rasa', 0.6935678720474243, 'llm', 3), ('deeppavlov/deeppavlov', 0.6284914016723633, 'nlp', 3), ('rcgai/simplyretrieve', 0.5998932123184204, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5911657214164734, 'nlp', 2), ('nomic-ai/gpt4all', 0.5897710919380188, 'llm', 1), ('nvidia/nemo', 0.5878262519836426, 'nlp', 0), ('embedchain/embedchain', 0.5760533809661865, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5590612888336182, 'llm', 1), ('lm-sys/fastchat', 0.5577590465545654, 'llm', 1), ('errbotio/errbot', 0.5522263646125793, 'nlp', 1), ('minimaxir/simpleaichat', 0.5406122207641602, 'llm', 0), ('databrickslabs/dolly', 0.5377427339553833, 'llm', 1), ('cheshire-cat-ai/core', 0.53544682264328, 'llm', 1), ('deepset-ai/haystack', 0.5347136855125427, 'llm', 1), ('pathwaycom/llm-app', 0.5325236916542053, 'llm', 2), ('openai/gpt-discord-bot', 0.5279523730278015, 'llm', 0), ('blinkdl/chatrwkv', 0.5218847393989563, 'llm', 1), ('kalliope-project/kalliope', 0.518312394618988, 'util', 1), ('krohling/bondai', 0.5118715763092041, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5103239417076111, 'llm', 0), ('chatarena/chatarena', 0.5086801052093506, 'llm', 0)]",103,5.0,,0.0,24,1,113,32,0,9,9,24.0,23.0,90.0,1.0,50
676,diffusion,https://github.com/compvis/latent-diffusion,"['diffusion', 'image-generation']",,[],[],,,,compvis/latent-diffusion,latent-diffusion,9627,1286,89,Jupyter Notebook,,High-Resolution Image Synthesis with Latent Diffusion Models,compvis,2024-01-14,2021-12-20,110,87.40466926070039,https://avatars.githubusercontent.com/u/30233788?v=4,High-Resolution Image Synthesis with Latent Diffusion Models,[],"['diffusion', 'image-generation']",2022-07-26,"[('stability-ai/stablediffusion', 1.0000004768371582, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.6906744837760925, 'diffusion', 0), ('compvis/stable-diffusion', 0.655044674873352, 'diffusion', 2), ('openai/glide-text2im', 0.6436025500297546, 'diffusion', 0), ('huggingface/diffusers', 0.6299404501914978, 'diffusion', 2), ('sharonzhou/long_stable_diffusion', 0.5753589272499084, 'diffusion', 0), ('openai/point-e', 0.5376940369606018, 'util', 0), ('timothybrooks/instruct-pix2pix', 0.5207135081291199, 'diffusion', 0), ('kakaobrain/rq-vae-transformer', 0.5072487592697144, 'ml-dl', 0)]",5,1.0,,0.0,67,13,25,18,0,0,0,67.0,147.0,90.0,2.2,50
135,ml-dl,https://github.com/keras-team/autokeras,[],,[],[],,,,keras-team/autokeras,autokeras,9016,1419,303,Python,http://autokeras.com/,AutoML library for deep learning,keras-team,2024-01-13,2017-11-19,323,27.888643393725143,https://avatars.githubusercontent.com/u/34455048?v=4,AutoML library for deep learning,"['autodl', 'automated-machine-learning', 'automl', 'deep-learning', 'keras', 'machine-learning', 'neural-architecture-search', 'tensorflow']","['autodl', 'automated-machine-learning', 'automl', 'deep-learning', 'keras', 'machine-learning', 'neural-architecture-search', 'tensorflow']",2023-10-02,"[('microsoft/nni', 0.8086925148963928, 'ml', 6), ('awslabs/autogluon', 0.7593497633934021, 'ml', 4), ('microsoft/flaml', 0.7519674897193909, 'ml', 4), ('rafiqhasan/auto-tensorflow', 0.6616485714912415, 'ml-dl', 3), ('mljar/mljar-supervised', 0.6605311036109924, 'ml', 3), ('winedarksea/autots', 0.6600058078765869, 'time-series', 3), ('alpa-projects/alpa', 0.6452708840370178, 'ml-dl', 2), ('nccr-itmo/fedot', 0.6287457346916199, 'ml-ops', 3), ('explosion/thinc', 0.6246259808540344, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.622309148311615, 'ml-rl', 2), ('automl/auto-sklearn', 0.616755485534668, 'ml', 2), ('huggingface/autotrain-advanced', 0.613167405128479, 'ml', 2), ('ggerganov/ggml', 0.6085128784179688, 'ml', 1), ('tensorflow/tensor2tensor', 0.5915209650993347, 'ml', 2), ('onnx/onnx', 0.5896108746528625, 'ml', 4), ('neuralmagic/sparseml', 0.5828292369842529, 'ml-dl', 3), ('karpathy/micrograd', 0.5800349712371826, 'study', 0), ('huggingface/transformers', 0.5780196189880371, 'nlp', 3), ('huggingface/datasets', 0.5751702785491943, 'nlp', 3), ('nvidia/deeplearningexamples', 0.5739009976387024, 'ml-dl', 2), ('activeloopai/deeplake', 0.5705196857452393, 'ml-ops', 3), ('tensorflow/tensorflow', 0.5696538090705872, 'ml-dl', 3), ('keras-team/keras', 0.5688300728797913, 'ml-dl', 3), ('featurelabs/featuretools', 0.5649420022964478, 'ml', 3), ('ray-project/ray', 0.5593129396438599, 'ml-ops', 4), ('rasbt/machine-learning-book', 0.5562219023704529, 'study', 2), ('ludwig-ai/ludwig', 0.5494021773338318, 'ml-ops', 2), ('apple/coremltools', 0.546789288520813, 'ml', 2), ('mosaicml/composer', 0.5466687083244324, 'ml-dl', 2), ('lutzroeder/netron', 0.5447315573692322, 'ml', 4), ('d2l-ai/d2l-en', 0.5389357805252075, 'study', 4), ('google/automl', 0.5386403799057007, 'ml', 1), ('keras-team/keras-nlp', 0.5352697372436523, 'nlp', 4), ('ashleve/lightning-hydra-template', 0.534925103187561, 'util', 1), ('mlflow/mlflow', 0.5325337052345276, 'ml-ops', 1), ('uber/petastorm', 0.5320855975151062, 'data', 3), ('google/trax', 0.5302404761314392, 'ml-dl', 2), ('shankarpandala/lazypredict', 0.5269849896430969, 'ml', 2), ('zenml-io/zenml', 0.5269325375556946, 'ml-ops', 4), ('huggingface/exporters', 0.5266240239143372, 'ml', 3), ('microsoft/deepspeed', 0.5261964201927185, 'ml-dl', 2), ('deepmind/dm-haiku', 0.5257297158241272, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.5244501233100891, 'ml', 2), ('polyaxon/polyaxon', 0.5219746232032776, 'ml-ops', 4), ('keras-rl/keras-rl', 0.5215295553207397, 'ml-rl', 3), ('xplainable/xplainable', 0.5181999802589417, 'ml-interpretability', 1), ('lucidrains/toolformer-pytorch', 0.516575276851654, 'llm', 1), ('pytorch/pytorch', 0.5157300233840942, 'ml-dl', 2), ('merantix-momentum/squirrel-core', 0.5147408246994019, 'ml', 3), ('microsoft/semi-supervised-learning', 0.514286458492279, 'ml', 2), ('horovod/horovod', 0.5139292478561401, 'ml-ops', 4), ('epistasislab/tpot', 0.5138368010520935, 'ml', 3), ('ourownstory/neural_prophet', 0.5104817748069763, 'ml', 2), ('aistream-peelout/flow-forecast', 0.5103901624679565, 'time-series', 1), ('oml-team/open-metric-learning', 0.5088566541671753, 'ml', 1), ('salesforce/deeptime', 0.5080786347389221, 'time-series', 1), ('microsoft/onnxruntime', 0.507604718208313, 'ml', 3), ('google/tf-quant-finance', 0.5068373680114746, 'finance', 1), ('googlecloudplatform/vertex-ai-samples', 0.5066843032836914, 'ml', 0), ('patchy631/machine-learning', 0.5066499710083008, 'ml', 0), ('pytorch/ignite', 0.5053526759147644, 'ml-dl', 2), ('iperov/deepfacelab', 0.5044635534286499, 'ml-dl', 2), ('amanchadha/coursera-deep-learning-specialization', 0.5036728382110596, 'study', 1), ('albumentations-team/albumentations', 0.5026241540908813, 'ml-dl', 2), ('torantulino/auto-gpt', 0.5023127198219299, 'llm', 0), ('lightly-ai/lightly', 0.5014397501945496, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.5002623200416565, 'perf', 2)]",143,4.0,,0.37,5,0,75,3,1,9,1,5.0,7.0,90.0,1.4,50
942,web,https://github.com/bottlepy/bottle,[],,[],[],,,,bottlepy/bottle,bottle,8217,1471,312,Python,http://bottlepy.org/,bottle.py is a fast and simple micro-framework for python web-applications.,bottlepy,2024-01-13,2009-06-30,761,10.797634691195794,https://avatars.githubusercontent.com/u/1019799?v=4,bottle.py is a fast and simple micro-framework for python web-applications.,"['bottle', 'rest', 'web-framework', 'wsgi']","['bottle', 'rest', 'web-framework', 'wsgi']",2024-01-03,"[('pallets/flask', 0.8060166835784912, 'web', 2), ('webpy/webpy', 0.7816590070724487, 'web', 0), ('pylons/pyramid', 0.7196846008300781, 'web', 2), ('cherrypy/cherrypy', 0.6888998746871948, 'web', 0), ('pallets/werkzeug', 0.6876417994499207, 'web', 1), ('masoniteframework/masonite', 0.6854233741760254, 'web', 0), ('falconry/falcon', 0.677134096622467, 'web', 2), ('willmcgugan/textual', 0.661994218826294, 'term', 0), ('reflex-dev/reflex', 0.6463294625282288, 'web', 0), ('scrapy/scrapy', 0.6345263123512268, 'data', 0), ('python-restx/flask-restx', 0.628285825252533, 'web', 1), ('eleutherai/pyfra', 0.6280529499053955, 'ml', 0), ('pypy/pypy', 0.623259961605072, 'util', 0), ('klen/muffin', 0.6195411086082458, 'web', 0), ('benoitc/gunicorn', 0.6153336763381958, 'web', 1), ('pyodide/pyodide', 0.6117225289344788, 'util', 0), ('pallets/quart', 0.5997056365013123, 'web', 0), ('pyeve/eve', 0.594473659992218, 'web', 1), ('pyinfra-dev/pyinfra', 0.590758204460144, 'util', 0), ('pylons/waitress', 0.5867997407913208, 'web', 0), ('neoteroi/blacksheep', 0.5855295062065125, 'web', 0), ('pyodide/micropip', 0.5800544023513794, 'util', 0), ('simple-salesforce/simple-salesforce', 0.5755710601806641, 'data', 0), ('r0x0r/pywebview', 0.5649958252906799, 'gui', 0), ('ets-labs/python-dependency-injector', 0.5632432699203491, 'util', 0), ('ethereum/web3.py', 0.5624367594718933, 'crypto', 0), ('flet-dev/flet', 0.5521423816680908, 'web', 0), ('1200wd/bitcoinlib', 0.5447421669960022, 'crypto', 0), ('requests/toolbelt', 0.5425326228141785, 'util', 0), ('hoffstadt/dearpygui', 0.541211724281311, 'gui', 0), ('stephenmcd/mezzanine', 0.5408180952072144, 'web', 0), ('encode/uvicorn', 0.5337467193603516, 'web', 0), ('timofurrer/awesome-asyncio', 0.5336396098136902, 'study', 0), ('indico/indico', 0.5327122211456299, 'web', 0), ('clips/pattern', 0.5306470990180969, 'nlp', 0), ('encode/httpx', 0.5296392440795898, 'web', 0), ('man-c/pycoingecko', 0.5294601917266846, 'crypto', 0), ('django/django', 0.5293058156967163, 'web', 0), ('dddomodossola/remi', 0.5286058187484741, 'gui', 0), ('klen/py-frameworks-bench', 0.5283790826797485, 'perf', 0), ('pypa/hatch', 0.5236046314239502, 'util', 0), ('plotly/dash', 0.5221378803253174, 'viz', 0), ('vitalik/django-ninja', 0.5206802487373352, 'web', 0), ('backtick-se/cowait', 0.5192388892173767, 'util', 0), ('fastai/fastcore', 0.5190497040748596, 'util', 0), ('tiangolo/sqlmodel', 0.5186842679977417, 'data', 0), ('tornadoweb/tornado', 0.5174039006233215, 'web', 0), ('dylanhogg/awesome-python', 0.5162600874900818, 'study', 0), ('holoviz/panel', 0.5157780647277832, 'viz', 0), ('pyglet/pyglet', 0.5153478384017944, 'gamedev', 0), ('indygreg/pyoxidizer', 0.514263391494751, 'util', 0), ('goldmansachs/gs-quant', 0.5141693949699402, 'finance', 0), ('feincms/feincms', 0.5126698613166809, 'web', 0), ('sqlalchemy/mako', 0.5123538970947266, 'template', 0), ('alirn76/panther', 0.5107240676879883, 'web', 0), ('voila-dashboards/voila', 0.5106417536735535, 'jupyter', 0), ('urwid/urwid', 0.5075056552886963, 'term', 0), ('pycqa/pylint-django', 0.5043782591819763, 'util', 0), ('rawheel/fastapi-boilerplate', 0.5016167759895325, 'web', 0)]",226,8.0,,0.02,12,6,177,5,0,6,6,12.0,27.0,90.0,2.2,50
96,perf,https://github.com/vaexio/vaex,[],,[],[],,,,vaexio/vaex,vaex,8106,590,145,Python,https://vaex.io,"Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀",vaexio,2024-01-14,2014-09-27,487,16.630128956623683,https://avatars.githubusercontent.com/u/45720408?v=4,"Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀","['bigdata', 'data-science', 'dataframe', 'hdf5', 'machine-learning', 'machinelearning', 'memory-mapped-file', 'pyarrow', 'tabular-data', 'visualization']","['bigdata', 'data-science', 'dataframe', 'hdf5', 'machine-learning', 'machinelearning', 'memory-mapped-file', 'pyarrow', 'tabular-data', 'visualization']",2023-07-21,"[('apache/arrow', 0.6739583611488342, 'data', 0), ('blaze/blaze', 0.6084710359573364, 'pandas', 0), ('ibis-project/ibis', 0.6082955598831177, 'data', 1), ('man-group/dtale', 0.6020064353942871, 'viz', 2), ('pytables/pytables', 0.6015515923500061, 'data', 0), ('holoviz/panel', 0.5968863368034363, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.592536449432373, 'viz', 1), ('fastai/fastcore', 0.5877453088760376, 'util', 0), ('pola-rs/polars', 0.5824528336524963, 'pandas', 1), ('jazzband/tablib', 0.575348973274231, 'data', 0), ('eventual-inc/daft', 0.5715435743331909, 'pandas', 3), ('graphistry/pygraphistry', 0.5663058161735535, 'data', 1), ('rapidsai/cudf', 0.56623375415802, 'pandas', 2), ('holoviz/datashader', 0.5661620497703552, 'gis', 0), ('polyaxon/datatile', 0.5635982155799866, 'pandas', 1), ('apache/spark', 0.5628867149353027, 'data', 0), ('mito-ds/monorepo', 0.5610413551330566, 'jupyter', 1), ('contextlab/hypertools', 0.548748791217804, 'ml', 1), ('scikit-hep/awkward-1.0', 0.5477538704872131, 'data', 0), ('huggingface/datasets', 0.545326828956604, 'nlp', 1), ('jmcarpenter2/swifter', 0.5449737906455994, 'pandas', 0), ('dylanhogg/awesome-python', 0.5425941944122314, 'study', 2), ('jakevdp/pythondatasciencehandbook', 0.5422857999801636, 'study', 0), ('mljar/mljar-supervised', 0.5394431352615356, 'ml', 2), ('gradio-app/gradio', 0.5373766422271729, 'viz', 2), ('residentmario/geoplot', 0.5356773734092712, 'gis', 0), ('pandas-dev/pandas', 0.5320824980735779, 'pandas', 2), ('cython/cython', 0.5234464406967163, 'util', 0), ('holoviz/holoviz', 0.5222266316413879, 'viz', 0), ('kanaries/pygwalker', 0.5217480063438416, 'pandas', 2), ('bokeh/bokeh', 0.5212001800537109, 'viz', 1), ('fugue-project/fugue', 0.5170252919197083, 'pandas', 1), ('quantopian/qgrid', 0.5162068009376526, 'jupyter', 0), ('hi-primus/optimus', 0.5148595571517944, 'ml-ops', 3), ('wesm/pydata-book', 0.5123580098152161, 'study', 0), ('nalepae/pandarallel', 0.5113261938095093, 'pandas', 0), ('astanin/python-tabulate', 0.5106703042984009, 'util', 0), ('ploomber/ploomber', 0.5101336240768433, 'ml-ops', 2), ('ydataai/ydata-synthetic', 0.5099949836730957, 'data', 1), ('lux-org/lux', 0.5093144178390503, 'viz', 2), ('kubeflow-kale/kale', 0.5090245008468628, 'ml-ops', 1), ('holoviz/hvplot', 0.5080651640892029, 'pandas', 0), ('saulpw/visidata', 0.5060981512069702, 'term', 2), ('tobymao/sqlglot', 0.5004581809043884, 'data', 0), ('uber/petastorm', 0.500319242477417, 'data', 2), ('plotly/dash', 0.5000450611114502, 'viz', 1)]",72,6.0,,0.52,19,2,113,6,0,40,40,19.0,23.0,90.0,1.2,50
321,web,https://github.com/graphql-python/graphene,[],,[],[],,,,graphql-python/graphene,graphene,7884,871,141,Python,http://graphene-python.org/,GraphQL framework for Python,graphql-python,2024-01-14,2015-09-24,435,18.094426229508198,https://avatars.githubusercontent.com/u/15002022?v=4,GraphQL framework for Python,"['framework', 'graphene', 'graphql', 'relay']","['framework', 'graphene', 'graphql', 'relay']",2023-10-06,"[('pygraphviz/pygraphviz', 0.5481510162353516, 'viz', 0), ('accenture/cymple', 0.5159372091293335, 'data', 0), ('westhealth/pyvis', 0.5054649114608765, 'graph', 0), ('pydot/pydot', 0.5014999508857727, 'viz', 0)]",207,6.0,,0.37,15,7,101,3,2,7,2,15.0,15.0,90.0,1.0,50
154,util,https://github.com/marshmallow-code/marshmallow,[],,[],[],,,,marshmallow-code/marshmallow,marshmallow,6778,640,82,Python,https://marshmallow.readthedocs.io/,A lightweight library for converting complex objects to and from simple Python datatypes.,marshmallow-code,2024-01-13,2013-11-10,533,12.709884811143851,https://avatars.githubusercontent.com/u/10334301?v=4,A lightweight library for converting complex objects to and from simple Python datatypes.,"['deserialization', 'marshalling', 'schema', 'serde', 'serialization', 'validation']","['deserialization', 'marshalling', 'schema', 'serde', 'serialization', 'validation']",2024-01-10,"[('pyeve/cerberus', 0.6555448174476624, 'data', 0), ('python-odin/odin', 0.6529213190078735, 'util', 1), ('pylons/colander', 0.6454058885574341, 'util', 3), ('yukinarit/pyserde', 0.6370522379875183, 'util', 2), ('jsonpickle/jsonpickle', 0.6203814744949341, 'data', 2), ('pydantic/pydantic', 0.606956422328949, 'util', 2), ('instagram/libcst', 0.6035540699958801, 'util', 0), ('pytoolz/toolz', 0.5878369808197021, 'util', 0), ('uqfoundation/dill', 0.5810075402259827, 'data', 0), ('lk-geimfari/mimesis', 0.5606078505516052, 'data', 1), ('instagram/monkeytype', 0.5554887056350708, 'typing', 0), ('facebook/pyre-check', 0.5461035966873169, 'typing', 0), ('pyston/pyston', 0.5445635914802551, 'util', 0), ('brokenloop/jsontopydantic', 0.5406548380851746, 'util', 0), ('tiangolo/sqlmodel', 0.539881706237793, 'data', 0), ('pandas-dev/pandas', 0.5381511449813843, 'pandas', 0), ('lidatong/dataclasses-json', 0.5334739089012146, 'util', 0), ('samuelcolvin/rtoml', 0.5252963900566101, 'data', 1), ('fabiocaccamo/python-benedict', 0.5224064588546753, 'util', 0), ('konradhalas/dacite', 0.5163299441337585, 'util', 0), ('xrudelis/pytrait', 0.5146151185035706, 'util', 0), ('strawberry-graphql/strawberry', 0.5134101510047913, 'web', 0), ('fastai/fastcore', 0.5120862722396851, 'util', 0), ('google/pytype', 0.5046632289886475, 'typing', 0), ('imageio/imageio', 0.503285825252533, 'util', 0), ('kubeflow/fairing', 0.5004829168319702, 'ml-ops', 0)]",208,1.0,,2.23,43,33,124,0,0,17,17,43.0,30.0,90.0,0.7,50
508,ml,https://github.com/google/automl,[],,[],[],,,,google/automl,automl,6083,1464,156,Jupyter Notebook,,Google Brain AutoML,google,2024-01-13,2020-03-12,202,30.007751937984494,https://avatars.githubusercontent.com/u/1342004?v=4,Google Brain AutoML,"['automl', 'efficientdet', 'efficientnet', 'efficientnetv2', 'object-detection']","['automl', 'efficientdet', 'efficientnet', 'efficientnetv2', 'object-detection']",2023-12-14,"[('rwightman/pytorch-image-models', 0.5850779414176941, 'ml-dl', 1), ('nvlabs/gcvit', 0.5431532859802246, 'diffusion', 1), ('keras-team/autokeras', 0.5386403799057007, 'ml-dl', 1), ('blakeblackshear/frigate', 0.5250091552734375, 'util', 1), ('deepmind/deepmind-research', 0.5214808583259583, 'ml', 0), ('deci-ai/super-gradients', 0.5212241411209106, 'ml-dl', 1), ('awslabs/autogluon', 0.5180317163467407, 'ml', 2), ('karpathy/micrograd', 0.5122846364974976, 'study', 0), ('googlecloudplatform/practical-ml-vision-book', 0.5014958381652832, 'study', 0)]",42,4.0,,0.38,14,6,47,1,0,1,1,14.0,11.0,90.0,0.8,50
300,data,https://github.com/kaggle/kaggle-api,[],,[],[],,,,kaggle/kaggle-api,kaggle-api,5734,1050,194,Python,,Official Kaggle API,kaggle,2024-01-14,2018-01-25,313,18.27777777777778,https://avatars.githubusercontent.com/u/1336944?v=4,Official Kaggle API,[],[],2024-01-08,[],37,2.0,,0.15,40,17,73,0,5,1,5,40.0,52.0,90.0,1.3,50
308,crypto,https://github.com/crytic/slither,[],,[],[],,,,crytic/slither,slither,4790,894,68,Python,https://blog.trailofbits.com/2018/10/19/slither-a-solidity-static-analysis-framework/,Static Analyzer for Solidity and Vyper,crytic,2024-01-13,2018-09-05,281,16.994424733907756,https://avatars.githubusercontent.com/u/48330002?v=4,Static Analyzer for Solidity and Vyper,"['ethereum', 'solidity', 'static-analysis', 'vyper']","['ethereum', 'solidity', 'static-analysis', 'vyper']",2023-10-18,"[('google/pytype', 0.6767980456352234, 'typing', 1), ('instagram/monkeytype', 0.5720254182815552, 'typing', 0), ('astral-sh/ruff', 0.5403991937637329, 'util', 1)]",126,1.0,,12.58,132,61,65,3,5,7,5,132.0,119.0,90.0,0.9,50
1670,testing,https://github.com/getsentry/responses,"['mocking', 'requests']",,[],[],,,,getsentry/responses,responses,3994,339,91,Python,,A utility for mocking out the Python Requests library.,getsentry,2024-01-13,2013-11-15,532,7.499463519313305,https://avatars.githubusercontent.com/u/1396951?v=4,A utility for mocking out the Python Requests library.,['tag-production'],"['mocking', 'requests', 'tag-production']",2024-01-10,"[('jamielennox/requests-mock', 0.7649958729743958, 'testing', 1), ('requests/toolbelt', 0.7001904249191284, 'util', 0), ('wolever/parameterized', 0.6193545460700989, 'testing', 0), ('psf/requests', 0.6119535565376282, 'web', 1), ('lundberg/respx', 0.5848559737205505, 'testing', 1), ('nedbat/coveragepy', 0.5841493606567383, 'testing', 0), ('lk-geimfari/mimesis', 0.5823182463645935, 'data', 0), ('taverntesting/tavern', 0.5727768540382385, 'testing', 0), ('pytest-dev/pytest-mock', 0.5724997520446777, 'testing', 0), ('kevin1024/vcrpy', 0.5679908990859985, 'testing', 1), ('eleutherai/pyfra', 0.5386306047439575, 'ml', 0), ('buildbot/buildbot', 0.5227794647216797, 'util', 0), ('pytoolz/toolz', 0.52274489402771, 'util', 0), ('pympler/pympler', 0.520066499710083, 'perf', 0), ('pmorissette/bt', 0.5153909921646118, 'finance', 0), ('mementum/backtrader', 0.5119891166687012, 'finance', 0), ('ionelmc/pytest-benchmark', 0.5085520148277283, 'testing', 0), ('google/python-fire', 0.5072945952415466, 'term', 0), ('encode/httpx', 0.5034039616584778, 'web', 0), ('snyk/faker-security', 0.5025610327720642, 'security', 0), ('locustio/locust', 0.5024783611297607, 'testing', 0), ('mkdocstrings/griffe', 0.5013567209243774, 'util', 0)]",124,5.0,,0.92,27,23,124,0,6,5,6,27.0,60.0,90.0,2.2,50
320,gui,https://github.com/r0x0r/pywebview,[],,[],[],,,,r0x0r/pywebview,pywebview,3981,507,62,Python,https://pywebview.flowrl.com,"Build GUI for your Python program with JavaScript, HTML, and CSS",r0x0r,2024-01-13,2014-11-20,479,8.298689696247767,,"Build GUI for your Python program with JavaScript, HTML, and CSS","['cef', 'cocoa', 'gtk', 'gui', 'html', 'javascript', 'linux', 'osx', 'qt', 'webkit', 'windows']","['cef', 'cocoa', 'gtk', 'gui', 'html', 'javascript', 'linux', 'osx', 'qt', 'webkit', 'windows']",2023-12-10,"[('willmcgugan/textual', 0.6927530765533447, 'term', 0), ('parthjadhav/tkinter-designer', 0.6899272799491882, 'gui', 1), ('kivy/kivy', 0.6854493618011475, 'util', 2), ('hoffstadt/dearpygui', 0.6651965379714966, 'gui', 3), ('beeware/toga', 0.6624377965927124, 'gui', 1), ('pallets/flask', 0.6464259624481201, 'web', 0), ('pysimplegui/pysimplegui', 0.6416908502578735, 'gui', 2), ('webpy/webpy', 0.6379135251045227, 'web', 0), ('masoniteframework/masonite', 0.6317577362060547, 'web', 0), ('urwid/urwid', 0.6302554607391357, 'term', 0), ('reflex-dev/reflex', 0.6069856882095337, 'web', 0), ('plotly/dash', 0.6030216217041016, 'viz', 0), ('flet-dev/flet', 0.5987342000007629, 'web', 0), ('pyglet/pyglet', 0.593908965587616, 'gamedev', 0), ('pypa/build', 0.5934130549430847, 'util', 0), ('bokeh/bokeh', 0.5825904607772827, 'viz', 1), ('klen/muffin', 0.5798044800758362, 'web', 0), ('pypy/pypy', 0.5696408748626709, 'util', 0), ('wxwidgets/phoenix', 0.5664918422698975, 'gui', 3), ('bottlepy/bottle', 0.5649958252906799, 'web', 0), ('jquast/blessed', 0.5557140707969666, 'term', 0), ('reactive-python/reactpy', 0.5532059073448181, 'web', 1), ('holoviz/panel', 0.5402711629867554, 'viz', 1), ('microsoft/playwright-python', 0.5271952748298645, 'testing', 1), ('seleniumbase/seleniumbase', 0.5268258452415466, 'testing', 1), ('pyodide/pyodide', 0.5263348817825317, 'util', 0), ('vizzuhq/ipyvizzu', 0.5254994034767151, 'jupyter', 0), ('python/cpython', 0.5236340165138245, 'util', 0), ('dylanhogg/awesome-python', 0.5234578251838684, 'study', 0), ('pygamelib/pygamelib', 0.5211628079414368, 'gamedev', 0), ('voila-dashboards/voila', 0.5208461880683899, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.520444393157959, 'jupyter', 0), ('pylons/pyramid', 0.5202688574790955, 'web', 0), ('maartenbreddels/ipyvolume', 0.5197725296020508, 'jupyter', 0), ('plotly/plotly.py', 0.5167466402053833, 'viz', 0), ('adafruit/circuitpython', 0.5162983536720276, 'util', 0), ('gradio-app/gradio', 0.5159224271774292, 'viz', 0), ('timofurrer/awesome-asyncio', 0.5146098136901855, 'study', 0), ('connorferster/handcalcs', 0.5135572552680969, 'jupyter', 0), ('1200wd/bitcoinlib', 0.5121883153915405, 'crypto', 0), ('cobrateam/splinter', 0.5121274590492249, 'testing', 0), ('pyscript/pyscript', 0.5112079381942749, 'web', 2), ('amaargiru/pyroad', 0.5109246969223022, 'study', 0), ('pallets/quart', 0.5082329511642456, 'web', 0), ('beeware/briefcase', 0.5074366927146912, 'util', 0), ('goldmansachs/gs-quant', 0.5072776079177856, 'finance', 0), ('clips/pattern', 0.5047861933708191, 'nlp', 0), ('jiffyclub/snakeviz', 0.5045416951179504, 'profiling', 0), ('dddomodossola/remi', 0.5024312138557434, 'gui', 1), ('eleutherai/pyfra', 0.5016577243804932, 'ml', 0)]",123,2.0,,3.92,72,56,111,1,13,7,13,72.0,148.0,90.0,2.1,50
100,ml,https://github.com/skvark/opencv-python,[],,[],[],,,,skvark/opencv-python,opencv-python,3923,812,86,Python,https://pypi.org/project/opencv-python/,"Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.",skvark,2024-01-14,2016-04-08,407,9.625306694707326,https://avatars.githubusercontent.com/u/5009934?v=4,"Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.","['manylinux', 'opencv', 'opencv-contrib-python', 'opencv-python', 'precompiled', 'wheel']","['manylinux', 'opencv', 'opencv-contrib-python', 'opencv-python', 'precompiled', 'wheel']",2023-12-31,"[('pypa/pipenv', 0.5230939984321594, 'util', 0)]",48,5.0,,0.69,77,50,95,0,6,9,6,77.0,113.0,90.0,1.5,50
742,diffusion,https://github.com/nateraw/stable-diffusion-videos,[],,[],[],,,,nateraw/stable-diffusion-videos,stable-diffusion-videos,3917,373,53,Python,,Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts,nateraw,2024-01-13,2022-09-06,73,53.657534246575345,,Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts,"['ai-art', 'huggingface', 'huggingface-diffusers', 'machine-learning', 'stable-diffusion']","['ai-art', 'huggingface', 'huggingface-diffusers', 'machine-learning', 'stable-diffusion']",2023-05-07,"[('saharmor/dalle-playground', 0.671584963798523, 'diffusion', 2), ('compvis/stable-diffusion', 0.6355494856834412, 'diffusion', 0), ('carson-katri/dream-textures', 0.6127192378044128, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6116631627082825, 'diffusion', 2), ('jina-ai/discoart', 0.5996933579444885, 'diffusion', 1), ('invoke-ai/invokeai', 0.5912322402000427, 'diffusion', 2), ('stability-ai/stability-sdk', 0.5674799680709839, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.5622400045394897, 'diffusion', 1), ('huggingface/diffusers', 0.5594053268432617, 'diffusion', 1), ('openai/glide-text2im', 0.5577892661094666, 'diffusion', 0), ('open-mmlab/mmediting', 0.5489683747291565, 'ml', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5448333024978638, 'web', 0), ('chenyangqiqi/fatezero', 0.543769896030426, 'diffusion', 1), ('lunarring/latentblending', 0.5257456302642822, 'diffusion', 1), ('facebookresearch/mmf', 0.5128837823867798, 'ml-dl', 0), ('sharonzhou/long_stable_diffusion', 0.512672483921051, 'diffusion', 0), ('thereforegames/unprompted', 0.5079180002212524, 'diffusion', 2), ('thudm/cogvideo', 0.5062693357467651, 'ml', 0)]",14,5.0,,0.33,8,1,16,8,1,13,1,8.0,10.0,90.0,1.2,50
1791,util,https://github.com/bogdanp/dramatiq,[],,[],[],,,,bogdanp/dramatiq,dramatiq,3884,277,63,Python,https://dramatiq.io,A fast and reliable background task processing library for Python 3.,bogdanp,2024-01-13,2017-05-30,348,11.160919540229886,,A fast and reliable background task processing library for Python 3.,"['distributed-lock', 'rabbit', 'redis', 'task', 'task-manager', 'task-runner', 'task-scheduler']","['distributed-lock', 'rabbit', 'redis', 'task', 'task-manager', 'task-runner', 'task-scheduler']",2024-01-13,"[('agronholm/apscheduler', 0.6053295135498047, 'util', 0), ('samuelcolvin/arq', 0.5857503414154053, 'data', 1), ('mher/flower', 0.5586143732070923, 'perf', 1), ('joblib/loky', 0.5552358031272888, 'perf', 0), ('airtai/faststream', 0.5551525354385376, 'perf', 1), ('tox-dev/py-filelock', 0.5492278337478638, 'util', 0), ('python-trio/trio', 0.5322513580322266, 'perf', 0), ('dask/dask', 0.5178118944168091, 'perf', 0), ('sumerc/yappi', 0.517719566822052, 'profiling', 0), ('dask/distributed', 0.5118077993392944, 'perf', 0)]",100,6.0,,1.15,29,14,81,0,4,10,4,29.0,39.0,90.0,1.3,50
1291,llm,https://github.com/ravenscroftj/turbopilot,[],,[],[],,,,ravenscroftj/turbopilot,turbopilot,3842,133,43,C++,,Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU,ravenscroftj,2024-01-12,2023-04-09,42,90.85810810810811,,Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU,"['code-completion', 'cpp', 'language-model', 'machine-learning']","['code-completion', 'cpp', 'language-model', 'machine-learning']",2023-09-30,"[('databrickslabs/dolly', 0.5751809477806091, 'llm', 0), ('modularml/mojo', 0.5523777604103088, 'util', 1), ('salesforce/codegen', 0.5407807230949402, 'nlp', 0), ('lianjiatech/belle', 0.5382049083709717, 'llm', 0), ('microsoft/pycodegpt', 0.524189293384552, 'llm', 0), ('salesforce/codet5', 0.5119407773017883, 'nlp', 1), ('titanml/takeoff', 0.5088446140289307, 'llm', 1), ('togethercomputer/redpajama-data', 0.5085808038711548, 'llm', 0), ('thudm/codegeex', 0.5072124600410461, 'llm', 0), ('conceptofmind/toolformer', 0.5007780194282532, 'llm', 1)]",7,1.0,,5.13,1,1,9,4,7,11,7,1.0,0.0,90.0,0.0,50
889,viz,https://github.com/has2k1/plotnine,[],,[],[],,,,has2k1/plotnine,plotnine,3682,207,65,Python,https://plotnine.org,A Grammar of Graphics for Python,has2k1,2024-01-13,2017-04-24,353,10.426375404530745,,A Grammar of Graphics for Python,"['data-analysis', 'grammar', 'graphics', 'plotting']","['data-analysis', 'grammar', 'graphics', 'plotting']",2024-01-12,"[('altair-viz/altair', 0.6832043528556824, 'viz', 0), ('matplotlib/matplotlib', 0.6249548196792603, 'viz', 1), ('holoviz/holoviz', 0.6090349555015564, 'viz', 0), ('plotly/plotly.py', 0.6062517762184143, 'viz', 0), ('mwaskom/seaborn', 0.6059504747390747, 'viz', 0), ('holoviz/geoviews', 0.6033970713615417, 'gis', 1), ('residentmario/geoplot', 0.5918059349060059, 'gis', 0), ('vizzuhq/ipyvizzu', 0.585931122303009, 'jupyter', 1), ('scitools/cartopy', 0.5810568332672119, 'gis', 0), ('holoviz/hvplot', 0.5800595879554749, 'pandas', 1), ('imageio/imageio', 0.5798242092132568, 'util', 0), ('bokeh/bokeh', 0.5768696069717407, 'viz', 1), ('man-group/dtale', 0.5764876008033752, 'viz', 1), ('python/cpython', 0.5753418803215027, 'util', 0), ('pandas-dev/pandas', 0.5737428069114685, 'pandas', 1), ('scitools/iris', 0.5705260634422302, 'gis', 1), ('artelys/geonetworkx', 0.569690465927124, 'gis', 0), ('pytoolz/toolz', 0.5640243887901306, 'util', 0), ('opengeos/leafmap', 0.5541620254516602, 'gis', 0), ('holoviz/panel', 0.5538381338119507, 'viz', 0), ('pyston/pyston', 0.5506923198699951, 'util', 0), ('pysal/pysal', 0.5488908290863037, 'gis', 0), ('sympy/sympy', 0.5483768582344055, 'math', 0), ('graphistry/pygraphistry', 0.5480146408081055, 'data', 0), ('westhealth/pyvis', 0.5456839203834534, 'graph', 0), ('gboeing/pynamical', 0.5370927453041077, 'sim', 0), ('wesm/pydata-book', 0.5339842438697815, 'study', 0), ('cuemacro/chartpy', 0.5336882472038269, 'viz', 1), ('albahnsen/pycircular', 0.5327418446540833, 'math', 0), ('brandtbucher/specialist', 0.5308057069778442, 'perf', 0), ('enthought/mayavi', 0.5297136902809143, 'viz', 0), ('giswqs/geemap', 0.5272516012191772, 'gis', 0), ('nschloe/perfplot', 0.5270527005195618, 'perf', 0), ('pyparsing/pyparsing', 0.5245786905288696, 'util', 0), ('zulko/moviepy', 0.524414598941803, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5220550894737244, 'study', 0), ('alexmojaki/heartrate', 0.5188327431678772, 'debug', 0), ('pygraphviz/pygraphviz', 0.5177363753318787, 'viz', 0), ('contextlab/hypertools', 0.5157314538955688, 'ml', 0), ('eleutherai/pyfra', 0.5156086683273315, 'ml', 0), ('dfki-ric/pytransform3d', 0.5153816342353821, 'math', 0), ('kanaries/pygwalker', 0.5138752460479736, 'pandas', 1), ('holoviz/holoviews', 0.5102058053016663, 'viz', 1), ('earthlab/earthpy', 0.5097348093986511, 'gis', 0), ('vispy/vispy', 0.5095175504684448, 'viz', 0), ('pyproj4/pyproj', 0.5081332921981812, 'gis', 0), ('federicoceratto/dashing', 0.5077523589134216, 'term', 0), ('rapidsai/cudf', 0.5067694783210754, 'pandas', 1), ('google/latexify_py', 0.5012533068656921, 'util', 0)]",105,3.0,,5.33,31,25,82,0,4,3,4,31.0,40.0,90.0,1.3,50
553,jupyter,https://github.com/executablebooks/jupyter-book,[],,[],[],,,,executablebooks/jupyter-book,jupyter-book,3592,649,63,Python,http://jupyterbook.org,"Create beautiful, publication-quality books and documents from computational content.",executablebooks,2024-01-13,2018-06-14,293,12.229571984435797,https://avatars.githubusercontent.com/u/57655115?v=4,"Create beautiful, publication-quality books and documents from computational content.","['documentation-generator', 'jupyter', 'sphinx-doc']","['documentation-generator', 'jupyter', 'sphinx-doc']",2023-12-05,"[('sphinx-doc/sphinx', 0.753897488117218, 'util', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.535974383354187, 'data', 0), ('mitmproxy/pdoc', 0.5281922817230225, 'util', 1), ('squidfunk/mkdocs-material', 0.5147403478622437, 'util', 0)]",134,5.0,,0.81,66,24,68,1,4,8,4,66.0,96.0,90.0,1.5,50
1603,llm,https://github.com/1rgs/jsonformer,"['prompt-engineering', 'json']",,[],[],,,,1rgs/jsonformer,jsonformer,3448,109,19,Jupyter Notebook,,A Bulletproof Way to Generate Structured JSON from Language Models,1rgs,2024-01-13,2023-04-29,39,87.44927536231884,,A Bulletproof Way to Generate Structured JSON from Language Models,[],"['json', 'prompt-engineering']",2023-05-30,"[('neulab/prompt2model', 0.6683309674263, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5909636616706848, 'llm', 1), ('brokenloop/jsontopydantic', 0.5837192535400391, 'util', 0), ('guidance-ai/guidance', 0.5747532248497009, 'llm', 1), ('srush/minichain', 0.5634762644767761, 'llm', 1), ('hazyresearch/ama_prompting', 0.5634579062461853, 'llm', 1), ('juncongmoo/pyllama', 0.5465362071990967, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5433797240257263, 'llm', 0), ('lidatong/dataclasses-json', 0.529080331325531, 'util', 1), ('hannibal046/awesome-llm', 0.5063848495483398, 'study', 0), ('ai21labs/lm-evaluation', 0.5037403702735901, 'llm', 0)]",6,4.0,,0.69,8,0,9,8,0,0,0,8.0,8.0,90.0,1.0,50
1564,llm,https://github.com/deep-diver/llm-as-chatbot,['chatbot'],,[],[],,,,deep-diver/llm-as-chatbot,LLM-As-Chatbot,3161,392,50,Python,,LLM as a Chatbot Service,deep-diver,2024-01-12,2023-02-27,48,65.65875370919882,,LLM as a Chatbot Service,[],['chatbot'],2023-11-20,"[('nomic-ai/gpt4all', 0.7740846276283264, 'llm', 1), ('pathwaycom/llm-app', 0.6957004070281982, 'llm', 1), ('hwchase17/langchain', 0.6912153363227844, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6634297966957092, 'perf', 1), ('embedchain/embedchain', 0.6422813534736633, 'llm', 0), ('deepset-ai/haystack', 0.6116864085197449, 'llm', 0), ('microsoft/promptcraft-robotics', 0.6085308194160461, 'sim', 0), ('chatarena/chatarena', 0.5871044397354126, 'llm', 0), ('thudm/chatglm2-6b', 0.5782683491706848, 'llm', 0), ('mmabrouk/chatgpt-wrapper', 0.5747789144515991, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5731402635574341, 'llm', 1), ('shishirpatil/gorilla', 0.5617109537124634, 'llm', 0), ('aws-samples/serverless-pdf-chat', 0.5605927109718323, 'llm', 0), ('rcgai/simplyretrieve', 0.5600287914276123, 'llm', 0), ('run-llama/rags', 0.5595909953117371, 'llm', 1), ('gunthercox/chatterbot', 0.5590612888336182, 'nlp', 1), ('berriai/litellm', 0.5576227903366089, 'llm', 0), ('chainlit/chainlit', 0.548169732093811, 'llm', 0), ('microsoft/semantic-kernel', 0.5478009581565857, 'llm', 0), ('microsoft/autogen', 0.5456553101539612, 'llm', 1), ('togethercomputer/openchatkit', 0.5419533848762512, 'nlp', 1), ('ajndkr/lanarky', 0.5414519309997559, 'llm', 0), ('young-geng/easylm', 0.5367242097854614, 'llm', 1), ('mnotgod96/appagent', 0.5356595516204834, 'llm', 0), ('fasteval/fasteval', 0.5321249961853027, 'llm', 0), ('microsoft/promptflow', 0.5296043753623962, 'llm', 0), ('zilliztech/gptcache', 0.5247036218643188, 'llm', 1), ('microsoft/jarvis', 0.5197903513908386, 'llm', 0), ('bigscience-workshop/petals', 0.5034541487693787, 'data', 1), ('eugeneyan/open-llms', 0.502346932888031, 'study', 0), ('agenta-ai/agenta', 0.5022589564323425, 'llm', 0), ('larsbaunwall/bricky', 0.5015654563903809, 'llm', 0), ('errbotio/errbot', 0.5012304186820984, 'nlp', 1), ('nebuly-ai/nebullvm', 0.5001731514930725, 'perf', 0)]",7,4.0,,6.19,2,0,11,2,0,0,0,2.0,1.0,90.0,0.5,50
899,ml,https://github.com/rucaibox/recbole,[],,[],[],,,,rucaibox/recbole,RecBole,3022,562,40,Python,https://recbole.io/,"A unified, comprehensive and efficient recommendation library",rucaibox,2024-01-13,2020-06-11,189,15.92921686746988,https://avatars.githubusercontent.com/u/54706620?v=4,"A unified, comprehensive and efficient recommendation library","['collaborative-filtering', 'ctr-prediction', 'deep-learning', 'graph-neural-networks', 'knowledge-graph', 'pytorch', 'recommendation-system', 'recommendations', 'recommender', 'recommender-systems', 'sequential-recommendation']","['collaborative-filtering', 'ctr-prediction', 'deep-learning', 'graph-neural-networks', 'knowledge-graph', 'pytorch', 'recommendation-system', 'recommendations', 'recommender', 'recommender-systems', 'sequential-recommendation']",2023-11-25,"[('pytorch/torchrec', 0.7371825575828552, 'ml-dl', 3), ('microsoft/recommenders', 0.5956623554229736, 'study', 3), ('nicolashug/surprise', 0.5934752821922302, 'ml', 1), ('pyg-team/pytorch_geometric', 0.566724419593811, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.5453563928604126, 'ml-rl', 1), ('a-r-j/graphein', 0.5301325917243958, 'sim', 3), ('explosion/thinc', 0.5200947523117065, 'ml-dl', 2)]",70,5.0,,2.96,86,35,44,2,1,2,1,86.0,136.0,90.0,1.6,50
822,study,https://github.com/huggingface/diffusion-models-class,[],,[],[],,,,huggingface/diffusion-models-class,diffusion-models-class,2931,309,71,Jupyter Notebook,,Materials for the Hugging Face Diffusion Models Course,huggingface,2024-01-14,2022-10-13,67,43.28481012658228,https://avatars.githubusercontent.com/u/25720743?v=4,Materials for the Hugging Face Diffusion Models Course,[],[],2023-12-18,"[('huggingface/notebooks', 0.5564571619033813, 'ml', 0), ('huggingface/deep-rl-class', 0.551882803440094, 'study', 0)]",20,5.0,,0.62,10,7,15,1,0,0,0,10.0,9.0,90.0,0.9,50
1362,util,https://github.com/pyo3/maturin,['rust'],,[],[],,,,pyo3/maturin,maturin,2916,195,24,Rust,https://maturin.rs,"Build and publish crates with pyo3, rust-cpython and cffi bindings as well as rust binaries as python packages",pyo3,2024-01-14,2018-07-21,288,10.109955423476968,https://avatars.githubusercontent.com/u/28156855?v=4,"Build and publish crates with pyo3, rust-cpython and cffi bindings as well as rust binaries as python packages","['cffi', 'cpython', 'cross-compile', 'manylinux', 'packaging', 'pyo3', 'pypy', 'rust-cpython', 'uniffi', 'wheels']","['cffi', 'cpython', 'cross-compile', 'manylinux', 'packaging', 'pyo3', 'pypy', 'rust', 'rust-cpython', 'uniffi', 'wheels']",2024-01-10,"[('pyo3/rust-numpy', 0.654156506061554, 'util', 1), ('pyo3/pyo3', 0.6540318131446838, 'util', 1), ('scikit-build/scikit-build', 0.6356386542320251, 'ml', 3), ('rustpython/rustpython', 0.6316028237342834, 'util', 1), ('pypa/installer', 0.6027328372001648, 'util', 0), ('pypy/pypy', 0.5836184024810791, 'util', 1), ('ofek/pyapp', 0.5643833875656128, 'util', 2), ('astral-sh/ruff', 0.556878387928009, 'util', 1), ('aswinnnn/pyscan', 0.552875280380249, 'security', 1), ('libtcod/python-tcod', 0.5518137812614441, 'gamedev', 1), ('eventual-inc/daft', 0.5351252555847168, 'pandas', 1), ('pyodide/micropip', 0.5349652171134949, 'util', 0), ('python/cpython', 0.5341805219650269, 'util', 1), ('delta-io/delta-rs', 0.532139241695404, 'pandas', 1), ('pdm-project/pdm', 0.5319200754165649, 'util', 1), ('pypa/hatch', 0.5255593061447144, 'util', 1), ('cython/cython', 0.5176377892494202, 'util', 1), ('pypa/virtualenv', 0.5136356353759766, 'util', 1), ('pytoolz/toolz', 0.5072370171546936, 'util', 0), ('pyodide/pyodide', 0.504960834980011, 'util', 1), ('pyo3/setuptools-rust', 0.5044507384300232, 'util', 1), ('ipython/ipython', 0.5035890936851501, 'util', 0)]",102,1.0,,7.62,121,103,67,0,29,52,29,121.0,171.0,90.0,1.4,50
1294,llm,https://github.com/microsoft/torchscale,[],,[],[],,,,microsoft/torchscale,torchscale,2793,185,44,Python,https://aka.ms/GeneralAI,Foundation Architecture for (M)LLMs,microsoft,2024-01-14,2022-11-17,62,44.535307517084284,https://avatars.githubusercontent.com/u/6154722?v=4,Foundation Architecture for (M)LLMs,"['computer-vision', 'machine-learning', 'multimodal', 'natural-language-processing', 'pretrained-language-model', 'speech-processing', 'transformer', 'translation']","['computer-vision', 'machine-learning', 'multimodal', 'natural-language-processing', 'pretrained-language-model', 'speech-processing', 'transformer', 'translation']",2023-12-27,"[('ludwig-ai/ludwig', 0.6496773958206177, 'ml-ops', 3), ('eugeneyan/open-llms', 0.633047878742218, 'study', 0), ('alpha-vllm/llama2-accessory', 0.602177619934082, 'llm', 0), ('bentoml/openllm', 0.5949733853340149, 'ml-ops', 0), ('iryna-kondr/scikit-llm', 0.5895041823387146, 'llm', 1), ('microsoft/lmops', 0.5832264423370361, 'llm', 0), ('vllm-project/vllm', 0.582655668258667, 'llm', 1), ('pathwaycom/llm-app', 0.5825396180152893, 'llm', 1), ('next-gpt/next-gpt', 0.582415759563446, 'llm', 1), ('salesforce/xgen', 0.5822688937187195, 'llm', 0), ('confident-ai/deepeval', 0.581218957901001, 'testing', 0), ('microsoft/jarvis', 0.5797202587127686, 'llm', 0), ('microsoft/semantic-kernel', 0.573233425617218, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5727461576461792, 'study', 1), ('intel/intel-extension-for-transformers', 0.570101261138916, 'perf', 0), ('ray-project/ray-llm', 0.5647855997085571, 'llm', 0), ('tigerlab-ai/tiger', 0.5640652179718018, 'llm', 0), ('explosion/spacy-llm', 0.5628166198730469, 'llm', 2), ('hiyouga/llama-factory', 0.5607731938362122, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5607730746269226, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5583703517913818, 'llm', 0), ('optimalscale/lmflow', 0.5554569959640503, 'llm', 1), ('young-geng/easylm', 0.5538792610168457, 'llm', 2), ('bobazooba/xllm', 0.5502038598060608, 'llm', 0), ('deepset-ai/haystack', 0.546305775642395, 'llm', 1), ('microsoft/promptflow', 0.542982816696167, 'llm', 0), ('nebuly-ai/nebullvm', 0.5394699573516846, 'perf', 0), ('hwchase17/langchain', 0.5352845788002014, 'llm', 0), ('nat/openplayground', 0.5345559120178223, 'llm', 0), ('mlc-ai/mlc-llm', 0.532260000705719, 'llm', 0), ('bigscience-workshop/petals', 0.5316954255104065, 'data', 2), ('nvidia/tensorrt-llm', 0.5302456617355347, 'viz', 0), ('agenta-ai/agenta', 0.5262821912765503, 'llm', 0), ('argilla-io/argilla', 0.5222489237785339, 'nlp', 2), ('artidoro/qlora', 0.5133755207061768, 'llm', 0), ('juncongmoo/pyllama', 0.5133620500564575, 'llm', 0), ('tsinghuadatabasegroup/db-gpt', 0.5131277441978455, 'llm', 0), ('dylanhogg/llmgraph', 0.5129197835922241, 'ml', 0), ('lancedb/lancedb', 0.5128360390663147, 'data', 0), ('tensorflow/tensorflow', 0.5050406455993652, 'ml-dl', 1), ('llmware-ai/llmware', 0.5042523741722107, 'llm', 1)]",16,3.0,,1.04,32,19,14,1,0,0,0,32.0,38.0,90.0,1.2,50
544,ml,https://github.com/lightly-ai/lightly,[],,[],[],,,,lightly-ai/lightly,lightly,2654,233,26,Python,https://docs.lightly.ai/self-supervised-learning/,A python library for self-supervised learning on images.,lightly-ai,2024-01-12,2020-10-13,172,15.430232558139535,https://avatars.githubusercontent.com/u/50146475?v=4,A python library for self-supervised learning on images.,"['computer-vision', 'contrastive-learning', 'deep-learning', 'embeddings', 'machine-learning', 'pytorch', 'self-supervised-learning']","['computer-vision', 'contrastive-learning', 'deep-learning', 'embeddings', 'machine-learning', 'pytorch', 'self-supervised-learning']",2024-01-11,"[('mdbloice/augmentor', 0.7019970417022705, 'ml', 2), ('pytorch/ignite', 0.6327634453773499, 'ml-dl', 3), ('deci-ai/super-gradients', 0.610939621925354, 'ml-dl', 3), ('facebookresearch/vissl', 0.6104288101196289, 'ml', 0), ('rasbt/mlxtend', 0.6017403602600098, 'ml', 1), ('featurelabs/featuretools', 0.5864541530609131, 'ml', 1), ('facebookresearch/dinov2', 0.5861289501190186, 'diffusion', 0), ('kevinmusgrave/pytorch-metric-learning', 0.581474244594574, 'ml', 7), ('imageio/imageio', 0.5790013670921326, 'util', 0), ('skorch-dev/skorch', 0.5767190456390381, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5627269148826599, 'perf', 3), ('google-research/deeplab2', 0.5617728233337402, 'ml', 0), ('weecology/deepforest', 0.5572022199630737, 'gis', 0), ('mrdbourke/pytorch-deep-learning', 0.5538014769554138, 'study', 3), ('pycaret/pycaret', 0.5519795417785645, 'ml', 1), ('ageron/handson-ml2', 0.5470166802406311, 'ml', 0), ('salesforce/blip', 0.5466323494911194, 'diffusion', 0), ('pytorch/rl', 0.5465163588523865, 'ml-rl', 2), ('xl0/lovely-tensors', 0.5458189845085144, 'ml-dl', 2), ('hysts/pytorch_image_classification', 0.5435891151428223, 'ml-dl', 2), ('python-pillow/pillow', 0.5426392555236816, 'util', 0), ('albumentations-team/albumentations', 0.5409029126167297, 'ml-dl', 2), ('kornia/kornia', 0.5407032370567322, 'ml-dl', 4), ('nvlabs/gcvit', 0.5394826531410217, 'diffusion', 1), ('pytorch/pytorch', 0.5388930439949036, 'ml-dl', 2), ('lucidrains/imagen-pytorch', 0.5377395153045654, 'ml-dl', 1), ('rasbt/machine-learning-book', 0.5357835292816162, 'study', 3), ('tensorflow/tensorflow', 0.5355916023254395, 'ml-dl', 2), ('cvxgrp/pymde', 0.5311744809150696, 'ml', 2), ('facebookresearch/pytorch3d', 0.5310624241828918, 'ml-dl', 0), ('ggerganov/ggml', 0.5310502052307129, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5261020660400391, 'ml', 4), ('roboflow/supervision', 0.5256815552711487, 'ml', 4), ('oml-team/open-metric-learning', 0.5244993567466736, 'ml', 4), ('allenai/allennlp', 0.5244608521461487, 'nlp', 2), ('scikit-image/scikit-image', 0.5242587924003601, 'util', 1), ('karpathy/micrograd', 0.5221474766731262, 'study', 0), ('lutzroeder/netron', 0.5200084447860718, 'ml', 3), ('luispedro/mahotas', 0.5195291638374329, 'viz', 1), ('uber/petastorm', 0.5194175243377686, 'data', 3), ('tensorlayer/tensorlayer', 0.5181266665458679, 'ml-rl', 1), ('azavea/raster-vision', 0.5178630352020264, 'gis', 4), ('scikit-learn/scikit-learn', 0.5172454714775085, 'ml', 1), ('gradio-app/gradio', 0.5170907378196716, 'viz', 2), ('microsoft/semi-supervised-learning', 0.5161562561988831, 'ml', 4), ('pyg-team/pytorch_geometric', 0.5151110887527466, 'ml-dl', 2), ('huggingface/datasets', 0.5144714713096619, 'nlp', 4), ('xl0/lovely-numpy', 0.5120965242385864, 'util', 1), ('earthlab/earthpy', 0.5085079669952393, 'gis', 0), ('jeshraghian/snntorch', 0.5078932046890259, 'ml-dl', 2), ('aleju/imgaug', 0.5071452260017395, 'ml', 2), ('huggingface/huggingface_hub', 0.5056184530258179, 'ml', 3), ('ddbourgin/numpy-ml', 0.5032593607902527, 'ml', 1), ('aws/sagemaker-python-sdk', 0.5022170543670654, 'ml', 2), ('keras-team/autokeras', 0.5014397501945496, 'ml-dl', 2), ('tensorflow/data-validation', 0.5010249018669128, 'ml-ops', 0)]",35,1.0,,4.71,72,46,40,0,35,34,35,72.0,116.0,90.0,1.6,50
1577,ml,https://github.com/zjunlp/deepke,['knowledge-graph'],,[],[],,,,zjunlp/deepke,DeepKE,2653,601,44,Python,http://deepke.zjukg.cn/,An Open Toolkit for Knowledge Graph Extraction and Construction published at EMNLP2022 System Demonstrations.,zjunlp,2024-01-14,2018-08-01,286,9.248505976095618,https://avatars.githubusercontent.com/u/41887875?v=4,An Open Toolkit for Knowledge Graph Extraction and Construction published at EMNLP2022 System Demonstrations.,"['attribute-extraction', 'bert', 'chinese', 'deep-learning', 'deepke', 'document-level', 'few-shot', 'information-extraction', 'kg', 'knowledge-graph', 'knowprompt', 'lightner', 'low-resource', 'multi-modal', 'named-entity-recognition', 'ner', 'nlp', 'prompt', 'pytorch', 'relation-extraction']","['attribute-extraction', 'bert', 'chinese', 'deep-learning', 'deepke', 'document-level', 'few-shot', 'information-extraction', 'kg', 'knowledge-graph', 'knowprompt', 'lightner', 'low-resource', 'multi-modal', 'named-entity-recognition', 'ner', 'nlp', 'prompt', 'pytorch', 'relation-extraction']",2024-01-10,"[('accenture/ampligraph', 0.593433678150177, 'data', 1), ('dylanhogg/llmgraph', 0.5825223922729492, 'ml', 1), ('microsoft/vert-papers', 0.5728095769882202, 'nlp', 3), ('awslabs/dgl-ke', 0.5722960233688354, 'ml', 1), ('babelscape/rebel', 0.5535876154899597, 'nlp', 2), ('alibaba/easynlp', 0.5432515144348145, 'nlp', 4), ('deepgraphlearning/ultra', 0.5096688270568848, 'ml', 1)]",31,1.0,,11.06,54,53,66,0,5,3,5,54.0,190.0,90.0,3.5,50
1755,util,https://github.com/pantsbuild/pex,"['executable', 'venv']",,[],[],,,,pantsbuild/pex,pex,2411,247,56,Python,https://pex.readthedocs.io,"A tool for generating .pex (Python EXecutable) files, lock files and venvs.",pantsbuild,2024-01-13,2014-07-21,497,4.849712643678161,https://avatars.githubusercontent.com/u/3065172?v=4,"A tool for generating .pex (Python EXecutable) files, lock files and venvs.",[],"['executable', 'venv']",2024-01-14,"[('pypa/virtualenv', 0.5755523443222046, 'util', 1), ('tox-dev/py-filelock', 0.5669477581977844, 'util', 0), ('pyenv/pyenv', 0.5503339171409607, 'util', 1), ('pypa/pipenv', 0.5425669550895691, 'util', 1), ('pypa/pipx', 0.5113623738288879, 'util', 1)]",119,6.0,,2.67,61,50,115,0,37,25,37,61.0,142.0,90.0,2.3,50
1189,diffusion,https://github.com/stability-ai/stability-sdk,[],,[],[],,,,stability-ai/stability-sdk,stability-sdk,2381,336,60,Jupyter Notebook,https://platform.stability.ai/,SDK for interacting with stability.ai APIs (e.g. stable diffusion inference),stability-ai,2024-01-12,2022-08-22,75,31.686311787072242,https://avatars.githubusercontent.com/u/100950301?v=4,SDK for interacting with stability.ai APIs (e.g. stable diffusion inference),"['ai-art', 'generative-art', 'latent-diffusion', 'multimodal', 'stable-diffusion']","['ai-art', 'generative-art', 'latent-diffusion', 'multimodal', 'stable-diffusion']",2023-11-20,"[('carson-katri/dream-textures', 0.7153577208518982, 'diffusion', 1), ('bentoml/onediffusion', 0.6665179133415222, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6460036039352417, 'diffusion', 2), ('comfyanonymous/comfyui', 0.6112304925918579, 'diffusion', 1), ('invoke-ai/invokeai', 0.5937549471855164, 'diffusion', 4), ('nateraw/stable-diffusion-videos', 0.5674799680709839, 'diffusion', 2), ('divamgupta/stable-diffusion-tensorflow', 0.5413241386413574, 'diffusion', 0), ('bentoml/bentoml', 0.5263645052909851, 'ml-ops', 0), ('jina-ai/jina', 0.524762749671936, 'ml', 1), ('thereforegames/unprompted', 0.5244125127792358, 'diffusion', 2), ('kubeflow/fairing', 0.5190809369087219, 'ml-ops', 0), ('google-research/torchsde', 0.5064745545387268, 'math', 0), ('activeloopai/deeplake', 0.5024917125701904, 'ml-ops', 0), ('huggingface/diffusers', 0.5022362470626831, 'diffusion', 1), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5016722679138184, 'web', 0)]",16,2.0,,0.42,15,6,17,2,11,18,11,15.0,28.0,90.0,1.9,50
195,ml-rl,https://github.com/pettingzoo-team/pettingzoo,[],,[],[],,,,pettingzoo-team/pettingzoo,PettingZoo,2196,360,20,Python,https://pettingzoo.farama.org,"An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities",pettingzoo-team,2024-01-12,2020-01-20,210,10.450033990482664,https://avatars.githubusercontent.com/u/62961550?v=4,"An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities","['api', 'gym', 'gymnasium', 'multi-agent-reinforcement-learning', 'multiagent-reinforcement-learning', 'reinforcement-learning']","['api', 'gym', 'gymnasium', 'multi-agent-reinforcement-learning', 'multiagent-reinforcement-learning', 'reinforcement-learning']",2024-01-11,"[('farama-foundation/gymnasium', 0.947864830493927, 'ml-rl', 3), ('nvidia-omniverse/isaacgymenvs', 0.6462188959121704, 'sim', 1), ('unity-technologies/ml-agents', 0.6170148849487305, 'ml-rl', 1), ('pytorch/rl', 0.6150112748146057, 'ml-rl', 2), ('deepmind/acme', 0.578000545501709, 'ml-rl', 1), ('facebookresearch/habitat-lab', 0.5756711363792419, 'sim', 1), ('google/dopamine', 0.5669017434120178, 'ml-rl', 0), ('inspirai/timechamber', 0.5665203332901001, 'sim', 1), ('thu-ml/tianshou', 0.556010365486145, 'ml-rl', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.5554696917533875, 'sim', 0), ('tensorlayer/tensorlayer', 0.554624080657959, 'ml-rl', 1), ('humancompatibleai/imitation', 0.5525628924369812, 'ml-rl', 1), ('facebookresearch/reagent', 0.5440720915794373, 'ml-rl', 0), ('openai/baselines', 0.5400475859642029, 'ml-rl', 0), ('openai/gym', 0.5399578213691711, 'ml-rl', 1), ('deepmind/pysc2', 0.5372393131256104, 'ml-rl', 1), ('operand/agency', 0.5311383605003357, 'llm', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5307071805000305, 'study', 1), ('salesforce/warp-drive', 0.5245906710624695, 'ml-rl', 2), ('huggingface/deep-rl-class', 0.5236347317695618, 'study', 1), ('ai4finance-foundation/finrl', 0.5186222791671753, 'finance', 1), ('kzl/decision-transformer', 0.5139691829681396, 'ml-rl', 1), ('projectmesa/mesa', 0.5047821998596191, 'sim', 0), ('openai/spinningup', 0.5047026872634888, 'study', 0)]",105,2.0,,3.52,44,39,48,0,6,10,6,44.0,96.0,90.0,2.2,50
64,sim,https://github.com/projectmesa/mesa,[],,[],[],,,,projectmesa/mesa,mesa,2089,802,91,Python,,"Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.",projectmesa,2024-01-14,2014-09-19,488,4.275730994152047,https://avatars.githubusercontent.com/u/8754505?v=4,"Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.","['agent-based-modeling', 'agent-based-simulation', 'complex-systems', 'complexity-analysis', 'gis', 'mesa', 'modeling-agents', 'simulation', 'simulation-environment', 'simulation-framework', 'spatial-models']","['agent-based-modeling', 'agent-based-simulation', 'complex-systems', 'complexity-analysis', 'gis', 'mesa', 'modeling-agents', 'simulation', 'simulation-environment', 'simulation-framework', 'spatial-models']",2024-01-13,"[('google-deepmind/concordia', 0.6392713785171509, 'sim', 1), ('zacwellmer/worldmodels', 0.5980151891708374, 'ml-rl', 1), ('ljvmiranda921/seagull', 0.5834044218063354, 'sim', 1), ('operand/agency', 0.5751315355300903, 'llm', 0), ('artemyk/dynpy', 0.5512898564338684, 'sim', 0), ('crowddynamics/crowddynamics', 0.5487060546875, 'sim', 0), ('pytorch/rl', 0.5338829159736633, 'ml-rl', 0), ('humanoidagents/humanoidagents', 0.5313592553138733, 'sim', 1), ('gboeing/pynamical', 0.530546247959137, 'sim', 0), ('pythonarcade/arcade', 0.5278759002685547, 'gamedev', 0), ('unity-technologies/ml-agents', 0.5214616060256958, 'ml-rl', 0), ('alephalpha/golly', 0.5125412344932556, 'sim', 0), ('scikit-mobility/scikit-mobility', 0.5076199769973755, 'gis', 1), ('lordmauve/pgzero', 0.5056121349334717, 'gamedev', 0), ('pettingzoo-team/pettingzoo', 0.5047821998596191, 'ml-rl', 0), ('transformeroptimus/superagi', 0.5027607083320618, 'llm', 0)]",134,2.0,,5.35,124,93,113,0,7,3,7,124.0,671.0,90.0,5.4,50
224,sim,https://github.com/google/brax,[],,[],[],,,,google/brax,brax,1940,219,39,Jupyter Notebook,,Massively parallel rigidbody physics simulation on accelerator hardware.,google,2024-01-13,2021-06-02,138,13.97119341563786,https://avatars.githubusercontent.com/u/1342004?v=4,Massively parallel rigidbody physics simulation on accelerator hardware.,"['jax', 'physics-simulation', 'reinforcement-learning', 'robotics']","['jax', 'physics-simulation', 'reinforcement-learning', 'robotics']",2024-01-03,"[('arise-initiative/robosuite', 0.5911247134208679, 'ml-rl', 3), ('deepmind/dm_control', 0.514367938041687, 'ml-rl', 2)]",31,7.0,,0.19,36,20,32,0,7,8,7,36.0,45.0,90.0,1.2,50
1282,viz,https://github.com/marcomusy/vedo,[],,[],[],,,,marcomusy/vedo,vedo,1847,245,30,Python,https://vedo.embl.es,A python module for scientific analysis of 3D data based on VTK and Numpy,marcomusy,2024-01-12,2017-11-10,324,5.690580985915493,,A python module for scientific analysis of 3D data based on VTK and Numpy,"['3d', '3d-graphics', 'dolfin', 'fenics', 'finite-elements', 'mesh', 'numpy', 'scientific-research', 'scientific-visualization', 'simulations', 'visualization', 'vtk']","['3d', '3d-graphics', 'dolfin', 'fenics', 'finite-elements', 'mesh', 'numpy', 'scientific-research', 'scientific-visualization', 'simulations', 'visualization', 'vtk']",2024-01-13,"[('enthought/mayavi', 0.742211639881134, 'viz', 2), ('pyvista/pyvista', 0.7296451330184937, 'viz', 6), ('pyqtgraph/pyqtgraph', 0.6459553241729736, 'viz', 3), ('contextlab/hypertools', 0.6358500719070435, 'ml', 1), ('numpy/numpy', 0.6334555745124817, 'math', 1), ('dfki-ric/pytransform3d', 0.6237989664077759, 'math', 1), ('scitools/iris', 0.6136019825935364, 'gis', 0), ('isl-org/open3d', 0.6037029027938843, 'sim', 2), ('matplotlib/matplotlib', 0.577059805393219, 'viz', 0), ('viblo/pymunk', 0.5741404294967651, 'sim', 0), ('maartenbreddels/ipyvolume', 0.5702053308486938, 'jupyter', 1), ('earthlab/earthpy', 0.5666988492012024, 'gis', 0), ('pysal/pysal', 0.560834527015686, 'gis', 0), ('altair-viz/altair', 0.5546357035636902, 'viz', 1), ('scikit-geometry/scikit-geometry', 0.5506011843681335, 'gis', 0), ('holoviz/holoviz', 0.5432279706001282, 'viz', 0), ('residentmario/geoplot', 0.5391072034835815, 'gis', 0), ('roban/cosmolopy', 0.537321925163269, 'sim', 0), ('mwaskom/seaborn', 0.5347036123275757, 'viz', 0), ('pokepetter/ursina', 0.5303018093109131, 'gamedev', 0), ('gboeing/pynamical', 0.5265044569969177, 'sim', 2), ('jakevdp/pythondatasciencehandbook', 0.5232061743736267, 'study', 1), ('cupy/cupy', 0.5182294845581055, 'math', 1), ('holoviz/hvplot', 0.5138098001480103, 'pandas', 0), ('eleutherai/pyfra', 0.5130149126052856, 'ml', 0), ('vispy/vispy', 0.5108841061592102, 'viz', 1), ('man-group/dtale', 0.5097540020942688, 'viz', 1), ('scipy/scipy', 0.5084977746009827, 'math', 0), ('wesm/pydata-book', 0.5054879784584045, 'study', 0), ('rasbt/mlxtend', 0.5048307180404663, 'ml', 0), ('albahnsen/pycircular', 0.5011388063430786, 'math', 0), ('makepath/xarray-spatial', 0.5006656646728516, 'gis', 0)]",34,7.0,,10.98,91,74,75,0,5,9,5,91.0,184.0,90.0,2.0,50
795,util,https://github.com/open-telemetry/opentelemetry-python,[],,[],[],,,,open-telemetry/opentelemetry-python,opentelemetry-python,1474,549,37,Python,https://opentelemetry.io,OpenTelemetry Python API and SDK ,open-telemetry,2024-01-13,2019-05-07,247,5.967611336032388,https://avatars.githubusercontent.com/u/49998002?v=4,OpenTelemetry Python API and SDK ,"['correlationcontext', 'distributed-tracing', 'logging', 'metrics', 'opentelemetry', 'sdk', 'tracecontext']","['correlationcontext', 'distributed-tracing', 'logging', 'metrics', 'opentelemetry', 'sdk', 'tracecontext']",2024-01-05,"[('open-telemetry/opentelemetry-python-contrib', 0.7430706024169922, 'util', 0), ('openai/openai-python', 0.5268572568893433, 'util', 0), ('cohere-ai/cohere-python', 0.5118368864059448, 'util', 1), ('kubeflow/fairing', 0.5018121004104614, 'ml-ops', 0), ('gaogaotiantian/viztracer', 0.5011972784996033, 'profiling', 1)]",209,8.0,,2.73,184,71,57,0,7,12,7,184.0,250.0,90.0,1.4,50
1392,llm,https://github.com/explosion/spacy-llm,[],,[],[],,,,explosion/spacy-llm,spacy-llm,780,56,14,Python,https://spacy.io/usage/large-language-models,🦙 Integrating LLMs into structured NLP pipelines,explosion,2024-01-13,2023-03-16,45,17.0625,https://avatars.githubusercontent.com/u/20011530?v=4,🦙 Integrating LLMs into structured NLP pipelines,"['anthropic', 'claude', 'cohere', 'dolly', 'falcon', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llm', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'openai', 'prompt-engineering', 'spacy', 'text-classification']","['anthropic', 'claude', 'cohere', 'dolly', 'falcon', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llm', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'openai', 'prompt-engineering', 'spacy', 'text-classification']",2023-12-28,"[('paddlepaddle/paddlenlp', 0.691947877407074, 'llm', 3), ('argilla-io/argilla', 0.6727258563041687, 'nlp', 5), 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0.5245431065559387, 'nlp', 2), ('explosion/spacy-transformers', 0.524055004119873, 'llm', 5), ('lm-sys/fastchat', 0.5232176780700684, 'llm', 0), ('eugeneyan/open-llms', 0.5212215185165405, 'study', 2), ('zilliztech/gptcache', 0.5211718082427979, 'llm', 4), ('ctlllll/llm-toolmaker', 0.520075261592865, 'llm', 0), ('huggingface/transformers', 0.519777774810791, 'nlp', 3), ('guardrails-ai/guardrails', 0.5183983445167542, 'llm', 3), ('run-llama/rags', 0.5166354775428772, 'llm', 2), ('princeton-nlp/alce', 0.5158350467681885, 'llm', 0), ('hannibal046/awesome-llm', 0.5151101350784302, 'study', 0), ('freedomintelligence/llmzoo', 0.5140294432640076, 'llm', 0), ('ibm/dromedary', 0.5127615928649902, 'llm', 0), ('ibm/transition-amr-parser', 0.5116180777549744, 'nlp', 2), ('next-gpt/next-gpt', 0.5109996199607849, 'llm', 3), ('bigscience-workshop/biomedical', 0.510345458984375, 'data', 0), ('jbesomi/texthero', 0.5079323053359985, 'nlp', 2), ('bentoml/openllm', 0.5071743726730347, 'ml-ops', 3), ('maartengr/bertopic', 0.507010817527771, 'nlp', 2), ('openlmlab/moss', 0.5049393773078918, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.504305899143219, 'study', 2), ('franck-dernoncourt/neuroner', 0.5040908455848694, 'nlp', 3), ('ludwig-ai/ludwig', 0.5036592483520508, 'ml-ops', 4), ('eth-sri/lmql', 0.502147376537323, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5020976662635803, 'nlp', 2), ('mlc-ai/web-llm', 0.5018036961555481, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5016040205955505, 'perf', 0), ('qanastek/drbert', 0.5015144348144531, 'llm', 2)]",16,5.0,,5.15,68,54,10,1,23,28,23,68.0,87.0,90.0,1.3,50
1852,util,https://github.com/lastmile-ai/aiconfig,"['config', 'llm']","AIConfig saves prompts, models and model parameters as source control friendly configs. This allows you to iterate on prompts and model parameters separately from your application code.",[],[],1.0,,,lastmile-ai/aiconfig,aiconfig,600,35,7,Python,https://aiconfig.lastmileai.dev,"aiconfig -- config-driven, source control friendly AI application development",lastmile-ai,2024-01-12,2023-09-01,21,27.814569536423843,https://avatars.githubusercontent.com/u/123273171?v=4,"aiconfig -- config-driven, source control friendly AI application development","['ai', 'developer-tools', 'generative-ai', 'llm', 'llm-ops']","['ai', 'config', 'developer-tools', 'generative-ai', 'llm', 'llm-ops']",2024-01-14,"[('microsoft/promptflow', 0.7025976181030273, 'llm', 2), ('antonosika/gpt-engineer', 0.6765998005867004, 'llm', 1), ('prefecthq/marvin', 0.6677407622337341, 'nlp', 2), ('bentoml/bentoml', 0.6600256562232971, 'ml-ops', 2), ('microsoft/lmops', 0.6332518458366394, 'llm', 1), ('cheshire-cat-ai/core', 0.6183017492294312, 'llm', 2), ('avaiga/taipy', 0.6097587943077087, 'data', 1), ('sweepai/sweep', 0.6068457365036011, 'llm', 3), ('pythagora-io/gpt-pilot', 0.5966111421585083, 'llm', 2), ('pathwaycom/llm-app', 0.5902220606803894, 'llm', 1), ('mlc-ai/mlc-llm', 0.588663637638092, 'llm', 1), ('microsoft/semantic-kernel', 0.5658584237098694, 'llm', 2), ('mindsdb/mindsdb', 0.5651490092277527, 'data', 2), ('transformeroptimus/superagi', 0.5631660223007202, 'llm', 2), ('ludwig-ai/ludwig', 0.5598220229148865, 'ml-ops', 1), ('operand/agency', 0.5538642406463623, 'llm', 2), ('smol-ai/developer', 0.5501940846443176, 'llm', 1), ('zenml-io/zenml', 0.5441376566886902, 'ml-ops', 2), ('netflix/metaflow', 0.5414328575134277, 'ml-ops', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5404054522514343, 'study', 1), ('microsoft/generative-ai-for-beginners', 0.5391389727592468, 'study', 2), ('h2oai/h2o-llmstudio', 0.5382325053215027, 'llm', 3), ('activeloopai/deeplake', 0.5284830331802368, 'ml-ops', 2), ('tigerlab-ai/tiger', 0.5260499715805054, 'llm', 1), ('allegroai/clearml', 0.5247846841812134, 'ml-ops', 1), ('arize-ai/phoenix', 0.5230908393859863, 'ml-interpretability', 0), ('iterative/dvc', 0.522969663143158, 'ml-ops', 2), ('giskard-ai/giskard', 0.5168980360031128, 'data', 0), ('pytorchlightning/pytorch-lightning', 0.5128691792488098, 'ml-dl', 1), ('embedchain/embedchain', 0.5092721581459045, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5029549598693848, 'llm', 0), ('polyaxon/polyaxon', 0.5021164417266846, 'ml-ops', 0), ('jina-ai/jina', 0.5008789300918579, 'ml', 1), ('salesforce/codet5', 0.500541627407074, 'nlp', 0)]",15,1.0,,5.6,900,762,4,0,5,18,5,900.0,610.0,90.0,0.7,50
1305,study,https://github.com/christoschristofidis/awesome-deep-learning,['awesome'],,[],[],,,,christoschristofidis/awesome-deep-learning,awesome-deep-learning,22183,5979,1213,,,"A curated list of awesome Deep Learning tutorials, projects and communities.",christoschristofidis,2024-01-13,2015-01-02,473,46.84193061840121,,"A curated list of awesome Deep Learning tutorials, projects and communities.","['awesome', 'awesome-list', 'deep-learning', 'deep-learning-tutorial', 'deep-networks', 'face-images', 'machine-learning', 'neural-network', 'recurrent-networks']","['awesome', 'awesome-list', 'deep-learning', 'deep-learning-tutorial', 'deep-networks', 'face-images', 'machine-learning', 'neural-network', 'recurrent-networks']",2022-11-14,"[('dylanhogg/awesome-python', 0.6466501355171204, 'study', 4), ('mrdbourke/pytorch-deep-learning', 0.5786244869232178, 'study', 2), ('tensorflow/tensorflow', 0.5731549263000488, 'ml-dl', 3), ('rasbt/deeplearning-models', 0.5625115036964417, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.55058354139328, 'ml-dl', 1), ('graykode/nlp-tutorial', 0.5480766296386719, 'study', 0), ('lutzroeder/netron', 0.5479288101196289, 'ml', 3), ('dylanhogg/crazy-awesome-crypto', 0.5464283227920532, 'crypto', 2), ('d2l-ai/d2l-en', 0.5462198853492737, 'study', 2), ('huggingface/transformers', 0.5454069972038269, 'nlp', 2), ('explosion/thinc', 0.5450423359870911, 'ml-dl', 2), ('udacity/deep-learning-v2-pytorch', 0.5436199903488159, 'study', 3), ('keras-team/keras', 0.5434898138046265, 'ml-dl', 2), ('deepfakes/faceswap', 0.5433439612388611, 'ml-dl', 2), ('timofurrer/awesome-asyncio', 0.5383524298667908, 'study', 2), ('roboflow/notebooks', 0.5378063917160034, 'study', 2), ('mosaicml/composer', 0.5376695990562439, 'ml-dl', 3), ('nyandwi/modernconvnets', 0.5358662605285645, 'ml-dl', 0), ('pytorch/ignite', 0.5337355136871338, 'ml-dl', 3), ('amanchadha/coursera-deep-learning-specialization', 0.5327649116516113, 'study', 2), ('luodian/otter', 0.5302937030792236, 'llm', 2), ('rasbt/machine-learning-book', 0.5299058556556702, 'study', 2), ('tensorlayer/tensorlayer', 0.5272840857505798, 'ml-rl', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5266667008399963, 'study', 2), ('ashleve/lightning-hydra-template', 0.52616947889328, 'util', 1), ('paddlepaddle/paddlenlp', 0.5255681872367859, 'llm', 0), ('rwightman/pytorch-image-models', 0.5237160921096802, 'ml-dl', 0), ('neuralmagic/sparseml', 0.5221423506736755, 'ml-dl', 0), ('aiqc/aiqc', 0.5192874670028687, 'ml-ops', 0), ('mrdbourke/tensorflow-deep-learning', 0.5140354037284851, 'study', 1), ('horovod/horovod', 0.5127199292182922, 'ml-ops', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.5087785124778748, 'study', 3), ('iperov/deepfacelab', 0.5062230825424194, 'ml-dl', 2), ('rasbt/stat453-deep-learning-ss20', 0.506165087223053, 'study', 0), ('gradio-app/gradio', 0.5050585269927979, 'viz', 2), ('aistream-peelout/flow-forecast', 0.5039076209068298, 'time-series', 1), ('huggingface/autotrain-advanced', 0.5007786750793457, 'ml', 2), ('deci-ai/super-gradients', 0.5002260804176331, 'ml-dl', 2)]",147,7.0,,0.0,3,1,110,14,0,0,0,3.0,0.0,90.0,0.0,49
241,ml,https://github.com/deepmind/deepmind-research,[],,[],[],,,,deepmind/deepmind-research,deepmind-research,12418,2508,337,Jupyter Notebook,,This repository contains implementations and illustrative code to accompany DeepMind publications,deepmind,2024-01-13,2019-01-15,263,47.21673003802282,https://avatars.githubusercontent.com/u/8596759?v=4,This repository contains implementations and illustrative code to accompany DeepMind publications,[],[],2023-06-02,"[('rasbt/machine-learning-book', 0.5797885060310364, 'study', 0), ('nvidia/deeplearningexamples', 0.5370939373970032, 'ml-dl', 0), ('deepmind/dm_control', 0.5342397689819336, 'ml-rl', 0), ('microsoft/deepspeed', 0.5327295660972595, 'ml-dl', 0), ('bigcode-project/starcoder', 0.5220165252685547, 'llm', 0), ('google/automl', 0.5214808583259583, 'ml', 0), ('google-research/deeplab2', 0.5203995704650879, 'ml', 0), ('iperov/deepfacelab', 0.5150367021560669, 'ml-dl', 0), ('allenai/allennlp', 0.514882504940033, 'nlp', 0), ('microsoft/semi-supervised-learning', 0.5121714472770691, 'ml', 0), ('lucidrains/imagen-pytorch', 0.5075194835662842, 'ml-dl', 0), ('pytorch/fairseq', 0.5041647553443909, 'nlp', 0), ('graykode/nlp-tutorial', 0.5026014447212219, 'study', 0)]",92,2.0,,0.27,34,9,61,8,0,0,0,34.0,17.0,90.0,0.5,49
167,sim,https://github.com/bulletphysics/bullet3,[],,[],[],,,,bulletphysics/bullet3,bullet3,11488,2812,404,C++,http://bulletphysics.org,"Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.",bulletphysics,2024-01-14,2011-04-12,668,17.197604790419163,https://avatars.githubusercontent.com/u/6955508?v=4,"Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.","['computer-animation', 'game-development', 'kinematics', 'pybullet', 'reinforcement-learning', 'robotics', 'simulation', 'simulator', 'virtual-reality']","['computer-animation', 'game-development', 'kinematics', 'pybullet', 'reinforcement-learning', 'robotics', 'simulation', 'simulator', 'virtual-reality']",2023-11-28,"[('viblo/pymunk', 0.5624234080314636, 'sim', 0), ('tensorlayer/tensorlayer', 0.5209137201309204, 'ml-rl', 1), ('arise-initiative/robosuite', 0.5200599431991577, 'ml-rl', 2), ('aimhubio/aim', 0.5082801580429077, 'ml-ops', 0), ('pytorch/rl', 0.505810022354126, 'ml-rl', 2), ('facebookresearch/habitat-lab', 0.5023512840270996, 'sim', 3)]",305,3.0,,0.08,47,7,155,2,0,2,2,47.0,38.0,90.0,0.8,49
649,util,https://github.com/magicstack/uvloop,[],,[],[],,,,magicstack/uvloop,uvloop,9768,572,226,Cython,,Ultra fast asyncio event loop.,magicstack,2024-01-13,2015-11-08,429,22.754076539101497,https://avatars.githubusercontent.com/u/14324950?v=4,Ultra fast asyncio event loop.,"['async', 'async-await', 'async-python', 'asyncio', 'event-loop', 'high-performance', 'libuv', 'networking']","['async', 'async-await', 'async-python', 'asyncio', 'event-loop', 'high-performance', 'libuv', 'networking']",2023-10-22,"[('agronholm/anyio', 0.7205407619476318, 'perf', 2), ('tiangolo/asyncer', 0.6868960857391357, 'perf', 2), ('aio-libs/aiohttp', 0.6718955636024475, 'web', 2), ('python-trio/trio', 0.6619237065315247, 'perf', 3), ('erdewit/nest_asyncio', 0.6544142365455627, 'util', 2), ('alex-sherman/unsync', 0.6438055634498596, 'util', 0), ('samuelcolvin/arq', 0.6330302953720093, 'data', 2), ('timofurrer/awesome-asyncio', 0.6274605989456177, 'study', 1), ('miguelgrinberg/python-socketio', 0.6088827252388, 'util', 1), ('sumerc/yappi', 0.6066219806671143, 'profiling', 1), ('airtai/faststream', 0.6053869128227234, 'perf', 1), ('pallets/quart', 0.5947157740592957, 'web', 1), ('alirn76/panther', 0.5940344929695129, 'web', 0), ('neoteroi/blacksheep', 0.5796182155609131, 'web', 1), ('noxdafox/pebble', 0.5719602108001709, 'perf', 1), ('pytest-dev/pytest-asyncio', 0.552464485168457, 'testing', 1), ('geeogi/async-python-lambda-template', 0.5491867065429688, 'template', 0), ('samuelcolvin/aioaws', 0.5443870425224304, 'data', 1), ('encode/httpx', 0.5275475978851318, 'web', 1), ('gbeced/basana', 0.5239098072052002, 'finance', 1), ('samuelcolvin/watchfiles', 0.5178032517433167, 'util', 1), ('fastai/fastcore', 0.5093868970870972, 'util', 0), ('tiangolo/fastapi', 0.5084176659584045, 'web', 2), ('huge-success/sanic', 0.5082210898399353, 'web', 1), ('encode/starlette', 0.5062094926834106, 'web', 1), ('klen/py-frameworks-bench', 0.5054547190666199, 'perf', 0)]",60,3.0,,0.21,24,11,100,3,2,10,2,24.0,25.0,90.0,1.0,49
1194,llm,https://github.com/thudm/glm-130b,[],,[],[],,,,thudm/glm-130b,GLM-130B,7447,602,96,Python,,GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023),thudm,2024-01-13,2022-08-03,77,95.64954128440367,https://avatars.githubusercontent.com/u/48590610?v=4,GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023),[],[],2023-07-25,"[('openai/finetune-transformer-lm', 0.590366780757904, 'llm', 0), ('thudm/chatglm-6b', 0.5821515321731567, 'llm', 0), ('microsoft/unilm', 0.5527222156524658, 'nlp', 0), ('cg123/mergekit', 0.542129635810852, 'llm', 0), ('yizhongw/self-instruct', 0.5371110439300537, 'llm', 0), ('salesforce/blip', 0.5314812064170837, 'diffusion', 0), ('qanastek/drbert', 0.5260924696922302, 'llm', 0), ('thudm/chatglm2-6b', 0.5260385274887085, 'llm', 0), ('ofa-sys/ofa', 0.5175337195396423, 'llm', 0), ('freedomintelligence/llmzoo', 0.5044916272163391, 'llm', 0), ('thudm/codegeex', 0.5032495856285095, 'llm', 0), ('lm-sys/fastchat', 0.5007140636444092, 'llm', 0)]",6,2.0,,0.33,8,1,18,6,0,0,0,8.0,4.0,90.0,0.5,49
1275,nlp,https://github.com/deeppavlov/deeppavlov,[],,[],[],,,,deeppavlov/deeppavlov,DeepPavlov,6439,1135,209,Python,https://deeppavlov.ai,An open source library for deep learning end-to-end dialog systems and chatbots.,deeppavlov,2024-01-13,2017-11-17,323,19.899779249448123,https://avatars.githubusercontent.com/u/29918795?v=4,An open source library for deep learning end-to-end dialog systems and chatbots.,"['ai', 'artificial-intelligence', 'bot', 'chatbot', 'chitchat', 'deep-learning', 'deep-neural-networks', 'dialogue-agents', 'dialogue-manager', 'dialogue-systems', 'entity-extraction', 'intent-classification', 'intent-detection', 'machine-learning', 'named-entity-recognition', 'nlp', 'nlp-machine-learning', 'question-answering', 'slot-filling', 'tensorflow']","['ai', 'artificial-intelligence', 'bot', 'chatbot', 'chitchat', 'deep-learning', 'deep-neural-networks', 'dialogue-agents', 'dialogue-manager', 'dialogue-systems', 'entity-extraction', 'intent-classification', 'intent-detection', 'machine-learning', 'named-entity-recognition', 'nlp', 'nlp-machine-learning', 'question-answering', 'slot-filling', 'tensorflow']",2023-12-27,"[('rasahq/rasa', 0.7903884053230286, 'llm', 4), ('nvidia/nemo', 0.7471092939376831, 'nlp', 3), ('openlmlab/moss', 0.6669769287109375, 'llm', 2), ('krohling/bondai', 0.6658996939659119, 'llm', 0), ('togethercomputer/openchatkit', 0.6648150086402893, 'nlp', 1), ('rcgai/simplyretrieve', 0.6620361804962158, 'llm', 3), ('embedchain/embedchain', 0.6491185426712036, 'llm', 1), ('nomic-ai/gpt4all', 0.6342305541038513, 'llm', 1), ('gunthercox/chatterbot', 0.6284914016723633, 'nlp', 3), ('facebookresearch/parlai', 0.6255822777748108, 'nlp', 0), ('gunthercox/chatterbot-corpus', 0.6254500150680542, 'nlp', 0), ('allenai/allennlp', 0.6174921989440918, 'nlp', 2), ('tensorlayer/tensorlayer', 0.6134109497070312, 'ml-rl', 4), ('lm-sys/fastchat', 0.606029212474823, 'llm', 1), ('databrickslabs/dolly', 0.5981650352478027, 'llm', 1), ('cheshire-cat-ai/core', 0.5946456789970398, 'llm', 2), ('explosion/thinc', 0.5923045873641968, 'ml-dl', 6), ('thilinarajapakse/simpletransformers', 0.5851011276245117, 'nlp', 2), ('nvidia/deeplearningexamples', 0.5808957815170288, 'ml-dl', 3), ('larsbaunwall/bricky', 0.5805360078811646, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5695449113845825, 'llm', 2), ('deepset-ai/haystack', 0.5695079565048218, 'llm', 4), ('franck-dernoncourt/neuroner', 0.5672761797904968, 'nlp', 5), ('prefecthq/marvin', 0.5636438131332397, 'nlp', 1), ('laion-ai/open-assistant', 0.563031792640686, 'llm', 2), ('minimaxir/simpleaichat', 0.559428870677948, 'llm', 1), ('espnet/espnet', 0.5584684610366821, 'nlp', 1), ('tensorflow/tensorflow', 0.5553365349769592, 'ml-dl', 4), ('dialogflow/dialogflow-python-client-v2', 0.5524868369102478, 'nlp', 1), ('huggingface/transformers', 0.5488126873970032, 'nlp', 4), ('blinkdl/chatrwkv', 0.5481441617012024, 'llm', 1), ('llmware-ai/llmware', 0.5467641353607178, 'llm', 4), ('fasteval/fasteval', 0.5457442402839661, 'llm', 0), ('keras-team/keras-nlp', 0.5425511002540588, 'nlp', 4), ('lucidrains/toolformer-pytorch', 0.5414809584617615, 'llm', 2), ('google-research/language', 0.5389516949653625, 'nlp', 1), ('run-llama/rags', 0.5382777452468872, 'llm', 1), ('graykode/nlp-tutorial', 0.529963493347168, 'study', 2), ('tensorflow/tensor2tensor', 0.5281538963317871, 'ml', 2), ('aiwaves-cn/agents', 0.5271100997924805, 'nlp', 0), ('alibaba/easynlp', 0.52702796459198, 'nlp', 3), ('facebookresearch/habitat-lab', 0.5260887145996094, 'sim', 2), ('langchain-ai/chat-langchain', 0.5239380598068237, 'llm', 1), ('argilla-io/argilla', 0.5165128111839294, 'nlp', 3), ('chatarena/chatarena', 0.5164510011672974, 'llm', 2), ('unity-technologies/ml-agents', 0.5153900980949402, 'ml-rl', 2), ('microsoft/autogen', 0.5114785432815552, 'llm', 1), ('uberi/speech_recognition', 0.509485125541687, 'ml', 0), ('pathwaycom/llm-app', 0.5093848705291748, 'llm', 2), ('neuml/txtai', 0.5076900720596313, 'nlp', 2), ('microsoft/generative-ai-for-beginners', 0.5074864625930786, 'study', 1), ('lupantech/chameleon-llm', 0.5067449808120728, 'llm', 1), ('jina-ai/clip-as-service', 0.5053911805152893, 'nlp', 1), ('explosion/spacy', 0.5052632689476013, 'nlp', 6), ('speechbrain/speechbrain', 0.5031290054321289, 'nlp', 1), ('mlc-ai/web-llm', 0.5006988644599915, 'llm', 1)]",76,3.0,,0.62,37,14,75,1,6,10,6,37.0,9.0,90.0,0.2,49
1822,nlp,https://github.com/facebookresearch/metaseq,['fairseq'],"A codebase for working with Open Pre-trained Transformers, originally forked from fairseq.",[],[],,,,facebookresearch/metaseq,metaseq,6297,711,110,Python,,Repo for external large-scale work,facebookresearch,2024-01-13,2022-05-02,91,69.08934169278997,https://avatars.githubusercontent.com/u/16943930?v=4,Repo for external large-scale work,[],['fairseq'],2023-06-08,[],54,3.0,,0.9,2,0,21,7,0,0,0,2.0,1.0,90.0,0.5,49
282,util,https://github.com/sdispater/pendulum,[],,[],[],,,,sdispater/pendulum,pendulum,5900,398,69,Python,https://pendulum.eustace.io,Python datetimes made easy,sdispater,2024-01-14,2016-06-27,396,14.893617021276595,,Python datetimes made easy,"['date', 'datetime', 'time', 'timezones']","['date', 'datetime', 'time', 'timezones']",2023-12-16,"[('dateutil/dateutil', 0.7966391444206238, 'util', 3), ('arrow-py/arrow', 0.7535154819488525, 'util', 4), ('scrapinghub/dateparser', 0.6702179908752441, 'util', 2), ('stub42/pytz', 0.646413266658783, 'util', 0), ('spulec/freezegun', 0.54648357629776, 'testing', 0), ('rjt1990/pyflux', 0.5265621542930603, 'time-series', 0)]",96,3.0,,0.9,67,45,92,1,2,7,2,67.0,82.0,90.0,1.2,49
1015,util,https://github.com/wireservice/csvkit,[],,[],[],,,,wireservice/csvkit,csvkit,5701,601,130,Python,https://csvkit.readthedocs.io,"A suite of utilities for converting to and working with CSV, the king of tabular file formats.",wireservice,2024-01-13,2011-04-01,669,8.514401536163858,https://avatars.githubusercontent.com/u/17111824?v=4,"A suite of utilities for converting to and working with CSV, the king of tabular file formats.",[],[],2023-12-21,"[('jazzband/prettytable', 0.5902390480041504, 'term', 0), ('jazzband/tablib', 0.5830636620521545, 'data', 0), ('saulpw/visidata', 0.5774484276771545, 'term', 0), ('camelot-dev/camelot', 0.567770779132843, 'util', 0), ('crunch-io/lazycsv', 0.561709463596344, 'perf', 0), ('astanin/python-tabulate', 0.5541922450065613, 'util', 0), ('dask/fastparquet', 0.5032108426094055, 'data', 0)]",107,9.0,,1.9,102,69,156,1,0,2,2,102.0,80.0,90.0,0.8,49
542,ml-dl,https://github.com/facebookresearch/mmf,[],,[],[],,,,facebookresearch/mmf,mmf,5352,928,117,Python,https://mmf.sh/,A modular framework for vision & language multimodal research from Facebook AI Research (FAIR),facebookresearch,2024-01-13,2018-06-27,291,18.337738619676944,https://avatars.githubusercontent.com/u/16943930?v=4,A modular framework for vision & language multimodal research from Facebook AI Research (FAIR),"['captioning', 'deep-learning', 'dialog', 'hateful-memes', 'multi-tasking', 'multimodal', 'pretrained-models', 'pytorch', 'textvqa', 'vqa']","['captioning', 'deep-learning', 'dialog', 'hateful-memes', 'multi-tasking', 'multimodal', 'pretrained-models', 'pytorch', 'textvqa', 'vqa']",2024-01-03,"[('pytorch/fairseq', 0.6375062465667725, 'nlp', 1), ('microsoft/i-code', 0.5485939383506775, 'ml', 0), ('rasahq/rasa', 0.5447145104408264, 'llm', 0), ('nvlabs/prismer', 0.5333836674690247, 'diffusion', 1), ('nvidia/nemo', 0.5225995779037476, 'nlp', 1), ('jina-ai/clip-as-service', 0.52168208360672, 'nlp', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5202192068099976, 'web', 0), ('docarray/docarray', 0.516020655632019, 'data', 3), ('nateraw/stable-diffusion-videos', 0.5128837823867798, 'diffusion', 0), ('luodian/otter', 0.5072057843208313, 'llm', 1), ('ofa-sys/ofa', 0.5046265721321106, 'llm', 2), ('salesforce/blip', 0.5001189112663269, 'diffusion', 0)]",116,5.0,,0.38,7,4,68,0,0,1,1,7.0,6.0,90.0,0.9,49
1887,util,https://github.com/rsalmei/alive-progress,"['progress-bar', 'cli']",,[],[],1.0,,,rsalmei/alive-progress,alive-progress,4854,191,49,Python,,"A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!",rsalmei,2024-01-14,2019-08-05,234,20.73093349603417,,"A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!","['alive', 'animated', 'animations', 'bar', 'cli', 'eta', 'feedback', 'monitoring', 'multi-threaded', 'progress', 'progress-bar', 'progressbar', 'repl', 'spinner', 'spinner-styles', 'spinners', 'terminal', 'throughput', 'visual']","['alive', 'animated', 'animations', 'bar', 'cli', 'eta', 'feedback', 'monitoring', 'multi-threaded', 'progress', 'progress-bar', 'progressbar', 'repl', 'spinner', 'spinner-styles', 'spinners', 'terminal', 'throughput', 'visual']",2023-12-02,"[('tqdm/tqdm', 0.6888198256492615, 'term', 5), ('wolph/python-progressbar', 0.6114795207977295, 'util', 7), ('rockhopper-technologies/enlighten', 0.5524495244026184, 'term', 0)]",7,3.0,,0.83,9,5,54,1,0,1,1,9.0,31.0,90.0,3.4,49
27,typing,https://github.com/google/pytype,['code-quality'],,[],[],,,,google/pytype,pytype,4452,279,57,Python,https://google.github.io/pytype,A static type analyzer for Python code,google,2024-01-13,2015-03-18,462,9.618518518518519,https://avatars.githubusercontent.com/u/1342004?v=4,A static type analyzer for Python code,"['linter', 'static-analysis', 'static-code-analysis', 'typechecker', 'types', 'typing']","['code-quality', 'linter', 'static-analysis', 'static-code-analysis', 'typechecker', 'types', 'typing']",2024-01-11,"[('microsoft/pyright', 0.8127192258834839, 'typing', 2), ('instagram/monkeytype', 0.8035555481910706, 'typing', 1), ('facebook/pyre-check', 0.7848848104476929, 'typing', 3), ('python/mypy', 0.7456424236297607, 'typing', 5), ('agronholm/typeguard', 0.7289842367172241, 'typing', 2), ('python/typeshed', 0.7091848254203796, 'typing', 3), ('astral-sh/ruff', 0.6849864721298218, 'util', 4), ('crytic/slither', 0.6767980456352234, 'crypto', 1), ('rubik/radon', 0.6680740714073181, 'util', 1), ('landscapeio/prospector', 0.6452606916427612, 'util', 0), ('grantjenks/blue', 0.6101509928703308, 'util', 1), ('psf/black', 0.5877479314804077, 'util', 1), ('pytoolz/toolz', 0.5861064195632935, 'util', 0), ('pycqa/mccabe', 0.5844917893409729, 'util', 0), ('nedbat/coveragepy', 0.5753951072692871, 'testing', 0), ('klen/pylama', 0.5735052824020386, 'util', 1), ('pyutils/line_profiler', 0.5699858069419861, 'profiling', 0), ('pycqa/pylint', 0.5689358115196228, 'util', 4), ('google/yapf', 0.5599178075790405, 'util', 1), ('pycqa/flake8', 0.5498300194740295, 'util', 4), ('xrudelis/pytrait', 0.5462278127670288, 'util', 0), ('tiangolo/typer', 0.5461122393608093, 'term', 0), ('eugeneyan/python-collab-template', 0.5415253043174744, 'template', 0), ('pycqa/pylint-django', 0.5338144898414612, 'util', 1), ('pympler/pympler', 0.524192750453949, 'perf', 0), ('patrick-kidger/torchtyping', 0.5230032205581665, 'typing', 1), ('benfred/py-spy', 0.5136662721633911, 'profiling', 0), ('gaogaotiantian/viztracer', 0.5134180784225464, 'profiling', 0), ('python-rope/rope', 0.5130378603935242, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5118113160133362, 'study', 0), ('pycqa/isort', 0.5101639032363892, 'util', 2), ('pycqa/pyflakes', 0.5093093514442444, 'util', 1), ('pandas-dev/pandas', 0.5089789628982544, 'pandas', 0), ('pythonspeed/filprofiler', 0.5083953738212585, 'profiling', 0), ('marshmallow-code/marshmallow', 0.5046632289886475, 'util', 0), ('alexmojaki/birdseye', 0.5039346218109131, 'debug', 0), ('ta-lib/ta-lib-python', 0.5031558275222778, 'finance', 0), ('aswinnnn/pyscan', 0.5005221366882324, 'security', 1)]",100,2.0,,10.46,57,44,107,0,0,22,22,57.0,27.0,90.0,0.5,49
209,web,https://github.com/pywebio/pywebio,[],,[],[],,,,pywebio/pywebio,PyWebIO,4234,363,54,Python,https://pywebio.readthedocs.io,Write interactive web app in script way.,pywebio,2024-01-13,2020-02-29,204,20.71139063591894,https://avatars.githubusercontent.com/u/83836839?v=4,Write interactive web app in script way.,['pywebio'],['pywebio'],2023-12-12,"[('webpy/webpy', 0.5835192799568176, 'web', 0), ('pyscript/pyscript', 0.5674092173576355, 'web', 0), ('reflex-dev/reflex', 0.5351123213768005, 'web', 0), ('masoniteframework/masonite', 0.5325572490692139, 'web', 0), ('pallets/flask', 0.5090134143829346, 'web', 0)]",19,3.0,,0.75,12,7,47,1,0,8,8,12.0,14.0,90.0,1.2,49
938,llm,https://github.com/microsoft/biogpt,[],,[],[],,,,microsoft/biogpt,BioGPT,4169,436,68,Python,,,microsoft,2024-01-12,2022-08-15,76,54.75234521575985,https://avatars.githubusercontent.com/u/6154722?v=4,microsoft/BioGPT,[],[],2023-11-13,[],9,4.0,,0.42,10,2,17,2,0,0,0,10.0,8.0,90.0,0.8,49
1598,nlp,https://github.com/thilinarajapakse/simpletransformers,[],,[],[],,,,thilinarajapakse/simpletransformers,simpletransformers,3901,723,62,Python,https://simpletransformers.ai/,"Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI",thilinarajapakse,2024-01-14,2019-10-04,225,17.29385687143762,,"Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI","['conversational-ai', 'named-entity-recognition', 'question-answering', 'text-classification', 'transformers']","['conversational-ai', 'named-entity-recognition', 'question-answering', 'text-classification', 'transformers']",2023-12-18,"[('huggingface/transformers', 0.713013768196106, 'nlp', 0), ('cdpierse/transformers-interpret', 0.6818839311599731, 'ml-interpretability', 1), ('nvidia/nemo', 0.6740487813949585, 'nlp', 0), ('keras-team/keras-nlp', 0.6119193434715271, 'nlp', 0), ('llmware-ai/llmware', 0.6119190454483032, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.6100896596908569, 'study', 1), ('lucidrains/toolformer-pytorch', 0.6100559234619141, 'llm', 1), ('deepset-ai/haystack', 0.5894189476966858, 'llm', 2), ('prefecthq/marvin', 0.5893362760543823, 'nlp', 0), ('deeppavlov/deeppavlov', 0.5851011276245117, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.5818993449211121, 'llm', 2), ('explosion/thinc', 0.5814999938011169, 'ml-dl', 0), ('docarray/docarray', 0.5803972482681274, 'data', 0), ('rasahq/rasa', 0.5802013874053955, 'llm', 1), ('bentoml/bentoml', 0.5780894160270691, 'ml-ops', 0), ('intellabs/fastrag', 0.5657897591590881, 'nlp', 2), ('explosion/spacy', 0.5573221445083618, 'nlp', 2), ('cheshire-cat-ai/core', 0.5490924715995789, 'llm', 0), ('lvwerra/trl', 0.5451595783233643, 'llm', 0), ('lm-sys/fastchat', 0.5447478890419006, 'llm', 0), ('sloria/textblob', 0.5410082936286926, 'nlp', 0), ('nvlabs/prismer', 0.5408389568328857, 'diffusion', 0), ('krohling/bondai', 0.534867525100708, 'llm', 0), ('makcedward/nlpaug', 0.5320999026298523, 'nlp', 0), ('ddbourgin/numpy-ml', 0.5308157801628113, 'ml', 0), ('graykode/nlp-tutorial', 0.5299626588821411, 'study', 0), ('deepset-ai/farm', 0.5268033742904663, 'nlp', 1), ('microsoft/lmops', 0.5261474251747131, 'llm', 0), ('explosion/spacy-transformers', 0.5258124470710754, 'llm', 0), ('nltk/nltk', 0.5254354476928711, 'nlp', 0), ('explosion/spacy-models', 0.5240768790245056, 'nlp', 0), ('milvus-io/bootcamp', 0.5199248790740967, 'data', 1), ('google/trax', 0.5190243721008301, 'ml-dl', 0), ('embedchain/embedchain', 0.518301248550415, 'llm', 0), ('rcgai/simplyretrieve', 0.5177233815193176, 'llm', 0), ('huggingface/autotrain-advanced', 0.517383873462677, 'ml', 0), ('extreme-bert/extreme-bert', 0.5170273184776306, 'llm', 0), ('mindsdb/mindsdb', 0.515864372253418, 'data', 0), ('alibaba/easynlp', 0.5156533122062683, 'nlp', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5155435800552368, 'study', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5146978497505188, 'study', 0), ('eleutherai/knowledge-neurons', 0.5122392177581787, 'ml-interpretability', 1), ('bigscience-workshop/megatron-deepspeed', 0.5100935697555542, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5100935697555542, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5096346735954285, 'ml-interpretability', 1), ('eugeneyan/obsidian-copilot', 0.5094960927963257, 'llm', 0), ('databrickslabs/dolly', 0.507858157157898, 'llm', 0), ('ofa-sys/ofa', 0.5072410702705383, 'llm', 0), ('franck-dernoncourt/neuroner', 0.5060073733329773, 'nlp', 1), ('jina-ai/finetuner', 0.5025129914283752, 'ml', 0), ('explosion/spacy-llm', 0.5020976662635803, 'llm', 2), ('modularml/mojo', 0.5019367933273315, 'util', 0), ('noahshinn/reflexion', 0.5012626051902771, 'llm', 0), ('espnet/espnet', 0.5008228421211243, 'nlp', 0), ('promptslab/awesome-prompt-engineering', 0.5007800459861755, 'study', 0), ('next-gpt/next-gpt', 0.5003290772438049, 'llm', 0)]",100,5.0,,0.25,10,3,52,1,0,62,62,10.0,7.0,90.0,0.7,49
1386,security,https://github.com/rhinosecuritylabs/pacu,[],,[],[],,,,rhinosecuritylabs/pacu,pacu,3872,640,111,Python,https://rhinosecuritylabs.com/aws/pacu-open-source-aws-exploitation-framework/,"The AWS exploitation framework, designed for testing the security of Amazon Web Services environments.",rhinosecuritylabs,2024-01-13,2018-06-13,293,13.176470588235293,https://avatars.githubusercontent.com/u/11430746?v=4,"The AWS exploitation framework, designed for testing the security of Amazon Web Services environments.","['aws', 'aws-security', 'penetration-testing', 'security']","['aws', 'aws-security', 'penetration-testing', 'security']",2024-01-09,"[('flipkart-incubator/astra', 0.5590912699699402, 'web', 2), ('newsapps/beeswithmachineguns', 0.5492108464241028, 'testing', 0), ('swisskyrepo/payloadsallthethings', 0.5338615775108337, 'security', 2), ('prefecthq/prefect-aws', 0.5137910842895508, 'data', 1), ('aws-samples/sagemaker-ssh-helper', 0.5007947087287903, 'util', 1)]",53,3.0,,1.79,30,26,68,0,11,4,11,30.0,12.0,90.0,0.4,49
797,util,https://github.com/miguelgrinberg/python-socketio,[],,[],[],,,,miguelgrinberg/python-socketio,python-socketio,3638,597,60,Python,,Python Socket.IO server and client,miguelgrinberg,2024-01-13,2015-07-15,445,8.159564242230054,,Python Socket.IO server and client,"['asyncio', 'eventlet', 'gevent', 'long-polling', 'low-latency', 'socket-io', 'socketio', 'socketio-server', 'web-server', 'websocket']","['asyncio', 'eventlet', 'gevent', 'long-polling', 'low-latency', 'socket-io', 'socketio', 'socketio-server', 'web-server', 'websocket']",2024-01-11,"[('websocket-client/websocket-client', 0.7025880217552185, 'web', 1), ('aio-libs/aiohttp', 0.6701440215110779, 'web', 1), ('encode/starlette', 0.6212427616119385, 'web', 0), ('magicstack/uvloop', 0.6088827252388, 'util', 1), ('encode/httpx', 0.5959307551383972, 'web', 1), ('bmoscon/cryptofeed', 0.595906138420105, 'crypto', 2), ('encode/uvicorn', 0.5958155989646912, 'web', 1), ('airtai/faststream', 0.5774848461151123, 'perf', 1), ('pallets/quart', 0.5742831826210022, 'web', 1), ('python-trio/trio', 0.5562794208526611, 'perf', 0), ('sumerc/yappi', 0.552412211894989, 'profiling', 2), ('neoteroi/blacksheep', 0.5502622127532959, 'web', 1), ('agronholm/anyio', 0.5345661640167236, 'perf', 1)]",69,4.0,,1.38,59,55,104,0,4,11,4,59.0,97.0,90.0,1.6,49
426,util,https://github.com/pycqa/flake8,['code-quality'],,[],[],,,,pycqa/flake8,flake8,3144,303,37,Python,https://flake8.pycqa.org,"flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.",pycqa,2024-01-14,2014-09-13,489,6.423817863397548,https://avatars.githubusercontent.com/u/8749848?v=4,"flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.","['complexity-analysis', 'flake8', 'linter', 'linter-flake8', 'pep8', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide', 'stylelint']","['code-quality', 'complexity-analysis', 'flake8', 'linter', 'linter-flake8', 'pep8', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide', 'stylelint']",2024-01-08,"[('pycqa/pycodestyle', 0.6883364915847778, 'util', 4), ('pycqa/mccabe', 0.6619266867637634, 'util', 3), ('grantjenks/blue', 0.6205138564109802, 'util', 1), ('hhatto/autopep8', 0.6079347729682922, 'util', 1), ('psf/black', 0.6002591848373413, 'util', 1), ('astral-sh/ruff', 0.5777422785758972, 'util', 7), ('rubik/radon', 0.5651803612709045, 'util', 1), ('google/pytype', 0.5498300194740295, 'typing', 4), ('google/yapf', 0.5436343550682068, 'util', 1), ('pycqa/pylint-django', 0.5301540493965149, 'util', 1), ('pytoolz/toolz', 0.5233083367347717, 'util', 0), ('landscapeio/prospector', 0.5223195552825928, 'util', 0), ('facebook/pyre-check', 0.5048815608024597, 'typing', 2), ('nedbat/coveragepy', 0.5013235807418823, 'testing', 0)]",174,6.0,,0.92,37,36,114,0,0,9,9,37.0,48.0,90.0,1.3,49
1297,nlp,https://github.com/promptslab/promptify,['prompt-engineering'],,[],[],,,,promptslab/promptify,Promptify,2835,205,46,Jupyter Notebook,https://discord.gg/m88xfYMbK6,"Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research",promptslab,2024-01-14,2022-12-12,59,47.93478260869565,https://avatars.githubusercontent.com/u/120981762?v=4,"Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research","['chatgpt', 'chatgpt-api', 'chatgpt-python', 'gpt-3', 'gpt-3-prompts', 'gpt-4', 'gpt-4-api', 'gpt3-library', 'large-language-models', 'machine-learning', 'nlp', 'openai', 'prompt-engineering', 'prompt-toolkit', 'prompt-tuning', 'prompt-versioning', 'prompting', 'prompts', 'promptversioning', 'transformers']","['chatgpt', 'chatgpt-api', 'chatgpt-python', 'gpt-3', 'gpt-3-prompts', 'gpt-4', 'gpt-4-api', 'gpt3-library', 'large-language-models', 'machine-learning', 'nlp', 'openai', 'prompt-engineering', 'prompt-toolkit', 'prompt-tuning', 'prompt-versioning', 'prompting', 'prompts', 'promptversioning', 'transformers']",2023-08-03,"[('promptslab/awesome-prompt-engineering', 0.7121634483337402, 'study', 8), ('guidance-ai/guidance', 0.6470388770103455, 'llm', 2), ('xtekky/gpt4free', 0.6027716398239136, 'llm', 5), ('microsoft/autogen', 0.5999529957771301, 'llm', 2), ('agenta-ai/agenta', 0.5992403030395508, 'llm', 3), ('neulab/prompt2model', 0.5863036513328552, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5769301056861877, 'llm', 1), ('killianlucas/open-interpreter', 0.5764856338500977, 'llm', 2), ('bigscience-workshop/promptsource', 0.5728100538253784, 'nlp', 2), ('hazyresearch/ama_prompting', 0.5550077557563782, 'llm', 1), ('run-llama/rags', 0.5508096814155579, 'llm', 2), ('microsoft/promptbase', 0.5429252982139587, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.541034460067749, 'llm', 0), ('hazyresearch/manifest', 0.5325891971588135, 'llm', 1), ('microsoft/promptflow', 0.531336784362793, 'llm', 2), ('lianjiatech/belle', 0.5284560322761536, 'llm', 0), ('tmbo/questionary', 0.5267842411994934, 'term', 0), ('microsoft/pycodegpt', 0.5242052674293518, 'llm', 0), ('stanfordnlp/dspy', 0.5224989056587219, 'llm', 1), ('farizrahman4u/loopgpt', 0.5138046741485596, 'llm', 1), ('minimaxir/gpt-2-simple', 0.5102137327194214, 'llm', 1), ('eth-sri/lmql', 0.5044764876365662, 'llm', 2)]",12,4.0,,8.29,8,1,13,5,0,1,1,8.0,4.0,90.0,0.5,49
245,util,https://github.com/pyston/pyston,[],,[],[],,,,pyston/pyston,pyston,2476,90,38,Python,https://www.pyston.org/,A faster and highly-compatible implementation of the Python programming language.,pyston,2024-01-14,2021-03-01,152,16.27417840375587,https://avatars.githubusercontent.com/u/9670621?v=4,A faster and highly-compatible implementation of the Python programming language.,[],[],2023-02-28,"[('pypy/pypy', 0.7871115207672119, 'util', 0), ('python/cpython', 0.7426720261573792, 'util', 0), ('pytoolz/toolz', 0.719200074672699, 'util', 0), ('cython/cython', 0.6975716352462769, 'util', 0), ('exaloop/codon', 0.6812407970428467, 'perf', 0), ('fastai/fastcore', 0.6732315421104431, 'util', 0), ('sympy/sympy', 0.6676157712936401, 'math', 0), ('eleutherai/pyfra', 0.650770366191864, 'ml', 0), ('micropython/micropython', 0.6496843695640564, 'util', 0), ('ipython/ipyparallel', 0.6226590275764465, 'perf', 0), ('hoffstadt/dearpygui', 0.6223757863044739, 'gui', 0), ('lcompilers/lpython', 0.6024731397628784, 'util', 0), ('joblib/joblib', 0.6001591086387634, 'util', 0), ('pytables/pytables', 0.5999751091003418, 'data', 0), ('pympler/pympler', 0.5993977189064026, 'perf', 0), ('faster-cpython/tools', 0.5962938070297241, 'perf', 0), ('primal100/pybitcointools', 0.5950328707695007, 'crypto', 0), ('ethereum/py-evm', 0.5943373441696167, 'crypto', 0), ('oracle/graalpython', 0.591853141784668, 'util', 0), ('adafruit/circuitpython', 0.5873665809631348, 'util', 0), ('google/latexify_py', 0.5858403444290161, 'util', 0), ('klen/py-frameworks-bench', 0.5815452933311462, 'perf', 0), ('paramiko/paramiko', 0.5798661708831787, 'util', 0), ('numba/llvmlite', 0.5717125535011292, 'util', 0), ('instagram/libcst', 0.5713760852813721, 'util', 0), ('google/jax', 0.5706031918525696, 'ml', 0), ('beeware/toga', 0.5686622262001038, 'gui', 0), ('1200wd/bitcoinlib', 0.5686086416244507, 'crypto', 0), ('ethereum/web3.py', 0.5668970942497253, 'crypto', 0), ('numpy/numpy', 0.5655171871185303, 'math', 0), ('brandtbucher/specialist', 0.5647898316383362, 'perf', 0), ('wxwidgets/phoenix', 0.5643559694290161, 'gui', 0), ('py4j/py4j', 0.5638821721076965, 'util', 0), ('faster-cpython/ideas', 0.5621926188468933, 'perf', 0), ('fredrik-johansson/mpmath', 0.5607538819313049, 'math', 0), ('agronholm/apscheduler', 0.5583381652832031, 'util', 0), ('connorferster/handcalcs', 0.5578861832618713, 'jupyter', 0), ('artemyk/dynpy', 0.5571711659431458, 'sim', 0), ('libtcod/python-tcod', 0.5553798079490662, 'gamedev', 0), ('pyglet/pyglet', 0.5540895462036133, 'gamedev', 0), ('dgilland/cacheout', 0.5537205338478088, 'perf', 0), ('norvig/pytudes', 0.5527113080024719, 'util', 0), ('pytransitions/transitions', 0.5525701642036438, 'util', 0), ('alexmojaki/snoop', 0.5509077310562134, 'debug', 0), ('erotemic/ubelt', 0.5508465766906738, 'util', 0), ('has2k1/plotnine', 0.5506923198699951, 'viz', 0), ('legrandin/pycryptodome', 0.5480275750160217, 'util', 0), ('marshmallow-code/marshmallow', 0.5445635914802551, 'util', 0), ('rustpython/rustpython', 0.543964684009552, 'util', 0), ('klen/muffin', 0.5434106588363647, 'web', 0), ('dosisod/refurb', 0.5432273149490356, 'util', 0), ('python-trio/trio', 0.5414426922798157, 'perf', 0), ('spotify/annoy', 0.5413413643836975, 'ml', 0), ('opengeos/leafmap', 0.5413333773612976, 'gis', 0), ('secdev/scapy', 0.5411231517791748, 'util', 0), ('facebook/pyre-check', 0.5409777760505676, 'typing', 0), ('urwid/urwid', 0.5381410121917725, 'term', 0), ('pexpect/pexpect', 0.5372031331062317, 'util', 0), ('crunch-io/lazycsv', 0.5371149182319641, 'perf', 0), ('google/pyglove', 0.5362030863761902, 'util', 0), ('python-rope/rope', 0.5357602834701538, 'util', 0), ('xrudelis/pytrait', 0.5349142551422119, 'util', 0), ('pandas-dev/pandas', 0.5344043970108032, 'pandas', 0), ('pythonspeed/filprofiler', 0.5313695073127747, 'profiling', 0), ('pyomo/pyomo', 0.5302437543869019, 'math', 0), ('pdm-project/pdm', 0.530032217502594, 'util', 0), ('webpy/webpy', 0.5299744606018066, 'web', 0), ('pypa/installer', 0.5297871828079224, 'util', 0), ('pyca/cryptography', 0.5291821360588074, 'util', 0), ('amaargiru/pyroad', 0.5288054347038269, 'study', 0), ('irmen/pyminiaudio', 0.5274853110313416, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5267325639724731, 'template', 0), ('stanfordnlp/dspy', 0.5251495838165283, 'llm', 0), ('altair-viz/altair', 0.5246127843856812, 'viz', 0), ('zerointensity/pointers.py', 0.523978590965271, 'perf', 0), ('mynameisfiber/high_performance_python_2e', 0.5236924290657043, 'study', 0), ('masoniteframework/masonite', 0.523208498954773, 'web', 0), ('ibis-project/ibis', 0.5222876667976379, 'data', 0), ('pyparsing/pyparsing', 0.522191047668457, 'util', 0), ('willmcgugan/textual', 0.5220023393630981, 'term', 0), ('imageio/imageio', 0.5213198661804199, 'util', 0), ('google/gin-config', 0.5207974910736084, 'util', 0), ('scipy/scipy', 0.5205775499343872, 'math', 0), ('pybind/pybind11', 0.5205539464950562, 'perf', 0), ('tiangolo/sqlmodel', 0.5203248858451843, 'data', 0), ('nvidia/warp', 0.5197428464889526, 'sim', 0), ('julienpalard/pipe', 0.5197369456291199, 'util', 0), ('astral-sh/ruff', 0.5196829438209534, 'util', 0), ('cherrypy/cherrypy', 0.5174131989479065, 'web', 0), ('pemistahl/lingua-py', 0.5171454548835754, 'nlp', 0), ('pygments/pygments', 0.5150144696235657, 'util', 0), ('nedbat/coveragepy', 0.5141503810882568, 'testing', 0), ('rasbt/watermark', 0.5138229131698608, 'util', 0), ('hhatto/autopep8', 0.5110304355621338, 'util', 0), ('evhub/coconut', 0.5107958316802979, 'util', 0), ('requests/toolbelt', 0.5103037357330322, 'util', 0), ('zarr-developers/zarr-python', 0.5102282762527466, 'data', 0), ('plasma-umass/scalene', 0.5100093483924866, 'profiling', 0), ('amzn/ion-python', 0.5089343786239624, 'data', 0), ('pyeve/cerberus', 0.5083901882171631, 'data', 0), ('kubeflow/fairing', 0.508223295211792, 'ml-ops', 0), ('pyscript/pyscript-cli', 0.5076680183410645, 'web', 0), ('gbeced/pyalgotrade', 0.507022500038147, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5064808130264282, 'study', 0), ('nmslib/hnswlib', 0.5059448480606079, 'ml', 0), ('pylons/pyramid', 0.5057663321495056, 'web', 0), ('collerek/ormar', 0.5057287812232971, 'data', 0), ('scrapy/scrapy', 0.5049728751182556, 'data', 0), ('pypa/hatch', 0.5036124587059021, 'util', 0), ('ethtx/ethtx', 0.5028896331787109, 'crypto', 0), ('holoviz/holoviz', 0.5020092725753784, 'viz', 0), ('encode/httpx', 0.5002996325492859, 'web', 0), ('google/yapf', 0.5001851320266724, 'util', 0)]",1037,6.0,,0.04,1,0,35,11,0,164,164,1.0,2.0,90.0,2.0,49
1739,ml-rl,https://github.com/eureka-research/eureka,['evolutionary-optimization'],,[],[],,,,eureka-research/eureka,Eureka,2376,197,22,Jupyter Notebook,https://eureka-research.github.io/,"Official Repository for ""Eureka: Human-Level Reward Design via Coding Large Language Models""",eureka-research,2024-01-14,2023-09-25,18,130.96062992125985,https://avatars.githubusercontent.com/u/145279285?v=4,"Official Repository for ""Eureka: Human-Level Reward Design via Coding Large Language Models""",[],['evolutionary-optimization'],2023-11-17,"[('yueyu1030/attrprompt', 0.522046685218811, 'llm', 0), ('lupantech/chameleon-llm', 0.5140032768249512, 'llm', 0)]",2,0.0,,0.12,36,8,4,2,0,0,0,36.0,43.0,90.0,1.2,49
1356,term,https://github.com/textualize/trogon,"['click', 'cli', 'terminal', 'textual']",,[],[],,,,textualize/trogon,trogon,2229,44,20,Python,,Easily turn your Click CLI into a powerful terminal application,textualize,2024-01-12,2023-04-18,41,54.36585365853659,https://avatars.githubusercontent.com/u/93378883?v=4,Easily turn your Click CLI into a powerful terminal application,[],"['cli', 'click', 'terminal', 'textual']",2023-08-21,"[('tiangolo/typer', 0.5765345692634583, 'term', 3), ('jquast/blessed', 0.5472238063812256, 'term', 2), ('pallets/click', 0.5391209125518799, 'term', 3), ('click-contrib/click-completion', 0.5366969704627991, 'term', 1), ('pexpect/pexpect', 0.5282593965530396, 'util', 0), ('python-poetry/cleo', 0.5179005265235901, 'term', 1), ('pyscript/pyscript-cli', 0.516979992389679, 'web', 0)]",6,3.0,,3.12,14,2,9,5,3,4,3,14.0,9.0,90.0,0.6,49
527,ml-dl,https://github.com/neuralmagic/sparseml,[],,[],[],,,,neuralmagic/sparseml,sparseml,1893,135,46,Python,,"Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models",neuralmagic,2024-01-13,2020-12-11,163,11.57292576419214,https://avatars.githubusercontent.com/u/68670575?v=4,"Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models","['automl', 'computer-vision-algorithms', 'deep-learning-algorithms', 'deep-learning-library', 'deep-learning-models', 'image-classification', 'keras', 'nlp', 'object-detection', 'onnx', 'pruning', 'pruning-algorithms', 'pytorch', 'smaller-models', 'sparsification', 'sparsification-recipes', 'sparsity', 'tensorflow', 'transfer-learning']","['automl', 'computer-vision-algorithms', 'deep-learning-algorithms', 'deep-learning-library', 'deep-learning-models', 'image-classification', 'keras', 'nlp', 'object-detection', 'onnx', 'pruning', 'pruning-algorithms', 'pytorch', 'smaller-models', 'sparsification', 'sparsification-recipes', 'sparsity', 'tensorflow', 'transfer-learning']",2024-01-12,"[('neuralmagic/deepsparse', 0.7135436534881592, 'nlp', 5), ('lutzroeder/netron', 0.6401857137680054, 'ml', 4), ('mosaicml/composer', 0.6215312480926514, 'ml-dl', 1), ('pytorch/ignite', 0.6128144860267639, 'ml-dl', 1), ('karpathy/micrograd', 0.597611129283905, 'study', 0), ('skorch-dev/skorch', 0.5961683988571167, 'ml-dl', 1), ('explosion/thinc', 0.5918089747428894, 'ml-dl', 3), ('huggingface/transformers', 0.5908825993537903, 'nlp', 3), ('rafiqhasan/auto-tensorflow', 0.5831721425056458, 'ml-dl', 2), ('keras-team/autokeras', 0.5828292369842529, 'ml-dl', 3), ('rasbt/machine-learning-book', 0.5774632692337036, 'study', 1), ('intel/intel-extension-for-pytorch', 0.5700653195381165, 'perf', 1), ('nyandwi/modernconvnets', 0.567727267742157, 'ml-dl', 4), ('microsoft/nni', 0.565841555595398, 'ml', 3), ('alpa-projects/alpa', 0.560257613658905, 'ml-dl', 0), ('ashleve/lightning-hydra-template', 0.5597487688064575, 'util', 1), ('uber/petastorm', 0.5541288256645203, 'data', 2), ('ludwig-ai/ludwig', 0.5519493222236633, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5509819984436035, 'ml-dl', 3), ('tensorflow/addons', 0.5490881204605103, 'ml', 1), ('ggerganov/ggml', 0.5481603741645813, 'ml', 0), ('huggingface/datasets', 0.5456334948539734, 'nlp', 3), ('awslabs/autogluon', 0.5449380278587341, 'ml', 5), ('albumentations-team/albumentations', 0.5420981645584106, 'ml-dl', 2), ('aleju/imgaug', 0.5411894917488098, 'ml', 0), ('arogozhnikov/einops', 0.5392698645591736, 'ml-dl', 3), ('cvxgrp/pymde', 0.5381171107292175, 'ml', 1), ('tensorflow/tensorflow', 0.5374124050140381, 'ml-dl', 1), ('oml-team/open-metric-learning', 0.5373204350471497, 'ml', 1), ('deepfakes/faceswap', 0.5372126698493958, 'ml-dl', 0), ('horovod/horovod', 0.5365691781044006, 'ml-ops', 3), ('microsoft/deepspeed', 0.5357862114906311, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5339189171791077, 'ml', 0), ('huggingface/exporters', 0.5338050127029419, 'ml', 2), ('explosion/spacy-streamlit', 0.5314697623252869, 'nlp', 1), ('pyg-team/pytorch_geometric', 0.5272446274757385, 'ml-dl', 1), ('onnx/onnx', 0.5271431803703308, 'ml', 4), ('explosion/spacy-transformers', 0.525894284248352, 'llm', 3), ('microsoft/flaml', 0.525197446346283, 'ml', 1), ('rwightman/pytorch-image-models', 0.5248225331306458, 'ml-dl', 1), ('fepegar/torchio', 0.5229683518409729, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5221423506736755, 'study', 0), ('aiqc/aiqc', 0.5212861895561218, 'ml-ops', 0), ('roboflow/notebooks', 0.5195624828338623, 'study', 3), ('tensorlayer/tensorlayer', 0.5187444090843201, 'ml-rl', 2), ('deci-ai/super-gradients', 0.5168516039848328, 'ml-dl', 4), ('tensorflow/tensor2tensor', 0.5167617201805115, 'ml', 0), ('tensorly/tensorly', 0.5156332850456238, 'ml-dl', 2), ('mrdbourke/pytorch-deep-learning', 0.5144563317298889, 'study', 1), ('lucidrains/imagen-pytorch', 0.512857973575592, 'ml-dl', 0), ('qdrant/quaterion', 0.5124584436416626, 'ml', 1), ('huggingface/optimum', 0.5119937658309937, 'ml', 2), ('roboflow/supervision', 0.5113904476165771, 'ml', 3), ('explosion/spacy-models', 0.5111375451087952, 'nlp', 1), ('aistream-peelout/flow-forecast', 0.5107213854789734, 'time-series', 2), ('towhee-io/towhee', 0.5096480250358582, 'ml-ops', 0), ('mdbloice/augmentor', 0.5088579654693604, 'ml', 0), ('keras-team/keras', 0.5088281631469727, 'ml-dl', 2), ('nccr-itmo/fedot', 0.508357048034668, 'ml-ops', 1), ('huggingface/neuralcoref', 0.5070856213569641, 'nlp', 2), ('iperov/deepfacelab', 0.5047088861465454, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5017151236534119, 'ml', 3)]",47,2.0,,6.75,220,198,38,0,11,12,11,219.0,79.0,90.0,0.4,49
433,data,https://github.com/sdv-dev/sdv,[],,[],[],,,,sdv-dev/sdv,SDV,1775,255,42,Python,https://docs.sdv.dev/sdv,Synthetic data generation for tabular data,sdv-dev,2024-01-13,2018-05-11,298,5.944976076555024,https://avatars.githubusercontent.com/u/59050321?v=4,Synthetic data generation for tabular data,"['data-generation', 'deep-learning', 'gan', 'gans', 'generative-adversarial-network', 'generative-ai', 'generative-model', 'generativeai', 'machine-learning', 'multi-table', 'relational-datasets', 'sdv', 'synthetic-data', 'synthetic-data-generation', 'time-series']","['data-generation', 'deep-learning', 'gan', 'gans', 'generative-adversarial-network', 'generative-ai', 'generative-model', 'generativeai', 'machine-learning', 'multi-table', 'relational-datasets', 'sdv', 'synthetic-data', 'synthetic-data-generation', 'time-series']",2024-01-11,"[('ydataai/ydata-synthetic', 0.9112098217010498, 'data', 7), ('nicolas-hbt/pygraft', 0.6006342172622681, 'ml', 2), ('awslabs/autogluon', 0.562446117401123, 'ml', 3), ('makcedward/nlpaug', 0.517977237701416, 'nlp', 1)]",46,5.0,,5.25,147,116,69,0,13,10,13,146.0,192.0,90.0,1.3,49
1529,llm,https://github.com/spcl/graph-of-thoughts,[],,[],[],1.0,,,spcl/graph-of-thoughts,graph-of-thoughts,1675,102,19,Python,https://arxiv.org/pdf/2308.09687.pdf,"Official Implementation of ""Graph of Thoughts: Solving Elaborate Problems with Large Language Models""",spcl,2024-01-14,2023-08-18,23,71.06060606060606,https://avatars.githubusercontent.com/u/31108244?v=4,"Official Implementation of ""Graph of Thoughts: Solving Elaborate Problems with Large Language Models""","['graph-of-thoughts', 'graph-structures', 'graphs', 'large-language-models', 'llm', 'prompt-engineering', 'prompting']","['graph-of-thoughts', 'graph-structures', 'graphs', 'large-language-models', 'llm', 'prompt-engineering', 'prompting']",2023-12-02,"[('kyegomez/tree-of-thoughts', 0.6711254715919495, 'llm', 1), ('dylanhogg/llmgraph', 0.6043452024459839, 'ml', 1), ('hannibal046/awesome-llm', 0.5740511417388916, 'study', 0), ('keirp/automatic_prompt_engineer', 0.5692050457000732, 'llm', 1), ('guidance-ai/guidance', 0.5539388060569763, 'llm', 1), ('lianjiatech/belle', 0.5425434708595276, 'llm', 0), ('lupantech/chameleon-llm', 0.5331078171730042, 'llm', 1), ('langchain-ai/langgraph', 0.5278857350349426, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5230525732040405, 'study', 1), ('microsoft/autogen', 0.5202249884605408, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5085147023200989, 'llm', 0)]",4,1.0,,0.37,16,14,5,1,2,5,2,16.0,12.0,90.0,0.8,49
1858,sim,https://github.com/nvidia/warp,"['simulation', 'gpu']",,[],[],,,,nvidia/warp,warp,1472,118,43,Python,https://nvidia.github.io/warp/,A Python framework for high performance GPU simulation and graphics,nvidia,2024-01-13,2022-03-18,97,15.086383601756955,https://avatars.githubusercontent.com/u/1728152?v=4,A Python framework for high performance GPU simulation and graphics,[],"['gpu', 'simulation']",2024-01-11,"[('pytorch/pytorch', 0.6113191843032837, 'ml-dl', 1), ('exaloop/codon', 0.603724479675293, 'perf', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5572924017906189, 'jupyter', 1), ('google/jax', 0.5563659071922302, 'ml', 0), ('nvidia/tensorrt-llm', 0.5474246740341187, 'viz', 1), ('joblib/joblib', 0.5375118851661682, 'util', 0), ('google/tf-quant-finance', 0.5373433828353882, 'finance', 1), ('klen/py-frameworks-bench', 0.5304505825042725, 'perf', 0), ('google/gin-config', 0.5256108641624451, 'util', 0), ('pokepetter/ursina', 0.5230095982551575, 'gamedev', 0), ('pyston/pyston', 0.5197428464889526, 'util', 0), ('ipython/ipyparallel', 0.5195223093032837, 'perf', 0), ('cupy/cupy', 0.518425464630127, 'math', 1), ('huggingface/accelerate', 0.5148420333862305, 'ml', 0), ('micropython/micropython', 0.512362003326416, 'util', 0), ('numpy/numpy', 0.5096368193626404, 'math', 0), ('fastai/fastcore', 0.5028382539749146, 'util', 0), ('facebookincubator/aitemplate', 0.502746045589447, 'ml-dl', 0), ('eleutherai/gpt-neo', 0.5011614561080933, 'llm', 0)]",34,2.0,,17.08,56,25,22,0,5,12,5,56.0,45.0,90.0,0.8,49
1069,llm,https://github.com/microsoft/megatron-deepspeed,[],,[],[],,,,microsoft/megatron-deepspeed,Megatron-DeepSpeed,1353,262,19,Python,,"Ongoing research training transformer language models at scale, including: BERT & GPT-2",microsoft,2024-01-13,2021-06-21,136,9.938090241343128,https://avatars.githubusercontent.com/u/6154722?v=4,"Ongoing research training transformer language models at scale, including: BERT & GPT-2",[],[],2024-01-10,"[('bigscience-workshop/megatron-deepspeed', 1.0000001192092896, 'llm', 0), ('nvidia/megatron-lm', 0.6671424508094788, 'llm', 0), ('lvwerra/trl', 0.6662755608558655, 'llm', 0), ('jonasgeiping/cramming', 0.6582860946655273, 'nlp', 0), ('huggingface/transformers', 0.6457441449165344, 'nlp', 0), ('explosion/spacy-transformers', 0.6363678574562073, 'llm', 0), ('hannibal046/awesome-llm', 0.6277967095375061, 'study', 0), ('extreme-bert/extreme-bert', 0.6164913773536682, 'llm', 0), ('graykode/nlp-tutorial', 0.6075314879417419, 'study', 0), ('karpathy/mingpt', 0.6039530634880066, 'llm', 0), ('lianjiatech/belle', 0.5846147537231445, 'llm', 0), ('huggingface/text-generation-inference', 0.5798518061637878, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.5679675936698914, 'study', 0), ('next-gpt/next-gpt', 0.5671409964561462, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5593957901000977, 'llm', 0), ('eleutherai/gpt-neo', 0.5555706024169922, 'llm', 0), ('eleutherai/knowledge-neurons', 0.548306405544281, 'ml-interpretability', 0), ('ai21labs/lm-evaluation', 0.5460460186004639, 'llm', 0), ('microsoft/lora', 0.5398515462875366, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5385422110557556, 'llm', 0), ('deepset-ai/farm', 0.5374853014945984, 'nlp', 0), ('jalammar/ecco', 0.5253652930259705, 'ml-interpretability', 0), ('promptslab/awesome-prompt-engineering', 0.5253517031669617, 'study', 0), ('bigscience-workshop/biomedical', 0.5243796706199646, 'data', 0), ('xtekky/gpt4free', 0.5237749814987183, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5230602622032166, 'llm', 0), ('ist-daslab/gptq', 0.5179560780525208, 'llm', 0), ('jina-ai/finetuner', 0.5171146988868713, 'ml', 0), ('bytedance/lightseq', 0.515959620475769, 'nlp', 0), ('lm-sys/fastchat', 0.51581871509552, 'llm', 0), ('alignmentresearch/tuned-lens', 0.515650749206543, 'ml-interpretability', 0), ('freedomintelligence/llmzoo', 0.5145018100738525, 'llm', 0), ('cdpierse/transformers-interpret', 0.5142018795013428, 'ml-interpretability', 0), ('bobazooba/xllm', 0.5139332413673401, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5127219557762146, 'llm', 0), ('llmware-ai/llmware', 0.5115044116973877, 'llm', 0), ('microsoft/autogen', 0.5109838843345642, 'llm', 0), ('salesforce/blip', 0.5101778507232666, 'diffusion', 0), ('thilinarajapakse/simpletransformers', 0.5100935697555542, 'nlp', 0), ('openai/finetune-transformer-lm', 0.5082074999809265, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5077611804008484, 'llm', 0), ('openai/gpt-2', 0.5042035579681396, 'llm', 0), ('alibaba/easynlp', 0.5024697184562683, 'nlp', 0), ('muennighoff/sgpt', 0.5024375915527344, 'llm', 0)]",122,3.0,,5.9,86,45,31,0,0,4,4,86.0,115.0,90.0,1.3,49
973,ml,https://github.com/googlecloudplatform/vertex-ai-samples,[],,[],[],,,,googlecloudplatform/vertex-ai-samples,vertex-ai-samples,1150,649,40,Jupyter Notebook,https://cloud.google.com/vertex-ai,"Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud",googlecloudplatform,2024-01-11,2021-05-27,139,8.231083844580777,https://avatars.githubusercontent.com/u/2810941?v=4,"Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud","['ai', 'data-science', 'gcp', 'google-cloud-platform', 'ml', 'mlops', 'notebook', 'samples', 'vertex-ai']","['ai', 'data-science', 'gcp', 'google-cloud-platform', 'ml', 'mlops', 'notebook', 'samples', 'vertex-ai']",2024-01-12,"[('google-research/google-research', 0.6550525426864624, 'ml', 1), ('mlflow/mlflow', 0.6433287262916565, 'ml-ops', 2), ('bentoml/bentoml', 0.6409500241279602, 'ml-ops', 2), ('pytorchlightning/pytorch-lightning', 0.6309098601341248, 'ml-dl', 2), 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1508,llm,https://github.com/ajndkr/lanarky,[],,[],[],,,,ajndkr/lanarky,lanarky,900,65,14,Python,https://lanarky.ajndkr.com/,The web framework for building LLM microservices,ajndkr,2024-01-12,2023-04-07,42,21.140939597315437,,The web framework for building LLM microservices,"['fastapi', 'llmops', 'web']","['fastapi', 'llmops', 'web']",2024-01-13,"[('janetech-inc/fast-api-admin-template', 0.6117317080497742, 'template', 0), ('berriai/litellm', 0.587331235408783, 'llm', 1), ('young-geng/easylm', 0.5861847996711731, 'llm', 0), ('pathwaycom/llm-app', 0.5696076154708862, 'llm', 1), ('tiangolo/fastapi', 0.5630785822868347, 'web', 2), ('shishirpatil/gorilla', 0.5609325766563416, 'llm', 0), ('run-llama/llama-hub', 0.5594974160194397, 'data', 0), ('alpha-vllm/llama2-accessory', 0.552966833114624, 'llm', 0), ('jerryjliu/llama_index', 0.5451417565345764, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5414519309997559, 'llm', 0), ('ml-tooling/opyrator', 0.5310590267181396, 'viz', 1), ('deepset-ai/haystack', 0.5300788283348083, 'llm', 0), ('run-llama/llama-lab', 0.5272144079208374, 'llm', 0), ('unionai-oss/unionml', 0.5264783501625061, 'ml-ops', 0), ('falconry/falcon', 0.5218005180358887, 'web', 1), ('eugeneyan/open-llms', 0.5215867757797241, 'study', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5205399394035339, 'template', 1), ('pallets/quart', 0.5203720331192017, 'web', 0), ('hwchase17/langchain', 0.5159770846366882, 'llm', 0), ('bentoml/openllm', 0.5158595442771912, 'ml-ops', 1), ('pallets/flask', 0.510615348815918, 'web', 0), ('microsoft/semantic-kernel', 0.5084496140480042, 'llm', 0), ('agenta-ai/agenta', 0.5057575702667236, 'llm', 1)]",13,2.0,,3.06,53,47,9,0,48,65,48,53.0,62.0,90.0,1.2,49
1318,ml-dl,https://github.com/nvidia/deeplearningexamples,[],,[],[],,,,nvidia/deeplearningexamples,DeepLearningExamples,12049,2999,295,Jupyter Notebook,,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.,nvidia,2024-01-14,2018-05-02,299,40.18246784182944,https://avatars.githubusercontent.com/u/1728152?v=4,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.,"['computer-vision', 'deep-learning', 'drug-discovery', 'forecasting', 'large-language-models', 'mxnet', 'nlp', 'paddlepaddle', 'pytorch', 'recommender-systems', 'speech-recognition', 'speech-synthesis', 'tensorflow', 'tensorflow2', 'translation']","['computer-vision', 'deep-learning', 'drug-discovery', 'forecasting', 'large-language-models', 'mxnet', 'nlp', 'paddlepaddle', 'pytorch', 'recommender-systems', 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115,nlp,https://github.com/allenai/allennlp,[],,[],[],,,,allenai/allennlp,allennlp,11631,2261,284,Python,http://www.allennlp.org,"An open-source NLP research library, built on PyTorch.",allenai,2024-01-13,2017-05-15,350,33.217870257037944,https://avatars.githubusercontent.com/u/5667695?v=4,"An open-source NLP research library, built on PyTorch.","['data-science', 'deep-learning', 'natural-language-processing', 'nlp', 'pytorch']","['data-science', 'deep-learning', 'natural-language-processing', 'nlp', 'pytorch']",2022-11-22,"[('nltk/nltk', 0.6935926675796509, 'nlp', 2), ('alibaba/easynlp', 0.6890572309494019, 'nlp', 3), ('flairnlp/flair', 0.6880818605422974, 'nlp', 3), ('graykode/nlp-tutorial', 0.6779881715774536, 'study', 3), ('pytorch/ignite', 0.6625421047210693, 'ml-dl', 2), ('explosion/spacy', 0.6516764163970947, 'nlp', 4), ('skorch-dev/skorch', 0.639916181564331, 'ml-dl', 1), ('huggingface/transformers', 0.6358655095100403, 'nlp', 4), ('pemistahl/lingua-py', 0.6268466711044312, 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0.514882504940033, 'ml', 0), ('karpathy/micrograd', 0.5140243768692017, 'study', 0), ('minimaxir/textgenrnn', 0.5124790072441101, 'nlp', 1), ('jbesomi/texthero', 0.5118191242218018, 'nlp', 1), ('tensorlayer/tensorlayer', 0.5114910006523132, 'ml-rl', 1), ('minimaxir/gpt-2-simple', 0.510489821434021, 'llm', 0), ('blackhc/toma', 0.5094995498657227, 'ml-dl', 2), ('aiwaves-cn/agents', 0.5092148184776306, 'nlp', 0), ('ferdinandzhong/punctuator', 0.5085445046424866, 'nlp', 3), ('neuml/txtai', 0.50567227602005, 'nlp', 1), ('thu-ml/tianshou', 0.5055130124092102, 'ml-rl', 1), ('cgpotts/cs224u', 0.5052735805511475, 'study', 1), ('extreme-bert/extreme-bert', 0.5051680207252502, 'llm', 4), ('faster-cpython/ideas', 0.5050787329673767, 'perf', 0), ('mooler0410/llmspracticalguide', 0.5047245025634766, 'study', 2), ('lm-sys/fastchat', 0.5036823153495789, 'llm', 0), ('xl0/lovely-tensors', 0.5030158162117004, 'ml-dl', 2), ('carla-recourse/carla', 0.5022084712982178, 'ml', 1), ('bigscience-workshop/promptsource', 0.5015190839767456, 'nlp', 2), ('koaning/human-learn', 0.501263439655304, 'data', 0)]",267,5.0,,0.0,0,0,81,14,0,10,10,0.0,0.0,90.0,0.0,48
1827,util,https://github.com/zulko/moviepy,[],,[],[],,,,zulko/moviepy,moviepy,11300,1501,256,Python,https://zulko.github.io/moviepy/,Video editing with Python,zulko,2024-01-14,2013-08-12,546,20.690557154067488,,Video editing with Python,"['animation', 'gif', 'video', 'video-editing', 'video-processing']","['animation', 'gif', 'video', 'video-editing', 'video-processing']",2023-07-11,"[('imageio/imageio', 0.6167894601821899, 'util', 1), ('soft-matter/pims', 0.6140278577804565, 'util', 1), ('scikit-image/scikit-image', 0.5875822305679321, 'util', 0), ('chenyangqiqi/fatezero', 0.5514541268348694, 'diffusion', 1), ('researchmm/sttn', 0.5262514352798462, 'ml-dl', 0), ('has2k1/plotnine', 0.524414598941803, 'viz', 0), ('pyglet/pyglet', 0.5143889784812927, 'gamedev', 0), ('renpy/renpy', 0.5059114098548889, 'viz', 0), ('open-mmlab/mmediting', 0.5014966726303101, 'ml', 0)]",160,1.0,,0.21,85,22,127,6,0,1,1,85.0,137.0,90.0,1.6,48
215,debug,https://github.com/gruns/icecream,[],,[],[],,,,gruns/icecream,icecream,8090,171,50,Python,,🍦 Never use print() to debug again.,gruns,2024-01-13,2018-02-13,311,26.012861736334404,,🍦 Never use print() to debug again.,"['debug', 'debugging', 'debugging-tool', 'inspects', 'print']","['debug', 'debugging', 'debugging-tool', 'inspects', 'print']",2022-12-04,"[('cool-rr/pysnooper', 0.8130260705947876, 'debug', 1)]",21,7.0,,0.0,26,4,72,14,0,1,1,26.0,38.0,90.0,1.5,48
1206,ml,https://github.com/uberi/speech_recognition,[],,[],[],,,,uberi/speech_recognition,speech_recognition,7796,2331,282,Python,https://pypi.python.org/pypi/SpeechRecognition/,"Speech recognition module for Python, supporting several engines and APIs, online and offline.",uberi,2024-01-14,2014-04-23,509,15.290557579153825,,"Speech recognition module for Python, supporting several engines and APIs, online and offline.","['audio', 'speech-recognition', 'speech-to-text']","['audio', 'speech-recognition', 'speech-to-text']",2023-12-06,"[('pndurette/gtts', 0.7022308707237244, 'util', 0), ('nateshmbhat/pyttsx3', 0.6923384070396423, 'util', 0), ('spotify/pedalboard', 0.6759282946586609, 'util', 1), ('speechbrain/speechbrain', 0.6739375591278076, 'nlp', 3), ('irmen/pyminiaudio', 0.659464955329895, 'util', 0), ('googleapis/python-speech', 0.6549380421638489, 'ml', 0), ('pemistahl/lingua-py', 0.6296498775482178, 'nlp', 0), ('m-bain/whisperx', 0.6138349771499634, 'nlp', 2), ('espnet/espnet', 0.6095865964889526, 'nlp', 1), ('bastibe/python-soundfile', 0.575599730014801, 'util', 0), ('taylorsmarks/playsound', 0.5477955341339111, 'util', 0), ('jamesturk/jellyfish', 0.545852541923523, 'nlp', 0), ('minimaxir/simpleaichat', 0.5369465351104736, 'llm', 0), ('quodlibet/mutagen', 0.5264820456504822, 'util', 0), ('pytoolz/toolz', 0.517691969871521, 'util', 0), ('clips/pattern', 0.5162601470947266, 'nlp', 0), ('cmusphinx/pocketsphinx', 0.5127110481262207, 'ml', 1), ('deeppavlov/deeppavlov', 0.509485125541687, 'nlp', 0), ('pypy/pypy', 0.5066007971763611, 'util', 0), ('rasbt/mlxtend', 0.5056686401367188, 'ml', 0), ('rasahq/rasa', 0.5011873245239258, 'llm', 0)]",49,4.0,,0.98,45,14,118,1,2,5,2,45.0,36.0,90.0,0.8,48
132,ml,https://github.com/automl/auto-sklearn,[],,[],[],,,,automl/auto-sklearn,auto-sklearn,7287,1268,215,Python,https://automl.github.io/auto-sklearn,Automated Machine Learning with scikit-learn,automl,2024-01-13,2015-07-02,447,16.276005105296747,https://avatars.githubusercontent.com/u/6469053?v=4,Automated Machine Learning with scikit-learn,"['automated-machine-learning', 'automl', 'bayesian-optimization', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'meta-learning', 'metalearning', 'scikit-learn', 'smac']","['automated-machine-learning', 'automl', 'bayesian-optimization', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'meta-learning', 'metalearning', 'scikit-learn', 'smac']",2023-04-18,"[('microsoft/nni', 0.7624291777610779, 'ml', 5), ('nccr-itmo/fedot', 0.7373944520950317, 'ml-ops', 3), ('microsoft/flaml', 0.7281423807144165, 'ml', 4), ('ray-project/tune-sklearn', 0.6818026304244995, 'ml', 4), ('winedarksea/autots', 0.6762388944625854, 'time-series', 1), ('awslabs/autogluon', 0.672629177570343, 'ml', 4), ('mljar/mljar-supervised', 0.6622180938720703, 'ml', 4), ('epistasislab/tpot', 0.6574358940124512, 'ml', 4), ('featurelabs/featuretools', 0.6524330377578735, 'ml', 3), ('districtdatalabs/yellowbrick', 0.6410435438156128, 'ml', 1), ('google/pyglove', 0.6234724521636963, 'util', 2), ('keras-team/autokeras', 0.616755485534668, 'ml-dl', 2), ('koaning/scikit-lego', 0.6153001189231873, 'ml', 1), ('scikit-optimize/scikit-optimize', 0.6117300391197205, 'ml', 5), ('scikit-learn/scikit-learn', 0.6095296144485474, 'ml', 0), ('determined-ai/determined', 0.6021798253059387, 'ml-ops', 3), ('rasbt/machine-learning-book', 0.6004539132118225, 'study', 1), ('patchy631/machine-learning', 0.6001100540161133, 'ml', 0), ('skops-dev/skops', 0.5991626381874084, 'ml-ops', 1), ('sktime/sktime', 0.5886750817298889, 'time-series', 1), ('intel/scikit-learn-intelex', 0.5883508920669556, 'perf', 1), ('koaning/human-learn', 0.5842374563217163, 'data', 1), ('scikit-learn-contrib/metric-learn', 0.576133131980896, 'ml', 1), ('shankarpandala/lazypredict', 0.5726633667945862, 'ml', 1), ('xplainable/xplainable', 0.5678261518478394, 'ml-interpretability', 0), ('gradio-app/gradio', 0.5600031614303589, 'viz', 0), ('onnx/onnx', 0.5575015544891357, 'ml', 1), ('huggingface/autotrain-advanced', 0.5570406317710876, 'ml', 0), ('iryna-kondr/scikit-llm', 0.5549347996711731, 'llm', 1), ('fatiando/verde', 0.5549225807189941, 'gis', 0), ('polyaxon/polyaxon', 0.5545597672462463, 'ml-ops', 1), ('firmai/atspy', 0.5523808598518372, 'time-series', 0), ('google/vizier', 0.5519843101501465, 'ml', 3), ('optuna/optuna', 0.5508047938346863, 'ml', 1), ('firmai/industry-machine-learning', 0.5466862320899963, 'study', 0), ('ml-tooling/opyrator', 0.546398937702179, 'viz', 0), ('huggingface/evaluate', 0.5426995158195496, 'ml', 0), ('wandb/client', 0.5397332906723022, 'ml', 3), ('teamhg-memex/eli5', 0.5375847220420837, 'ml', 1), ('csinva/imodels', 0.5375404953956604, 'ml', 1), ('mlflow/mlflow', 0.5365729928016663, 'ml-ops', 0), ('alpa-projects/alpa', 0.535767674446106, 'ml-dl', 0), ('ageron/handson-ml2', 0.531454861164093, 'ml', 0), ('mosaicml/composer', 0.5304707288742065, 'ml-dl', 0), ('eugeneyan/testing-ml', 0.528367280960083, 'testing', 0), ('huggingface/datasets', 0.5262554883956909, 'nlp', 0), ('online-ml/river', 0.5197332501411438, 'ml', 0), ('kubeflow/pipelines', 0.5186381936073303, 'ml-ops', 0), ('ddbourgin/numpy-ml', 0.5156731605529785, 'ml', 0), ('kubeflow/katib', 0.5148612260818481, 'ml', 0), ('tensorflow/tensorflow', 0.5126420259475708, 'ml-dl', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5101978778839111, 'study', 0), ('rasbt/mlxtend', 0.5089040994644165, 'ml', 0), ('dask/dask-ml', 0.5055193305015564, 'ml', 0), ('google-research/google-research', 0.5043892860412598, 'ml', 0), ('ray-project/ray', 0.5039158463478088, 'ml-ops', 3), ('ourownstory/neural_prophet', 0.5033456683158875, 'ml', 0), ('tensorflow/data-validation', 0.5028933882713318, 'ml-ops', 0), ('unionai-oss/unionml', 0.5017037987709045, 'ml-ops', 0)]",88,7.0,,0.02,32,12,104,9,1,4,1,32.0,50.0,90.0,1.6,48
594,gis,https://github.com/domlysz/blendergis,[],,[],[],,,,domlysz/blendergis,BlenderGIS,6989,1302,257,Python,,Blender addons to make the bridge between Blender and geographic data,domlysz,2024-01-13,2014-05-08,507,13.765616207090602,,Blender addons to make the bridge between Blender and geographic data,"['3d', '3d-map', '3dgis', 'addon', 'basemap', 'blender', 'delaunay-triangulation', 'dem', 'geodata', 'georeferencing', 'geospatial', 'gis', 'importexport', 'map', 'openstreetmap', 'raster', 'shapefile', 'terrain-model']","['3d', '3d-map', '3dgis', 'addon', 'basemap', 'blender', 'delaunay-triangulation', 'dem', 'geodata', 'georeferencing', 'geospatial', 'gis', 'importexport', 'map', 'openstreetmap', 'raster', 'shapefile', 'terrain-model']",2024-01-08,"[('raphaelquast/eomaps', 0.5599436163902283, 'gis', 2), ('darribas/gds_env', 0.5147411823272705, 'gis', 0), ('isl-org/open3d', 0.5055702924728394, 'sim', 1)]",15,3.0,,0.02,49,9,118,0,0,2,2,49.0,81.0,90.0,1.7,48
637,util,https://github.com/theskumar/python-dotenv,[],,[],[],,,,theskumar/python-dotenv,python-dotenv,6790,434,34,Python,https://saurabh-kumar.com/python-dotenv/,Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.,theskumar,2024-01-14,2014-09-06,490,13.845033498397903,,Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.,"['12-factor-app', 'configuration', 'devops-tools', 'dotenv', 'env', 'environment-variables']","['12-factor-app', 'configuration', 'devops-tools', 'dotenv', 'env', 'environment-variables']",2023-07-07,[],93,6.0,,0.17,17,9,114,6,2,5,2,17.0,34.0,90.0,2.0,48
223,nlp,https://github.com/kingoflolz/mesh-transformer-jax,[],,[],[],1.0,,,kingoflolz/mesh-transformer-jax,mesh-transformer-jax,6165,897,109,Python,,Model parallel transformers in JAX and Haiku,kingoflolz,2024-01-13,2021-03-13,150,40.98290598290598,,Model parallel transformers in JAX and Haiku,[],[],2023-01-12,"[('huggingface/transformers', 0.5089766979217529, 'nlp', 0)]",23,3.0,,0.0,7,0,35,12,0,0,0,7.0,18.0,90.0,2.6,48
437,perf,https://github.com/mher/flower,[],,[],[],,,,mher/flower,flower,6007,1046,143,Python,https://flower.readthedocs.io,Real-time monitor and web admin for Celery distributed task queue,mher,2024-01-14,2012-07-08,603,9.957139474307365,,Real-time monitor and web admin for Celery distributed task queue,"['administration', 'asynchronous', 'celery', 'monitoring', 'rabbitmq', 'redis', 'task-queue', 'workers']","['administration', 'asynchronous', 'celery', 'monitoring', 'rabbitmq', 'redis', 'task-queue', 'workers']",2023-12-17,"[('celery/celery', 0.6660754680633545, 'perf', 1), ('bogdanp/dramatiq', 0.5586143732070923, 'util', 1), ('samuelcolvin/arq', 0.5143932104110718, 'data', 1), ('sumerc/yappi', 0.5045316219329834, 'profiling', 1), ('airtai/faststream', 0.5014379620552063, 'perf', 2)]",211,6.0,,1.08,29,11,140,1,0,2,2,29.0,26.0,90.0,0.9,48
1387,security,https://github.com/nccgroup/scoutsuite,[],,[],[],,,,nccgroup/scoutsuite,ScoutSuite,5923,980,125,Python,,Multi-Cloud Security Auditing Tool,nccgroup,2024-01-13,2018-10-30,274,21.616788321167885,https://avatars.githubusercontent.com/u/4067082?v=4,Multi-Cloud Security Auditing Tool,"['auditing', 'aws', 'azure', 'cloud', 'gcp', 'security']","['auditing', 'aws', 'azure', 'cloud', 'gcp', 'security']",2023-09-22,"[('jorgebastida/awslogs', 0.5164755582809448, 'util', 0)]",119,3.0,,0.81,27,3,63,4,3,13,3,27.0,18.0,90.0,0.7,48
566,util,https://github.com/jd/tenacity,[],,[],[],1.0,,,jd/tenacity,tenacity,5616,285,48,Python,http://tenacity.readthedocs.io,Retrying library for Python,jd,2024-01-14,2016-08-11,389,14.410557184750733,,Retrying library for Python,"['failure', 'retry', 'retry-library']","['failure', 'retry', 'retry-library']",2023-12-18,[],87,3.0,,0.56,17,8,90,1,0,9,9,17.0,12.0,90.0,0.7,48
600,jupyter,https://github.com/connorferster/handcalcs,[],,[],[],,,,connorferster/handcalcs,handcalcs,5300,444,84,CSS,,Python library for converting Python calculations into rendered latex.,connorferster,2024-01-12,2020-02-19,205,25.746009715475363,,Python library for converting Python calculations into rendered latex.,[],[],2023-11-12,"[('google/latexify_py', 0.7623890042304993, 'util', 0), ('pytoolz/toolz', 0.6247959733009338, 'util', 0), ('pypy/pypy', 0.5907471179962158, 'util', 0), ('fredrik-johansson/mpmath', 0.5756496787071228, 'math', 0), ('pyscf/pyscf', 0.5601664185523987, 'sim', 0), ('sympy/sympy', 0.5598889589309692, 'math', 0), ('pyston/pyston', 0.5578861832618713, 'util', 0), ('wtforms/wtforms', 0.5505093336105347, 'web', 0), ('eleutherai/pyfra', 0.5502340793609619, 'ml', 0), ('zoomeranalytics/xlwings', 0.5488008856773376, 'data', 0), ('python/cpython', 0.5454192161560059, 'util', 0), ('julienpalard/pipe', 0.5392968058586121, 'util', 0), ('pmorissette/ffn', 0.5378669500350952, 'finance', 0), ('numpy/numpy', 0.5350318551063538, 'math', 0), ('imageio/imageio', 0.5331127047538757, 'util', 0), ('altair-viz/altair', 0.531620442867279, 'viz', 0), ('google/yapf', 0.5293661952018738, 'util', 0), ('hhatto/autopep8', 0.5234463810920715, 'util', 0), ('maartenbreddels/ipyvolume', 0.5224195122718811, 'jupyter', 0), ('pyglet/pyglet', 0.5195719003677368, 'gamedev', 0), ('jmcnamara/xlsxwriter', 0.5153928399085999, 'data', 0), ('r0x0r/pywebview', 0.5135572552680969, 'gui', 0), ('gbeced/pyalgotrade', 0.5120189189910889, 'finance', 0), ('beeware/briefcase', 0.5107748508453369, 'util', 0), ('webpy/webpy', 0.5106810331344604, 'web', 0), ('sqlalchemy/mako', 0.5080623626708984, 'template', 0), ('hgrecco/pint', 0.506892740726471, 'util', 0), ('cython/cython', 0.5067312121391296, 'util', 0), ('google/jax', 0.5066884160041809, 'ml', 0), ('dfki-ric/pytransform3d', 0.5065004229545593, 'math', 0), ('urwid/urwid', 0.5055436491966248, 'term', 0), ('python-markdown/markdown', 0.5013054609298706, 'util', 0), ('pyfpdf/fpdf2', 0.5012051463127136, 'util', 0), ('pympler/pympler', 0.5004320740699768, 'perf', 0)]",11,5.0,,0.04,9,0,48,2,0,2,2,9.0,14.0,90.0,1.6,48
1868,util,https://github.com/pdfminer/pdfminer.six,[],,[],[],,,,pdfminer/pdfminer.six,pdfminer.six,5083,863,121,Python,https://pdfminersix.readthedocs.io,Community maintained fork of pdfminer - we fathom PDF,pdfminer,2024-01-13,2014-08-29,491,10.340308049985468,https://avatars.githubusercontent.com/u/22586632?v=4,Community maintained fork of pdfminer - we fathom PDF,"['parser', 'pdf']","['parser', 'pdf']",2024-01-12,"[('py-pdf/pypdf2', 0.5491688847541809, 'util', 1), ('pyfpdf/fpdf2', 0.5335804224014282, 'util', 1), ('allenai/s2orc-doc2json', 0.5287355184555054, 'nlp', 0), ('unstructured-io/pipeline-paddleocr', 0.5148302316665649, 'data', 1)]",137,4.0,,0.35,62,37,114,0,1,2,1,62.0,74.0,90.0,1.2,48
102,ml,https://github.com/rasbt/mlxtend,[],,[],[],,,,rasbt/mlxtend,mlxtend,4676,839,117,Python,https://rasbt.github.io/mlxtend/,A library of extension and helper modules for Python's data analysis and machine learning libraries.,rasbt,2024-01-13,2014-08-14,493,9.471064814814815,,A library of extension and helper modules for Python's data analysis and machine learning libraries.,"['association-rules', 'data-mining', 'data-science', 'machine-learning', 'supervised-learning', 'unsupervised-learning']","['association-rules', 'data-mining', 'data-science', 'machine-learning', 'supervised-learning', 'unsupervised-learning']",2024-01-05,"[('scikit-learn/scikit-learn', 0.7584848403930664, 'ml', 2), ('pycaret/pycaret', 0.7207165956497192, 'ml', 2), ('featurelabs/featuretools', 0.6914775371551514, 'ml', 2), ('tensorflow/data-validation', 0.6768656373023987, 'ml-ops', 0), ('gradio-app/gradio', 0.6547028422355652, 'viz', 2), ('scikit-learn-contrib/imbalanced-learn', 0.651149570941925, 'ml', 2), ('pandas-dev/pandas', 0.6480407118797302, 'pandas', 1), ('clips/pattern', 0.6350256204605103, 'nlp', 1), ('alkaline-ml/pmdarima', 0.6218876838684082, 'time-series', 1), ('jovianml/opendatasets', 0.6177619695663452, 'data', 2), ('huggingface/evaluate', 0.6174339056015015, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.610371470451355, 'study', 2), ('dylanhogg/awesome-python', 0.6053752899169922, 'study', 2), ('yzhao062/pyod', 0.6038527488708496, 'data', 4), ('lightly-ai/lightly', 0.6017403602600098, 'ml', 1), ('pytoolz/toolz', 0.6010292172431946, 'util', 0), ('scikit-learn-contrib/metric-learn', 0.6002252101898193, 'ml', 1), ('ta-lib/ta-lib-python', 0.5990067720413208, 'finance', 0), ('wesm/pydata-book', 0.5947810411453247, 'study', 0), ('google/temporian', 0.58907151222229, 'time-series', 0), ('epistasislab/tpot', 0.5866554379463196, 'ml', 2), ('pyeve/cerberus', 0.5806938409805298, 'data', 0), ('firmai/industry-machine-learning', 0.5761663913726807, 'study', 2), ('probml/pyprobml', 0.5730016827583313, 'ml', 1), ('mljar/mljar-supervised', 0.5722818374633789, 'ml', 2), ('online-ml/river', 0.5713738799095154, 'ml', 2), ('mdbloice/augmentor', 0.5698684453964233, 'ml', 1), ('nicolashug/surprise', 0.5660281777381897, 'ml', 1), ('polyaxon/datatile', 0.5656301975250244, 'pandas', 1), ('ageron/handson-ml2', 0.564663290977478, 'ml', 0), ('districtdatalabs/yellowbrick', 0.5595728754997253, 'ml', 1), ('eleutherai/pyfra', 0.5588405728340149, 'ml', 0), ('scikit-learn-contrib/lightning', 0.5572295188903809, 'ml', 1), ('tdameritrade/stumpy', 0.5554875135421753, 'time-series', 1), ('merantix-momentum/squirrel-core', 0.5547300577163696, 'ml', 2), ('teamhg-memex/eli5', 0.553990364074707, 'ml', 2), ('sktime/sktime', 0.5530063509941101, 'time-series', 3), ('firmai/atspy', 0.5510342121124268, 'time-series', 0), ('ggerganov/ggml', 0.5479773283004761, 'ml', 1), ('sentinel-hub/eo-learn', 0.5460903644561768, 'gis', 1), ('pemistahl/lingua-py', 0.5422750115394592, 'nlp', 0), ('selfexplainml/piml-toolbox', 0.5372369885444641, 'ml-interpretability', 0), ('unit8co/darts', 0.5370567440986633, 'time-series', 2), ('microsoft/flaml', 0.5349463224411011, 'ml', 2), ('rasbt/machine-learning-book', 0.5330138206481934, 'study', 1), ('gerdm/prml', 0.5303501486778259, 'study', 1), ('dagworks-inc/hamilton', 0.5286049842834473, 'ml-ops', 2), ('carla-recourse/carla', 0.5272185802459717, 'ml', 1), ('goldmansachs/gs-quant', 0.5270822644233704, 'finance', 0), ('pyutils/line_profiler', 0.5252503156661987, 'profiling', 0), ('catboost/catboost', 0.5251424908638, 'ml', 3), ('patchy631/machine-learning', 0.5243169665336609, 'ml', 0), ('scikit-mobility/scikit-mobility', 0.5235726833343506, 'gis', 1), ('sympy/sympy', 0.5225716829299927, 'math', 0), ('statsmodels/statsmodels', 0.5218582153320312, 'ml', 1), ('quantecon/quantecon.py', 0.5213393568992615, 'sim', 0), ('altair-viz/altair', 0.5204195976257324, 'viz', 0), ('ddbourgin/numpy-ml', 0.5202142000198364, 'ml', 1), 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('guyallard/markov_clustering', 0.5017951130867004, 'graph', 0), ('skorch-dev/skorch', 0.5017459988594055, 'ml-dl', 1), ('pyparsing/pyparsing', 0.5005236864089966, 'util', 0), ('scipy/scipy', 0.5000782012939453, 'math', 0)]",103,5.0,,1.08,7,4,115,0,3,3,3,7.0,11.0,90.0,1.6,48
1813,study,https://github.com/mrdbourke/tensorflow-deep-learning,[],,[],[],,,,mrdbourke/tensorflow-deep-learning,tensorflow-deep-learning,4631,2271,149,Jupyter Notebook,https://dbourke.link/ZTMTFcourse,All course materials for the Zero to Mastery Deep Learning with TensorFlow course.,mrdbourke,2024-01-13,2020-11-23,166,27.87360275150473,,All course materials for the Zero to Mastery Deep Learning with TensorFlow course.,"['curriculum', 'deep-learning', 'deep-neural-networks', 'tensorflow', 'tensorflow-course', 'tensorflow-tutorials', 'tensorflow2']","['curriculum', 'deep-learning', 'deep-neural-networks', 'tensorflow', 'tensorflow-course', 'tensorflow-tutorials', 'tensorflow2']",2023-11-09,"[('mrdbourke/zero-to-mastery-ml', 0.7862590551376343, 'study', 1), ('mrdbourke/pytorch-deep-learning', 0.7342724800109863, 'study', 1), ('amanchadha/coursera-deep-learning-specialization', 0.592336118221283, 'study', 1), ('tensorlayer/tensorlayer', 0.576848566532135, 'ml-rl', 3), ('d2l-ai/d2l-en', 0.5674564242362976, 'study', 2), ('udacity/deep-learning-v2-pytorch', 0.5660417079925537, 'study', 1), ('tensorly/tensorly', 0.5285704731941223, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5140354037284851, 'study', 1), ('keras-team/keras', 0.5046241283416748, 'ml-dl', 2), ('google/tf-quant-finance', 0.5032038688659668, 'finance', 1), ('keras-rl/keras-rl', 0.5023423433303833, 'ml-rl', 1), ('nyandwi/modernconvnets', 0.5007387399673462, 'ml-dl', 1)]",27,3.0,,0.73,11,0,38,2,0,0,0,11.0,23.0,90.0,2.1,48
1626,typing,https://github.com/instagram/monkeytype,"['static', 'annotation', 'runtime', 'code-quality']",,[],[],1.0,,,instagram/monkeytype,MonkeyType,4445,168,54,Python,,A Python library that generates static type annotations by collecting runtime types,instagram,2024-01-13,2017-07-11,342,12.997076023391813,https://avatars.githubusercontent.com/u/549085?v=4,A Python library that generates static type annotations by collecting runtime types,[],"['annotation', 'code-quality', 'runtime', 'static']",2024-01-13,"[('google/pytype', 0.8035555481910706, 'typing', 1), ('microsoft/pyright', 0.7293500900268555, 'typing', 1), ('python/typeshed', 0.723702609539032, 'typing', 1), ('python/mypy', 0.7129145264625549, 'typing', 1), ('facebook/pyre-check', 0.6643034815788269, 'typing', 1), ('agronholm/typeguard', 0.6599376201629639, 'typing', 1), ('patrick-kidger/torchtyping', 0.6300653219223022, 'typing', 0), ('astral-sh/ruff', 0.6186012029647827, 'util', 1), ('strawberry-graphql/strawberry', 0.6149357557296753, 'web', 0), ('pytoolz/toolz', 0.5914798974990845, 'util', 0), ('psf/black', 0.5771250128746033, 'util', 1), ('landscapeio/prospector', 0.5743587017059326, 'util', 0), ('grantjenks/blue', 0.5733786225318909, 'util', 1), ('crytic/slither', 0.5720254182815552, 'crypto', 0), ('marshmallow-code/marshmallow', 0.5554887056350708, 'util', 0), ('xrudelis/pytrait', 0.5478743314743042, 'util', 0), ('google/latexify_py', 0.5287166237831116, 'util', 0), ('numba/llvmlite', 0.5182361602783203, 'util', 0), ('google/yapf', 0.5119272470474243, 'util', 1), ('rubik/radon', 0.5105746984481812, 'util', 0), ('getpelican/pelican', 0.5085812211036682, 'web', 0), ('instagram/libcst', 0.5069942474365234, 'util', 0), ('pyutils/line_profiler', 0.5013983845710754, 'profiling', 0)]",48,5.0,,0.27,18,12,79,0,0,3,3,18.0,19.0,90.0,1.1,48
711,data,https://github.com/jazzband/tablib,[],,[],[],,,,jazzband/tablib,tablib,4426,588,137,Python,https://tablib.readthedocs.io/,"Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, &c.",jazzband,2024-01-13,2011-03-28,670,6.604561927094436,https://avatars.githubusercontent.com/u/15129049?v=4,"Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, &c.",[],[],2024-01-01,"[('astanin/python-tabulate', 0.6442596316337585, 'util', 0), ('zoomeranalytics/xlwings', 0.5972012877464294, 'data', 0), ('jmcnamara/xlsxwriter', 0.5911999344825745, 'data', 0), ('wireservice/csvkit', 0.5830636620521545, 'util', 0), ('vaexio/vaex', 0.575348973274231, 'perf', 0), ('saulpw/visidata', 0.5593103170394897, 'term', 0), ('python-odin/odin', 0.5585665106773376, 'util', 0), ('tkrabel/bamboolib', 0.5476372241973877, 'pandas', 0), ('camelot-dev/camelot', 0.5369576811790466, 'util', 0), ('mljar/mljar-supervised', 0.531550407409668, 'ml', 0), ('wesm/pydata-book', 0.5241658687591553, 'study', 0), ('koaning/drawdata', 0.5231077075004578, 'jupyter', 0), ('tiangolo/sqlmodel', 0.5218853950500488, 'data', 0), ('quantopian/qgrid', 0.5186475515365601, 'jupyter', 0), ('sqlalchemy/sqlalchemy', 0.5141265392303467, 'data', 0), ('bloomberg/ipydatagrid', 0.5118611454963684, 'jupyter', 0), ('jakevdp/pythondatasciencehandbook', 0.5034573078155518, 'study', 0), ('scikit-hep/awkward-1.0', 0.5033981800079346, 'data', 0), ('jazzband/prettytable', 0.5020918250083923, 'term', 0)]",125,8.0,,0.63,15,14,156,0,2,4,2,15.0,32.0,90.0,2.1,48
786,ml-ops,https://github.com/orchest/orchest,[],,[],[],,,,orchest/orchest,orchest,4005,253,43,TypeScript,https://orchest.readthedocs.io/en/stable/,"Build data pipelines, the easy way 🛠️",orchest,2024-01-14,2020-05-21,192,20.782060785767236,https://avatars.githubusercontent.com/u/62945975?v=4,"Build data pipelines, the easy way 🛠️","['airflow', 'cloud', 'dag', 'data-pipelines', 'data-science', 'deployment', 'docker', 'etl', 'etl-pipeline', 'ide', 'jupyter', 'jupyterlab', 'kubernetes', 'machine-learning', 'notebooks', 'orchest', 'pipelines', 'self-hosted']","['airflow', 'cloud', 'dag', 'data-pipelines', 'data-science', 'deployment', 'docker', 'etl', 'etl-pipeline', 'ide', 'jupyter', 'jupyterlab', 'kubernetes', 'machine-learning', 'notebooks', 'orchest', 'pipelines', 'self-hosted']",2023-06-06,"[('ploomber/ploomber', 0.862511932849884, 'ml-ops', 5), ('mage-ai/mage-ai', 0.7309496402740479, 'ml-ops', 5), ('airbytehq/airbyte', 0.7260224223136902, 'data', 2), ('bodywork-ml/bodywork-core', 0.6645437479019165, 'ml-ops', 3), ('dagster-io/dagster', 0.6582160592079163, 'ml-ops', 3), ('netflix/metaflow', 0.6532567739486694, 'ml-ops', 3), ('flyteorg/flyte', 0.6491807699203491, 'ml-ops', 3), ('zenml-io/zenml', 0.6482174396514893, 'ml-ops', 3), ('backtick-se/cowait', 0.6478020548820496, 'util', 3), ('kubeflow/pipelines', 0.6418040990829468, 'ml-ops', 3), ('kestra-io/kestra', 0.6342953443527222, 'ml-ops', 1), ('linealabs/lineapy', 0.6248028874397278, 'jupyter', 0), ('avaiga/taipy', 0.6196687817573547, 'data', 1), ('dagworks-inc/hamilton', 0.6139141917228699, 'ml-ops', 5), ('meltano/meltano', 0.6131107211112976, 'ml-ops', 2), ('kubeflow-kale/kale', 0.6008453965187073, 'ml-ops', 1), ('darribas/gds_env', 0.5977623462677002, 'gis', 1), ('polyaxon/polyaxon', 0.5914933681488037, 'ml-ops', 6), ('pypa/pipenv', 0.5898057818412781, 'util', 0), ('getindata/kedro-kubeflow', 0.5747969746589661, 'ml-ops', 0), ('apache/airflow', 0.57388836145401, 'ml-ops', 6), ('simonw/datasette', 0.5611031651496887, 'data', 1), ('allegroai/clearml', 0.5581900477409363, 'ml-ops', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5576450228691101, 'template', 1), ('hi-primus/optimus', 0.5557096004486084, 'ml-ops', 2), ('jina-ai/jina', 0.5534289479255676, 'ml', 3), ('merantix-momentum/squirrel-core', 0.5455144643783569, 'ml', 2), ('astronomer/astro-sdk', 0.5454973578453064, 'ml-ops', 3), ('gefyrahq/gefyra', 0.5443967580795288, 'util', 2), ('featureform/embeddinghub', 0.5408744215965271, 'nlp', 2), ('eventual-inc/daft', 0.5366385579109192, 'pandas', 2), ('polyaxon/datatile', 0.5361875891685486, 'pandas', 1), ('pytest-dev/pytest-testinfra', 0.5304024815559387, 'testing', 2), ('bentoml/bentoml', 0.52862948179245, 'ml-ops', 2), ('zenml-io/mlstacks', 0.5279688239097595, 'ml-ops', 0), ('skypilot-org/skypilot', 0.5275050401687622, 'llm', 2), ('willmcgugan/textual', 0.526781439781189, 'term', 0), ('whylabs/whylogs', 0.5236711502075195, 'util', 2), ('tox-dev/tox', 0.523241400718689, 'testing', 0), ('pydoit/doit', 0.522449254989624, 'util', 1), ('kubeflow/fairing', 0.5192087292671204, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5189650058746338, 'ml-ops', 1), ('koaning/scikit-partial', 0.5157948732376099, 'data', 0), ('streamlit/streamlit', 0.5133765935897827, 'viz', 2), ('fmind/mlops-python-package', 0.5126034617424011, 'template', 0), ('pypa/hatch', 0.510188639163971, 'util', 0), ('localstack/localstack', 0.5089136362075806, 'util', 1), ('thoth-station/micropipenv', 0.5059201121330261, 'util', 0), ('martinheinz/python-project-blueprint', 0.5042280554771423, 'template', 2), ('prefecthq/server', 0.5037789344787598, 'util', 0), ('spotify/luigi', 0.5029619336128235, 'ml-ops', 0), ('prefecthq/prefect', 0.5009967684745789, 'ml-ops', 1), ('fastai/fastcore', 0.5004292130470276, 'util', 0)]",31,4.0,,0.38,2,0,44,7,5,56,5,2.0,7.0,90.0,3.5,48
1279,viz,https://github.com/pyqtgraph/pyqtgraph,[],,[],[],,,,pyqtgraph/pyqtgraph,pyqtgraph,3554,1054,153,Python,https://www.pyqtgraph.org,Fast data visualization and GUI tools for scientific / engineering applications,pyqtgraph,2024-01-14,2013-09-12,541,6.560654008438819,https://avatars.githubusercontent.com/u/5440571?v=4,Fast data visualization and GUI tools for scientific / engineering applications,"['numpy', 'qt', 'scientific-visualization', 'visualization']","['numpy', 'qt', 'scientific-visualization', 'visualization']",2023-12-21,"[('enthought/mayavi', 0.734231173992157, 'viz', 2), ('holoviz/holoviz', 0.6720275282859802, 'viz', 0), ('altair-viz/altair', 0.6555560827255249, 'viz', 1), ('marcomusy/vedo', 0.6459553241729736, 'viz', 3), ('mwaskom/seaborn', 0.6456829905509949, 'viz', 0), ('numpy/numpy', 0.6338181495666504, 'math', 1), ('matplotlib/matplotlib', 0.6332533955574036, 'viz', 1), ('holoviz/panel', 0.6308204531669617, 'viz', 0), ('scitools/iris', 0.6289077401161194, 'gis', 0), ('man-group/dtale', 0.6243569850921631, 'viz', 1), ('contextlab/hypertools', 0.6197599172592163, 'ml', 1), ('pyvista/pyvista', 0.6187593936920166, 'viz', 2), ('holoviz/hvplot', 0.5959815979003906, 'pandas', 0), ('holoviz/datashader', 0.5956966876983643, 'gis', 0), ('vaexio/vaex', 0.592536449432373, 'perf', 1), ('residentmario/geoplot', 0.5889087915420532, 'gis', 0), ('bokeh/bokeh', 0.5844684839248657, 'viz', 1), ('graphistry/pygraphistry', 0.5679578185081482, 'data', 1), ('kanaries/pygwalker', 0.5670520663261414, 'pandas', 1), ('jakevdp/pythondatasciencehandbook', 0.5645138621330261, 'study', 1), ('blaze/blaze', 0.5560141205787659, 'pandas', 0), ('lux-org/lux', 0.5438263416290283, 'viz', 1), ('gregorhd/mapcompare', 0.5434110760688782, 'gis', 0), ('plotly/plotly.py', 0.5377339720726013, 'viz', 1), ('mckinsey/vizro', 0.5374338626861572, 'viz', 1), ('pandas-dev/pandas', 0.5296177268028259, 'pandas', 0), ('mito-ds/monorepo', 0.5288783311843872, 'jupyter', 0), ('wesm/pydata-book', 0.5280112624168396, 'study', 0), ('beeware/toga', 0.5267707109451294, 'gui', 0), ('xl0/lovely-numpy', 0.5235827565193176, 'util', 2), ('pytables/pytables', 0.5200861692428589, 'data', 0), ('districtdatalabs/yellowbrick', 0.5196901559829712, 'ml', 1), ('alexmojaki/heartrate', 0.5196880102157593, 'debug', 1), ('gradio-app/gradio', 0.5161272287368774, 'viz', 0), ('tqdm/tqdm', 0.5137172341346741, 'term', 0), ('bloomberg/ipydatagrid', 0.5133781433105469, 'jupyter', 0), ('dfki-ric/pytransform3d', 0.5104587078094482, 'math', 1), ('wxwidgets/phoenix', 0.509281575679779, 'gui', 0), ('polyaxon/datatile', 0.5080159902572632, 'pandas', 0), ('plotly/dash', 0.5079091191291809, 'viz', 0), ('quantopian/qgrid', 0.5077592730522156, 'jupyter', 0), ('ipython/ipyparallel', 0.5072664022445679, 'perf', 0), ('hoffstadt/dearpygui', 0.5063014626502991, 'gui', 0), ('earthlab/earthpy', 0.506048321723938, 'gis', 0), ('faster-cpython/tools', 0.5059671998023987, 'perf', 0), ('westhealth/pyvis', 0.5053408741950989, 'graph', 0), ('vizzuhq/ipyvizzu', 0.5050925612449646, 'jupyter', 0), ('fastai/fastcore', 0.5031821727752686, 'util', 0), ('makepath/xarray-spatial', 0.503014862537384, 'gis', 0), ('wandb/client', 0.5011712312698364, 'ml', 0), ('nomic-ai/deepscatter', 0.5000237822532654, 'viz', 1)]",267,5.0,,3.69,83,42,126,1,2,3,2,83.0,140.0,90.0,1.7,48
1060,ml-rl,https://github.com/deepmind/dm_control,[],,[],[],,,,deepmind/dm_control,dm_control,3414,638,128,Python,,"Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.",deepmind,2024-01-13,2017-12-29,317,10.75033738191633,https://avatars.githubusercontent.com/u/8596759?v=4,"Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.","['artificial-intelligence', 'deep-learning', 'machine-learning', 'mujoco', 'neural-networks', 'physics-simulation', 'reinforcement-learning']","['artificial-intelligence', 'deep-learning', 'machine-learning', 'mujoco', 'neural-networks', 'physics-simulation', 'reinforcement-learning']",2024-01-12,"[('tensorlayer/tensorlayer', 0.654015064239502, 'ml-rl', 3), ('google/trax', 0.6313595771789551, 'ml-dl', 3), ('unity-technologies/ml-agents', 0.6150110363960266, 'ml-rl', 4), ('openai/mujoco-py', 0.5940796732902527, 'sim', 0), ('tensorflow/tensor2tensor', 0.5927808880805969, 'ml', 3), ('keras-rl/keras-rl', 0.590694010257721, 'ml-rl', 3), ('googlecloudplatform/vertex-ai-samples', 0.569223940372467, 'ml', 0), ('google/dopamine', 0.562454104423523, 'ml-rl', 0), ('salesforce/warp-drive', 0.5524762272834778, 'ml-rl', 2), ('bentoml/bentoml', 0.5436348915100098, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5414921641349792, 'study', 2), ('deepmind/deepmind-research', 0.5342397689819336, 'ml', 0), ('ddbourgin/numpy-ml', 0.5336541533470154, 'ml', 3), ('determined-ai/determined', 0.5311621427536011, 'ml-ops', 2), ('thu-ml/tianshou', 0.5307285189628601, 'ml-rl', 1), ('wandb/client', 0.5283645987510681, 'ml', 3), ('polyaxon/polyaxon', 0.5232641100883484, 'ml-ops', 4), ('apache/incubator-mxnet', 0.5190567374229431, 'ml-dl', 0), ('explosion/thinc', 0.5177432894706726, 'ml-dl', 3), ('microsoft/deepspeed', 0.5161496996879578, 'ml-dl', 2), ('keras-team/keras', 0.5160452723503113, 'ml-dl', 3), ('onnx/onnx', 0.5158249139785767, 'ml', 2), ('google/brax', 0.514367938041687, 'sim', 2), ('microsoft/onnxruntime', 0.5139978528022766, 'ml', 3), ('pytorchlightning/pytorch-lightning', 0.5117486715316772, 'ml-dl', 3), ('arise-initiative/robosuite', 0.5112686157226562, 'ml-rl', 2), ('denys88/rl_games', 0.5099033117294312, 'ml-rl', 2), ('tensorflow/tensorflow', 0.5096217393875122, 'ml-dl', 2), ('pytorch/rl', 0.507379412651062, 'ml-rl', 2), ('ai4finance-foundation/finrl', 0.5040066838264465, 'finance', 1), ('facebookresearch/habitat-lab', 0.5033023357391357, 'sim', 2), ('adap/flower', 0.502716064453125, 'ml-ops', 3), ('jina-ai/jina', 0.5015859603881836, 'ml', 2), ('deepmodeling/deepmd-kit', 0.5011904239654541, 'sim', 1)]",41,3.0,,1.04,28,17,74,0,7,4,7,27.0,34.0,90.0,1.3,48
1695,util,https://github.com/asottile/pyupgrade,"['pre-commit', 'code-quality']",,[],[],,,,asottile/pyupgrade,pyupgrade,3044,171,35,Python,,A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.,asottile,2024-01-12,2017-02-28,361,8.43213296398892,,A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.,"['linter', 'pre-commit']","['code-quality', 'linter', 'pre-commit']",2024-01-08,"[('pre-commit/pre-commit', 0.6998698115348816, 'util', 3), ('callowayproject/bump-my-version', 0.5788130760192871, 'util', 1), ('psf/black', 0.5597057342529297, 'util', 1), ('thudm/codegeex', 0.5523767471313477, 'llm', 0)]",35,4.0,,1.52,23,19,84,0,0,24,24,23.0,36.0,90.0,1.6,48
1773,jupyter,https://github.com/jupyter-widgets/ipywidgets,[],,[],[],,,,jupyter-widgets/ipywidgets,ipywidgets,2976,942,76,TypeScript,https://ipywidgets.readthedocs.io,Interactive Widgets for the Jupyter Notebook,jupyter-widgets,2024-01-13,2015-04-17,458,6.489719626168224,https://avatars.githubusercontent.com/u/25869250?v=4,Interactive Widgets for the Jupyter Notebook,"['jupyter-notebooks', 'jupyterlab-extension']","['jupyter-notebooks', 'jupyterlab-extension']",2023-12-19,"[('jupyter/notebook', 0.8538753390312195, 'jupyter', 0), ('voila-dashboards/voila', 0.7114137411117554, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.7082852721214294, 'jupyter', 0), ('jupyter/nbformat', 0.7056740522384644, 'jupyter', 0), ('aws/graph-notebook', 0.687077522277832, 'jupyter', 0), ('bloomberg/ipydatagrid', 0.6741766929626465, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.6638791561126709, 'jupyter', 0), ('mwouts/jupytext', 0.6579967737197876, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.6541346311569214, 'jupyter', 0), ('jupyter-widgets/ipyleaflet', 0.6435815095901489, 'gis', 1), ('cohere-ai/notebooks', 0.6400914192199707, 'llm', 0), ('xiaohk/stickyland', 0.6346691250801086, 'jupyter', 1), ('mamba-org/gator', 0.6345846056938171, 'jupyter', 1), ('quantopian/qgrid', 0.6274958848953247, 'jupyter', 0), ('giswqs/mapwidget', 0.6259199976921082, 'gis', 0), ('computationalmodelling/nbval', 0.6210037469863892, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.6188204884529114, 'jupyter', 0), ('chaoleili/jupyterlab_tensorboard', 0.6089569330215454, 'jupyter', 1), ('jupyter/nbconvert', 0.6067924499511719, 'jupyter', 0), ('ipython/ipykernel', 0.6043174862861633, 'util', 0), ('ipython/ipyparallel', 0.5989540219306946, 'perf', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5988917946815491, 'jupyter', 1), ('tkrabel/bamboolib', 0.5929217338562012, 'pandas', 0), ('jupyterlite/jupyterlite', 0.5854482650756836, 'jupyter', 1), ('jupyter/nbdime', 0.5793547034263611, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5679361820220947, 'study', 0), ('jupyter/nbviewer', 0.5482261180877686, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5422195792198181, 'study', 0), ('koaning/drawdata', 0.5359898805618286, 'jupyter', 0), ('pysimplegui/pysimplegui', 0.5297706127166748, 'gui', 0), ('jupyter/nbgrader', 0.5242305397987366, 'jupyter', 0), ('wxwidgets/phoenix', 0.5223656296730042, 'gui', 0), ('nteract/testbook', 0.5169227719306946, 'jupyter', 0), ('bokeh/bokeh', 0.5115343928337097, 'viz', 0), ('plotly/plotly.py', 0.5096937417984009, 'viz', 0), ('nteract/papermill', 0.5082428455352783, 'jupyter', 0), ('ageron/handson-ml2', 0.5040411949157715, 'ml', 0), ('jupyterlab/jupyter-ai', 0.5035024285316467, 'jupyter', 1)]",212,9.0,,1.75,61,5,106,1,5,143,5,61.0,62.0,90.0,1.0,48
577,ml-dl,https://github.com/alpa-projects/alpa,[],,[],[],,,,alpa-projects/alpa,alpa,2921,343,46,Python,https://alpa.ai,Training and serving large-scale neural networks with auto parallelization.,alpa-projects,2024-01-14,2021-02-22,153,19.073694029850746,https://avatars.githubusercontent.com/u/82711759?v=4,Training and serving large-scale neural networks with auto parallelization.,"['alpa', 'auto-parallelization', 'compiler', 'deep-learning', 'distributed-computing', 'distributed-training', 'high-performance-computing', 'jax', 'llm', 'machine-learning']","['alpa', 'auto-parallelization', 'compiler', 'deep-learning', 'distributed-computing', 'distributed-training', 'high-performance-computing', 'jax', 'llm', 'machine-learning']",2023-12-09,"[('paddlepaddle/paddle', 0.7082504034042358, 'ml-dl', 3), ('horovod/horovod', 0.6610665321350098, 'ml-ops', 2), ('microsoft/nni', 0.6484193205833435, 'ml', 2), ('keras-team/autokeras', 0.6452708840370178, 'ml-dl', 2), ('ray-project/ray', 0.6443263292312622, 'ml-ops', 2), ('microsoft/onnxruntime', 0.6340340971946716, 'ml', 2), ('onnx/onnx', 0.6325767636299133, 'ml', 2), ('bigscience-workshop/petals', 0.6318992972373962, 'data', 2), ('hpcaitech/colossalai', 0.6304028034210205, 'llm', 2), ('tensorflow/tensorflow', 0.6228885650634766, 'ml-dl', 2), ('deepmind/dm-haiku', 0.6117678284645081, 'ml-dl', 3), ('keras-team/keras', 0.6106972098350525, 'ml-dl', 3), ('pytorch/glow', 0.6077716946601868, 'ml', 0), ('neuralmagic/deepsparse', 0.6077420711517334, 'nlp', 0), ('microsoft/deepspeed', 0.6055509448051453, 'ml-dl', 2), ('explosion/thinc', 0.5971328020095825, 'ml-dl', 3), ('apache/incubator-mxnet', 0.5928085446357727, 'ml-dl', 0), ('uber/fiber', 0.5923050045967102, 'data', 2), ('google/trax', 0.5918993949890137, 'ml-dl', 3), ('aiqc/aiqc', 0.5878965854644775, 'ml-ops', 0), ('mosaicml/composer', 0.5876685976982117, 'ml-dl', 2), ('bentoml/bentoml', 0.5799961686134338, 'ml-ops', 2), ('awslabs/autogluon', 0.5790343284606934, 'ml', 2), ('huggingface/transformers', 0.57871413230896, 'nlp', 3), ('winedarksea/autots', 0.5752678513526917, 'time-series', 2), ('determined-ai/determined', 0.571927547454834, 'ml-ops', 3), ('nvidia/deeplearningexamples', 0.5713871717453003, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5670627951622009, 'ml-dl', 2), ('nccr-itmo/fedot', 0.5670198202133179, 'ml-ops', 1), ('ludwig-ai/ludwig', 0.5621179938316345, 'ml-ops', 3), ('karpathy/micrograd', 0.5604699850082397, 'study', 0), ('neuralmagic/sparseml', 0.560257613658905, 'ml-dl', 0), ('microsoft/flaml', 0.5595440864562988, 'ml', 2), ('ml-tooling/opyrator', 0.5594131350517273, 'viz', 1), ('googlecloudplatform/vertex-ai-samples', 0.5572735667228699, 'ml', 0), ('opentensor/bittensor', 0.556601345539093, 'ml', 2), ('tensorflow/tensor2tensor', 0.5523074865341187, 'ml', 2), ('mlc-ai/mlc-llm', 0.5512301921844482, 'llm', 1), ('ray-project/ray-educational-materials', 0.5506226420402527, 'study', 2), ('polyaxon/polyaxon', 0.5499585866928101, 'ml-ops', 2), ('jina-ai/jina', 0.5415318012237549, 'ml', 2), ('huggingface/autotrain-advanced', 0.5402303338050842, 'ml', 2), ('huggingface/datasets', 0.5375392436981201, 'nlp', 2), ('automl/auto-sklearn', 0.535767674446106, 'ml', 0), ('deepfakes/faceswap', 0.5312798619270325, 'ml-dl', 2), ('uber/petastorm', 0.5310642719268799, 'data', 2), ('adap/flower', 0.5309455990791321, 'ml-ops', 2), ('skypilot-org/skypilot', 0.5307222008705139, 'llm', 3), ('titanml/takeoff', 0.5302854776382446, 'llm', 1), ('mlflow/mlflow', 0.52925705909729, 'ml-ops', 1), ('tlkh/tf-metal-experiments', 0.5292550921440125, 'perf', 1), ('optuna/optuna', 0.5211974382400513, 'ml', 1), ('amanchadha/coursera-deep-learning-specialization', 0.5197420120239258, 'study', 1), ('keras-rl/keras-rl', 0.5167292356491089, 'ml-rl', 1), ('ddbourgin/numpy-ml', 0.5153135657310486, 'ml', 1), ('rafiqhasan/auto-tensorflow', 0.5152245759963989, 'ml-dl', 1), ('google/mediapipe', 0.5148690938949585, 'ml', 2), ('iperov/deepfacelab', 0.511199414730072, 'ml-dl', 2), ('samuela/git-re-basin', 0.507290780544281, 'ml-dl', 3), ('rwightman/pytorch-image-models', 0.507267415523529, 'ml-dl', 1), ('microsoft/semi-supervised-learning', 0.5067688822746277, 'ml', 2), ('unionai-oss/unionml', 0.50523841381073, 'ml-ops', 1), ('nyandwi/modernconvnets', 0.5051084160804749, 'ml-dl', 0), ('tensorlayer/tensorlayer', 0.5041244029998779, 'ml-rl', 1), ('xplainable/xplainable', 0.5035876035690308, 'ml-interpretability', 1), ('bobazooba/xllm', 0.5013929605484009, 'llm', 2), ('towhee-io/towhee', 0.5001883506774902, 'ml-ops', 2)]",45,6.0,,0.67,28,6,35,1,1,5,1,28.0,26.0,90.0,0.9,48
702,ml,https://github.com/mljar/mljar-supervised,[],,[],[],,,,mljar/mljar-supervised,mljar-supervised,2858,372,45,Python,https://mljar.com,"Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation",mljar,2024-01-13,2018-11-05,273,10.463389121338912,https://avatars.githubusercontent.com/u/20522384?v=4,"Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation","['automated-machine-learning', 'automatic-machine-learning', 'automl', 'catboost', 'data-science', 'decision-tree', 'ensemble', 'feature-engineering', 'hyper-parameters', 'hyperparameter-optimization', 'lightgbm', 'machine-learning', 'mljar', 'models-tuning', 'neural-network', 'random-forest', 'scikit-learn', 'shap', 'tuning-algorithm', 'xgboost']","['automated-machine-learning', 'automatic-machine-learning', 'automl', 'catboost', 'data-science', 'decision-tree', 'ensemble', 'feature-engineering', 'hyper-parameters', 'hyperparameter-optimization', 'lightgbm', 'machine-learning', 'mljar', 'models-tuning', 'neural-network', 'random-forest', 'scikit-learn', 'shap', 'tuning-algorithm', 'xgboost']",2024-01-08,"[('microsoft/flaml', 0.7940219044685364, 'ml', 7), ('awslabs/autogluon', 0.7640585899353027, 'ml', 6), ('microsoft/nni', 0.6952623724937439, 'ml', 7), ('automl/auto-sklearn', 0.6622180938720703, 'ml', 4), ('keras-team/autokeras', 0.6605311036109924, 'ml-dl', 3), ('featurelabs/featuretools', 0.645880401134491, 'ml', 6), ('epistasislab/tpot', 0.6145864129066467, 'ml', 8), ('winedarksea/autots', 0.578948438167572, 'time-series', 3), ('nccr-itmo/fedot', 0.5752649307250977, 'ml-ops', 4), ('rasbt/mlxtend', 0.5722818374633789, 'ml', 2), ('google/pyglove', 0.5717796683311462, 'util', 2), ('ray-project/tune-sklearn', 0.5580164194107056, 'ml', 2), ('shankarpandala/lazypredict', 0.5433629155158997, 'ml', 2), ('vaexio/vaex', 0.5394431352615356, 'perf', 2), ('gradio-app/gradio', 0.5382276773452759, 'viz', 2), ('jazzband/tablib', 0.531550407409668, 'data', 0), ('firmai/atspy', 0.5243828296661377, 'time-series', 0), ('paperswithcode/axcell', 0.5180116891860962, 'util', 0), ('dylanhogg/awesome-python', 0.5141728520393372, 'study', 2), ('alkaline-ml/pmdarima', 0.509310781955719, 'time-series', 1), ('google/vizier', 0.5090547204017639, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5052536129951477, 'ml-interpretability', 0), ('ydataai/ydata-synthetic', 0.5016704797744751, 'data', 1), ('astanin/python-tabulate', 0.5009972453117371, 'util', 0)]",25,3.0,,2.15,37,11,63,0,6,11,6,37.0,68.0,90.0,1.8,48
1109,llm,https://github.com/juncongmoo/pyllama,[],,[],['pyllama'],,,Start 2023-04-13,juncongmoo/pyllama,pyllama,2732,309,36,Python,,LLaMA: Open and Efficient Foundation Language Models,juncongmoo,2024-01-14,2023-02-28,48,56.916666666666664,,LLaMA: Open and Efficient Foundation Language Models,[],[],2023-04-25,"[('ai21labs/lm-evaluation', 0.699636697769165, 'llm', 0), ('hannibal046/awesome-llm', 0.6920731067657471, 'study', 0), ('optimalscale/lmflow', 0.6721161007881165, 'llm', 0), ('cg123/mergekit', 0.6644551753997803, 'llm', 0), ('freedomintelligence/llmzoo', 0.6618118286132812, 'llm', 0), ('ctlllll/llm-toolmaker', 0.6501331925392151, 'llm', 0), ('lianjiatech/belle', 0.6371904611587524, 'llm', 0), ('guardrails-ai/guardrails', 0.6344968676567078, 'llm', 0), ('lm-sys/fastchat', 0.6291965842247009, 'llm', 0), ('salesforce/xgen', 0.6149995923042297, 'llm', 0), ('reasoning-machines/pal', 0.6026532053947449, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.6021994948387146, 'llm', 0), ('guidance-ai/guidance', 0.6007868051528931, 'llm', 0), ('eleutherai/the-pile', 0.5945956707000732, 'data', 0), ('young-geng/easylm', 0.5928237438201904, 'llm', 0), ('openlmlab/leval', 0.5909538269042969, 'llm', 0), ('artidoro/qlora', 0.5865978002548218, 'llm', 0), ('jonasgeiping/cramming', 0.5846665501594543, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5793993473052979, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5714971423149109, 'llm', 0), ('hiyouga/llama-factory', 0.5714971423149109, 'llm', 0), ('srush/minichain', 0.5712380409240723, 'llm', 0), ('bobazooba/xllm', 0.5698232054710388, 'llm', 0), ('explosion/spacy-llm', 0.5681570768356323, 'llm', 0), ('hazyresearch/h3', 0.567010223865509, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5613843202590942, 'study', 0), ('infinitylogesh/mutate', 0.5594502687454224, 'nlp', 0), ('mit-han-lab/streaming-llm', 0.5574493408203125, 'llm', 0), ('aiwaves-cn/agents', 0.5563294887542725, 'nlp', 0), ('yizhongw/self-instruct', 0.5553812384605408, 'llm', 0), ('openbmb/toolbench', 0.5528221130371094, 'llm', 0), ('facebookresearch/seamless_communication', 0.5527052879333496, 'nlp', 0), ('facebookresearch/codellama', 0.5494055151939392, 'llm', 0), ('facebookresearch/llama', 0.5485102534294128, 'llm', 0), ('bigscience-workshop/biomedical', 0.5467652678489685, 'data', 0), ('1rgs/jsonformer', 0.5465362071990967, 'llm', 0), ('next-gpt/next-gpt', 0.5461319088935852, 'llm', 0), ('prefecthq/langchain-prefect', 0.5437901616096497, 'llm', 0), ('neulab/prompt2model', 0.5409372448921204, 'llm', 0), ('microsoft/autogen', 0.5394055247306824, 'llm', 0), ('dylanhogg/llmgraph', 0.5386449694633484, 'ml', 0), ('conceptofmind/toolformer', 0.5372142195701599, 'llm', 0), ('databrickslabs/dolly', 0.5352316498756409, 'llm', 0), ('oobabooga/text-generation-webui', 0.5334741473197937, 'llm', 0), ('lupantech/chameleon-llm', 0.5328553915023804, 'llm', 0), ('togethercomputer/redpajama-data', 0.5303771495819092, 'llm', 0), ('stanfordnlp/dspy', 0.5301841497421265, 'llm', 0), ('explosion/spacy-models', 0.5298112630844116, 'nlp', 0), ('llmware-ai/llmware', 0.5293798446655273, 'llm', 0), ('salesforce/codet5', 0.5293552875518799, 'nlp', 0), ('sjtu-ipads/powerinfer', 0.5285684466362, 'llm', 0), ('cstankonrad/long_llama', 0.5282744765281677, 'llm', 0), ('timdettmers/bitsandbytes', 0.5255274772644043, 'util', 0), ('huggingface/text-generation-inference', 0.5241082310676575, 'llm', 0), ('karpathy/llama2.c', 0.5219447016716003, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.518515944480896, 'llm', 0), ('predibase/llm_distillation_playbook', 0.5181902647018433, 'llm', 0), ('microsoft/lora', 0.5170261263847351, 'llm', 0), ('microsoft/llama-2-onnx', 0.5146937966346741, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5146656036376953, 'nlp', 0), ('epfllm/meditron', 0.5138061046600342, 'llm', 0), ('openlm-research/open_llama', 0.5135765671730042, 'llm', 0), ('microsoft/torchscale', 0.5133620500564575, 'llm', 0), ('fasteval/fasteval', 0.5125579833984375, 'llm', 0), ('confident-ai/deepeval', 0.5091282725334167, 'testing', 0), ('nvidia/tensorrt-llm', 0.5075156092643738, 'viz', 0), ('eth-sri/lmql', 0.5070822834968567, 'llm', 0), ('thudm/chatglm2-6b', 0.5058130621910095, 'llm', 0), ('ray-project/ray-llm', 0.505372941493988, 'llm', 0), ('openai/gpt-2', 0.504024863243103, 'llm', 0), ('thudm/codegeex', 0.503732442855835, 'llm', 0), ('facebookresearch/shepherd', 0.5019182562828064, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5018168091773987, 'llm', 0)]",11,3.0,,0.83,7,2,11,9,1,1,1,7.0,7.0,90.0,1.0,48
677,util,https://github.com/spotify/basic-pitch,[],,[],[],,,,spotify/basic-pitch,basic-pitch,2654,186,48,Python,https://basicpitch.io,A lightweight yet powerful audio-to-MIDI converter with pitch bend detection,spotify,2024-01-14,2022-05-03,91,29.164835164835164,https://avatars.githubusercontent.com/u/251374?v=4,A lightweight yet powerful audio-to-MIDI converter with pitch bend detection,"['audio', 'lightweight', 'machine-learning', 'midi', 'music', 'pitch-detection', 'polyphonic', 'transcription', 'typescript']","['audio', 'lightweight', 'machine-learning', 'midi', 'music', 'pitch-detection', 'polyphonic', 'transcription', 'typescript']",2023-09-12,"[('espnet/espnet', 0.5158132910728455, 'nlp', 0), ('facebookresearch/audiocraft', 0.5119118094444275, 'util', 1)]",17,4.0,,0.67,14,5,21,4,3,2,3,14.0,18.0,90.0,1.3,48
850,jupyter,https://github.com/jupyter/nbdime,[],,[],[],,,,jupyter/nbdime,nbdime,2562,164,42,TypeScript,http://nbdime.readthedocs.io,Tools for diffing and merging of Jupyter notebooks.,jupyter,2024-01-13,2015-11-16,428,5.983983983983984,https://avatars.githubusercontent.com/u/7388996?v=4,Tools for diffing and merging of Jupyter notebooks.,"['diff', 'diffing', 'git', 'hg', 'jupyter', 'jupyter-notebook', 'jupyterlab-extension', 'mercurial', 'merge', 'merge-driver', 'mergetool', 'vcs', 'version-control']","['diff', 'diffing', 'git', 'hg', 'jupyter', 'jupyter-notebook', 'jupyterlab-extension', 'mercurial', 'merge', 'merge-driver', 'mergetool', 'vcs', 'version-control']",2023-11-21,"[('mwouts/jupytext', 0.6408078074455261, 'jupyter', 3), ('jupyter/nbformat', 0.6054288148880005, 'jupyter', 0), ('quantopian/qgrid', 0.5866443514823914, 'jupyter', 0), ('voila-dashboards/voila', 0.5848777890205383, 'jupyter', 3), ('jupyter-widgets/ipywidgets', 0.5793547034263611, 'jupyter', 1), ('jupyter/nbconvert', 0.5780813694000244, 'jupyter', 0), ('cohere-ai/notebooks', 0.559609591960907, 'llm', 0), ('aws/graph-notebook', 0.5575152039527893, 'jupyter', 2), ('jupyterlab/jupyterlab-desktop', 0.552423357963562, 'jupyter', 2), ('jupyter/notebook', 0.5463511943817139, 'jupyter', 2), ('nbqa-dev/nbqa', 0.5352687239646912, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5264706611633301, 'study', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5217457413673401, 'study', 0), ('jupyter/nbgrader', 0.5187388062477112, 'jupyter', 2), ('nteract/papermill', 0.5114229917526245, 'jupyter', 1), ('computationalmodelling/nbval', 0.5095821022987366, 'jupyter', 1)]",50,5.0,,2.46,47,36,99,2,6,11,6,47.0,182.0,90.0,3.9,48
1159,util,https://github.com/whylabs/whylogs,[],,[],[],,,,whylabs/whylogs,whylogs,2444,109,32,Jupyter Notebook,https://whylogs.readthedocs.io/,"An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈",whylabs,2024-01-13,2020-08-14,180,13.534810126582279,https://avatars.githubusercontent.com/u/56651354?v=4,"An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈","['ai-pipelines', 'analytics', 'approximate-statistics', 'calculate-statistics', 'constraints', 'data-constraints', 'data-pipeline', 'data-quality', 'data-science', 'dataops', 'dataset', 'logging', 'machine-learning', 'ml-pipelines', 'mlops', 'model-performance', 'statistical-properties']","['ai-pipelines', 'analytics', 'approximate-statistics', 'calculate-statistics', 'constraints', 'data-constraints', 'data-pipeline', 'data-quality', 'data-science', 'dataops', 'dataset', 'logging', 'machine-learning', 'ml-pipelines', 'mlops', 'model-performance', 'statistical-properties']",2024-01-11,"[('salesforce/logai', 0.7454851269721985, 'util', 1), ('polyaxon/datatile', 0.5969440937042236, 'pandas', 4), ('wandb/client', 0.5798064470291138, 'ml', 3), ('aimhubio/aim', 0.5764767527580261, 'ml-ops', 3), ('mlflow/mlflow', 0.5756088495254517, 'ml-ops', 1), ('cleanlab/cleanlab', 0.5660983324050903, 'ml', 3), ('netflix/metaflow', 0.5658851861953735, 'ml-ops', 3), ('huggingface/datasets', 0.5516629815101624, 'nlp', 1), ('activeloopai/deeplake', 0.5506848096847534, 'ml-ops', 3), ('feast-dev/feast', 0.5498430132865906, 'ml-ops', 4), ('meltano/meltano', 0.5477058291435242, 'ml-ops', 1), ('merantix-momentum/squirrel-core', 0.5451236367225647, 'ml', 3), ('mage-ai/mage-ai', 0.5448665022850037, 'ml-ops', 2), ('google/ml-metadata', 0.5445284247398376, 'ml-ops', 0), ('csinva/imodels', 0.5419551730155945, 'ml', 2), ('ploomber/ploomber', 0.5393771529197693, 'ml-ops', 3), ('oegedijk/explainerdashboard', 0.5336712598800659, 'ml-interpretability', 0), ('polyaxon/polyaxon', 0.5290948152542114, 'ml-ops', 3), ('orchest/orchest', 0.5236711502075195, 'ml-ops', 2), ('airbytehq/airbyte', 0.5231224298477173, 'data', 1), ('bentoml/bentoml', 0.5217410922050476, 'ml-ops', 2), ('googlecloudplatform/vertex-ai-samples', 0.5152586102485657, 'ml', 2), ('avaiga/taipy', 0.5133393406867981, 'data', 1), ('dagworks-inc/hamilton', 0.5131041407585144, 'ml-ops', 3), ('microsoft/nni', 0.5119295120239258, 'ml', 3), ('teamhg-memex/eli5', 0.511576235294342, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5081930756568909, 'ml-interpretability', 0), ('kubeflow/pipelines', 0.5075204372406006, 'ml-ops', 3), ('interpretml/interpret', 0.5045889019966125, 'ml-interpretability', 1), ('zenml-io/zenml', 0.5043908357620239, 'ml-ops', 3), ('great-expectations/great_expectations', 0.5034822821617126, 'ml-ops', 3), ('simonw/datasette', 0.5017004609107971, 'data', 0), ('mindsdb/mindsdb', 0.5011435151100159, 'data', 1)]",23,3.0,,5.04,64,57,42,0,54,43,54,64.0,16.0,90.0,0.2,48
914,profiling,https://github.com/pyutils/line_profiler,[],,[],[],,,,pyutils/line_profiler,line_profiler,2304,112,14,Python,,Line-by-line profiling for Python,pyutils,2024-01-14,2019-12-10,216,10.666666666666666,https://avatars.githubusercontent.com/u/58752944?v=4,Line-by-line profiling for Python,[],[],2023-12-05,"[('benfred/py-spy', 0.6891393065452576, 'profiling', 0), ('pythonspeed/filprofiler', 0.6149646043777466, 'profiling', 0), ('landscapeio/prospector', 0.5992322564125061, 'util', 0), ('gaogaotiantian/viztracer', 0.5905494689941406, 'profiling', 0), ('pympler/pympler', 0.5873364806175232, 'perf', 0), ('klen/py-frameworks-bench', 0.5825835466384888, 'perf', 0), ('nedbat/coveragepy', 0.5808612108230591, 'testing', 0), ('pythonprofilers/memory_profiler', 0.577487051486969, 'profiling', 0), ('rubik/radon', 0.5716840028762817, 'util', 0), ('google/pytype', 0.5699858069419861, 'typing', 0), ('jiffyclub/snakeviz', 0.563589870929718, 'profiling', 0), ('p403n1x87/austin', 0.5507018566131592, 'profiling', 0), ('ionelmc/pytest-benchmark', 0.5505008101463318, 'testing', 0), ('pysal/pysal', 0.5387760400772095, 'gis', 0), ('nschloe/perfplot', 0.535541832447052, 'perf', 0), ('eleutherai/pyfra', 0.5344709157943726, 'ml', 0), ('sumerc/yappi', 0.5341673493385315, 'profiling', 0), ('alexmojaki/snoop', 0.5304473638534546, 'debug', 0), ('csurfer/pyheat', 0.5279625654220581, 'profiling', 0), ('altair-viz/altair', 0.5267626643180847, 'viz', 0), ('rasbt/mlxtend', 0.5252503156661987, 'ml', 0), ('xrudelis/pytrait', 0.5251006484031677, 'util', 0), ('pypy/pypy', 0.5241230726242065, 'util', 0), ('alexmojaki/heartrate', 0.522416353225708, 'debug', 0), ('joerick/pyinstrument', 0.5132960677146912, 'profiling', 0), ('lcompilers/lpython', 0.5114628076553345, 'util', 0), ('pytoolz/toolz', 0.5109219551086426, 'util', 0), ('reloadware/reloadium', 0.5102075338363647, 'profiling', 0), ('carla-recourse/carla', 0.5099355578422546, 'ml', 0), ('google/yapf', 0.5072764158248901, 'util', 0), ('instagram/monkeytype', 0.5013983845710754, 'typing', 0)]",43,5.0,,2.12,20,13,50,1,4,5,4,20.0,44.0,90.0,2.2,48
1073,ml,https://github.com/google-research/t5x,[],,[],[],,,,google-research/t5x,t5x,2278,275,36,Python,,,google-research,2024-01-13,2021-11-01,117,19.446341463414633,https://avatars.githubusercontent.com/u/43830688?v=4,google-research/t5x,[],[],2024-01-13,"[('google-research/byt5', 0.8195183277130127, 'nlp', 0), ('google-research/google-research', 0.6067818999290466, 'ml', 0)]",106,3.0,,4.54,81,45,27,0,0,0,0,81.0,25.0,90.0,0.3,48
1395,llm,https://github.com/civitai/sd_civitai_extension,[],,[],[],,,,civitai/sd_civitai_extension,sd_civitai_extension,2139,405,73,Python,,All of the Civitai models inside Automatic 1111 Stable Diffusion Web UI,civitai,2024-01-14,2022-12-06,60,35.65,https://avatars.githubusercontent.com/u/117393426?v=4,All of the Civitai models inside Automatic 1111 Stable Diffusion Web UI,[],[],2023-12-21,"[('mlc-ai/web-stable-diffusion', 0.6748091578483582, 'diffusion', 0), ('automatic1111/stable-diffusion-webui', 0.6364338397979736, 'diffusion', 0), ('thereforegames/unprompted', 0.6360564827919006, 'diffusion', 0), ('comfyanonymous/comfyui', 0.6050902009010315, 'diffusion', 0), ('carson-katri/dream-textures', 0.5569747686386108, 'diffusion', 0), ('bentoml/onediffusion', 0.5044161081314087, 'diffusion', 0)]",10,5.0,,1.0,24,3,13,1,0,0,0,24.0,20.0,90.0,0.8,48
1327,llm,https://github.com/young-geng/easylm,[],,[],[],,,,young-geng/easylm,EasyLM,2093,209,36,Python,,"Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.",young-geng,2024-01-13,2022-11-22,62,33.75806451612903,,"Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.","['chatbot', 'deep-learning', 'flax', 'jax', 'language-model', 'large-language-models', 'llama', 'natural-language-processing', 'transformer']","['chatbot', 'deep-learning', 'flax', 'jax', 'language-model', 'large-language-models', 'llama', 'natural-language-processing', 'transformer']",2023-08-31,"[('hiyouga/llama-factory', 0.6924945116043091, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.6924943923950195, 'llm', 3), ('nomic-ai/gpt4all', 0.6724932789802551, 'llm', 2), ('salesforce/xgen', 0.6715541481971741, 'llm', 2), ('bigscience-workshop/petals', 0.6689819693565369, 'data', 5), ('deepset-ai/haystack', 0.6550629734992981, 'llm', 2), ('lm-sys/fastchat', 0.6548444628715515, 'llm', 2), ('cg123/mergekit', 0.6466503143310547, 'llm', 1), ('mooler0410/llmspracticalguide', 0.6447182893753052, 'study', 2), ('paddlepaddle/paddlenlp', 0.6431230902671814, 'llm', 1), ('mlc-ai/web-llm', 0.6428095102310181, 'llm', 2), ('hwchase17/langchain', 0.6422194838523865, 'llm', 2), ('eugeneyan/open-llms', 0.6238888502120972, 'study', 1), ('lianjiatech/belle', 0.6193146705627441, 'llm', 1), ('ctlllll/llm-toolmaker', 0.6142538785934448, 'llm', 1), ('night-chen/toolqa', 0.6132665276527405, 'llm', 1), ('microsoft/autogen', 0.6095622181892395, 'llm', 1), ('explosion/spacy-llm', 0.6080474257469177, 'llm', 3), ('oobabooga/text-generation-webui', 0.6048070788383484, 'llm', 1), ('agenta-ai/agenta', 0.6034758687019348, 'llm', 1), ('confident-ai/deepeval', 0.6033726334571838, 'testing', 1), ('dylanhogg/llmgraph', 0.6026452779769897, 'ml', 0), ('fasteval/fasteval', 0.6013676524162292, 'llm', 0), ('thudm/chatglm2-6b', 0.601020097732544, 'llm', 1), ('h2oai/h2o-llmstudio', 0.6009706258773804, 'llm', 2), ('tigerlab-ai/tiger', 0.6003850698471069, 'llm', 1), ('ai21labs/lm-evaluation', 0.5989466309547424, 'llm', 1), ('jzhang38/tinyllama', 0.597965657711029, 'llm', 2), ('bobazooba/xllm', 0.5975850224494934, 'llm', 3), ('llmware-ai/llmware', 0.593721330165863, 'llm', 1), ('freedomintelligence/llmzoo', 0.5935439467430115, 'llm', 1), ('nat/openplayground', 0.593249499797821, 'llm', 1), ('juncongmoo/pyllama', 0.5928237438201904, 'llm', 0), ('ray-project/ray-llm', 0.5914445519447327, 'llm', 1), ('salesforce/codet5', 0.591428816318512, 'nlp', 2), ('alpha-vllm/llama2-accessory', 0.5873928070068359, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5872876048088074, 'perf', 1), ('pathwaycom/llm-app', 0.5865856409072876, 'llm', 1), ('titanml/takeoff', 0.5864971876144409, 'llm', 2), ('ajndkr/lanarky', 0.5861847996711731, 'llm', 0), ('conceptofmind/toolformer', 0.5841966867446899, 'llm', 1), ('argilla-io/argilla', 0.5817759037017822, 'nlp', 1), ('ludwig-ai/ludwig', 0.5782345533370972, 'ml-ops', 3), ('next-gpt/next-gpt', 0.5767945647239685, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5753515362739563, 'llm', 2), ('artidoro/qlora', 0.574920654296875, 'llm', 1), ('huggingface/transformers', 0.571175754070282, 'nlp', 6), ('infinitylogesh/mutate', 0.566120445728302, 'nlp', 1), ('citadel-ai/langcheck', 0.5656929612159729, 'llm', 1), ('hegelai/prompttools', 0.5647485256195068, 'llm', 2), ('eleutherai/the-pile', 0.5631842613220215, 'data', 0), ('nebuly-ai/nebullvm', 0.5627244114875793, 'perf', 1), ('vllm-project/vllm', 0.5622703433036804, 'llm', 2), ('hannibal046/awesome-llm', 0.5593286752700806, 'study', 1), ('microsoft/torchscale', 0.5538792610168457, 'llm', 2), ('salesforce/jaxformer', 0.5536481142044067, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5518462657928467, 'nlp', 0), ('microsoft/unilm', 0.5508254170417786, 'nlp', 0), ('openbmb/toolbench', 0.5505062341690063, 'llm', 0), ('jina-ai/thinkgpt', 0.5500134825706482, 'llm', 1), ('aiwaves-cn/agents', 0.5498647689819336, 'nlp', 1), ('microsoft/llama-2-onnx', 0.5490400791168213, 'llm', 2), ('bentoml/openllm', 0.5489903688430786, 'ml-ops', 1), ('facebookresearch/llama-recipes', 0.5477281212806702, 'llm', 2), ('squeezeailab/squeezellm', 0.5474568605422974, 'llm', 4), ('lightning-ai/lit-llama', 0.5471640825271606, 'llm', 2), ('langchain-ai/langgraph', 0.5411015748977661, 'llm', 0), ('run-llama/llama-lab', 0.5406877398490906, 'llm', 2), ('optimalscale/lmflow', 0.5390665531158447, 'llm', 3), ('ibm/dromedary', 0.5381739139556885, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.536756694316864, 'llm', 2), ('deep-diver/llm-as-chatbot', 0.5367242097854614, 'llm', 1), ('databrickslabs/dolly', 0.5357629656791687, 'llm', 1), ('google/flax', 0.5341013073921204, 'ml-dl', 1), ('openlmlab/moss', 0.5339535474777222, 'llm', 4), ('ml-tooling/opyrator', 0.5335082411766052, 'viz', 0), ('microsoft/jarvis', 0.5291217565536499, 'llm', 1), ('shishirpatil/gorilla', 0.5281785130500793, 'llm', 0), ('iryna-kondr/scikit-llm', 0.5262447595596313, 'llm', 1), ('microsoft/lmops', 0.5255038738250732, 'llm', 1), ('epfllm/meditron', 0.5253199934959412, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5250315070152283, 'llm', 2), ('deepmind/dm-haiku', 0.5241255164146423, 'ml-dl', 2), ('microsoft/semantic-kernel', 0.5219286680221558, 'llm', 0), ('predibase/lorax', 0.5218080878257751, 'llm', 1), ('reasoning-machines/pal', 0.5207021832466125, 'llm', 2), ('guidance-ai/guidance', 0.5203404426574707, 'llm', 1), ('extreme-bert/extreme-bert', 0.5198579430580139, 'llm', 4), ('microsoft/lora', 0.519779622554779, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5186941027641296, 'study', 1), ('cheshire-cat-ai/core', 0.5178155899047852, 'llm', 1), ('alphasecio/langchain-examples', 0.5165916681289673, 'llm', 0), ('embedchain/embedchain', 0.5164496302604675, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5126963257789612, 'llm', 2), ('berriai/litellm', 0.5113945603370667, 'llm', 0), ('cstankonrad/long_llama', 0.5092800259590149, 'llm', 2), ('keirp/automatic_prompt_engineer', 0.5091218948364258, 'llm', 1), ('rasahq/rasa', 0.5090000033378601, 'llm', 2), ('deepset-ai/farm', 0.5065217614173889, 'nlp', 1), ('predibase/llm_distillation_playbook', 0.5040218234062195, 'llm', 0), ('google/trax', 0.5037261843681335, 'ml-dl', 3), ('run-llama/rags', 0.5016451478004456, 'llm', 1), ('zilliztech/gptcache', 0.5008785724639893, 'llm', 2), ('tsinghuadatabasegroup/db-gpt', 0.5007253289222717, 'llm', 1), ('lupantech/chameleon-llm', 0.5002449154853821, 'llm', 1)]",11,6.0,,2.85,10,2,14,5,0,0,0,10.0,14.0,90.0,1.4,48
799,web,https://github.com/python-restx/flask-restx,[],,[],[],,,,python-restx/flask-restx,flask-restx,2020,326,66,Python,https://flask-restx.readthedocs.io/en/latest/,"Fork of Flask-RESTPlus: Fully featured framework for fast, easy and documented API development with Flask",python-restx,2024-01-13,2020-01-09,211,9.541160593792172,https://avatars.githubusercontent.com/u/59693083?v=4,"Fork of Flask-RESTPlus: Fully featured framework for fast, easy and documented API development with Flask","['api', 'flask', 'json', 'rest', 'restful', 'restplus', 'restx', 'swagger']","['api', 'flask', 'json', 'rest', 'restful', 'restplus', 'restx', 'swagger']",2023-12-10,"[('pyeve/eve', 0.7703225612640381, 'web', 2), ('vitalik/django-ninja', 0.7110880613327026, 'web', 1), ('tiangolo/fastapi', 0.6711971163749695, 'web', 4), ('starlite-api/starlite', 0.6593479514122009, 'web', 3), ('pallets/flask', 0.6463286280632019, 'web', 1), ('falconry/falcon', 0.6462195515632629, 'web', 2), ('bottlepy/bottle', 0.628285825252533, 'web', 1), ('alirn76/panther', 0.6230086088180542, 'web', 0), ('hugapi/hug', 0.6121255159378052, 'util', 0), ('flet-dev/flet', 0.6032120585441589, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5995645523071289, 'data', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5876633524894714, 'template', 2), ('asacristani/fastapi-rocket-boilerplate', 0.5854663848876953, 'template', 0), ('willmcgugan/textual', 0.5673668384552002, 'term', 0), ('ets-labs/python-dependency-injector', 0.551591694355011, 'util', 1), ('klen/muffin', 0.5422064065933228, 'web', 0), ('taverntesting/tavern', 0.5409488081932068, 'testing', 0), ('s3rius/fastapi-template', 0.5399068593978882, 'web', 0), ('pallets/quart', 0.5398460626602173, 'web', 0), ('pallets/werkzeug', 0.5365567803382874, 'web', 0), ('timofurrer/awesome-asyncio', 0.5342033505439758, 'study', 0), ('fastai/fastcore', 0.5335445404052734, 'util', 0), ('plotly/dash', 0.5263261795043945, 'viz', 1), ('huge-success/sanic', 0.5204745531082153, 'web', 0), ('neoteroi/blacksheep', 0.519770085811615, 'web', 0), ('masoniteframework/masonite', 0.5184113383293152, 'web', 0), ('fastai/ghapi', 0.5162189602851868, 'util', 0), ('awtkns/fastapi-crudrouter', 0.5144665837287903, 'web', 2), ('rawheel/fastapi-boilerplate', 0.5100096464157104, 'web', 0), ('klen/py-frameworks-bench', 0.5092925429344177, 'perf', 0), ('pylons/pyramid', 0.5075467824935913, 'web', 0), ('snyk-labs/pysnyk', 0.5058279633522034, 'security', 1), ('nficano/python-lambda', 0.505323588848114, 'util', 0), ('backtick-se/cowait', 0.5034772157669067, 'util', 0)]",147,7.0,,0.56,34,18,49,1,4,4,4,34.0,47.0,90.0,1.4,48
1758,ml,https://github.com/rom1504/clip-retrieval,[],,[],[],,,,rom1504/clip-retrieval,clip-retrieval,1917,181,22,Jupyter Notebook,https://rom1504.github.io/clip-retrieval/,Easily compute clip embeddings and build a clip retrieval system with them,rom1504,2024-01-14,2021-06-07,138,13.87693898655636,,Easily compute clip embeddings and build a clip retrieval system with them,"['ai', 'clip', 'deep-learning', 'knn', 'multimodal', 'semantic-search']","['ai', 'clip', 'deep-learning', 'knn', 'multimodal', 'semantic-search']",2024-01-13,"[('jina-ai/clip-as-service', 0.6420944929122925, 'nlp', 1), ('openai/clip', 0.5981614589691162, 'ml-dl', 2), ('albumentations-team/albumentations', 0.5307724475860596, 'ml-dl', 1), ('nomic-ai/nomic', 0.5256919264793396, 'nlp', 0), ('chroma-core/chroma', 0.5229653716087341, 'data', 0), ('qdrant/fastembed', 0.5138350129127502, 'ml', 0)]",26,3.0,,1.08,123,69,32,0,9,33,9,123.0,80.0,90.0,0.7,48
1050,util,https://github.com/home-assistant/supervisor,[],,[],[],,,,home-assistant/supervisor,supervisor,1559,553,85,Python,https://home-assistant.io/hassio/,:house_with_garden: Home Assistant Supervisor,home-assistant,2024-01-12,2017-03-14,359,4.342618384401114,https://avatars.githubusercontent.com/u/13844975?v=4,🏡 Home Assistant Supervisor,"['docker', 'home-assistant', 'home-automation', 'orchestrator']","['docker', 'home-assistant', 'home-automation', 'orchestrator']",2024-01-13,"[('prefecthq/server', 0.5141927599906921, 'util', 0)]",76,3.0,,8.54,239,205,83,0,37,61,37,239.0,452.0,90.0,1.9,48
714,math,https://github.com/facebookresearch/theseus,[],,[],[],,,,facebookresearch/theseus,theseus,1523,116,29,Python,,A library for differentiable nonlinear optimization,facebookresearch,2024-01-12,2021-11-18,114,13.276463262764633,https://avatars.githubusercontent.com/u/16943930?v=4,A library for differentiable nonlinear optimization,"['bilevel-optimization', 'computer-vision', 'deep-learning', 'differentiable-optimization', 'embodied-ai', 'gauss-newton', 'implicit-differentiation', 'levenberg-marquardt', 'nonlinear-least-squares', 'pytorch', 'robotics']","['bilevel-optimization', 'computer-vision', 'deep-learning', 'differentiable-optimization', 'embodied-ai', 'gauss-newton', 'implicit-differentiation', 'levenberg-marquardt', 'nonlinear-least-squares', 'pytorch', 'robotics']",2023-12-22,"[('tensorlayer/tensorlayer', 0.553860068321228, 'ml-rl', 1), ('pytorch/rl', 0.5316617488861084, 'ml-rl', 2), ('explosion/thinc', 0.5256298184394836, 'ml-dl', 2), ('thu-ml/tianshou', 0.5228813886642456, 'ml-rl', 1), ('pytorch/ignite', 0.5099302530288696, 'ml-dl', 2), ('tensorflow/tensor2tensor', 0.5072576403617859, 'ml', 1)]",25,4.0,,2.0,24,17,26,1,3,5,3,24.0,67.0,90.0,2.8,48
632,perf,https://github.com/dask/distributed,[],,[],[],,,,dask/distributed,distributed,1513,706,56,Python,https://distributed.dask.org,A distributed task scheduler for Dask,dask,2024-01-13,2015-09-13,437,3.4599803985625615,https://avatars.githubusercontent.com/u/17131925?v=4,A distributed task scheduler for Dask,"['dask', 'distributed-computing', 'pydata']","['dask', 'distributed-computing', 'pydata']",2024-01-12,"[('dask/dask', 0.719694972038269, 'perf', 2), ('prefecthq/prefect-dask', 0.6549785137176514, 'util', 1), ('agronholm/apscheduler', 0.5948215126991272, 'util', 0), ('fugue-project/fugue', 0.5652367472648621, 'pandas', 2), ('dask/dask-ml', 0.5617966055870056, 'ml', 0), ('backtick-se/cowait', 0.56135493516922, 'util', 1), ('autoviml/auto_ts', 0.5237195491790771, 'time-series', 0), ('bogdanp/dramatiq', 0.5118077993392944, 'util', 0)]",322,2.0,,10.19,243,164,101,0,0,26,26,242.0,567.0,90.0,2.3,48
1652,llm,https://github.com/farizrahman4u/loopgpt,[],"Re-implementation of Auto-GPT as a python package, written with modularity and extensibility in mind.",[],[],,,,farizrahman4u/loopgpt,loopgpt,1339,128,34,Python,,Modular Auto-GPT Framework,farizrahman4u,2024-01-12,2023-04-14,41,32.20962199312715,,Modular Auto-GPT Framework,"['chatgpt', 'gpt', 'gpt4', 'llms']","['chatgpt', 'gpt', 'gpt4', 'llms']",2023-10-20,"[('mmabrouk/chatgpt-wrapper', 0.6415730118751526, 'llm', 2), ('xtekky/gpt4free', 0.617682158946991, 'llm', 3), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5821303129196167, 'llm', 1), ('microsoft/autogen', 0.5695356726646423, 'llm', 2), ('killianlucas/open-interpreter', 0.5448848605155945, 'llm', 1), ('eth-sri/lmql', 0.5417187809944153, 'llm', 1), ('run-llama/rags', 0.5412454605102539, 'llm', 1), ('karpathy/nanogpt', 0.5409718155860901, 'llm', 0), ('openai/openai-cookbook', 0.533822238445282, 'ml', 1), ('eleutherai/gpt-neo', 0.5264042019844055, 'llm', 1), ('mnotgod96/appagent', 0.5192574858665466, 'llm', 2), ('shishirpatil/gorilla', 0.5184142589569092, 'llm', 1), ('promptslab/promptify', 0.5138046741485596, 'nlp', 1), ('vision-cair/minigpt-4', 0.5081561207771301, 'llm', 0), ('torantulino/auto-gpt', 0.5069693922996521, 'llm', 0), ('opengenerativeai/genossgpt', 0.5030877590179443, 'llm', 1), ('continuum-llms/chatgpt-memory', 0.5028600096702576, 'llm', 1)]",12,4.0,,5.17,1,0,9,3,7,9,7,1.0,1.0,90.0,1.0,48
1241,ml,https://github.com/visual-layer/fastdup,[],,[],[],,,,visual-layer/fastdup,fastdup,1302,67,20,Python,,fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.,visual-layer,2024-01-12,2022-05-11,89,14.48966613672496,https://avatars.githubusercontent.com/u/116299338?v=4,fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.,"['data-augmentation', 'data-curation', 'dataset', 'deep-learning', 'image', 'image-analysis', 'image-classfication', 'image-classification', 'image-duplicate-detection', 'image-processing', 'image-similarity', 'machine-learning', 'novelty-detection', 'object-detection', 'outlier-detection', 'visual-search', 'visualization', 'visualization-tools']","['data-augmentation', 'data-curation', 'dataset', 'deep-learning', 'image', 'image-analysis', 'image-classfication', 'image-classification', 'image-duplicate-detection', 'image-processing', 'image-similarity', 'machine-learning', 'novelty-detection', 'object-detection', 'outlier-detection', 'visual-search', 'visualization', 'visualization-tools']",2024-01-09,"[('albumentations-team/albumentations', 0.5445998311042786, 'ml-dl', 5), ('towhee-io/towhee', 0.5181317925453186, 'ml-ops', 2), ('huggingface/datasets', 0.5084423422813416, 'nlp', 2)]",20,1.0,,12.88,28,18,20,0,61,83,61,28.0,31.0,90.0,1.1,48
971,ml,https://github.com/google/vizier,[],,[],[],,,,google/vizier,vizier,1138,71,20,Python,https://oss-vizier.readthedocs.io,"Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.",google,2024-01-14,2022-02-16,101,11.17251051893408,https://avatars.githubusercontent.com/u/1342004?v=4,"Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.","['algorithm', 'bayesian-optimization', 'blackbox-optimization', 'deep-learning', 'distributed-computing', 'distributed-systems', 'evolutionary-algorithms', 'google', 'grpc', 'hyperparameter-optimization', 'hyperparameter-tuning', 'machine-learning', 'open-source', 'optimization', 'tuning', 'tuning-parameters', 'vizier']","['algorithm', 'bayesian-optimization', 'blackbox-optimization', 'deep-learning', 'distributed-computing', 'distributed-systems', 'evolutionary-algorithms', 'google', 'grpc', 'hyperparameter-optimization', 'hyperparameter-tuning', 'machine-learning', 'open-source', 'optimization', 'tuning', 'tuning-parameters', 'vizier']",2024-01-12,"[('scikit-optimize/scikit-optimize', 0.6410875916481018, 'ml', 5), ('determined-ai/determined', 0.6237358450889587, 'ml-ops', 4), ('epistasislab/tpot', 0.6137571930885315, 'ml', 2), ('microsoft/nni', 0.5949028134346008, 'ml', 5), ('ray-project/ray', 0.5806607604026794, 'ml-ops', 4), ('hyperopt/hyperopt', 0.5750223994255066, 'ml', 0), ('gradio-app/gradio', 0.5744260549545288, 'viz', 2), ('microsoft/deepspeed', 0.5668622851371765, 'ml-dl', 2), ('wandb/client', 0.5621833801269531, 'ml', 4), ('automl/auto-sklearn', 0.5519843101501465, 'ml', 3), ('kubeflow/katib', 0.5503707528114319, 'ml', 0), ('microsoft/flaml', 0.5446196794509888, 'ml', 4), ('ray-project/tune-sklearn', 0.5394313931465149, 'ml', 2), ('polyaxon/polyaxon', 0.5294651389122009, 'ml-ops', 3), ('kubeflow/fairing', 0.5291571021080017, 'ml-ops', 0), ('ml-tooling/opyrator', 0.528394877910614, 'viz', 1), ('plasma-umass/scalene', 0.5277619957923889, 'profiling', 0), ('optuna/optuna', 0.5277183055877686, 'ml', 2), ('tensorflow/tensor2tensor', 0.5147285461425781, 'ml', 2), ('radiantearth/radiant-mlhub', 0.5126688480377197, 'gis', 1), ('oegedijk/explainerdashboard', 0.5101515650749207, 'ml-interpretability', 0), ('mljar/mljar-supervised', 0.5090547204017639, 'ml', 2), ('dialogflow/dialogflow-python-client-v2', 0.5073549151420593, 'nlp', 1), ('google/gin-config', 0.5030581951141357, 'util', 0), ('rasbt/machine-learning-book', 0.5023799538612366, 'study', 2), ('ageron/handson-ml2', 0.5004328489303589, 'ml', 0)]",21,4.0,,9.52,99,94,23,0,15,17,15,99.0,25.0,90.0,0.3,48
1573,data,https://github.com/pathwaycom/pathway,['llmops'],,[],[],,,,pathwaycom/pathway,pathway,1079,42,18,Python,https://pathway.com,"Pathway is a high-throughput, low-latency data processing framework that handles live data & streaming for you. Made with ❤️ for Python & ML/AI developers.",pathwaycom,2024-01-13,2022-11-27,61,17.606060606060606,https://avatars.githubusercontent.com/u/25750857?v=4,"Pathway is a high-throughput, low-latency data processing framework that handles live data & streaming for you. Made with ❤️ for Python & ML/AI developers.","['batch-processing', 'kafka', 'machine-learning-algorithms', 'pathway', 'real-time', 'streaming']","['batch-processing', 'kafka', 'llmops', 'machine-learning-algorithms', 'pathway', 'real-time', 'streaming']",2024-01-12,"[('airtai/faststream', 0.6327610611915588, 'perf', 1), ('google/mediapipe', 0.5712288022041321, 'ml', 0), ('mage-ai/mage-ai', 0.5671728849411011, 'ml-ops', 0), ('activeloopai/deeplake', 0.5419109463691711, 'ml-ops', 0), ('online-ml/river', 0.5360531210899353, 'ml', 1), ('dagworks-inc/hamilton', 0.5315225720405579, 'ml-ops', 1), ('polyaxon/datatile', 0.5242671370506287, 'pandas', 0), ('kestra-io/kestra', 0.5241698026657104, 'ml-ops', 0), ('streamlit/streamlit', 0.519947350025177, 'viz', 0), ('ml-tooling/opyrator', 0.5166861414909363, 'viz', 0), ('superduperdb/superduperdb', 0.5140187740325928, 'data', 1), ('mlflow/mlflow', 0.5128524899482727, 'ml-ops', 0), ('ploomber/ploomber', 0.5119403004646301, 'ml-ops', 0), ('pathwaycom/llm-app', 0.5075528621673584, 'llm', 3), ('polyaxon/polyaxon', 0.5067856311798096, 'ml-ops', 0), ('wandb/client', 0.5043047666549683, 'ml', 0), ('flyteorg/flyte', 0.503392219543457, 'ml-ops', 0), ('airbytehq/airbyte', 0.5019164681434631, 'data', 0)]",13,5.0,,1.0,1,1,14,0,22,19,22,1.0,2.0,90.0,2.0,48
1895,time-series,https://github.com/google/temporian,"['feature-engineering', 'temporal-data']",,[],[],1.0,,,google/temporian,temporian,445,28,11,Python,https://temporian.readthedocs.io,Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖,google,2024-01-12,2023-01-17,54,8.24074074074074,https://avatars.githubusercontent.com/u/1342004?v=4,Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖,"['cpp', 'feature-engineering', 'temporal-data', 'time-series']","['cpp', 'feature-engineering', 'temporal-data', 'time-series']",2024-01-12,"[('featurelabs/featuretools', 0.7070109844207764, 'ml', 1), ('pycaret/pycaret', 0.6051017045974731, 'ml', 1), ('firmai/atspy', 0.6023882031440735, 'time-series', 1), ('rasbt/mlxtend', 0.58907151222229, 'ml', 0), ('alkaline-ml/pmdarima', 0.5879765152931213, 'time-series', 1), ('gradio-app/gradio', 0.5871459245681763, 'viz', 0), ('blue-yonder/tsfresh', 0.5776029229164124, 'time-series', 1), ('sktime/sktime', 0.5772302150726318, 'time-series', 1), ('dateutil/dateutil', 0.5748814344406128, 'util', 0), ('rjt1990/pyflux', 0.5719876289367676, 'time-series', 1), ('awslabs/gluonts', 0.5661502480506897, 'time-series', 1), ('ta-lib/ta-lib-python', 0.5626396536827087, 'finance', 0), ('unit8co/darts', 0.5616124272346497, 'time-series', 1), ('tdameritrade/stumpy', 0.5609158277511597, 'time-series', 0), ('scikit-learn/scikit-learn', 0.5564255714416504, 'ml', 0), ('kubeflow/fairing', 0.5490253567695618, 'ml-ops', 0), ('rasbt/machine-learning-book', 0.5215150117874146, 'study', 0), ('pandas-dev/pandas', 0.5212157368659973, 'pandas', 0), ('pytoolz/toolz', 0.5204028487205505, 'util', 0), ('huggingface/huggingface_hub', 0.5130770802497864, 'ml', 0), ('winedarksea/autots', 0.5130333304405212, 'time-series', 2), ('jovianml/opendatasets', 0.512967050075531, 'data', 0), ('stan-dev/pystan', 0.5121117234230042, 'ml', 0), ('microsoft/flaml', 0.511090874671936, 'ml', 0), ('pastas/pastas', 0.5104494094848633, 'time-series', 0), ('microsoft/nni', 0.5072931051254272, 'ml', 1), ('online-ml/river', 0.5042147040367126, 'ml', 0), ('crflynn/stochastic', 0.5019282698631287, 'sim', 0)]",12,3.0,,36.23,60,56,12,0,7,9,7,60.0,122.0,90.0,2.0,48
491,ml-dl,https://github.com/rasbt/deeplearning-models,[],,[],[],,,,rasbt/deeplearning-models,deeplearning-models,16133,3949,599,Jupyter Notebook,,"A collection of various deep learning architectures, models, and tips",rasbt,2024-01-14,2019-06-05,242,66.43,,"A collection of various deep learning architectures, models, and tips",[],[],2023-02-16,"[('pytorch/ignite', 0.5799865126609802, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5731253623962402, 'ml', 0), ('tensorflow/tensorflow', 0.5665931105613708, 'ml-dl', 0), ('christoschristofidis/awesome-deep-learning', 0.5625115036964417, 'study', 0), ('mrdbourke/pytorch-deep-learning', 0.5607303977012634, 'study', 0), ('nvidia/deeplearningexamples', 0.554612934589386, 'ml-dl', 0), ('xl0/lovely-tensors', 0.5497564673423767, 'ml-dl', 0), ('mosaicml/composer', 0.5479599833488464, 'ml-dl', 0), ('udlbook/udlbook', 0.5459153056144714, 'study', 0), ('aiqc/aiqc', 0.5450917482376099, 'ml-ops', 0), ('calculatedcontent/weightwatcher', 0.5426604747772217, 'ml-dl', 0), ('microsoft/jarvis', 0.5421392321586609, 'llm', 0), ('facebookresearch/ppuda', 0.5405725240707397, 'ml-dl', 0), ('nyandwi/modernconvnets', 0.5375327467918396, 'ml-dl', 0), ('datasystemslab/geotorch', 0.5235190391540527, 'gis', 0), ('karpathy/nn-zero-to-hero', 0.5198577642440796, 'study', 0), ('intellabs/bayesian-torch', 0.516359806060791, 'ml', 0), ('huggingface/accelerate', 0.5161780714988708, 'ml', 0), ('determined-ai/determined', 0.5105359554290771, 'ml-ops', 0), ('keras-rl/keras-rl', 0.5067861080169678, 'ml-rl', 0), ('tatsu-lab/stanford_alpaca', 0.5054455399513245, 'llm', 0), ('keras-team/keras', 0.5051916837692261, 'ml-dl', 0), ('karpathy/micrograd', 0.5046243667602539, 'study', 0), ('denys88/rl_games', 0.5041263103485107, 'ml-rl', 0), ('ggerganov/ggml', 0.5030370354652405, 'ml', 0), ('unity-technologies/ml-agents', 0.501521110534668, 'ml-rl', 0)]",13,6.0,,0.08,0,0,56,11,0,0,0,0.0,0.0,90.0,0.0,47
24,ml-rl,https://github.com/google/dopamine,[],,[],[],,,,google/dopamine,dopamine,10288,1410,435,Jupyter Notebook,https://github.com/google/dopamine,Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. ,google,2024-01-11,2018-07-26,287,35.75769612711023,https://avatars.githubusercontent.com/u/1342004?v=4,Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. ,"['ai', 'google', 'ml', 'rl', 'tensorflow']","['ai', 'google', 'ml', 'rl', 'tensorflow']",2023-11-27,"[('pytorch/rl', 0.6595585942268372, 'ml-rl', 2), ('unity-technologies/ml-agents', 0.6415248513221741, 'ml-rl', 0), ('thu-ml/tianshou', 0.6199951171875, 'ml-rl', 1), ('tensorlayer/tensorlayer', 0.5985347628593445, 'ml-rl', 2), ('keras-rl/keras-rl', 0.5969773530960083, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5846539735794067, 'ml-rl', 0), ('humancompatibleai/imitation', 0.576420783996582, 'ml-rl', 0), ('facebookresearch/habitat-lab', 0.5727390646934509, 'sim', 1), ('denys88/rl_games', 0.5697341561317444, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5669017434120178, 'ml-rl', 0), ('openai/baselines', 0.5650805830955505, 'ml-rl', 0), ('ai4finance-foundation/finrl', 0.5640981793403625, 'finance', 0), ('deepmind/dm_control', 0.562454104423523, 'ml-rl', 0), ('openai/gym', 0.5587803721427917, 'ml-rl', 0), ('openai/spinningup', 0.5561296939849854, 'study', 0), ('facebookresearch/reagent', 0.5503111481666565, 'ml-rl', 0), ('prefecthq/marvin', 0.547942042350769, 'nlp', 1), ('googlecloudplatform/vertex-ai-samples', 0.5409380793571472, 'ml', 2), ('google-research/google-research', 0.5344747304916382, 'ml', 1), ('tensorflow/tensor2tensor', 0.5282601118087769, 'ml', 0), ('operand/agency', 0.5216368436813354, 'llm', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5188839435577393, 'study', 0), ('pytorchlightning/pytorch-lightning', 0.5187596082687378, 'ml-dl', 1), ('oegedijk/explainerdashboard', 0.5178472399711609, 'ml-interpretability', 0), ('microsoft/lmops', 0.5175570249557495, 'llm', 0), ('deepmind/acme', 0.5139473080635071, 'ml-rl', 0), ('google/trax', 0.5098437666893005, 'ml-dl', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.5081252455711365, 'sim', 0), ('mlflow/mlflow', 0.5052387118339539, 'ml-ops', 2), ('explosion/thinc', 0.5020156502723694, 'ml-dl', 2), ('salesforce/warp-drive', 0.5014780759811401, 'ml-rl', 0), ('nvidia-omniverse/isaacgymenvs', 0.5014722943305969, 'sim', 0), ('inspirai/timechamber', 0.5013951063156128, 'sim', 0)]",15,4.0,,0.19,5,2,67,2,0,0,0,5.0,3.0,90.0,0.6,47
1107,ml,https://github.com/twitter/the-algorithm-ml,[],,[],[],,,,twitter/the-algorithm-ml,the-algorithm-ml,9797,2242,100,Python,https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm,Source code for Twitter's Recommendation Algorithm,twitter,2024-01-13,2023-03-27,44,221.93851132686083,https://avatars.githubusercontent.com/u/50278?v=4,Source code for Twitter's Recommendation Algorithm,[],[],2023-04-06,[],0,-1.0,23.0,1.0,2,2,10,9,0,0,0,2.0,0.0,90.0,0.0,47
996,finance,https://github.com/ta-lib/ta-lib-python,[],,[],[],,,,ta-lib/ta-lib-python,ta-lib-python,8658,1703,325,Cython,http://ta-lib.github.io/ta-lib-python,Python wrapper for TA-Lib (http://ta-lib.org/).,ta-lib,2024-01-14,2012-03-23,618,13.99676674364896,https://avatars.githubusercontent.com/u/21127168?v=4,Python wrapper for TA-Lib (http://ta-lib.org/).,"['finance', 'pattern-recognition', 'quantitative-finance', 'ta-lib', 'technical-analysis']","['finance', 'pattern-recognition', 'quantitative-finance', 'ta-lib', 'technical-analysis']",2023-12-30,"[('goldmansachs/gs-quant', 0.6905857920646667, 'finance', 0), ('pytoolz/toolz', 0.653429388999939, 'util', 0), ('cuemacro/finmarketpy', 0.6374157071113586, 'finance', 0), ('twopirllc/pandas-ta', 0.6217855215072632, 'finance', 2), ('alkaline-ml/pmdarima', 0.6164563894271851, 'time-series', 0), ('rasbt/mlxtend', 0.5990067720413208, 'ml', 0), ('gbeced/pyalgotrade', 0.5967674851417542, 'finance', 0), ('dylanhogg/awesome-python', 0.59423828125, 'study', 0), ('pmorissette/ffn', 0.5941312909126282, 'finance', 0), ('wesm/pydata-book', 0.5871008038520813, 'study', 0), ('firmai/atspy', 0.5853879451751709, 'time-series', 1), ('domokane/financepy', 0.5799912810325623, 'finance', 1), ('probml/pyprobml', 0.5762322545051575, 'ml', 0), ('pandas-dev/pandas', 0.5740821957588196, 'pandas', 0), ('pycaret/pycaret', 0.5695496201515198, 'ml', 0), ('ranaroussi/quantstats', 0.566260814666748, 'finance', 2), ('gradio-app/gradio', 0.5636385679244995, 'viz', 0), ('google/temporian', 0.5626396536827087, 'time-series', 0), ('mito-ds/monorepo', 0.5616872906684875, 'jupyter', 0), ('mementum/backtrader', 0.560478925704956, 'finance', 0), ('pypy/pypy', 0.5588146448135376, 'util', 0), ('timofurrer/awesome-asyncio', 0.5574406981468201, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5513738393783569, 'study', 0), ('krzjoa/awesome-python-data-science', 0.5507447123527527, 'study', 0), ('quantconnect/lean', 0.5490601062774658, 'finance', 1), ('google/tf-quant-finance', 0.5487057566642761, 'finance', 2), ('hydrosquall/tiingo-python', 0.5465307831764221, 'finance', 1), ('featurelabs/featuretools', 0.5429303646087646, 'ml', 0), ('eleutherai/pyfra', 0.5423515439033508, 'ml', 0), ('quantecon/quantecon.py', 0.54204261302948, 'sim', 0), ('quantopian/zipline', 0.5401220321655273, 'finance', 0), ('jovianml/opendatasets', 0.5373624563217163, 'data', 0), ('1200wd/bitcoinlib', 0.5371884703636169, 'crypto', 0), ('man-c/pycoingecko', 0.5362427234649658, 'crypto', 0), ('landscapeio/prospector', 0.5358104109764099, 'util', 0), ('mementum/bta-lib', 0.5354213714599609, 'finance', 0), ('brandon-rhodes/python-patterns', 0.5266119837760925, 'util', 0), ('huggingface/huggingface_hub', 0.5248498916625977, 'ml', 0), ('scikit-learn/scikit-learn', 0.5245856642723083, 'ml', 0), ('mynameisfiber/high_performance_python_2e', 0.5237243175506592, 'study', 0), ('rjt1990/pyflux', 0.5227634906768799, 'time-series', 0), ('plotly/dash', 0.522579550743103, 'viz', 1), ('python/cpython', 0.5212801098823547, 'util', 0), ('ageron/handson-ml2', 0.5182675123214722, 'ml', 0), ('malloydata/malloy-py', 0.5163371562957764, 'data', 0), ('scikit-mobility/scikit-mobility', 0.5160692930221558, 'gis', 0), ('ai4finance-foundation/fingpt', 0.5139918327331543, 'finance', 2), ('erotemic/ubelt', 0.513969361782074, 'util', 0), ('tensorly/tensorly', 0.5138649344444275, 'ml-dl', 0), ('wilsonrljr/sysidentpy', 0.5129168033599854, 'time-series', 0), ('kernc/backtesting.py', 0.512697160243988, 'finance', 1), ('masoniteframework/masonite', 0.512328028678894, 'web', 0), ('sqlalchemy/mako', 0.511563241481781, 'template', 0), ('legrandin/pycryptodome', 0.5114703178405762, 'util', 0), ('tdameritrade/stumpy', 0.509051501750946, 'time-series', 0), ('pyscf/pyscf', 0.5085233449935913, 'sim', 0), ('clips/pattern', 0.5062140226364136, 'nlp', 0), ('fastai/fastcore', 0.5056002140045166, 'util', 0), ('lballabio/quantlib-swig', 0.505255401134491, 'finance', 1), ('openai/openai-python', 0.5044464468955994, 'util', 0), ('pyca/cryptography', 0.5043237209320068, 'util', 0), ('dit/dit', 0.5039918422698975, 'math', 0), ('imageio/imageio', 0.5035238862037659, 'util', 0), ('google/pytype', 0.5031558275222778, 'typing', 0), ('fredrik-johansson/mpmath', 0.5021244287490845, 'math', 0), ('astral-sh/ruff', 0.5013483166694641, 'util', 0), ('aswinnnn/pyscan', 0.501020610332489, 'security', 0), ('polyaxon/datatile', 0.500678539276123, 'pandas', 0)]",29,1.0,,0.58,25,10,144,0,0,2,2,25.0,47.0,90.0,1.9,47
838,time-series,https://github.com/blue-yonder/tsfresh,[],,[],[],,,,blue-yonder/tsfresh,tsfresh,7953,1199,167,Jupyter Notebook,http://tsfresh.readthedocs.io,Automatic extraction of relevant features from time series:,blue-yonder,2024-01-13,2016-10-26,378,20.992081447963802,https://avatars.githubusercontent.com/u/6234170?v=4,Automatic extraction of relevant features from time series:,"['data-science', 'feature-extraction', 'time-series']","['data-science', 'feature-extraction', 'time-series']",2023-10-24,"[('salesforce/merlion', 0.6107531189918518, 'time-series', 1), ('sktime/sktime', 0.5950053930282593, 'time-series', 2), ('tdameritrade/stumpy', 0.5777381062507629, 'time-series', 1), ('google/temporian', 0.5776029229164124, 'time-series', 1), ('alkaline-ml/pmdarima', 0.5346757173538208, 'time-series', 1), ('unit8co/darts', 0.5309851765632629, 'time-series', 2), ('winedarksea/autots', 0.5182939171791077, 'time-series', 1)]",91,3.0,,0.48,6,2,88,3,1,4,1,6.0,6.0,90.0,1.0,47
1128,ml,https://github.com/scikit-learn-contrib/imbalanced-learn,[],,[],[],,,,scikit-learn-contrib/imbalanced-learn,imbalanced-learn,6603,1274,140,Python,https://imbalanced-learn.org, A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning,scikit-learn-contrib,2024-01-12,2014-08-16,493,13.381876085697742,https://avatars.githubusercontent.com/u/17349883?v=4, A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning,"['data-analysis', 'data-science', 'machine-learning', 'statistics']","['data-analysis', 'data-science', 'machine-learning', 'statistics']",2023-10-23,"[('pycaret/pycaret', 0.6684343814849854, 'ml', 2), ('scikit-learn/scikit-learn', 0.6626807451248169, 'ml', 4), ('rasbt/mlxtend', 0.651149570941925, 'ml', 2), ('yzhao062/pyod', 0.589371383190155, 'data', 3), ('facebookresearch/balance', 0.5837095379829407, 'ml', 0), ('jovianml/opendatasets', 0.5723727941513062, 'data', 2), ('gradio-app/gradio', 0.571931004524231, 'viz', 3), ('featurelabs/featuretools', 0.5710511207580566, 'ml', 2), ('scikit-learn-contrib/lightning', 0.5669341087341309, 'ml', 1), ('tensorflow/data-validation', 0.5666349530220032, 'ml-ops', 0), ('huggingface/evaluate', 0.557458758354187, 'ml', 1), ('epistasislab/tpot', 0.545037567615509, 'ml', 2), ('krzjoa/awesome-python-data-science', 0.535544753074646, 'study', 4), ('scikit-learn-contrib/metric-learn', 0.5348906517028809, 'ml', 1), ('unit8co/darts', 0.5324269533157349, 'time-series', 2), ('dask/dask-ml', 0.5264381170272827, 'ml', 0), ('rasbt/machine-learning-book', 0.5242375731468201, 'study', 1), ('firmai/industry-machine-learning', 0.5154035687446594, 'study', 2), ('merantix-momentum/squirrel-core', 0.5136831998825073, 'ml', 2), ('online-ml/river', 0.5116428732872009, 'ml', 2), ('dylanhogg/awesome-python', 0.5090502500534058, 'study', 2), ('huggingface/datasets', 0.507771372795105, 'nlp', 1), ('teamhg-memex/eli5', 0.5050359964370728, 'ml', 2)]",80,6.0,,0.83,14,10,115,3,1,3,1,14.0,13.0,90.0,0.9,47
421,util,https://github.com/tebelorg/rpa-python,[],,[],[],,,,tebelorg/rpa-python,RPA-Python,4316,648,103,Python,,Python package for doing RPA,tebelorg,2024-01-13,2019-03-30,252,17.097906055461234,https://avatars.githubusercontent.com/u/10379612?v=4,Python package for doing RPA,"['cross-platform', 'opencv', 'rpa', 'sikuli', 'tagui', 'tesseract']","['cross-platform', 'opencv', 'rpa', 'sikuli', 'tagui', 'tesseract']",2023-12-24,"[('openai/openai-python', 0.5686379671096802, 'util', 0), ('earthlab/earthpy', 0.5264238715171814, 'gis', 0), ('pypy/pypy', 0.5048558115959167, 'util', 0), ('imageio/imageio', 0.502187967300415, 'util', 0)]",4,2.0,,0.44,25,23,58,1,2,11,2,25.0,79.0,90.0,3.2,47
1800,ml,https://github.com/nv-tlabs/get3d,"['generative-model', '3d']",Generative Model of High Quality 3D Textured Shapes Learned from Images,[],[],,,,nv-tlabs/get3d,GET3D,4002,364,142,Python,,,nv-tlabs,2024-01-13,2022-09-08,72,55.03732809430255,https://avatars.githubusercontent.com/u/49653101?v=4,Generative Model of High Quality 3D Textured Shapes Learned from Images,[],"['3d', 'generative-model']",2023-10-23,"[('openai/image-gpt', 0.5187845826148987, 'llm', 0), ('sharonzhou/long_stable_diffusion', 0.5064239501953125, 'diffusion', 0)]",4,1.0,,0.08,12,6,16,3,0,0,0,12.0,20.0,90.0,1.7,47
610,testing,https://github.com/spulec/freezegun,[],,[],[],,,,spulec/freezegun,freezegun,3890,267,34,Python,,Let your Python tests travel through time,spulec,2024-01-12,2012-12-11,581,6.695352839931153,,Let your Python tests travel through time,[],[],2023-12-19,"[('pmorissette/bt', 0.5981432199478149, 'finance', 0), ('wolever/parameterized', 0.5846211314201355, 'testing', 0), ('sdispater/pendulum', 0.54648357629776, 'util', 0), ('ionelmc/pytest-benchmark', 0.5428466200828552, 'testing', 0), ('nedbat/coveragepy', 0.5298979878425598, 'testing', 0), ('arrow-py/arrow', 0.5130565166473389, 'util', 0)]",112,7.0,,0.42,56,21,135,0,3,5,3,56.0,50.0,90.0,0.9,47
441,gis,https://github.com/shapely/shapely,"['geometric-algorithms', 'geometry']",,[],[],1.0,,,shapely/shapely,shapely,3549,554,88,Python,https://shapely.readthedocs.io/en/stable/,Manipulation and analysis of geometric objects,shapely,2024-01-12,2011-12-31,630,5.629503738953093,https://avatars.githubusercontent.com/u/59894073?v=4,Manipulation and analysis of geometric objects,[],"['geometric-algorithms', 'geometry']",2024-01-04,"[('benbovy/spherely', 0.873152494430542, 'gis', 2), ('scikit-geometry/scikit-geometry', 0.5902884602546692, 'gis', 2), ('google-deepmind/alphageometry', 0.5421801805496216, 'math', 1)]",151,5.0,,1.4,67,20,147,0,2,8,2,67.0,123.0,90.0,1.8,47
657,util,https://github.com/zeromq/pyzmq,[],,[],[],,,,zeromq/pyzmq,pyzmq,3498,666,103,Python,http://zguide.zeromq.org/py:all,PyZMQ:  Python bindings for zeromq,zeromq,2024-01-13,2010-07-21,705,4.955676988463874,https://avatars.githubusercontent.com/u/109777?v=4,PyZMQ:  Python bindings for zeromq,"['cython', 'zeromq']","['cython', 'zeromq']",2024-01-04,"[('quantumlib/cirq', 0.5248025059700012, 'sim', 0), ('pyscf/pyscf', 0.5071917772293091, 'sim', 0)]",196,5.0,,2.38,23,17,164,0,0,6,6,23.0,37.0,90.0,1.6,47
249,web,https://github.com/websocket-client/websocket-client,[],,[],[],,,,websocket-client/websocket-client,websocket-client,3381,805,86,Python,https://github.com/websocket-client/websocket-client,WebSocket client for Python,websocket-client,2024-01-12,2010-12-28,683,4.950219619326501,https://avatars.githubusercontent.com/u/24536015?v=4,WebSocket client for Python,"['rfc-6455', 'websocket', 'websocket-client', 'websockets', 'websockets-client']","['rfc-6455', 'websocket', 'websocket-client', 'websockets', 'websockets-client']",2024-01-12,"[('miguelgrinberg/python-socketio', 0.7025880217552185, 'util', 1), ('encode/httpx', 0.6006039381027222, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5527551770210266, 'data', 0), ('bmoscon/cryptofeed', 0.5356486439704895, 'crypto', 2), ('aio-libs/aiohttp', 0.5178495049476624, 'web', 0), ('paramiko/paramiko', 0.5153641700744629, 'util', 0), ('masoniteframework/masonite', 0.5101653933525085, 'web', 0), ('encode/uvicorn', 0.5029712319374084, 'web', 0)]",221,5.0,,1.31,23,15,159,0,10,6,10,23.0,28.0,90.0,1.2,47
94,web,https://github.com/unbit/uwsgi,[],,[],[],,,,unbit/uwsgi,uwsgi,3374,681,111,C,http://projects.unbit.it/uwsgi,uWSGI application server container,unbit,2024-01-13,2011-10-09,642,5.253113879003559,,uWSGI application server container,[],[],2023-12-26,[],359,5.0,,0.48,54,24,149,1,0,10,10,54.0,88.0,90.0,1.6,47
618,util,https://github.com/more-itertools/more-itertools,[],,[],[],,,,more-itertools/more-itertools,more-itertools,3314,266,40,Python,https://more-itertools.rtfd.io,"More routines for operating on iterables, beyond itertools",more-itertools,2024-01-13,2012-04-26,613,5.399906890130354,https://avatars.githubusercontent.com/u/61018589?v=4,"More routines for operating on iterables, beyond itertools",[],[],2024-01-12,"[('fluentpython/example-code-2e', 0.5874441862106323, 'study', 0)]",114,5.0,,3.12,41,33,143,0,6,4,6,41.0,42.0,90.0,1.0,47
1499,ml-rl,https://github.com/deepmind/acme,[],,[],[],1.0,,,deepmind/acme,acme,3302,410,83,Python,,A library of reinforcement learning components and agents,deepmind,2024-01-12,2020-05-01,195,16.883856829802777,https://avatars.githubusercontent.com/u/8596759?v=4,A library of reinforcement learning components and agents,"['agents', 'reinforcement-learning', 'research']","['agents', 'reinforcement-learning', 'research']",2024-01-03,"[('pytorch/rl', 0.6701556444168091, 'ml-rl', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.6519054770469666, 'study', 1), ('openai/gym', 0.6127493381500244, 'ml-rl', 1), ('thu-ml/tianshou', 0.5823108553886414, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.578000545501709, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5646693110466003, 'ml-rl', 1), ('humancompatibleai/imitation', 0.5565671920776367, 'ml-rl', 0), ('tensorlayer/tensorlayer', 0.5421755909919739, 'ml-rl', 1), ('facebookresearch/reagent', 0.536990761756897, 'ml-rl', 0), ('denys88/rl_games', 0.5312319397926331, 'ml-rl', 1), ('facebookresearch/habitat-lab', 0.52789306640625, 'sim', 2), ('google/dopamine', 0.5139473080635071, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5082459449768066, 'ml-rl', 1), ('salesforce/warp-drive', 0.5006597638130188, 'ml-rl', 1)]",84,3.0,,0.6,9,2,45,0,0,3,3,9.0,6.0,90.0,0.7,47
718,util,https://github.com/ashleve/lightning-hydra-template,[],,[],[],,,,ashleve/lightning-hydra-template,lightning-hydra-template,3296,545,25,Python,,PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation.  ⚡🔥⚡,ashleve,2024-01-14,2020-11-04,168,19.519458544839257,,PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation.  ⚡🔥⚡,"['best-practices', 'config', 'deep-learning', 'hydra', 'mlops', 'project-structure', 'pytorch', 'pytorch-lightning', 'reproducibility', 'template']","['best-practices', 'config', 'deep-learning', 'hydra', 'mlops', 'project-structure', 'pytorch', 'pytorch-lightning', 'reproducibility', 'template']",2023-09-25,"[('pytorch/ignite', 0.6497684717178345, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.6405739188194275, 'perf', 2), ('determined-ai/determined', 0.6221935153007507, 'ml-ops', 3), ('rasbt/machine-learning-book', 0.619256317615509, 'study', 2), ('aws/sagemaker-python-sdk', 0.6082078814506531, 'ml', 1), ('mrdbourke/pytorch-deep-learning', 0.605148196220398, 'study', 2), ('skorch-dev/skorch', 0.6017202138900757, 'ml-dl', 1), ('huggingface/transformers', 0.5985631942749023, 'nlp', 2), ('tlkh/tf-metal-experiments', 0.5975523591041565, 'perf', 1), ('facebookresearch/hydra', 0.5850319266319275, 'util', 0), ('pytorch/data', 0.5815867781639099, 'data', 0), ('horovod/horovod', 0.5749367475509644, 'ml-ops', 2), ('uber/petastorm', 0.5747993588447571, 'data', 2), ('facebookresearch/pytorch3d', 0.565503716468811, 'ml-dl', 0), ('kubeflow/fairing', 0.5602784752845764, 'ml-ops', 0), ('neuralmagic/sparseml', 0.5597487688064575, 'ml-dl', 1), ('huggingface/huggingface_hub', 0.5572559237480164, 'ml', 2), ('karpathy/micrograd', 0.5562566518783569, 'study', 0), ('nicolas-chaulet/torch-points3d', 0.5534338355064392, 'ml', 0), ('nvidia/apex', 0.553119957447052, 'ml-dl', 0), ('gradio-app/gradio', 0.5525809526443481, 'viz', 1), ('denys88/rl_games', 0.550940990447998, 'ml-rl', 2), ('arogozhnikov/einops', 0.5508671998977661, 'ml-dl', 2), ('microsoft/onnxruntime', 0.5507823824882507, 'ml', 2), ('oml-team/open-metric-learning', 0.5497283339500427, 'ml', 3), ('rafiqhasan/auto-tensorflow', 0.5461589694023132, 'ml-dl', 0), ('blackhc/toma', 0.545914351940155, 'ml-dl', 1), ('wandb/client', 0.5431339740753174, 'ml', 4), ('fastai/fastcore', 0.5420973300933838, 'util', 0), ('huggingface/accelerate', 0.5420671105384827, 'ml', 0), ('microsoft/nni', 0.5410720109939575, 'ml', 3), ('allenai/allennlp', 0.5402539968490601, 'nlp', 2), ('mosaicml/composer', 0.5401220917701721, 'ml-dl', 2), ('xl0/lovely-tensors', 0.539811372756958, 'ml-dl', 2), ('lutzroeder/netron', 0.5372906923294067, 'ml', 2), ('huggingface/datasets', 0.5349999666213989, 'nlp', 2), ('keras-team/autokeras', 0.534925103187561, 'ml-dl', 1), ('aistream-peelout/flow-forecast', 0.5330670475959778, 'time-series', 2), ('tensorlayer/tensorlayer', 0.5330002307891846, 'ml-rl', 1), ('google/gin-config', 0.5326757431030273, 'util', 0), ('tensorflow/tensor2tensor', 0.5318635106086731, 'ml', 1), ('nvidia/deeplearningexamples', 0.5269266963005066, 'ml-dl', 2), ('christoschristofidis/awesome-deep-learning', 0.52616947889328, 'study', 1), ('pytorch/rl', 0.5253444314002991, 'ml-rl', 1), ('explosion/thinc', 0.5251020193099976, 'ml-dl', 2), ('microsoft/flaml', 0.5247229337692261, 'ml', 1), ('zenml-io/zenml', 0.5233268141746521, 'ml-ops', 3), ('rentruewang/koila', 0.5227289199829102, 'ml', 2), ('microsoft/deepspeed', 0.5205578207969666, 'ml-dl', 2), ('merantix-momentum/squirrel-core', 0.5204800367355347, 'ml', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5156070590019226, 'study', 0), ('streamlit/streamlit', 0.5148152112960815, 'viz', 1), ('apache/incubator-mxnet', 0.5147703886032104, 'ml-dl', 0), ('ludwig-ai/ludwig', 0.5141164064407349, 'ml-ops', 2), ('tensorflow/tensorflow', 0.5114924311637878, 'ml-dl', 1), ('google/tf-quant-finance', 0.5111380219459534, 'finance', 0), ('pyg-team/pytorch_geometric', 0.5103349089622498, 'ml-dl', 2), ('tensorflow/addons', 0.5103277564048767, 'ml', 1), ('ray-project/ray', 0.510144054889679, 'ml-ops', 2), ('pytorch/captum', 0.5099735260009766, 'ml-interpretability', 0), ('bobazooba/xllm', 0.5093933939933777, 'llm', 2), ('kubeflow-kale/kale', 0.5069912075996399, 'ml-ops', 0), ('deepmodeling/deepmd-kit', 0.5063115358352661, 'sim', 1), ('graykode/nlp-tutorial', 0.5006478428840637, 'study', 1), ('polyaxon/datatile', 0.5006352066993713, 'pandas', 2), ('teamhg-memex/eli5', 0.5004812479019165, 'ml', 0), ('huggingface/exporters', 0.5000954270362854, 'ml', 2)]",32,6.0,,0.56,19,4,39,4,7,4,7,19.0,6.0,90.0,0.3,47
1003,finance,https://github.com/matplotlib/mplfinance,[],,[],[],,,,matplotlib/mplfinance,mplfinance,3155,589,85,Python,https://pypi.org/project/mplfinance/,Financial Markets Data Visualization using Matplotlib,matplotlib,2024-01-13,2019-12-05,216,14.558338826631509,https://avatars.githubusercontent.com/u/215947?v=4,Financial Markets Data Visualization using Matplotlib,"['candlestick', 'candlestick-chart', 'candlestickchart', 'finance', 'intraday-data', 'market-data', 'matplotlib', 'mplfinance', 'ohlc', 'ohlc-chart', 'ohlc-data', 'ohlc-plot', 'ohlcv', 'trading-days']","['candlestick', 'candlestick-chart', 'candlestickchart', 'finance', 'intraday-data', 'market-data', 'matplotlib', 'mplfinance', 'ohlc', 'ohlc-chart', 'ohlc-data', 'ohlc-plot', 'ohlcv', 'trading-days']",2023-08-01,"[('mwaskom/seaborn', 0.6046485900878906, 'viz', 1), ('cuemacro/chartpy', 0.574644148349762, 'viz', 1), ('hydrosquall/tiingo-python', 0.5679528713226318, 'finance', 1), ('ranaroussi/yfinance', 0.5448886156082153, 'finance', 1), ('holoviz/hvplot', 0.5378217101097107, 'pandas', 0), ('matplotlib/matplotlib', 0.5346719622612, 'viz', 1), ('man-group/dtale', 0.5290730595588684, 'viz', 0), ('holoviz/panel', 0.5236456394195557, 'viz', 1), ('kanaries/pygwalker', 0.517193078994751, 'pandas', 1), ('bokeh/bokeh', 0.5142570734024048, 'viz', 0), ('netflix/flamescope', 0.5054373741149902, 'viz', 0), ('ranaroussi/quantstats', 0.5049977898597717, 'finance', 1), ('residentmario/geoplot', 0.5038678050041199, 'gis', 1), ('cuemacro/findatapy', 0.5023597478866577, 'finance', 1)]",48,7.0,,0.98,17,8,50,6,1,3,1,17.0,41.0,90.0,2.4,47
474,gis,https://github.com/holoviz/datashader,[],,[],[],1.0,,,holoviz/datashader,datashader,3127,366,91,Python,http://datashader.org,Quickly and accurately render even the largest data.,holoviz,2024-01-12,2015-12-23,422,7.394932432432433,https://avatars.githubusercontent.com/u/51678735?v=4,Quickly and accurately render even the largest data.,"['data-visualizations', 'datashader', 'holoviz', 'rasterization']","['data-visualizations', 'datashader', 'holoviz', 'rasterization']",2024-01-08,"[('nomic-ai/deepscatter', 0.6156784296035767, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5956966876983643, 'viz', 0), ('holoviz/hvplot', 0.5903522372245789, 'pandas', 2), ('holoviz/holoviz', 0.5855922698974609, 'viz', 2), ('vaexio/vaex', 0.5661620497703552, 'perf', 0), ('man-group/dtale', 0.5459538698196411, 'viz', 0), ('visgl/deck.gl', 0.521821916103363, 'viz', 0), ('altair-viz/altair', 0.5193212032318115, 'viz', 0), ('holoviz/holoviews', 0.5166183710098267, 'viz', 1), ('contextlab/hypertools', 0.5120874643325806, 'ml', 0), ('hazyresearch/meerkat', 0.5083599090576172, 'viz', 0), ('holoviz/panel', 0.5079904198646545, 'viz', 1), ('enthought/mayavi', 0.504320502281189, 'viz', 0), ('facebookresearch/hiplot', 0.5003904104232788, 'viz', 0)]",54,6.0,,1.88,44,19,98,0,5,13,5,44.0,38.0,90.0,0.9,47
1210,llm,https://github.com/freedomintelligence/llmzoo,['language-model'],,[],[],,,,freedomintelligence/llmzoo,LLMZoo,2786,189,50,Python,,"⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡",freedomintelligence,2024-01-12,2023-04-01,43,64.15131578947368,https://avatars.githubusercontent.com/u/127706844?v=4,"⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡",[],['language-model'],2023-07-25,"[('ai21labs/lm-evaluation', 0.7427138090133667, 'llm', 1), ('hannibal046/awesome-llm', 0.7315229177474976, 'study', 1), ('lm-sys/fastchat', 0.6950333714485168, 'llm', 1), ('ctlllll/llm-toolmaker', 0.6779150366783142, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.6675116419792175, 'llm', 1), ('fasteval/fasteval', 0.6659313440322876, 'llm', 0), ('juncongmoo/pyllama', 0.6618118286132812, 'llm', 0), ('lianjiatech/belle', 0.6501920819282532, 'llm', 0), ('cg123/mergekit', 0.641423225402832, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.640003502368927, 'llm', 0), ('togethercomputer/redpajama-data', 0.6302767992019653, 'llm', 0), ('openlmlab/leval', 0.6185758113861084, 'llm', 1), ('reasoning-machines/pal', 0.6142875552177429, 'llm', 1), ('paddlepaddle/paddlenlp', 0.6110307574272156, 'llm', 0), ('bigscience-workshop/biomedical', 0.6107045412063599, 'data', 0), ('guidance-ai/guidance', 0.5987256169319153, 'llm', 1), ('thudm/chatglm2-6b', 0.5987119078636169, 'llm', 0), ('young-geng/easylm', 0.5935439467430115, 'llm', 1), ('next-gpt/next-gpt', 0.5918599963188171, 'llm', 0), ('jonasgeiping/cramming', 0.5916814804077148, 'nlp', 1), ('prefecthq/langchain-prefect', 0.5867060422897339, 'llm', 0), ('oobabooga/text-generation-webui', 0.5807399153709412, 'llm', 1), ('hazyresearch/h3', 0.5786004066467285, 'llm', 0), ('explosion/spacy-models', 0.5769272446632385, 'nlp', 0), ('thudm/chatglm-6b', 0.5757870674133301, 'llm', 1), ('infinitylogesh/mutate', 0.5752876996994019, 'nlp', 1), ('mit-han-lab/streaming-llm', 0.5750225782394409, 'llm', 0), ('srush/minichain', 0.5740912556648254, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5728333592414856, 'nlp', 0), ('sjtu-ipads/powerinfer', 0.5668209195137024, 'llm', 0), ('databrickslabs/dolly', 0.5654436945915222, 'llm', 0), ('salesforce/xgen', 0.5646532773971558, 'llm', 1), ('nvlabs/prismer', 0.5624253153800964, 'diffusion', 1), ('tatsu-lab/stanford_alpaca', 0.5588739514350891, 'llm', 1), ('microsoft/lora', 0.5567244291305542, 'llm', 1), ('lupantech/chameleon-llm', 0.5542495250701904, 'llm', 1), ('microsoft/autogen', 0.553227961063385, 'llm', 0), ('eleutherai/the-pile', 0.5475942492485046, 'data', 0), ('lucidrains/toolformer-pytorch', 0.5474002957344055, 'llm', 1), ('yizhongw/self-instruct', 0.5435721278190613, 'llm', 1), ('ai21labs/in-context-ralm', 0.5410796403884888, 'llm', 1), ('cstankonrad/long_llama', 0.5405339002609253, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5354942083358765, 'llm', 1), ('openai/gpt-2', 0.532707691192627, 'llm', 0), ('paperswithcode/galai', 0.5310094356536865, 'llm', 1), ('jalammar/ecco', 0.5306503772735596, 'ml-interpretability', 0), ('openbmb/toolbench', 0.5291275978088379, 'llm', 0), ('hiyouga/llama-factory', 0.5281153321266174, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5281152129173279, 'llm', 1), ('yueyu1030/attrprompt', 0.5263165831565857, 'llm', 0), ('huggingface/text-generation-inference', 0.523625373840332, 'llm', 0), ('paddlepaddle/rocketqa', 0.5235476493835449, 'nlp', 0), ('conceptofmind/toolformer', 0.5184057950973511, 'llm', 1), ('mlc-ai/web-llm', 0.517315149307251, 'llm', 1), ('extreme-bert/extreme-bert', 0.5171335935592651, 'llm', 1), ('bigscience-workshop/megatron-deepspeed', 0.5145018100738525, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5145018100738525, 'llm', 0), ('explosion/spacy-llm', 0.5140294432640076, 'llm', 0), ('bobazooba/xllm', 0.5108060240745544, 'llm', 0), ('facebookresearch/codellama', 0.508786141872406, 'llm', 1), ('openlmlab/moss', 0.5085688233375549, 'llm', 1), ('bytedance/lightseq', 0.5059230327606201, 'nlp', 0), ('llmware-ai/llmware', 0.5058205127716064, 'llm', 0), ('thudm/glm-130b', 0.5044916272163391, 'llm', 0), ('aiwaves-cn/agents', 0.5030314922332764, 'nlp', 1), ('mooler0410/llmspracticalguide', 0.5019903182983398, 'study', 0), ('jina-ai/finetuner', 0.5019564628601074, 'ml', 0)]",10,3.0,,3.96,2,1,10,6,0,0,0,2.0,1.0,90.0,0.5,47
863,profiling,https://github.com/reloadware/reloadium,[],,[],[],1.0,,,reloadware/reloadium,reloadium,2621,57,25,Python,https://reloadium.io,"Hot Reloading, Profiling and AI debugging for Python",reloadware,2024-01-14,2022-01-15,106,24.62684563758389,https://avatars.githubusercontent.com/u/85869255?v=4,"Hot Reloading, Profiling and AI debugging for Python","['ai', 'artificial-intelligence', 'chatgpt', 'django', 'edit-and-continue', 'flask', 'hot-reload', 'hot-reloading', 'pandas']","['ai', 'artificial-intelligence', 'chatgpt', 'django', 'edit-and-continue', 'flask', 'hot-reload', 'hot-reloading', 'pandas']",2024-01-04,"[('carla-recourse/carla', 0.5905767679214478, 'ml', 1), ('fastai/fastcore', 0.5807570219039917, 'util', 0), ('sourcery-ai/sourcery', 0.5745614767074585, 'util', 1), ('alexmojaki/snoop', 0.5736259818077087, 'debug', 0), ('eleutherai/pyfra', 0.5577932596206665, 'ml', 0), ('willmcgugan/textual', 0.5569230318069458, 'term', 0), ('google/gin-config', 0.5562544465065002, 'util', 0), ('google/pyglove', 0.5510817766189575, 'util', 0), ('nedbat/coveragepy', 0.5490651726722717, 'testing', 0), ('polyaxon/datatile', 0.5487061738967896, 'pandas', 1), ('gradio-app/gradio', 0.5461754202842712, 'viz', 0), ('oegedijk/explainerdashboard', 0.5406351089477539, 'ml-interpretability', 0), ('kubeflow/fairing', 0.5393650531768799, 'ml-ops', 0), ('pytorchlightning/pytorch-lightning', 0.5348438024520874, 'ml-dl', 2), ('dylanhogg/awesome-python', 0.5307287573814392, 'study', 2), ('cheshire-cat-ai/core', 0.5294227004051208, 'llm', 1), ('faster-cpython/ideas', 0.5288839936256409, 'perf', 0), ('klen/py-frameworks-bench', 0.5263360142707825, 'perf', 0), ('pympler/pympler', 0.5263128876686096, 'perf', 0), ('sumerc/yappi', 0.5243247151374817, 'profiling', 0), ('fmind/mlops-python-package', 0.5241882801055908, 'template', 1), ('python-rope/rope', 0.5215712785720825, 'util', 0), ('holoviz/panel', 0.5191416144371033, 'viz', 0), ('python-cachier/cachier', 0.5175240635871887, 'perf', 0), ('avaiga/taipy', 0.5162569284439087, 'data', 0), ('online-ml/river', 0.5157492160797119, 'ml', 0), ('teamhg-memex/eli5', 0.5111801624298096, 'ml', 0), ('sweepai/sweep', 0.5105124711990356, 'llm', 1), ('pyutils/line_profiler', 0.5102075338363647, 'profiling', 0), ('minimaxir/simpleaichat', 0.509559154510498, 'llm', 2), ('gventuri/pandas-ai', 0.5095576047897339, 'pandas', 2), ('mindsdb/mindsdb', 0.5066758990287781, 'data', 2), ('plotly/dash', 0.5045337080955505, 'viz', 1), ('plasma-umass/scalene', 0.5005995035171509, 'profiling', 0), ('huggingface/datasets', 0.500510036945343, 'nlp', 1), ('merantix-momentum/squirrel-core', 0.5004510283470154, 'ml', 1), ('amaargiru/pyroad', 0.5002206563949585, 'study', 0), ('allrod5/injectable', 0.5002021193504333, 'util', 0)]",3,2.0,,0.27,14,11,24,0,0,3,3,14.0,19.0,90.0,1.4,47
307,util,https://github.com/legrandin/pycryptodome,[],,[],[],,,,legrandin/pycryptodome,pycryptodome,2582,468,63,C,https://www.pycryptodome.org,A self-contained cryptographic library for Python,legrandin,2024-01-14,2014-05-02,508,5.0769662921348315,,A self-contained cryptographic library for Python,"['cryptography', 'security']","['cryptography', 'security']",2024-01-13,"[('pyca/cryptography', 0.8198734521865845, 'util', 1), ('pyca/pynacl', 0.7229923605918884, 'util', 1), ('primal100/pybitcointools', 0.6471469402313232, 'crypto', 0), ('1200wd/bitcoinlib', 0.6432069540023804, 'crypto', 0), ('snyk/faker-security', 0.5843793153762817, 'security', 0), ('pytoolz/toolz', 0.5827405452728271, 'util', 0), ('man-c/pycoingecko', 0.5711352229118347, 'crypto', 0), ('pyupio/safety', 0.5586642622947693, 'security', 1), ('fredrik-johansson/mpmath', 0.5532434582710266, 'math', 0), ('pyeve/cerberus', 0.5516589283943176, 'data', 0), ('pyston/pyston', 0.5480275750160217, 'util', 0), ('paramiko/paramiko', 0.5410947203636169, 'util', 0), ('pypy/pypy', 0.5345046520233154, 'util', 0), ('trailofbits/pip-audit', 0.5239244103431702, 'security', 1), ('gbeced/pyalgotrade', 0.5236385464668274, 'finance', 0), ('pypa/installer', 0.5224993228912354, 'util', 0), ('sympy/sympy', 0.5150558352470398, 'math', 0), ('ta-lib/ta-lib-python', 0.5114703178405762, 'finance', 0), ('mkdocstrings/griffe', 0.5096718668937683, 'util', 0), ('erotemic/ubelt', 0.5003699660301208, 'util', 0)]",146,4.0,,2.42,27,20,118,0,10,12,10,27.0,46.0,90.0,1.7,47
130,viz,https://github.com/holoviz/holoviews,[],,[],[],,,,holoviz/holoviews,holoviews,2550,386,58,Python,https://holoviews.org,"With Holoviews, your data visualizes itself.",holoviz,2024-01-13,2014-05-07,507,5.0210970464135025,https://avatars.githubusercontent.com/u/51678735?v=4,"With Holoviews, your data visualizes itself.","['holoviews', 'holoviz', 'plotting']","['holoviews', 'holoviz', 'plotting']",2023-12-22,"[('holoviz/hvplot', 0.682068407535553, 'pandas', 3), ('holoviz/geoviews', 0.6530880331993103, 'gis', 3), ('holoviz/holoviz', 0.585200309753418, 'viz', 2), ('matplotlib/matplotlib', 0.564393937587738, 'viz', 1), ('facebookresearch/hiplot', 0.5553148984909058, 'viz', 0), ('holoviz/datashader', 0.5166183710098267, 'gis', 1), ('enthought/mayavi', 0.5158518552780151, 'viz', 0), ('altair-viz/altair', 0.5143608450889587, 'viz', 0), ('mwaskom/seaborn', 0.513229489326477, 'viz', 0), ('has2k1/plotnine', 0.5102058053016663, 'viz', 1)]",140,4.0,,5.21,176,82,118,1,8,41,8,174.0,226.0,90.0,1.3,47
1731,testing,https://github.com/kevin1024/vcrpy,[],,[],[],,,,kevin1024/vcrpy,vcrpy,2547,363,38,Python,,Automatically mock your HTTP interactions to simplify and speed up testing,kevin1024,2024-01-12,2012-05-29,609,4.182266009852217,,Automatically mock your HTTP interactions to simplify and speed up testing,"['http', 'mocking', 'testing']","['http', 'mocking', 'testing']",2024-01-05,"[('jamielennox/requests-mock', 0.6570014953613281, 'testing', 1), ('lundberg/respx', 0.6465907692909241, 'testing', 2), ('getsentry/responses', 0.5679908990859985, 'testing', 1)]",139,5.0,,2.52,66,40,142,0,5,5,5,66.0,125.0,90.0,1.9,47
867,perf,https://github.com/ipython/ipyparallel,[],,[],[],,,,ipython/ipyparallel,ipyparallel,2518,1051,121,Jupyter Notebook,https://ipyparallel.readthedocs.io/,IPython Parallel: Interactive Parallel Computing in Python,ipython,2024-01-13,2015-04-09,459,5.477315102548166,https://avatars.githubusercontent.com/u/230453?v=4,IPython Parallel: Interactive Parallel Computing in Python,"['jupyter', 'parallel']","['jupyter', 'parallel']",2024-01-05,"[('jupyterlab/jupyterlab', 0.6818181872367859, 'jupyter', 1), ('ipython/ipykernel', 0.6646348237991333, 'util', 1), ('dask/dask', 0.64837247133255, 'perf', 0), ('pypy/pypy', 0.6435779333114624, 'util', 0), ('joblib/joblib', 0.6388868689537048, 'util', 0), ('maartenbreddels/ipyvolume', 0.6384920477867126, 'jupyter', 1), ('python/cpython', 0.6357694268226624, 'util', 0), ('faster-cpython/tools', 0.6260746717453003, 'perf', 0), ('pyston/pyston', 0.6226590275764465, 'util', 0), ('jupyter/notebook', 0.6222683191299438, 'jupyter', 1), ('quantopian/qgrid', 0.611356794834137, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.604579508304596, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.5989540219306946, 'jupyter', 0), ('faster-cpython/ideas', 0.5969278216362, 'perf', 0), ('nalepae/pandarallel', 0.585684597492218, 'pandas', 1), ('jupyterlab/jupyterlab-desktop', 0.5839833617210388, 'jupyter', 1), ('micropython/micropython', 0.5837920904159546, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5817736983299255, 'study', 0), ('opengeos/leafmap', 0.580319344997406, 'gis', 1), ('jupyter/nbformat', 0.5791874527931213, 'jupyter', 0), ('wesm/pydata-book', 0.579038143157959, 'study', 0), ('brandtbucher/specialist', 0.5722348093986511, 'perf', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5656928420066833, 'study', 0), ('cohere-ai/notebooks', 0.5586426258087158, 'llm', 0), ('eleutherai/pyfra', 0.5585790276527405, 'ml', 0), ('voila-dashboards/voila', 0.5572918057441711, 'jupyter', 1), ('numba/numba', 0.5564903020858765, 'perf', 1), ('gotcha/ipdb', 0.5557621717453003, 'debug', 0), ('adafruit/circuitpython', 0.5501038432121277, 'util', 0), ('fastai/fastcore', 0.5499588251113892, 'util', 0), ('python-trio/trio', 0.549788773059845, 'perf', 0), ('p403n1x87/austin', 0.5462062954902649, 'profiling', 0), ('cython/cython', 0.5451430678367615, 'util', 0), ('numpy/numpy', 0.5446184277534485, 'math', 0), ('exaloop/codon', 0.5431182980537415, 'perf', 0), ('ageron/handson-ml2', 0.5427348017692566, 'ml', 0), ('markshannon/faster-cpython', 0.541959285736084, 'perf', 0), ('eventlet/eventlet', 0.5408223271369934, 'perf', 0), ('computationalmodelling/nbval', 0.5368309617042542, 'jupyter', 0), ('wxwidgets/phoenix', 0.5365442633628845, 'gui', 0), ('bloomberg/ipydatagrid', 0.5364363193511963, 'jupyter', 0), ('aws/graph-notebook', 0.5359687805175781, 'jupyter', 1), ('koaning/drawdata', 0.5354899168014526, 'jupyter', 1), ('agronholm/apscheduler', 0.529596745967865, 'util', 0), ('klen/muffin', 0.5273743271827698, 'web', 0), ('manrajgrover/halo', 0.5247726440429688, 'term', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5247416496276855, 'jupyter', 1), ('jupyter/nbconvert', 0.5246542096138, 'jupyter', 0), ('rasbt/watermark', 0.5231406688690186, 'util', 1), ('nvidia/warp', 0.5195223093032837, 'sim', 0), ('hyperopt/hyperopt', 0.5190962553024292, 'ml', 0), ('holoviz/holoviz', 0.5176970362663269, 'viz', 0), ('pytorch/data', 0.5157786011695862, 'data', 0), ('google/jax', 0.514338493347168, 'ml', 0), ('ethereum/py-evm', 0.5111911296844482, 'crypto', 0), ('backtick-se/cowait', 0.5108761787414551, 'util', 0), ('hoffstadt/dearpygui', 0.5102288126945496, 'gui', 0), ('sumerc/yappi', 0.5100932121276855, 'profiling', 0), ('joblib/loky', 0.5099025964736938, 'perf', 0), ('tkrabel/bamboolib', 0.5092272162437439, 'pandas', 0), ('pypa/virtualenv', 0.5087124109268188, 'util', 0), ('pyqtgraph/pyqtgraph', 0.5072664022445679, 'viz', 0), ('tqdm/tqdm', 0.5037369728088379, 'term', 2), ('sympy/sympy', 0.5034547448158264, 'math', 0), ('holoviz/panel', 0.5028029084205627, 'viz', 1), ('klen/py-frameworks-bench', 0.5014100074768066, 'perf', 0)]",113,6.0,,1.63,21,12,107,0,0,7,7,21.0,36.0,90.0,1.7,47
439,gis,https://github.com/rasterio/rasterio,[],,[],[],1.0,,,rasterio/rasterio,rasterio,2074,521,147,Python,https://rasterio.readthedocs.io/,Rasterio reads and writes geospatial raster datasets,rasterio,2024-01-13,2013-11-04,534,3.882856378710885,https://avatars.githubusercontent.com/u/46967650?v=4,Rasterio reads and writes geospatial raster datasets,"['cli', 'cython', 'gdal', 'gis', 'mapbox-satellite-oss', 'raster']","['cli', 'cython', 'gdal', 'gis', 'mapbox-satellite-oss', 'raster']",2024-01-09,"[('cogeotiff/rio-tiler', 0.7036706209182739, 'gis', 2), ('toblerity/fiona', 0.5375838279724121, 'gis', 4), ('corteva/rioxarray', 0.5225644707679749, 'gis', 3)]",155,5.0,,2.65,101,80,124,0,8,17,8,101.0,134.0,90.0,1.3,47
116,perf,https://github.com/h5py/h5py,['hdf5'],,[],[],,,,h5py/h5py,h5py,1965,545,57,Python,http://www.h5py.org,HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.,h5py,2024-01-12,2012-09-21,592,3.316055930568949,https://avatars.githubusercontent.com/u/2389852?v=4,HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.,[],['hdf5'],2024-01-12,[],199,6.0,,2.65,49,27,138,0,3,4,3,49.0,164.0,90.0,3.3,47
1234,llm,https://github.com/lucidrains/toolformer-pytorch,"['toolformer', 'language-model']",,[],[],,,,lucidrains/toolformer-pytorch,toolformer-pytorch,1802,111,38,Python,,"Implementation of Toolformer, Language Models That Can Use Tools, by MetaAI",lucidrains,2024-01-14,2023-02-10,50,35.632768361581924,,"Implementation of Toolformer, Language Models That Can Use Tools, by MetaAI","['api-calling', 'artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'transformers']","['api-calling', 'artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'language-model', 'toolformer', 'transformers']",2023-12-21,"[('conceptofmind/toolformer', 0.7667592167854309, 'llm', 2), ('ctlllll/llm-toolmaker', 0.6329768300056458, 'llm', 1), ('lm-sys/fastchat', 0.6176435947418213, 'llm', 1), ('huggingface/transformers', 0.6121810674667358, 'nlp', 2), ('thilinarajapakse/simpletransformers', 0.6100559234619141, 'nlp', 1), ('oobabooga/text-generation-webui', 0.5891088843345642, 'llm', 1), ('openbmb/toolbench', 0.5833466649055481, 'llm', 0), ('explosion/thinc', 0.5822931528091431, 'ml-dl', 2), ('ml-tooling/opyrator', 0.5760772228240967, 'viz', 0), ('nvidia/deeplearningexamples', 0.576062798500061, 'ml-dl', 1), ('young-geng/easylm', 0.5753515362739563, 'llm', 2), ('microsoft/lmops', 0.5752981901168823, 'llm', 1), ('deepset-ai/haystack', 0.5719588398933411, 'llm', 2), ('huggingface/datasets', 0.5622266530990601, 'nlp', 1), ('cheshire-cat-ai/core', 0.5618926882743835, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.559565544128418, 'study', 2), ('paddlepaddle/paddlenlp', 0.5539929866790771, 'llm', 1), ('keras-team/keras-nlp', 0.5536922216415405, 'nlp', 1), ('eugeneyan/obsidian-copilot', 0.5504752397537231, 'llm', 0), ('rasahq/rasa', 0.5501740574836731, 'llm', 0), ('lianjiatech/belle', 0.5494219064712524, 'llm', 0), ('llmware-ai/llmware', 0.5493955016136169, 'llm', 1), ('kubeflow/fairing', 0.5491804480552673, 'ml-ops', 0), ('fastai/fastcore', 0.5489647388458252, 'util', 0), ('freedomintelligence/llmzoo', 0.5474002957344055, 'llm', 1), ('prefecthq/marvin', 0.5462851524353027, 'nlp', 0), ('tigerlab-ai/tiger', 0.5445385575294495, 'llm', 0), ('explosion/spacy-transformers', 0.5443832278251648, 'llm', 1), ('operand/agency', 0.5433422923088074, 'llm', 1), ('deeppavlov/deeppavlov', 0.5414809584617615, 'nlp', 2), ('apple/coremltools', 0.5380272269248962, 'ml', 0), ('bentoml/bentoml', 0.5346093773841858, 'ml-ops', 1), ('microsoft/nni', 0.531758725643158, 'ml', 1), ('nccr-itmo/fedot', 0.5307614803314209, 'ml-ops', 0), ('wandb/client', 0.5302769541740417, 'ml', 1), ('pythagora-io/gpt-pilot', 0.5301491022109985, 'llm', 0), ('nvlabs/prismer', 0.5290699005126953, 'diffusion', 1), ('vitalik/django-ninja', 0.528582751750946, 'web', 0), ('hannibal046/awesome-llm', 0.5266475677490234, 'study', 1), ('mlc-ai/mlc-llm', 0.5253450274467468, 'llm', 1), ('transformeroptimus/superagi', 0.5244137048721313, 'llm', 1), ('lupantech/chameleon-llm', 0.5233466029167175, 'llm', 1), ('argilla-io/argilla', 0.5221193432807922, 'nlp', 0), ('gradio-app/gradio', 0.5205651521682739, 'viz', 1), ('microsoft/autogen', 0.5196950435638428, 'llm', 0), ('explosion/spacy', 0.5190293192863464, 'nlp', 2), ('xtekky/gpt4free', 0.5187653303146362, 'llm', 1), ('cdpierse/transformers-interpret', 0.5184912085533142, 'ml-interpretability', 2), ('espnet/espnet', 0.5174741744995117, 'nlp', 1), ('keras-team/autokeras', 0.516575276851654, 'ml-dl', 1), ('google/pyglove', 0.5139380097389221, 'util', 0), ('tiangolo/fastapi', 0.5137580037117004, 'web', 0), ('bigscience-workshop/megatron-deepspeed', 0.5127219557762146, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5127219557762146, 'llm', 0), ('next-gpt/next-gpt', 0.5125038027763367, 'llm', 0), ('mlflow/mlflow', 0.5123224258422852, 'ml-ops', 0), ('mosaicml/composer', 0.5117185115814209, 'ml-dl', 1), ('nvidia/nemo', 0.5109055042266846, 'nlp', 2), ('kalliope-project/kalliope', 0.5108852982521057, 'util', 0), ('explosion/spacy-streamlit', 0.5099772214889526, 'nlp', 0), ('modularml/mojo', 0.5094813108444214, 'util', 0), ('tatsu-lab/stanford_alpaca', 0.5094295740127563, 'llm', 2), ('openlmlab/moss', 0.508660614490509, 'llm', 2), ('huggingface/huggingface_hub', 0.5079225301742554, 'ml', 1), ('polyaxon/polyaxon', 0.5062516927719116, 'ml-ops', 2), ('mindsdb/mindsdb', 0.5057101249694824, 'data', 1), ('selfexplainml/piml-toolbox', 0.5051028728485107, 'ml-interpretability', 0), ('night-chen/toolqa', 0.5044512152671814, 'llm', 0), ('alibaba/easynlp', 0.5038707852363586, 'nlp', 2), ('pathwaycom/llm-app', 0.5037291049957275, 'llm', 0), ('langchain-ai/opengpts', 0.5033729672431946, 'llm', 0), ('lastmile-ai/aiconfig', 0.5029549598693848, 'util', 0), ('starlite-api/starlite', 0.5022919774055481, 'web', 0), ('google-research/language', 0.5021114945411682, 'nlp', 0), ('openlm-research/open_llama', 0.5018919110298157, 'llm', 1), ('mlc-ai/web-llm', 0.5002791881561279, 'llm', 2)]",3,1.0,,1.17,9,2,11,1,24,27,24,9.0,10.0,90.0,1.1,47
1093,ml-interpretability,https://github.com/eleutherai/pythia,"['interpretability', 'interpretable-ml']",Interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers,[],[],,,,eleutherai/pythia,pythia,1801,117,29,Jupyter Notebook,,The hub for EleutherAI's work on interpretability and learning dynamics,eleutherai,2024-01-13,2021-12-25,109,16.45822454308094,https://avatars.githubusercontent.com/u/68924597?v=4,The hub for EleutherAI's work on interpretability and learning dynamics,[],"['interpretability', 'interpretable-ml']",2023-12-31,"[('pair-code/lit', 0.641786515712738, 'ml-interpretability', 0), ('csinva/imodels', 0.5875741243362427, 'ml', 1), ('tensorflow/lucid', 0.5809341669082642, 'ml-interpretability', 1), ('marcotcr/lime', 0.5768492817878723, 'ml-interpretability', 1), ('interpretml/interpret', 0.5574303269386292, 'ml-interpretability', 2), ('pytorch/captum', 0.5508965253829956, 'ml-interpretability', 2), ('seldonio/alibi', 0.5343512296676636, 'ml-interpretability', 1), ('maif/shapash', 0.5036888122558594, 'ml', 1)]",17,4.0,,3.37,30,23,25,0,0,0,0,30.0,36.0,90.0,1.2,47
1116,web,https://github.com/cherrypy/cherrypy,[],,[],[],,,,cherrypy/cherrypy,cherrypy,1748,359,55,Python,https://docs.cherrypy.dev,"CherryPy is a pythonic, object-oriented HTTP framework.      https://cherrypy.dev",cherrypy,2024-01-10,2016-04-30,404,4.322147651006712,https://avatars.githubusercontent.com/u/6617466?v=4,"CherryPy is a pythonic, object-oriented HTTP framework.      https://cherrypy.dev","['cherrypy', 'cross-platform', 'daemon-mode', 'http', 'http-server', 'http-streaming', 'https', 'idiomatic-python', 'jython', 'pure-python', 'pypy', 'pypy3']","['cherrypy', 'cross-platform', 'daemon-mode', 'http', 'http-server', 'http-streaming', 'https', 'idiomatic-python', 'jython', 'pure-python', 'pypy', 'pypy3']",2024-01-05,"[('bottlepy/bottle', 0.6888998746871948, 'web', 0), ('webpy/webpy', 0.6774104833602905, 'web', 0), ('encode/httpx', 0.65858393907547, 'web', 1), ('encode/uvicorn', 0.6493438482284546, 'web', 2), ('masoniteframework/masonite', 0.6259151697158813, 'web', 0), ('pallets/flask', 0.6162318587303162, 'web', 0), ('neoteroi/blacksheep', 0.6155569553375244, 'web', 2), ('scrapy/scrapy', 0.6140583157539368, 'data', 0), ('falconry/falcon', 0.6051623821258545, 'web', 1), ('benoitc/gunicorn', 0.5961728096008301, 'web', 2), ('klen/muffin', 0.5874270796775818, 'web', 0), ('pallets/werkzeug', 0.5869470834732056, 'web', 1), ('pypy/pypy', 0.5867733955383301, 'util', 0), ('requests/toolbelt', 0.5835244059562683, 'util', 1), ('pyodide/pyodide', 0.5807443857192993, 'util', 0), ('pallets/quart', 0.5793529748916626, 'web', 1), ('psf/requests', 0.5770725607872009, 'web', 1), ('simple-salesforce/simple-salesforce', 0.5702207088470459, 'data', 0), ('pylons/pyramid', 0.5556930303573608, 'web', 0), ('reflex-dev/reflex', 0.553268313407898, 'web', 0), ('1200wd/bitcoinlib', 0.5514862537384033, 'crypto', 0), ('pylons/waitress', 0.5498244762420654, 'web', 1), ('eleutherai/pyfra', 0.5470208525657654, 'ml', 0), ('aio-libs/aiohttp', 0.5461557507514954, 'web', 2), ('jupyterlite/jupyterlite', 0.5396081805229187, 'jupyter', 0), ('python/cpython', 0.5312963724136353, 'util', 0), ('paramiko/paramiko', 0.5295555591583252, 'util', 0), ('timofurrer/awesome-asyncio', 0.5200496912002563, 'study', 0), ('willmcgugan/textual', 0.5189430117607117, 'term', 0), ('pyston/pyston', 0.5174131989479065, 'util', 0), ('fastai/fastcore', 0.5150647759437561, 'util', 0), ('hugapi/hug', 0.5129648447036743, 'util', 2), ('voila-dashboards/voila', 0.5070238709449768, 'jupyter', 0), ('ethereum/web3.py', 0.5029838681221008, 'crypto', 0), ('clips/pattern', 0.500634491443634, 'nlp', 0)]",143,7.0,,0.5,22,15,94,0,0,17,17,22.0,54.0,90.0,2.5,47
1824,llm,https://github.com/noahshinn/reflexion,[],,[],[],,,,noahshinn/reflexion,reflexion,1697,160,29,Python,,[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning,noahshinn,2024-01-14,2023-03-22,44,37.8312101910828,,[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning,"['ai', 'artificial-intelligence', 'llm']","['ai', 'artificial-intelligence', 'llm']",2023-11-26,"[('aiwaves-cn/agents', 0.5806130170822144, 'nlp', 1), ('jina-ai/thinkgpt', 0.5254539847373962, 'llm', 0), ('lupantech/scienceqa', 0.5062936544418335, 'llm', 0), ('oliveirabruno01/babyagi-asi', 0.5022252798080444, 'llm', 1), ('humanoidagents/humanoidagents', 0.5013625621795654, 'sim', 0), ('thilinarajapakse/simpletransformers', 0.5012626051902771, 'nlp', 0), ('prefecthq/marvin', 0.5003121495246887, 'nlp', 2)]",7,3.0,,3.15,13,9,10,2,0,0,0,13.0,16.0,90.0,1.2,47
427,jupyter,https://github.com/jupyter-lsp/jupyterlab-lsp,[],,[],[],,,,jupyter-lsp/jupyterlab-lsp,jupyterlab-lsp,1663,134,20,TypeScript,,Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol,jupyter-lsp,2024-01-13,2019-08-17,232,7.154886293792256,https://avatars.githubusercontent.com/u/92232904?v=4,Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol,"['autocompletion', 'ipython', 'julia-language', 'jupyter', 'jupyter-lab', 'jupyter-notebook', 'jupyterlab', 'jupyterlab-extension', 'language-server-protocol', 'linter', 'lsp', 'notebook', 'notebook-jupyter', 'r']","['autocompletion', 'ipython', 'julia-language', 'jupyter', 'jupyter-lab', 'jupyter-notebook', 'jupyterlab', 'jupyterlab-extension', 'language-server-protocol', 'linter', 'lsp', 'notebook', 'notebook-jupyter', 'r']",2023-11-26,"[('mwouts/jupytext', 0.6457168459892273, 'jupyter', 3), ('jupyter-widgets/ipywidgets', 0.5988917946815491, 'jupyter', 1), ('cohere-ai/notebooks', 0.5917092561721802, 'llm', 0), ('jupyter/notebook', 0.5912204384803772, 'jupyter', 3), ('jupyterlab/jupyterlab', 0.5912031531333923, 'jupyter', 2), ('jupyterlab/jupyterlab-desktop', 0.5716159343719482, 'jupyter', 3), ('aws/graph-notebook', 0.5679754018783569, 'jupyter', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5593620538711548, 'study', 0), ('voila-dashboards/voila', 0.5473379492759705, 'jupyter', 3), ('jupyter-widgets/ipyleaflet', 0.5437476634979248, 'gis', 2), ('jupyter/nbformat', 0.5423336029052734, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.539960503578186, 'jupyter', 3), ('opengeos/leafmap', 0.5345747470855713, 'gis', 2), ('jupyterlab/jupyter-ai', 0.5275683403015137, 'jupyter', 3), ('ipython/ipykernel', 0.5254985690116882, 'util', 3), ('jupyterlite/jupyterlite', 0.5251054763793945, 'jupyter', 3), ('ipython/ipyparallel', 0.5247416496276855, 'perf', 1), ('python/cpython', 0.523478627204895, 'util', 0), ('nteract/papermill', 0.5029893517494202, 'jupyter', 3)]",51,5.0,,5.02,49,34,54,2,12,12,12,49.0,75.0,90.0,1.5,47
1075,util,https://github.com/rhettbull/osxphotos,[],,[],[],,,,rhettbull/osxphotos,osxphotos,1521,85,21,Python,,"Python app to export pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata. ",rhettbull,2024-01-13,2019-06-16,241,6.303730017761989,,"Python app to export pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata. ","['apple', 'apple-photos', 'apple-photos-export', 'library-photos', 'macos', 'macosx', 'osx', 'photos', 'photos-database', 'photos-export', 'pictures']","['apple', 'apple-photos', 'apple-photos-export', 'library-photos', 'macos', 'macosx', 'osx', 'photos', 'photos-database', 'photos-export', 'pictures']",2024-01-13,"[('imageio/imageio', 0.6166835427284241, 'util', 0), ('python-pillow/pillow', 0.5487352609634399, 'util', 0), ('python-odin/odin', 0.5156180262565613, 'util', 0), ('erotemic/ubelt', 0.5059564709663391, 'util', 0)]",35,2.0,,6.46,129,99,56,0,50,98,50,129.0,231.0,90.0,1.8,47
1559,nlp,https://github.com/marella/ctransformers,[],,[],[],,,,marella/ctransformers,ctransformers,1510,118,18,C,,Python bindings for the Transformer models implemented in C/C++ using GGML library.,marella,2024-01-14,2023-05-14,37,40.49808429118774,,Python bindings for the Transformer models implemented in C/C++ using GGML library.,"['ai', 'ctransformers', 'llm', 'transformers']","['ai', 'ctransformers', 'llm', 'transformers']",2023-09-10,"[('alignmentresearch/tuned-lens', 0.5659533143043518, 'ml-interpretability', 1), ('huggingface/transformers', 0.5550124645233154, 'nlp', 0), ('nielsrogge/transformers-tutorials', 0.553908109664917, 'study', 1), ('google/jax', 0.5531230568885803, 'ml', 0), ('eleutherai/gpt-neox', 0.5529630184173584, 'llm', 1), ('pybind/pybind11', 0.5483258962631226, 'perf', 0), ('opengeos/earthformer', 0.5367976427078247, 'gis', 0), ('eleutherai/gpt-neo', 0.5227762460708618, 'llm', 1), ('nvidia/cuda-python', 0.5207217931747437, 'ml', 0), ('pytoolz/toolz', 0.5103341341018677, 'util', 0)]",6,0.0,,2.77,56,10,8,4,30,46,30,56.0,72.0,90.0,1.3,47
402,perf,https://github.com/agronholm/anyio,[],,[],[],,,,agronholm/anyio,anyio,1482,117,27,Python,,High level asynchronous concurrency and networking framework that works on top of either trio or asyncio,agronholm,2024-01-13,2018-08-19,284,5.213065326633166,,High level asynchronous concurrency and networking framework that works on top of either trio or asyncio,"['async-await', 'asyncio', 'curio', 'trio']","['async-await', 'asyncio', 'curio', 'trio']",2024-01-13,"[('python-trio/trio', 0.8090512156486511, 'perf', 2), ('magicstack/uvloop', 0.7205407619476318, 'util', 2), ('tiangolo/asyncer', 0.7173997759819031, 'perf', 2), ('aio-libs/aiohttp', 0.6634643077850342, 'web', 1), ('samuelcolvin/arq', 0.6350996494293213, 'data', 1), ('noxdafox/pebble', 0.6101366281509399, 'perf', 1), ('pallets/quart', 0.6049391627311707, 'web', 1), ('sumerc/yappi', 0.593978226184845, 'profiling', 1), ('alirn76/panther', 0.5871341824531555, 'web', 0), ('airtai/faststream', 0.5833213329315186, 'perf', 1), ('encode/starlette', 0.5785104036331177, 'web', 0), ('eventlet/eventlet', 0.5704464912414551, 'perf', 0), ('tornadoweb/tornado', 0.5639466643333435, 'web', 0), ('encode/httpx', 0.558357298374176, 'web', 2), ('alex-sherman/unsync', 0.5539295673370361, 'util', 0), ('klen/muffin', 0.5409852266311646, 'web', 3), ('miguelgrinberg/python-socketio', 0.5345661640167236, 'util', 1), ('timofurrer/awesome-asyncio', 0.5322125554084778, 'study', 1), ('neoteroi/blacksheep', 0.5259332656860352, 'web', 1), ('samuelcolvin/aioaws', 0.5206239223480225, 'data', 1), ('python-greenlet/greenlet', 0.517914354801178, 'perf', 0), ('tiangolo/fastapi', 0.5019605159759521, 'web', 1)]",46,4.0,,2.98,56,48,66,0,3,9,3,56.0,200.0,90.0,3.6,47
621,data,https://github.com/zarr-developers/zarr-python,[],,[],[],,,,zarr-developers/zarr-python,zarr-python,1274,263,46,Python,http://zarr.readthedocs.io/,"An implementation of chunked, compressed, N-dimensional arrays for Python.",zarr-developers,2024-01-13,2015-12-15,424,3.0047169811320753,https://avatars.githubusercontent.com/u/35050297?v=4,"An implementation of chunked, compressed, N-dimensional arrays for Python.","['compressed', 'ndimensional-arrays', 'zarr']","['compressed', 'ndimensional-arrays', 'zarr']",2024-01-10,"[('google/tensorstore', 0.668194591999054, 'data', 0), ('blosc/python-blosc', 0.5569503903388977, 'perf', 0), ('pyston/pyston', 0.5102282762527466, 'util', 0), ('pydata/xarray', 0.5044090151786804, 'util', 0)]",95,8.0,,2.67,148,88,98,0,11,11,11,148.0,242.0,90.0,1.6,47
764,data,https://github.com/google/tensorstore,[],,[],[],,,,google/tensorstore,tensorstore,1248,103,31,C++,https://google.github.io/tensorstore/,Library for reading and writing large multi-dimensional arrays.,google,2024-01-12,2020-03-30,200,6.235546038543897,https://avatars.githubusercontent.com/u/1342004?v=4,Library for reading and writing large multi-dimensional arrays.,[],[],2024-01-04,"[('zarr-developers/zarr-python', 0.668194591999054, 'data', 0), ('xl0/lovely-numpy', 0.5052934288978577, 'util', 0)]",22,3.0,,8.33,18,10,46,0,0,14,14,18.0,54.0,90.0,3.0,47
572,util,https://github.com/fsspec/filesystem_spec,[],,[],[],,,,fsspec/filesystem_spec,filesystem_spec,723,308,20,Python,,A specification that python filesystems should adhere to.,fsspec,2024-01-14,2018-04-23,301,2.400853889943074,https://avatars.githubusercontent.com/u/92825505?v=4,A specification that python filesystems should adhere to.,[],[],2024-01-13,"[('pyfilesystem/pyfilesystem2', 0.7090234160423279, 'util', 0), ('tox-dev/py-filelock', 0.6173799633979797, 'util', 0), ('platformdirs/platformdirs', 0.5413647294044495, 'util', 0), ('grantjenks/python-diskcache', 0.5200872421264648, 'util', 0), ('pytoolz/toolz', 0.5183253288269043, 'util', 0), ('google/yapf', 0.5129982233047485, 'util', 0), ('pyupio/safety', 0.5129398107528687, 'security', 0), ('python-odin/odin', 0.5112189054489136, 'util', 0), ('drivendataorg/cloudpathlib', 0.5109065175056458, 'data', 0), ('scikit-hep/uproot5', 0.5076752305030823, 'data', 0)]",223,9.0,,3.63,124,87,70,0,0,13,13,124.0,296.0,90.0,2.4,47
1324,util,https://github.com/anthropics/anthropic-sdk-python,"['sdk', 'language-model', 'api']",SDK providing access to Anthropic's safety-first language model APIs,[],[],,,,anthropics/anthropic-sdk-python,anthropic-sdk-python,584,65,42,Python,,,anthropics,2024-01-13,2023-01-17,54,10.814814814814815,https://avatars.githubusercontent.com/u/76263028?v=4,SDK providing access to Anthropic's safety-first language model APIs,[],"['api', 'language-model', 'sdk']",2024-01-08,"[('langchain-ai/langsmith-sdk', 0.5629613995552063, 'llm', 1), ('cohere-ai/cohere-python', 0.5114951133728027, 'util', 1), ('kubeflow/fairing', 0.5095229148864746, 'ml-ops', 0)]",16,5.0,,4.38,120,119,12,0,42,43,42,120.0,57.0,90.0,0.5,47
1733,ml,https://github.com/qdrant/fastembed,['vectordb'],,[],[],,,,qdrant/fastembed,fastembed,503,27,7,Jupyter Notebook,https://qdrant.github.io/fastembed/,"Fast, Accurate, Lightweight Python library to make State of the Art Embedding",qdrant,2024-01-12,2023-07-14,28,17.605,https://avatars.githubusercontent.com/u/73504361?v=4,"Fast, Accurate, Lightweight Python library to make State of the Art Embedding","['embeddings', 'openai', 'rag', 'retrieval', 'retrieval-augmented-generation', 'vector-search']","['embeddings', 'openai', 'rag', 'retrieval', 'retrieval-augmented-generation', 'vector-search', 'vectordb']",2023-12-13,"[('chroma-core/chroma', 0.7453431487083435, 'data', 2), ('jina-ai/vectordb', 0.7195001244544983, 'data', 2), ('neuml/txtai', 0.6601556539535522, 'nlp', 4), ('kagisearch/vectordb', 0.6594275832176208, 'data', 1), ('plasticityai/magnitude', 0.6370154619216919, 'nlp', 1), ('koaning/embetter', 0.6213396787643433, 'data', 0), ('koaning/whatlies', 0.5915380120277405, 'nlp', 1), ('jina-ai/clip-as-service', 0.584290087223053, 'nlp', 1), ('castorini/pyserini', 0.556861937046051, 'ml', 0), ('jina-ai/finetuner', 0.5480974912643433, 'ml', 0), ('cvxgrp/pymde', 0.5445080995559692, 'ml', 0), ('facebookresearch/faiss', 0.5384175777435303, 'ml', 2), ('erotemic/ubelt', 0.5371770262718201, 'util', 0), ('mcfunley/pugsql', 0.5356664657592773, 'data', 0), ('qdrant/qdrant-client', 0.5288180708885193, 'util', 1), ('pytoolz/toolz', 0.5285203456878662, 'util', 0), ('llmware-ai/llmware', 0.5254819989204407, 'llm', 3), ('milvus-io/bootcamp', 0.5193023681640625, 'data', 1), ('qdrant/vector-db-benchmark', 0.519282341003418, 'perf', 1), ('pola-rs/polars', 0.5184158682823181, 'pandas', 0), ('rom1504/clip-retrieval', 0.5138350129127502, 'ml', 0), ('ukplab/sentence-transformers', 0.5122830867767334, 'nlp', 0), ('activeloopai/deeplake', 0.5088616609573364, 'ml-ops', 1), ('lancedb/lancedb', 0.507724940776825, 'data', 1), ('accenture/ampligraph', 0.5070711970329285, 'data', 0), ('pytorch/torchrec', 0.5069307088851929, 'ml-dl', 0), ('paddlepaddle/paddlenlp', 0.5066239833831787, 'llm', 0), ('awslabs/dgl-ke', 0.5057823061943054, 'ml', 0), ('dgilland/cacheout', 0.5054611563682556, 'perf', 0), ('sebischair/lbl2vec', 0.5038244128227234, 'nlp', 0), ('ddangelov/top2vec', 0.5031333565711975, 'nlp', 0), ('intellabs/fastrag', 0.5029743909835815, 'nlp', 0), ('nomic-ai/semantic-search-app-template', 0.5012847781181335, 'study', 1), ('pytorch/data', 0.5008647441864014, 'data', 0), ('alphasecio/langchain-examples', 0.5004814267158508, 'llm', 2)]",7,3.0,,5.75,70,46,6,1,3,20,3,70.0,125.0,90.0,1.8,47
1606,llm,https://github.com/opengvlab/omniquant,[],,[],[],,,,opengvlab/omniquant,OmniQuant,457,36,13,Python,,OmniQuant is a simple and powerful quantization technique for LLMs. ,opengvlab,2024-01-12,2023-08-22,23,19.869565217391305,https://avatars.githubusercontent.com/u/94522163?v=4,OmniQuant is a simple and powerful quantization technique for LLMs. ,"['large-language-models', 'llm', 'quantization']","['large-language-models', 'llm', 'quantization']",2023-12-27,"[('artidoro/qlora', 0.7345274090766907, 'llm', 0), ('squeezeailab/squeezellm', 0.6535307168960571, 'llm', 3), ('vahe1994/spqr', 0.6055932641029358, 'llm', 1), ('bobazooba/xllm', 0.5902096033096313, 'llm', 2), ('lightning-ai/lit-gpt', 0.5481755137443542, 'llm', 1), ('lightning-ai/lit-llama', 0.5302090048789978, 'llm', 0), ('timdettmers/bitsandbytes', 0.5289829969406128, 'util', 0), ('salesforce/xgen', 0.5267922878265381, 'llm', 2), ('ray-project/ray-llm', 0.5080553293228149, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5039510726928711, 'llm', 3), ('hiyouga/llama-factory', 0.5039510726928711, 'llm', 3), ('vllm-project/vllm', 0.5003920197486877, 'llm', 1)]",12,6.0,,0.63,38,29,5,1,1,2,1,38.0,99.0,90.0,2.6,47
782,study,https://github.com/wesm/pydata-book,[],,[],[],,,,wesm/pydata-book,pydata-book,20766,14692,1476,Jupyter Notebook,,"Materials and IPython notebooks for ""Python for Data Analysis"" by Wes McKinney, published by O'Reilly Media",wesm,2024-01-14,2012-06-30,604,34.35641692271331,,"Materials and IPython notebooks for ""Python for Data Analysis"" by Wes McKinney, published by O'Reilly Media",[],[],2023-04-12,"[('jakevdp/pythondatasciencehandbook', 0.7202770709991455, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.6737366318702698, 'study', 0), ('python/cpython', 0.6508677005767822, 'util', 0), ('pandas-dev/pandas', 0.6384697556495667, 'pandas', 0), ('mynameisfiber/high_performance_python_2e', 0.6341950297355652, 'study', 0), ('eleutherai/pyfra', 0.631885826587677, 'ml', 0), ('ageron/handson-ml2', 0.6252664923667908, 'ml', 0), ('jupyter/nbformat', 0.6051349639892578, 'jupyter', 0), ('ipython/ipython', 0.6048235893249512, 'util', 0), ('imageio/imageio', 0.6035079956054688, 'util', 0), ('pytoolz/toolz', 0.599173903465271, 'util', 0), ('cuemacro/finmarketpy', 0.5989004969596863, 'finance', 0), ('man-group/dtale', 0.5984211564064026, 'viz', 0), ('rasbt/mlxtend', 0.5947810411453247, 'ml', 0), ('cohere-ai/notebooks', 0.5929689407348633, 'llm', 0), ('pypy/pypy', 0.5926802158355713, 'util', 0), ('holoviz/panel', 0.5906403064727783, 'viz', 0), ('opengeos/leafmap', 0.5878331065177917, 'gis', 0), ('ta-lib/ta-lib-python', 0.5871008038520813, 'finance', 0), ('ipython/ipyparallel', 0.579038143157959, 'perf', 0), ('goldmansachs/gs-quant', 0.5769721269607544, 'finance', 0), ('krzjoa/awesome-python-data-science', 0.5766783952713013, 'study', 0), ('altair-viz/altair', 0.5764631032943726, 'viz', 0), ('plotly/dash', 0.5707026124000549, 'viz', 0), ('mwaskom/seaborn', 0.5627793073654175, 'viz', 0), ('scikit-mobility/scikit-mobility', 0.5627614259719849, 'gis', 0), ('realpython/python-guide', 0.5613322854042053, 'study', 0), ('kanaries/pygwalker', 0.5609186887741089, 'pandas', 0), ('faster-cpython/ideas', 0.5588380694389343, 'perf', 0), ('residentmario/geoplot', 0.5581743121147156, 'gis', 0), ('tkrabel/bamboolib', 0.557572066783905, 'pandas', 0), ('landscapeio/prospector', 0.5570473670959473, 'util', 0), ('rasbt/watermark', 0.5561281442642212, 'util', 0), ('gotcha/ipdb', 0.5557295083999634, 'debug', 0), ('probml/pyprobml', 0.5555208325386047, 'ml', 0), ('plotly/plotly.py', 0.5537784099578857, 'viz', 0), ('mito-ds/monorepo', 0.5535025000572205, 'jupyter', 0), ('faster-cpython/tools', 0.5520884990692139, 'perf', 0), ('dylanhogg/awesome-python', 0.548250138759613, 'study', 0), ('quantecon/quantecon.py', 0.5465633273124695, 'sim', 0), ('ranaroussi/quantstats', 0.5430867671966553, 'finance', 0), ('numpy/numpy', 0.5418587327003479, 'math', 0), ('mementum/bta-lib', 0.5407599806785583, 'finance', 0), ('vizzuhq/ipyvizzu', 0.5378664135932922, 'jupyter', 0), ('jovianml/opendatasets', 0.5370580554008484, 'data', 0), ('maartenbreddels/ipyvolume', 0.5356112122535706, 'jupyter', 0), ('alkaline-ml/pmdarima', 0.5350579619407654, 'time-series', 0), ('contextlab/hypertools', 0.5346917510032654, 'ml', 0), ('has2k1/plotnine', 0.5339842438697815, 'viz', 0), ('python-odin/odin', 0.5320025682449341, 'util', 0), ('geopandas/geopandas', 0.531495213508606, 'gis', 0), ('gradio-app/gradio', 0.5288758873939514, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5280112624168396, 'viz', 0), ('brandon-rhodes/python-patterns', 0.5279967188835144, 'util', 0), ('lux-org/lux', 0.5268304944038391, 'viz', 0), ('cython/cython', 0.5262758135795593, 'util', 0), ('jazzband/tablib', 0.5241658687591553, 'data', 0), ('quantopian/qgrid', 0.5240903496742249, 'jupyter', 0), ('gerdm/prml', 0.5239573121070862, 'study', 0), ('amaargiru/pyroad', 0.5237447023391724, 'study', 0), ('gbeced/pyalgotrade', 0.5229116082191467, 'finance', 0), ('graphistry/pygraphistry', 0.522087812423706, 'data', 0), ('holoviz/holoviz', 0.5204950571060181, 'viz', 0), ('timofurrer/awesome-asyncio', 0.5189331769943237, 'study', 0), ('malloydata/malloy-py', 0.5186687707901001, 'data', 0), ('ipython/ipykernel', 0.5184985995292664, 'util', 0), ('bokeh/bokeh', 0.5152010321617126, 'viz', 0), ('clips/pattern', 0.5136392116546631, 'nlp', 0), ('albahnsen/pycircular', 0.5135537981987, 'math', 0), ('polyaxon/datatile', 0.513410747051239, 'pandas', 0), ('rjt1990/pyflux', 0.5128363966941833, 'time-series', 0), ('earthlab/earthpy', 0.512826681137085, 'gis', 0), ('vaexio/vaex', 0.5123580098152161, 'perf', 0), ('pysal/pysal', 0.5121508836746216, 'gis', 0), ('statsmodels/statsmodels', 0.5118904709815979, 'ml', 0), ('pyglet/pyglet', 0.5112947225570679, 'gamedev', 0), ('sympy/sympy', 0.511038601398468, 'math', 0), ('pytorch/data', 0.5109310746192932, 'data', 0), ('dlt-hub/dlt', 0.5088979005813599, 'data', 0), ('firmai/industry-machine-learning', 0.5071250200271606, 'study', 0), ('marcomusy/vedo', 0.5054879784584045, 'viz', 0), ('pympler/pympler', 0.505323588848114, 'perf', 0), ('scikit-learn/scikit-learn', 0.5047890543937683, 'ml', 0), ('1200wd/bitcoinlib', 0.5045524835586548, 'crypto', 0), ('pytables/pytables', 0.5043383836746216, 'data', 0), ('twopirllc/pandas-ta', 0.5033674240112305, 'finance', 0), ('federicoceratto/dashing', 0.5012456178665161, 'term', 0), ('fastai/fastcore', 0.5009101629257202, 'util', 0)]",9,5.0,,0.06,6,3,140,9,0,0,0,6.0,4.0,90.0,0.7,46
187,ml,https://github.com/harisiqbal88/plotneuralnet,"['diagrams', 'latex']",,[],[],,,,harisiqbal88/plotneuralnet,PlotNeuralNet,20540,2735,229,TeX,,Latex code for making neural networks diagrams,harisiqbal88,2024-01-13,2018-07-24,288,71.31944444444444,,Latex code for making neural networks diagrams,"['deep-neural-networks', 'latex']","['deep-neural-networks', 'diagrams', 'latex']",2020-11-06,"[('lutzroeder/netron', 0.542097270488739, 'ml', 0)]",13,6.0,,0.0,0,0,67,39,0,0,0,0.0,0.0,90.0,0.0,46
145,llm,https://github.com/openai/gpt-2,[],,[],[],,,,openai/gpt-2,gpt-2,20469,5247,635,Python,https://openai.com/blog/better-language-models/,"Code for the paper ""Language Models are Unsupervised Multitask Learners""",openai,2024-01-13,2019-02-11,259,78.98732083792723,https://avatars.githubusercontent.com/u/14957082?v=4,"Code for the paper ""Language Models are Unsupervised Multitask Learners""",['paper'],['paper'],2020-12-02,"[('openai/finetune-transformer-lm', 0.6553829312324524, 'llm', 1), ('yueyu1030/attrprompt', 0.5717838406562805, 'llm', 0), ('jonasgeiping/cramming', 0.5695840716362, 'nlp', 0), ('hannibal046/awesome-llm', 0.5629648566246033, 'study', 0), ('tatsu-lab/stanford_alpaca', 0.5557239055633545, 'llm', 0), ('facebookresearch/codellama', 0.550112247467041, 'llm', 0), ('srush/minichain', 0.5330092310905457, 'llm', 0), ('freedomintelligence/llmzoo', 0.532707691192627, 'llm', 0), ('next-gpt/next-gpt', 0.5251544713973999, 'llm', 0), ('facebookresearch/llama', 0.5193938612937927, 'llm', 0), ('nvlabs/prismer', 0.5184028744697571, 'diffusion', 0), ('bigscience-workshop/megatron-deepspeed', 0.5042035579681396, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5042035579681396, 'llm', 0), ('juncongmoo/pyllama', 0.504024863243103, 'llm', 0), ('facebookresearch/shepherd', 0.5036864280700684, 'llm', 0), ('explosion/spacy-models', 0.5017908215522766, 'nlp', 0), ('hazyresearch/h3', 0.5015887022018433, 'llm', 0)]",16,2.0,,0.0,3,1,60,38,0,0,0,3.0,1.0,90.0,0.3,46
149,data,https://github.com/twintproject/twint,[],,[],[],,,,twintproject/twint,twint,15365,2744,322,Python,,"An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.",twintproject,2024-01-13,2017-06-10,346,44.35257731958763,https://avatars.githubusercontent.com/u/40190352?v=4,"An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.","['elasticsearch', 'kibana', 'osint', 'scrape', 'scrape-followers', 'scrape-following', 'scrape-likes', 'tweep', 'tweets', 'twint', 'twitter']","['elasticsearch', 'kibana', 'osint', 'scrape', 'scrape-followers', 'scrape-following', 'scrape-likes', 'tweep', 'tweets', 'twint', 'twitter']",2021-03-02,"[('sherlock-project/sherlock', 0.5957009196281433, 'web', 1), ('alirezamika/autoscraper', 0.5726699233055115, 'data', 1), ('scrapy/scrapy', 0.5525391101837158, 'data', 0), ('roniemartinez/dude', 0.5376254916191101, 'util', 0)]",65,4.0,,0.0,1,1,80,35,0,4,4,1.0,0.0,90.0,0.0,46
1058,ml,https://github.com/ddbourgin/numpy-ml,[],,[],[],,,,ddbourgin/numpy-ml,numpy-ml,14370,3641,452,Python,https://numpy-ml.readthedocs.io/,"Machine learning, in numpy",ddbourgin,2024-01-14,2019-04-06,251,57.15340909090909,,"Machine learning, in numpy","['attention', 'bayesian-inference', 'gaussian-mixture-models', 'gaussian-processes', 'good-turing-smoothing', 'gradient-boosting', 'hidden-markov-models', 'knn', 'lstm', 'machine-learning', 'mfcc', 'neural-networks', 'reinforcement-learning', 'resnet', 'topic-modeling', 'vae', 'wavenet', 'wgan-gp', 'word2vec']","['attention', 'bayesian-inference', 'gaussian-mixture-models', 'gaussian-processes', 'good-turing-smoothing', 'gradient-boosting', 'hidden-markov-models', 'knn', 'lstm', 'machine-learning', 'mfcc', 'neural-networks', 'reinforcement-learning', 'resnet', 'topic-modeling', 'vae', 'wavenet', 'wgan-gp', 'word2vec']",2022-01-08,"[('mosaicml/composer', 0.6701366305351257, 'ml-dl', 2), ('huggingface/datasets', 0.6540278196334839, 'nlp', 1), ('google/trax', 0.6432744264602661, 'ml-dl', 2), ('huggingface/transformers', 0.6313506960868835, 'nlp', 1), ('onnx/onnx', 0.6284279227256775, 'ml', 1), ('tensorflow/tensorflow', 0.6283783912658691, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.6215345859527588, 'ml-dl', 0), ('gradio-app/gradio', 0.6104564666748047, 'viz', 1), ('keras-team/keras', 0.6089706420898438, 'ml-dl', 2), ('keras-rl/keras-rl', 0.6088154911994934, 'ml-rl', 3), ('explosion/thinc', 0.6082127094268799, 'ml-dl', 1), ('tensorly/tensorly', 0.6033033728599548, 'ml-dl', 1), ('online-ml/river', 0.6021139025688171, 'ml', 1), ('ageron/handson-ml2', 0.6011914014816284, 'ml', 0), ('lutzroeder/netron', 0.6000135540962219, 'ml', 1), ('rwightman/pytorch-image-models', 0.5986382961273193, 'ml-dl', 1), ('scikit-learn/scikit-learn', 0.5975611209869385, 'ml', 1), ('tensorlayer/tensorlayer', 0.5864402651786804, 'ml-rl', 1), ('nyandwi/modernconvnets', 0.5848450660705566, 'ml-dl', 1), ('xl0/lovely-numpy', 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218,ml,https://github.com/spotify/annoy,[],,[],[],1.0,,,spotify/annoy,annoy,12337,1171,322,C++,,Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk,spotify,2024-01-13,2013-04-01,565,21.829878665318503,https://avatars.githubusercontent.com/u/251374?v=4,Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk,"['approximate-nearest-neighbor-search', 'c-plus-plus', 'golang', 'locality-sensitive-hashing', 'lua', 'nearest-neighbor-search']","['approximate-nearest-neighbor-search', 'c-plus-plus', 'golang', 'locality-sensitive-hashing', 'lua', 'nearest-neighbor-search']",2023-08-20,"[('nmslib/hnswlib', 0.7858440279960632, 'ml', 0), ('spotify/voyager', 0.6272151470184326, 'ml', 1), ('lmcinnes/pynndescent', 0.6271777749061584, 'ml', 2), ('dgilland/cacheout', 0.5922878980636597, 'perf', 0), ('erotemic/ubelt', 0.5622727870941162, 'util', 0), ('cython/cython', 0.5542510151863098, 'util', 0), ('pyston/pyston', 0.5413413643836975, 'util', 0), ('python-cachier/cachier', 0.5307378768920898, 'perf', 0), ('plasma-umass/scalene', 0.5279016494750977, 'profiling', 0), ('joblib/joblib', 0.5218635201454163, 'util', 0), ('pypy/pypy', 0.5199677348136902, 'util', 0), ('grantjenks/python-diskcache', 0.5176307559013367, 'util', 0), ('facebookresearch/faiss', 0.5121656060218811, 'ml', 0), ('life4/textdistance', 0.5062219500541687, 'nlp', 0), ('pythonspeed/filprofiler', 0.5058916807174683, 'profiling', 0)]",88,2.0,,0.6,3,0,131,5,2,3,2,3.0,3.0,90.0,1.0,46
1006,finance,https://github.com/mementum/backtrader,[],,[],[],1.0,,,mementum/backtrader,backtrader,12322,3618,601,Python,https://www.backtrader.com,Python Backtesting library for trading strategies,mementum,2024-01-14,2015-01-10,472,26.082249773208346,,Python Backtesting library for trading strategies,"['backtesting', 'metaclass', 'trading']","['backtesting', 'metaclass', 'trading']",2023-04-19,"[('cuemacro/finmarketpy', 0.8932955265045166, 'finance', 0), ('gbeced/pyalgotrade', 0.6685662269592285, 'finance', 0), ('kernc/backtesting.py', 0.6531647443771362, 'finance', 2), ('pmorissette/bt', 0.6390605568885803, 'finance', 0), ('robcarver17/pysystemtrade', 0.6113889813423157, 'finance', 0), ('goldmansachs/gs-quant', 0.5926412343978882, 'finance', 0), ('gbeced/basana', 0.5649062991142273, 'finance', 1), ('ta-lib/ta-lib-python', 0.560478925704956, 'finance', 0), ('quantopian/zipline', 0.5593721866607666, 'finance', 0), ('pytoolz/toolz', 0.5593503713607788, 'util', 0), ('domokane/financepy', 0.5463677048683167, 'finance', 0), ('mementum/bta-lib', 0.5395269989967346, 'finance', 0), ('eleutherai/pyfra', 0.5332545638084412, 'ml', 0), ('wolever/parameterized', 0.5309708714485168, 'testing', 0), ('pmorissette/ffn', 0.5245431661605835, 'finance', 0), ('google/pyglove', 0.5199276208877563, 'util', 0), ('klen/py-frameworks-bench', 0.5150114893913269, 'perf', 0), ('polakowo/vectorbt', 0.5124863982200623, 'finance', 2), ('getsentry/responses', 0.5119891166687012, 'testing', 0), ('python-rope/rope', 0.50078284740448, 'util', 0)]",56,2.0,,0.31,6,3,110,9,0,15,15,6.0,1.0,90.0,0.2,46
111,ml-interpretability,https://github.com/marcotcr/lime,['interpretable-ml'],,[],[],,,,marcotcr/lime,lime,11075,1798,264,JavaScript,,Lime: Explaining the predictions of any machine learning classifier,marcotcr,2024-01-13,2016-03-15,411,26.94647201946472,,Lime: Explaining the predictions of any machine learning classifier,[],['interpretable-ml'],2021-07-29,"[('seldonio/alibi', 0.7131057977676392, 'ml-interpretability', 0), ('pair-code/lit', 0.6946200132369995, 'ml-interpretability', 0), ('maif/shapash', 0.6499969363212585, 'ml', 0), ('csinva/imodels', 0.6466513276100159, 'ml', 0), ('teamhg-memex/eli5', 0.6452601552009583, 'ml', 0), ('slundberg/shap', 0.6387524604797363, 'ml-interpretability', 0), ('interpretml/interpret', 0.634106457233429, 'ml-interpretability', 1), ('tensorflow/lucid', 0.61471027135849, 'ml-interpretability', 0), ('huggingface/evaluate', 0.6089439392089844, 'ml', 0), ('eleutherai/pythia', 0.5768492817878723, 'ml-interpretability', 1), ('pytorch/captum', 0.5743399262428284, 'ml-interpretability', 1), ('tensorflow/data-validation', 0.5638388395309448, 'ml-ops', 0), ('linkedin/fasttreeshap', 0.5298793315887451, 'ml', 0), ('patchy631/machine-learning', 0.5150882601737976, 'ml', 0), ('xplainable/xplainable', 0.5037121176719666, 'ml-interpretability', 0)]",62,5.0,,0.0,9,2,95,30,0,2,2,9.0,8.0,90.0,0.9,46
746,study,https://github.com/karpathy/nn-zero-to-hero,[],,[],[],,,,karpathy/nn-zero-to-hero,nn-zero-to-hero,9163,1068,259,Jupyter Notebook,,Neural Networks: Zero to Hero,karpathy,2024-01-13,2022-09-08,72,126.01375245579568,,Neural Networks: Zero to Hero,[],[],2023-01-17,"[('rasbt/deeplearning-models', 0.5198577642440796, 'ml-dl', 0), ('mosaicml/composer', 0.5013008713722229, 'ml-dl', 0)]",2,0.0,,0.0,4,3,16,12,0,0,0,4.0,3.0,90.0,0.8,46
230,template,https://github.com/drivendata/cookiecutter-data-science,[],,[],[],1.0,,,drivendata/cookiecutter-data-science,cookiecutter-data-science,7324,2282,120,Python,http://drivendata.github.io/cookiecutter-data-science/,"A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.",drivendata,2024-01-13,2015-10-30,430,17.009953550099535,,"A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.","['ai', 'cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'data-science', 'machine-learning']","['ai', 'cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'data-science', 'machine-learning']",2023-09-22,"[('crmne/cookiecutter-modern-datascience', 0.7191076874732971, 'template', 3), ('airbnb/knowledge-repo', 0.5590470433235168, 'data', 1), ('netflix/metaflow', 0.5439575910568237, 'ml-ops', 3), ('onnx/onnx', 0.5203287601470947, 'ml', 1), ('avaiga/taipy', 0.519536018371582, 'data', 0), ('meltano/meltano', 0.5092251300811768, 'ml-ops', 0), ('google-research/google-research', 0.5077729225158691, 'ml', 2), ('firmai/industry-machine-learning', 0.5075307488441467, 'study', 2), ('merantix-momentum/squirrel-core', 0.5045228004455566, 'ml', 3)]",46,6.0,,0.02,23,11,100,4,0,0,0,24.0,22.0,90.0,0.9,46
453,ml-rl,https://github.com/tensorlayer/tensorlayer,[],,[],[],,,,tensorlayer/tensorlayer,TensorLayer,7264,1636,461,Python,http://tensorlayerx.com,Deep Learning and Reinforcement Learning Library for Scientists and Engineers ,tensorlayer,2024-01-12,2016-06-07,399,18.205513784461154,https://avatars.githubusercontent.com/u/32261543?v=4,Deep Learning and Reinforcement Learning Library for Scientists and Engineers ,"['a3c', 'artificial-intelligence', 'chatbot', 'deep-learning', 'dqn', 'gan', 'google', 'imagenet', 'neural-network', 'object-detection', 'reinforcement-learning', 'tensorflow', 'tensorflow-tutorial', 'tensorflow-tutorials', 'tensorlayer']","['a3c', 'artificial-intelligence', 'chatbot', 'deep-learning', 'dqn', 'gan', 'google', 'imagenet', 'neural-network', 'object-detection', 'reinforcement-learning', 'tensorflow', 'tensorflow-tutorial', 'tensorflow-tutorials', 'tensorlayer']",2023-02-18,"[('pytorch/rl', 0.7300659418106079, 'ml-rl', 1), ('keras-rl/keras-rl', 0.6922048926353455, 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'ml-dl', 1), ('uber/petastorm', 0.5072025060653687, 'data', 2), ('mlflow/mlflow', 0.5055288672447205, 'ml-ops', 0), ('fepegar/torchio', 0.5055205225944519, 'ml-dl', 1), ('arogozhnikov/einops', 0.5044419765472412, 'ml-dl', 2), ('alpa-projects/alpa', 0.5041244029998779, 'ml-dl', 1), ('docarray/docarray', 0.5022443532943726, 'data', 1), ('roboflow/supervision', 0.502228319644928, 'ml', 3), ('dylanhogg/awesome-python', 0.5007988810539246, 'study', 1), ('csinva/imodels', 0.5001160502433777, 'ml', 1)]",134,6.0,,0.02,2,0,93,11,0,11,11,2.0,1.0,90.0,0.5,46
1462,util,https://github.com/hugapi/hug,[],,[],[],,,,hugapi/hug,hug,6756,389,161,Python,,"Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.",hugapi,2024-01-13,2015-07-17,445,15.162552100032062,https://avatars.githubusercontent.com/u/49378345?v=4,"Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.","['command-line', 'falcon', 'http', 'http-server', 'hug-api', 'python-api']","['command-line', 'falcon', 'http', 'http-server', 'hug-api', 'python-api']",2023-06-30,"[('vitalik/django-ninja', 0.6697272658348083, 'web', 0), ('tiangolo/fastapi', 0.6468743085861206, 'web', 0), ('falconry/falcon', 0.6300604343414307, 'web', 1), ('simple-salesforce/simple-salesforce', 0.6207526922225952, 'data', 0), ('python-restx/flask-restx', 0.6121255159378052, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5935749411582947, 'template', 0), ('encode/httpx', 0.5931265950202942, 'web', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5920613408088684, 'template', 0), ('ml-tooling/opyrator', 0.5820281505584717, 'viz', 0), ('psf/requests', 0.5775818228721619, 'web', 1), ('willmcgugan/textual', 0.5758522152900696, 'term', 0), ('huggingface/huggingface_hub', 0.5746172070503235, 'ml', 0), ('starlite-api/starlite', 0.573395848274231, 'web', 0), ('requests/toolbelt', 0.5709561705589294, 'util', 1), ('fastai/ghapi', 0.5626925230026245, 'util', 0), ('masoniteframework/masonite', 0.5562730431556702, 'web', 0), ('taverntesting/tavern', 0.5526415705680847, 'testing', 1), ('shishirpatil/gorilla', 0.5522815585136414, 'llm', 0), ('pallets/quart', 0.533208429813385, 'web', 1), ('pallets/flask', 0.5281922221183777, 'web', 0), ('pallets/werkzeug', 0.5272501111030579, 'web', 1), ('radiantearth/radiant-mlhub', 0.5193449854850769, 'gis', 0), ('alirn76/panther', 0.5171224474906921, 'web', 0), ('snyk-labs/pysnyk', 0.515166163444519, 'security', 0), ('cherrypy/cherrypy', 0.5129648447036743, 'web', 2), ('prefecthq/server', 0.5119301676750183, 'util', 0), ('googleapis/google-api-python-client', 0.5113233327865601, 'util', 0), ('encode/uvicorn', 0.5106011629104614, 'web', 2), ('eternnoir/pytelegrambotapi', 0.507483184337616, 'util', 1), ('hydrosquall/tiingo-python', 0.5074604749679565, 'finance', 0), ('openai/openai-python', 0.5053588151931763, 'util', 0), ('aio-libs/aiohttp', 0.5045132040977478, 'web', 2), ('urwid/urwid', 0.5001529455184937, 'term', 0)]",120,6.0,,0.08,1,0,103,7,0,7,7,1.0,1.0,90.0,1.0,46
1117,web,https://github.com/webpy/webpy,[],,[],[],,,,webpy/webpy,webpy,5856,1325,337,Python,http://webpy.org,web.py is a web framework for python that is as simple as it is powerful. ,webpy,2024-01-13,2008-09-29,800,7.31869309051955,https://avatars.githubusercontent.com/u/26682?v=4,web.py is a web framework for python that is as simple as it is powerful. ,[],[],2024-01-12,"[('bottlepy/bottle', 0.7816590070724487, 'web', 0), ('pallets/flask', 0.7347891926765442, 'web', 0), ('masoniteframework/masonite', 0.7172554731369019, 'web', 0), ('reflex-dev/reflex', 0.702220618724823, 'web', 0), ('cherrypy/cherrypy', 0.6774104833602905, 'web', 0), ('clips/pattern', 0.6514905095100403, 'nlp', 0), ('pylons/pyramid', 0.6500197052955627, 'web', 0), ('pypy/pypy', 0.6487240195274353, 'util', 0), ('klen/muffin', 0.6442074179649353, 'web', 0), ('r0x0r/pywebview', 0.6379135251045227, 'gui', 0), ('pyodide/pyodide', 0.6373624205589294, 'util', 0), ('scrapy/scrapy', 0.6368110775947571, 'data', 0), ('willmcgugan/textual', 0.6224280595779419, 'term', 0), ('falconry/falcon', 0.6171799302101135, 'web', 0), ('eleutherai/pyfra', 0.6089206337928772, 'ml', 0), ('pallets/werkzeug', 0.5957249999046326, 'web', 0), ('pyglet/pyglet', 0.5836023688316345, 'gamedev', 0), ('pywebio/pywebio', 0.5835192799568176, 'web', 0), ('python/cpython', 0.5767532587051392, 'util', 0), ('hoffstadt/dearpygui', 0.5751984119415283, 'gui', 0), ('pyodide/micropip', 0.5706849694252014, 'util', 0), ('encode/uvicorn', 0.5704232454299927, 'web', 0), ('alirezamika/autoscraper', 0.5700419545173645, 'data', 0), ('simple-salesforce/simple-salesforce', 0.5696903467178345, 'data', 0), ('neoteroi/blacksheep', 0.5648735761642456, 'web', 0), ('requests/toolbelt', 0.563819169998169, 'util', 0), ('voila-dashboards/voila', 0.5608404278755188, 'jupyter', 0), ('seleniumbase/seleniumbase', 0.5600611567497253, 'testing', 0), ('flet-dev/flet', 0.5566127896308899, 'web', 0), ('pallets/quart', 0.5523453950881958, 'web', 0), ('reactive-python/reactpy', 0.5520175695419312, 'web', 0), ('roniemartinez/dude', 0.5446486473083496, 'util', 0), ('holoviz/panel', 0.5416973829269409, 'viz', 0), ('encode/httpx', 0.5403591394424438, 'web', 0), ('jiffyclub/snakeviz', 0.5397558808326721, 'profiling', 0), ('bokeh/bokeh', 0.5360361933708191, 'viz', 0), ('urwid/urwid', 0.533157467842102, 'term', 0), ('pyston/pyston', 0.5299744606018066, 'util', 0), ('1200wd/bitcoinlib', 0.5290544033050537, 'crypto', 0), ('plotly/dash', 0.5255879163742065, 'viz', 0), ('cobrateam/splinter', 0.5233193635940552, 'testing', 0), ('pytoolz/toolz', 0.522210419178009, 'util', 0), ('pyinfra-dev/pyinfra', 0.5194896459579468, 'util', 0), ('dylanhogg/awesome-python', 0.5156476497650146, 'study', 0), ('pygamelib/pygamelib', 0.5152013301849365, 'gamedev', 0), ('amaargiru/pyroad', 0.5149017572402954, 'study', 0), ('connorferster/handcalcs', 0.5106810331344604, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5092483162879944, 'jupyter', 0), ('timofurrer/awesome-asyncio', 0.5086891651153564, 'study', 0), ('binux/pyspider', 0.5072404742240906, 'data', 0), ('ethereum/web3.py', 0.5061323046684265, 'crypto', 0), ('pygments/pygments', 0.5060795545578003, 'util', 0), ('microsoft/playwright-python', 0.505174994468689, 'testing', 0), ('jquast/blessed', 0.5048279166221619, 'term', 0), ('psf/requests', 0.5032954812049866, 'web', 0), ('jupyterlite/jupyterlite', 0.5002906918525696, 'jupyter', 0)]",90,5.0,,0.17,14,7,186,0,1,1,1,14.0,31.0,90.0,2.2,46
757,ml-dl,https://github.com/xpixelgroup/basicsr,[],,[],[],,,,xpixelgroup/basicsr,BasicSR,5775,1036,96,Python,https://basicsr.readthedocs.io/en/latest/,"Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.",xpixelgroup,2024-01-13,2018-04-19,301,19.140625,https://avatars.githubusercontent.com/u/104772975?v=4,"Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.","['basicsr', 'basicvsr', 'dfdnet', 'ecbsr', 'edsr', 'edvr', 'esrgan', 'pytorch', 'rcan', 'restoration', 'srgan', 'srresnet', 'stylegan2', 'super-resolution', 'swinir']","['basicsr', 'basicvsr', 'dfdnet', 'ecbsr', 'edsr', 'edvr', 'esrgan', 'pytorch', 'rcan', 'restoration', 'srgan', 'srresnet', 'stylegan2', 'super-resolution', 'swinir']",2023-02-02,"[('xinntao/real-esrgan', 0.6734346151351929, 'ml-dl', 3), ('tencentarc/gfpgan', 0.6321846842765808, 'ml', 2), ('open-mmlab/mmediting', 0.5552972555160522, 'ml', 2)]",19,7.0,,0.06,32,2,70,12,0,6,6,32.0,25.0,90.0,0.8,46
1646,util,https://github.com/prompt-toolkit/ptpython,"['repl', 'cli']",,[],[],1.0,,,prompt-toolkit/ptpython,ptpython,4969,316,66,Python,,A better Python REPL,prompt-toolkit,2024-01-13,2014-09-29,487,10.200293255131966,https://avatars.githubusercontent.com/u/44159252?v=4,A better Python REPL,[],"['cli', 'repl']",2023-12-14,"[('dosisod/refurb', 0.573533296585083, 'util', 1), ('google/python-fire', 0.5544407367706299, 'term', 1), ('urwid/urwid', 0.5443150997161865, 'term', 0), ('python/cpython', 0.5359960794448853, 'util', 0), ('sourcery-ai/sourcery', 0.5356951355934143, 'util', 0), ('pypy/pypy', 0.532114565372467, 'util', 0), ('pypi/warehouse', 0.5278775691986084, 'util', 0), ('pytoolz/toolz', 0.5264323949813843, 'util', 0), ('pdm-project/pdm', 0.518380880355835, 'util', 0), ('pypa/hatch', 0.5076357126235962, 'util', 1), ('erotemic/ubelt', 0.5056498646736145, 'util', 0), ('pyscript/pyscript-cli', 0.5022722482681274, 'web', 0)]",58,4.0,,0.52,24,13,113,1,2,4,2,24.0,30.0,90.0,1.2,46
636,util,https://github.com/pycqa/pycodestyle,[],,[],[],,,,pycqa/pycodestyle,pycodestyle,4941,809,117,Python,https://pycodestyle.pycqa.org,Simple Python style checker in one Python file,pycqa,2024-01-13,2009-10-02,747,6.609401872730747,https://avatars.githubusercontent.com/u/8749848?v=4,Simple Python style checker in one Python file,"['flake8-plugin', 'linter-flake8', 'linter-plugin', 'pep8', 'style-guide', 'styleguide']","['flake8-plugin', 'linter-flake8', 'linter-plugin', 'pep8', 'style-guide', 'styleguide']",2024-01-08,"[('pycqa/flake8', 0.6883364915847778, 'util', 4), ('pycqa/mccabe', 0.5681533813476562, 'util', 3), ('hhatto/autopep8', 0.5678965449333191, 'util', 1), ('pycqa/pyflakes', 0.5580477118492126, 'util', 0), ('landscapeio/prospector', 0.531697690486908, 'util', 0), ('microsoft/pyright', 0.5176783204078674, 'typing', 0), ('agronholm/typeguard', 0.5046524405479431, 'typing', 0)]",133,4.0,,0.88,24,21,174,0,0,3,3,24.0,35.0,90.0,1.5,46
1268,perf,https://github.com/ultrajson/ultrajson,[],,[],[],,,,ultrajson/ultrajson,ultrajson,4180,374,87,C,https://pypi.org/project/ujson/,Ultra fast JSON decoder and encoder written in C with Python bindings,ultrajson,2024-01-12,2011-02-27,674,6.1991525423728815,https://avatars.githubusercontent.com/u/61062879?v=4,Ultra fast JSON decoder and encoder written in C with Python bindings,"['c', 'decoder', 'encoder', 'json', 'ujson', 'ultrajson']","['c', 'decoder', 'encoder', 'json', 'ujson', 'ultrajson']",2024-01-05,"[('cython/cython', 0.5291088223457336, 'util', 1), ('pypy/pypy', 0.5242108106613159, 'util', 0), ('blosc/python-blosc', 0.5216162204742432, 'perf', 0)]",87,4.0,,0.9,11,10,157,0,2,2,2,11.0,34.0,90.0,3.1,46
59,gamedev,https://github.com/panda3d/panda3d,[],,[],[],,,,panda3d/panda3d,panda3d,4140,780,199,C++,https://www.panda3d.org/,"Powerful, mature open-source cross-platform game engine for Python and C++, developed by Disney and CMU",panda3d,2024-01-13,2013-09-30,539,7.678855325914149,https://avatars.githubusercontent.com/u/590956?v=4,"Powerful, mature open-source cross-platform game engine for Python and C++, developed by Disney and CMU","['c-plus-plus', 'cross-platform', 'game-development', 'game-engine', 'gamedev', 'multi-platform', 'open-source', 'opengl', 'panda3d', 'panda3d-game-engine']","['c-plus-plus', 'cross-platform', 'game-development', 'game-engine', 'gamedev', 'multi-platform', 'open-source', 'opengl', 'panda3d', 'panda3d-game-engine']",2024-01-08,"[('pokepetter/ursina', 0.8077520132064819, 'gamedev', 2), ('kitao/pyxel', 0.6634293794631958, 'gamedev', 3), ('pygame/pygame', 0.609826922416687, 'gamedev', 2), ('lordmauve/pgzero', 0.5944162011146545, 'gamedev', 0), ('pythonarcade/arcade', 0.5839753746986389, 'gamedev', 1), ('pygamelib/pygamelib', 0.5582475662231445, 'gamedev', 2), ('renpy/renpy', 0.5438269972801208, 'viz', 0), ('cython/cython', 0.5402704477310181, 'util', 0), ('isl-org/open3d', 0.5318998694419861, 'sim', 1), ('scikit-build/scikit-build', 0.5191662907600403, 'ml', 1), ('fastai/fastcore', 0.5172508955001831, 'util', 0), ('pyglet/pyglet', 0.5137691497802734, 'gamedev', 2), ('sail-sg/envpool', 0.5131605267524719, 'sim', 0), ('exaloop/codon', 0.5107043981552124, 'perf', 0), ('polyaxon/datatile', 0.5070898532867432, 'pandas', 0), ('eventual-inc/daft', 0.5059236288070679, 'pandas', 0), ('kivy/kivy', 0.5057852268218994, 'util', 0), ('quantconnect/lean', 0.5038788318634033, 'finance', 0)]",162,1.0,,5.98,71,28,125,0,1,2,1,71.0,121.0,90.0,1.7,46
1154,data,https://github.com/mongodb/mongo-python-driver,[],,[],[],,,,mongodb/mongo-python-driver,mongo-python-driver,3982,1192,240,Python,https://pymongo.readthedocs.io,PyMongo - the Official MongoDB Python driver,mongodb,2024-01-13,2009-01-15,784,5.074458401602039,https://avatars.githubusercontent.com/u/45120?v=4,PyMongo - the Official MongoDB Python driver,"['mongodb', 'mongodb-driver', 'pymongo']","['mongodb', 'mongodb-driver', 'pymongo']",2024-01-12,"[('pyeve/eve', 0.5024893879890442, 'web', 1), ('mause/duckdb_engine', 0.5003612041473389, 'data', 0)]",206,2.0,,5.9,94,87,183,0,6,10,6,92.0,109.0,90.0,1.2,46
775,diffusion,https://github.com/jina-ai/discoart,[],,[],[],,,,jina-ai/discoart,discoart,3834,254,34,Python,,🪩 Create Disco Diffusion artworks in one line,jina-ai,2024-01-13,2022-06-30,82,46.35233160621762,https://avatars.githubusercontent.com/u/60539444?v=4,🪩 Create Disco Diffusion artworks in one line,"['clip-guided-diffusion', 'creative-ai', 'creative-art', 'cross-modal', 'dalle', 'diffusion', 'disco-diffusion', 'discodiffusion', 'generative-art', 'imgen', 'latent-diffusion', 'midjourney', 'multimodal', 'prompts', 'stable-diffusion']","['clip-guided-diffusion', 'creative-ai', 'creative-art', 'cross-modal', 'dalle', 'diffusion', 'disco-diffusion', 'discodiffusion', 'generative-art', 'imgen', 'latent-diffusion', 'midjourney', 'multimodal', 'prompts', 'stable-diffusion']",2023-05-16,"[('nateraw/stable-diffusion-videos', 0.5996933579444885, 'diffusion', 1), ('carson-katri/dream-textures', 0.5772116184234619, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.560263454914093, 'diffusion', 2), ('invoke-ai/invokeai', 0.55684894323349, 'diffusion', 3), ('compvis/stable-diffusion', 0.5315988659858704, 'diffusion', 1), ('lkwq007/stablediffusion-infinity', 0.5142841935157776, 'diffusion', 1), ('huggingface/diffusers', 0.5044229030609131, 'diffusion', 2)]",7,2.0,,0.06,2,2,19,8,1,77,1,2.0,3.0,90.0,1.5,46
692,util,https://github.com/joblib/joblib,[],,[],[],,,,joblib/joblib,joblib,3544,436,61,Python,http://joblib.readthedocs.org,Computing with Python functions.,joblib,2024-01-13,2010-05-07,716,4.945773524720893,https://avatars.githubusercontent.com/u/332661?v=4,Computing with Python functions.,"['caching', 'memoization', 'multiprocessing', 'parallel-computing', 'threading']","['caching', 'memoization', 'multiprocessing', 'parallel-computing', 'threading']",2023-12-01,"[('dgilland/cacheout', 0.6794201135635376, 'perf', 2), ('python-cachier/cachier', 0.6731811165809631, 'perf', 2), ('noxdafox/pebble', 0.6440830826759338, 'perf', 2), ('ipython/ipyparallel', 0.6388868689537048, 'perf', 0), ('dask/dask', 0.6337271332740784, 'perf', 0), ('sumerc/yappi', 0.6218010783195496, 'profiling', 0), ('pympler/pympler', 0.6179777979850769, 'perf', 0), ('exaloop/codon', 0.6015269160270691, 'perf', 0), ('fastai/fastcore', 0.6007087826728821, 'util', 0), ('pyston/pyston', 0.6001591086387634, 'util', 0), ('pythonspeed/filprofiler', 0.5940987467765808, 'profiling', 0), ('pypy/pypy', 0.5741433501243591, 'util', 0), ('python-trio/trio', 0.5737351179122925, 'perf', 0), ('cython/cython', 0.5678399801254272, 'util', 0), ('sympy/sympy', 0.5607999563217163, 'math', 0), ('jmcarpenter2/swifter', 0.5563712120056152, 'pandas', 1), ('pythonprofilers/memory_profiler', 0.5550679564476013, 'profiling', 0), ('nalepae/pandarallel', 0.5519195199012756, 'pandas', 0), ('numpy/numpy', 0.5501325130462646, 'math', 0), ('blackhc/toma', 0.5492387413978577, 'ml-dl', 0), ('samuelcolvin/arq', 0.5469869375228882, 'data', 0), ('google/jax', 0.5429615378379822, 'ml', 0), ('klen/py-frameworks-bench', 0.540972888469696, 'perf', 0), ('agronholm/apscheduler', 0.5400955080986023, 'util', 0), ('nvidia/warp', 0.5375118851661682, 'sim', 0), ('locustio/locust', 0.534275233745575, 'testing', 0), ('grantjenks/python-diskcache', 0.5340135097503662, 'util', 0), ('plasma-umass/scalene', 0.5323008298873901, 'profiling', 0), ('micropython/micropython', 0.5310302376747131, 'util', 0), ('pytoolz/toolz', 0.5292420983314514, 'util', 0), ('python/cpython', 0.5285337567329407, 'util', 0), ('eleutherai/pyfra', 0.5282385349273682, 'ml', 0), ('google/pyglove', 0.526533305644989, 'util', 0), ('joblib/loky', 0.5221757292747498, 'perf', 0), ('spotify/annoy', 0.5218635201454163, 'ml', 0), ('klen/muffin', 0.5203728675842285, 'web', 0), ('hyperopt/hyperopt', 0.5118980407714844, 'ml', 0), ('erotemic/ubelt', 0.5093076825141907, 'util', 0), ('geeogi/async-python-lambda-template', 0.5088009834289551, 'template', 0), ('scipy/scipy', 0.508256196975708, 'math', 0), ('lcompilers/lpython', 0.5082236528396606, 'util', 0), ('baruchel/tco', 0.5076671838760376, 'perf', 0), ('evhub/coconut', 0.5061070322990417, 'util', 0), ('eventlet/eventlet', 0.5059096217155457, 'perf', 0), ('pytables/pytables', 0.5035998225212097, 'data', 0), ('fluentpython/example-code-2e', 0.5024613738059998, 'study', 0), ('jeshraghian/snntorch', 0.5017874836921692, 'ml-dl', 0), ('fugue-project/fugue', 0.5016358494758606, 'pandas', 0), ('adafruit/circuitpython', 0.5005473494529724, 'util', 0)]",127,6.0,,1.27,46,21,167,1,3,6,3,46.0,103.0,90.0,2.2,46
419,ml-rl,https://github.com/facebookresearch/reagent,[],,[],[],,,,facebookresearch/reagent,ReAgent,3495,534,152,Python,https://reagent.ai,"A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)",facebookresearch,2024-01-14,2017-07-27,339,10.288057190916737,https://avatars.githubusercontent.com/u/16943930?v=4,"A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)",[],[],2024-01-09,"[('openai/gym', 0.573969841003418, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.5644688606262207, 'ml-rl', 0), ('google/dopamine', 0.5503111481666565, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5440720915794373, 'ml-rl', 0), ('deepmind/acme', 0.536990761756897, 'ml-rl', 0), ('thu-ml/tianshou', 0.5355173945426941, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5251111388206482, 'ml-rl', 0), ('ai4finance-foundation/finrl', 0.5226213932037354, 'finance', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5210154056549072, 'study', 0), ('stanfordnlp/dspy', 0.5081221461296082, 'llm', 0), ('pytorch/rl', 0.5019720196723938, 'ml-rl', 0)]",164,5.0,,1.31,1,0,79,0,0,0,0,1.0,2.0,90.0,2.0,46
139,ml-interpretability,https://github.com/pair-code/lit,[],,[],[],1.0,,,pair-code/lit,lit,3273,339,71,TypeScript,https://pair-code.github.io/lit,The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.,pair-code,2024-01-14,2020-07-28,183,17.885245901639344,https://avatars.githubusercontent.com/u/29804435?v=4,The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.,"['machine-learning', 'natural-language-processing', 'visualization']","['machine-learning', 'natural-language-processing', 'visualization']",2023-11-08,"[('marcotcr/lime', 0.6946200132369995, 'ml-interpretability', 0), ('tensorflow/lucid', 0.6758688688278198, 'ml-interpretability', 2), ('csinva/imodels', 0.6702791452407837, 'ml', 1), ('seldonio/alibi', 0.6469577550888062, 'ml-interpretability', 1), ('eleutherai/pythia', 0.641786515712738, 'ml-interpretability', 0), ('maif/shapash', 0.62184739112854, 'ml', 1), ('selfexplainml/piml-toolbox', 0.6214944124221802, 'ml-interpretability', 0), ('interpretml/interpret', 0.6197747588157654, 'ml-interpretability', 1), ('huggingface/evaluate', 0.5728373527526855, 'ml', 1), ('teamhg-memex/eli5', 0.5625553727149963, 'ml', 1), ('pytorch/captum', 0.5618991851806641, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.5486710071563721, 'ml', 2), ('tensorflow/data-validation', 0.5432097315788269, 'ml-ops', 0), ('hazyresearch/meerkat', 0.5387915968894958, 'viz', 1), ('xplainable/xplainable', 0.5334827303886414, 'ml-interpretability', 1), ('slundberg/shap', 0.532273530960083, 'ml-interpretability', 1)]",34,3.0,,5.25,34,16,42,2,2,3,2,33.0,13.0,90.0,0.4,46
1200,ml,https://github.com/huggingface/notebooks,[],,[],[],,,,huggingface/notebooks,notebooks,3012,1308,73,Jupyter Notebook,,Notebooks using the Hugging Face libraries 🤗,huggingface,2024-01-14,2020-06-15,189,15.924471299093655,https://avatars.githubusercontent.com/u/25720743?v=4,Notebooks using the Hugging Face libraries 🤗,[],[],2024-01-05,"[('huggingface/huggingface_hub', 0.5788795948028564, 'ml', 0), ('koaning/calm-notebooks', 0.5623078942298889, 'study', 0), ('huggingface/diffusion-models-class', 0.5564571619033813, 'study', 0), ('cohere-ai/notebooks', 0.529309868812561, 'llm', 0)]",75,1.0,,4.69,35,12,44,0,0,0,0,35.0,49.0,90.0,1.4,46
1717,util,https://github.com/jendrikseipp/vulture,['code-quality'],,[],[],,,,jendrikseipp/vulture,vulture,2874,175,26,Python,,Find dead Python code,jendrikseipp,2024-01-13,2017-03-06,360,7.980166600555335,,Find dead Python code,['dead-code-removal'],"['code-quality', 'dead-code-removal']",2024-01-06,"[('facebookincubator/bowler', 0.5705008506774902, 'util', 0), ('dosisod/refurb', 0.5567197203636169, 'util', 0), ('rubik/radon', 0.5527566075325012, 'util', 0), ('agronholm/typeguard', 0.5461918711662292, 'typing', 1), ('google/yapf', 0.5393766164779663, 'util', 1), ('microsoft/pyright', 0.536612331867218, 'typing', 1), ('sourcery-ai/sourcery', 0.5342533588409424, 'util', 1), ('nedbat/coveragepy', 0.5239750146865845, 'testing', 0), ('pythonprofilers/memory_profiler', 0.5172761678695679, 'profiling', 0)]",40,4.0,,0.62,17,10,84,0,4,7,4,17.0,29.0,90.0,1.7,46
99,data,https://github.com/zoomeranalytics/xlwings,[],,[],[],,,,zoomeranalytics/xlwings,xlwings,2773,482,122,Python,https://www.xlwings.org,xlwings is a Python library that makes it easy to call Python from Excel and vice versa. It works with Excel on Windows and macOS as well as with Google Sheets and Excel on the web. ,zoomeranalytics,2024-01-13,2014-03-17,515,5.382972823072657,https://avatars.githubusercontent.com/u/6239016?v=4,xlwings is a Python library that makes it easy to call Python from Excel and vice versa. It works with Excel on Windows and macOS as well as with Google Sheets and Excel on the web. ,"['automation', 'excel', 'google-sheets', 'googlesheets', 'reporting']","['automation', 'excel', 'google-sheets', 'googlesheets', 'reporting']",2024-01-05,"[('jmcnamara/xlsxwriter', 0.7553142309188843, 'data', 0), ('jazzband/tablib', 0.5972012877464294, 'data', 0), ('connorferster/handcalcs', 0.5488008856773376, 'jupyter', 0), ('plotly/dash', 0.5458297729492188, 'viz', 0), ('tkrabel/bamboolib', 0.5089218616485596, 'pandas', 0)]",64,2.0,,3.02,47,27,120,0,17,16,17,48.0,84.0,90.0,1.8,46
1437,util,https://github.com/lxml/lxml,['xml'],,[],[],,,,lxml/lxml,lxml,2512,586,80,Python,https://lxml.de/,The lxml XML toolkit for Python,lxml,2024-01-14,2011-02-11,676,3.7128378378378377,https://avatars.githubusercontent.com/u/612230?v=4,The lxml XML toolkit for Python,[],['xml'],2024-01-12,"[('roniemartinez/dude', 0.5113477110862732, 'util', 0)]",156,5.0,,5.48,19,16,157,0,9,11,9,19.0,26.0,90.0,1.4,46
1067,nlp,https://github.com/bigscience-workshop/promptsource,[],,[],[],,,,bigscience-workshop/promptsource,promptsource,2325,320,28,Python,,"Toolkit for creating, sharing and using natural language prompts.",bigscience-workshop,2024-01-14,2021-05-19,140,16.506085192697768,https://avatars.githubusercontent.com/u/82455566?v=4,"Toolkit for creating, sharing and using natural language prompts.","['machine-learning', 'natural-language-processing', 'nlp']","['machine-learning', 'natural-language-processing', 'nlp']",2023-10-23,"[('promptslab/awesome-prompt-engineering', 0.5903843641281128, 'study', 1), ('rasahq/rasa', 0.5880197882652283, 'llm', 3), ('promptslab/promptify', 0.5728100538253784, 'nlp', 2), ('srush/minichain', 0.5696084499359131, 'llm', 0), ('nltk/nltk', 0.5687624216079712, 'nlp', 3), ('gunthercox/chatterbot-corpus', 0.5633988976478577, 'nlp', 0), ('keirp/automatic_prompt_engineer', 0.5572788715362549, 'llm', 0), ('stanfordnlp/dspy', 0.5486031770706177, 'llm', 0), ('tmbo/questionary', 0.5423410534858704, 'term', 0), ('conceptofmind/toolformer', 0.5377352237701416, 'llm', 0), ('openlmlab/moss', 0.5367189049720764, 'llm', 1), ('rcgai/simplyretrieve', 0.5266952514648438, 'llm', 3), ('hegelai/prompttools', 0.526086688041687, 'llm', 1), ('killianlucas/open-interpreter', 0.5243964195251465, 'llm', 0), ('neulab/prompt2model', 0.5239977240562439, 'llm', 0), ('agenta-ai/agenta', 0.52383953332901, 'llm', 0), ('alibaba/easynlp', 0.5199021697044373, 'nlp', 2), ('argilla-io/argilla', 0.5188122987747192, 'nlp', 3), ('lm-sys/fastchat', 0.514387309551239, 'llm', 0), ('flairnlp/flair', 0.5140012502670288, 'nlp', 3), ('hazyresearch/ama_prompting', 0.512933611869812, 'llm', 0), ('guidance-ai/guidance', 0.5123276114463806, 'llm', 0), ('allenai/allennlp', 0.5015190839767456, 'nlp', 2)]",65,6.0,,0.13,11,11,32,3,0,2,2,11.0,10.0,90.0,0.9,46
1367,sim,https://github.com/rdkit/rdkit,['chemistry'],,[],[],,,,rdkit/rdkit,rdkit,2305,808,85,HTML,,The official sources for the RDKit library,rdkit,2024-01-12,2013-05-12,559,4.121328224776501,https://avatars.githubusercontent.com/u/2018047?v=4,The official sources for the RDKit library,"['c-plus-plus', 'cheminformatics', 'rdkit']","['c-plus-plus', 'cheminformatics', 'chemistry', 'rdkit']",2024-01-11,"[('espressomd/espresso', 0.5796418786048889, 'sim', 1), ('rasbt/machine-learning-book', 0.5607836246490479, 'study', 0), ('skorch-dev/skorch', 0.510020911693573, 'ml-dl', 0)]",208,1.0,,6.33,220,142,130,0,11,16,11,220.0,304.0,90.0,1.4,46
377,ml-interpretability,https://github.com/oegedijk/explainerdashboard,[],,[],[],,,,oegedijk/explainerdashboard,explainerdashboard,2123,305,22,Python,http://explainerdashboard.readthedocs.io,"Quickly build Explainable AI dashboards that show the inner workings of so-called ""blackbox"" machine learning models.",oegedijk,2024-01-11,2019-10-30,221,9.569220862846104,,"Quickly build Explainable AI dashboards that show the inner workings of so-called ""blackbox"" machine learning models.","['dash', 'dashboard', 'data-scientists', 'explainer', 'inner-workings', 'interactive-dashboards', 'interactive-plots', 'model-predictions', 'permutation-importances', 'plotly', 'shap', 'shap-values', 'xai', 'xai-library']","['dash', 'dashboard', 'data-scientists', 'explainer', 'inner-workings', 'interactive-dashboards', 'interactive-plots', 'model-predictions', 'permutation-importances', 'plotly', 'shap', 'shap-values', 'xai', 'xai-library']",2023-12-18,"[('interpretml/interpret', 0.6743864417076111, 'ml-interpretability', 1), ('xplainable/xplainable', 0.627983570098877, 'ml-interpretability', 2), ('seldonio/alibi', 0.6191368699073792, 'ml-interpretability', 1), ('teamhg-memex/eli5', 0.602367103099823, 'ml', 0), ('polyaxon/datatile', 0.5954424142837524, 'pandas', 1), ('mindsdb/mindsdb', 0.5860490798950195, 'data', 0), ('maif/shapash', 0.5845269560813904, 'ml', 1), ('prefecthq/marvin', 0.584237813949585, 'nlp', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5830463171005249, 'study', 0), ('rafiqhasan/auto-tensorflow', 0.5813491940498352, 'ml-dl', 0), ('googlecloudplatform/vertex-ai-samples', 0.5758024454116821, 'ml', 0), ('csinva/imodels', 0.5566130876541138, 'ml', 0), ('gradio-app/gradio', 0.5561123490333557, 'viz', 0), ('bentoml/bentoml', 0.553854763507843, 'ml-ops', 0), ('cheshire-cat-ai/core', 0.5511989593505859, 'llm', 0), ('tensorflow/lucid', 0.5506213903427124, 'ml-interpretability', 0), ('unity-technologies/ml-agents', 0.5465452671051025, 'ml-rl', 0), ('antonosika/gpt-engineer', 0.545367956161499, 'llm', 0), ('slundberg/shap', 0.5450636148452759, 'ml-interpretability', 1), ('carla-recourse/carla', 0.5436306595802307, 'ml', 0), ('lutzroeder/netron', 0.54157954454422, 'ml', 0), ('reloadware/reloadium', 0.5406351089477539, 'profiling', 0), ('wandb/client', 0.5388295650482178, 'ml', 0), ('pytorchlightning/pytorch-lightning', 0.5369613766670227, 'ml-dl', 0), ('google-research/google-research', 0.5351460576057434, 'ml', 0), ('aimhubio/aim', 0.5350830554962158, 'ml-ops', 0), ('whylabs/whylogs', 0.5336712598800659, 'util', 0), ('explosion/thinc', 0.5325929522514343, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5324363708496094, 'ml', 0), ('sweepai/sweep', 0.5306786298751831, 'llm', 0), ('transformeroptimus/superagi', 0.5287240147590637, 'llm', 0), ('ml-tooling/opyrator', 0.5270505547523499, 'viz', 0), ('mosaicml/composer', 0.5244570374488831, 'ml-dl', 0), ('districtdatalabs/yellowbrick', 0.5237336158752441, 'ml', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.52154141664505, 'study', 0), ('activeloopai/deeplake', 0.5188158750534058, 'ml-ops', 0), ('google/dopamine', 0.5178472399711609, 'ml-rl', 0), ('oneil512/insight', 0.5162001848220825, 'ml', 0), ('salesforce/logai', 0.5160885453224182, 'util', 0), ('mlc-ai/mlc-llm', 0.5132462978363037, 'llm', 0), ('tensorlayer/tensorlayer', 0.5132309794425964, 'ml-rl', 0), ('ray-project/ray', 0.5122550129890442, 'ml-ops', 0), ('hpcaitech/colossalai', 0.5112443566322327, 'llm', 0), ('mlflow/mlflow', 0.5103710889816284, 'ml-ops', 0), ('google/vizier', 0.5101515650749207, 'ml', 0), ('huggingface/datasets', 0.5090382695198059, 'nlp', 0), ('google-research/language', 0.5075442790985107, 'nlp', 0), ('microsoft/nni', 0.507343053817749, 'ml', 0), ('nvidia/deeplearningexamples', 0.5068669319152832, 'ml-dl', 0)]",21,6.0,,1.04,14,5,51,1,6,19,6,14.0,22.0,90.0,1.6,46
1136,util,https://github.com/libaudioflux/audioflux,[],,[],[],,,,libaudioflux/audioflux,audioFlux,1957,95,26,C,https://audioflux.top,"A library for audio and music analysis, feature extraction.",libaudioflux,2024-01-13,2023-01-16,54,36.145118733509236,https://avatars.githubusercontent.com/u/105165315?v=4,"A library for audio and music analysis, feature extraction.","['audio', 'audio-analysis', 'audio-features', 'audio-processing', 'deep-learning', 'machine-learning', 'mfcc', 'mir', 'music', 'music-analysis', 'music-information-retrieval', 'pitch', 'signal-processing', 'spectral-analysis', 'spectrogram', 'time-frequency-analysis', 'wavelet-analysis', 'wavelet-transform']","['audio', 'audio-analysis', 'audio-features', 'audio-processing', 'deep-learning', 'machine-learning', 'mfcc', 'mir', 'music', 'music-analysis', 'music-information-retrieval', 'pitch', 'signal-processing', 'spectral-analysis', 'spectrogram', 'time-frequency-analysis', 'wavelet-analysis', 'wavelet-transform']",2023-12-22,"[('bastibe/python-soundfile', 0.6440500617027283, 'util', 0), ('spotify/pedalboard', 0.5974409580230713, 'util', 2), ('facebookresearch/audiocraft', 0.551815927028656, 'util', 1), ('speechbrain/speechbrain', 0.5162292718887329, 'nlp', 3), ('quodlibet/mutagen', 0.5058978796005249, 'util', 1)]",5,1.0,,1.33,6,3,12,1,8,8,8,6.0,4.0,90.0,0.7,46
955,gis,https://github.com/azavea/raster-vision,[],,[],[],,,,azavea/raster-vision,raster-vision,1956,374,74,Python,https://docs.rastervision.io,An open source library and framework for deep learning on satellite and aerial imagery.,azavea,2024-01-11,2017-02-02,364,5.36310223266745,https://avatars.githubusercontent.com/u/595231?v=4,An open source library and framework for deep learning on satellite and aerial imagery.,"['classification', 'computer-vision', 'deep-learning', 'geospatial', 'machine-learning', 'object-detection', 'pytorch', 'remote-sensing', 'semantic-segmentation']","['classification', 'computer-vision', 'deep-learning', 'geospatial', 'machine-learning', 'object-detection', 'pytorch', 'remote-sensing', 'semantic-segmentation']",2024-01-11,"[('datasystemslab/geotorch', 0.6860873103141785, 'gis', 1), ('developmentseed/label-maker', 0.6791407465934753, 'gis', 3), ('microsoft/torchgeo', 0.6176372766494751, 'gis', 5), ('remotesensinglab/raster4ml', 0.5898652076721191, 'gis', 2), ('tensorflow/tensorflow', 0.5648720860481262, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5499731302261353, 'ml-dl', 5), ('kevinmusgrave/pytorch-metric-learning', 0.5324239134788513, 'ml', 4), ('tensorlayer/tensorlayer', 0.5277007818222046, 'ml-rl', 2), ('kornia/kornia', 0.5268778204917908, 'ml-dl', 4), ('weecology/deepforest', 0.5204096436500549, 'gis', 0), ('google-research/deeplab2', 0.5186462998390198, 'ml', 0), ('lightly-ai/lightly', 0.5178630352020264, 'ml', 4), ('sentinelsat/sentinelsat', 0.5150529742240906, 'gis', 1), ('plant99/felicette', 0.5094917416572571, 'gis', 1), ('nvlabs/gcvit', 0.5082259178161621, 'diffusion', 3), ('tensorflow/tensor2tensor', 0.5076959133148193, 'ml', 2), ('albumentations-team/albumentations', 0.50620436668396, 'ml-dl', 3)]",35,5.0,,4.73,77,56,85,0,6,3,6,77.0,80.0,90.0,1.0,46
940,nlp,https://github.com/alibaba/easynlp,[],,[],[],,,,alibaba/easynlp,EasyNLP,1872,238,37,Python,,EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit,alibaba,2024-01-13,2022-04-06,94,19.734939759036145,https://avatars.githubusercontent.com/u/1961952?v=4,EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit,"['bert', 'deep-learning', 'fewshot-learning', 'knowledge-distillation', 'knowledge-pretraining', 'machine-learning', 'nlp', 'pretrained-models', 'pytorch', 'text-classification', 'text-image-retrieval', 'text-to-image-synthesis', 'transfer-learning', 'transformers']","['bert', 'deep-learning', 'fewshot-learning', 'knowledge-distillation', 'knowledge-pretraining', 'machine-learning', 'nlp', 'pretrained-models', 'pytorch', 'text-classification', 'text-image-retrieval', 'text-to-image-synthesis', 'transfer-learning', 'transformers']",2024-01-10,"[('allenai/allennlp', 0.6890572309494019, 'nlp', 3), ('paddlepaddle/paddlenlp', 0.6783795952796936, 'llm', 4), ('graykode/nlp-tutorial', 0.6614455580711365, 'study', 3), ('huggingface/transformers', 0.6499559879302979, 'nlp', 6), ('deepset-ai/farm', 0.6495715975761414, 'nlp', 6), ('norskregnesentral/skweak', 0.6338717341423035, 'nlp', 0), ('jina-ai/finetuner', 0.6169459819793701, 'ml', 3), ('nltk/nltk', 0.6162857413291931, 'nlp', 2), ('extreme-bert/extreme-bert', 0.614682674407959, 'llm', 5), ('explosion/spacy', 0.6040478348731995, 'nlp', 4), ('jina-ai/clip-as-service', 0.6006197333335876, 'nlp', 3), ('llmware-ai/llmware', 0.592271625995636, 'llm', 5), ('lucidrains/imagen-pytorch', 0.5900583267211914, 'ml-dl', 1), ('maartengr/bertopic', 0.5850814580917358, 'nlp', 4), ('flairnlp/flair', 0.5845892429351807, 'nlp', 3), ('huggingface/datasets', 0.5757067799568176, 'nlp', 4), ('explosion/spacy-transformers', 0.5717461109161377, 'llm', 5), ('jbesomi/texthero', 0.5708900690078735, 'nlp', 2), ('ddangelov/top2vec', 0.5686218738555908, 'nlp', 1), ('openai/clip', 0.5683876872062683, 'ml-dl', 2), ('koaning/whatlies', 0.5676613450050354, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5610079169273376, 'ml-dl', 3), ('rasahq/rasa', 0.5567002892494202, 'llm', 2), ('salesforce/blip', 0.553688108921051, 'diffusion', 0), ('ofa-sys/ofa', 0.5513916611671448, 'llm', 2), ('huggingface/setfit', 0.5513603091239929, 'nlp', 1), ('ukplab/sentence-transformers', 0.551360011100769, 'nlp', 0), ('jalammar/ecco', 0.549338698387146, 'ml-interpretability', 2), ('explosion/spacy-models', 0.5481346845626831, 'nlp', 2), ('huggingface/text-generation-inference', 0.5467448234558105, 'llm', 3), ('google-research/electra', 0.5465848445892334, 'ml-dl', 2), ('explosion/spacy-llm', 0.5462448596954346, 'llm', 3), ('minimaxir/textgenrnn', 0.5449576377868652, 'nlp', 1), ('zjunlp/deepke', 0.5432515144348145, 'ml', 4), ('makcedward/nlpaug', 0.5402962565422058, 'nlp', 2), ('infinitylogesh/mutate', 0.5399907231330872, 'nlp', 0), ('keras-team/keras-nlp', 0.539397120475769, 'nlp', 3), ('microsoft/unilm', 0.5370928645133972, 'nlp', 1), ('qanastek/drbert', 0.5349388718605042, 'llm', 3), ('yueyu1030/attrprompt', 0.5339798331260681, 'llm', 1), ('nvidia/nemo', 0.527443528175354, 'nlp', 2), ('jonasgeiping/cramming', 0.5274117588996887, 'nlp', 1), ('sloria/textblob', 0.5273472666740417, 'nlp', 1), ('deeppavlov/deeppavlov', 0.52702796459198, 'nlp', 3), ('awslabs/autogluon', 0.5265514850616455, 'ml', 4), ('neuml/txtai', 0.5239185690879822, 'nlp', 3), ('huggingface/huggingface_hub', 0.5210217237472534, 'ml', 4), ('eleutherai/lm-evaluation-harness', 0.520174503326416, 'llm', 0), ('bigscience-workshop/promptsource', 0.5199021697044373, 'nlp', 2), ('lucidrains/dalle2-pytorch', 0.5197792649269104, 'diffusion', 1), ('jaidedai/easyocr', 0.5166882872581482, 'data', 3), ('thilinarajapakse/simpletransformers', 0.5156533122062683, 'nlp', 2), ('open-mmlab/mmediting', 0.5134133696556091, 'ml', 2), ('intellabs/fastrag', 0.5131124258041382, 'nlp', 2), ('huggingface/autotrain-advanced', 0.5123465657234192, 'ml', 2), ('speechbrain/speechbrain', 0.5123403072357178, 'nlp', 3), ('amansrivastava17/embedding-as-service', 0.5122250318527222, 'nlp', 3), ('argilla-io/argilla', 0.5120981335639954, 'nlp', 2), ('explosion/thinc', 0.5110622048377991, 'ml-dl', 4), ('milvus-io/bootcamp', 0.5068036317825317, 'data', 2), ('espnet/espnet', 0.5063716173171997, 'nlp', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5053616762161255, 'web', 0), ('prithivirajdamodaran/styleformer', 0.504541277885437, 'nlp', 1), ('saharmor/dalle-playground', 0.5042204260826111, 'diffusion', 2), ('lucidrains/toolformer-pytorch', 0.5038707852363586, 'llm', 2), ('bigscience-workshop/megatron-deepspeed', 0.5024697184562683, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5024697184562683, 'llm', 0)]",39,5.0,,1.33,11,5,22,0,0,1,1,11.0,4.0,90.0,0.4,46
1135,math,https://github.com/pyomo/pyomo,[],,[],[],,,,pyomo/pyomo,pyomo,1749,479,61,Python,https://www.pyomo.org,An object-oriented algebraic modeling language in Python for structured optimization problems.,pyomo,2024-01-12,2016-05-27,400,4.366262482168331,https://avatars.githubusercontent.com/u/10505959?v=4,An object-oriented algebraic modeling language in Python for structured optimization problems.,"['linear-programming', 'mathematical-programming', 'modeling-language', 'nonlinear-programming', 'optimization']","['linear-programming', 'mathematical-programming', 'modeling-language', 'nonlinear-programming', 'optimization']",2024-01-13,"[('sympy/sympy', 0.5923652052879333, 'math', 0), ('keon/algorithms', 0.5572477579116821, 'util', 0), ('google/pyglove', 0.5334599018096924, 'util', 0), ('scikit-optimize/scikit-optimize', 0.5328642129898071, 'ml', 1), ('pyston/pyston', 0.5302437543869019, 'util', 0), ('pytoolz/toolz', 0.5213468670845032, 'util', 0), ('eth-sri/lmql', 0.5141502022743225, 'llm', 0), ('python/cpython', 0.5133528709411621, 'util', 0), ('cma-es/pycma', 0.5060227513313293, 'math', 0), ('artemyk/dynpy', 0.5054152011871338, 'sim', 0), ('pymc-devs/pymc3', 0.5023778080940247, 'ml', 0), ('evhub/coconut', 0.5014179944992065, 'util', 0)]",137,1.0,,48.38,186,132,93,0,5,8,5,186.0,238.0,90.0,1.3,46
660,ml,https://github.com/huggingface/evaluate,[],,[],[],,,,huggingface/evaluate,evaluate,1673,206,48,Python,https://huggingface.co/docs/evaluate,🤗 Evaluate: A library for easily evaluating machine learning models and datasets.,huggingface,2024-01-13,2022-03-30,95,17.453055141579732,https://avatars.githubusercontent.com/u/25720743?v=4,🤗 Evaluate: A library for easily evaluating machine learning models and datasets.,"['evaluation', 'machine-learning']","['evaluation', 'machine-learning']",2023-12-27,"[('tensorflow/data-validation', 0.7562239170074463, 'ml-ops', 0), ('anthropics/evals', 0.7150794267654419, 'llm', 0), ('teamhg-memex/eli5', 0.6364562511444092, 'ml', 1), ('districtdatalabs/yellowbrick', 0.6335552334785461, 'ml', 1), ('rasbt/mlxtend', 0.6174339056015015, 'ml', 1), ('eugeneyan/testing-ml', 0.6167011260986328, 'testing', 1), ('marcotcr/lime', 0.6089439392089844, 'ml-interpretability', 0), ('scikit-learn/scikit-learn', 0.5982382893562317, 'ml', 1), ('selfexplainml/piml-toolbox', 0.5961371660232544, 'ml-interpretability', 0), ('csinva/imodels', 0.5934129357337952, 'ml', 1), ('ai21labs/lm-evaluation', 0.5928609371185303, 'llm', 0), ('patchy631/machine-learning', 0.5767751932144165, 'ml', 0), ('pair-code/lit', 0.5728373527526855, 'ml-interpretability', 1), ('firmai/industry-machine-learning', 0.5610095858573914, 'study', 1), ('jovianml/opendatasets', 0.5602533221244812, 'data', 1), ('huggingface/datasets', 0.5580374598503113, 'nlp', 1), ('scikit-learn-contrib/imbalanced-learn', 0.557458758354187, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.556303083896637, 'ml', 1), ('seldonio/alibi', 0.5494807958602905, 'ml-interpretability', 1), ('microsoft/flaml', 0.5472498536109924, 'ml', 1), ('gradio-app/gradio', 0.5471292734146118, 'viz', 1), ('evidentlyai/evidently', 0.5467391014099121, 'ml-ops', 1), ('pytorch/ignite', 0.5455392003059387, 'ml-dl', 1), ('microsoft/nni', 0.5446441173553467, 'ml', 1), ('paperswithcode/axcell', 0.5434747338294983, 'util', 0), ('automl/auto-sklearn', 0.5426995158195496, 'ml', 0), ('eleutherai/lm-evaluation-harness', 0.5417475700378418, 'llm', 1), ('rasbt/stat451-machine-learning-fs20', 0.5357676148414612, 'study', 0), ('determined-ai/determined', 0.5355773568153381, 'ml-ops', 1), ('scikit-learn-contrib/lightning', 0.5336165428161621, 'ml', 1), ('maif/shapash', 0.5302979946136475, 'ml', 1), ('rasbt/machine-learning-book', 0.5270928740501404, 'study', 1), ('wandb/client', 0.5268489122390747, 'ml', 1), ('openai/evals', 0.5261046886444092, 'llm', 1), ('tensorflow/lucid', 0.5247843861579895, 'ml-interpretability', 1), ('openbmb/toolbench', 0.5237149000167847, 'llm', 1), ('mlflow/mlflow', 0.5217682123184204, 'ml-ops', 1), ('oml-team/open-metric-learning', 0.5213491320610046, 'ml', 0), ('kubeflow/fairing', 0.5212286114692688, 'ml-ops', 0), ('dask/dask-ml', 0.5205085873603821, 'ml', 0), ('featurelabs/featuretools', 0.5203360915184021, 'ml', 1), ('bigscience-workshop/biomedical', 0.5192505121231079, 'data', 0), ('carla-recourse/carla', 0.5139058232307434, 'ml', 1), ('hazyresearch/meerkat', 0.5127301216125488, 'viz', 1), ('pycaret/pycaret', 0.5119611024856567, 'ml', 1), ('openlmlab/leval', 0.5112590193748474, 'llm', 1), ('ggerganov/ggml', 0.5103128552436829, 'ml', 1), ('hazyresearch/domino', 0.5064350366592407, 'ml', 0), ('shankarpandala/lazypredict', 0.5062326788902283, 'ml', 1), ('cleverhans-lab/cleverhans', 0.5058858394622803, 'ml', 1), ('truera/trulens', 0.5006502270698547, 'llm', 2)]",124,3.0,,0.46,51,12,22,1,1,5,1,51.0,51.0,90.0,1.0,46
853,jupyter,https://github.com/jupyter/nbconvert,[],,[],[],,,,jupyter/nbconvert,nbconvert,1610,547,51,Python,https://nbconvert.readthedocs.io/,Jupyter Notebook Conversion,jupyter,2024-01-12,2015-04-09,459,3.502175264139217,https://avatars.githubusercontent.com/u/7388996?v=4,Jupyter Notebook Conversion,[],[],2024-01-11,"[('jupyter/nbformat', 0.810336709022522, 'jupyter', 0), ('jupyter/notebook', 0.7077092528343201, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.6312793493270874, 'jupyter', 0), ('jupyter/nbgrader', 0.6126564145088196, 'jupyter', 0), ('cohere-ai/notebooks', 0.6110220551490784, 'llm', 0), ('jupyter-widgets/ipywidgets', 0.6067924499511719, 'jupyter', 0), ('mwouts/jupytext', 0.6044052839279175, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.5970379710197449, 'jupyter', 0), ('jakevdp/pythondatasciencehandbook', 0.5953406095504761, 'study', 0), ('voila-dashboards/voila', 0.5891522169113159, 'jupyter', 0), ('ipython/ipykernel', 0.5823147892951965, 'util', 0), ('jupyter/nbdime', 0.5780813694000244, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5641415119171143, 'study', 0), ('nteract/testbook', 0.5553418397903442, 'jupyter', 0), ('ageron/handson-ml2', 0.5394284725189209, 'ml', 0), ('quantopian/qgrid', 0.538368284702301, 'jupyter', 0), ('xiaohk/stickyland', 0.536399781703949, 'jupyter', 0), ('ipython/ipyparallel', 0.5246542096138, 'perf', 0), ('nteract/papermill', 0.5243469476699829, 'jupyter', 0), ('aws/graph-notebook', 0.522909939289093, 'jupyter', 0), ('computationalmodelling/nbval', 0.5174958109855652, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5078595280647278, 'jupyter', 0)]",270,7.0,,1.9,67,31,107,0,24,10,24,67.0,63.0,90.0,0.9,46
1076,ml,https://github.com/kubeflow/katib,[],,[],[],,,,kubeflow/katib,katib,1391,394,67,Go,,Repository for hyperparameter tuning,kubeflow,2024-01-13,2018-04-03,304,4.5756578947368425,https://avatars.githubusercontent.com/u/33164907?v=4,Repository for hyperparameter tuning,[],[],2024-01-09,"[('optuna/optuna', 0.6405646800994873, 'ml', 0), ('ray-project/tune-sklearn', 0.626979410648346, 'ml', 0), ('microsoft/flaml', 0.6054434776306152, 'ml', 0), ('hyperopt/hyperopt', 0.6022725701332092, 'ml', 0), ('determined-ai/determined', 0.5883219242095947, 'ml-ops', 0), ('google/vizier', 0.5503707528114319, 'ml', 0), ('scikit-optimize/scikit-optimize', 0.5325116515159607, 'ml', 0), ('huggingface/peft', 0.5282931327819824, 'llm', 0), ('automl/auto-sklearn', 0.5148612260818481, 'ml', 0), ('microsoft/nni', 0.5062976479530334, 'ml', 0)]",111,7.0,,1.79,53,26,70,0,2,4,2,53.0,125.0,90.0,2.4,46
926,nlp,https://github.com/jonasgeiping/cramming,[],,[],[],,,,jonasgeiping/cramming,cramming,1191,90,21,Python,,Cramming the training of a (BERT-type) language model into limited compute.,jonasgeiping,2024-01-11,2022-12-29,56,21.0,,Cramming the training of a (BERT-type) language model into limited compute.,"['english-language', 'language-model', 'machine-learning']","['english-language', 'language-model', 'machine-learning']",2023-09-03,"[('bigscience-workshop/megatron-deepspeed', 0.6582860946655273, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6582860946655273, 'llm', 0), ('extreme-bert/extreme-bert', 0.6568642258644104, 'llm', 2), ('deepset-ai/farm', 0.6375002861022949, 'nlp', 0), ('ai21labs/lm-evaluation', 0.6214629411697388, 'llm', 1), ('reasoning-machines/pal', 0.6135402917861938, 'llm', 1), ('hannibal046/awesome-llm', 0.603162944316864, 'study', 1), ('whu-zqh/chatgpt-vs.-bert', 0.6024624109268188, 'llm', 0), ('ctlllll/llm-toolmaker', 0.593092143535614, 'llm', 1), ('maartengr/bertopic', 0.5925445556640625, 'nlp', 1), ('tatsu-lab/stanford_alpaca', 0.5919219851493835, 'llm', 1), ('freedomintelligence/llmzoo', 0.5916814804077148, 'llm', 1), ('llmware-ai/llmware', 0.5896017551422119, 'llm', 1), ('openai/finetune-transformer-lm', 0.5850217342376709, 'llm', 0), ('juncongmoo/pyllama', 0.5846665501594543, 'llm', 0), ('qanastek/drbert', 0.5827434659004211, 'llm', 1), ('databrickslabs/dolly', 0.5738639831542969, 'llm', 0), ('lianjiatech/belle', 0.5717350244522095, 'llm', 0), ('huggingface/text-generation-inference', 0.5711867213249207, 'llm', 0), ('openai/gpt-2', 0.5695840716362, 'llm', 0), ('huggingface/transformers', 0.568610429763794, 'nlp', 2), ('eleutherai/lm-evaluation-harness', 0.5666804909706116, 'llm', 1), ('jina-ai/finetuner', 0.5652620792388916, 'ml', 0), ('graykode/nlp-tutorial', 0.5574339628219604, 'study', 0), ('bigscience-workshop/biomedical', 0.5536470413208008, 'data', 0), ('explosion/spacy-transformers', 0.5527690649032593, 'llm', 2), ('srush/minichain', 0.5501981973648071, 'llm', 0), ('lm-sys/fastchat', 0.5438570976257324, 'llm', 1), ('ddangelov/top2vec', 0.5433996915817261, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5425843596458435, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5425389409065247, 'llm', 0), ('yueyu1030/attrprompt', 0.5370364189147949, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5355748534202576, 'nlp', 0), ('explosion/spacy-models', 0.5299116373062134, 'nlp', 1), ('lvwerra/trl', 0.5296485424041748, 'llm', 0), ('alibaba/easynlp', 0.5274117588996887, 'nlp', 1), ('explosion/spacy-llm', 0.526769757270813, 'llm', 1), ('flairnlp/flair', 0.5266839265823364, 'nlp', 1), ('openlmlab/leval', 0.5251019597053528, 'llm', 1), ('guidance-ai/guidance', 0.5238267779350281, 'llm', 1), ('timdettmers/bitsandbytes', 0.5229673981666565, 'util', 0), ('bytedance/lightseq', 0.5221551656723022, 'nlp', 0), ('openbmb/toolbench', 0.521796703338623, 'llm', 0), ('cg123/mergekit', 0.5206159353256226, 'llm', 0), ('yizhongw/self-instruct', 0.5202336311340332, 'llm', 1), ('hazyresearch/h3', 0.5188775062561035, 'llm', 0), ('optimalscale/lmflow', 0.514919102191925, 'llm', 1), ('infinitylogesh/mutate', 0.5127670168876648, 'nlp', 1), ('maartengr/keybert', 0.5119235515594482, 'nlp', 0), ('google-research/electra', 0.5113855600357056, 'ml-dl', 0), ('keirp/automatic_prompt_engineer', 0.5112678408622742, 'llm', 1), ('mit-han-lab/streaming-llm', 0.5054171681404114, 'llm', 0), ('norskregnesentral/skweak', 0.505377471446991, 'nlp', 0)]",7,3.0,,1.08,6,6,13,4,2,2,2,6.0,20.0,90.0,3.3,46
1266,perf,https://github.com/intel/intel-extension-for-pytorch,[],,[],[],,,,intel/intel-extension-for-pytorch,intel-extension-for-pytorch,1150,167,34,Python,,A Python package for extending the official PyTorch that can easily obtain performance on Intel platform,intel,2024-01-14,2020-04-15,197,5.812274368231047,https://avatars.githubusercontent.com/u/17888862?v=4,A Python package for extending the official PyTorch that can easily obtain performance on Intel platform,"['deep-learning', 'intel', 'machine-learning', 'neural-network', 'pytorch', 'quantization']","['deep-learning', 'intel', 'machine-learning', 'neural-network', 'pytorch', 'quantization']",2024-01-11,"[('pytorch/ignite', 0.7741976380348206, 'ml-dl', 4), ('skorch-dev/skorch', 0.7512941360473633, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.7345353960990906, 'study', 3), ('nvidia/apex', 0.7110769152641296, 'ml-dl', 0), ('karpathy/micrograd', 0.6794243454933167, 'study', 0), ('pytorch/data', 0.6773589849472046, 'data', 0), ('arogozhnikov/einops', 0.6702998876571655, 'ml-dl', 2), ('tlkh/tf-metal-experiments', 0.6522815227508545, 'perf', 1), ('huggingface/transformers', 0.6460942625999451, 'nlp', 3), ('ashleve/lightning-hydra-template', 0.6405739188194275, 'util', 2), ('mrdbourke/pytorch-deep-learning', 0.6339874267578125, 'study', 3), ('microsoft/onnxruntime', 0.6330754160881042, 'ml', 3), ('huggingface/accelerate', 0.6329745650291443, 'ml', 0), ('determined-ai/determined', 0.6318769454956055, 'ml-ops', 3), ('intel/scikit-learn-intelex', 0.6315147280693054, 'perf', 2), ('pyg-team/pytorch_geometric', 0.6297549605369568, 'ml-dl', 2), ('rentruewang/koila', 0.6254847645759583, 'ml', 4), ('google/tf-quant-finance', 0.6181202530860901, 'finance', 0), ('blackhc/toma', 0.6170483231544495, 'ml-dl', 2), ('pytorch/pytorch', 0.6112003922462463, 'ml-dl', 3), ('xl0/lovely-tensors', 0.610737144947052, 'ml-dl', 2), ('ageron/handson-ml2', 0.6076744198799133, 'ml', 0), ('pytorch/rl', 0.6046189069747925, 'ml-rl', 2), ('tensorflow/addons', 0.6045843958854675, 'ml', 3), ('denys88/rl_games', 0.6032094359397888, 'ml-rl', 2), ('pytorch/glow', 0.6005612015724182, 'ml', 0), ('facebookresearch/pytorch3d', 0.5983558893203735, 'ml-dl', 0), ('nvlabs/gcvit', 0.5956222414970398, 'diffusion', 1), ('allenai/allennlp', 0.5950837731361389, 'nlp', 2), ('horovod/horovod', 0.5949805974960327, 'ml-ops', 3), ('plasma-umass/scalene', 0.5931693315505981, 'profiling', 0), ('fastai/fastcore', 0.5917688012123108, 'util', 0), ('pypy/pypy', 0.5893913507461548, 'util', 0), ('laekov/fastmoe', 0.5892179012298584, 'ml', 0), ('faster-cpython/tools', 0.5863217115402222, 'perf', 0), ('intellabs/bayesian-torch', 0.582083523273468, 'ml', 2), ('huggingface/datasets', 0.5820187926292419, 'nlp', 3), ('mrdbourke/m1-machine-learning-test', 0.5805241465568542, 'ml', 1), ('huggingface/huggingface_hub', 0.5788046717643738, 'ml', 3), ('fchollet/deep-learning-with-python-notebooks', 0.5782514214515686, 'study', 0), ('nicolas-chaulet/torch-points3d', 0.5780234932899475, 'ml', 0), ('explosion/thinc', 0.5743740200996399, 'ml-dl', 3), ('gradio-app/gradio', 0.5713984966278076, 'viz', 2), ('hysts/pytorch_image_classification', 0.5713286399841309, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5710954070091248, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5700653195381165, 'ml-dl', 1), ('pytorch/captum', 0.5694814324378967, 'ml-interpretability', 0), ('microsoft/deepspeed', 0.5665271878242493, 'ml-dl', 3), ('lucidrains/imagen-pytorch', 0.5663982033729553, 'ml-dl', 1), ('neuralmagic/deepsparse', 0.5653654932975769, 'nlp', 1), ('tensorlayer/tensorlayer', 0.5644388198852539, 'ml-rl', 2), ('nyandwi/modernconvnets', 0.564269483089447, 'ml-dl', 0), ('uber/petastorm', 0.5632723569869995, 'data', 3), ('lightly-ai/lightly', 0.5627269148826599, 'ml', 3), ('ggerganov/ggml', 0.5619326233863831, 'ml', 1), ('cython/cython', 0.5606357455253601, 'util', 0), ('huggingface/optimum', 0.560483992099762, 'ml', 3), ('aws/sagemaker-python-sdk', 0.5577678680419922, 'ml', 2), ('keras-team/keras', 0.5548680424690247, 'ml-dl', 3), ('dmlc/dgl', 0.5548177361488342, 'ml-dl', 1), ('micropython/micropython', 0.5541401505470276, 'util', 0), ('kubeflow/fairing', 0.5540038347244263, 'ml-ops', 0), ('mdbloice/augmentor', 0.5521341562271118, 'ml', 2), ('koaning/human-learn', 0.5494535565376282, 'data', 1), ('tensorly/tensorly', 0.5494438409805298, 'ml-dl', 2), ('ray-project/ray', 0.5487441420555115, 'ml-ops', 3), ('tensorflow/tensorflow', 0.548033595085144, 'ml-dl', 3), ('thu-ml/tianshou', 0.5456005930900574, 'ml-rl', 1), ('speechbrain/speechbrain', 0.5442495942115784, 'nlp', 2), ('rafiqhasan/auto-tensorflow', 0.5420622825622559, 'ml-dl', 1), ('pycaret/pycaret', 0.5417818427085876, 'ml', 1), ('tensorflow/similarity', 0.5410365462303162, 'ml-dl', 2), ('pyro-ppl/pyro', 0.538422167301178, 'ml-dl', 3), ('salesforce/blip', 0.5368949770927429, 'diffusion', 0), ('kshitij12345/torchnnprofiler', 0.5366755723953247, 'profiling', 0), ('timdettmers/bitsandbytes', 0.5321269631385803, 'util', 0), ('ddbourgin/numpy-ml', 0.5318039059638977, 'ml', 1), ('google/gin-config', 0.5311468839645386, 'util', 0), ('google/jax', 0.5305117964744568, 'ml', 0), ('merantix-momentum/squirrel-core', 0.5278235077857971, 'ml', 3), ('lutzroeder/netron', 0.5267831683158875, 'ml', 4), ('klen/py-frameworks-bench', 0.5263614654541016, 'perf', 0), ('pytorch/torchrec', 0.5260089635848999, 'ml-dl', 2), ('pytorch-labs/gpt-fast', 0.5219361782073975, 'llm', 1), ('numpy/numpy', 0.5218780636787415, 'math', 0), ('catboost/catboost', 0.5203407406806946, 'ml', 1), ('mosaicml/composer', 0.5193430185317993, 'ml-dl', 4), ('onnx/onnx', 0.5180797576904297, 'ml', 4), ('adafruit/circuitpython', 0.5163028240203857, 'util', 0), ('faster-cpython/ideas', 0.5159723162651062, 'perf', 0), ('karpathy/mingpt', 0.5158076882362366, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.5152040123939514, 'ml-dl', 3), ('cvxgrp/pymde', 0.5150303244590759, 'ml', 2), ('wandb/client', 0.5133212208747864, 'ml', 3), ('featurelabs/featuretools', 0.512016773223877, 'ml', 1), ('jeshraghian/snntorch', 0.5118179321289062, 'ml-dl', 2), ('facebookresearch/dinov2', 0.5111134052276611, 'diffusion', 0), ('microsoft/nni', 0.5098321437835693, 'ml', 4), ('oml-team/open-metric-learning', 0.506502091884613, 'ml', 2), ('tensorflow/tensor2tensor', 0.5063350200653076, 'ml', 2), ('lucidrains/dalle2-pytorch', 0.5061784982681274, 'diffusion', 1), ('nvidia/tensorrt-llm', 0.505849301815033, 'viz', 0), ('pytoolz/toolz', 0.5057084560394287, 'util', 0), ('wxwidgets/phoenix', 0.5054412484169006, 'gui', 0), ('lucidrains/vit-pytorch', 0.502902626991272, 'ml-dl', 0), ('exaloop/codon', 0.5024413466453552, 'perf', 0), ('hoffstadt/dearpygui', 0.5012747645378113, 'gui', 0), ('markshannon/faster-cpython', 0.5006858706474304, 'perf', 0), ('keras-team/autokeras', 0.5002623200416565, 'ml-dl', 2)]",60,2.0,,8.48,101,24,46,0,8,9,8,101.0,198.0,90.0,2.0,46
694,perf,https://github.com/intel/scikit-learn-intelex,[],,[],[],,,,intel/scikit-learn-intelex,scikit-learn-intelex,1105,167,30,Python,https://intel.github.io/scikit-learn-intelex/,Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application,intel,2024-01-12,2018-08-07,286,3.8636363636363638,https://avatars.githubusercontent.com/u/17888862?v=4,Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application,"['ai-inference', 'ai-machine-learning', 'ai-training', 'analytics', 'big-data', 'data-analysis', 'gpu', 'intel', 'machine-learning', 'machine-learning-algorithms', 'oneapi', 'scikit-learn', 'swrepo']","['ai-inference', 'ai-machine-learning', 'ai-training', 'analytics', 'big-data', 'data-analysis', 'gpu', 'intel', 'machine-learning', 'machine-learning-algorithms', 'oneapi', 'scikit-learn', 'swrepo']",2024-01-11,"[('intel/intel-extension-for-pytorch', 0.6315147280693054, 'perf', 2), ('automl/auto-sklearn', 0.5883508920669556, 'ml', 1), ('iryna-kondr/scikit-llm', 0.5876615047454834, 'llm', 2), ('koaning/human-learn', 0.5819746255874634, 'data', 2), ('skops-dev/skops', 0.5795713067054749, 'ml-ops', 2), ('microsoft/onnxruntime', 0.5744372010231018, 'ml', 2), ('rasbt/machine-learning-book', 0.5701589584350586, 'study', 2), ('koaning/scikit-lego', 0.5534452199935913, 'ml', 2), ('determined-ai/determined', 0.534125566482544, 'ml-ops', 1), ('google/tf-quant-finance', 0.5270321369171143, 'finance', 1), ('ray-project/ray', 0.5234288573265076, 'ml-ops', 1), ('explosion/thinc', 0.5210903882980347, 'ml-dl', 1), ('gradio-app/gradio', 0.5180376172065735, 'viz', 2), ('wandb/client', 0.5164636969566345, 'ml', 1), ('tlkh/tf-metal-experiments', 0.5120803713798523, 'perf', 1), ('huggingface/datasets', 0.5119993090629578, 'nlp', 1), ('districtdatalabs/yellowbrick', 0.5062663555145264, 'ml', 2), ('pytorch/glow', 0.5033442378044128, 'ml', 0), ('microsoft/nni', 0.5030348300933838, 'ml', 2), ('sweepai/sweep', 0.502382218837738, 'llm', 0)]",77,2.0,,5.44,136,105,66,0,6,5,6,136.0,532.0,90.0,3.9,46
1727,llm,https://github.com/truera/trulens,['evaluation'],,[],[],,,,truera/trulens,trulens,1042,83,12,Jupyter Notebook,https://www.trulens.org/,Evaluation and Tracking for LLM Experiments,truera,2024-01-13,2020-11-02,169,6.160472972972973,https://avatars.githubusercontent.com/u/51224128?v=4,Evaluation and Tracking for LLM Experiments,"['explainable-ml', 'llm', 'llmops', 'machine-learning', 'neural-networks']","['evaluation', 'explainable-ml', 'llm', 'llmops', 'machine-learning', 'neural-networks']",2024-01-12,"[('bentoml/openllm', 0.5837077498435974, 'ml-ops', 2), ('vllm-project/vllm', 0.5575326681137085, 'llm', 2), ('citadel-ai/langcheck', 0.5569631457328796, 'llm', 1), ('microsoft/jarvis', 0.5391638278961182, 'llm', 0), ('arize-ai/phoenix', 0.532617449760437, 'ml-interpretability', 1), ('wandb/client', 0.5260670185089111, 'ml', 1), ('tigerlab-ai/tiger', 0.5201952457427979, 'llm', 1), ('iryna-kondr/scikit-llm', 0.5199382901191711, 'llm', 2), ('argilla-io/argilla', 0.5192816853523254, 'nlp', 2), ('confident-ai/deepeval', 0.5170626640319824, 'testing', 3), ('alpha-vllm/llama2-accessory', 0.5145223140716553, 'llm', 0), ('nebuly-ai/nebullvm', 0.5128412842750549, 'perf', 1), ('openai/evals', 0.5125577449798584, 'llm', 1), ('determined-ai/determined', 0.510200023651123, 'ml-ops', 1), ('huggingface/evaluate', 0.5006502270698547, 'ml', 2)]",32,1.0,,10.73,306,286,39,0,23,10,23,306.0,461.0,90.0,1.5,46
1893,util,https://github.com/ofek/pyapp,"['installer', 'bundle', 'packaging']",,[],[],,,,ofek/pyapp,pyapp,883,17,6,Rust,https://ofek.dev/pyapp/,Runtime installer for Python applications,ofek,2024-01-13,2023-05-07,38,23.063432835820894,,Runtime installer for Python applications,"['application', 'build', 'cli', 'packaging', 'rust']","['application', 'build', 'bundle', 'cli', 'installer', 'packaging', 'rust']",2024-01-01,"[('pyodide/micropip', 0.6668835282325745, 'util', 0), ('beeware/briefcase', 0.6602802276611328, 'util', 1), ('indygreg/pyoxidizer', 0.6547517776489258, 'util', 1), ('pypa/hatch', 0.6210417747497559, 'util', 3), ('pypa/pipx', 0.604620099067688, 'util', 1), ('pyinstaller/pyinstaller', 0.5914837121963501, 'util', 1), ('mitsuhiko/rye', 0.575480043888092, 'util', 1), ('pypi/warehouse', 0.5665706992149353, 'util', 0), ('pyo3/maturin', 0.5643833875656128, 'util', 2), ('python-poetry/poetry', 0.5570515394210815, 'util', 1), ('pypa/virtualenv', 0.5487356781959534, 'util', 0), ('pypa/flit', 0.5471957921981812, 'util', 1), ('linkedin/shiv', 0.5412315130233765, 'util', 0), ('pomponchik/instld', 0.5397077202796936, 'util', 0), ('pypa/installer', 0.5315470695495605, 'util', 0), ('pypa/build', 0.5296021103858948, 'util', 1), ('pypa/pipenv', 0.5295817852020264, 'util', 1), ('conda/constructor', 0.5201569199562073, 'util', 0), ('pdm-project/pdm', 0.514813244342804, 'util', 1), ('willmcgugan/textual', 0.5099067091941833, 'term', 1), ('conda/conda', 0.5070095658302307, 'util', 1), ('jazzband/pip-tools', 0.5037299394607544, 'util', 1)]",5,1.0,,1.75,21,11,8,0,15,23,15,21.0,24.0,90.0,1.1,46
720,web,https://github.com/rstudio/py-shiny,[],,[],[],,,,rstudio/py-shiny,py-shiny,817,49,29,Python,https://shiny.posit.co/py/,Shiny for Python,rstudio,2024-01-13,2021-07-27,131,6.236641221374046,https://avatars.githubusercontent.com/u/107264312?v=4,Shiny for Python,[],[],2024-01-12,"[('python/cpython', 0.536689817905426, 'util', 0), ('holoviz/panel', 0.5275716781616211, 'viz', 0), ('plotly/dash', 0.5270806550979614, 'viz', 0), ('pypy/pypy', 0.5167948603630066, 'util', 0), ('hoffstadt/dearpygui', 0.5147618651390076, 'gui', 0), ('eleutherai/pyfra', 0.5123597979545593, 'ml', 0)]",18,4.0,,7.56,272,164,30,0,11,9,11,272.0,269.0,90.0,1.0,46
343,data,https://github.com/scikit-hep/awkward-1.0,[],,[],[],,,,scikit-hep/awkward-1.0,awkward,770,77,21,Python,https://awkward-array.org,Manipulate JSON-like data with NumPy-like idioms.,scikit-hep,2024-01-14,2019-08-14,232,3.3067484662576687,https://avatars.githubusercontent.com/u/23454624?v=4,Manipulate JSON-like data with NumPy-like idioms.,"['apache-arrow', 'cern-root', 'columnar-format', 'data-analysis', 'jagged-array', 'json', 'numba', 'numpy', 'pandas', 'ragged-array', 'rdataframe', 'scikit-hep']","['apache-arrow', 'cern-root', 'columnar-format', 'data-analysis', 'jagged-array', 'json', 'numba', 'numpy', 'pandas', 'ragged-array', 'rdataframe', 'scikit-hep']",2024-01-12,"[('apache/arrow', 0.559539258480072, 'data', 3), ('vaexio/vaex', 0.5477538704872131, 'perf', 0), ('man-group/dtale', 0.5412405729293823, 'viz', 2), ('kellyjonbrazil/jello', 0.5297543406486511, 'util', 1), ('brokenloop/jsontopydantic', 0.5177565813064575, 'util', 0), ('pandas-dev/pandas', 0.5098268389701843, 'pandas', 2), ('jazzband/tablib', 0.5033981800079346, 'data', 0), ('jsonpickle/jsonpickle', 0.500355064868927, 'data', 1)]",40,4.0,,9.77,197,165,54,0,36,62,36,196.0,380.0,90.0,1.9,46
881,gis,https://github.com/osgeo/grass,[],,[],[],,,,osgeo/grass,grass,720,255,43,C,https://grass.osgeo.org,GRASS GIS - free and open-source geospatial processing engine,osgeo,2024-01-13,2019-05-17,245,2.931937172774869,https://avatars.githubusercontent.com/u/1058467?v=4,GRASS GIS - free and open-source geospatial processing engine,"['arrays', 'data-science', 'earth-observation', 'geospatial', 'geospatial-analysis', 'gis', 'grass-gis', 'image-processing', 'jupyter', 'machine-learning', 'open-science', 'parallel-computing', 'raster', 'remote-sensing', 'science', 'spatial', 'timeseries-analysis', 'vector']","['arrays', 'data-science', 'earth-observation', 'geospatial', 'geospatial-analysis', 'gis', 'grass-gis', 'image-processing', 'jupyter', 'machine-learning', 'open-science', 'parallel-computing', 'raster', 'remote-sensing', 'science', 'spatial', 'timeseries-analysis', 'vector']",2024-01-14,"[('remotesensinglab/raster4ml', 0.6746719479560852, 'gis', 3), ('fatiando/verde', 0.6331599950790405, 'gis', 2), ('microsoft/torchgeo', 0.6095272302627563, 'gis', 3), ('earthlab/earthpy', 0.5645433068275452, 'gis', 2), ('osgeo/gdal', 0.5594460368156433, 'gis', 3), ('giswqs/geemap', 0.5491253137588501, 'gis', 6), ('apache/incubator-sedona', 0.5388128757476807, 'gis', 1), ('opengeos/segment-geospatial', 0.5276463627815247, 'gis', 2), ('opengeos/leafmap', 0.5151031017303467, 'gis', 5), ('r-barnes/richdem', 0.5100005269050598, 'gis', 1), ('determined-ai/determined', 0.5073267817497253, 'ml-ops', 2), ('perrygeo/python-rasterstats', 0.5056399703025818, 'gis', 0), ('darribas/gds_env', 0.502344012260437, 'gis', 0)]",107,6.0,,8.48,316,166,57,0,6,30,6,317.0,588.0,90.0,1.9,46
1409,math,https://github.com/lean-dojo/leandojo,[],,[],[],,,,lean-dojo/leandojo,LeanDojo,389,45,13,Python,https://leandojo.org,Tool for data extraction and interacting with Lean programmatically.,lean-dojo,2024-01-14,2023-06-13,33,11.787878787878787,https://avatars.githubusercontent.com/u/136513911?v=4,Tool for data extraction and interacting with Lean programmatically.,"['lean', 'lean4', 'machine-learning', 'theorem-proving']","['lean', 'lean4', 'machine-learning', 'theorem-proving']",2024-01-10,"[('lean-dojo/reprover', 0.6379106640815735, 'math', 3), ('intake/intake', 0.5443071722984314, 'data', 0), ('paperswithcode/axcell', 0.5234335660934448, 'util', 0), ('linealabs/lineapy', 0.5060582756996155, 'jupyter', 0)]",11,5.0,,5.35,45,43,7,0,16,28,16,45.0,33.0,90.0,0.7,46
998,finance,https://github.com/quantopian/zipline,[],,[],[],,,,quantopian/zipline,zipline,16792,4710,1000,Python,https://www.zipline.io,"Zipline, a Pythonic Algorithmic Trading Library",quantopian,2024-01-14,2012-10-19,588,28.53009708737864,https://avatars.githubusercontent.com/u/1393215?v=4,"Zipline, a Pythonic Algorithmic Trading Library","['algorithmic-trading', 'quant', 'zipline']","['algorithmic-trading', 'quant', 'zipline']",2020-10-14,"[('gbeced/pyalgotrade', 0.8662933707237244, 'finance', 0), ('quantconnect/lean', 0.6676159501075745, 'finance', 0), ('robcarver17/pysystemtrade', 0.6582158803939819, 'finance', 0), ('gbeced/basana', 0.6551344394683838, 'finance', 1), ('ranaroussi/quantstats', 0.5925479531288147, 'finance', 2), ('cuemacro/finmarketpy', 0.5911790132522583, 'finance', 0), ('goldmansachs/gs-quant', 0.5875362753868103, 'finance', 0), ('cuemacro/findatapy', 0.5615792870521545, 'finance', 0), ('mementum/backtrader', 0.5593721866607666, 'finance', 0), ('quantecon/quantecon.py', 0.5450539588928223, 'sim', 0), ('keon/algorithms', 0.5438115000724792, 'util', 0), ('kernc/backtesting.py', 0.540707528591156, 'finance', 1), ('ta-lib/ta-lib-python', 0.5401220321655273, 'finance', 0), ('pmorissette/ffn', 0.5246074199676514, 'finance', 0), ('primal100/pybitcointools', 0.5228780508041382, 'crypto', 0), ('zvtvz/zvt', 0.5200475454330444, 'finance', 2), ('sympy/sympy', 0.5198596119880676, 'math', 0), ('polakowo/vectorbt', 0.519047737121582, 'finance', 1), ('pytoolz/toolz', 0.5173577070236206, 'util', 0), ('erotemic/ubelt', 0.5161089897155762, 'util', 0), ('1200wd/bitcoinlib', 0.5117334723472595, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5056824684143066, 'finance', 0), ('linkedin/shiv', 0.5038162469863892, 'util', 0), ('scipy/scipy', 0.5005958080291748, 'math', 0), ('thealgorithms/python', 0.5000721216201782, 'study', 0)]",160,5.0,,0.0,3,0,137,40,0,2,2,3.0,1.0,90.0,0.3,45
540,ml,https://github.com/aleju/imgaug,[],,[],[],,,,aleju/imgaug,imgaug,13972,2422,232,Python,http://imgaug.readthedocs.io,Image augmentation for machine learning experiments.,aleju,2024-01-12,2015-07-10,446,31.28726807421625,,Image augmentation for machine learning experiments.,"['affine-transformation', 'augment-images', 'augmentation', 'bounding-boxes', 'contrast', 'crop', 'deep-learning', 'heatmap', 'image-augmentation', 'images', 'keypoints', 'machine-learning', 'polygon', 'segmentation-maps']","['affine-transformation', 'augment-images', 'augmentation', 'bounding-boxes', 'contrast', 'crop', 'deep-learning', 'heatmap', 'image-augmentation', 'images', 'keypoints', 'machine-learning', 'polygon', 'segmentation-maps']",2020-06-01,"[('mdbloice/augmentor', 0.7141932845115662, 'ml', 3), ('albumentations-team/albumentations', 0.6503161787986755, 'ml-dl', 4), ('fepegar/torchio', 0.5931549072265625, 'ml-dl', 3), ('roboflow/supervision', 0.5748479962348938, 'ml', 2), ('facebookresearch/augly', 0.5716978311538696, 'data', 0), ('lutzroeder/netron', 0.5660139322280884, 'ml', 2), ('makcedward/nlpaug', 0.5599479079246521, 'nlp', 2), ('microsoft/torchgeo', 0.5551947355270386, 'gis', 1), ('awslabs/autogluon', 0.5523484349250793, 'ml', 2), ('open-mmlab/mmediting', 0.5496745109558105, 'ml', 1), ('neuralmagic/sparseml', 0.5411894917488098, 'ml-dl', 0), ('huggingface/datasets', 0.5358902812004089, 'nlp', 2), ('rwightman/pytorch-image-models', 0.5356476902961731, 'ml-dl', 0), ('open-mmlab/mmsegmentation', 0.5349984169006348, 'ml', 0), ('onnx/onnx', 0.5340151786804199, 'ml', 2), ('developmentseed/label-maker', 0.5313600897789001, 'gis', 1), ('mosaicml/composer', 0.517113983631134, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5129750370979309, 'ml-dl', 1), ('lightly-ai/lightly', 0.5071452260017395, 'ml', 2), ('keras-team/keras-cv', 0.5068478584289551, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.506173312664032, 'ml', 2), ('ddbourgin/numpy-ml', 0.5041775107383728, 'ml', 1)]",36,6.0,,0.0,2,2,104,44,0,2,2,2.0,1.0,90.0,0.5,45
281,util,https://github.com/arrow-py/arrow,[],,[],[],,,,arrow-py/arrow,arrow,8455,689,135,Python,https://arrow.readthedocs.io,🏹 Better dates & times for Python,arrow-py,2024-01-12,2012-11-18,584,14.470660146699267,https://avatars.githubusercontent.com/u/68518399?v=4,🏹 Better dates & times for Python,"['arrow', 'date', 'datetime', 'time', 'timestamp', 'timezones']","['arrow', 'date', 'datetime', 'time', 'timestamp', 'timezones']",2023-09-30,"[('sdispater/pendulum', 0.7535154819488525, 'util', 4), ('dateutil/dateutil', 0.7106708884239197, 'util', 3), ('scrapinghub/dateparser', 0.5817139744758606, 'util', 2), ('stub42/pytz', 0.5504962205886841, 'util', 0), ('spulec/freezegun', 0.5130565166473389, 'testing', 0)]",270,4.0,,0.12,7,0,136,4,1,5,1,7.0,8.0,90.0,1.1,45
777,diffusion,https://github.com/xavierxiao/dreambooth-stable-diffusion,[],,[],[],,,,xavierxiao/dreambooth-stable-diffusion,Dreambooth-Stable-Diffusion,7295,775,95,Jupyter Notebook,,Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion,xavierxiao,2024-01-13,2022-09-06,73,99.93150684931507,,Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion,"['pytorch', 'pytorch-lightning', 'stable-diffusion', 'text-to-image']","['pytorch', 'pytorch-lightning', 'stable-diffusion', 'text-to-image']",2022-09-21,"[('carson-katri/dream-textures', 0.5931910872459412, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.5680199861526489, 'diffusion', 2), ('ashawkey/stable-dreamfusion', 0.5586757659912109, 'diffusion', 1), ('huggingface/diffusers', 0.5447068810462952, 'diffusion', 2), ('comfyanonymous/comfyui', 0.5401220321655273, 'diffusion', 2)]",1,1.0,,0.0,8,0,16,16,0,0,0,8.0,6.0,90.0,0.8,45
134,ml,https://github.com/hyperopt/hyperopt,[],,[],[],,,,hyperopt/hyperopt,hyperopt,6976,1073,126,Python,http://hyperopt.github.io/hyperopt,Distributed Asynchronous Hyperparameter Optimization in Python,hyperopt,2024-01-13,2011-09-06,647,10.782071097372489,https://avatars.githubusercontent.com/u/5280805?v=4,Distributed Asynchronous Hyperparameter Optimization in Python,[],[],2023-09-29,"[('optuna/optuna', 0.7380708456039429, 'ml', 0), ('kubeflow/katib', 0.6022725701332092, 'ml', 0), ('google/vizier', 0.5750223994255066, 'ml', 0), ('samuelcolvin/arq', 0.5642699599266052, 'data', 0), ('baruchel/tco', 0.5423157215118408, 'perf', 0), ('scikit-optimize/scikit-optimize', 0.5412236452102661, 'ml', 0), ('determined-ai/determined', 0.5405532121658325, 'ml-ops', 0), ('dask/dask', 0.5399159789085388, 'perf', 0), ('klen/py-frameworks-bench', 0.5207222700119019, 'perf', 0), ('ipython/ipyparallel', 0.5190962553024292, 'perf', 0), ('joblib/joblib', 0.5118980407714844, 'util', 0), ('python-trio/trio', 0.510098934173584, 'perf', 0), ('geeogi/async-python-lambda-template', 0.5066941976547241, 'template', 0), ('alirn76/panther', 0.5043962597846985, 'web', 0)]",102,5.0,,0.44,75,50,150,5,0,1,1,75.0,49.0,90.0,0.7,45
681,util,https://github.com/bndr/pipreqs,[],,[],[],,,,bndr/pipreqs,pipreqs,5580,367,56,Python,,pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.,bndr,2024-01-13,2015-04-22,457,12.187207488299531,,pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.,[],[],2023-10-08,"[('thoth-station/micropipenv', 0.625019907951355, 'util', 0), ('pypa/pipenv', 0.550599217414856, 'util', 0), ('pdm-project/pdm', 0.5109939575195312, 'util', 0), ('pomponchik/instld', 0.5049751996994019, 'util', 0)]",60,2.0,,0.23,42,22,106,3,2,3,2,42.0,76.0,90.0,1.8,45
1673,data,https://github.com/madmaze/pytesseract,['ocr'],,[],[],,,,madmaze/pytesseract,pytesseract,5291,717,107,Python,,A Python wrapper for Google Tesseract,madmaze,2024-01-13,2010-10-27,691,7.6475325211645675,,A Python wrapper for Google Tesseract,[],['ocr'],2024-01-10,"[('rapidai/rapidocr', 0.5656213164329529, 'data', 1), ('jaidedai/easyocr', 0.5150726437568665, 'data', 1), ('hrnet/hrnet-semantic-segmentation', 0.5132443308830261, 'ml', 0)]",45,2.0,,0.96,13,9,161,0,3,2,3,13.0,28.0,90.0,2.2,45
264,util,https://github.com/pytransitions/transitions,[],,[],[],,,,pytransitions/transitions,transitions,5203,519,92,Python,,"A lightweight, object-oriented finite state machine implementation in Python with many extensions",pytransitions,2024-01-14,2014-10-12,485,10.721518987341772,https://avatars.githubusercontent.com/u/26332704?v=4,"A lightweight, object-oriented finite state machine implementation in Python with many extensions","['hierarchical-state-machine', 'nested-states', 'state-diagram', 'state-machine']","['hierarchical-state-machine', 'nested-states', 'state-diagram', 'state-machine']",2023-09-20,"[('artemyk/dynpy', 0.55495285987854, 'sim', 0), ('pyston/pyston', 0.5525701642036438, 'util', 0), ('sympy/sympy', 0.523476243019104, 'math', 0), ('ethereum/py-evm', 0.5112031698226929, 'crypto', 0), ('citadel-ai/langcheck', 0.5025991201400757, 'llm', 0)]",76,6.0,,0.48,7,3,113,4,0,5,5,7.0,7.0,90.0,1.0,45
311,util,https://github.com/indygreg/pyoxidizer,"['package-manager', 'packaging']",,[],[],,,,indygreg/pyoxidizer,PyOxidizer,5016,212,62,Rust,,A modern Python application packaging and distribution tool,indygreg,2024-01-13,2018-12-18,267,18.786516853932586,,A modern Python application packaging and distribution tool,[],"['package-manager', 'packaging']",2023-01-21,"[('pypa/flit', 0.8746299147605896, 'util', 2), ('mitsuhiko/rye', 0.8726885914802551, 'util', 2), ('python-poetry/poetry', 0.8143705725669861, 'util', 2), ('pdm-project/pdm', 0.7249577045440674, 'util', 2), ('pomponchik/instld', 0.7080777883529663, 'util', 1), ('regebro/pyroma', 0.7064893245697021, 'util', 1), ('pypa/hatch', 0.7048346400260925, 'util', 2), ('mamba-org/mamba', 0.6980676651000977, 'util', 2), ('pyodide/micropip', 0.6752342581748962, 'util', 0), ('pypi/warehouse', 0.6707281470298767, 'util', 0), ('ofek/pyapp', 0.6547517776489258, 'util', 1), ('beeware/briefcase', 0.6296486258506775, 'util', 0), ('pypa/pipenv', 0.6146742701530457, 'util', 1), ('conda/conda', 0.5986080169677734, 'util', 2), ('jazzband/pip-tools', 0.5879077315330505, 'util', 1), ('pypa/installer', 0.5681904554367065, 'util', 0), ('tezromach/python-package-template', 0.5635542273521423, 'template', 0), ('spack/spack', 0.5544406771659851, 'util', 1), ('tiangolo/poetry-version-plugin', 0.5493191480636597, 'util', 1), ('dosisod/refurb', 0.5480595827102661, 'util', 0), ('pytables/pytables', 0.5478392839431763, 'data', 0), ('omry/omegaconf', 0.5404947400093079, 'util', 0), ('mamba-org/gator', 0.5344242453575134, 'jupyter', 0), ('pypa/gh-action-pypi-publish', 0.5328114628791809, 'util', 0), ('pyinstaller/pyinstaller', 0.5304632782936096, 'util', 0), ('pympler/pympler', 0.5277453064918518, 'perf', 0), ('linkedin/shiv', 0.5276411175727844, 'util', 0), ('grahamdumpleton/wrapt', 0.5274959206581116, 'util', 0), ('hoffstadt/dearpygui', 0.5257773995399475, 'gui', 0), ('malloydata/malloy-py', 0.5247126221656799, 'data', 0), ('python-injector/injector', 0.5219050645828247, 'util', 0), ('beeware/toga', 0.5212326645851135, 'gui', 0), ('conda/conda-pack', 0.5181369781494141, 'util', 0), ('mgedmin/check-manifest', 0.5157642364501953, 'util', 0), ('bottlepy/bottle', 0.514263391494751, 'web', 0), ('thoth-station/micropipenv', 0.513746976852417, 'util', 0), ('pyscaffold/pyscaffold', 0.5098890662193298, 'template', 0), ('urwid/urwid', 0.5094347596168518, 'term', 0), ('ethtx/ethtx', 0.5071747303009033, 'crypto', 0), ('eleutherai/pyfra', 0.503706157207489, 'ml', 0), ('pallets/flask', 0.5036942958831787, 'web', 0), ('lukasschwab/arxiv.py', 0.5008288621902466, 'util', 0), ('conda/conda-build', 0.5000386834144592, 'util', 0)]",54,3.0,,0.0,21,3,62,12,0,72,72,21.0,11.0,90.0,0.5,45
536,nlp,https://github.com/salesforce/codegen,[],,[],[],,,,salesforce/codegen,CodeGen,4596,353,77,Python,,CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.,salesforce,2024-01-13,2022-03-28,96,47.803863298662705,https://avatars.githubusercontent.com/u/453694?v=4,CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.,"['codex', 'generativemodel', 'languagemodel', 'llm', 'programsynthesis', 'tpu-acceleration']","['codex', 'generativemodel', 'languagemodel', 'llm', 'programsynthesis', 'tpu-acceleration']",2023-11-21,"[('salesforce/codet5', 0.6593608856201172, 'nlp', 0), ('thudm/codegeex', 0.6133092045783997, 'llm', 0), ('bigcode-project/starcoder', 0.601254940032959, 'llm', 0), ('openai/image-gpt', 0.5758861303329468, 'llm', 0), ('ravenscroftj/turbopilot', 0.5407807230949402, 'llm', 0), ('conceptofmind/toolformer', 0.5336177945137024, 'llm', 0), ('microsoft/pycodegpt', 0.5231809020042419, 'llm', 0), ('lupantech/chameleon-llm', 0.5142695307731628, 'llm', 1), ('next-gpt/next-gpt', 0.5129567384719849, 'llm', 1), ('pytorch/glow', 0.5098628997802734, 'ml', 0), ('modularml/mojo', 0.5068298578262329, 'util', 0), ('ludwig-ai/ludwig', 0.5055090188980103, 'ml-ops', 1)]",10,2.0,,0.65,5,1,22,2,0,0,0,5.0,2.0,90.0,0.4,45
146,ml,https://github.com/nmslib/hnswlib,[],,[],[],,,,nmslib/hnswlib,hnswlib,3773,593,64,C++,https://github.com/nmslib/hnswlib,Header-only C++/python library for fast approximate nearest neighbors,nmslib,2024-01-13,2017-07-06,342,11.00917048770321,https://avatars.githubusercontent.com/u/37882366?v=4,Header-only C++/python library for fast approximate nearest neighbors,[],[],2023-12-03,"[('spotify/annoy', 0.7858440279960632, 'ml', 0), ('lmcinnes/pynndescent', 0.6369488835334778, 'ml', 0), ('spotify/voyager', 0.5946098566055298, 'ml', 0), ('pyston/pyston', 0.5059448480606079, 'util', 0), ('facebookresearch/faiss', 0.5014215111732483, 'ml', 0)]",72,4.0,,0.65,30,11,79,1,2,2,2,30.0,28.0,90.0,0.9,45
758,diffusion,https://github.com/lkwq007/stablediffusion-infinity,[],,[],[],,,,lkwq007/stablediffusion-infinity,stablediffusion-infinity,3732,295,41,Python,,Outpainting with Stable Diffusion on an infinite canvas,lkwq007,2024-01-14,2022-09-02,73,50.72621359223301,,Outpainting with Stable Diffusion on an infinite canvas,"['gui', 'inpainting', 'outpainting', 'stable-diffusion', 'stablediffusion']","['gui', 'inpainting', 'outpainting', 'stable-diffusion', 'stablediffusion']",2023-01-24,"[('carson-katri/dream-textures', 0.5580441951751709, 'diffusion', 1), ('sanster/lama-cleaner', 0.5446968078613281, 'ml-dl', 2), ('timothybrooks/instruct-pix2pix', 0.5145807266235352, 'diffusion', 0), ('jina-ai/discoart', 0.5142841935157776, 'diffusion', 1), ('comfyanonymous/comfyui', 0.5033169388771057, 'diffusion', 2)]",7,2.0,,0.13,4,0,17,12,0,2,2,4.0,3.0,90.0,0.8,45
1169,llm,https://github.com/instruction-tuning-with-gpt-4/gpt-4-llm,[],,[],[],,,,instruction-tuning-with-gpt-4/gpt-4-llm,GPT-4-LLM,3721,272,44,HTML,https://instruction-tuning-with-gpt-4.github.io/,Instruction Tuning with GPT-4,instruction-tuning-with-gpt-4,2024-01-14,2023-04-06,42,87.11371237458194,,Instruction Tuning with GPT-4,"['alpaca', 'chatgpt', 'gpt-4', 'instruction-tuning', 'llama']","['alpaca', 'chatgpt', 'gpt-4', 'instruction-tuning', 'llama']",2023-06-11,"[('declare-lab/instruct-eval', 0.653624415397644, 'llm', 0), ('haotian-liu/llava', 0.6272305250167847, 'llm', 4), ('farizrahman4u/loopgpt', 0.5821303129196167, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5739951133728027, 'llm', 2), ('hiyouga/llama-factory', 0.5739949345588684, 'llm', 2), ('tiger-ai-lab/mammoth', 0.5739008784294128, 'llm', 1), ('zrrskywalker/llama-adapter', 0.5689181685447693, 'llm', 2), ('tloen/alpaca-lora', 0.5178021788597107, 'llm', 1)]",7,3.0,,0.63,0,0,9,7,0,0,0,0.0,0.0,90.0,0.0,45
259,util,https://github.com/python-markdown/markdown,[],,[],[],,,,python-markdown/markdown,markdown,3472,865,76,Python,https://python-markdown.github.io/,A Python implementation of John Gruber’s Markdown with Extension support.,python-markdown,2024-01-12,2010-05-29,713,4.866639967961554,https://avatars.githubusercontent.com/u/11278576?v=4,A Python implementation of John Gruber’s Markdown with Extension support.,"['markdown', 'markdown-parser', 'markdown-to-html', 'python-markdown']","['markdown', 'markdown-parser', 'markdown-to-html', 'python-markdown']",2024-01-10,"[('getpelican/pelican', 0.6716626882553101, 'web', 0), ('hhatto/autopep8', 0.5607690811157227, 'util', 0), ('google/yapf', 0.5518056154251099, 'util', 0), ('mwouts/jupytext', 0.5461102724075317, 'jupyter', 1), ('google/latexify_py', 0.5334113240242004, 'util', 0), ('pygments/pygments', 0.5284628868103027, 'util', 0), ('roniemartinez/dude', 0.5186324715614319, 'util', 0), ('pytoolz/toolz', 0.5181722640991211, 'util', 0), ('connorferster/handcalcs', 0.5013054609298706, 'jupyter', 0), ('feincms/feincms', 0.500464141368866, 'web', 0)]",173,2.0,,0.87,49,40,166,0,2,4,2,48.0,167.0,90.0,3.5,45
360,ml-ops,https://github.com/polyaxon/polyaxon,[],,[],[],,,,polyaxon/polyaxon,polyaxon,3432,321,79,,https://polyaxon.com,MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle,polyaxon,2024-01-12,2016-12-26,370,9.272095715939791,https://avatars.githubusercontent.com/u/24544827?v=4,MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle,"['artificial-intelligence', 'caffe', 'data-science', 'deep-learning', 'hyperparameter-optimization', 'jupyter', 'jupyterlab', 'k8s', 'keras', 'kubernetes', 'machine-learning', 'ml', 'mlops', 'mxnet', 'notebook', 'pipelines', 'pytorch', 'reinforcement-learning', 'tensorflow', 'workflow']","['artificial-intelligence', 'caffe', 'data-science', 'deep-learning', 'hyperparameter-optimization', 'jupyter', 'jupyterlab', 'k8s', 'keras', 'kubernetes', 'machine-learning', 'ml', 'mlops', 'mxnet', 'notebook', 'pipelines', 'pytorch', 'reinforcement-learning', 'tensorflow', 'workflow']",2024-01-12,"[('kubeflow/pipelines', 0.7241019010543823, 'ml-ops', 4), ('mlflow/mlflow', 0.7237421870231628, 'ml-ops', 2), ('bentoml/bentoml', 0.70576012134552, 'ml-ops', 4), ('netflix/metaflow', 0.6915358901023865, 'ml-ops', 5), ('onnx/onnx', 0.6912744641304016, 'ml', 7), ('fmind/mlops-python-package', 0.6891217231750488, 'template', 2), ('bodywork-ml/bodywork-core', 0.6861110925674438, 'ml-ops', 4), ('feast-dev/feast', 0.6851301193237305, 'ml-ops', 4), ('microsoft/nni', 0.6834843754768372, 'ml', 7), ('flyteorg/flyte', 0.6784146428108215, 'ml-ops', 5), ('determined-ai/determined', 0.6687636971473694, 'ml-ops', 9), ('wandb/client', 0.6643576622009277, 'ml', 9), ('zenml-io/zenml', 0.6621537804603577, 'ml-ops', 9), ('allegroai/clearml', 0.6574744582176208, 'ml-ops', 4), ('unionai-oss/unionml', 0.6547122597694397, 'ml-ops', 2), ('huggingface/datasets', 0.6493980884552002, 'nlp', 4), ('skypilot-org/skypilot', 0.6188631057739258, 'llm', 3), ('merantix-momentum/squirrel-core', 0.6144108772277832, 'ml', 6), ('googlecloudplatform/vertex-ai-samples', 0.6127983331680298, 'ml', 4), ('tensorflow/tensorflow', 0.6108132600784302, 'ml-dl', 4), ('avaiga/taipy', 0.6045488119125366, 'data', 3), ('ml-tooling/opyrator', 0.6038118600845337, 'viz', 1), ('lutzroeder/netron', 0.5946682095527649, 'ml', 8), ('microsoft/onnxruntime', 0.5915482044219971, 'ml', 4), ('orchest/orchest', 0.5914933681488037, 'ml-ops', 6), ('polyaxon/datatile', 0.5872251987457275, 'pandas', 4), ('getindata/kedro-kubeflow', 0.5841188430786133, 'ml-ops', 1), ('ploomber/ploomber', 0.5840852856636047, 'ml-ops', 6), ('dagster-io/dagster', 0.5835554003715515, 'ml-ops', 3), ('firmai/industry-machine-learning', 0.5819257497787476, 'study', 2), ('horovod/horovod', 0.5797879695892334, 'ml-ops', 6), ('skops-dev/skops', 0.5779671669006348, 'ml-ops', 2), ('kubeflow/fairing', 0.5774126648902893, 'ml-ops', 0), ('apple/coremltools', 0.5726978778839111, 'ml', 3), ('iterative/dvc', 0.5695563554763794, 'ml-ops', 2), ('roboflow/supervision', 0.5695512890815735, 'ml', 4), ('mage-ai/mage-ai', 0.5694826245307922, 'ml-ops', 4), ('apache/airflow', 0.5673277974128723, 'ml-ops', 4), ('deepchecks/deepchecks', 0.5667403340339661, 'data', 6), ('alirezadir/machine-learning-interview-enlightener', 0.564633309841156, 'study', 2), ('kubeflow-kale/kale', 0.5640344619750977, 'ml-ops', 1), ('nccr-itmo/fedot', 0.5636411905288696, 'ml-ops', 2), ('kestra-io/kestra', 0.561455249786377, 'ml-ops', 1), ('gradio-app/gradio', 0.5608937740325928, 'viz', 3), ('activeloopai/deeplake', 0.5602334141731262, 'ml-ops', 7), ('doccano/doccano', 0.5601885914802551, 'nlp', 1), ('aimhubio/aim', 0.5590616464614868, 'ml-ops', 6), ('adap/flower', 0.5568473935127258, 'ml-ops', 5), ('jina-ai/jina', 0.5560136437416077, 'ml', 4), ('automl/auto-sklearn', 0.5545597672462463, 'ml', 1), ('dagworks-inc/hamilton', 0.5525692701339722, 'ml-ops', 3), ('ddbourgin/numpy-ml', 0.5511565208435059, 'ml', 2), ('alpa-projects/alpa', 0.5499585866928101, 'ml-dl', 2), ('xplainable/xplainable', 0.5472549796104431, 'ml-interpretability', 2), ('ray-project/ray', 0.5454540252685547, 'ml-ops', 7), ('unity-technologies/ml-agents', 0.5450904965400696, 'ml-rl', 3), ('explosion/thinc', 0.5428985357284546, 'ml-dl', 6), ('nvidia/deeplearningexamples', 0.5416098833084106, 'ml-dl', 4), ('aws/sagemaker-python-sdk', 0.5382318496704102, 'ml', 4), ('hpcaitech/colossalai', 0.5358098745346069, 'llm', 1), ('google/mediapipe', 0.5345292091369629, 'ml', 2), ('mosaicml/composer', 0.5328403115272522, 'ml-dl', 3), ('titanml/takeoff', 0.5318194031715393, 'llm', 0), ('featureform/embeddinghub', 0.5308116674423218, 'nlp', 4), ('pytorchlightning/pytorch-lightning', 0.529716968536377, 'ml-dl', 5), ('evidentlyai/evidently', 0.5295282006263733, 'ml-ops', 3), ('google/vizier', 0.5294651389122009, 'ml', 3), ('whylabs/whylogs', 0.5290948152542114, 'util', 3), ('backtick-se/cowait', 0.5269318222999573, 'util', 2), ('districtdatalabs/yellowbrick', 0.5260629653930664, 'ml', 1), 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1), ('zenml-io/mlstacks', 0.5073304176330566, 'ml-ops', 2), ('eventual-inc/daft', 0.5069470405578613, 'pandas', 3), ('awslabs/autogluon', 0.5069301724433899, 'ml', 5), ('pathwaycom/pathway', 0.5067856311798096, 'data', 0), ('lucidrains/toolformer-pytorch', 0.5062516927719116, 'llm', 2), ('tensorflow/tensor2tensor', 0.5061532258987427, 'ml', 3), ('kedro-org/kedro', 0.5058719515800476, 'ml-ops', 2), ('neuralmagic/deepsparse', 0.504664957523346, 'nlp', 0), ('towhee-io/towhee', 0.5038055181503296, 'ml-ops', 1), ('spack/spack', 0.5023648738861084, 'util', 0), ('giskard-ai/giskard', 0.5021538734436035, 'data', 3), ('lastmile-ai/aiconfig', 0.5021164417266846, 'util', 0), ('keras-rl/keras-rl', 0.5010080337524414, 'ml-rl', 4), ('superduperdb/superduperdb', 0.5004454851150513, 'data', 3), ('microsoft/flaml', 0.5002272129058838, 'ml', 4)]",98,3.0,,3.77,3,1,86,0,0,29,29,3.0,1.0,90.0,0.3,45
35,data,https://github.com/jmcnamara/xlsxwriter,[],,[],[],,,,jmcnamara/xlsxwriter,XlsxWriter,3402,624,118,Python,https://xlsxwriter.readthedocs.io,A Python module for creating Excel XLSX files.,jmcnamara,2024-01-13,2013-01-04,577,5.890180558990848,,A Python module for creating Excel XLSX files.,"['charts', 'libxlsxwriter', 'pandas', 'spreadsheet', 'xlsx', 'xlsx-files', 'xlsxwriter']","['charts', 'libxlsxwriter', 'pandas', 'spreadsheet', 'xlsx', 'xlsx-files', 'xlsxwriter']",2023-11-08,"[('zoomeranalytics/xlwings', 0.7553142309188843, 'data', 0), ('jazzband/tablib', 0.5911999344825745, 'data', 0), ('tkrabel/bamboolib', 0.5247606039047241, 'pandas', 1), ('connorferster/handcalcs', 0.5153928399085999, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5122716426849365, 'jupyter', 1), ('holoviz/hvplot', 0.5030190348625183, 'pandas', 0), ('cuemacro/chartpy', 0.500167191028595, 'viz', 0)]",52,2.0,,1.31,27,19,134,2,0,15,15,27.0,87.0,90.0,3.2,45
365,ml,https://github.com/facebookresearch/vissl,[],,[],[],,,,facebookresearch/vissl,vissl,3180,328,53,Jupyter Notebook,https://vissl.ai,"VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.",facebookresearch,2024-01-13,2020-04-09,198,16.002875629043853,https://avatars.githubusercontent.com/u/16943930?v=4,"VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.",[],[],2024-01-05,"[('deci-ai/super-gradients', 0.6394485831260681, 'ml-dl', 0), ('lightly-ai/lightly', 0.6104288101196289, 'ml', 0), ('roboflow/notebooks', 0.5875295996665955, 'study', 0), ('google-research/maxvit', 0.5404923558235168, 'ml', 0), ('lucidrains/vit-pytorch', 0.5331419110298157, 'ml-dl', 0), ('paperswithcode/sota-extractor', 0.5020662546157837, 'data', 0)]",37,5.0,,0.19,2,0,46,0,0,1,1,2.0,2.0,90.0,1.0,45
654,ml,https://github.com/pytorch/glow,[],,[],[],,,,pytorch/glow,glow,3085,690,157,C++,,Compiler for Neural Network hardware accelerators,pytorch,2024-01-13,2017-09-29,330,9.332324978392394,https://avatars.githubusercontent.com/u/21003710?v=4,Compiler for Neural Network hardware accelerators,[],[],2024-01-09,"[('microsoft/onnxruntime', 0.6179055571556091, 'ml', 0), ('alpa-projects/alpa', 0.6077716946601868, 'ml-dl', 0), ('karpathy/micrograd', 0.6073175668716431, 'study', 0), ('intel/intel-extension-for-pytorch', 0.6005612015724182, 'perf', 0), ('exaloop/codon', 0.5580657720565796, 'perf', 0), ('microsoft/olive', 0.5527094006538391, 'ml', 0), ('plasma-umass/scalene', 0.5508031249046326, 'profiling', 0), ('facebookincubator/aitemplate', 0.542003870010376, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5354295372962952, 'study', 0), ('microsoft/deepspeed', 0.5229150652885437, 'ml-dl', 0), ('cython/cython', 0.5203021168708801, 'util', 0), ('pytorch/pytorch', 0.5188764929771423, 'ml-dl', 0), ('huggingface/optimum', 0.5153691172599792, 'ml', 0), ('neuralmagic/deepsparse', 0.514583945274353, 'nlp', 0), ('openai/triton', 0.5112419724464417, 'util', 0), ('salesforce/codegen', 0.5098628997802734, 'nlp', 0), ('fastai/fastcore', 0.509026288986206, 'util', 0), ('google/tf-quant-finance', 0.5075867772102356, 'finance', 0), ('denys88/rl_games', 0.5074953436851501, 'ml-rl', 0), ('bobazooba/xllm', 0.5073339343070984, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.5054729580879211, 'ml-dl', 0), ('skorch-dev/skorch', 0.5048933029174805, 'ml-dl', 0), ('determined-ai/determined', 0.5047574639320374, 'ml-ops', 0), ('intel/scikit-learn-intelex', 0.5033442378044128, 'perf', 0)]",353,1.0,,1.23,8,1,77,0,0,0,0,8.0,27.0,90.0,3.4,45
669,diffusion,https://github.com/saharmor/dalle-playground,[],,[],[],,,,saharmor/dalle-playground,dalle-playground,2751,603,32,JavaScript,,A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini),saharmor,2024-01-12,2021-09-13,124,22.15995397008055,,A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini),"['artificial', 'artificial-intelligence', 'dall-e', 'dalle', 'dalle-mini', 'gan', 'machine-learning', 'openai', 'stable-diffusion', 'text-to-image', 'transformers']","['artificial', 'artificial-intelligence', 'dall-e', 'dalle', 'dalle-mini', 'gan', 'machine-learning', 'openai', 'stable-diffusion', 'text-to-image', 'transformers']",2023-12-29,"[('borisdayma/dalle-mini', 0.7689334750175476, 'diffusion', 0), ('nateraw/stable-diffusion-videos', 0.671584963798523, 'diffusion', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.6557318568229675, 'web', 1), ('lucidrains/deep-daze', 0.6547111868858337, 'ml', 3), ('automatic1111/stable-diffusion-webui', 0.6440442204475403, 'diffusion', 1), ('compvis/stable-diffusion', 0.6419358253479004, 'diffusion', 0), ('sharonzhou/long_stable_diffusion', 0.6195039749145508, 'diffusion', 0), ('invoke-ai/invokeai', 0.6060563921928406, 'diffusion', 2), ('openai/glide-text2im', 0.6006641387939453, 'diffusion', 0), ('lucidrains/imagen-pytorch', 0.5938807725906372, 'ml-dl', 2), ('lucidrains/dalle2-pytorch', 0.5927163362503052, 'diffusion', 2), ('thereforegames/unprompted', 0.5805418491363525, 'diffusion', 1), ('promptslab/awesome-prompt-engineering', 0.5731377005577087, 'study', 3), ('laion-ai/dalle2-laion', 0.570446252822876, 'diffusion', 1), ('open-mmlab/mmediting', 0.5508294105529785, 'ml', 0), ('huggingface/diffusers', 0.539817750453949, 'diffusion', 1), ('thudm/cogvideo', 0.529645562171936, 'ml', 0), ('openai/clip', 0.5254179239273071, 'ml-dl', 1), ('carson-katri/dream-textures', 0.5181798934936523, 'diffusion', 1), ('alibaba/easynlp', 0.5042204260826111, 'nlp', 2)]",11,4.0,,0.13,2,2,28,1,0,0,0,2.0,4.0,90.0,2.0,45
868,study,https://github.com/rasbt/machine-learning-book,[],,[],[],,,,rasbt/machine-learning-book,machine-learning-book,2524,923,45,Jupyter Notebook,https://sebastianraschka.com/books/#machine-learning-with-pytorch-and-scikit-learn,Code Repository for Machine Learning with PyTorch and Scikit-Learn,rasbt,2024-01-13,2021-12-19,110,22.8860103626943,,Code Repository for Machine Learning with PyTorch and Scikit-Learn,"['deep-learning', 'machine-learning', 'neural-networks', 'pytorch', 'scikit-learn']","['deep-learning', 'machine-learning', 'neural-networks', 'pytorch', 'scikit-learn']",2023-12-27,"[('skorch-dev/skorch', 0.777802050113678, 'ml-dl', 3), ('intel/intel-extension-for-pytorch', 0.7345353960990906, 'perf', 3), ('pytorch/ignite', 0.7284553050994873, 'ml-dl', 3), ('mrdbourke/pytorch-deep-learning', 0.6849406957626343, 'study', 3), ('fchollet/deep-learning-with-python-notebooks', 0.6557565331459045, 'study', 0), ('ageron/handson-ml2', 0.6461301445960999, 'ml', 0), ('pyg-team/pytorch_geometric', 0.6416996121406555, 'ml-dl', 2), ('determined-ai/determined', 0.6383908987045288, 'ml-ops', 3), ('pycaret/pycaret', 0.6377236843109131, 'ml', 1), ('nvidia/apex', 0.6338503956794739, 'ml-dl', 0), ('ashleve/lightning-hydra-template', 0.619256317615509, 'util', 2), ('patchy631/machine-learning', 0.6192141175270081, 'ml', 0), ('huggingface/transformers', 0.6170870065689087, 'nlp', 3), ('tensorflow/tensorflow', 0.6161754131317139, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.6144220232963562, 'ml-rl', 1), ('pytorch/data', 0.6106820106506348, 'data', 0), ('teamhg-memex/eli5', 0.607665479183197, 'ml', 2), ('koaning/human-learn', 0.6072452068328857, 'data', 2), ('gradio-app/gradio', 0.607107937335968, 'viz', 2), ('allenai/allennlp', 0.6064896583557129, 'nlp', 2), ('microsoft/semi-supervised-learning', 0.6046102046966553, 'ml', 3), ('microsoft/nni', 0.603572428226471, 'ml', 3), ('huggingface/huggingface_hub', 0.6020623445510864, 'ml', 3), ('automl/auto-sklearn', 0.6004539132118225, 'ml', 1), ('karpathy/micrograd', 0.597282350063324, 'study', 0), ('featurelabs/featuretools', 0.5927860736846924, 'ml', 2), ('tensorflow/tensor2tensor', 0.5888902544975281, 'ml', 2), ('horovod/horovod', 0.5868951082229614, 'ml-ops', 3), ('nvidia/deeplearningexamples', 0.5868580937385559, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.586579442024231, 'ml', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5862606167793274, 'study', 2), ('intellabs/bayesian-torch', 0.5859395861625671, 'ml', 2), ('koaning/scikit-lego', 0.5857502818107605, 'ml', 2), ('iryna-kondr/scikit-llm', 0.5804186463356018, 'llm', 3), ('deepmind/deepmind-research', 0.5797885060310364, 'ml', 0), ('oml-team/open-metric-learning', 0.5796335339546204, 'ml', 2), ('neuralmagic/sparseml', 0.5774632692337036, 'ml-dl', 1), ('facebookresearch/pytorch3d', 0.574776828289032, 'ml-dl', 0), ('rentruewang/koila', 0.5745282769203186, 'ml', 3), ('pyro-ppl/pyro', 0.5722088813781738, 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'ml', 5), ('keras-team/autokeras', 0.5562219023704529, 'ml-dl', 2), ('keras-team/keras', 0.5560621619224548, 'ml-dl', 4), ('tlkh/tf-metal-experiments', 0.5555431246757507, 'perf', 1), ('ggerganov/ggml', 0.5550714135169983, 'ml', 1), ('skops-dev/skops', 0.5548707246780396, 'ml-ops', 2), ('kubeflow/fairing', 0.5534403324127197, 'ml-ops', 0), ('explosion/thinc', 0.5526840686798096, 'ml-dl', 3), ('tensorflow/data-validation', 0.5481884479522705, 'ml-ops', 0), ('wandb/client', 0.5468006134033203, 'ml', 3), ('ddbourgin/numpy-ml', 0.5466738343238831, 'ml', 2), ('facebookresearch/dinov2', 0.5447684526443481, 'diffusion', 0), ('onnx/onnx', 0.5438082814216614, 'ml', 4), ('pytorch/torchrec', 0.5437749624252319, 'ml-dl', 2), ('rafiqhasan/auto-tensorflow', 0.5379175543785095, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5370543003082275, 'study', 3), ('lightly-ai/lightly', 0.5357835292816162, 'ml', 3), ('pytorch/glow', 0.5354295372962952, 'ml', 0), ('rasbt/mlxtend', 0.5330138206481934, 'ml', 1), ('xl0/lovely-tensors', 0.5321751236915588, 'ml-dl', 2), ('scikit-learn/scikit-learn', 0.5305891036987305, 'ml', 1), ('dmlc/dgl', 0.5301172733306885, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5299058556556702, 'study', 2), ('pytorch/captum', 0.5297553539276123, 'ml-interpretability', 0), ('scikit-learn-contrib/metric-learn', 0.5294020175933838, 'ml', 2), ('google-research/language', 0.5288284420967102, 'nlp', 1), ('nyandwi/modernconvnets', 0.5279679298400879, 'ml-dl', 1), ('dask/dask-ml', 0.5278743505477905, 'ml', 0), ('jeshraghian/snntorch', 0.5273086428642273, 'ml-dl', 3), ('huggingface/evaluate', 0.5270928740501404, 'ml', 1), ('hysts/pytorch_image_classification', 0.5264284610748291, 'ml-dl', 1), ('microsoft/flaml', 0.5261116623878479, 'ml', 3), ('nvlabs/gcvit', 0.5256971716880798, 'diffusion', 1), ('huggingface/accelerate', 0.5255182385444641, 'ml', 0), ('mdbloice/augmentor', 0.5249707102775574, 'ml', 3), ('scikit-learn-contrib/imbalanced-learn', 0.5242375731468201, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5238132476806641, 'ml', 3), ('speechbrain/speechbrain', 0.5237654447555542, 'nlp', 2), ('csinva/imodels', 0.5234281420707703, 'ml', 2), ('google/tf-quant-finance', 0.523298442363739, 'finance', 0), ('googlecloudplatform/vertex-ai-samples', 0.5225531458854675, 'ml', 0), ('thu-ml/tianshou', 0.5220516920089722, 'ml-rl', 1), ('google/temporian', 0.5215150117874146, 'time-series', 0), ('lucidrains/imagen-pytorch', 0.5199929475784302, 'ml-dl', 1), ('aiqc/aiqc', 0.519234299659729, 'ml-ops', 0), ('salesforce/blip', 0.5187211036682129, 'diffusion', 0), ('graykode/nlp-tutorial', 0.518619179725647, 'study', 1), ('lutzroeder/netron', 0.5182120203971863, 'ml', 3), ('udacity/deep-learning-v2-pytorch', 0.517561674118042, 'study', 2), ('deepmind/dm-haiku', 0.5160495638847351, 'ml-dl', 3), ('nicolas-chaulet/torch-points3d', 0.5115610957145691, 'ml', 0), ('epistasislab/tpot', 0.5111925601959229, 'ml', 2), ('catboost/catboost', 0.5105630159378052, 'ml', 1), ('weecology/deepforest', 0.5097160339355469, 'gis', 0), ('neuralmagic/deepsparse', 0.5080384612083435, 'nlp', 0), ('tensorflow/lucid', 0.5070700645446777, 'ml-interpretability', 1), ('cvxgrp/pymde', 0.5070154070854187, 'ml', 2), ('dylanhogg/awesome-python', 0.5065024495124817, 'study', 2), ('keras-rl/keras-rl', 0.5053036212921143, 'ml-rl', 2), ('carla-recourse/carla', 0.5052952766418457, 'ml', 2), ('unity-technologies/ml-agents', 0.5040963292121887, 'ml-rl', 3), ('deepmodeling/deepmd-kit', 0.5035223960876465, 'sim', 1), ('adafruit/circuitpython', 0.5035032033920288, 'util', 0), ('ray-project/tune-sklearn', 0.5028691291809082, 'ml', 1), ('google/vizier', 0.5023799538612366, 'ml', 2), ('nltk/nltk', 0.5020496249198914, 'nlp', 1), ('blackhc/toma', 0.5006940960884094, 'ml-dl', 2), ('ludwig-ai/ludwig', 0.5003655552864075, 'ml-ops', 3)]",14,3.0,,0.38,7,4,25,1,0,1,1,7.0,7.0,90.0,1.0,45
1793,util,https://github.com/pyinfra-dev/pyinfra,[],,[],[],,,,pyinfra-dev/pyinfra,pyinfra,2467,319,37,Python,https://pyinfra.com,"pyinfra automates infrastructure using Python. It’s fast and scales from one server to thousands. Great for ad-hoc command execution, service deployment, configuration management and more.",pyinfra-dev,2024-01-13,2014-10-19,484,5.0941002949852505,https://avatars.githubusercontent.com/u/146648081?v=4,"pyinfra automates infrastructure using Python. It’s fast and scales from one server to thousands. Great for ad-hoc command execution, service deployment, configuration management and more.","['cloud-management', 'configuration-management', 'high-performance', 'infrastructure', 'pyinfra', 'remote-execution']","['cloud-management', 'configuration-management', 'high-performance', 'infrastructure', 'pyinfra', 'remote-execution']",2024-01-13,"[('willmcgugan/textual', 0.6321539878845215, 'term', 0), ('pypy/pypy', 0.6099465489387512, 'util', 0), ('backtick-se/cowait', 0.5932263135910034, 'util', 0), ('bottlepy/bottle', 0.590758204460144, 'web', 0), ('cython/cython', 0.5551347732543945, 'util', 0), ('pypa/hatch', 0.5454100966453552, 'util', 0), ('pallets/flask', 0.5409476161003113, 'web', 0), ('pallets/quart', 0.5390889048576355, 'web', 0), ('dddomodossola/remi', 0.5348232984542847, 'gui', 0), ('pytables/pytables', 0.5320051312446594, 'data', 0), ('micropython/micropython', 0.5298929214477539, 'util', 0), ('eventual-inc/daft', 0.5292609333992004, 'pandas', 0), ('eleutherai/pyfra', 0.5274806022644043, 'ml', 0), ('falconry/falcon', 0.5231207609176636, 'web', 0), ('hoffstadt/dearpygui', 0.5215190649032593, 'gui', 0), ('klen/py-frameworks-bench', 0.5210394263267517, 'perf', 0), ('nficano/python-lambda', 0.5205470323562622, 'util', 0), ('fastai/fastcore', 0.5203496217727661, 'util', 0), ('webpy/webpy', 0.5194896459579468, 'web', 0), ('masoniteframework/masonite', 0.5186633467674255, 'web', 0), ('aws/chalice', 0.5128588080406189, 'web', 0), ('pyodide/micropip', 0.5114571452140808, 'util', 0), ('pypa/pipenv', 0.5107925534248352, 'util', 0), ('flet-dev/flet', 0.5072339773178101, 'web', 0), ('neoteroi/blacksheep', 0.5058210492134094, 'web', 0), ('google/gin-config', 0.5022099614143372, 'util', 1), ('klen/muffin', 0.5019458532333374, 'web', 0)]",104,5.0,,1.31,61,25,112,0,4,22,4,61.0,52.0,90.0,0.9,45
585,util,https://github.com/hgrecco/pint,[],,[],[],,,,hgrecco/pint,pint,2171,492,41,Python,http://pint.readthedocs.org/,Operate and manipulate physical quantities in Python,hgrecco,2024-01-12,2012-07-13,602,3.602892366050261,,Operate and manipulate physical quantities in Python,"['science', 'units']","['science', 'units']",2024-01-03,"[('fredrik-johansson/mpmath', 0.5482094883918762, 'math', 0), ('numpy/numpy', 0.5478784441947937, 'math', 0), ('sympy/sympy', 0.534096896648407, 'math', 1), ('artemyk/dynpy', 0.5099499225616455, 'sim', 0), ('connorferster/handcalcs', 0.506892740726471, 'jupyter', 0)]",208,3.0,,3.29,80,28,140,0,0,4,4,80.0,169.0,90.0,2.1,45
1872,ml,https://github.com/freedmand/semantra,[],Semantra is a multipurpose tool for semantically searching documents. Query by meaning rather than just by matching text.,[],[],,,,freedmand/semantra,semantra,2152,117,31,Python,,Multi-tool for semantic search,freedmand,2024-01-13,2023-03-31,43,49.390163934426226,,Multi-tool for semantic search,"['cli', 'machine-learning', 'semantic-search']","['cli', 'machine-learning', 'semantic-search']",2023-12-16,"[('docarray/docarray', 0.5385406017303467, 'data', 2), ('nomic-ai/semantic-search-app-template', 0.5121299624443054, 'study', 1)]",4,1.0,,0.87,7,5,10,1,0,0,0,7.0,7.0,90.0,1.0,45
297,finance,https://github.com/blankly-finance/blankly,[],,[],[],,,,blankly-finance/blankly,blankly,1864,250,39,Python,https://package.blankly.finance,"🚀 💸  Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.",blankly-finance,2024-01-14,2021-03-09,151,12.344370860927153,https://avatars.githubusercontent.com/u/82687739?v=4,"🚀 💸  Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.","['algotrading', 'binance', 'blankly', 'bot', 'bot-framework', 'bots', 'coinbase', 'crypto', 'cryptocurrency', 'framework', 'investment', 'platform', 'stocks', 'trading', 'trading-bot', 'trading-strategies']","['algotrading', 'binance', 'blankly', 'bot', 'bot-framework', 'bots', 'coinbase', 'crypto', 'cryptocurrency', 'framework', 'investment', 'platform', 'stocks', 'trading', 'trading-bot', 'trading-strategies']",2023-12-23,"[('ccxt/ccxt', 0.6164409518241882, 'crypto', 4), ('kernc/backtesting.py', 0.5919058918952942, 'finance', 5), ('gbeced/basana', 0.5776981711387634, 'finance', 3), ('freqtrade/freqtrade', 0.5714588165283203, 'crypto', 2), ('idanya/algo-trader', 0.5535969138145447, 'finance', 2), ('quantconnect/lean', 0.5336184501647949, 'finance', 3)]",18,5.0,,0.35,7,3,35,1,0,10,10,7.0,6.0,90.0,0.9,45
507,ml,https://github.com/tensorflow/addons,[],,[],[],,,,tensorflow/addons,addons,1680,612,58,Python,,Useful extra functionality for TensorFlow 2.x maintained by SIG-addons,tensorflow,2024-01-09,2018-11-26,270,6.21893178212586,https://avatars.githubusercontent.com/u/15658638?v=4,Useful extra functionality for TensorFlow 2.x maintained by SIG-addons,"['deep-learning', 'machine-learning', 'neural-network', 'tensorflow', 'tensorflow-addons']","['deep-learning', 'machine-learning', 'neural-network', 'tensorflow', 'tensorflow-addons']",2023-12-13,"[('arogozhnikov/einops', 0.6348954439163208, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.6045843958854675, 'perf', 3), ('blackhc/toma', 0.5717829465866089, 'ml-dl', 1), ('tlkh/tf-metal-experiments', 0.5677783489227295, 'perf', 2), ('nyandwi/modernconvnets', 0.567768931388855, 'ml-dl', 1), ('google/tf-quant-finance', 0.5670149326324463, 'finance', 1), ('tensorflow/similarity', 0.5603650212287903, 'ml-dl', 3), ('tensorly/tensorly', 0.5590072274208069, 'ml-dl', 2), ('danielegrattarola/spektral', 0.5564872026443481, 'ml-dl', 2), ('explosion/thinc', 0.5554784536361694, 'ml-dl', 3), ('microsoft/onnxruntime', 0.5536020994186401, 'ml', 3), ('neuralmagic/sparseml', 0.5490881204605103, 'ml-dl', 1), ('pytorch/pytorch', 0.5459487438201904, 'ml-dl', 3), ('xl0/lovely-tensors', 0.5444281697273254, 'ml-dl', 1), ('horovod/horovod', 0.5429335832595825, 'ml-ops', 3), ('huggingface/transformers', 0.5373498797416687, 'nlp', 3), ('lutzroeder/netron', 0.5334285497665405, 'ml', 4), ('ageron/handson-ml2', 0.5312256813049316, 'ml', 0), ('rafiqhasan/auto-tensorflow', 0.530368983745575, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5303577780723572, 'ml-dl', 2), ('huggingface/datasets', 0.5287928581237793, 'nlp', 3), ('pytorch/ignite', 0.5261369943618774, 'ml-dl', 3), ('ggerganov/ggml', 0.525490939617157, 'ml', 1), ('tensorlayer/tensorlayer', 0.5212689638137817, 'ml-rl', 3), ('nvidia/tensorrt-llm', 0.5190548300743103, 'viz', 0), ('tensorflow/tensorflow', 0.5162330865859985, 'ml-dl', 4), ('onnx/onnx', 0.515902578830719, 'ml', 4), ('mdbloice/augmentor', 0.5146579742431641, 'ml', 2), ('keras-team/keras-nlp', 0.5104029178619385, 'nlp', 3), ('keras-team/keras', 0.5103944540023804, 'ml-dl', 3), ('ashleve/lightning-hydra-template', 0.5103277564048767, 'util', 1), ('determined-ai/determined', 0.5096710920333862, 'ml-ops', 3), ('google/gin-config', 0.5091912150382996, 'util', 1), ('dmlc/dgl', 0.5086209774017334, 'ml-dl', 1), ('google-research/deeplab2', 0.5076053738594055, 'ml', 0), ('ddbourgin/numpy-ml', 0.5075183510780334, 'ml', 1)]",207,7.0,,0.54,16,13,62,1,4,8,4,16.0,25.0,90.0,1.6,45
212,data,https://github.com/sfu-db/connector-x,[],,[],[],1.0,,,sfu-db/connector-x,connector-x,1656,121,30,Rust,https://sfu-db.github.io/connector-x/intro.html,Fastest library to load data from DB to DataFrames in Rust and Python,sfu-db,2024-01-12,2021-01-13,158,10.424460431654676,https://avatars.githubusercontent.com/u/18023593?v=4,Fastest library to load data from DB to DataFrames in Rust and Python,"['database', 'dataframe', 'rust', 'sql']","['database', 'dataframe', 'rust', 'sql']",2024-01-11,"[('pola-rs/polars', 0.6430513262748718, 'pandas', 2), ('delta-io/delta-rs', 0.5911102890968323, 'pandas', 1), ('ibis-project/ibis', 0.5806707143783569, 'data', 2), ('eventual-inc/daft', 0.5663425326347351, 'pandas', 2), ('tobymao/sqlglot', 0.5419861674308777, 'data', 1), ('jmcarpenter2/swifter', 0.5345582962036133, 'pandas', 0), ('tiangolo/sqlmodel', 0.5301145911216736, 'data', 1), ('klen/py-frameworks-bench', 0.5208754539489746, 'perf', 0), ('sqlalchemy/sqlalchemy', 0.5162760019302368, 'data', 1), ('samuelcolvin/rtoml', 0.5036177039146423, 'data', 1), ('mcfunley/pugsql', 0.5034618377685547, 'data', 1)]",43,5.0,,1.71,33,9,36,0,1,3,1,33.0,38.0,90.0,1.2,45
303,nlp,https://github.com/featureform/embeddinghub,[],,[],[],,,,featureform/embeddinghub,featureform,1627,91,14,Jupyter Notebook,https://www.featureform.com,The Virtual Feature Store. Turn your existing data infrastructure into a feature store.,featureform,2024-01-13,2020-10-16,171,9.48293089092423,https://avatars.githubusercontent.com/u/72954069?v=4,The Virtual Feature Store. Turn your existing data infrastructure into a feature store.,"['data-quality', 'data-science', 'embeddings', 'embeddings-similarity', 'feature-engineering', 'feature-store', 'machine-learning', 'ml', 'mlops', 'vector-database']","['data-quality', 'data-science', 'embeddings', 'embeddings-similarity', 'feature-engineering', 'feature-store', 'machine-learning', 'ml', 'mlops', 'vector-database']",2023-12-15,"[('feast-dev/feast', 0.7385993599891663, 'ml-ops', 6), ('lancedb/lancedb', 0.6071080565452576, 'data', 1), ('activeloopai/deeplake', 0.6054278016090393, 'ml-ops', 5), ('superduperdb/superduperdb', 0.6010522246360779, 'data', 2), ('airbytehq/airbyte', 0.5929150581359863, 'data', 0), ('mage-ai/mage-ai', 0.5914458632469177, 'ml-ops', 2), ('dgarnitz/vectorflow', 0.5690073370933533, 'data', 2), ('streamlit/streamlit', 0.5492863059043884, 'viz', 2), ('jina-ai/vectordb', 0.549279510974884, 'data', 1), ('netflix/metaflow', 0.5473335981369019, 'ml-ops', 4), ('orchest/orchest', 0.5408744215965271, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5308116674423218, 'ml-ops', 4), ('ploomber/ploomber', 0.529184103012085, 'ml-ops', 3), ('milvus-io/bootcamp', 0.5258285403251648, 'data', 2), ('avaiga/taipy', 0.5199906229972839, 'data', 1), ('meltano/meltano', 0.5155656337738037, 'ml-ops', 0), ('dagster-io/dagster', 0.512913703918457, 'ml-ops', 2), ('flyteorg/flyte', 0.5021466612815857, 'ml-ops', 3), ('huggingface/datasets', 0.5000424981117249, 'nlp', 1)]",30,1.0,,11.08,201,147,40,1,20,12,20,201.0,85.0,90.0,0.4,45
1686,perf,https://github.com/faster-cpython/ideas,['cpython'],Discussion and work tracker for Faster CPython project.,[],[],,,,faster-cpython/ideas,ideas,1618,53,130,,,,faster-cpython,2024-01-13,2021-03-02,152,10.644736842105264,https://avatars.githubusercontent.com/u/81193161?v=4,Discussion and work tracker for Faster CPython project.,[],['cpython'],2024-01-03,"[('faster-cpython/tools', 0.8179801106452942, 'perf', 1), ('python/cpython', 0.7051501274108887, 'util', 1), ('brandtbucher/specialist', 0.6769110560417175, 'perf', 1), ('markshannon/faster-cpython', 0.6732155084609985, 'perf', 0), ('pypy/pypy', 0.6248586773872375, 'util', 1), ('p403n1x87/austin', 0.6115438938140869, 'profiling', 0), ('ipython/ipyparallel', 0.5969278216362, 'perf', 0), ('cohere-ai/notebooks', 0.5839570760726929, 'llm', 0), ('cython/cython', 0.5725173354148865, 'util', 1), ('facebookincubator/cinder', 0.5721518397331238, 'perf', 1), ('agronholm/apscheduler', 0.5676429867744446, 'util', 0), ('wxwidgets/phoenix', 0.563103973865509, 'gui', 0), ('pyston/pyston', 0.5621926188468933, 'util', 0), ('wesm/pydata-book', 0.5588380694389343, 'study', 0), ('scikit-build/scikit-build', 0.5575029253959656, 'ml', 1), ('fastai/fastcore', 0.5519487857818604, 'util', 0), ('ipython/ipython', 0.5474236607551575, 'util', 0), ('gotcha/ipdb', 0.5395191311836243, 'debug', 0), ('eleutherai/pyfra', 0.5366376042366028, 'ml', 0), ('pytorch/data', 0.5340155959129333, 'data', 0), ('reloadware/reloadium', 0.5288839936256409, 'profiling', 0), ('tqdm/tqdm', 0.5260686874389648, 'term', 0), ('rasbt/watermark', 0.5214296579360962, 'util', 0), ('hoffstadt/dearpygui', 0.5212865471839905, 'gui', 0), ('sumerc/yappi', 0.5191786289215088, 'profiling', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5187180638313293, 'study', 0), ('intel/intel-extension-for-pytorch', 0.5159723162651062, 'perf', 0), ('willmcgugan/textual', 0.5151427388191223, 'term', 0), ('alexmojaki/snoop', 0.5135080814361572, 'debug', 0), ('erotemic/ubelt', 0.5061014890670776, 'util', 0), ('allenai/allennlp', 0.5050787329673767, 'nlp', 0), ('mynameisfiber/high_performance_python_2e', 0.5015802979469299, 'study', 0), ('timofurrer/awesome-asyncio', 0.500760018825531, 'study', 0)]",11,2.0,,4.48,25,6,35,0,0,0,0,25.0,97.0,90.0,3.9,45
1866,data,https://github.com/rapidai/rapidocr,[],,[],[],,,,rapidai/rapidocr,RapidOCR,1614,266,33,Python,https://rapidai.github.io/RapidOCRDocs/docs/,A cross platform OCR Library based on PaddleOCR & OnnxRuntime & OpenVINO.,rapidai,2024-01-13,2021-01-04,160,10.078501338090991,https://avatars.githubusercontent.com/u/87350760?v=4,A cross platform OCR Library based on PaddleOCR & OnnxRuntime & OpenVINO.,"['chineseocr', 'crnn', 'dbnet', 'easyocr', 'ocr', 'onnxruntime', 'openvino', 'paddleocr', 'rapidocr']","['chineseocr', 'crnn', 'dbnet', 'easyocr', 'ocr', 'onnxruntime', 'openvino', 'paddleocr', 'rapidocr']",2023-12-28,"[('jaidedai/easyocr', 0.6983810663223267, 'data', 3), ('hrnet/hrnet-semantic-segmentation', 0.5680984258651733, 'ml', 0), ('madmaze/pytesseract', 0.5656213164329529, 'data', 1), ('unstructured-io/pipeline-paddleocr', 0.5100756287574768, 'data', 1)]",14,4.0,,4.33,12,9,37,1,4,4,4,12.0,19.0,90.0,1.6,45
1789,web,https://github.com/indico/indico,[],,[],[],,,,indico/indico,indico,1591,397,63,Python,https://getindico.io,"Indico - A feature-rich event management system, made @ CERN, the place where the Web was born.",indico,2024-01-13,2011-07-27,652,2.436980306345733,https://avatars.githubusercontent.com/u/715236?v=4,"Indico - A feature-rich event management system, made @ CERN, the place where the Web was born.","['conferences', 'events', 'flask', 'sqlalchemy']","['conferences', 'events', 'flask', 'sqlalchemy']",2024-01-11,"[('masoniteframework/masonite', 0.5639700293540955, 'web', 0), ('pallets/flask', 0.5564385056495667, 'web', 1), ('bottlepy/bottle', 0.5327122211456299, 'web', 0), ('brettkromkamp/contextualise', 0.5198062658309937, 'data', 0), ('django/django', 0.514659583568573, 'web', 0), ('dylanhogg/awesome-python', 0.5107914209365845, 'study', 0), ('wagtail/wagtail', 0.5091932415962219, 'web', 0), ('klen/muffin', 0.5037544369697571, 'web', 0), ('emmett-framework/emmett', 0.5001763701438904, 'web', 0), ('feincms/feincms', 0.5001288652420044, 'web', 0)]",142,5.0,,9.62,181,120,152,0,6,10,6,181.0,266.0,90.0,1.5,45
1718,util,https://github.com/codespell-project/codespell,['code-quality'],,[],[],,,,codespell-project/codespell,codespell,1590,470,24,Python,,check code for common misspellings,codespell-project,2024-01-14,2011-01-28,678,2.343157894736842,https://avatars.githubusercontent.com/u/39140587?v=4,check code for common misspellings,[],['code-quality'],2024-01-13,[],188,5.0,,8.06,188,147,158,0,4,3,4,188.0,287.0,90.0,1.5,45
263,data,https://github.com/collerek/ormar,[],,[],[],,,,collerek/ormar,ormar,1506,78,17,Python,https://collerek.github.io/ormar/,python async orm with fastapi in mind and pydantic validation,collerek,2024-01-13,2020-08-02,182,8.261755485893417,,python async orm with fastapi in mind and pydantic validation,"['alembic', 'async-orm', 'databases', 'fastapi', 'orm', 'pydantic', 'python-orm', 'sqlalchemy']","['alembic', 'async-orm', 'databases', 'fastapi', 'orm', 'pydantic', 'python-orm', 'sqlalchemy']",2024-01-11,"[('tiangolo/sqlmodel', 0.6308665871620178, 'data', 3), ('mcfunley/pugsql', 0.6217651963233948, 'data', 1), ('sqlalchemy/sqlalchemy', 0.6017918586730957, 'data', 1), ('python-trio/trio', 0.5920240879058838, 'perf', 0), ('andialbrecht/sqlparse', 0.5768142938613892, 'data', 0), ('s3rius/fastapi-template', 0.5686522722244263, 'web', 2), ('fastai/fastcore', 0.5605081915855408, 'util', 0), ('rawheel/fastapi-boilerplate', 0.5596618056297302, 'web', 5), ('ibis-project/ibis', 0.5441567301750183, 'data', 1), ('pypy/pypy', 0.5436598658561707, 'util', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5430356860160828, 'web', 3), ('pyeve/cerberus', 0.5298268795013428, 'data', 0), ('pydantic/pydantic', 0.5280044078826904, 'util', 1), ('aminalaee/sqladmin', 0.5242454409599304, 'data', 2), ('python-cachier/cachier', 0.5154281258583069, 'perf', 0), ('pyston/pyston', 0.5057287812232971, 'util', 0), ('eleutherai/pyfra', 0.5003811717033386, 'ml', 0)]",35,5.0,,2.88,36,20,42,0,2,22,2,36.0,32.0,90.0,0.9,45
801,web,https://github.com/s3rius/fastapi-template,[],,[],[],,,,s3rius/fastapi-template,FastAPI-template,1421,126,24,Python,,Feature rich robust FastAPI template.,s3rius,2024-01-14,2020-10-05,173,8.207095709570957,,Feature rich robust FastAPI template.,"['aerich', 'alembic', 'asynchronous', 'asyncio', 'cookiecutter', 'cookiecutter-python3', 'cookiecutter-template', 'fastapi', 'fastapi-boilerplate', 'fastapi-template', 'graphql', 'opentelemetry', 'ormar', 'prometheus', 'sentry', 'sqlalchemy-orm', 'strawberry-graphql', 'tortoise-orm']","['aerich', 'alembic', 'asynchronous', 'asyncio', 'cookiecutter', 'cookiecutter-python3', 'cookiecutter-template', 'fastapi', 'fastapi-boilerplate', 'fastapi-template', 'graphql', 'opentelemetry', 'ormar', 'prometheus', 'sentry', 'sqlalchemy-orm', 'strawberry-graphql', 'tortoise-orm']",2023-09-12,"[('rawheel/fastapi-boilerplate', 0.6903481483459473, 'web', 4), ('asacristani/fastapi-rocket-boilerplate', 0.6634681224822998, 'template', 1), ('tiangolo/fastapi', 0.6399540305137634, 'web', 2), ('buuntu/fastapi-react', 0.6214925646781921, 'template', 2), ('fastai/fastcore', 0.6101469993591309, 'util', 0), ('fastapi-admin/fastapi-admin', 0.6024838089942932, 'web', 2), ('dmontagu/fastapi_client', 0.6021080613136292, 'web', 0), ('aminalaee/sqladmin', 0.5958586931228638, 'data', 2), ('fastapi-users/fastapi-users', 0.595072329044342, 'web', 2), ('vitalik/django-ninja', 0.5742310285568237, 'web', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5711674690246582, 'web', 3), ('collerek/ormar', 0.5686522722244263, 'data', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.5562103986740112, 'template', 2), ('starlite-api/starlite', 0.5500728487968445, 'web', 1), ('pallets/jinja', 0.5498924255371094, 'util', 0), ('python-restx/flask-restx', 0.5399068593978882, 'web', 0), ('tiangolo/sqlmodel', 0.5149979591369629, 'data', 1), ('awtkns/fastapi-crudrouter', 0.514696478843689, 'web', 2), ('tedivm/robs_awesome_python_template', 0.5110516548156738, 'template', 1), ('klen/muffin', 0.5076808333396912, 'web', 1), ('strawberry-graphql/strawberry', 0.5046817064285278, 'web', 2), ('sumerc/yappi', 0.5003270506858826, 'profiling', 2)]",18,6.0,,0.73,13,3,40,4,14,11,14,13.0,37.0,90.0,2.8,45
1472,data,https://github.com/aminalaee/sqladmin,[],,[],[],,,,aminalaee/sqladmin,sqladmin,1391,141,12,Python,https://aminalaee.dev/sqladmin/,SQLAlchemy Admin for FastAPI and Starlette,aminalaee,2024-01-12,2021-12-22,109,12.661898569570871,,SQLAlchemy Admin for FastAPI and Starlette,"['admin', 'admin-dashboard', 'asgi', 'asyncio', 'fastapi', 'sqlalchemy', 'starlette', 'web', 'wsgi']","['admin', 'admin-dashboard', 'asgi', 'asyncio', 'fastapi', 'sqlalchemy', 'starlette', 'web', 'wsgi']",2023-12-13,"[('piccolo-orm/piccolo_admin', 0.6254207491874695, 'data', 5), ('fastapi-admin/fastapi-admin', 0.6240462064743042, 'web', 3), ('sqlalchemy/sqlalchemy', 0.6173149347305298, 'data', 1), ('aeternalis-ingenium/fastapi-backend-template', 0.6071468591690063, 'web', 2), ('s3rius/fastapi-template', 0.5958586931228638, 'web', 2), ('rawheel/fastapi-boilerplate', 0.5917928218841553, 'web', 2), ('tiangolo/sqlmodel', 0.5850541591644287, 'data', 2), ('mause/duckdb_engine', 0.5703755021095276, 'data', 1), ('sqlalchemy/alembic', 0.5552855730056763, 'data', 1), ('starlite-api/starlite', 0.5524939894676208, 'web', 2), ('fastapi-users/fastapi-users', 0.5336757302284241, 'web', 3), ('collerek/ormar', 0.5242454409599304, 'data', 2), ('neoteroi/blacksheep', 0.5168036818504333, 'web', 3), ('ibis-project/ibis', 0.512384831905365, 'data', 1), ('agronholm/sqlacodegen', 0.5044848918914795, 'data', 0), ('pallets/werkzeug', 0.5044029355049133, 'web', 1)]",46,1.0,,2.08,45,32,25,1,14,17,14,45.0,55.0,90.0,1.2,45
1185,ml,https://github.com/castorini/pyserini,[],,[],[],,,,castorini/pyserini,pyserini,1269,283,16,Python,http://pyserini.io/,Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.,castorini,2024-01-12,2019-11-01,221,5.7272727272727275,https://avatars.githubusercontent.com/u/26842848?v=4,Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.,['information-retrieval'],['information-retrieval'],2024-01-11,"[('facebookresearch/dpr-scale', 0.5861307978630066, 'nlp', 0), ('qdrant/fastembed', 0.556861937046051, 'ml', 0), ('harangju/wikinet', 0.5286003351211548, 'data', 0), ('kagisearch/vectordb', 0.5248305201530457, 'data', 0), ('paddlepaddle/rocketqa', 0.5224674940109253, 'nlp', 1), ('qdrant/qdrant-client', 0.5034437775611877, 'util', 0)]",148,2.0,,4.27,99,89,51,0,6,8,6,99.0,93.0,90.0,0.9,45
743,data,https://github.com/pytorch/data,[],,[],[],,,,pytorch/data,data,1044,136,33,Python,,A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.,pytorch,2024-01-13,2021-05-12,141,7.359516616314199,https://avatars.githubusercontent.com/u/21003710?v=4,A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.,[],[],2024-01-13,"[('intel/intel-extension-for-pytorch', 0.6773589849472046, 'perf', 0), ('nvidia/apex', 0.6665452122688293, 'ml-dl', 0), ('pytorch/ignite', 0.655742347240448, 'ml-dl', 0), ('skorch-dev/skorch', 0.631018877029419, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.6106820106506348, 'study', 0), ('parallel-domain/pd-sdk', 0.6059823036193848, 'data', 0), ('erotemic/ubelt', 0.5979729294776917, 'util', 0), ('allenai/allennlp', 0.5899907946586609, 'nlp', 0), ('pytorch/torchrec', 0.5872920751571655, 'ml-dl', 0), ('fastai/fastcore', 0.5834670066833496, 'util', 0), ('ashleve/lightning-hydra-template', 0.5815867781639099, 'util', 0), ('pytorch-labs/gpt-fast', 0.5759943723678589, 'llm', 0), ('karpathy/micrograd', 0.5738345980644226, 'study', 0), ('rentruewang/koila', 0.5698388814926147, 'ml', 0), ('faster-cpython/tools', 0.5645531415939331, 'perf', 0), ('mrdbourke/pytorch-deep-learning', 0.5634875893592834, 'study', 0), ('kshitij12345/torchnnprofiler', 0.5611929297447205, 'profiling', 0), ('huggingface/accelerate', 0.5591470003128052, 'ml', 0), ('blackhc/toma', 0.5554980635643005, 'ml-dl', 0), ('pytoolz/toolz', 0.5517788529396057, 'util', 0), ('pyg-team/pytorch_geometric', 0.5495861768722534, 'ml-dl', 0), ('nvidia/cuda-python', 0.5482061505317688, 'ml', 0), ('pytorch/captum', 0.5359891057014465, 'ml-interpretability', 0), ('faster-cpython/ideas', 0.5340155959129333, 'perf', 0), ('facebookresearch/pytorch3d', 0.5338151454925537, 'ml-dl', 0), ('arogozhnikov/einops', 0.5333592891693115, 'ml-dl', 0), ('google/gin-config', 0.5325197577476501, 'util', 0), ('huggingface/transformers', 0.5293325185775757, 'nlp', 0), ('pytorch/rl', 0.5270489454269409, 'ml-rl', 0), ('p403n1x87/austin', 0.5269789695739746, 'profiling', 0), ('laekov/fastmoe', 0.5255650877952576, 'ml', 0), ('pypy/pypy', 0.5254920721054077, 'util', 0), ('gotcha/ipdb', 0.5220991969108582, 'debug', 0), ('dlt-hub/dlt', 0.5167723298072815, 'data', 0), ('ipython/ipyparallel', 0.5157786011695862, 'perf', 0), ('graphistry/pygraphistry', 0.5149250030517578, 'data', 0), ('xl0/lovely-tensors', 0.5113422274589539, 'ml-dl', 0), ('jovianml/opendatasets', 0.5112810730934143, 'data', 0), ('imageio/imageio', 0.5112005472183228, 'util', 0), ('wesm/pydata-book', 0.5109310746192932, 'study', 0), ('denys88/rl_games', 0.5077896118164062, 'ml-rl', 0), ('uber/petastorm', 0.5076653361320496, 'data', 0), ('timdettmers/bitsandbytes', 0.5031000375747681, 'util', 0), ('kubeflow/fairing', 0.5028988122940063, 'ml-ops', 0), ('qdrant/fastembed', 0.5008647441864014, 'ml', 0)]",79,7.0,,2.08,14,8,33,0,4,9,4,14.0,15.0,90.0,1.1,45
1836,ml,https://github.com/spotify/voyager,[],,[],[],,,,spotify/voyager,voyager,1024,33,10,C++,https://spotify.github.io/voyager/,"🛰️ Voyager is an approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.",spotify,2024-01-14,2023-04-13,41,24.54794520547945,https://avatars.githubusercontent.com/u/251374?v=4,"🛰️ Voyager is an approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.","['hnsw', 'hnswlib', 'java', 'machine-learning', 'nearest-neighbor-search']","['hnsw', 'hnswlib', 'java', 'machine-learning', 'nearest-neighbor-search']",2023-12-07,"[('lmcinnes/pynndescent', 0.6540524363517761, 'ml', 1), ('spotify/annoy', 0.6272151470184326, 'ml', 1), ('nmslib/hnswlib', 0.5946098566055298, 'ml', 0), ('erotemic/ubelt', 0.5305669903755188, 'util', 0), ('scikit-learn-contrib/metric-learn', 0.503491997718811, 'ml', 1), ('pycaret/pycaret', 0.502596914768219, 'ml', 1), ('radiantearth/radiant-mlhub', 0.5025808811187744, 'gis', 1)]",5,2.0,,1.15,23,13,9,1,7,11,7,23.0,19.0,90.0,0.8,45
1321,ml-dl,https://github.com/keras-team/keras-cv,"['keras', 'computer-vision']",,[],[],,,,keras-team/keras-cv,keras-cv,888,285,32,Python,,Industry-strength Computer Vision workflows with Keras,keras-team,2024-01-13,2020-05-18,193,4.597633136094674,https://avatars.githubusercontent.com/u/34455048?v=4,Industry-strength Computer Vision workflows with Keras,[],"['computer-vision', 'keras']",2024-01-12,"[('roboflow/supervision', 0.640568733215332, 'ml', 1), ('nyandwi/modernconvnets', 0.6089338064193726, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5779148936271667, 'ml-dl', 1), ('keras-team/keras-nlp', 0.5433396697044373, 'nlp', 1), ('lutzroeder/netron', 0.5374981760978699, 'ml', 1), ('huggingface/datasets', 0.535764217376709, 'nlp', 1), ('hysts/pytorch_image_classification', 0.5079523921012878, 'ml-dl', 1), ('aleju/imgaug', 0.5068478584289551, 'ml', 0), ('onnx/onnx', 0.5009073615074158, 'ml', 1), ('nvidia/deeplearningexamples', 0.5008230805397034, 'ml-dl', 1)]",89,2.0,,8.73,251,196,44,0,21,12,21,251.0,321.0,90.0,1.3,45
324,security,https://github.com/trailofbits/pip-audit,[],,[],[],,,,trailofbits/pip-audit,pip-audit,877,60,25,Python,https://pypi.org/project/pip-audit/,Audits Python environments and dependency trees for known vulnerabilities,trailofbits,2024-01-13,2021-09-02,125,6.976136363636364,https://avatars.githubusercontent.com/u/647025?v=4,Audits Python environments and dependency trees for known vulnerabilities,"['pip', 'security', 'security-audit', 'supply-chain']","['pip', 'security', 'security-audit', 'supply-chain']",2024-01-12,"[('pyupio/safety', 0.7114713788032532, 'security', 1), ('aswinnnn/pyscan', 0.6042621731758118, 'security', 2), ('pdm-project/pdm', 0.54820716381073, 'util', 0), ('tiiuae/sbomnix', 0.5466781258583069, 'util', 1), ('alexmojaki/snoop', 0.5462668538093567, 'debug', 0), ('klen/pylama', 0.5406633019447327, 'util', 0), ('pypa/pipenv', 0.531186044216156, 'util', 1), ('legrandin/pycryptodome', 0.5239244103431702, 'util', 1), ('pypa/hatch', 0.5167754292488098, 'util', 0), ('jazzband/pip-tools', 0.5135653614997864, 'util', 1), ('thoth-station/micropipenv', 0.5130209922790527, 'util', 1), ('ethereum/web3.py', 0.5048879981040955, 'crypto', 0), ('tox-dev/pipdeptree', 0.5004677176475525, 'util', 1)]",27,4.0,,3.29,47,40,29,0,14,23,14,47.0,40.0,90.0,0.9,45
1888,ml,https://github.com/oml-team/open-metric-learning,"['pytorch', 'embeddings']",OML is a PyTorch-based framework to train and validate the models producing high-quality embeddings.,[],[],,,,oml-team/open-metric-learning,open-metric-learning,716,45,10,Jupyter Notebook,https://open-metric-learning.readthedocs.io/en/latest/index.html,Library for metric learning pipelines and models.,oml-team,2024-01-12,2022-06-04,86,8.284297520661157,https://avatars.githubusercontent.com/u/104944039?v=4,Library for metric learning pipelines and models.,"['computer-vision', 'data-science', 'deep-learning', 'hacktoberfest-2023', 'hacktoberfest2023', 'metric-learning', 'pytorch', 'pytorch-lightning', 'representation-learning', 'similarity-learning']","['computer-vision', 'data-science', 'deep-learning', 'embeddings', 'hacktoberfest-2023', 'hacktoberfest2023', 'metric-learning', 'pytorch', 'pytorch-lightning', 'representation-learning', 'similarity-learning']",2023-12-30,"[('kevinmusgrave/pytorch-metric-learning', 0.7173192501068115, 'ml', 5), ('scikit-learn-contrib/metric-learn', 0.6861700415611267, 'ml', 1), ('qdrant/quaterion', 0.618319571018219, 'ml', 5), ('pytorch/ignite', 0.6061093807220459, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.5796335339546204, 'study', 2), ('ashleve/lightning-hydra-template', 0.5497283339500427, 'util', 3), ('roboflow/supervision', 0.5494734644889832, 'ml', 3), ('uber/petastorm', 0.5485543012619019, 'data', 2), ('ggerganov/ggml', 0.5477278232574463, 'ml', 0), ('tensorflow/tensorflow', 0.5407246947288513, 'ml-dl', 1), ('pytorch/torchrec', 0.5388403534889221, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5373204350471497, 'ml-dl', 1), ('tensorflow/similarity', 0.5347641706466675, 'ml-dl', 3), ('huggingface/transformers', 0.5327936410903931, 'nlp', 2), ('featurelabs/featuretools', 0.5298222899436951, 'ml', 1), ('catboost/catboost', 0.5297994017601013, 'ml', 1), ('huggingface/datasets', 0.528002142906189, 'nlp', 3), ('dmlc/xgboost', 0.5269960761070251, 'ml', 0), ('pyg-team/pytorch_geometric', 0.5263068079948425, 'ml-dl', 2), ('lightly-ai/lightly', 0.5244993567466736, 'ml', 4), ('tensorflow/data-validation', 0.5238116979598999, 'ml-ops', 0), ('huggingface/evaluate', 0.5213491320610046, 'ml', 0), ('aws/sagemaker-python-sdk', 0.5199127197265625, 'ml', 1), ('skorch-dev/skorch', 0.5191598534584045, 'ml-dl', 1), ('deci-ai/super-gradients', 0.5170210599899292, 'ml-dl', 3), ('pycaret/pycaret', 0.5165910124778748, 'ml', 1), ('tensorflow/tensor2tensor', 0.5146539211273193, 'ml', 1), ('tensorlayer/tensorlayer', 0.5133138298988342, 'ml-rl', 1), ('gradio-app/gradio', 0.5132337808609009, 'viz', 2), ('microsoft/flaml', 0.5119624733924866, 'ml', 2), ('keras-team/autokeras', 0.5088566541671753, 'ml-dl', 1), ('cvxgrp/pymde', 0.5077699422836304, 'ml', 1), ('microsoft/nni', 0.5069662928581238, 'ml', 3), ('intel/intel-extension-for-pytorch', 0.506502091884613, 'perf', 2)]",20,4.0,,2.87,56,52,20,0,0,12,12,56.0,52.0,90.0,0.9,45
948,ml,https://github.com/facebookresearch/balance,[],,[],[],,,,facebookresearch/balance,balance,656,42,6,Python,https://import-balance.org,The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.,facebookresearch,2024-01-11,2022-11-15,63,10.412698412698413,https://avatars.githubusercontent.com/u/16943930?v=4,The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.,[],[],2023-12-07,"[('scikit-learn-contrib/imbalanced-learn', 0.5837095379829407, 'ml', 0)]",19,5.0,,1.88,1,1,14,1,9,9,9,1.0,3.0,90.0,3.0,45
1422,sim,https://github.com/bowang-lab/scgpt,[],scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI,[],[],,,,bowang-lab/scgpt,scGPT,584,91,29,Jupyter Notebook,https://scgpt.readthedocs.io/en/latest/,,bowang-lab,2024-01-11,2023-04-23,40,14.49645390070922,https://avatars.githubusercontent.com/u/50999261?v=4,scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI,"['foundation-model', 'gpt', 'single-cell']","['foundation-model', 'gpt', 'single-cell']",2024-01-13,[],6,1.0,,2.83,56,43,9,0,7,9,7,56.0,109.0,90.0,1.9,45
1882,sim,https://github.com/google-deepmind/concordia,[],,[],[],,,,google-deepmind/concordia,concordia,207,24,13,Python,,A library for generative social simulation,google-deepmind,2024-01-13,2023-11-21,10,20.7,https://avatars.githubusercontent.com/u/8596759?v=4,A library for generative social simulation,"['agent-based-simulation', 'generative-agents', 'multi-agent', 'social-simulation']","['agent-based-simulation', 'generative-agents', 'multi-agent', 'social-simulation']",2024-01-09,"[('humanoidagents/humanoidagents', 0.640636682510376, 'sim', 0), ('projectmesa/mesa', 0.6392713785171509, 'sim', 1), ('crowddynamics/crowddynamics', 0.5502240657806396, 'sim', 1)]",11,6.0,,2.15,17,16,2,0,1,6,1,17.0,18.0,90.0,1.1,45
963,ml-rl,https://github.com/openai/baselines,[],,[],[],,,,openai/baselines,baselines,15098,4861,648,Python,,OpenAI Baselines: high-quality implementations of reinforcement learning algorithms,openai,2024-01-14,2017-05-24,348,43.27846027846028,https://avatars.githubusercontent.com/u/14957082?v=4,OpenAI Baselines: high-quality implementations of reinforcement learning algorithms,[],[],2020-01-31,"[('thu-ml/tianshou', 0.6823237538337708, 'ml-rl', 0), ('denys88/rl_games', 0.5995540618896484, 'ml-rl', 0), ('salesforce/warp-drive', 0.5866561532020569, 'ml-rl', 0), ('humancompatibleai/imitation', 0.5837662816047668, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.5754907131195068, 'ml-rl', 0), ('google/dopamine', 0.5650805830955505, 'ml-rl', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.5605931282043457, 'sim', 0), ('unity-technologies/ml-agents', 0.5591613054275513, 'ml-rl', 0), ('openai/gym', 0.5574614405632019, 'ml-rl', 0), ('keras-rl/keras-rl', 0.5544887781143188, 'ml-rl', 0), ('pytorch/rl', 0.5460628867149353, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5400475859642029, 'ml-rl', 0), ('google/trax', 0.5311444401741028, 'ml-dl', 0), ('nvidia-omniverse/isaacgymenvs', 0.5117769837379456, 'sim', 0), ('kzl/decision-transformer', 0.5097741484642029, 'ml-rl', 0), ('openai/spinningup', 0.5069130063056946, 'study', 0), ('inspirai/timechamber', 0.5040830969810486, 'sim', 0)]",115,3.0,,0.0,5,2,81,48,0,0,0,5.0,1.0,90.0,0.2,44
156,data,https://github.com/s0md3v/photon,[],,[],[],,,,s0md3v/photon,Photon,10251,1459,324,Python,,Incredibly fast crawler designed for OSINT.,s0md3v,2024-01-14,2018-03-30,304,33.657129455909946,,Incredibly fast crawler designed for OSINT.,"['crawler', 'information-gathering', 'osint', 'spider']","['crawler', 'information-gathering', 'osint', 'spider']",2022-12-20,"[('binux/pyspider', 0.7611035108566284, 'data', 1), ('scrapy/scrapy', 0.6149922609329224, 'data', 1)]",21,3.0,,0.0,4,0,70,13,0,3,3,4.0,1.0,90.0,0.2,44
1057,ml-rl,https://github.com/deepmind/pysc2,[],,[],[],,,,deepmind/pysc2,pysc2,7863,1164,352,Python,,StarCraft II Learning Environment,deepmind,2024-01-12,2017-07-25,340,23.126470588235293,https://avatars.githubusercontent.com/u/8596759?v=4,StarCraft II Learning Environment,"['blizzard-api', 'deepmind', 'machine-learning', 'reinforcement-learning', 'starcraft-ii', 'starcraft-ii-replays']","['blizzard-api', 'deepmind', 'machine-learning', 'reinforcement-learning', 'starcraft-ii', 'starcraft-ii-replays']",2023-04-19,"[('pettingzoo-team/pettingzoo', 0.5372393131256104, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5224155187606812, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.5204218029975891, 'ml-rl', 2), ('google/trax', 0.5190588235855103, 'ml-dl', 2), ('keras-rl/keras-rl', 0.5143329501152039, 'ml-rl', 2)]",39,4.0,,0.13,4,1,79,9,0,1,1,4.0,3.0,90.0,0.8,44
675,ml,https://github.com/hips/autograd,[],,[],[],,,,hips/autograd,autograd,6644,908,218,Python,,Efficiently computes derivatives of numpy code.,hips,2024-01-13,2014-11-24,479,13.866428145497913,https://avatars.githubusercontent.com/u/7935606?v=4,Efficiently computes derivatives of numpy code.,[],[],2023-11-16,"[('google/jax', 0.5986758470535278, 'ml', 0), ('numpy/numpy', 0.5689885020256042, 'math', 0), ('numba/numba', 0.5151181817054749, 'perf', 0), ('cupy/cupy', 0.5063801407814026, 'math', 0), ('andgoldschmidt/derivative', 0.5045579075813293, 'math', 0)]",56,7.0,,0.54,37,3,111,2,0,0,0,37.0,8.0,90.0,0.2,44
1647,util,https://github.com/evhub/coconut,['functional'],,[],[],,,,evhub/coconut,coconut,3868,116,62,Python,http://coconut-lang.org,"Simple, elegant, Pythonic functional programming.",evhub,2024-01-13,2014-10-04,486,7.951835535976505,,"Simple, elegant, Pythonic functional programming.","['coconut', 'compiler', 'functional', 'functional-language', 'functional-programming', 'language', 'programming-language', 'xonsh', 'xontrib']","['coconut', 'compiler', 'functional', 'functional-language', 'functional-programming', 'language', 'programming-language', 'xonsh', 'xontrib']",2023-11-28,"[('pytoolz/toolz', 0.6476520895957947, 'util', 0), ('suor/funcy', 0.6461945176124573, 'util', 1), ('google/pyglove', 0.583031415939331, 'util', 0), ('fastai/fastcore', 0.5812950730323792, 'util', 1), ('modularml/mojo', 0.5474485754966736, 'util', 2), ('pypy/pypy', 0.544915497303009, 'util', 1), ('dylanhogg/awesome-python', 0.5391582250595093, 'study', 0), ('lukaszahradnik/pyneuralogic', 0.5328680872917175, 'math', 0), ('python/cpython', 0.5319302678108215, 'util', 0), ('stanfordnlp/dspy', 0.5305084586143494, 'llm', 0), ('explosion/thinc', 0.5264431834220886, 'ml-dl', 1), ('1200wd/bitcoinlib', 0.5255511403083801, 'crypto', 0), ('pygamelib/pygamelib', 0.5140511989593506, 'gamedev', 0), ('fluentpython/example-code-2e', 0.5133896470069885, 'study', 0), ('pyston/pyston', 0.5107958316802979, 'util', 0), ('gondolav/pyfuncol', 0.5076735019683838, 'util', 1), ('xonsh/xonsh', 0.5075111985206604, 'util', 1), ('norvig/pytudes', 0.5068784356117249, 'util', 0), ('joblib/joblib', 0.5061070322990417, 'util', 0), ('tiangolo/typer', 0.5052512884140015, 'term', 0), ('pyparsing/pyparsing', 0.504447877407074, 'util', 0), ('sloria/textblob', 0.5033293962478638, 'nlp', 0), ('pyomo/pyomo', 0.5014179944992065, 'math', 0)]",33,3.0,,6.79,36,24,113,2,5,4,5,36.0,25.0,90.0,0.7,44
907,util,https://github.com/ets-labs/python-dependency-injector,[],,[],[],1.0,,,ets-labs/python-dependency-injector,python-dependency-injector,3414,307,48,Python,https://python-dependency-injector.ets-labs.org/,Dependency injection framework for Python,ets-labs,2024-01-13,2015-01-04,473,7.213401750679143,https://avatars.githubusercontent.com/u/11329744?v=4,Dependency injection framework for Python,"['aiohttp', 'asyncio', 'dependency-injection', 'dependency-injection-container', 'dependency-injection-framework', 'design-patterns', 'factory', 'flask', 'flask-application', 'flask-restful', 'ioc', 'ioc-container', 'singleton', 'threadlocal']","['aiohttp', 'asyncio', 'dependency-injection', 'dependency-injection-container', 'dependency-injection-framework', 'design-patterns', 'factory', 'flask', 'flask-application', 'flask-restful', 'ioc', 'ioc-container', 'singleton', 'threadlocal']",2022-12-19,"[('allrod5/injectable', 0.6628846526145935, 'util', 2), ('python-injector/injector', 0.6611223220825195, 'util', 2), ('ivankorobkov/python-inject', 0.6299859881401062, 'util', 1), ('aio-libs/aiohttp', 0.5857105255126953, 'web', 2), ('pallets/flask', 0.5796188712120056, 'web', 1), ('timofurrer/awesome-asyncio', 0.5701932311058044, 'study', 1), ('bottlepy/bottle', 0.5632432699203491, 'web', 0), ('klen/muffin', 0.5598342418670654, 'web', 1), ('python-restx/flask-restx', 0.551591694355011, 'web', 1), ('falconry/falcon', 0.5372539758682251, 'web', 0), ('neoteroi/blacksheep', 0.5310226678848267, 'web', 1), ('pallets/quart', 0.5255832672119141, 'web', 1), ('alirn76/panther', 0.516931414604187, 'web', 0), ('eleutherai/pyfra', 0.5152551531791687, 'ml', 0), ('python-poetry/poetry', 0.510571300983429, 'util', 0), ('sumerc/yappi', 0.5105165839195251, 'profiling', 1), ('encode/uvicorn', 0.5008002519607544, 'web', 1), ('backtick-se/cowait', 0.500067412853241, 'util', 0)]",29,7.0,,0.0,35,6,110,13,0,36,36,35.0,80.0,90.0,2.3,44
1219,ml,https://github.com/py-why/econml,[],,[],[],,,,py-why/econml,EconML,3385,655,75,Jupyter Notebook,https://www.microsoft.com/en-us/research/project/alice/,"ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its  goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.",py-why,2024-01-13,2018-04-30,300,11.277962874821513,https://avatars.githubusercontent.com/u/101266056?v=4,"ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its  goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.","['causal-inference', 'causality', 'econometrics', 'economics', 'machine-learning', 'treatment-effects']","['causal-inference', 'causality', 'econometrics', 'economics', 'machine-learning', 'treatment-effects']",2024-01-12,"[('py-why/dowhy', 0.5789477229118347, 'ml', 4), ('uber/causalml', 0.5542822480201721, 'ml', 2), ('mckinsey/causalnex', 0.5422161221504211, 'math', 2)]",34,3.0,,0.96,35,18,69,0,1,5,1,35.0,27.0,90.0,0.8,44
271,data,https://github.com/praw-dev/praw,[],,[],[],,,,praw-dev/praw,praw,3248,461,73,Python,http://praw.readthedocs.io/,"PRAW, an acronym for ""Python Reddit API Wrapper"", is a python package that allows for simple access to Reddit's API.",praw-dev,2024-01-13,2010-08-19,701,4.628664495114006,https://avatars.githubusercontent.com/u/1696888?v=4,"PRAW, an acronym for ""Python Reddit API Wrapper"", is a python package that allows for simple access to Reddit's API.","['api', 'oauth', 'praw', 'reddit', 'reddit-api']","['api', 'oauth', 'praw', 'reddit', 'reddit-api']",2024-01-10,"[('praw-dev/asyncpraw', 0.8785129189491272, 'ml-dl', 5)]",226,4.0,,1.31,36,27,163,0,2,12,2,36.0,48.0,90.0,1.3,44
690,ml-dl,https://github.com/deepmind/dm-haiku,[],,[],[],,,,deepmind/dm-haiku,dm-haiku,2675,222,39,Python,https://dm-haiku.readthedocs.io,JAX-based neural network library,deepmind,2024-01-14,2020-02-18,206,12.985436893203884,https://avatars.githubusercontent.com/u/8596759?v=4,JAX-based neural network library,"['deep-learning', 'deep-neural-networks', 'jax', 'machine-learning', 'neural-networks']","['deep-learning', 'deep-neural-networks', 'jax', 'machine-learning', 'neural-networks']",2023-11-30,"[('deepmind/synjax', 0.7001689076423645, 'math', 1), ('google/flax', 0.6950955986976624, 'ml-dl', 1), ('google/trax', 0.6491378545761108, 'ml-dl', 3), ('keras-team/keras', 0.6441159844398499, 'ml-dl', 4), ('explosion/thinc', 0.6371207237243652, 'ml-dl', 3), ('huggingface/transformers', 0.6224076747894287, 'nlp', 3), ('sanchit-gandhi/whisper-jax', 0.6133875846862793, 'ml', 2), ('alpa-projects/alpa', 0.6117678284645081, 'ml-dl', 3), ('deepmind/kfac-jax', 0.5966999530792236, 'math', 2), ('google/evojax', 0.5894790291786194, 'sim', 1), ('deepmind/chex', 0.5875384211540222, 'ml-dl', 1), ('tensorflow/tensorflow', 0.5688497424125671, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.5642551183700562, 'ml-rl', 1), ('uber/petastorm', 0.557473361492157, 'data', 2), ('onnx/onnx', 0.542344868183136, 'ml', 3), ('microsoft/onnxruntime', 0.540787935256958, 'ml', 3), ('ml-tooling/opyrator', 0.5379303097724915, 'viz', 1), ('karpathy/micrograd', 0.5368869304656982, 'study', 0), ('pytorch/ignite', 0.5363015532493591, 'ml-dl', 2), ('horovod/horovod', 0.5358175039291382, 'ml-ops', 2), ('keras-team/autokeras', 0.5257297158241272, 'ml-dl', 2), ('young-geng/easylm', 0.5241255164146423, 'llm', 2), ('aiqc/aiqc', 0.5235515236854553, 'ml-ops', 0), ('apache/incubator-mxnet', 0.522907018661499, 'ml-dl', 0), ('microsoft/deepspeed', 0.5188496112823486, 'ml-dl', 2), ('microsoft/nni', 0.5166778564453125, 'ml', 2), ('rasbt/machine-learning-book', 0.5160495638847351, 'study', 3), ('titanml/takeoff', 0.5155620574951172, 'llm', 0), ('tensorflow/tensor2tensor', 0.5139560103416443, 'ml', 2), ('arogozhnikov/einops', 0.5126287341117859, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5113754868507385, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.5113567113876343, 'study', 3), ('denys88/rl_games', 0.507714569568634, 'ml-rl', 1), ('mlflow/mlflow', 0.50584477186203, 'ml-ops', 1), ('adap/flower', 0.5038338303565979, 'ml-ops', 2)]",79,5.0,,1.83,23,19,48,2,1,3,1,23.0,4.0,90.0,0.2,44
1812,llm,https://github.com/paperswithcode/galai,"['scientific', 'citations', 'language-model']",,[],[],,,,paperswithcode/galai,galai,2613,274,44,Jupyter Notebook,,Model API for GALACTICA,paperswithcode,2024-01-13,2022-11-15,63,41.476190476190474,https://avatars.githubusercontent.com/u/40305508?v=4,Model API for GALACTICA,[],"['citations', 'language-model', 'scientific']",2023-02-14,"[('princeton-nlp/alce', 0.6015400290489197, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5739641785621643, 'llm', 1), ('hannibal046/awesome-llm', 0.5614985823631287, 'study', 1), ('freedomintelligence/llmzoo', 0.5310094356536865, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5114402174949646, 'llm', 1), ('urschrei/pyzotero', 0.5094993710517883, 'util', 1)]",7,3.0,,0.1,1,0,14,11,0,3,3,1.0,1.0,90.0,1.0,44
1431,util,https://github.com/tox-dev/pipdeptree,"['cli', 'dependencies', 'packages']",,[],[],,,,tox-dev/pipdeptree,pipdeptree,2606,141,31,Python,https://pypi.python.org/pypi/pipdeptree,A command line utility to display dependency tree of the installed Python packages,tox-dev,2024-01-13,2014-02-02,521,4.999177856947108,https://avatars.githubusercontent.com/u/20345659?v=4,A command line utility to display dependency tree of the installed Python packages,"['dependency-graph', 'pip']","['cli', 'dependencies', 'dependency-graph', 'packages', 'pip']",2024-01-10,"[('mitsuhiko/rye', 0.5925803780555725, 'util', 0), ('pypi/warehouse', 0.5815759897232056, 'util', 0), ('python-poetry/poetry', 0.5638055205345154, 'util', 0), ('pdm-project/pdm', 0.5571689605712891, 'util', 0), ('hugovk/pypistats', 0.5279873609542847, 'util', 1), ('pypa/hatch', 0.5165544152259827, 'util', 1), ('pyodide/micropip', 0.5127100944519043, 'util', 0), ('trailofbits/pip-audit', 0.5004677176475525, 'security', 1)]",43,5.0,,1.63,21,21,121,0,23,5,23,21.0,16.0,90.0,0.8,44
201,util,https://github.com/camelot-dev/camelot,[],,[],[],,,,camelot-dev/camelot,camelot,2490,408,45,Python,https://camelot-py.readthedocs.io,A Python library to extract tabular data from PDFs,camelot-dev,2024-01-14,2019-07-01,239,10.412186379928315,https://avatars.githubusercontent.com/u/43926448?v=4,A Python library to extract tabular data from PDFs,[],[],2023-10-02,"[('py-pdf/pypdf2', 0.6539286971092224, 'util', 0), ('astanin/python-tabulate', 0.6331050395965576, 'util', 0), ('pyfpdf/fpdf2', 0.5940113663673401, 'util', 0), ('paperswithcode/axcell', 0.577544093132019, 'util', 0), ('wireservice/csvkit', 0.567770779132843, 'util', 0), ('unstructured-io/pipeline-paddleocr', 0.5457990169525146, 'data', 0), ('jazzband/tablib', 0.5369576811790466, 'data', 0), ('pypdfium2-team/pypdfium2', 0.5336915850639343, 'util', 0), ('jorisschellekens/borb', 0.5273860692977905, 'util', 0), ('jazzband/prettytable', 0.5252398252487183, 'term', 0)]",46,4.0,,0.65,50,17,55,3,0,6,6,50.0,37.0,90.0,0.7,44
1622,data,https://github.com/kayak/pypika,['sql'],,[],[],,,,kayak/pypika,pypika,2260,273,35,Python,http://pypika.readthedocs.io/en/latest/,PyPika is a python SQL query builder that exposes the full richness of the SQL language using a syntax that reflects the resulting query. PyPika excels at all sorts of SQL queries but is especially useful for data analysis.,kayak,2024-01-13,2016-07-06,394,5.723589001447178,https://avatars.githubusercontent.com/u/521891?v=4,PyPika is a python SQL query builder that exposes the full richness of the SQL language using a syntax that reflects the resulting query. PyPika excels at all sorts of SQL queries but is especially useful for data analysis.,"['builder', 'data', 'functional', 'pythonic', 'query', 'sql']","['builder', 'data', 'functional', 'pythonic', 'query', 'sql']",2023-12-10,"[('ibis-project/ibis', 0.6313249468803406, 'data', 1), ('tiangolo/sqlmodel', 0.6139056086540222, 'data', 1), ('sqlalchemy/sqlalchemy', 0.5847384333610535, 'data', 1), ('macbre/sql-metadata', 0.5715281367301941, 'data', 1), ('andialbrecht/sqlparse', 0.5649843811988831, 'data', 0), ('tobymao/sqlglot', 0.5631858706474304, 'data', 1), ('mcfunley/pugsql', 0.5262505412101746, 'data', 1)]",98,3.0,,0.21,29,4,92,1,0,10,10,28.0,57.0,90.0,2.0,44
761,ml-dl,https://github.com/fepegar/torchio,[],,[],[],,,,fepegar/torchio,torchio,1902,219,18,Python,http://www.torchio.org,Medical imaging toolkit for deep learning,fepegar,2024-01-13,2019-11-26,218,8.724770642201834,,Medical imaging toolkit for deep learning,"['augmentation', 'data-augmentation', 'deep-learning', 'machine-learning', 'medical-image-analysis', 'medical-image-computing', 'medical-image-processing', 'medical-images', 'medical-imaging-datasets', 'medical-imaging-with-deep-learning', 'pytorch']","['augmentation', 'data-augmentation', 'deep-learning', 'machine-learning', 'medical-image-analysis', 'medical-image-computing', 'medical-image-processing', 'medical-images', 'medical-imaging-datasets', 'medical-imaging-with-deep-learning', 'pytorch']",2024-01-13,"[('project-monai/monai', 0.8480987548828125, 'ml', 4), ('aleju/imgaug', 0.5931549072265625, 'ml', 3), ('albumentations-team/albumentations', 0.5927706956863403, 'ml-dl', 3), ('mdbloice/augmentor', 0.5896494388580322, 'ml', 3), ('keras-team/keras', 0.5828122496604919, 'ml-dl', 3), ('open-mmlab/mmsegmentation', 0.5752494931221008, 'ml', 1), ('huggingface/datasets', 0.574800968170166, 'nlp', 3), ('onnx/onnx', 0.5600517392158508, 'ml', 3), ('microsoft/onnxruntime', 0.5472385883331299, 'ml', 3), ('tensorflow/tensor2tensor', 0.5404090285301208, 'ml', 2), ('deepfakes/faceswap', 0.5368450880050659, 'ml-dl', 2), ('mosaicml/composer', 0.5322839617729187, 'ml-dl', 3), ('lutzroeder/netron', 0.531164288520813, 'ml', 3), ('explosion/thinc', 0.5309528112411499, 'ml-dl', 3), ('nvidia/deeplearningexamples', 0.5291038155555725, 'ml-dl', 2), ('tensorflow/tensorflow', 0.5249353647232056, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5229683518409729, 'ml-dl', 1), ('open-mmlab/mmediting', 0.5203762650489807, 'ml', 2), ('iperov/deepfacelab', 0.5164421200752258, 'ml-dl', 2), ('microsoft/deepspeed', 0.5152624845504761, 'ml-dl', 3), ('nyandwi/modernconvnets', 0.5130273699760437, 'ml-dl', 0), ('roboflow/supervision', 0.5101572871208191, 'ml', 3), ('ddbourgin/numpy-ml', 0.507611870765686, 'ml', 1), ('rwightman/pytorch-image-models', 0.5073763728141785, 'ml-dl', 1), ('tensorlayer/tensorlayer', 0.5055205225944519, 'ml-rl', 1), ('lucidrains/imagen-pytorch', 0.5043638348579407, 'ml-dl', 1), ('awslabs/autogluon', 0.5024552941322327, 'ml', 3), ('neuralmagic/deepsparse', 0.5005324482917786, 'nlp', 0)]",47,2.0,,1.6,28,24,50,0,1,64,1,28.0,49.0,90.0,1.8,44
670,gis,https://github.com/apache/incubator-sedona,[],,[],[],,,,apache/incubator-sedona,sedona,1654,622,102,Java,https://sedona.apache.org/,A cluster computing framework for processing large-scale geospatial data,apache,2024-01-11,2015-04-24,457,3.6147361848267248,https://avatars.githubusercontent.com/u/47359?v=4,A cluster computing framework for processing large-scale geospatial data,"['cluster-computing', 'geospatial', 'java', 'scala', 'spatial-analysis', 'spatial-query', 'spatial-sql']","['cluster-computing', 'geospatial', 'java', 'scala', 'spatial-analysis', 'spatial-query', 'spatial-sql']",2024-01-14,"[('apache/spark', 0.5984252095222473, 'data', 2), ('giswqs/geog-414', 0.560769259929657, 'study', 1), ('osgeo/grass', 0.5388128757476807, 'gis', 1)]",110,4.0,,7.0,158,150,106,0,3,9,3,159.0,120.0,90.0,0.8,44
1245,sim,https://github.com/facebookresearch/habitat-lab,[],,[],[],,,,facebookresearch/habitat-lab,habitat-lab,1538,432,48,Python,https://aihabitat.org/,A modular high-level library to train embodied AI agents across a variety of tasks and environments.,facebookresearch,2024-01-13,2019-02-04,260,5.912136188907194,https://avatars.githubusercontent.com/u/16943930?v=4,A modular high-level library to train embodied AI agents across a variety of tasks and environments.,"['ai', 'computer-vision', 'deep-learning', 'deep-reinforcement-learning', 'reinforcement-learning', 'research', 'robotics', 'sim2real', 'simulator']","['ai', 'computer-vision', 'deep-learning', 'deep-reinforcement-learning', 'reinforcement-learning', 'research', 'robotics', 'sim2real', 'simulator']",2024-01-12,"[('facebookresearch/droidlet', 0.6688793897628784, 'sim', 0), ('unity-technologies/ml-agents', 0.6679417490959167, 'ml-rl', 3), ('tensorlayer/tensorlayer', 0.6519606113433838, 'ml-rl', 2), ('minedojo/voyager', 0.6467283964157104, 'llm', 0), ('pytorch/rl', 0.6235378384590149, 'ml-rl', 3), ('arise-initiative/robosuite', 0.596968412399292, 'ml-rl', 2), ('prefecthq/marvin', 0.5811783671379089, 'nlp', 1), ('pettingzoo-team/pettingzoo', 0.5756711363792419, 'ml-rl', 1), ('google/dopamine', 0.5727390646934509, 'ml-rl', 1), ('luodian/otter', 0.5644159913063049, 'llm', 1), ('farama-foundation/gymnasium', 0.5635895729064941, 'ml-rl', 1), ('nvidia-omniverse/orbit', 0.5620828866958618, 'sim', 1), ('thu-ml/tianshou', 0.5616220831871033, 'ml-rl', 0), ('inspirai/timechamber', 0.5581255555152893, 'sim', 2), ('tensorflow/tensor2tensor', 0.5538010597229004, 'ml', 2), ('explosion/thinc', 0.5424818396568298, 'ml-dl', 2), ('operand/agency', 0.5416178107261658, 'llm', 1), ('salesforce/warp-drive', 0.5386927127838135, 'ml-rl', 2), ('deepmind/acme', 0.52789306640625, 'ml-rl', 2), ('deeppavlov/deeppavlov', 0.5260887145996094, 'nlp', 2), ('humanoidagents/humanoidagents', 0.5243606567382812, 'sim', 0), ('denys88/rl_games', 0.5146901607513428, 'ml-rl', 2), ('nvidia-omniverse/omniisaacgymenvs', 0.5088497400283813, 'sim', 0), ('huggingface/deep-rl-class', 0.5081996917724609, 'study', 3), ('openai/spinningup', 0.5063529014587402, 'study', 0), ('google/trax', 0.5037897825241089, 'ml-dl', 3), ('deepmind/dm_control', 0.5033023357391357, 'ml-rl', 2), ('kornia/kornia', 0.502574622631073, 'ml-dl', 3), ('bulletphysics/bullet3', 0.5023512840270996, 'sim', 3), ('smol-ai/developer', 0.5017600059509277, 'llm', 1)]",71,3.0,,3.02,142,78,60,0,3,5,3,142.0,104.0,90.0,0.7,44
1625,util,https://github.com/instagram/libcst,"['ast', 'cst', 'serializer']",,[],[],,,,instagram/libcst,LibCST,1341,166,42,Python,https://libcst.readthedocs.io/,A concrete syntax tree parser and serializer library for Python that preserves many aspects of Python's abstract syntax tree,instagram,2024-01-14,2019-08-06,234,5.730769230769231,https://avatars.githubusercontent.com/u/549085?v=4,A concrete syntax tree parser and serializer library for Python that preserves many aspects of Python's abstract syntax tree,[],"['ast', 'cst', 'serializer']",2024-01-08,"[('pyparsing/pyparsing', 0.6272467970848083, 'util', 0), ('marshmallow-code/marshmallow', 0.6035540699958801, 'util', 0), ('pytoolz/toolz', 0.6008638739585876, 'util', 0), ('python-rope/rope', 0.5846632719039917, 'util', 1), ('pyston/pyston', 0.5713760852813721, 'util', 0), ('python-odin/odin', 0.5643008351325989, 'util', 0), ('python/cpython', 0.5573931932449341, 'util', 0), ('pygments/pygments', 0.5501353144645691, 'util', 0), ('ibm/transition-amr-parser', 0.5405070781707764, 'nlp', 0), ('uqfoundation/dill', 0.5361902713775635, 'data', 0), ('google/latexify_py', 0.5354775786399841, 'util', 0), ('andialbrecht/sqlparse', 0.5294908285140991, 'data', 0), ('hhatto/autopep8', 0.5244827270507812, 'util', 0), ('psf/black', 0.5167094469070435, 'util', 0), ('pypy/pypy', 0.5154047608375549, 'util', 0), ('microsoft/pycodegpt', 0.5071039795875549, 'llm', 0), ('instagram/monkeytype', 0.5069942474365234, 'typing', 0), ('pydantic/pydantic', 0.5014991164207458, 'util', 0), ('mkdocstrings/griffe', 0.5002207159996033, 'util', 0)]",75,3.0,,2.02,63,41,54,0,4,9,4,63.0,94.0,90.0,1.5,44
613,testing,https://github.com/pytest-dev/pytest-asyncio,[],,[],[],,,,pytest-dev/pytest-asyncio,pytest-asyncio,1264,131,38,Python,https://pytest-asyncio.readthedocs.io,Asyncio support for pytest,pytest-dev,2024-01-12,2015-04-11,459,2.7512437810945274,https://avatars.githubusercontent.com/u/8897583?v=4,Asyncio support for pytest,"['asyncio', 'pytest-plugin', 'testing']","['asyncio', 'pytest-plugin', 'testing']",2024-01-10,"[('pytest-dev/pytest-xdist', 0.6232805252075195, 'testing', 1), ('pytest-dev/pytest-mock', 0.6129404306411743, 'testing', 0), ('pytest-dev/pytest-cov', 0.6025922894477844, 'testing', 0), ('computationalmodelling/nbval', 0.5843405723571777, 'jupyter', 2), ('timofurrer/awesome-asyncio', 0.5779024958610535, 'study', 1), ('ionelmc/pytest-benchmark', 0.5777239203453064, 'testing', 0), ('aio-libs/aiohttp', 0.5665972828865051, 'web', 1), ('pytest-dev/pytest', 0.5557228922843933, 'testing', 1), ('teemu/pytest-sugar', 0.5544842481613159, 'testing', 2), ('magicstack/uvloop', 0.552464485168457, 'util', 1), ('samuelcolvin/aioaws', 0.5367757081985474, 'data', 1), ('alex-sherman/unsync', 0.5358930230140686, 'util', 0), ('aio-libs/aiobotocore', 0.5174840688705444, 'util', 1), ('erdewit/nest_asyncio', 0.5122057199478149, 'util', 1), ('samuelcolvin/arq', 0.5120292901992798, 'data', 1), ('encode/httpx', 0.5099304914474487, 'web', 1)]",44,4.0,,4.63,179,153,107,0,13,6,13,179.0,267.0,90.0,1.5,44
1382,ml,https://github.com/laekov/fastmoe,['mixture-of-experts'],,[],[],,,,laekov/fastmoe,fastmoe,1240,152,13,Python,https://fastmoe.ai,A fast MoE impl for PyTorch,laekov,2024-01-13,2021-01-25,157,7.890909090909091,,A fast MoE impl for PyTorch,[],['mixture-of-experts'],2023-10-08,"[('nvidia/apex', 0.6603500247001648, 'ml-dl', 0), ('pytorch/ignite', 0.6223480701446533, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5892179012298584, 'perf', 0), ('davidmrau/mixture-of-experts', 0.5889419913291931, 'ml', 1), ('huggingface/accelerate', 0.5827643275260925, 'ml', 0), ('pytorch/botorch', 0.5626605749130249, 'ml-dl', 0), ('skorch-dev/skorch', 0.5523069500923157, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5278257131576538, 'study', 0), ('pytorch/data', 0.5255650877952576, 'data', 0)]",23,7.0,,0.58,9,3,36,3,2,3,2,9.0,35.0,90.0,3.9,44
1491,math,https://github.com/dynamicslab/pysindy/,[],,[],[],,,,dynamicslab/pysindy/,pysindy,1176,282,33,Python,https://pysindy.readthedocs.io/en/latest/,A package for the sparse identification of nonlinear dynamical systems from data,dynamicslab,2024-01-12,2019-05-10,246,4.769409038238702,https://avatars.githubusercontent.com/u/59835780?v=4,A package for the sparse identification of nonlinear dynamical systems from data,"['dynamical-systems', 'machine-learning', 'model-discovery', 'nonlinear-dynamics', 'sparse-regression', 'system-identification']","['dynamical-systems', 'machine-learning', 'model-discovery', 'nonlinear-dynamics', 'sparse-regression', 'system-identification']",2023-12-01,"[('wilsonrljr/sysidentpy', 0.6205930113792419, 'time-series', 3)]",26,5.0,,2.23,51,21,57,0,3,10,3,51.0,114.0,90.0,2.2,44
1148,util,https://github.com/aio-libs/yarl,[],,[],[],,,,aio-libs/yarl,yarl,1081,151,31,Python,https://yarl.aio-libs.org,Yet another URL library,aio-libs,2024-01-14,2016-08-02,391,2.764705882352941,https://avatars.githubusercontent.com/u/7049303?v=4,Yet another URL library,"['aiohttp', 'url-parsing', 'urls']","['aiohttp', 'url-parsing', 'urls']",2024-01-01,"[('magicstack/httptools', 0.5892772674560547, 'web', 0)]",84,6.0,,5.52,62,44,91,0,4,15,4,62.0,128.0,90.0,2.1,44
1801,ml,https://github.com/microsoft/olive,"['onnx', 'gpu', 'toolchain', 'performance']",,[],[],,,,microsoft/olive,Olive,1064,102,25,Python,,"Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. ",microsoft,2024-01-12,2019-08-12,233,4.563725490196078,https://avatars.githubusercontent.com/u/6154722?v=4,"Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. ",[],"['gpu', 'onnx', 'performance', 'toolchain']",2024-01-12,"[('pytorch/glow', 0.5527094006538391, 'ml', 0), ('microsoft/nni', 0.5166267156600952, 'ml', 0), ('plasma-umass/scalene', 0.503036618232727, 'profiling', 1), ('eleutherai/oslo', 0.5006281733512878, 'ml', 0)]",30,1.0,,10.9,248,223,54,0,8,2,8,248.0,420.0,90.0,1.7,44
1244,ml,https://github.com/microsoft/semi-supervised-learning,[],,[],[],,,,microsoft/semi-supervised-learning,Semi-supervised-learning,1056,143,19,Python,https://usb.readthedocs.io,A Unified Semi-Supervised Learning Codebase (NeurIPS'22),microsoft,2024-01-14,2022-05-05,90,11.640944881889764,https://avatars.githubusercontent.com/u/6154722?v=4,A Unified Semi-Supervised Learning Codebase (NeurIPS'22),"['audio-classification', 'classification', 'computer-vision', 'deep-learning', 'low-resource', 'machine-learning', 'natural-language-processing', 'pytorch', 'semi-supervised-learning', 'semisupervised-learning', 'transformer']","['audio-classification', 'classification', 'computer-vision', 'deep-learning', 'low-resource', 'machine-learning', 'natural-language-processing', 'pytorch', 'semi-supervised-learning', 'semisupervised-learning', 'transformer']",2023-11-10,"[('rasbt/machine-learning-book', 0.6046102046966553, 'study', 3), ('huggingface/transformers', 0.5814858078956604, 'nlp', 5), ('tensorflow/tensorflow', 0.5672532916069031, 'ml-dl', 2), ('ludwig-ai/ludwig', 0.5534067749977112, 'ml-ops', 5), ('nvidia/deeplearningexamples', 0.5403677821159363, 'ml-dl', 3), ('milvus-io/bootcamp', 0.5314620733261108, 'data', 1), ('speechbrain/speechbrain', 0.5235595107078552, 'nlp', 2), ('lightly-ai/lightly', 0.5161562561988831, 'ml', 4), ('onnx/onnx', 0.5151509642601013, 'ml', 3), ('keras-team/autokeras', 0.514286458492279, 'ml-dl', 2), ('ddbourgin/numpy-ml', 0.5134571194648743, 'ml', 1), ('deepmind/deepmind-research', 0.5121714472770691, 'ml', 0), ('alpa-projects/alpa', 0.5067688822746277, 'ml-dl', 2), ('aiqc/aiqc', 0.5059452652931213, 'ml-ops', 0), ('huggingface/datasets', 0.505528450012207, 'nlp', 5), ('nyandwi/modernconvnets', 0.5045065879821777, 'ml-dl', 1), ('salesforce/blip', 0.5032647848129272, 'diffusion', 0), ('keras-team/keras', 0.5026553869247437, 'ml-dl', 3), ('pycaret/pycaret', 0.50245600938797, 'ml', 2), ('explosion/thinc', 0.5021923780441284, 'ml-dl', 4), ('huggingface/autotrain-advanced', 0.5021505355834961, 'ml', 3), ('keras-team/keras-nlp', 0.5014702677726746, 'nlp', 3)]",20,5.0,,0.92,30,20,21,2,0,1,1,30.0,55.0,90.0,1.8,44
1641,llm,https://github.com/linksoul-ai/autoagents,['autonomous-agents'],,[],[],,,,linksoul-ai/autoagents,AutoAgents,889,107,20,Python,https://huggingface.co/spaces/LinkSoul/AutoAgents,Generate different roles for GPTs to form a collaborative entity for complex tasks.,linksoul-ai,2024-01-13,2023-08-21,23,38.41358024691358,https://avatars.githubusercontent.com/u/147458898?v=4,Generate different roles for GPTs to form a collaborative entity for complex tasks.,[],['autonomous-agents'],2023-11-24,"[('assafelovic/gpt-researcher', 0.6728865504264832, 'llm', 0), ('yoheinakajima/babyagi', 0.6577290892601013, 'llm', 1), ('geekan/metagpt', 0.6280576586723328, 'llm', 0), ('torantulino/auto-gpt', 0.5497490167617798, 'llm', 1), ('langchain-ai/opengpts', 0.5377379655838013, 'llm', 0), ('operand/agency', 0.5123705267906189, 'llm', 1)]",11,3.0,,1.44,17,6,5,2,0,0,0,17.0,9.0,90.0,0.5,44
684,util,https://github.com/pyfpdf/fpdf2,[],,[],[],,,,pyfpdf/fpdf2,fpdf2,860,211,23,Python,https://py-pdf.github.io/fpdf2/,Simple PDF generation for Python,pyfpdf,2024-01-12,2017-03-15,358,2.3964968152866244,https://avatars.githubusercontent.com/u/102914013?v=4,Simple PDF generation for Python,"['barcode', 'markdown', 'pdf', 'pdf-generation', 'pdf-library', 'svg']","['barcode', 'markdown', 'pdf', 'pdf-generation', 'pdf-library', 'svg']",2024-01-02,"[('py-pdf/pypdf2', 0.6898808479309082, 'util', 1), ('pypdfium2-team/pypdfium2', 0.6115421652793884, 'util', 1), ('jorisschellekens/borb', 0.6073178052902222, 'util', 3), ('camelot-dev/camelot', 0.5940113663673401, 'util', 0), ('google/latexify_py', 0.5399512052536011, 'util', 0), ('pdfminer/pdfminer.six', 0.5335804224014282, 'util', 1), ('unstructured-io/pipeline-paddleocr', 0.531453549861908, 'data', 1), ('lukasschwab/arxiv.py', 0.5308734774589539, 'util', 1), ('google/yapf', 0.5062951445579529, 'util', 0), ('microsoft/genalog', 0.5020582675933838, 'data', 0), ('connorferster/handcalcs', 0.5012051463127136, 'jupyter', 0)]",116,3.0,,5.4,152,122,83,0,7,6,7,152.0,399.0,90.0,2.6,44
898,data,https://github.com/googleapis/python-bigquery,[],,[],[],,,,googleapis/python-bigquery,python-bigquery,686,316,54,Python,,,googleapis,2024-01-11,2019-12-10,216,3.175925925925926,https://avatars.githubusercontent.com/u/16785467?v=4,googleapis/python-bigquery,[],[],2024-01-12,"[('pytables/pytables', 0.5208421349525452, 'data', 0), ('ibis-project/ibis', 0.5185703635215759, 'data', 0), ('googleapis/google-api-python-client', 0.5121608376502991, 'util', 0), ('ofek/pypinfo', 0.5070151686668396, 'util', 0)]",142,5.0,,2.87,152,115,50,0,20,34,20,152.0,196.0,90.0,1.3,44
796,util,https://github.com/open-telemetry/opentelemetry-python-contrib,[],,[],[],,,,open-telemetry/opentelemetry-python-contrib,opentelemetry-python-contrib,554,469,16,Python,https://opentelemetry.io,OpenTelemetry instrumentation for Python modules,open-telemetry,2024-01-13,2019-11-08,220,2.511658031088083,https://avatars.githubusercontent.com/u/49998002?v=4,OpenTelemetry instrumentation for Python modules,[],[],2024-01-11,"[('open-telemetry/opentelemetry-python', 0.7430706024169922, 'util', 0), ('pympler/pympler', 0.5808881521224976, 'perf', 0), ('gaogaotiantian/viztracer', 0.5290429592132568, 'profiling', 0), ('openai/openai-python', 0.5244234204292297, 'util', 0)]",226,6.0,,2.98,177,78,51,0,7,10,7,177.0,243.0,90.0,1.4,44
1607,llm,https://github.com/zhudotexe/kani,[],,[],[],,,,zhudotexe/kani,kani,499,24,9,Python,https://kani.readthedocs.io,kani (カニ) is a highly hackable microframework for chat-based language models with tool use/function calling. (NLP-OSS @ EMNLP 2023),zhudotexe,2024-01-14,2023-07-14,28,17.465,,kani (カニ) is a highly hackable microframework for chat-based language models with tool use/function calling. (NLP-OSS @ EMNLP 2023),"['chatgpt', 'claude-2', 'framework', 'function-calling', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llama-2', 'llms', 'microframework', 'openai', 'tool-use']","['chatgpt', 'claude-2', 'framework', 'function-calling', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llama-2', 'llms', 'microframework', 'openai', 'tool-use']",2023-12-04,"[('lm-sys/fastchat', 0.579633891582489, 'llm', 0), ('thudm/chatglm2-6b', 0.5553779602050781, 'llm', 1), ('openai/tiktoken', 0.528551459312439, 'nlp', 1), ('next-gpt/next-gpt', 0.5206196904182434, 'llm', 3), ('embedchain/embedchain', 0.5120741724967957, 'llm', 1), ('microsoft/autogen', 0.5002747178077698, 'llm', 2)]",6,3.0,,4.69,7,6,6,1,18,36,18,7.0,4.0,90.0,0.6,44
1860,sim,https://github.com/nvidia-omniverse/orbit,['robot-learning'],,[],[],,,,nvidia-omniverse/orbit,orbit,494,135,23,Python,https://isaac-orbit.github.io/orbit/,Unified framework for robot learning built on NVIDIA Isaac Sim,nvidia-omniverse,2024-01-13,2022-11-16,62,7.859090909090909,https://avatars.githubusercontent.com/u/57824658?v=4,Unified framework for robot learning built on NVIDIA Isaac Sim,"['omniverse-kit-extension', 'robot-learning', 'robotics']","['omniverse-kit-extension', 'robot-learning', 'robotics']",2024-01-11,"[('nvidia-omniverse/omniisaacgymenvs', 0.6811214685440063, 'sim', 1), ('arise-initiative/robosuite', 0.6593456268310547, 'ml-rl', 2), ('unity-technologies/ml-agents', 0.5719271898269653, 'ml-rl', 0), ('facebookresearch/habitat-lab', 0.5620828866958618, 'sim', 1), ('pytorch/rl', 0.5298061966896057, 'ml-rl', 1), ('salesforce/warp-drive', 0.513428270816803, 'ml-rl', 0)]",18,4.0,,4.58,80,44,14,0,1,2,1,80.0,160.0,90.0,2.0,44
424,study,https://github.com/fchollet/deep-learning-with-python-notebooks,[],,[],[],,,,fchollet/deep-learning-with-python-notebooks,deep-learning-with-python-notebooks,17496,8431,651,Jupyter Notebook,,"Jupyter notebooks for the code samples of the book ""Deep Learning with Python""",fchollet,2024-01-13,2017-09-05,334,52.383233532934135,,"Jupyter notebooks for the code samples of the book ""Deep Learning with Python""",[],[],2023-02-13,"[('ageron/handson-ml2', 0.8291416168212891, 'ml', 0), ('cohere-ai/notebooks', 0.7323339581489563, 'llm', 0), ('wesm/pydata-book', 0.6737366318702698, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.6630170941352844, 'study', 0), ('jupyter/nbformat', 0.6576974987983704, 'jupyter', 0), ('rasbt/machine-learning-book', 0.6557565331459045, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.6521231532096863, 'study', 0), ('adafruit/circuitpython', 0.6453861594200134, 'util', 0), ('probml/pyprobml', 0.6373811364173889, 'ml', 0), ('python/cpython', 0.6243636608123779, 'util', 0), ('pypy/pypy', 0.5973843932151794, 'util', 0), ('mrdbourke/pytorch-deep-learning', 0.5927961468696594, 'study', 0), ('gradio-app/gradio', 0.5826172232627869, 'viz', 0), ('intel/intel-extension-for-pytorch', 0.5782514214515686, 'perf', 0), ('d2l-ai/d2l-en', 0.5766072273254395, 'study', 0), ('mwouts/jupytext', 0.5749877095222473, 'jupyter', 0), ('ipython/ipyparallel', 0.5656928420066833, 'perf', 0), ('dylanhogg/awesome-python', 0.5646929144859314, 'study', 0), ('jupyter/nbconvert', 0.5641415119171143, 'jupyter', 0), ('pytoolz/toolz', 0.5609208941459656, 'util', 0), ('uber/petastorm', 0.5607595443725586, 'data', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5593620538711548, 'jupyter', 0), ('gerdm/prml', 0.5584812760353088, 'study', 0), ('brandon-rhodes/python-patterns', 0.55435711145401, 'util', 0), ('ta-lib/ta-lib-python', 0.5513738393783569, 'finance', 0), ('jupyterlab/jupyterlab-desktop', 0.5508671402931213, 'jupyter', 0), ('nvidia/deeplearningexamples', 0.5502544045448303, 'ml-dl', 0), ('kubeflow/fairing', 0.5467697381973267, 'ml-ops', 0), ('opengeos/leafmap', 0.5463659763336182, 'gis', 0), ('pytorch/ignite', 0.5449861288070679, 'ml-dl', 0), ('jupyterlab/jupyterlab', 0.5447031259536743, 'jupyter', 0), ('amaargiru/pyroad', 0.5438991785049438, 'study', 0), ('ipython/ipykernel', 0.5428215265274048, 'util', 0), ('goldmansachs/gs-quant', 0.5427274107933044, 'finance', 0), ('jupyter-widgets/ipywidgets', 0.5422195792198181, 'jupyter', 0), ('arogozhnikov/einops', 0.5417112112045288, 'ml-dl', 0), ('cuemacro/finmarketpy', 0.541059136390686, 'finance', 0), ('huggingface/huggingface_hub', 0.5410536527633667, 'ml', 0), ('firmai/industry-machine-learning', 0.5408152937889099, 'study', 0), ('rafiqhasan/auto-tensorflow', 0.5406836867332458, 'ml-dl', 0), ('graykode/nlp-tutorial', 0.5380004644393921, 'study', 0), ('tatsu-lab/stanford_alpaca', 0.5365834832191467, 'llm', 0), ('google/gin-config', 0.5364246964454651, 'util', 0), ('jupyter/notebook', 0.5353759527206421, 'jupyter', 0), ('pycaret/pycaret', 0.5345378518104553, 'ml', 0), ('jupyter/nbgrader', 0.5345233678817749, 'jupyter', 0), ('faster-cpython/tools', 0.533972442150116, 'perf', 0), ('eleutherai/pyfra', 0.5332986116409302, 'ml', 0), ('koaning/calm-notebooks', 0.5319899320602417, 'study', 0), ('tensorlayer/tensorlayer', 0.5313844680786133, 'ml-rl', 0), ('jeshraghian/snntorch', 0.5287100076675415, 'ml-dl', 0), ('mito-ds/monorepo', 0.525928795337677, 'jupyter', 0), ('aws/graph-notebook', 0.525762140750885, 'jupyter', 0), ('ipython/ipython', 0.525562584400177, 'util', 0), ('cython/cython', 0.5218517780303955, 'util', 0), ('jupyter/nbdime', 0.5217457413673401, 'jupyter', 0), ('nedbat/coveragepy', 0.521513819694519, 'testing', 0), ('imageio/imageio', 0.5206165909767151, 'util', 0), ('faster-cpython/ideas', 0.5187180638313293, 'perf', 0), ('voila-dashboards/voila', 0.5183944702148438, 'jupyter', 0), ('klen/muffin', 0.5174252390861511, 'web', 0), ('udacity/deep-learning-v2-pytorch', 0.5173661708831787, 'study', 0), ('skorch-dev/skorch', 0.5172905325889587, 'ml-dl', 0), ('numba/llvmlite', 0.5170167088508606, 'util', 0), ('huggingface/transformers', 0.5168095827102661, 'nlp', 0), ('maartenbreddels/ipyvolume', 0.516755223274231, 'jupyter', 0), ('ashleve/lightning-hydra-template', 0.5156070590019226, 'util', 0), ('google/latexify_py', 0.5139066576957703, 'util', 0), ('googlecloudplatform/vertex-ai-samples', 0.5130940079689026, 'ml', 0), ('tensorly/tensorly', 0.5126520395278931, 'ml-dl', 0), ('nteract/papermill', 0.5121607780456543, 'jupyter', 0), ('quantopian/qgrid', 0.5115215182304382, 'jupyter', 0), ('masoniteframework/masonite', 0.5106345415115356, 'web', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5104230642318726, 'study', 0), ('p403n1x87/austin', 0.5097584128379822, 'profiling', 0), ('timofurrer/awesome-asyncio', 0.5076543092727661, 'study', 0), ('atsushisakai/pythonrobotics', 0.5075343251228333, 'sim', 0), ('ggerganov/ggml', 0.5067695379257202, 'ml', 0), ('computationalmodelling/nbval', 0.5067647099494934, 'jupyter', 0), ('realpython/python-guide', 0.5066047310829163, 'study', 0), ('facebookresearch/pytorch3d', 0.5057374238967896, 'ml-dl', 0), ('dmlc/dgl', 0.504264235496521, 'ml-dl', 0), ('astral-sh/ruff', 0.5036055445671082, 'util', 0), ('pyro-ppl/pyro', 0.5035637617111206, 'ml-dl', 0), ('nbqa-dev/nbqa', 0.5034378170967102, 'jupyter', 0), ('grantjenks/blue', 0.5027827024459839, 'util', 0), ('psf/black', 0.5026202201843262, 'util', 0), ('jupyterlab/jupyter-ai', 0.5025471448898315, 'jupyter', 0), ('willmcgugan/textual', 0.5023050904273987, 'term', 0), ('fastai/fastcore', 0.5009275078773499, 'util', 0)]",9,1.0,,0.02,3,1,77,11,0,0,0,3.0,1.0,90.0,0.3,43
1331,study,https://github.com/graykode/nlp-tutorial,[],,[],[],,,,graykode/nlp-tutorial,nlp-tutorial,13361,3865,291,Jupyter Notebook,https://www.reddit.com/r/MachineLearning/comments/amfinl/project_nlptutoral_repository_who_is_studying/,Natural Language Processing Tutorial for Deep Learning Researchers,graykode,2024-01-14,2019-01-09,263,50.63724959393611,,Natural Language Processing Tutorial for Deep Learning Researchers,"['attention', 'bert', 'natural-language-processing', 'nlp', 'paper', 'pytorch', 'tensorflow', 'transformer', 'tutorial']","['attention', 'bert', 'natural-language-processing', 'nlp', 'paper', 'pytorch', 'tensorflow', 'transformer', 'tutorial']",2021-07-25,"[('allenai/allennlp', 0.6779881715774536, 'nlp', 3), ('keras-team/keras-nlp', 0.6779394149780273, 'nlp', 3), ('huggingface/transformers', 0.6622538566589355, 'nlp', 6), ('alibaba/easynlp', 0.6614455580711365, 'nlp', 3), ('extreme-bert/extreme-bert', 0.6246617436408997, 'llm', 5), ('deepset-ai/farm', 0.6138063669204712, 'nlp', 3), ('bigscience-workshop/megatron-deepspeed', 0.6075314879417419, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6075314879417419, 'llm', 0), ('mrdbourke/pytorch-deep-learning', 0.6003298163414001, 'study', 1), ('paddlepaddle/paddlenlp', 0.5823931097984314, 'llm', 2), ('nltk/nltk', 0.5764945149421692, 'nlp', 2), ('salesforce/blip', 0.5725541710853577, 'diffusion', 0), ('nvidia/deeplearningexamples', 0.569402277469635, 'ml-dl', 3), ('udacity/deep-learning-v2-pytorch', 0.5692509412765503, 'study', 1), ('flairnlp/flair', 0.561720073223114, 'nlp', 3), ('llmware-ai/llmware', 0.559558093547821, 'llm', 3), ('jonasgeiping/cramming', 0.5574339628219604, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5533468723297119, 'llm', 0), ('ageron/handson-ml2', 0.5493988394737244, 'ml', 0), ('jina-ai/finetuner', 0.5485501885414124, 'ml', 1), ('rasahq/rasa', 0.5484858155250549, 'llm', 2), ('ddangelov/top2vec', 0.5482358932495117, 'nlp', 1), ('christoschristofidis/awesome-deep-learning', 0.5480766296386719, 'study', 0), ('explosion/spacy', 0.5477665066719055, 'nlp', 2), ('jina-ai/clip-as-service', 0.5440134406089783, 'nlp', 2), ('rafiqhasan/auto-tensorflow', 0.5383457541465759, 'ml-dl', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5380004644393921, 'study', 0), ('maartengr/bertopic', 0.5367704629898071, 'nlp', 2), ('pytorch/ignite', 0.5355981588363647, 'ml-dl', 1), ('norskregnesentral/skweak', 0.5352105498313904, 'nlp', 1), ('d2l-ai/d2l-en', 0.5312547087669373, 'study', 3), ('deeppavlov/deeppavlov', 0.529963493347168, 'nlp', 2), ('thilinarajapakse/simpletransformers', 0.5299626588821411, 'nlp', 0), ('explosion/spacy-models', 0.5290482044219971, 'nlp', 2), ('google-research/electra', 0.5271270871162415, 'ml-dl', 2), ('whu-zqh/chatgpt-vs.-bert', 0.5263599753379822, 'llm', 1), ('explosion/thinc', 0.5204659104347229, 'ml-dl', 4), ('rasbt/machine-learning-book', 0.518619179725647, 'study', 1), ('keras-team/keras', 0.5174597501754761, 'ml-dl', 2), ('nvidia/nemo', 0.5165086388587952, 'nlp', 1), ('sloria/textblob', 0.5135754942893982, 'nlp', 2), ('huggingface/text-generation-inference', 0.5133894681930542, 'llm', 3), ('microsoft/generative-ai-for-beginners', 0.5113995671272278, 'study', 0), ('openai/finetune-transformer-lm', 0.5100629329681396, 'llm', 1), ('mooler0410/llmspracticalguide', 0.510050356388092, 'study', 2), ('nvlabs/gcvit', 0.5081471800804138, 'diffusion', 0), ('jalammar/ecco', 0.5071130990982056, 'ml-interpretability', 3), ('amanchadha/coursera-deep-learning-specialization', 0.5051466226577759, 'study', 0), ('arogozhnikov/einops', 0.5046815276145935, 'ml-dl', 2), ('amansrivastava17/embedding-as-service', 0.5044365525245667, 'nlp', 4), ('udlbook/udlbook', 0.5029069185256958, 'study', 0), ('deepmind/deepmind-research', 0.5026014447212219, 'ml', 0), ('tensorly/tensorly', 0.5011732578277588, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.5006478428840637, 'util', 1)]",14,4.0,,0.0,1,0,61,30,0,0,0,1.0,0.0,90.0,0.0,43
152,nlp,https://github.com/sloria/textblob,[],,[],[],,,,sloria/textblob,TextBlob,8820,1134,267,Python,https://textblob.readthedocs.io/,"Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.",sloria,2024-01-13,2013-06-30,552,15.969994826694258,,"Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.","['natural-language-processing', 'nlp', 'nltk', 'pattern']","['natural-language-processing', 'nlp', 'nltk', 'pattern']",2023-03-11,"[('explosion/spacy', 0.7043011784553528, 'nlp', 2), ('nltk/nltk', 0.6578431129455566, 'nlp', 3), ('flairnlp/flair', 0.6279769539833069, 'nlp', 2), ('clips/pattern', 0.6183449029922485, 'nlp', 1), ('keras-team/keras-nlp', 0.5880878567695618, 'nlp', 2), ('vi3k6i5/flashtext', 0.5837533473968506, 'data', 1), ('explosion/spacy-models', 0.5832897424697876, 'nlp', 2), ('pemistahl/lingua-py', 0.5679237246513367, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.559609591960907, 'llm', 1), ('lexpredict/lexpredict-lexnlp', 0.5583809018135071, 'nlp', 1), ('pyparsing/pyparsing', 0.5576520562171936, 'util', 0), ('pandas-dev/pandas', 0.554803729057312, 'pandas', 0), ('eliasdabbas/advertools', 0.5545390248298645, 'data', 0), ('norskregnesentral/skweak', 0.5448392033576965, 'nlp', 1), ('explosion/spacy-streamlit', 0.5445199012756348, 'nlp', 2), ('jbesomi/texthero', 0.5416422486305237, 'nlp', 1), ('ddbourgin/numpy-ml', 0.5411363244056702, 'ml', 0), ('thilinarajapakse/simpletransformers', 0.5410082936286926, 'nlp', 0), ('ranaroussi/quantstats', 0.5343949794769287, 'finance', 0), ('milvus-io/bootcamp', 0.5331091284751892, 'data', 1), ('thealgorithms/python', 0.531756579875946, 'study', 0), ('plotly/dash', 0.5282818078994751, 'viz', 0), ('alibaba/easynlp', 0.5273472666740417, 'nlp', 1), ('explosion/spacy-llm', 0.5245431065559387, 'llm', 2), ('rasahq/rasa', 0.5231644511222839, 'llm', 2), ('allenai/allennlp', 0.5219146013259888, 'nlp', 2), ('rare-technologies/gensim', 0.5176156759262085, 'nlp', 2), ('gradio-app/gradio', 0.5139216780662537, 'viz', 0), ('graykode/nlp-tutorial', 0.5135754942893982, 'study', 2), ('dylanhogg/awesome-python', 0.5117655992507935, 'study', 2), ('fatiando/verde', 0.5090445876121521, 'gis', 0), ('rasbt/mlxtend', 0.5048877596855164, 'ml', 0), ('scikit-learn/scikit-learn', 0.5042425990104675, 'ml', 0), ('evhub/coconut', 0.5033293962478638, 'util', 0)]",36,4.0,,0.02,7,1,128,10,0,4,4,7.0,4.0,90.0,0.6,43
107,ml,https://github.com/nicolashug/surprise,[],,[],[],,,,nicolashug/surprise,Surprise,6098,1043,146,Python,http://surpriselib.com,A Python scikit for building and analyzing recommender systems,nicolashug,2024-01-13,2016-10-23,379,16.07758945386064,,A Python scikit for building and analyzing recommender systems,"['factorization', 'machine-learning', 'matrix', 'recommendation', 'recommender', 'svd', 'systems']","['factorization', 'machine-learning', 'matrix', 'recommendation', 'recommender', 'svd', 'systems']",2023-01-27,"[('rucaibox/recbole', 0.5934752821922302, 'ml', 1), ('jacopotagliabue/reclist', 0.5908733010292053, 'ml', 1), ('microsoft/recommenders', 0.5881096720695496, 'study', 3), ('pytorch/torchrec', 0.5874725580215454, 'ml-dl', 0), ('rasbt/mlxtend', 0.5660281777381897, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.5304577946662903, 'ml', 1)]",45,3.0,,0.02,7,3,88,12,0,2,2,7.0,10.0,90.0,1.4,43
379,data,https://github.com/alirezamika/autoscraper,[],,[],[],,,,alirezamika/autoscraper,autoscraper,5757,618,125,Python,,"A Smart, Automatic, Fast and Lightweight Web Scraper for Python",alirezamika,2024-01-14,2020-08-31,178,32.316760224538896,,"A Smart, Automatic, Fast and Lightweight Web Scraper for Python","['ai', 'artificial-intelligence', 'automation', 'crawler', 'machine-learning', 'scrape', 'scraper', 'scraping', 'web-scraping', 'webautomation', 'webscraping']","['ai', 'artificial-intelligence', 'automation', 'crawler', 'machine-learning', 'scrape', 'scraper', 'scraping', 'web-scraping', 'webautomation', 'webscraping']",2022-07-17,"[('scrapy/scrapy', 0.8083213567733765, 'data', 3), ('roniemartinez/dude', 0.781152606010437, 'util', 5), ('nv7-github/googlesearch', 0.7142531275749207, 'util', 0), ('clips/pattern', 0.7112637758255005, 'nlp', 1), ('binux/pyspider', 0.6465148329734802, 'data', 1), ('requests/toolbelt', 0.5791863799095154, 'util', 0), ('twintproject/twint', 0.5726699233055115, 'data', 1), ('webpy/webpy', 0.5700419545173645, 'web', 0), ('seleniumbase/seleniumbase', 0.5415842533111572, 'testing', 0), ('psf/requests', 0.5295840501785278, 'web', 0), ('cobrateam/splinter', 0.5292969346046448, 'testing', 1), ('serpapi/google-search-results-python', 0.5220003724098206, 'util', 2), ('jovianml/opendatasets', 0.5161617398262024, 'data', 1), ('falconry/falcon', 0.513289749622345, 'web', 0), ('masoniteframework/masonite', 0.5066377520561218, 'web', 0)]",8,2.0,,0.0,1,0,41,18,0,5,5,1.0,1.0,90.0,1.0,43
1023,finance,https://github.com/kernc/backtesting.py,[],,[],[],,,,kernc/backtesting.py,backtesting.py,4436,875,109,Python,https://kernc.github.io/backtesting.py/,:mag_right: :chart_with_upwards_trend: :snake: :moneybag:  Backtest trading strategies in Python.,kernc,2024-01-14,2019-01-02,264,16.748651564185543,,:mag_right: :chart_with_upwards_trend: 🐍 :moneybag:  Backtest trading strategies in Python.,"['algo-trading', 'algorithmic-trading', 'backtesting', 'backtesting-engine', 'backtesting-frameworks', 'backtesting-trading-strategies', 'finance', 'financial-markets', 'forex', 'forex-trading', 'framework', 'investing', 'investment', 'investment-strategies', 'stocks', 'trading', 'trading-algorithms', 'trading-simulator', 'trading-strategies']","['algo-trading', 'algorithmic-trading', 'backtesting', 'backtesting-engine', 'backtesting-frameworks', 'backtesting-trading-strategies', 'finance', 'financial-markets', 'forex', 'forex-trading', 'framework', 'investing', 'investment', 'investment-strategies', 'stocks', 'trading', 'trading-algorithms', 'trading-simulator', 'trading-strategies']",2023-01-15,"[('cuemacro/finmarketpy', 0.6904587149620056, 'finance', 2), ('mementum/backtrader', 0.6531647443771362, 'finance', 2), ('polakowo/vectorbt', 0.6390688419342041, 'finance', 5), ('ranaroussi/quantstats', 0.62286376953125, 'finance', 3), ('quantconnect/lean', 0.5955772995948792, 'finance', 5), ('blankly-finance/blankly', 0.5919058918952942, 'finance', 5), ('goldmansachs/gs-quant', 0.5561110377311707, 'finance', 1), ('gbeced/pyalgotrade', 0.5535092949867249, 'finance', 0), ('robcarver17/pysystemtrade', 0.5506398677825928, 'finance', 0), ('gbeced/basana', 0.5409364700317383, 'finance', 2), ('quantopian/zipline', 0.540707528591156, 'finance', 1), ('zvtvz/zvt', 0.5392759442329407, 'finance', 3), ('bashtage/arch', 0.5312038064002991, 'time-series', 1), ('ai4finance-foundation/finrl', 0.5252265334129333, 'finance', 2), ('idanya/algo-trader', 0.522881269454956, 'finance', 3), ('ta-lib/ta-lib-python', 0.512697160243988, 'finance', 1), ('firmai/atspy', 0.5046423673629761, 'time-series', 1)]",19,4.0,,0.0,30,3,61,12,0,4,4,30.0,23.0,90.0,0.8,43
1840,finance,https://github.com/twopirllc/pandas-ta,[],,[],[],,,,twopirllc/pandas-ta,pandas-ta,4335,871,92,Python,https://twopirllc.github.io/pandas-ta/,Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators,twopirllc,2024-01-14,2019-02-19,258,16.802325581395348,,Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators,"['dataframe', 'finance', 'fundamental-analysis', 'jupyter-notebook', 'pandas', 'pandas-dataframe-extension', 'pandas-extension', 'pandas-ta', 'stock-market', 'technical', 'technical-analysis', 'technical-analysis-indicators', 'technical-analysis-library', 'technical-indicators', 'trading', 'trading-algorithms']","['dataframe', 'finance', 'fundamental-analysis', 'jupyter-notebook', 'pandas', 'pandas-dataframe-extension', 'pandas-extension', 'pandas-ta', 'stock-market', 'technical', 'technical-analysis', 'technical-analysis-indicators', 'technical-analysis-library', 'technical-indicators', 'trading', 'trading-algorithms']",2022-09-24,"[('mementum/bta-lib', 0.6815423369407654, 'finance', 0), ('ta-lib/ta-lib-python', 0.6217855215072632, 'finance', 2), ('adamerose/pandasgui', 0.5920196771621704, 'pandas', 2), ('pydata/pandas-datareader', 0.5820935368537903, 'pandas', 2), ('lux-org/lux', 0.5814751386642456, 'viz', 1), ('tkrabel/bamboolib', 0.5758503675460815, 'pandas', 2), ('rapidsai/cudf', 0.542910099029541, 'pandas', 2), ('mito-ds/monorepo', 0.5410447716712952, 'jupyter', 1), ('jmcarpenter2/swifter', 0.539636492729187, 'pandas', 1), ('goldmansachs/gs-quant', 0.5390924215316772, 'finance', 0), ('kanaries/pygwalker', 0.5264495611190796, 'pandas', 2), ('ranaroussi/quantstats', 0.5218469500541687, 'finance', 1), ('nalepae/pandarallel', 0.517140805721283, 'pandas', 1), ('man-group/dtale', 0.5126270651817322, 'viz', 2), ('alkaline-ml/pmdarima', 0.5053380131721497, 'time-series', 0), ('stefmolin/stock-analysis', 0.5048727989196777, 'finance', 2), ('pandas-dev/pandas', 0.5036715269088745, 'pandas', 2), ('wesm/pydata-book', 0.5033674240112305, 'study', 0), ('holoviz/panel', 0.5016809105873108, 'viz', 0)]",45,1.0,,0.0,45,32,60,16,0,1,1,46.0,136.0,90.0,3.0,43
1025,finance,https://github.com/ranaroussi/quantstats,[],,[],[],,,,ranaroussi/quantstats,quantstats,3886,748,93,Python,,"Portfolio analytics for quants, written in Python",ranaroussi,2024-01-14,2019-05-01,247,15.678386167146973,,"Portfolio analytics for quants, written in Python","['algo-trading', 'algorithmic-trading', 'algotrading', 'finance', 'plotting', 'quant', 'quantitative-analysis', 'quantitative-finance', 'quantitative-trading', 'visualization']","['algo-trading', 'algorithmic-trading', 'algotrading', 'finance', 'plotting', 'quant', 'quantitative-analysis', 'quantitative-finance', 'quantitative-trading', 'visualization']",2023-07-06,"[('goldmansachs/gs-quant', 0.7339354157447815, 'finance', 0), ('plotly/dash', 0.6857461333274841, 'viz', 1), ('quantconnect/lean', 0.6603469848632812, 'finance', 1), ('quantopian/pyfolio', 0.6542462706565857, 'finance', 0), ('zvtvz/zvt', 0.6442165970802307, 'finance', 4), ('polakowo/vectorbt', 0.6319853663444519, 'finance', 4), ('statsmodels/statsmodels', 0.6285594701766968, 'ml', 0), ('kernc/backtesting.py', 0.62286376953125, 'finance', 3), ('gbeced/pyalgotrade', 0.6159399747848511, 'finance', 0), ('scikit-mobility/scikit-mobility', 0.6154873967170715, 'gis', 0), ('polyaxon/datatile', 0.6079445481300354, 'pandas', 0), ('cuemacro/finmarketpy', 0.6077700853347778, 'finance', 0), ('quantopian/zipline', 0.5925479531288147, 'finance', 2), ('holoviz/panel', 0.5793597102165222, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5782219767570496, 'study', 0), ('thealgorithms/python', 0.5772646069526672, 'study', 0), ('firmai/atspy', 0.5767538547515869, 'time-series', 1), ('ta-lib/ta-lib-python', 0.566260814666748, 'finance', 2), ('pandas-dev/pandas', 0.5643635988235474, 'pandas', 0), ('man-group/dtale', 0.55287766456604, 'viz', 1), ('google/tf-quant-finance', 0.5515581369400024, 'finance', 2), ('scikit-learn/scikit-learn', 0.5498625636100769, 'ml', 0), ('clips/pattern', 0.5454891324043274, 'nlp', 0), ('wesm/pydata-book', 0.5430867671966553, 'study', 0), ('robcarver17/pysystemtrade', 0.5409232378005981, 'finance', 0), ('sloria/textblob', 0.5343949794769287, 'nlp', 0), ('eleutherai/pyfra', 0.5325418710708618, 'ml', 0), ('openbb-finance/openbbterminal', 0.53244549036026, 'finance', 2), ('stefmolin/stock-analysis', 0.5322091579437256, 'finance', 0), ('networkx/networkx', 0.5321804881095886, 'graph', 0), ('1200wd/bitcoinlib', 0.5297749638557434, 'crypto', 0), ('gbeced/basana', 0.5287092328071594, 'finance', 1), ('bokeh/bokeh', 0.5271790027618408, 'viz', 2), ('dagworks-inc/hamilton', 0.5268656015396118, 'ml-ops', 0), ('domokane/financepy', 0.526004433631897, 'finance', 1), ('dylanhogg/awesome-python', 0.5245246887207031, 'study', 0), ('ydataai/ydata-profiling', 0.5242244005203247, 'pandas', 0), ('alkaline-ml/pmdarima', 0.522229790687561, 'time-series', 0), ('twopirllc/pandas-ta', 0.5218469500541687, 'finance', 1), ('plotly/plotly.py', 0.5214852094650269, 'viz', 1), ('malloydata/malloy-py', 0.5185449123382568, 'data', 0), ('numerai/example-scripts', 0.5179154872894287, 'finance', 0), ('hydrosquall/tiingo-python', 0.5174421072006226, 'finance', 1), ('microsoft/qlib', 0.5154274702072144, 'finance', 5), ('quantecon/quantecon.py', 0.513916015625, 'sim', 0), ('gradio-app/gradio', 0.5128483176231384, 'viz', 0), ('keon/algorithms', 0.5114973187446594, 'util', 0), ('mementum/bta-lib', 0.5088109970092773, 'finance', 0), ('ai4finance-foundation/finrl', 0.5057669878005981, 'finance', 2), ('matplotlib/mplfinance', 0.5049977898597717, 'finance', 1), ('bashtage/arch', 0.5018336772918701, 'time-series', 1), ('federicoceratto/dashing', 0.5010005235671997, 'term', 0), ('online-ml/river', 0.5004301071166992, 'ml', 0)]",32,3.0,,0.52,25,6,57,6,3,3,3,25.0,20.0,90.0,0.8,43
1112,web,https://github.com/pylons/pyramid,[],,[],[],,,,pylons/pyramid,pyramid,3875,931,161,Python,https://trypyramid.com/,Pyramid - A Python web framework,pylons,2024-01-13,2010-10-24,692,5.597399917457697,https://avatars.githubusercontent.com/u/452227?v=4,Pyramid - A Python web framework,"['pylons', 'pyramid', 'web-framework', 'wsgi']","['pylons', 'pyramid', 'web-framework', 'wsgi']",2023-09-14,"[('pallets/flask', 0.7600159049034119, 'web', 2), ('pallets/werkzeug', 0.7471210360527039, 'web', 1), ('bottlepy/bottle', 0.7196846008300781, 'web', 2), ('masoniteframework/masonite', 0.6714824438095093, 'web', 0), ('webpy/webpy', 0.6500197052955627, 'web', 0), ('klen/muffin', 0.6305515766143799, 'web', 0), ('falconry/falcon', 0.6222757697105408, 'web', 1), ('neoteroi/blacksheep', 0.5955870151519775, 'web', 0), ('pallets/quart', 0.5922417640686035, 'web', 0), ('scrapy/scrapy', 0.5863667726516724, 'data', 0), ('eleutherai/pyfra', 0.5796028971672058, 'ml', 0), ('reflex-dev/reflex', 0.5728359818458557, 'web', 0), ('pypy/pypy', 0.5644940137863159, 'util', 0), ('encode/uvicorn', 0.5620464086532593, 'web', 0), ('benoitc/gunicorn', 0.5611777305603027, 'web', 1), ('timofurrer/awesome-asyncio', 0.5598770380020142, 'study', 0), ('cherrypy/cherrypy', 0.5556930303573608, 'web', 0), ('feincms/feincms', 0.5519199371337891, 'web', 0), ('willmcgugan/textual', 0.5482103228569031, 'term', 0), ('emmett-framework/emmett', 0.5466519594192505, 'web', 1), ('pyglet/pyglet', 0.5355060696601868, 'gamedev', 0), ('pyodide/pyodide', 0.5352792739868164, 'util', 0), ('pylons/waitress', 0.5299732089042664, 'web', 0), ('python/cpython', 0.5238844752311707, 'util', 0), ('django/django', 0.5236054062843323, 'web', 0), ('pycqa/pylint-django', 0.5214040279388428, 'util', 0), ('r0x0r/pywebview', 0.5202688574790955, 'gui', 0), ('sqlalchemy/mako', 0.5201253890991211, 'template', 0), ('encode/httpx', 0.5153858661651611, 'web', 0), ('clips/pattern', 0.5145288705825806, 'nlp', 0), ('starlite-api/starlite', 0.5109481811523438, 'web', 0), ('holoviz/panel', 0.507599949836731, 'viz', 0), ('python-restx/flask-restx', 0.5075467824935913, 'web', 0), ('alirn76/panther', 0.5073546767234802, 'web', 0), ('pyston/pyston', 0.5057663321495056, 'util', 0), ('dylanhogg/awesome-python', 0.5040434002876282, 'study', 0), ('pytoolz/toolz', 0.5017217993736267, 'util', 0), ('requests/toolbelt', 0.5011575222015381, 'util', 0)]",349,4.0,,0.48,8,0,161,4,0,11,11,8.0,12.0,90.0,1.5,43
928,ml-dl,https://github.com/williamyang1991/vtoonify,[],,[],[],,,,williamyang1991/vtoonify,VToonify,3408,434,64,Jupyter Notebook,,[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer,williamyang1991,2024-01-12,2022-09-09,72,46.960629921259844,,[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer,"['face', 'siggraph-asia', 'style-transfer', 'stylegan2', 'toonify', 'video-style-transfer']","['face', 'siggraph-asia', 'style-transfer', 'stylegan2', 'toonify', 'video-style-transfer']",2023-02-24,"[('chenyangqiqi/fatezero', 0.5721680521965027, 'diffusion', 1), ('thudm/cogvideo', 0.5548344850540161, 'ml', 0), ('mchong6/jojogan', 0.515015184879303, 'data', 0)]",4,3.0,,0.04,5,1,16,11,0,0,0,5.0,4.0,90.0,0.8,43
1708,util,https://github.com/tartley/colorama,"['terminal', 'ansi']",,[],[],,,,tartley/colorama,colorama,3328,239,45,Python,,Simple cross-platform colored terminal text in Python,tartley,2024-01-12,2014-04-17,510,6.516363636363637,,Simple cross-platform colored terminal text in Python,[],"['ansi', 'terminal']",2023-12-01,"[('willmcgugan/rich', 0.7069451808929443, 'term', 1), ('jquast/blessed', 0.5634984970092773, 'term', 1), ('carpedm20/emoji', 0.5459503531455994, 'util', 0), ('urwid/urwid', 0.5115954279899597, 'term', 0)]",51,5.0,,0.06,3,2,119,1,0,2,2,3.0,7.0,90.0,2.3,43
1180,nlp,https://github.com/errbotio/errbot,[],,[],[],,,,errbotio/errbot,errbot,3012,612,74,Python,http://errbot.io,"Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.",errbotio,2024-01-12,2012-05-20,610,4.935393258426966,https://avatars.githubusercontent.com/u/15802630?v=4,"Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.","['automation', 'chat', 'chatbot', 'chatbots', 'chatops', 'devops', 'hacktoberfest2020']","['automation', 'chat', 'chatbot', 'chatbots', 'chatops', 'devops', 'hacktoberfest2020']",2024-01-01,"[('gunthercox/chatterbot', 0.5522263646125793, 'nlp', 1), ('togethercomputer/openchatkit', 0.5380634665489197, 'nlp', 1), ('deep-diver/llm-as-chatbot', 0.5012304186820984, 'llm', 1)]",214,5.0,,0.56,44,38,142,0,1,7,1,44.0,40.0,90.0,0.9,43
968,ml,https://github.com/lucidrains/musiclm-pytorch,[],,[],[],1.0,,,lucidrains/musiclm-pytorch,musiclm-pytorch,2873,240,96,Python,,"Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch",lucidrains,2024-01-13,2023-01-27,52,54.64945652173913,,"Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch","['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'music-synthesis', 'transformers']","['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'music-synthesis', 'transformers']",2023-09-06,"[('huggingface/diffusers', 0.54345703125, 'diffusion', 1)]",2,0.0,,1.37,3,1,12,4,38,39,38,3.0,1.0,90.0,0.3,43
95,term,https://github.com/urwid/urwid,[],,[],[],,,,urwid/urwid,urwid,2681,313,61,Python,urwid.org,Console user interface library for Python (official repo),urwid,2024-01-13,2010-02-25,726,3.68920778454885,https://avatars.githubusercontent.com/u/6749304?v=4,Console user interface library for Python (official repo),[],[],2024-01-12,"[('jquast/blessed', 0.7145931720733643, 'term', 0), ('pygamelib/pygamelib', 0.700258731842041, 'gamedev', 0), ('hoffstadt/dearpygui', 0.6700049042701721, 'gui', 0), ('beeware/toga', 0.6368706822395325, 'gui', 0), ('pytoolz/toolz', 0.6324057579040527, 'util', 0), ('r0x0r/pywebview', 0.6302554607391357, 'gui', 0), ('google/python-fire', 0.6262136101722717, 'term', 0), ('pypy/pypy', 0.6176603436470032, 'util', 0), ('python/cpython', 0.613335907459259, 'util', 0), ('willmcgugan/textual', 0.6095877885818481, 'term', 0), ('samuelcolvin/python-devtools', 0.5885550379753113, 'debug', 0), ('landscapeio/prospector', 0.586963951587677, 'util', 0), ('pyglet/pyglet', 0.5836073160171509, 'gamedev', 0), ('openai/openai-python', 0.5712311267852783, 'util', 0), ('tmbo/questionary', 0.5709437727928162, 'term', 0), ('pyscript/pyscript-cli', 0.5667005181312561, 'web', 0), ('rockhopper-technologies/enlighten', 0.5648614764213562, 'term', 0), ('federicoceratto/dashing', 0.5645572543144226, 'term', 0), ('kivy/kivy', 0.5640290975570679, 'util', 0), ('pdm-project/pdm', 0.5620359778404236, 'util', 0), ('dddomodossola/remi', 0.5620279312133789, 'gui', 0), ('alexmojaki/snoop', 0.5546684265136719, 'debug', 0), ('pexpect/pexpect', 0.553383469581604, 'util', 0), ('inducer/pudb', 0.5516170859336853, 'debug', 0), ('hugovk/pypistats', 0.5486549735069275, 'util', 0), ('parthjadhav/tkinter-designer', 0.5480352640151978, 'gui', 0), ('ethereum/web3.py', 0.5449756979942322, 'crypto', 0), ('googleapis/google-api-python-client', 0.54488605260849, 'util', 0), ('ipython/ipython', 0.5444232821464539, 'util', 0), ('prompt-toolkit/ptpython', 0.5443150997161865, 'util', 0), ('tiangolo/typer', 0.5432376861572266, 'term', 0), ('pypa/hatch', 0.5425727963447571, 'util', 0), ('xonsh/xonsh', 0.5422582030296326, 'util', 0), ('eleutherai/pyfra', 0.5409858822822571, 'ml', 0), ('masoniteframework/masonite', 0.5398597121238708, 'web', 0), ('jiffyclub/snakeviz', 0.5396429300308228, 'profiling', 0), ('willmcgugan/rich', 0.538774847984314, 'term', 0), ('pyston/pyston', 0.5381410121917725, 'util', 0), ('paramiko/paramiko', 0.5337045192718506, 'util', 0), ('webpy/webpy', 0.533157467842102, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5318827629089355, 'data', 0), ('pypi/warehouse', 0.5293185710906982, 'util', 0), ('pysimplegui/pysimplegui', 0.5266430974006653, 'gui', 0), ('plotly/plotly.py', 0.5260567665100098, 'viz', 0), ('pallets/click', 0.5238144993782043, 'term', 0), ('reactive-python/reactpy', 0.5233248472213745, 'web', 0), ('irmen/pyminiaudio', 0.51679927110672, 'util', 0), ('pygame/pygame', 0.5163580775260925, 'gamedev', 0), ('pythonarcade/arcade', 0.5146381258964539, 'gamedev', 0), ('tartley/colorama', 0.5115954279899597, 'util', 0), ('kellyjonbrazil/jc', 0.5103952884674072, 'util', 0), ('indygreg/pyoxidizer', 0.5094347596168518, 'util', 0), ('microsoft/playwright-python', 0.5090146660804749, 'testing', 0), ('bottlepy/bottle', 0.5075056552886963, 'web', 0), ('bokeh/bokeh', 0.5072845816612244, 'viz', 0), ('amaargiru/pyroad', 0.5068714618682861, 'study', 0), ('brandtbucher/specialist', 0.5062293410301208, 'perf', 0), ('connorferster/handcalcs', 0.5055436491966248, 'jupyter', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5037996172904968, 'template', 0), ('pypa/pipenv', 0.5031107664108276, 'util', 0), ('plotly/dash', 0.5028428435325623, 'viz', 0), ('pypa/installer', 0.5019884705543518, 'util', 0), ('agronholm/apscheduler', 0.501419186592102, 'util', 0), ('cython/cython', 0.5010424852371216, 'util', 0), ('hugapi/hug', 0.5001529455184937, 'util', 0)]",138,1.0,,3.56,117,104,169,0,7,4,7,117.0,159.0,90.0,1.4,43
198,ml,https://github.com/maif/shapash,[],,[],[],,,,maif/shapash,shapash,2555,311,39,Jupyter Notebook,https://maif.github.io/shapash/,🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models,maif,2024-01-14,2020-04-29,195,13.045222465353756,https://avatars.githubusercontent.com/u/33632930?v=4,🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models,"['ethical-artificial-intelligence', 'explainability', 'explainable-ml', 'interpretability', 'lime', 'machine-learning', 'shap', 'transparency']","['ethical-artificial-intelligence', 'explainability', 'explainable-ml', 'interpretability', 'lime', 'machine-learning', 'shap', 'transparency']",2023-12-08,"[('seldonio/alibi', 0.6979689002037048, 'ml-interpretability', 2), ('slundberg/shap', 0.67304927110672, 'ml-interpretability', 4), ('csinva/imodels', 0.6528401374816895, 'ml', 3), ('interpretml/interpret', 0.6510236859321594, 'ml-interpretability', 5), ('marcotcr/lime', 0.6499969363212585, 'ml-interpretability', 0), ('linkedin/fasttreeshap', 0.6388174295425415, 'ml', 3), ('pair-code/lit', 0.62184739112854, 'ml-interpretability', 1), ('oegedijk/explainerdashboard', 0.5845269560813904, 'ml-interpretability', 1), ('tensorflow/lucid', 0.5549944639205933, 'ml-interpretability', 2), ('xplainable/xplainable', 0.551527738571167, 'ml-interpretability', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5307745337486267, 'study', 1), ('huggingface/evaluate', 0.5302979946136475, 'ml', 1), ('selfexplainml/piml-toolbox', 0.527449905872345, 'ml-interpretability', 0), ('carla-recourse/carla', 0.5270743370056152, 'ml', 3), ('teamhg-memex/eli5', 0.5209477543830872, 'ml', 1), ('tensorflow/data-validation', 0.5037372708320618, 'ml-ops', 0), ('eleutherai/pythia', 0.5036888122558594, 'ml-interpretability', 1), ('microsoft/robustlearn', 0.5023934841156006, 'time-series', 0)]",35,1.0,,2.33,29,17,45,1,10,9,10,29.0,6.0,90.0,0.2,43
792,web,https://github.com/pallets/quart,[],,[],[],,,,pallets/quart,quart,2431,137,30,Python,https://quart.palletsprojects.com,An async Python micro framework for building web applications. ,pallets,2024-01-12,2017-11-10,324,7.48987676056338,https://avatars.githubusercontent.com/u/16748505?v=4,An async Python micro framework for building web applications. ,"['asgi', 'asyncio', 'http-server', 'quart']","['asgi', 'asyncio', 'http-server', 'quart']",2024-01-03,"[('neoteroi/blacksheep', 0.8334370851516724, 'web', 3), ('encode/uvicorn', 0.8250173926353455, 'web', 3), ('aio-libs/aiohttp', 0.7800691723823547, 'web', 2), ('alirn76/panther', 0.7484593391418457, 'web', 0), ('encode/httpx', 0.748035192489624, 'web', 1), ('pallets/flask', 0.7176867127418518, 'web', 0), ('klen/muffin', 0.7141019701957703, 'web', 2), ('encode/starlette', 0.7032433152198792, 'web', 0), ('huge-success/sanic', 0.7020831108093262, 'web', 2), ('falconry/falcon', 0.6842796206474304, 'web', 1), ('timofurrer/awesome-asyncio', 0.6550799608230591, 'study', 1), ('python-trio/trio', 0.6473005414009094, 'perf', 0), ('starlite-api/starlite', 0.6257473230361938, 'web', 2), ('jordaneremieff/mangum', 0.6127312183380127, 'web', 3), ('masoniteframework/masonite', 0.607140302658081, 'web', 0), ('agronholm/anyio', 0.6049391627311707, 'perf', 1), ('bottlepy/bottle', 0.5997056365013123, 'web', 0), ('magicstack/uvloop', 0.5947157740592957, 'util', 1), ('pylons/pyramid', 0.5922417640686035, 'web', 0), ('sumerc/yappi', 0.5867199897766113, 'profiling', 2), ('samuelcolvin/aioaws', 0.5853649377822876, 'data', 1), ('aws/chalice', 0.5842033624649048, 'web', 0), ('cherrypy/cherrypy', 0.5793529748916626, 'web', 1), ('pylons/waitress', 0.5782569050788879, 'web', 1), ('geeogi/async-python-lambda-template', 0.5774820446968079, 'template', 0), ('tiangolo/asyncer', 0.5767269134521484, 'perf', 1), ('emmett-framework/emmett', 0.5764337778091431, 'web', 2), ('tornadoweb/tornado', 0.5757850408554077, 'web', 0), ('samuelcolvin/arq', 0.5751336216926575, 'data', 1), ('miguelgrinberg/python-socketio', 0.5742831826210022, 'util', 1), ('nficano/python-lambda', 0.5740982890129089, 'util', 0), ('reflex-dev/reflex', 0.5711527466773987, 'web', 0), ('flet-dev/flet', 0.5679628252983093, 'web', 0), ('airtai/faststream', 0.5664022564888, 'perf', 1), ('micropython/micropython', 0.5557764768600464, 'util', 0), ('webpy/webpy', 0.5523453950881958, 'web', 0), ('willmcgugan/textual', 0.5513238906860352, 'term', 0), ('pallets/werkzeug', 0.5403130054473877, 'web', 0), ('python-restx/flask-restx', 0.5398460626602173, 'web', 0), ('pyinfra-dev/pyinfra', 0.5390889048576355, 'util', 0), ('psf/requests', 0.5377407670021057, 'web', 0), ('aio-libs/aiobotocore', 0.5356045961380005, 'util', 1), ('hugapi/hug', 0.533208429813385, 'util', 1), ('ets-labs/python-dependency-injector', 0.5255832672119141, 'util', 1), ('backtick-se/cowait', 0.5232675671577454, 'util', 0), ('ajndkr/lanarky', 0.5203720331192017, 'llm', 0), ('fastai/fastcore', 0.5118736624717712, 'util', 0), ('locustio/locust', 0.5093166828155518, 'testing', 0), ('r0x0r/pywebview', 0.5082329511642456, 'gui', 0), ('requests/toolbelt', 0.5077387094497681, 'util', 0), ('gbeced/basana', 0.5044360160827637, 'finance', 1), ('eleutherai/pyfra', 0.5026024580001831, 'ml', 0)]",99,2.0,,1.56,35,19,75,1,0,10,10,35.0,21.0,90.0,0.6,43
1535,util,https://github.com/chaostoolkit/chaostoolkit,['devops'],,[],[],,,,chaostoolkit/chaostoolkit,chaostoolkit,1805,184,42,Python,https://chaostoolkit.org,Chaos Engineering Toolkit & Orchestration for Developers,chaostoolkit,2024-01-13,2017-09-24,331,5.448469167744718,https://avatars.githubusercontent.com/u/32068152?v=4,Chaos Engineering Toolkit & Orchestration for Developers,"['automation', 'chaos-engineering', 'chaostoolkit', 'devops-tools', 'reliability', 'reliability-engineering', 'resiliency', 'sre']","['automation', 'chaos-engineering', 'chaostoolkit', 'devops', 'devops-tools', 'reliability', 'reliability-engineering', 'resiliency', 'sre']",2024-01-11,"[('flyteorg/flyte', 0.5509274005889893, 'ml-ops', 0), ('dagster-io/dagster', 0.5302633047103882, 'ml-ops', 0), ('pydoit/doit', 0.5246021747589111, 'util', 0), ('zenml-io/zenml', 0.5238291621208191, 'ml-ops', 1), ('tiiuae/sbomnix', 0.5200450420379639, 'util', 0), ('avaiga/taipy', 0.5186023712158203, 'data', 1), ('polyaxon/polyaxon', 0.5090064406394958, 'ml-ops', 0), ('aquasecurity/trivy', 0.5057849287986755, 'security', 0), ('pytest-dev/pytest-testinfra', 0.5015678405761719, 'testing', 2)]",20,4.0,,0.87,4,2,77,0,7,11,7,4.0,11.0,90.0,2.8,43
1850,util,https://github.com/python-rope/rope,[],,[],[],,,,python-rope/rope,rope,1782,210,28,Python,,a python refactoring library,python-rope,2024-01-12,2013-11-30,530,3.3595475356854294,https://avatars.githubusercontent.com/u/6073454?v=4,a python refactoring library,"['ast', 'refactoring', 'refactoring-tools']","['ast', 'refactoring', 'refactoring-tools']",2024-01-11,"[('facebookincubator/bowler', 0.7546546459197998, 'util', 1), ('pytoolz/toolz', 0.649603009223938, 'util', 0), ('asottile/reorder-python-imports', 0.5853264331817627, 'util', 1), ('instagram/libcst', 0.5846632719039917, 'util', 1), ('instagram/fixit', 0.5731984972953796, 'util', 0), ('dosisod/refurb', 0.5674479007720947, 'util', 0), ('landscapeio/prospector', 0.5660980939865112, 'util', 0), ('grahamdumpleton/wrapt', 0.5636062026023865, 'util', 0), ('google/pyglove', 0.5538163781166077, 'util', 0), ('eugeneyan/python-collab-template', 0.5535590648651123, 'template', 0), ('pypy/pypy', 0.5428261160850525, 'util', 0), ('eleutherai/pyfra', 0.5410192608833313, 'ml', 0), ('pyston/pyston', 0.5357602834701538, 'util', 0), ('amaargiru/pyroad', 0.528800904750824, 'study', 0), ('google/latexify_py', 0.5269980430603027, 'util', 0), ('python/cpython', 0.5254427194595337, 'util', 0), ('hhatto/autopep8', 0.5218623876571655, 'util', 0), ('reloadware/reloadium', 0.5215712785720825, 'profiling', 0), ('xrudelis/pytrait', 0.5170307159423828, 'util', 0), ('facebook/pyre-check', 0.5157984495162964, 'typing', 0), ('fastai/fastcore', 0.5134739875793457, 'util', 0), ('google/pytype', 0.5130378603935242, 'typing', 0), ('pandas-dev/pandas', 0.5107076168060303, 'pandas', 0), ('pympler/pympler', 0.5101566910743713, 'perf', 0), ('beeware/toga', 0.5071592330932617, 'gui', 0), ('mkdocstrings/griffe', 0.5066107511520386, 'util', 0), ('psf/black', 0.504595935344696, 'util', 0), ('pdm-project/pdm', 0.5023258328437805, 'util', 0), ('mementum/backtrader', 0.50078284740448, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5005401968955994, 'study', 0)]",81,3.0,,2.19,35,23,123,0,0,5,5,35.0,77.0,90.0,2.2,43
395,web,https://github.com/neoteroi/blacksheep,[],,[],[],,,,neoteroi/blacksheep,BlackSheep,1584,68,28,Python,https://www.neoteroi.dev/blacksheep/,Fast ASGI web framework for Python,neoteroi,2024-01-13,2018-11-22,270,5.851187335092348,https://avatars.githubusercontent.com/u/72765587?v=4,Fast ASGI web framework for Python,"['asgi', 'asyncio', 'blacksheep', 'framework', 'http', 'http-server', 'server', 'web']","['asgi', 'asyncio', 'blacksheep', 'framework', 'http', 'http-server', 'server', 'web']",2024-01-12,"[('encode/uvicorn', 0.8586666584014893, 'web', 4), ('pallets/quart', 0.8334370851516724, 'web', 3), ('klen/muffin', 0.7668511271476746, 'web', 2), ('huge-success/sanic', 0.7380421161651611, 'web', 4), ('aio-libs/aiohttp', 0.7317296862602234, 'web', 3), ('encode/httpx', 0.7281930446624756, 'web', 2), ('alirn76/panther', 0.7121951580047607, 'web', 1), ('falconry/falcon', 0.6875892877578735, 'web', 4), ('encode/starlette', 0.6760811805725098, 'web', 1), ('starlite-api/starlite', 0.6740537881851196, 'web', 2), ('pallets/flask', 0.6553666591644287, 'web', 0), ('jordaneremieff/mangum', 0.6414300203323364, 'web', 2), ('cherrypy/cherrypy', 0.6155569553375244, 'web', 2), ('timofurrer/awesome-asyncio', 0.6109622716903687, 'study', 1), ('pallets/werkzeug', 0.5996673703193665, 'web', 1), ('masoniteframework/masonite', 0.5978954434394836, 'web', 2), ('klen/py-frameworks-bench', 0.5975432395935059, 'perf', 0), ('pylons/pyramid', 0.5955870151519775, 'web', 0), ('bottlepy/bottle', 0.5855295062065125, 'web', 0), ('magicstack/uvloop', 0.5796182155609131, 'util', 1), ('sumerc/yappi', 0.577142596244812, 'profiling', 2), ('geeogi/async-python-lambda-template', 0.5758123397827148, 'template', 0), ('locustio/locust', 0.5740907192230225, 'testing', 1), ('pylons/waitress', 0.5685455799102783, 'web', 1), ('emmett-framework/emmett', 0.5676200985908508, 'web', 2), ('webpy/webpy', 0.5648735761642456, 'web', 0), ('fastai/fastcore', 0.5601794719696045, 'util', 0), ('psf/requests', 0.551236629486084, 'web', 1), ('miguelgrinberg/python-socketio', 0.5502622127532959, 'util', 1), ('python-trio/trio', 0.5497913956642151, 'perf', 0), ('benoitc/gunicorn', 0.542283296585083, 'web', 2), ('reflex-dev/reflex', 0.5400927066802979, 'web', 1), ('samuelcolvin/arq', 0.5355557799339294, 'data', 1), ('ets-labs/python-dependency-injector', 0.5310226678848267, 'util', 1), ('scrapy/scrapy', 0.53035569190979, 'data', 1), ('requests/toolbelt', 0.5285502672195435, 'util', 1), ('agronholm/anyio', 0.5259332656860352, 'perf', 1), ('airtai/faststream', 0.5211659073829651, 'perf', 1), ('tornadoweb/tornado', 0.5203468203544617, 'web', 0), ('python-restx/flask-restx', 0.519770085811615, 'web', 0), ('tiangolo/fastapi', 0.5194063782691956, 'web', 3), ('pypy/pypy', 0.5189310908317566, 'util', 0), ('aminalaee/sqladmin', 0.5168036818504333, 'data', 3), ('aws/chalice', 0.5133174657821655, 'web', 0), ('flet-dev/flet', 0.5072730183601379, 'web', 1), ('pyinfra-dev/pyinfra', 0.5058210492134094, 'util', 0), ('grantjenks/python-diskcache', 0.5029258728027344, 'util', 0)]",16,2.0,,0.96,49,46,63,0,29,12,29,49.0,80.0,90.0,1.6,43
1366,ml,https://github.com/microsoft/i-code,[],"The ambition of the i-Code project is to build integrative and composable multimodal AI. The ""i"" stands for integrative multimodal learning.",[],[],,,,microsoft/i-code,i-Code,1551,159,39,Jupyter Notebook,,,microsoft,2024-01-12,2022-12-07,59,25.911694510739856,https://avatars.githubusercontent.com/u/6154722?v=4,"The ambition of the i-Code project is to build integrative and composable multimodal AI. The ""i"" stands for integrative multimodal learning.",[],[],2023-11-30,"[('facebookresearch/mmf', 0.5485939383506775, 'ml-dl', 0), ('invoke-ai/invokeai', 0.5126618146896362, 'diffusion', 0)]",13,2.0,,3.71,11,4,13,1,0,0,0,11.0,6.0,90.0,0.5,43
1593,data,https://github.com/milvus-io/bootcamp,"['vector-database', 'question-answering', 'embeddings']",,[],[],1.0,,,milvus-io/bootcamp,bootcamp,1510,519,30,HTML,https://milvus.io,"Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.",milvus-io,2024-01-13,2019-08-09,233,6.464831804281346,https://avatars.githubusercontent.com/u/51735404?v=4,"Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.","['audio-search', 'benchmark-testing', 'deep-learning', 'image-classification', 'image-recognition', 'image-search', 'milvus', 'nlp', 'question-answering', 'unstructured-data']","['audio-search', 'benchmark-testing', 'deep-learning', 'embeddings', 'image-classification', 'image-recognition', 'image-search', 'milvus', 'nlp', 'question-answering', 'unstructured-data', 'vector-database']",2024-01-09,"[('docarray/docarray', 0.677416205406189, 'data', 1), ('jina-ai/vectordb', 0.6278480887413025, 'data', 1), ('activeloopai/deeplake', 0.6203997731208801, 'ml-ops', 2), ('neuml/txtai', 0.6126426458358765, 'nlp', 3), ('nomic-ai/nomic', 0.6042595505714417, 'nlp', 0), ('koaning/embetter', 0.6010968089103699, 'data', 0), ('qdrant/qdrant', 0.5713267922401428, 'data', 2), ('marqo-ai/marqo', 0.5712332725524902, 'ml', 1), ('lancedb/lancedb', 0.566419780254364, 'data', 2), ('chroma-core/chroma', 0.5605462789535522, 'data', 1), ('awslabs/autogluon', 0.5507424473762512, 'ml', 2), ('huggingface/datasets', 0.545052170753479, 'nlp', 2), ('sloria/textblob', 0.5331091284751892, 'nlp', 1), ('microsoft/semi-supervised-learning', 0.5314620733261108, 'ml', 1), ('ddbourgin/numpy-ml', 0.5297070741653442, 'ml', 0), ('paddlepaddle/paddlenlp', 0.528664767742157, 'llm', 2), ('featureform/embeddinghub', 0.5258285403251648, 'nlp', 2), ('explosion/thinc', 0.5245603919029236, 'ml-dl', 2), ('feast-dev/feast', 0.5231591463088989, 'ml-ops', 0), ('thilinarajapakse/simpletransformers', 0.5199248790740967, 'nlp', 1), ('qdrant/fastembed', 0.5193023681640625, 'ml', 1), ('llmware-ai/llmware', 0.518172025680542, 'llm', 4), ('koaning/human-learn', 0.5165793895721436, 'data', 0), ('fatiando/verde', 0.5155651569366455, 'gis', 0), ('koaning/whatlies', 0.5146546363830566, 'nlp', 2), ('amanchadha/coursera-deep-learning-specialization', 0.5138573050498962, 'study', 1), ('jina-ai/clip-as-service', 0.5121859312057495, 'nlp', 1), ('rare-technologies/gensim', 0.5120472311973572, 'nlp', 1), ('onnx/onnx', 0.5098041892051697, 'ml', 1), ('qdrant/vector-db-benchmark', 0.5074758529663086, 'perf', 1), ('alibaba/easynlp', 0.5068036317825317, 'nlp', 2), ('superduperdb/superduperdb', 0.5059596300125122, 'data', 0), ('firmai/industry-machine-learning', 0.5041669011116028, 'study', 0), ('mosaicml/composer', 0.5024623870849609, 'ml-dl', 1), ('tensorflow/tensorflow', 0.5004434585571289, 'ml-dl', 1)]",80,3.0,,2.42,79,73,54,0,1,3,1,79.0,85.0,90.0,1.1,43
653,ml-dl,https://github.com/tensorly/tensorly,[],,[],[],,,,tensorly/tensorly,tensorly,1466,324,45,Python,http://tensorly.org,TensorLy: Tensor Learning in Python.,tensorly,2024-01-12,2016-10-21,379,3.8622506586375613,https://avatars.githubusercontent.com/u/22989719?v=4,TensorLy: Tensor Learning in Python.,"['cupy', 'decomposition', 'jax', 'machine-learning', 'mxnet', 'numpy', 'pytorch', 'regression', 'tensor', 'tensor-algebra', 'tensor-decomposition', 'tensor-factorization', 'tensor-learning', 'tensor-methods', 'tensor-regression', 'tensorflow', 'tensorly']","['cupy', 'decomposition', 'jax', 'machine-learning', 'mxnet', 'numpy', 'pytorch', 'regression', 'tensor', 'tensor-algebra', 'tensor-decomposition', 'tensor-factorization', 'tensor-learning', 'tensor-methods', 'tensor-regression', 'tensorflow', 'tensorly']",2024-01-08,"[('arogozhnikov/einops', 0.7486700415611267, 'ml-dl', 6), ('ggerganov/ggml', 0.7053402066230774, 'ml', 2), ('google/tf-quant-finance', 0.636419951915741, 'finance', 1), ('pytorch/pytorch', 0.6296373605728149, 'ml-dl', 3), ('huggingface/transformers', 0.6177733540534973, 'nlp', 4), ('xl0/lovely-tensors', 0.61644446849823, 'ml-dl', 1), ('rafiqhasan/auto-tensorflow', 0.6096346974372864, 'ml-dl', 2), ('tensorflow/similarity', 0.6049955487251282, 'ml-dl', 2), ('ddbourgin/numpy-ml', 0.6033033728599548, 'ml', 1), ('horovod/horovod', 0.5948300361633301, 'ml-ops', 4), ('patrick-kidger/torchtyping', 0.5934515595436096, 'typing', 1), ('explosion/thinc', 0.5860464572906494, 'ml-dl', 5), ('keras-team/keras', 0.5832876563072205, 'ml-dl', 4), ('huggingface/exporters', 0.5597980618476868, 'ml', 3), ('tensorflow/addons', 0.5590072274208069, 'ml', 2), ('nvidia/tensorrt-llm', 0.5586928129196167, 'viz', 0), ('ageron/handson-ml2', 0.5573654770851135, 'ml', 0), ('dylanhogg/awesome-python', 0.5549004673957825, 'study', 1), ('online-ml/river', 0.5535548329353333, 'ml', 1), ('intel/intel-extension-for-pytorch', 0.5494438409805298, 'perf', 2), ('tensorflow/tensorflow', 0.5459080934524536, 'ml-dl', 2), ('huggingface/huggingface_hub', 0.5400019884109497, 'ml', 2), ('pytorch/ignite', 0.5390286445617676, 'ml-dl', 2), ('gradio-app/gradio', 0.5336702466011047, 'viz', 1), ('mrdbourke/tensorflow-deep-learning', 0.5285704731941223, 'study', 1), ('onnx/onnx', 0.5244054198265076, 'ml', 4), ('skorch-dev/skorch', 0.5220605134963989, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5216432213783264, 'study', 5), ('google/gin-config', 0.5206177830696106, 'util', 1), ('probml/pyprobml', 0.5191105604171753, 'ml', 4), ('explosion/spacy', 0.5184281468391418, 'nlp', 1), ('nyandwi/modernconvnets', 0.5165544748306274, 'ml-dl', 1), ('mosaicml/composer', 0.5158124566078186, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5156332850456238, 'ml-dl', 2), ('adap/flower', 0.5153002142906189, 'ml-ops', 3), ('ta-lib/ta-lib-python', 0.5138649344444275, 'finance', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5126520395278931, 'study', 0), ('tensorflow/lucid', 0.5121048092842102, 'ml-interpretability', 2), ('nvidia/deeplearningexamples', 0.5106650590896606, 'ml-dl', 3), ('cupy/cupy', 0.5093604922294617, 'math', 3), ('rasbt/mlxtend', 0.5090686082839966, 'ml', 1), ('tensorlayer/tensorlayer', 0.508705198764801, 'ml-rl', 1), ('huggingface/datasets', 0.50432950258255, 'nlp', 4), ('danielegrattarola/spektral', 0.5032951831817627, 'ml-dl', 1), ('merantix-momentum/squirrel-core', 0.5030725002288818, 'ml', 4), ('lutzroeder/netron', 0.5027545094490051, 'ml', 4), ('activeloopai/deeplake', 0.5025835037231445, 'ml-ops', 3), ('keras-team/keras-nlp', 0.5025812387466431, 'nlp', 2), ('nccr-itmo/fedot', 0.5016763806343079, 'ml-ops', 1), ('ai4finance-foundation/finrl', 0.5015732645988464, 'finance', 0), ('goldmansachs/gs-quant', 0.5013471841812134, 'finance', 0), ('graykode/nlp-tutorial', 0.5011732578277588, 'study', 2)]",66,6.0,,1.58,18,11,88,0,1,3,1,18.0,48.0,90.0,2.7,43
920,util,https://github.com/p0dalirius/coercer,[],,[],[],,,,p0dalirius/coercer,Coercer,1465,169,21,Python,https://podalirius.net/,A python script to automatically coerce a Windows server to authenticate on an arbitrary machine through 12 methods.,p0dalirius,2024-01-12,2022-06-30,82,17.711571675302245,,A python script to automatically coerce a Windows server to authenticate on an arbitrary machine through 12 methods.,"['authentication', 'automatic', 'call', 'coerce', 'fuzzing', 'ntlm', 'privilege-escalation', 'rpc']","['authentication', 'automatic', 'call', 'coerce', 'fuzzing', 'ntlm', 'privilege-escalation', 'rpc']",2023-12-24,[],7,3.0,,0.52,9,6,19,1,2,7,2,9.0,9.0,90.0,1.0,43
1904,math,https://github.com/google-deepmind/alphageometry,"['geometry', 'theorem-prover']",Solving Olympiad Geometry without Human Demonstrations,[],[],,,,google-deepmind/alphageometry,alphageometry,1134,96,16,Python,,,google-deepmind,2024-01-18,2023-10-09,16,70.24778761061947,https://avatars.githubusercontent.com/u/8596759?v=4,Solving Olympiad Geometry without Human Demonstrations,[],"['geometry', 'theorem-prover']",2024-01-12,"[('shapely/shapely', 0.5421801805496216, 'gis', 1)]",2,0.0,,0.08,11,3,3,0,0,0,0,11.0,5.0,90.0,0.5,43
1394,math,https://github.com/geomstats/geomstats,[],,[],[],,,,geomstats/geomstats,geomstats,1108,231,36,Jupyter Notebook,http://geomstats.ai,Computations and statistics on manifolds with geometric structures.,geomstats,2024-01-13,2017-10-25,326,3.38986013986014,https://avatars.githubusercontent.com/u/39272386?v=4,Computations and statistics on manifolds with geometric structures.,"['deep-learning', 'geodesic', 'geometry', 'gpu-programming', 'lie-groups', 'machine-learning', 'manifold', 'neural-networks', 'riemannian-geometry', 'statistics']","['deep-learning', 'geodesic', 'geometry', 'gpu-programming', 'lie-groups', 'machine-learning', 'manifold', 'neural-networks', 'riemannian-geometry', 'statistics']",2024-01-12,"[('lmcinnes/umap', 0.5977250933647156, 'ml', 1), ('xl0/lovely-tensors', 0.5296072959899902, 'ml-dl', 2), ('kornia/kornia', 0.5275140404701233, 'ml-dl', 2), ('isl-org/open3d', 0.5070082545280457, 'sim', 1)]",87,4.0,,25.23,83,39,76,0,2,5,2,83.0,76.0,90.0,0.9,43
1531,util,https://github.com/oracle/graalpython,"['java', 'jvm']",,[],[],,,,oracle/graalpython,graalpython,1059,98,57,Python,,A Python 3 implementation built on GraalVM,oracle,2024-01-13,2018-04-17,302,3.506622516556291,https://avatars.githubusercontent.com/u/4430336?v=4,A Python 3 implementation built on GraalVM,[],"['java', 'jvm']",2024-01-12,"[('pyston/pyston', 0.591853141784668, 'util', 0), ('exaloop/codon', 0.5912636518478394, 'perf', 0), ('numba/llvmlite', 0.586859405040741, 'util', 0), ('py4j/py4j', 0.5628305077552795, 'util', 1), ('ethereum/py-evm', 0.54329913854599, 'crypto', 0), ('pypy/pypy', 0.5413466095924377, 'util', 0), ('paramiko/paramiko', 0.5263757109642029, 'util', 0), ('pytoolz/toolz', 0.5168983936309814, 'util', 0), ('amzn/ion-python', 0.5121100544929504, 'data', 0)]",64,2.0,,57.15,19,13,70,0,4,18,4,19.0,27.0,90.0,1.4,43
879,sim,https://github.com/pyscf/pyscf,[],,[],[],,,,pyscf/pyscf,pyscf,1051,569,78,Python,,Python module for quantum chemistry,pyscf,2024-01-13,2014-05-02,508,2.0665730337078654,https://avatars.githubusercontent.com/u/38367334?v=4,Python module for quantum chemistry,[],[],2024-01-11,"[('cqcl/tket', 0.677206814289093, 'util', 0), ('cqcl/lambeq', 0.6749314069747925, 'nlp', 0), ('quantumlib/cirq', 0.6541039347648621, 'sim', 0), ('mnooner256/pyqrcode', 0.5908879637718201, 'util', 0), ('pytoolz/toolz', 0.5605663657188416, 'util', 0), ('connorferster/handcalcs', 0.5601664185523987, 'jupyter', 0), ('pypy/pypy', 0.5428182482719421, 'util', 0), ('numpy/numpy', 0.5385183095932007, 'math', 0), ('primal100/pybitcointools', 0.5276904106140137, 'crypto', 0), ('google/latexify_py', 0.5187242031097412, 'util', 0), ('goldmansachs/gs-quant', 0.5135458707809448, 'finance', 0), ('ta-lib/ta-lib-python', 0.5085233449935913, 'finance', 0), ('zeromq/pyzmq', 0.5071917772293091, 'util', 0)]",152,4.0,,3.52,158,96,118,0,4,7,4,157.0,347.0,90.0,2.2,43
1139,nlp,https://github.com/nomic-ai/nomic,[],,[],[],,,,nomic-ai/nomic,nomic,833,121,24,Python,https://atlas.nomic.ai,"Interact, analyze and structure massive text, image, embedding, audio and video datasets",nomic-ai,2024-01-13,2022-07-21,79,10.449820788530467,https://avatars.githubusercontent.com/u/102670180?v=4,"Interact, analyze and structure massive text, image, embedding, audio and video datasets","['database', 'neural']","['database', 'neural']",2024-01-11,"[('milvus-io/bootcamp', 0.6042595505714417, 'data', 0), ('chroma-core/chroma', 0.5949238538742065, 'data', 0), ('koaning/embetter', 0.5922350287437439, 'data', 0), ('docarray/docarray', 0.5800225734710693, 'data', 0), ('jina-ai/vectordb', 0.5595861077308655, 'data', 0), ('jina-ai/clip-as-service', 0.5556612610816956, 'nlp', 0), ('activeloopai/deeplake', 0.5401791930198669, 'ml-ops', 0), ('facebookresearch/augly', 0.528243899345398, 'data', 0), ('lucidrains/imagen-pytorch', 0.5279374718666077, 'ml-dl', 0), ('rom1504/clip-retrieval', 0.5256919264793396, 'ml', 0), ('openai/clip', 0.5130395889282227, 'ml-dl', 0)]",13,3.0,,7.21,34,22,18,0,3,2,3,34.0,16.0,90.0,0.5,43
1346,util,https://github.com/milvus-io/pymilvus,[],,[],[],,,,milvus-io/pymilvus,pymilvus,785,329,16,Python,,Python SDK for Milvus.,milvus-io,2024-01-11,2019-06-13,241,3.2476359338061465,https://avatars.githubusercontent.com/u/51735404?v=4,Python SDK for Milvus.,"['anns', 'database', 'milvus', 'sdk', 'vector']","['anns', 'database', 'milvus', 'sdk', 'vector']",2024-01-11,"[('kubeflow/fairing', 0.5737506151199341, 'ml-ops', 0)]",95,2.0,,4.33,149,110,56,0,22,12,22,149.0,245.0,90.0,1.6,43
1853,sim,https://github.com/google-deepmind/materials_discovery,['materials-science'],Graph Networks for Materials Science (GNoME) is a project centered around scaling machine learning methods to tackle materials science.,[],[],,,,google-deepmind/materials_discovery,materials_discovery,689,148,35,Python,,,google-deepmind,2024-01-12,2023-11-28,9,76.55555555555556,https://avatars.githubusercontent.com/u/8596759?v=4,Graph Networks for Materials Science (GNoME) is a project centered around scaling machine learning methods to tackle materials science.,[],['materials-science'],2023-12-02,"[('stellargraph/stellargraph', 0.6555060148239136, 'graph', 0), ('benedekrozemberczki/tigerlily', 0.5921242237091064, 'ml-dl', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5740145444869995, 'study', 0), ('pyg-team/pytorch_geometric', 0.5600719451904297, 'ml-dl', 0), ('whitead/dmol-book', 0.5389830470085144, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5381258130073547, 'ml-dl', 0), ('dmlc/dgl', 0.5183952450752258, 'ml-dl', 0), ('graphistry/pygraphistry', 0.5010610818862915, 'data', 0)]",2,0.0,,0.15,14,4,2,1,0,0,0,14.0,15.0,90.0,1.1,43
1322,nlp,https://github.com/keras-team/keras-nlp,"['keras', 'natural-language-processing']",,[],[],,,,keras-team/keras-nlp,keras-nlp,622,180,28,Python,,Modular Natural Language Processing workflows with Keras,keras-team,2024-01-13,2020-05-28,191,3.2444113263785397,https://avatars.githubusercontent.com/u/34455048?v=4,Modular Natural Language Processing workflows with Keras,"['deep-learning', 'keras', 'machine-learning', 'nlp', 'tensorflow']","['deep-learning', 'keras', 'machine-learning', 'natural-language-processing', 'nlp', 'tensorflow']",2024-01-14,"[('graykode/nlp-tutorial', 0.6779394149780273, 'study', 3), ('explosion/spacy', 0.6470063924789429, 'nlp', 4), ('huggingface/transformers', 0.6460193395614624, 'nlp', 5), ('nltk/nltk', 0.6287647485733032, 'nlp', 3), ('nvidia/deeplearningexamples', 0.6211730241775513, 'ml-dl', 3), ('flairnlp/flair', 0.6184731125831604, 'nlp', 3), ('thilinarajapakse/simpletransformers', 0.6119193434715271, 'nlp', 0), ('rasahq/rasa', 0.6086421012878418, 'llm', 3), ('allenai/allennlp', 0.605678379535675, 'nlp', 3), ('explosion/thinc', 0.5930647253990173, 'ml-dl', 5), ('sloria/textblob', 0.5880878567695618, 'nlp', 2), ('explosion/spacy-llm', 0.5786953568458557, 'llm', 3), ('huggingface/datasets', 0.5684626698493958, 'nlp', 5), ('arogozhnikov/einops', 0.5635877251625061, 'ml-dl', 3), ('keras-rl/keras-rl', 0.5594122409820557, 'ml-rl', 3), ('horovod/horovod', 0.5583081245422363, 'ml-ops', 4), ('ddbourgin/numpy-ml', 0.5563119053840637, 'ml', 1), ('franck-dernoncourt/neuroner', 0.5547518134117126, 'nlp', 4), ('nvidia/nemo', 0.5545443892478943, 'nlp', 2), ('lucidrains/toolformer-pytorch', 0.5536922216415405, 'llm', 1), ('rafiqhasan/auto-tensorflow', 0.5517141819000244, 'ml-dl', 2), ('keras-team/keras-cv', 0.5433396697044373, 'ml-dl', 1), ('nyandwi/modernconvnets', 0.5431491136550903, 'ml-dl', 2), ('deeppavlov/deeppavlov', 0.5425511002540588, 'nlp', 4), ('alibaba/easynlp', 0.539397120475769, 'nlp', 3), ('tensorflow/tensorflow', 0.5365269780158997, 'ml-dl', 3), ('databrickslabs/dolly', 0.5354406237602234, 'llm', 0), ('keras-team/autokeras', 0.5352697372436523, 'ml-dl', 4), ('explosion/spacy-models', 0.5345762372016907, 'nlp', 3), ('llmware-ai/llmware', 0.5331376194953918, 'llm', 2), ('onnx/onnx', 0.531283974647522, 'ml', 4), ('deepset-ai/farm', 0.5295949578285217, 'nlp', 2), ('ageron/handson-ml2', 0.528778076171875, 'ml', 0), ('tatsu-lab/stanford_alpaca', 0.5273944735527039, 'llm', 1), ('norskregnesentral/skweak', 0.5260323882102966, 'nlp', 1), ('google-research/electra', 0.5258187651634216, 'ml-dl', 3), ('paddlepaddle/paddlenlp', 0.5255599021911621, 'llm', 1), ('lm-sys/fastchat', 0.5216904878616333, 'llm', 0), ('kubeflow/pipelines', 0.5210731625556946, 'ml-ops', 1), ('makcedward/nlpaug', 0.5205011367797852, 'nlp', 3), ('polyaxon/polyaxon', 0.5162709355354309, 'ml-ops', 4), ('ggerganov/ggml', 0.5159798860549927, 'ml', 1), ('gradio-app/gradio', 0.5152199864387512, 'viz', 2), ('optimalscale/lmflow', 0.5128912329673767, 'llm', 1), ('danielegrattarola/spektral', 0.5112001299858093, 'ml-dl', 3), ('tensorflow/addons', 0.5104029178619385, 'ml', 3), ('keras-team/keras', 0.5086270570755005, 'ml-dl', 3), ('merantix-momentum/squirrel-core', 0.5084515810012817, 'ml', 5), ('bentoml/bentoml', 0.5048489570617676, 'ml-ops', 2), ('huggingface/text-generation-inference', 0.5031775236129761, 'llm', 2), ('tensorly/tensorly', 0.5025812387466431, 'ml-dl', 2), ('microsoft/semi-supervised-learning', 0.5014702677726746, 'ml', 3)]",61,3.0,,6.85,221,177,44,0,15,11,15,221.0,257.0,90.0,1.2,43
110,data,https://github.com/binux/pyspider,[],,[],[],,,,binux/pyspider,pyspider,16155,3730,903,Python,http://docs.pyspider.org/,A Powerful Spider(Web Crawler) System in Python.,binux,2024-01-13,2014-02-21,518,31.15289256198347,,A Powerful Spider(Web Crawler) System in Python.,['crawler'],['crawler'],2020-08-02,"[('s0md3v/photon', 0.7611035108566284, 'data', 1), ('scrapy/scrapy', 0.7435339093208313, 'data', 1), ('alirezamika/autoscraper', 0.6465148329734802, 'data', 1), ('nv7-github/googlesearch', 0.6098272204399109, 'util', 0), ('roniemartinez/dude', 0.5958371758460999, 'util', 1), ('clips/pattern', 0.5396731495857239, 'nlp', 0), ('psf/requests', 0.5332942605018616, 'web', 0), ('webpy/webpy', 0.5072404742240906, 'web', 0)]",62,1.0,,0.0,2,0,120,42,0,1,1,2.0,2.0,90.0,1.0,42
68,web,https://github.com/pyeve/eve,[],,[],[],,,,pyeve/eve,eve,6650,754,226,Python,https://python-eve.org,REST API framework designed for human beings,pyeve,2024-01-13,2012-10-22,588,11.306776779208162,https://avatars.githubusercontent.com/u/26229868?v=4,REST API framework designed for human beings,"['flask', 'mongodb', 'rest']","['flask', 'mongodb', 'rest']",2023-07-10,"[('python-restx/flask-restx', 0.7703225612640381, 'web', 2), ('bottlepy/bottle', 0.594473659992218, 'web', 1), ('simple-salesforce/simple-salesforce', 0.5759877562522888, 'data', 0), ('falconry/falcon', 0.5698334574699402, 'web', 1), ('vitalik/django-ninja', 0.563342809677124, 'web', 0), ('pallets/flask', 0.5500431656837463, 'web', 1), ('tiangolo/fastapi', 0.5388421416282654, 'web', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5053060054779053, 'template', 0), ('mongodb/mongo-python-driver', 0.5024893879890442, 'data', 1), ('pynamodb/pynamodb', 0.5015949010848999, 'data', 0)]",214,4.0,,0.35,5,1,137,6,0,4,4,5.0,1.0,90.0,0.2,42
919,util,https://github.com/openai/point-e,[],,[],[],,,,openai/point-e,point-e,6106,726,216,Python,,Point cloud diffusion for 3D model synthesis,openai,2024-01-13,2022-12-06,60,101.76666666666667,https://avatars.githubusercontent.com/u/14957082?v=4,Point cloud diffusion for 3D model synthesis,[],[],2022-12-20,"[('stability-ai/stablediffusion', 0.5376940965652466, 'diffusion', 0), ('compvis/latent-diffusion', 0.5376940369606018, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.5226495265960693, 'diffusion', 0), ('ashawkey/stable-dreamfusion', 0.5210638642311096, 'diffusion', 0), ('nicolas-chaulet/torch-points3d', 0.5189539790153503, 'ml', 0)]",2,0.0,,0.0,6,2,13,13,0,0,0,6.0,2.0,90.0,0.3,42
575,ml,https://github.com/probml/pyprobml,[],,[],[],,,,probml/pyprobml,pyprobml,6092,1455,187,Jupyter Notebook,,"Python code for ""Probabilistic Machine learning"" book by Kevin Murphy",probml,2024-01-13,2016-08-17,388,15.66642174871418,https://avatars.githubusercontent.com/u/6309387?v=4,"Python code for ""Probabilistic Machine learning"" book by Kevin Murphy","['blackjax', 'colab', 'flax', 'jax', 'jupyter-notebooks', 'machine-learning', 'numpyro', 'pml', 'probabilistic-programming', 'pymc3', 'pyro', 'pytorch', 'tensorflow']","['blackjax', 'colab', 'flax', 'jax', 'jupyter-notebooks', 'machine-learning', 'numpyro', 'pml', 'probabilistic-programming', 'pymc3', 'pyro', 'pytorch', 'tensorflow']",2023-12-19,"[('pymc-devs/pymc3', 0.6565049886703491, 'ml', 1), ('gerdm/prml', 0.6533145308494568, 'study', 1), ('pyro-ppl/pyro', 0.6461431980133057, 'ml-dl', 3), ('fchollet/deep-learning-with-python-notebooks', 0.6373811364173889, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.6130873560905457, 'study', 0), ('ageron/handson-ml2', 0.6119535565376282, 'ml', 0), ('gbeced/pyalgotrade', 0.5891293883323669, 'finance', 0), ('scikit-learn/scikit-learn', 0.5849363207817078, 'ml', 1), ('awslabs/gluonts', 0.5823290348052979, 'time-series', 2), ('pycaret/pycaret', 0.582277238368988, 'ml', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.576326847076416, 'study', 1), ('ta-lib/ta-lib-python', 0.5762322545051575, 'finance', 0), ('crflynn/stochastic', 0.5735928416252136, 'sim', 0), ('rasbt/mlxtend', 0.5730016827583313, 'ml', 1), ('rasbt/machine-learning-book', 0.5608888268470764, 'study', 2), ('patchy631/machine-learning', 0.5563993453979492, 'ml', 0), ('wesm/pydata-book', 0.5555208325386047, 'study', 0), ('d2l-ai/d2l-en', 0.5489851236343384, 'study', 4), ('python/cpython', 0.5431109666824341, 'util', 0), ('dylanhogg/awesome-python', 0.5428842306137085, 'study', 1), ('pypy/pypy', 0.5381896495819092, 'util', 0), ('ggerganov/ggml', 0.536136269569397, 'ml', 1), ('uber/orbit', 0.5333145260810852, 'time-series', 4), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5301855802536011, 'study', 0), ('rasbt/stat451-machine-learning-fs20', 0.5270578861236572, 'study', 0), ('gradio-app/gradio', 0.5261308550834656, 'viz', 1), ('firmai/industry-machine-learning', 0.5219587087631226, 'study', 1), ('cuemacro/finmarketpy', 0.5201766490936279, 'finance', 0), ('tensorly/tensorly', 0.5191105604171753, 'ml-dl', 4), ('sympy/sympy', 0.5161436200141907, 'math', 0), ('pytoolz/toolz', 0.5156102180480957, 'util', 0), ('goldmansachs/gs-quant', 0.5155179500579834, 'finance', 0), ('pytorch/rl', 0.514581561088562, 'ml-rl', 2), ('selfexplainml/piml-toolbox', 0.512611985206604, 'ml-interpretability', 0), ('online-ml/river', 0.5124793648719788, 'ml', 1), ('brandon-rhodes/python-patterns', 0.5112978219985962, 'util', 0), ('ddbourgin/numpy-ml', 0.508565366268158, 'ml', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5084415078163147, 'study', 0), ('scipy/scipy', 0.5070646405220032, 'math', 0), ('huggingface/transformers', 0.5070176124572754, 'nlp', 5), ('skorch-dev/skorch', 0.5041447877883911, 'ml-dl', 2), ('huggingface/huggingface_hub', 0.5018727779388428, 'ml', 2), ('jovianml/opendatasets', 0.5017038583755493, 'data', 1), ('stan-dev/pystan', 0.5016106963157654, 'ml', 0), ('clips/pattern', 0.5005698204040527, 'nlp', 1)]",66,8.0,,0.38,0,0,90,1,0,0,0,0.0,0.0,90.0,0.0,42
1685,util,https://github.com/hhatto/autopep8,[],,[],[],,,,hhatto/autopep8,autopep8,4468,296,73,Python,https://pypi.org/project/autopep8/,A tool that automatically formats Python code to conform to the PEP 8 style guide.,hhatto,2024-01-13,2010-12-29,682,6.5430962343096235,,A tool that automatically formats Python code to conform to the PEP 8 style guide.,"['codeformatter', 'formatter', 'pep8']","['codeformatter', 'formatter', 'pep8']",2023-10-27,"[('grantjenks/blue', 0.7624608278274536, 'util', 2), ('psf/black', 0.7485063672065735, 'util', 2), ('danielnoord/pydocstringformatter', 0.7380919456481934, 'util', 2), ('google/yapf', 0.7038267850875854, 'util', 1), ('pycqa/flake8', 0.6079347729682922, 'util', 1), ('pygments/pygments', 0.600104570388794, 'util', 0), ('pycqa/docformatter', 0.591210126876831, 'util', 1), ('google/latexify_py', 0.5800272822380066, 'util', 0), ('landscapeio/prospector', 0.5784581303596497, 'util', 0), ('pdm-project/pdm', 0.5722745656967163, 'util', 0), ('pycqa/pycodestyle', 0.5678965449333191, 'util', 1), ('python-markdown/markdown', 0.5607690811157227, 'util', 0), ('rubik/radon', 0.5594862699508667, 'util', 0), ('nedbat/coveragepy', 0.5550077557563782, 'testing', 0), ('python/cpython', 0.5486846566200256, 'util', 0), ('msaelices/py2mojo', 0.5481264591217041, 'util', 0), ('pytoolz/toolz', 0.5430788397789001, 'util', 0), ('astral-sh/ruff', 0.5378404259681702, 'util', 1), ('hoffstadt/dearpygui', 0.5373272895812988, 'gui', 0), ('eugeneyan/python-collab-template', 0.5339161157608032, 'template', 0), ('dosisod/refurb', 0.5332099795341492, 'util', 0), ('grahamdumpleton/wrapt', 0.5304956436157227, 'util', 0), ('pypy/pypy', 0.5292234420776367, 'util', 0), ('instagram/libcst', 0.5244827270507812, 'util', 0), ('connorferster/handcalcs', 0.5234463810920715, 'jupyter', 0), ('python-rope/rope', 0.5218623876571655, 'util', 0), ('pdoc3/pdoc', 0.5197219252586365, 'util', 0), ('facebookincubator/bowler', 0.5189270973205566, 'util', 0), ('pycqa/pylint-django', 0.5179296731948853, 'util', 0), ('brandon-rhodes/python-patterns', 0.5134424567222595, 'util', 0), ('willmcgugan/rich', 0.5127493739128113, 'term', 0), ('google/python-fire', 0.5113070011138916, 'term', 0), ('pyston/pyston', 0.5110304355621338, 'util', 0), ('sourcery-ai/sourcery', 0.5095126628875732, 'util', 0), ('tiangolo/typer', 0.5092093348503113, 'term', 0), ('pycqa/isort', 0.5075655579566956, 'util', 1), ('sqlalchemy/mako', 0.5068827867507935, 'template', 0), ('mkdocstrings/python', 0.5049331784248352, 'util', 0), ('mnooner256/pyqrcode', 0.5037751793861389, 'util', 0)]",60,5.0,,0.4,14,5,159,3,3,6,3,14.0,17.0,90.0,1.2,42
206,ml,https://github.com/lucidrains/deep-daze,[],,[],[],,,,lucidrains/deep-daze,deep-daze,4378,334,75,Python,,Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun,lucidrains,2024-01-12,2021-01-17,158,27.65884476534296,,Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun,"['artificial-intelligence', 'deep-learning', 'implicit-neural-representation', 'multi-modality', 'siren', 'text-to-image', 'transformers']","['artificial-intelligence', 'deep-learning', 'implicit-neural-representation', 'multi-modality', 'siren', 'text-to-image', 'transformers']",2022-03-13,"[('saharmor/dalle-playground', 0.6547111868858337, 'diffusion', 3), ('lucidrains/dalle2-pytorch', 0.631841242313385, 'diffusion', 3), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.625627875328064, 'web', 1), ('lucidrains/imagen-pytorch', 0.588476300239563, 'ml-dl', 3), ('thudm/cogvideo', 0.5634084939956665, 'ml', 0), ('borisdayma/dalle-mini', 0.5611160397529602, 'diffusion', 0), ('google/sentencepiece', 0.5594016909599304, 'nlp', 0), ('open-mmlab/mmediting', 0.5528916716575623, 'ml', 1), ('sharonzhou/long_stable_diffusion', 0.5463470220565796, 'diffusion', 0), ('minimaxir/gpt-2-simple', 0.5265099406242371, 'llm', 0), ('openai/clip', 0.5211201906204224, 'ml-dl', 1), ('invoke-ai/invokeai', 0.5158758163452148, 'diffusion', 1), ('minimaxir/textgenrnn', 0.5096532106399536, 'nlp', 1)]",14,5.0,,0.0,0,0,36,22,0,23,23,0.0,0.0,90.0,0.0,42
1747,util,https://github.com/pyinvoke/invoke,['execution'],,[],[],,,,pyinvoke/invoke,invoke,4163,384,93,Python,http://pyinvoke.org,Pythonic task management & command execution.,pyinvoke,2024-01-13,2012-02-29,621,6.694463588329888,https://avatars.githubusercontent.com/u/1486921?v=4,Pythonic task management & command execution.,[],['execution'],2023-12-01,"[('agronholm/apscheduler', 0.6340285539627075, 'util', 0), ('dbader/schedule', 0.514901876449585, 'util', 0)]",60,3.0,,1.42,20,2,145,1,0,6,6,20.0,19.0,90.0,0.9,42
1352,util,https://github.com/jorisschellekens/borb,[],,[],[],,,,jorisschellekens/borb,borb,3219,143,33,Python,https://borbpdf.com/,"borb is a library for reading, creating and manipulating PDF files in python.",jorisschellekens,2024-01-13,2020-11-07,168,19.111959287531807,,"borb is a library for reading, creating and manipulating PDF files in python.","['pdf', 'pdf-conversion', 'pdf-converter', 'pdf-generation', 'pdf-library', 'sdk', 'typesetting']","['pdf', 'pdf-conversion', 'pdf-converter', 'pdf-generation', 'pdf-library', 'sdk', 'typesetting']",2023-12-17,"[('py-pdf/pypdf2', 0.6551130414009094, 'util', 1), ('pyfpdf/fpdf2', 0.6073178052902222, 'util', 3), ('camelot-dev/camelot', 0.5273860692977905, 'util', 0), ('geospatialpython/pyshp', 0.5256045460700989, 'gis', 0), ('pypdfium2-team/pypdfium2', 0.5246109366416931, 'util', 1), ('bhaskatripathi/pdfgpt', 0.5003618597984314, 'llm', 0)]",1,0.0,,0.17,13,9,39,1,10,21,10,13.0,25.0,90.0,1.9,42
260,time-series,https://github.com/salesforce/merlion,[],,[],[],,,,salesforce/merlion,Merlion,3181,280,52,Python,,Merlion: A Machine Learning Framework for Time Series Intelligence,salesforce,2024-01-13,2021-07-28,130,24.308951965065503,https://avatars.githubusercontent.com/u/453694?v=4,Merlion: A Machine Learning Framework for Time Series Intelligence,"['anomaly-detection', 'automl', 'benchmarking', 'ensemble-learning', 'forecasting', 'machine-learning', 'time-series']","['anomaly-detection', 'automl', 'benchmarking', 'ensemble-learning', 'forecasting', 'machine-learning', 'time-series']",2023-03-22,"[('sktime/sktime', 0.7518776059150696, 'time-series', 3), ('unit8co/darts', 0.7470134496688843, 'time-series', 4), ('winedarksea/autots', 0.682404637336731, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.6415663361549377, 'time-series', 3), ('blue-yonder/tsfresh', 0.6107531189918518, 'time-series', 1), ('alkaline-ml/pmdarima', 0.589963436126709, 'time-series', 3), ('salesforce/deeptime', 0.5836126804351807, 'time-series', 2), ('awslabs/autogluon', 0.5632773637771606, 'ml', 5), ('pycaret/pycaret', 0.5579712986946106, 'ml', 3), ('tdameritrade/stumpy', 0.5562719106674194, 'time-series', 1), ('firmai/atspy', 0.5541254281997681, 'time-series', 2), ('microsoft/flaml', 0.5453396439552307, 'ml', 2), ('yzhao062/pyod', 0.5439088940620422, 'data', 2), ('linkedin/greykite', 0.5402796864509583, 'ml', 0), ('awslabs/gluonts', 0.5388320684432983, 'time-series', 3), ('nixtla/statsforecast', 0.5316644906997681, 'time-series', 4), ('ourownstory/neural_prophet', 0.5225098133087158, 'ml', 3), ('xplainable/xplainable', 0.5224214196205139, 'ml-interpretability', 1), ('microprediction/microprediction', 0.5122169852256775, 'time-series', 1), ('facebookresearch/kats', 0.5117724537849426, 'time-series', 1), ('microsoft/nni', 0.503190279006958, 'ml', 2)]",14,6.0,,0.06,2,0,30,10,2,7,2,2.0,1.0,90.0,0.5,42
368,nlp,https://github.com/bytedance/lightseq,[],,[],[],,,,bytedance/lightseq,lightseq,3021,324,60,C++,,LightSeq: A High Performance Library for Sequence Processing and Generation,bytedance,2024-01-12,2019-12-06,216,13.949208443271768,https://avatars.githubusercontent.com/u/4158466?v=4,LightSeq: A High Performance Library for Sequence Processing and Generation,"['accelerate', 'bart', 'beam-search', 'bert', 'cuda', 'diverse-decoding', 'gpt', 'inference', 'multilingual-nmt', 'sampling', 'training', 'transformer']","['accelerate', 'bart', 'beam-search', 'bert', 'cuda', 'diverse-decoding', 'gpt', 'inference', 'multilingual-nmt', 'sampling', 'training', 'transformer']",2023-05-10,"[('huggingface/text-generation-inference', 0.672805666923523, 'llm', 3), ('ferdinandzhong/punctuator', 0.5860391855239868, 'nlp', 1), ('amazon-science/dq-bart', 0.564974844455719, 'nlp', 0), ('salesforce/xgen', 0.5642438530921936, 'llm', 0), ('infinitylogesh/mutate', 0.562773585319519, 'nlp', 0), ('ofa-sys/ofa', 0.5597769021987915, 'llm', 0), ('huggingface/transformers', 0.5596634149551392, 'nlp', 2), ('mit-han-lab/streaming-llm', 0.5517858862876892, 'llm', 0), ('allenai/allennlp', 0.5493444800376892, 'nlp', 0), ('minimaxir/gpt-2-simple', 0.5469551682472229, 'llm', 0), ('google-research/electra', 0.5465645790100098, 'ml-dl', 0), ('bigcode-project/starcoder', 0.5453761219978333, 'llm', 0), ('databrickslabs/dolly', 0.5437467098236084, 'llm', 1), ('jina-ai/finetuner', 0.5338277220726013, 'ml', 1), ('togethercomputer/redpajama-data', 0.5332623720169067, 'llm', 0), ('hazyresearch/safari', 0.5279725790023804, 'ml', 0), ('jonasgeiping/cramming', 0.5221551656723022, 'nlp', 0), ('norskregnesentral/skweak', 0.5218310952186584, 'nlp', 0), ('bigscience-workshop/megatron-deepspeed', 0.515959620475769, 'llm', 0), ('microsoft/megatron-deepspeed', 0.515959620475769, 'llm', 0), ('lianjiatech/belle', 0.5151085257530212, 'llm', 0), ('hannibal046/awesome-llm', 0.5142962336540222, 'study', 1), ('minimaxir/aitextgen', 0.5134293437004089, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5104448199272156, 'llm', 1), ('extreme-bert/extreme-bert', 0.5086089968681335, 'llm', 2), ('cqcl/lambeq', 0.5084514617919922, 'nlp', 0), ('bigscience-workshop/biomedical', 0.5078558325767517, 'data', 0), ('bobazooba/xllm', 0.5065972208976746, 'llm', 1), ('freedomintelligence/llmzoo', 0.5059230327606201, 'llm', 0), ('blinkdl/rwkv-lm', 0.5059091448783875, 'llm', 2), ('srush/minichain', 0.5048473477363586, 'llm', 0), ('timdettmers/bitsandbytes', 0.5029526948928833, 'util', 1), ('salesforce/blip', 0.5009039044380188, 'diffusion', 0), ('deepset-ai/farm', 0.5006200671195984, 'nlp', 1), ('microsoft/lora', 0.5001585483551025, 'llm', 0), ('ai21labs/lm-evaluation', 0.5000201463699341, 'llm', 0)]",17,7.0,,0.46,7,0,50,8,0,3,3,7.0,5.0,90.0,0.7,42
1493,llm,https://github.com/mshumer/gpt-llm-trainer,[],"Input a description of your task, and the system will generate a dataset, parse it, and fine-tune a LLaMA 2 model for you",[],[],,,,mshumer/gpt-llm-trainer,gpt-llm-trainer,2739,347,51,Jupyter Notebook,,,mshumer,2024-01-14,2023-08-09,24,110.1896551724138,,"Input a description of your task, and the system will generate a dataset, parse it, and fine-tune a LLaMA 2 model for you",[],[],2023-08-23,"[('microsoft/llama-2-onnx', 0.6853927969932556, 'llm', 0), ('zrrskywalker/llama-adapter', 0.684531569480896, 'llm', 0), ('facebookresearch/llama-recipes', 0.6829999685287476, 'llm', 0), ('tloen/alpaca-lora', 0.663759171962738, 'llm', 0), ('run-llama/llama-lab', 0.6018344759941101, 'llm', 0), ('facebookresearch/llama', 0.6008679866790771, 'llm', 0), ('jzhang38/tinyllama', 0.5883111953735352, 'llm', 0), ('tairov/llama2.mojo', 0.5809900760650635, 'llm', 0), ('karpathy/llama2.c', 0.5627032518386841, 'llm', 0), ('lightning-ai/lit-llama', 0.5464016199111938, 'llm', 0), ('openlm-research/open_llama', 0.5152719616889954, 'llm', 0), ('facebookresearch/codellama', 0.509406328201294, 'llm', 0), ('run-llama/llama-hub', 0.5054514408111572, 'data', 0), ('jerryjliu/llama_index', 0.5029345154762268, 'llm', 0)]",1,0.0,,0.15,2,0,5,5,0,0,0,2.0,1.0,90.0,0.5,42
1130,ml,https://github.com/scikit-learn-contrib/category_encoders,[],,[],[],,,,scikit-learn-contrib/category_encoders,category_encoders,2322,397,39,Python,http://contrib.scikit-learn.org/category_encoders/,A library of sklearn compatible categorical variable encoders,scikit-learn-contrib,2024-01-11,2015-11-29,426,5.447050938337801,https://avatars.githubusercontent.com/u/17349883?v=4,A library of sklearn compatible categorical variable encoders,[],[],2023-12-13,[],70,2.0,,0.96,6,4,99,1,4,4,4,6.0,16.0,90.0,2.7,42
296,util,https://github.com/pyparsing/pyparsing,[],,[],[],,,,pyparsing/pyparsing,pyparsing,2028,268,24,Python,,Python library for creating PEG parsers,pyparsing,2024-01-13,2017-05-14,350,5.789559543230016,https://avatars.githubusercontent.com/u/28690438?v=4,Python library for creating PEG parsers,"['parser-combinators', 'parsing', 'parsing-expression-grammar', 'parsing-library', 'peg-parsers', 'text-processing']","['parser-combinators', 'parsing', 'parsing-expression-grammar', 'parsing-library', 'peg-parsers', 'text-processing']",2023-11-20,"[('instagram/libcst', 0.6272467970848083, 'util', 0), ('pytoolz/toolz', 0.6166950464248657, 'util', 0), ('tobymao/sqlglot', 0.6120659708976746, 'data', 0), ('pandas-dev/pandas', 0.5993269681930542, 'pandas', 0), ('google/latexify_py', 0.5657850503921509, 'util', 0), ('sloria/textblob', 0.5576520562171936, 'nlp', 0), ('fastai/fastcore', 0.5457038283348083, 'util', 0), ('andialbrecht/sqlparse', 0.5442314147949219, 'data', 0), ('pypy/pypy', 0.5254474878311157, 'util', 0), ('has2k1/plotnine', 0.5245786905288696, 'viz', 0), ('pyston/pyston', 0.522191047668457, 'util', 0), ('sympy/sympy', 0.521645188331604, 'math', 0), ('dylanhogg/awesome-python', 0.5186076164245605, 'study', 0), ('python/cpython', 0.5173945426940918, 'util', 0), ('joowani/binarytree', 0.5169668197631836, 'util', 0), ('1200wd/bitcoinlib', 0.5124039053916931, 'crypto', 0), ('tiangolo/sqlmodel', 0.5065826773643494, 'data', 0), ('evhub/coconut', 0.504447877407074, 'util', 0), ('ibis-project/ibis', 0.5017038583755493, 'data', 0), ('rasbt/mlxtend', 0.5005236864089966, 'ml', 0)]",62,4.0,,1.73,24,10,81,2,5,9,5,24.0,31.0,90.0,1.3,42
1799,gamedev,https://github.com/pokepetter/ursina,[],,[],[],,,,pokepetter/ursina,ursina,2006,318,48,Python,https://pokepetter.github.io/ursina/,A game engine powered by python and panda3d.,pokepetter,2024-01-12,2017-07-19,340,5.885163453478626,,A game engine powered by python and panda3d.,"['3d-game-engine', 'game-development', 'game-engine']","['3d-game-engine', 'game-development', 'game-engine']",2024-01-02,"[('panda3d/panda3d', 0.8077520132064819, 'gamedev', 2), ('kitao/pyxel', 0.6735712885856628, 'gamedev', 2), ('lordmauve/pgzero', 0.6621728539466858, 'gamedev', 0), ('renpy/renpy', 0.6157830357551575, 'viz', 0), ('pygame/pygame', 0.5800055861473083, 'gamedev', 1), ('pythonarcade/arcade', 0.5775792598724365, 'gamedev', 0), ('isl-org/open3d', 0.5535932183265686, 'sim', 0), ('ljvmiranda921/seagull', 0.5359201431274414, 'sim', 0), ('marcomusy/vedo', 0.5303018093109131, 'viz', 0), ('nvidia/warp', 0.5230095982551575, 'sim', 0), ('pygamelib/pygamelib', 0.5143521428108215, 'gamedev', 1), ('alephalpha/golly', 0.5002817511558533, 'sim', 0)]",41,3.0,,8.52,58,40,79,0,0,0,0,58.0,62.0,90.0,1.1,42
309,data,https://github.com/graphistry/pygraphistry,[],,[],[],,,,graphistry/pygraphistry,pygraphistry,1983,202,49,Python,,"PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer",graphistry,2024-01-13,2015-06-02,452,4.3871681415929205,https://avatars.githubusercontent.com/u/6157633?v=4,"PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer","['csv', 'cudf', 'cugraph', 'gpu', 'graph', 'graph-visualization', 'graphistry', 'igraph', 'jupyter', 'neo4j', 'network-analysis', 'network-visualization', 'networkx', 'pandas', 'rapids', 'splunk', 'tigergraph', 'visualization', 'webgl']","['csv', 'cudf', 'cugraph', 'gpu', 'graph', 'graph-visualization', 'graphistry', 'igraph', 'jupyter', 'neo4j', 'network-analysis', 'network-visualization', 'networkx', 'pandas', 'rapids', 'splunk', 'tigergraph', 'visualization', 'webgl']",2023-12-27,"[('h4kor/graph-force', 0.6990000605583191, 'graph', 0), ('pygraphviz/pygraphviz', 0.678774356842041, 'viz', 1), ('pyg-team/pytorch_geometric', 0.6707996129989624, 'ml-dl', 0), ('dmlc/dgl', 0.6582305431365967, 'ml-dl', 0), ('westhealth/pyvis', 0.6478259563446045, 'graph', 2), ('networkx/networkx', 0.6410859823226929, 'graph', 1), ('plotly/plotly.py', 0.6395604610443115, 'viz', 2), ('a-r-j/graphein', 0.6311023235321045, 'sim', 0), ('artelys/geonetworkx', 0.6271733045578003, 'gis', 0), ('holoviz/hvplot', 0.6067784428596497, 'pandas', 0), ('stellargraph/stellargraph', 0.5959926247596741, 'graph', 1), ('cvxgrp/pymde', 0.5851930379867554, 'ml', 2), ('man-group/dtale', 0.5765081644058228, 'viz', 2), ('holoviz/holoviz', 0.5680197477340698, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5679578185081482, 'viz', 1), ('holoviz/panel', 0.567793607711792, 'viz', 1), ('vaexio/vaex', 0.5663058161735535, 'perf', 1), ('altair-viz/altair', 0.5656019449234009, 'viz', 1), ('residentmario/geoplot', 0.56211256980896, 'gis', 0), ('benedekrozemberczki/tigerlily', 0.5483189225196838, 'ml-dl', 2), ('has2k1/plotnine', 0.5480146408081055, 'viz', 0), ('pyglet/pyglet', 0.5440356135368347, 'gamedev', 0), ('kanaries/pygwalker', 0.5422446727752686, 'pandas', 2), ('vispy/vispy', 0.5377211570739746, 'viz', 1), ('jsonpickle/jsonpickle', 0.535290539264679, 'data', 0), ('accenture/ampligraph', 0.532095193862915, 'data', 0), ('scitools/iris', 0.5314244627952576, 'gis', 0), ('danielegrattarola/spektral', 0.5304725170135498, 'ml-dl', 0), ('contextlab/hypertools', 0.5276858806610107, 'ml', 1), ('hazyresearch/hgcn', 0.525396466255188, 'ml', 0), ('pydot/pydot', 0.5253562331199646, 'viz', 0), ('rapidsai/cudf', 0.5241773724555969, 'pandas', 4), ('bokeh/bokeh', 0.5225502252578735, 'viz', 2), ('wesm/pydata-book', 0.522087812423706, 'study', 0), ('matplotlib/matplotlib', 0.5203686356544495, 'viz', 0), ('pypy/pypy', 0.519489049911499, 'util', 0), ('kuanb/peartree', 0.5183374285697937, 'gis', 1), ('rampasek/graphgps', 0.5173921585083008, 'graph', 0), ('mwaskom/seaborn', 0.5153336524963379, 'viz', 1), ('pytorch/data', 0.5149250030517578, 'data', 0), ('scitools/cartopy', 0.5080820918083191, 'gis', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5049978494644165, 'study', 0), ('comfyanonymous/comfyui', 0.503555953502655, 'diffusion', 0), ('cuemacro/chartpy', 0.5035216212272644, 'viz', 0), ('rapidsai/jupyterlab-nvdashboard', 0.503430187702179, 'jupyter', 1), ('enthought/mayavi', 0.5026683807373047, 'viz', 1), ('google-deepmind/materials_discovery', 0.5010610818862915, 'sim', 0), ('vizzuhq/ipyvizzu', 0.5005484819412231, 'jupyter', 1)]",40,3.0,,7.06,34,19,105,1,0,21,21,34.0,25.0,90.0,0.7,42
975,pandas,https://github.com/fugue-project/fugue,['duckdb'],,[],[],,,,fugue-project/fugue,fugue,1810,92,22,Python,https://fugue-tutorials.readthedocs.io/,"A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.",fugue-project,2024-01-14,2020-03-24,201,9.00497512437811,https://avatars.githubusercontent.com/u/65140352?v=4,"A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.","['dask', 'data-practitioners', 'distributed', 'distributed-computing', 'distributed-systems', 'machine-learning', 'pandas', 'spark', 'sql']","['dask', 'data-practitioners', 'distributed', 'distributed-computing', 'distributed-systems', 'duckdb', 'machine-learning', 'pandas', 'spark', 'sql']",2024-01-07,"[('eventual-inc/daft', 0.6778815388679504, 'pandas', 2), ('backtick-se/cowait', 0.6694428324699402, 'util', 2), ('apache/spark', 0.6404576897621155, 'data', 2), ('ibis-project/ibis', 0.5997548699378967, 'data', 4), ('dagworks-inc/hamilton', 0.5906447768211365, 'ml-ops', 2), ('airbytehq/airbyte', 0.5778406858444214, 'data', 0), ('mage-ai/mage-ai', 0.5678737163543701, 'ml-ops', 3), ('dask/distributed', 0.5652367472648621, 'perf', 2), ('aws/aws-sdk-pandas', 0.5624109506607056, 'pandas', 1), ('hi-primus/optimus', 0.5615660548210144, 'ml-ops', 3), ('kestra-io/kestra', 0.5582082271575928, 'ml-ops', 0), ('polyaxon/datatile', 0.5508334040641785, 'pandas', 3), ('flyteorg/flyte', 0.5496802926063538, 'ml-ops', 1), ('pola-rs/polars', 0.5422793030738831, 'pandas', 0), ('merantix-momentum/squirrel-core', 0.5387210845947266, 'ml', 2), ('jmcarpenter2/swifter', 0.5381412506103516, 'pandas', 2), ('lithops-cloud/lithops', 0.53661048412323, 'ml-ops', 1), ('ray-project/ray', 0.5340755581855774, 'ml-ops', 2), ('dagster-io/dagster', 0.5329538583755493, 'ml-ops', 0), ('tobymao/sqlglot', 0.5296240448951721, 'data', 3), ('airbnb/omniduct', 0.5275896787643433, 'data', 0), ('fastai/fastcore', 0.527552604675293, 'util', 0), ('skypilot-org/skypilot', 0.5239272117614746, 'llm', 1), ('meltano/meltano', 0.5231815576553345, 'ml-ops', 0), ('astronomer/astro-sdk', 0.5199980735778809, 'ml-ops', 2), ('uber/fiber', 0.5197455883026123, 'data', 2), ('tiangolo/sqlmodel', 0.5192634463310242, 'data', 1), ('vaexio/vaex', 0.5170252919197083, 'perf', 1), ('spotify/luigi', 0.5144204497337341, 'ml-ops', 0), ('avaiga/taipy', 0.5130361318588257, 'data', 0), ('horovod/horovod', 0.5121609568595886, 'ml-ops', 2), ('prefecthq/prefect-dask', 0.5114060044288635, 'util', 1), ('dask/dask', 0.5079086422920227, 'perf', 2), ('airtai/faststream', 0.5063053369522095, 'perf', 1), ('streamlit/streamlit', 0.5049965381622314, 'viz', 1), ('ml-tooling/opyrator', 0.504669725894928, 'viz', 1), ('joblib/joblib', 0.5016358494758606, 'util', 0)]",22,1.0,,0.98,22,18,46,0,30,34,30,22.0,19.0,90.0,0.9,42
1749,util,https://github.com/mitmproxy/pdoc,[],,[],[],,,,mitmproxy/pdoc,pdoc,1715,233,27,Python,https://pdoc.dev,API Documentation for Python Projects,mitmproxy,2024-01-13,2013-08-04,547,3.133646567475855,https://avatars.githubusercontent.com/u/4652787?v=4,API Documentation for Python Projects,"['api', 'api-documentation', 'docs', 'docstring', 'docstrings', 'documentation', 'documentation-generator', 'documentation-tool', 'pdoc']","['api', 'api-documentation', 'docs', 'docstring', 'docstrings', 'documentation', 'documentation-generator', 'documentation-tool', 'pdoc']",2024-01-11,"[('pdoc3/pdoc', 0.7915372252464294, 'util', 8), ('sphinx-doc/sphinx', 0.7031545042991638, 'util', 3), ('squidfunk/mkdocs-material', 0.6519834399223328, 'util', 1), ('mkdocstrings/griffe', 0.6451767683029175, 'util', 2), ('vitalik/django-ninja', 0.5793086886405945, 'web', 0), ('landscapeio/prospector', 0.5606246590614319, 'util', 0), ('eugeneyan/python-collab-template', 0.5599040389060974, 'template', 0), ('mkdocstrings/mkdocstrings', 0.5541725158691406, 'util', 1), ('amaargiru/pyroad', 0.5501058101654053, 'study', 0), ('eternnoir/pytelegrambotapi', 0.5450152158737183, 'util', 0), ('mkdocs/mkdocs', 0.5321336984634399, 'util', 1), ('executablebooks/jupyter-book', 0.5281922817230225, 'jupyter', 1), ('googleapis/google-api-python-client', 0.5224719047546387, 'util', 0), ('pypa/hatch', 0.520322322845459, 'util', 0), ('martinheinz/python-project-blueprint', 0.514594554901123, 'template', 0), ('openai/openai-python', 0.5083382725715637, 'util', 0), ('mkdocstrings/python', 0.5048332214355469, 'util', 1), ('requests/toolbelt', 0.5041638016700745, 'util', 0), ('simple-salesforce/simple-salesforce', 0.5029121041297913, 'data', 1), ('danielnoord/pydocstringformatter', 0.5013178586959839, 'util', 1), ('python-odin/odin', 0.5004577040672302, 'util', 0)]",48,4.0,,2.46,36,28,127,0,0,8,8,36.0,48.0,90.0,1.3,42
715,util,https://github.com/omry/omegaconf,[],,[],[],,,,omry/omegaconf,omegaconf,1700,91,18,Python,,Flexible Python configuration system. The last one you will ever need.,omry,2024-01-13,2018-09-03,282,6.025316455696203,,Flexible Python configuration system. The last one you will ever need.,"['configuration-files', 'configuration-loader', 'python-types', 'schema-validator', 'yaml', 'yaml-configuration']","['configuration-files', 'configuration-loader', 'python-types', 'schema-validator', 'yaml', 'yaml-configuration']",2023-11-18,"[('python-odin/odin', 0.5967031121253967, 'util', 1), ('google/gin-config', 0.5957804322242737, 'util', 0), ('pypa/hatch', 0.5926797986030579, 'util', 0), ('mkdocstrings/griffe', 0.5919175744056702, 'util', 0), ('pomponchik/instld', 0.5507912039756775, 'util', 0), ('indygreg/pyoxidizer', 0.5404947400093079, 'util', 0), ('eugeneyan/python-collab-template', 0.5363547205924988, 'template', 0), ('pdm-project/pdm', 0.5336859822273254, 'util', 0), ('jazzband/pip-tools', 0.527951180934906, 'util', 0), ('python-poetry/poetry', 0.5274845361709595, 'util', 0), ('pydantic/pydantic', 0.5237911343574524, 'util', 0), ('malloydata/malloy-py', 0.5221198201179504, 'data', 0), ('pypa/pipenv', 0.5201141834259033, 'util', 0), ('pytoolz/toolz', 0.5165718197822571, 'util', 0), ('pypi/warehouse', 0.5100882053375244, 'util', 0), ('mitsuhiko/rye', 0.5054756999015808, 'util', 0), ('google/python-fire', 0.5032675862312317, 'term', 0)]",31,6.0,,0.42,21,11,65,2,0,3,3,21.0,35.0,90.0,1.7,42
317,gamedev,https://github.com/pythonarcade/arcade,[],,[],[],1.0,,,pythonarcade/arcade,arcade,1589,302,58,Python,http://arcade.academy,Easy to use Python library for creating 2D arcade games.,pythonarcade,2024-01-12,2016-01-04,421,3.773066485753053,https://avatars.githubusercontent.com/u/39569439?v=4,Easy to use Python library for creating 2D arcade games.,"['arcade-api', 'arcade-framework', 'arcade-learning-environment', 'educational-resources', 'educational-technology', 'opengl']","['arcade-api', 'arcade-framework', 'arcade-learning-environment', 'educational-resources', 'educational-technology', 'opengl']",2024-01-11,"[('pygame/pygame', 0.6259638071060181, 'gamedev', 0), ('lordmauve/pgzero', 0.6107795238494873, 'gamedev', 0), ('pygamelib/pygamelib', 0.5959578156471252, 'gamedev', 0), ('viblo/pymunk', 0.584841787815094, 'sim', 0), ('panda3d/panda3d', 0.5839753746986389, 'gamedev', 1), ('pokepetter/ursina', 0.5775792598724365, 'gamedev', 0), ('kitao/pyxel', 0.5633159875869751, 'gamedev', 0), ('projectmesa/mesa', 0.5278759002685547, 'sim', 0), ('pyglet/pyglet', 0.5264350175857544, 'gamedev', 1), ('urwid/urwid', 0.5146381258964539, 'term', 0), ('ljvmiranda921/seagull', 0.5029021501541138, 'sim', 0)]",150,5.0,,6.87,53,37,98,0,1,16,1,53.0,26.0,90.0,0.5,42
1696,util,https://github.com/mkdocstrings/mkdocstrings,[],,[],[],,,,mkdocstrings/mkdocstrings,mkdocstrings,1467,102,14,Python,https://mkdocstrings.github.io/,":blue_book: Automatic documentation from sources, for MkDocs.",mkdocstrings,2024-01-13,2019-12-09,216,6.7871777924653,https://avatars.githubusercontent.com/u/75664361?v=4,"📘 Automatic documentation from sources, for MkDocs.","['autodoc', 'docstrings', 'material-theme', 'mkdocs', 'mkdocs-plugin', 'mkdocstrings']","['autodoc', 'docstrings', 'material-theme', 'mkdocs', 'mkdocs-plugin', 'mkdocstrings']",2024-01-04,"[('mkdocstrings/python', 0.6996007561683655, 'util', 3), ('squidfunk/mkdocs-material', 0.6913550496101379, 'util', 1), ('mkdocs/mkdocs', 0.6345070600509644, 'util', 1), ('pdoc3/pdoc', 0.6134604215621948, 'util', 1), ('sphinx-doc/sphinx', 0.5597922801971436, 'util', 0), ('mitmproxy/pdoc', 0.5541725158691406, 'util', 1), ('pycqa/docformatter', 0.5333818793296814, 'util', 0)]",41,2.0,,1.33,19,16,50,0,3,19,3,19.0,34.0,90.0,1.8,42
238,data,https://github.com/simonw/sqlite-utils,[],,[],[],,,,simonw/sqlite-utils,sqlite-utils,1386,101,21,Python,https://sqlite-utils.datasette.io,Python CLI utility and library for manipulating SQLite databases,simonw,2024-01-12,2018-07-14,289,4.788746298124383,,Python CLI utility and library for manipulating SQLite databases,"['cli', 'click', 'datasette', 'datasette-io', 'datasette-tool', 'sqlite', 'sqlite-database']","['cli', 'click', 'datasette', 'datasette-io', 'datasette-tool', 'sqlite', 'sqlite-database']",2023-12-08,"[('sqlalchemy/sqlalchemy', 0.6196154952049255, 'data', 0), ('tiangolo/sqlmodel', 0.5946550965309143, 'data', 0), ('ibis-project/ibis', 0.540537416934967, 'data', 1), ('tconbeer/harlequin', 0.5377789735794067, 'term', 0), ('andialbrecht/sqlparse', 0.5041010975837708, 'data', 0)]",36,5.0,,1.92,19,13,67,1,8,23,8,19.0,25.0,90.0,1.3,42
1428,llm,https://github.com/cstankonrad/long_llama,"['llama', 'language-model']",,[],[],,,,cstankonrad/long_llama,long_llama,1381,88,26,Python,,LongLLaMA is a large language model capable of handling long contexts. It is based on OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.,cstankonrad,2024-01-13,2023-07-06,29,46.47596153846154,,LongLLaMA is a large language model capable of handling long contexts. It is based on OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.,[],"['language-model', 'llama']",2023-11-07,"[('openlmlab/leval', 0.5901378989219666, 'llm', 1), ('freedomintelligence/llmzoo', 0.5405339002609253, 'llm', 1), ('juncongmoo/pyllama', 0.5282744765281677, 'llm', 0), ('lightning-ai/lit-llama', 0.5269233584403992, 'llm', 2), ('next-gpt/next-gpt', 0.5235233306884766, 'llm', 0), ('facebookresearch/codellama', 0.5162266492843628, 'llm', 2), ('salesforce/xgen', 0.5117976665496826, 'llm', 1), ('thudm/chatglm2-6b', 0.5114611387252808, 'llm', 0), ('ai21labs/lm-evaluation', 0.5108537077903748, 'llm', 1), ('young-geng/easylm', 0.5092800259590149, 'llm', 2), ('hiyouga/llama-factory', 0.5056300163269043, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5056299567222595, 'llm', 2), ('cg123/mergekit', 0.5025786757469177, 'llm', 1), ('hannibal046/awesome-llm', 0.5006245970726013, 'study', 1)]",3,1.0,,1.17,6,4,6,2,0,0,0,6.0,3.0,90.0,0.5,42
449,util,https://github.com/imageio/imageio,[],,[],[],,,,imageio/imageio,imageio,1362,271,32,Python,https://imageio.readthedocs.io,Python library for reading and writing image data,imageio,2024-01-13,2013-05-04,560,2.4302829467244456,https://avatars.githubusercontent.com/u/3678179?v=4,Python library for reading and writing image data,"['animated-gif', 'dicom', 'imageio', 'scientific-formats', 'video', 'webcam-capture']","['animated-gif', 'dicom', 'imageio', 'scientific-formats', 'video', 'webcam-capture']",2023-12-11,"[('python-pillow/pillow', 0.6861987113952637, 'util', 0), ('zulko/moviepy', 0.6167894601821899, 'util', 1), ('rhettbull/osxphotos', 0.6166835427284241, 'util', 0), ('wesm/pydata-book', 0.6035079956054688, 'study', 0), ('geospatialpython/pyshp', 0.5908835530281067, 'gis', 0), ('pandas-dev/pandas', 0.5883070826530457, 'pandas', 0), ('pytoolz/toolz', 0.5862778425216675, 'util', 0), ('earthlab/earthpy', 0.5848910808563232, 'gis', 0), ('pypy/pypy', 0.5812743902206421, 'util', 0), ('soft-matter/pims', 0.5806184411048889, 'util', 1), ('has2k1/plotnine', 0.5798242092132568, 'viz', 0), ('lightly-ai/lightly', 0.5790013670921326, 'ml', 0), ('scikit-image/scikit-image', 0.5782924890518188, 'util', 0), ('pyglet/pyglet', 0.5611344575881958, 'gamedev', 0), ('scitools/cartopy', 0.5579615831375122, 'gis', 0), ('erotemic/ubelt', 0.5557732582092285, 'util', 0), ('python/cpython', 0.5454882979393005, 'util', 0), ('eleutherai/pyfra', 0.538760244846344, 'ml', 0), ('altair-viz/altair', 0.5385008454322815, 'viz', 0), ('google/yapf', 0.5354976654052734, 'util', 0), ('connorferster/handcalcs', 0.5331127047538757, 'jupyter', 0), ('mdbloice/augmentor', 0.5311002135276794, 'ml', 0), ('python-odin/odin', 0.5272129774093628, 'util', 0), ('plotly/dash', 0.5265047550201416, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5262821316719055, 'study', 0), ('irmen/pyminiaudio', 0.5260134339332581, 'util', 0), ('residentmario/geoplot', 0.525743305683136, 'gis', 0), ('hoffstadt/dearpygui', 0.5239776968955994, 'gui', 0), ('matplotlib/matplotlib', 0.5237370133399963, 'viz', 0), ('pyston/pyston', 0.5213198661804199, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5206165909767151, 'study', 0), ('holoviz/holoviz', 0.5204654335975647, 'viz', 0), ('dylanhogg/awesome-python', 0.5201471447944641, 'study', 0), ('1200wd/bitcoinlib', 0.517402708530426, 'crypto', 0), ('raphaelquast/eomaps', 0.514751672744751, 'gis', 0), ('bokeh/bokeh', 0.5136635303497314, 'viz', 0), ('plotly/plotly.py', 0.5120216012001038, 'viz', 0), ('jovianml/opendatasets', 0.5112266540527344, 'data', 0), ('pytorch/data', 0.5112005472183228, 'data', 0), ('scitools/iris', 0.5107640027999878, 'gis', 0), ('pyeve/cerberus', 0.5100802183151245, 'data', 0), ('pytables/pytables', 0.5082108378410339, 'data', 0), ('geopandas/geopandas', 0.5076414942741394, 'gis', 0), ('mito-ds/monorepo', 0.5070230960845947, 'jupyter', 0), ('adafruit/circuitpython', 0.506452739238739, 'util', 0), ('dlt-hub/dlt', 0.5059615969657898, 'data', 0), ('giswqs/geemap', 0.505904495716095, 'gis', 0), ('jaidedai/easyocr', 0.5057637095451355, 'data', 0), ('gradio-app/gradio', 0.5046327710151672, 'viz', 0), ('holoviz/panel', 0.504224419593811, 'viz', 0), ('ta-lib/ta-lib-python', 0.5035238862037659, 'finance', 0), ('marshmallow-code/marshmallow', 0.503285825252533, 'util', 0), ('man-group/dtale', 0.5025433897972107, 'viz', 0), ('tebelorg/rpa-python', 0.502187967300415, 'util', 0), ('cython/cython', 0.5013166666030884, 'util', 0)]",109,7.0,,0.98,23,15,130,1,19,8,19,23.0,48.0,90.0,2.1,42
953,util,https://github.com/fabiocaccamo/python-benedict,[],,[],[],,,,fabiocaccamo/python-benedict,python-benedict,1341,47,12,Python,,":blue_book: dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.",fabiocaccamo,2024-01-14,2019-05-17,245,5.460732984293194,,"📘 dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.","['base64', 'csv', 'decode', 'dict', 'dictionary', 'encode', 'filter', 'flatten', 'json', 'keypath', 'pickle', 'plist', 'query-string', 'subset', 'toml', 'traverse', 'xls', 'xml', 'yaml']","['base64', 'csv', 'decode', 'dict', 'dictionary', 'encode', 'filter', 'flatten', 'json', 'keypath', 'pickle', 'plist', 'query-string', 'subset', 'toml', 'traverse', 'xls', 'xml', 'yaml']",2024-01-08,"[('python-odin/odin', 0.5445603728294373, 'util', 4), ('konradhalas/dacite', 0.5441532731056213, 'util', 0), ('marshmallow-code/marshmallow', 0.5224064588546753, 'util', 0), ('yukinarit/pyserde', 0.5148563981056213, 'util', 3)]",7,2.0,,3.37,32,31,57,0,10,14,10,32.0,45.0,90.0,1.4,42
1642,term,https://github.com/tmbo/questionary,['cli'],,[],[],,,,tmbo/questionary,questionary,1314,79,21,Python,,"Python library to build pretty command line user prompts ✨Easy to use multi-select lists, confirmations, free text prompts ...",tmbo,2024-01-13,2018-12-01,269,4.876988335100743,,"Python library to build pretty command line user prompts ✨Easy to use multi-select lists, confirmations, free text prompts ...",[],['cli'],2024-01-12,"[('jquast/blessed', 0.6140781044960022, 'term', 1), ('pexpect/pexpect', 0.6019250750541687, 'util', 0), ('tiangolo/typer', 0.5755541324615479, 'term', 1), ('urwid/urwid', 0.5709437727928162, 'term', 0), ('google/python-fire', 0.5444633960723877, 'term', 1), ('bigscience-workshop/promptsource', 0.5423410534858704, 'nlp', 0), ('hoffstadt/dearpygui', 0.5394479632377625, 'gui', 0), ('promptslab/promptify', 0.5267842411994934, 'nlp', 0), ('xonsh/xonsh', 0.5194598436355591, 'util', 1)]",40,4.0,,0.83,35,24,62,0,0,5,5,35.0,34.0,90.0,1.0,42
641,data,https://github.com/pytables/pytables,[],,[],[],,,,pytables/pytables,PyTables,1245,267,61,Python,http://www.pytables.org,A Python package to manage extremely large amounts of data,pytables,2024-01-12,2011-06-03,660,1.8847318339100345,https://avatars.githubusercontent.com/u/828302?v=4,A Python package to manage extremely large amounts of data,[],[],2024-01-12,"[('eleutherai/pyfra', 0.6154819130897522, 'ml', 0), ('erotemic/ubelt', 0.615244448184967, 'util', 0), ('vaexio/vaex', 0.6015515923500061, 'perf', 0), ('pyston/pyston', 0.5999751091003418, 'util', 0), ('blaze/blaze', 0.5941234230995178, 'pandas', 0), ('ibis-project/ibis', 0.5871632695198059, 'data', 0), ('pympler/pympler', 0.5767551064491272, 'perf', 0), ('kagisearch/vectordb', 0.5746976137161255, 'data', 0), ('pypa/hatch', 0.5594925880432129, 'util', 0), ('fastai/fastcore', 0.5568447113037109, 'util', 0), ('cython/cython', 0.549433171749115, 'util', 0), ('saulpw/visidata', 0.5493369102478027, 'term', 0), ('datapane/datapane', 0.5492084622383118, 'viz', 0), ('pypy/pypy', 0.547862708568573, 'util', 0), ('indygreg/pyoxidizer', 0.5478392839431763, 'util', 0), ('pyjanitor-devs/pyjanitor', 0.5451061129570007, 'pandas', 0), ('holoviz/panel', 0.544653058052063, 'viz', 0), ('mitsuhiko/rye', 0.54390549659729, 'util', 0), ('koaning/clumper', 0.5409725904464722, 'util', 0), ('dddomodossola/remi', 0.5401478409767151, 'gui', 0), ('pytoolz/toolz', 0.5390780568122864, 'util', 0), ('dgilland/cacheout', 0.5385268926620483, 'perf', 0), ('grantjenks/python-diskcache', 0.5363883376121521, 'util', 0), ('pandas-dev/pandas', 0.5361529588699341, 'pandas', 0), ('dlt-hub/dlt', 0.5339342355728149, 'data', 0), ('pyinfra-dev/pyinfra', 0.5320051312446594, 'util', 0), ('sqlalchemy/sqlalchemy', 0.531848132610321, 'data', 0), ('airbnb/omniduct', 0.5307878851890564, 'data', 0), ('malloydata/malloy-py', 0.5297611951828003, 'data', 0), ('lk-geimfari/mimesis', 0.5296199321746826, 'data', 0), ('pythonspeed/filprofiler', 0.5271565914154053, 'profiling', 0), ('python-cachier/cachier', 0.5254138112068176, 'perf', 0), ('willmcgugan/textual', 0.5240064263343811, 'term', 0), ('tiangolo/sqlmodel', 0.5233675241470337, 'data', 0), ('pyodide/micropip', 0.5223343968391418, 'util', 0), ('amzn/ion-python', 0.5214452147483826, 'data', 0), ('googleapis/python-bigquery', 0.5208421349525452, 'data', 0), ('pyqtgraph/pyqtgraph', 0.5200861692428589, 'viz', 0), ('eventual-inc/daft', 0.5193186402320862, 'pandas', 0), ('python-poetry/poetry', 0.517038106918335, 'util', 0), ('jmcarpenter2/swifter', 0.5137337446212769, 'pandas', 0), ('modin-project/modin', 0.5136765837669373, 'perf', 0), ('micropython/micropython', 0.5130558013916016, 'util', 0), ('contextlab/hypertools', 0.5119327306747437, 'ml', 0), ('falconry/falcon', 0.5114437937736511, 'web', 0), ('pythonprofilers/memory_profiler', 0.5096424221992493, 'profiling', 0), ('kestra-io/kestra', 0.5085448026657104, 'ml-ops', 0), ('imageio/imageio', 0.5082108378410339, 'util', 0), ('wesm/pydata-book', 0.5043383836746216, 'study', 0), ('pyeve/cerberus', 0.5037830471992493, 'data', 0), ('joblib/joblib', 0.5035998225212097, 'util', 0), ('pynamodb/pynamodb', 0.5032399892807007, 'data', 0), ('merantix-momentum/squirrel-core', 0.5015002489089966, 'ml', 0)]",127,5.0,,7.06,42,23,154,0,3,4,3,42.0,81.0,90.0,1.9,42
416,pandas,https://github.com/pyjanitor-devs/pyjanitor,[],,[],[],1.0,,,pyjanitor-devs/pyjanitor,pyjanitor,1224,164,18,Python,https://pyjanitor-devs.github.io/pyjanitor,Clean APIs for data cleaning. Python implementation of R package Janitor,pyjanitor-devs,2024-01-12,2018-03-04,308,3.9703429101019463,https://avatars.githubusercontent.com/u/53411673?v=4,Clean APIs for data cleaning. Python implementation of R package Janitor,"['cleaning-data', 'data', 'data-engineering', 'dataframe', 'pandas', 'pydata']","['cleaning-data', 'data', 'data-engineering', 'dataframe', 'pandas', 'pydata']",2024-01-13,"[('hi-primus/optimus', 0.6122784614562988, 'ml-ops', 0), ('pandas-dev/pandas', 0.5571015477180481, 'pandas', 2), ('pytables/pytables', 0.5451061129570007, 'data', 0), ('python-odin/odin', 0.51002436876297, 'util', 0)]",107,4.0,,1.29,40,35,71,0,2,9,2,40.0,61.0,90.0,1.5,42
16,perf,https://github.com/eventlet/eventlet,[],,[],[],,,,eventlet/eventlet,eventlet,1223,371,64,Python,https://eventlet.net,Concurrent networking library for Python,eventlet,2024-01-09,2012-12-11,581,2.1049913941480205,https://avatars.githubusercontent.com/u/3017635?v=4,Concurrent networking library for Python,"['c10k', 'concurrency', 'greenlet', 'network', 'production-ready']","['c10k', 'concurrency', 'greenlet', 'network', 'production-ready']",2024-01-11,"[('python-trio/trio', 0.6357116103172302, 'perf', 0), ('agronholm/anyio', 0.5704464912414551, 'perf', 0), ('samuelcolvin/arq', 0.5532472729682922, 'data', 1), ('ipython/ipyparallel', 0.5408223271369934, 'perf', 0), ('dask/dask', 0.5214966535568237, 'perf', 0), ('backtick-se/cowait', 0.519990086555481, 'util', 0), ('dddomodossola/remi', 0.5185775756835938, 'gui', 0), ('joblib/joblib', 0.5059096217155457, 'util', 0), ('sumerc/yappi', 0.5047085285186768, 'profiling', 1)]",191,2.0,,0.75,114,71,135,0,0,5,5,114.0,360.0,90.0,3.2,42
1672,util,https://github.com/jaraco/keyring,"['security', 'keyring']",,[],[],,,,jaraco/keyring,keyring,1149,148,19,Python,,,jaraco,2024-01-13,2015-02-24,466,2.4656652360515023,,jaraco/keyring,[],"['keyring', 'security']",2024-01-07,[],118,7.0,,1.87,29,16,108,0,6,22,6,29.0,35.0,90.0,1.2,42
480,gis,https://github.com/sentinel-hub/eo-learn,[],,[],[],,,,sentinel-hub/eo-learn,eo-learn,1064,288,45,Python,https://eo-learn.readthedocs.io/en/latest/,Earth observation processing framework for machine learning in Python,sentinel-hub,2024-01-10,2018-05-31,295,3.5980676328502414,https://avatars.githubusercontent.com/u/31830596?v=4,Earth observation processing framework for machine learning in Python,"['eo-data', 'eo-research', 'machine-learning', 'python-package']","['eo-data', 'eo-research', 'machine-learning', 'python-package']",2024-01-10,"[('pytroll/satpy', 0.6639524102210999, 'gis', 0), ('scitools/iris', 0.623789370059967, 'gis', 0), ('opengeos/earthformer', 0.5971592664718628, 'gis', 0), ('scikit-learn/scikit-learn', 0.581174910068512, 'ml', 1), ('radiantearth/radiant-mlhub', 0.5761727094650269, 'gis', 1), ('weecology/deepforest', 0.5667561888694763, 'gis', 0), ('pycaret/pycaret', 0.5666598677635193, 'ml', 1), ('giswqs/geemap', 0.5640432834625244, 'gis', 0), ('cloudsen12/easystac', 0.5572369694709778, 'gis', 0), ('rasbt/mlxtend', 0.5460903644561768, 'ml', 1), ('fatiando/verde', 0.5445219874382019, 'gis', 1), ('opengeos/segment-geospatial', 0.5391361117362976, 'gis', 1), ('earthlab/earthpy', 0.5327595472335815, 'gis', 0), ('scikit-learn-contrib/metric-learn', 0.5314062237739563, 'ml', 1), ('featurelabs/featuretools', 0.5274897813796997, 'ml', 1), ('online-ml/river', 0.5141457915306091, 'ml', 1), ('gradio-app/gradio', 0.5121914744377136, 'viz', 1), ('remotesensinglab/raster4ml', 0.5058966279029846, 'gis', 1), ('developmentseed/label-maker', 0.5026171803474426, 'gis', 0)]",53,5.0,,4.42,28,28,68,0,7,8,7,28.0,20.0,90.0,0.7,42
1369,llm,https://github.com/ibm/dromedary,['language-model'],,[],[],,,,ibm/dromedary,Dromedary,1038,79,19,Python,,"Dromedary: towards helpful, ethical and reliable LLMs.",ibm,2024-01-12,2023-05-03,38,26.71323529411765,https://avatars.githubusercontent.com/u/1459110?v=4,"Dromedary: towards helpful, ethical and reliable LLMs.",[],['language-model'],2023-10-26,"[('jina-ai/thinkgpt', 0.6295039057731628, 'llm', 1), ('eugeneyan/open-llms', 0.6077130436897278, 'study', 0), ('mooler0410/llmspracticalguide', 0.5917999148368835, 'study', 0), ('night-chen/toolqa', 0.5732406377792358, 'llm', 0), ('hwchase17/langchain', 0.5653164386749268, 'llm', 1), ('citadel-ai/langcheck', 0.5524911880493164, 'llm', 1), ('young-geng/easylm', 0.5381739139556885, 'llm', 1), ('confident-ai/deepeval', 0.5251967906951904, 'testing', 1), ('salesforce/xgen', 0.5233420729637146, 'llm', 1), ('agenta-ai/agenta', 0.5226309895515442, 'llm', 0), ('rlancemartin/auto-evaluator', 0.521746814250946, 'llm', 0), ('aiwaves-cn/agents', 0.5198720097541809, 'nlp', 1), ('salesforce/codet5', 0.5191128253936768, 'nlp', 1), ('explosion/spacy-llm', 0.5127615928649902, 'llm', 0), ('deep-diver/pingpong', 0.510129988193512, 'llm', 0), ('tigerlab-ai/tiger', 0.5087694525718689, 'llm', 0), ('nomic-ai/gpt4all', 0.5083171129226685, 'llm', 1), ('artidoro/qlora', 0.5037577748298645, 'llm', 1), ('thudm/chatglm2-6b', 0.50322425365448, 'llm', 0), ('epfllm/meditron', 0.5021689534187317, 'llm', 1)]",4,2.0,,1.58,6,3,8,3,0,0,0,6.0,13.0,90.0,2.2,42
1145,util,https://github.com/aio-libs/aiobotocore,[],,[],[],,,,aio-libs/aiobotocore,aiobotocore,1034,175,26,Python,https://aiobotocore.rtfd.io,asyncio support for botocore library using aiohttp,aio-libs,2024-01-08,2015-05-31,452,2.286165508528111,https://avatars.githubusercontent.com/u/7049303?v=4,asyncio support for botocore library using aiohttp,"['aiohttp', 'asyncio', 'aws', 'aws-sdk', 'botocore', 'cloud', 'cloud-management']","['aiohttp', 'asyncio', 'aws', 'aws-sdk', 'botocore', 'cloud', 'cloud-management']",2023-12-13,"[('samuelcolvin/aioaws', 0.6990792155265808, 'data', 2), ('terrycain/aioboto3', 0.6956399083137512, 'util', 1), ('aio-libs/aiohttp', 0.6436625719070435, 'web', 2), ('geeogi/async-python-lambda-template', 0.6242508292198181, 'template', 0), ('timofurrer/awesome-asyncio', 0.5803163647651672, 'study', 1), ('jordaneremieff/mangum', 0.546834409236908, 'web', 2), ('aio-libs/aiokafka', 0.5398765802383423, 'data', 1), ('encode/httpx', 0.536460280418396, 'web', 1), ('pallets/quart', 0.5356045961380005, 'web', 1), ('pytest-dev/pytest-asyncio', 0.5174840688705444, 'testing', 1), ('samuelcolvin/arq', 0.5054838061332703, 'data', 1)]",60,7.0,,0.5,49,37,105,1,9,10,9,49.0,132.0,90.0,2.7,42
1348,nlp,https://github.com/abertsch72/unlimiformer,"['transformers', 'attention-mechanism']",,[],[],,,,abertsch72/unlimiformer,unlimiformer,1004,70,23,Python,,"Public repo for the NeurIPS 2023 paper ""Unlimiformer: Long-Range Transformers with Unlimited Length Input""",abertsch72,2024-01-12,2023-05-03,38,25.83823529411765,,"Public repo for the NeurIPS 2023 paper ""Unlimiformer: Long-Range Transformers with Unlimited Length Input""",[],"['attention-mechanism', 'transformers']",2023-10-03,"[('facebookresearch/xformers', 0.5475772023200989, 'ml', 1), ('microsoft/focal-transformer', 0.5153154134750366, 'ml', 0)]",7,1.0,,1.92,13,3,9,3,0,0,0,13.0,34.0,90.0,2.6,42
1103,diffusion,https://github.com/chenyangqiqi/fatezero,[],,[],[],,,,chenyangqiqi/fatezero,FateZero,976,92,13,Jupyter Notebook,http://fate-zero-edit.github.io/,"[ICCV 2023 Oral] ""FateZero: Fusing Attentions for Zero-shot Text-based Video Editing""",chenyangqiqi,2024-01-12,2023-03-16,45,21.35,,"[ICCV 2023 Oral] ""FateZero: Fusing Attentions for Zero-shot Text-based Video Editing""","['image-editing', 'stable-diffusion', 'text-driven-editing', 'video-editing', 'video-style-transfer']","['image-editing', 'stable-diffusion', 'text-driven-editing', 'video-editing', 'video-style-transfer']",2023-08-14,"[('thudm/cogvideo', 0.6052513718605042, 'ml', 0), ('williamyang1991/vtoonify', 0.5721680521965027, 'ml-dl', 1), ('zulko/moviepy', 0.5514541268348694, 'util', 1), ('nateraw/stable-diffusion-videos', 0.543769896030426, 'diffusion', 1), ('openai/glide-text2im', 0.5413234233856201, 'diffusion', 0), ('open-mmlab/mmediting', 0.5141303539276123, 'ml', 1)]",5,4.0,,2.08,5,3,10,5,2,2,2,5.0,4.0,90.0,0.8,42
1864,sim,https://github.com/sail-sg/envpool,[],,[],[],,,,sail-sg/envpool,envpool,965,92,21,C++,https://envpool.readthedocs.io,C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.,sail-sg,2024-01-12,2021-10-20,118,8.118990384615385,https://avatars.githubusercontent.com/u/85740051?v=4,C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.,"['atari-games', 'box2d', 'cpp17', 'dm-control', 'dm-env', 'gym', 'high-performance-computing', 'lock-free-queue', 'mujoco', 'parallel-processing', 'pybind11', 'reinforcement-learning', 'reinforcement-learning-environments', 'robotics', 'threadpool', 'vizdoom']","['atari-games', 'box2d', 'cpp17', 'dm-control', 'dm-env', 'gym', 'high-performance-computing', 'lock-free-queue', 'mujoco', 'parallel-processing', 'pybind11', 'reinforcement-learning', 'reinforcement-learning-environments', 'robotics', 'threadpool', 'vizdoom']",2023-10-30,"[('ray-project/ray', 0.529890775680542, 'ml-ops', 1), ('exaloop/codon', 0.5291399359703064, 'perf', 0), ('panda3d/panda3d', 0.5131605267524719, 'gamedev', 0), ('denys88/rl_games', 0.5019115805625916, 'ml-rl', 1), ('salesforce/warp-drive', 0.5001153945922852, 'ml-rl', 1)]",17,8.0,,0.37,14,8,27,2,3,12,3,14.0,25.0,90.0,1.8,42
555,jupyter,https://github.com/vizzuhq/ipyvizzu,[],,[],[],,,,vizzuhq/ipyvizzu,ipyvizzu,898,87,15,Python,https://ipyvizzu.vizzuhq.com,Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax.,vizzuhq,2024-01-13,2022-01-05,107,8.325827814569536,https://avatars.githubusercontent.com/u/79846421?v=4,Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax.,"['animation', 'chart', 'charting', 'charts', 'data-visualization', 'dataviz', 'graphing', 'graphs', 'ipython', 'jupyter', 'jupyter-notebook', 'plotting', 'storytelling', 'vizzu']","['animation', 'chart', 'charting', 'charts', 'data-visualization', 'dataviz', 'graphing', 'graphs', 'ipython', 'jupyter', 'jupyter-notebook', 'plotting', 'storytelling', 'vizzu']",2024-01-09,"[('plotly/plotly.py', 0.7362504601478577, 'viz', 1), ('maartenbreddels/ipyvolume', 0.7133920788764954, 'jupyter', 4), ('bokeh/bokeh', 0.6746152639389038, 'viz', 2), ('jupyter-widgets/ipywidgets', 0.6638791561126709, 'jupyter', 0), ('voila-dashboards/voila', 0.6425187587738037, 'jupyter', 2), ('holoviz/panel', 0.6363608837127686, 'viz', 2), ('cuemacro/chartpy', 0.6341903209686279, 'viz', 2), ('opengeos/leafmap', 0.6331526637077332, 'gis', 3), ('jupyter/notebook', 0.6270691156387329, 'jupyter', 2), ('jupyterlab/jupyterlab-desktop', 0.6113208532333374, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.6107296943664551, 'study', 1), ('jupyter-widgets/ipyleaflet', 0.6106564402580261, 'gis', 1), ('plotly/dash', 0.6095137596130371, 'viz', 3), ('man-group/dtale', 0.6090724468231201, 'viz', 3), ('quantopian/qgrid', 0.608697235584259, 'jupyter', 0), ('ipython/ipyparallel', 0.604579508304596, 'perf', 1), ('federicoceratto/dashing', 0.5991188883781433, 'term', 1), ('kanaries/pygwalker', 0.5989693403244019, 'pandas', 0), ('mwouts/jupytext', 0.5986511707305908, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.5963035821914673, 'jupyter', 1), ('aws/graph-notebook', 0.5957169532775879, 'jupyter', 2), ('has2k1/plotnine', 0.585931122303009, 'viz', 1), ('matplotlib/matplotlib', 0.5835548043251038, 'viz', 2), ('tkrabel/bamboolib', 0.5739924311637878, 'pandas', 1), ('holoviz/holoviz', 0.5736362338066101, 'viz', 0), ('xiaohk/stickyland', 0.5726215243339539, 'jupyter', 1), ('jupyter/nbformat', 0.5726070404052734, 'jupyter', 0), ('koaning/drawdata', 0.5688337683677673, 'jupyter', 1), ('lux-org/lux', 0.5684981346130371, 'viz', 1), ('cohere-ai/notebooks', 0.5615155100822449, 'llm', 0), ('altair-viz/altair', 0.5488370656967163, 'viz', 0), ('westhealth/pyvis', 0.5483621954917908, 'graph', 0), ('giswqs/mapwidget', 0.5461418032646179, 'gis', 1), ('brandtbucher/specialist', 0.5436729192733765, 'perf', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5411252975463867, 'jupyter', 0), ('jupyter-lsp/jupyterlab-lsp', 0.539960503578186, 'jupyter', 3), ('wesm/pydata-book', 0.5378664135932922, 'study', 0), ('jalammar/ecco', 0.5353596806526184, 'ml-interpretability', 0), ('pygraphviz/pygraphviz', 0.5348131060600281, 'viz', 0), ('mwaskom/seaborn', 0.5341234803199768, 'viz', 1), ('jupyterlite/jupyterlite', 0.5260722041130066, 'jupyter', 1), ('r0x0r/pywebview', 0.5254994034767151, 'gui', 0), ('datapane/datapane', 0.5247305035591125, 'viz', 1), ('giswqs/geemap', 0.5243290066719055, 'gis', 3), ('bloomberg/ipydatagrid', 0.522794783115387, 'jupyter', 0), ('holoviz/hvplot', 0.5226277112960815, 'pandas', 1), ('jmcnamara/xlsxwriter', 0.5122716426849365, 'data', 1), ('holoviz/geoviews', 0.5115540623664856, 'gis', 1), ('jupyter/nbviewer', 0.5107673406600952, 'jupyter', 2), ('ipython/ipykernel', 0.5079095363616943, 'util', 3), ('residentmario/geoplot', 0.5058284997940063, 'gis', 0), ('pyqtgraph/pyqtgraph', 0.5050925612449646, 'viz', 0), ('enthought/mayavi', 0.501654863357544, 'viz', 0), ('nschloe/perfplot', 0.5008931756019592, 'perf', 0), ('graphistry/pygraphistry', 0.5005484819412231, 'data', 1)]",20,4.0,,6.46,15,14,25,0,6,12,6,15.0,2.0,90.0,0.1,42
1632,util,https://github.com/pypa/setuptools_scm,"['hg', 'git', 'sdist', 'versioning']",,[],[],,,,pypa/setuptools_scm,setuptools_scm,790,215,27,Python,https://setuptools-scm.readthedocs.io/en/latest/,the blessed package to manage your versions by scm tags,pypa,2024-01-12,2015-07-01,447,1.7639553429027113,https://avatars.githubusercontent.com/u/647025?v=4,the blessed package to manage your versions by scm tags,"['metadata', 'packaging', 'version-control']","['git', 'hg', 'metadata', 'packaging', 'sdist', 'version-control', 'versioning']",2024-01-08,"[('mtkennerly/dunamai', 0.6616849303245544, 'util', 2), ('callowayproject/bump-my-version', 0.6197443604469299, 'util', 1), ('mtkennerly/poetry-dynamic-versioning', 0.6003091931343079, 'util', 2), ('spack/spack', 0.5813215970993042, 'util', 0), ('python-versioneer/python-versioneer', 0.5380630493164062, 'util', 0), ('conda/conda', 0.532427966594696, 'util', 1), ('pomponchik/instld', 0.5312846302986145, 'util', 0), ('pypa/hatch', 0.5210332274436951, 'util', 2), ('pypa/gh-action-pypi-publish', 0.5103867650032043, 'util', 0)]",131,7.0,,3.81,61,44,104,0,4,14,4,61.0,100.0,90.0,1.6,42
841,util,https://github.com/fsspec/s3fs,[],,[],[],,,,fsspec/s3fs,s3fs,773,258,18,Python,http://s3fs.readthedocs.io/en/latest/,S3 Filesystem ,fsspec,2024-01-12,2016-03-16,410,1.881432545201669,https://avatars.githubusercontent.com/u/92825505?v=4,S3 Filesystem ,[],[],2023-12-16,[],134,8.0,,1.12,46,33,95,1,0,8,8,46.0,125.0,90.0,2.7,42
578,gis,https://github.com/developmentseed/titiler,[],,[],[],,,,developmentseed/titiler,titiler,640,130,18,Python,https://developmentseed.org/titiler/,Build your own Raster dynamic map tile services,developmentseed,2024-01-12,2019-06-28,239,2.6714370900417412,https://avatars.githubusercontent.com/u/92384?v=4,Build your own Raster dynamic map tile services,"['aws-cdk', 'aws-lambda', 'cog', 'cogeotiff', 'dynamic', 'fastapi', 'gdal', 'map-tile-server', 'map-tiles', 'mosaicjson', 'raster', 'rasterio', 'rest', 'server', 'stac', 'tile']","['aws-cdk', 'aws-lambda', 'cog', 'cogeotiff', 'dynamic', 'fastapi', 'gdal', 'map-tile-server', 'map-tiles', 'mosaicjson', 'raster', 'rasterio', 'rest', 'server', 'stac', 'tile']",2024-01-10,"[('jordaneremieff/mangum', 0.5069487690925598, 'web', 2), ('localstack/localstack', 0.5016547441482544, 'util', 0)]",45,7.0,,2.54,25,22,55,0,0,17,17,25.0,36.0,90.0,1.4,42
584,ml-ops,https://github.com/kedro-org/kedro-viz,[],,[],[],,,,kedro-org/kedro-viz,kedro-viz,616,100,11,JavaScript,https://demo.kedro.org,Visualise your Kedro data and machine-learning pipelines and track your experiments. ,kedro-org,2024-01-14,2019-05-09,246,2.4968152866242037,https://avatars.githubusercontent.com/u/93382166?v=4,Visualise your Kedro data and machine-learning pipelines and track your experiments. ,"['data-visualization', 'experiment-tracking', 'kedro', 'kedro-plugin', 'react']","['data-visualization', 'experiment-tracking', 'kedro', 'kedro-plugin', 'react']",2024-01-12,"[('kedro-org/kedro', 0.7322895526885986, 'ml-ops', 2), ('getindata/kedro-kubeflow', 0.578177273273468, 'ml-ops', 2), ('wandb/client', 0.5440914034843445, 'ml', 0), ('kubeflow-kale/kale', 0.5434872508049011, 'ml-ops', 0), ('aimhubio/aim', 0.5116142630577087, 'ml-ops', 2)]",47,4.0,,3.92,174,117,57,0,19,15,19,172.0,236.0,90.0,1.4,42
984,nlp,https://github.com/intellabs/fastrag,"['retrieval-augmentation', 'knowledge-graph', 'haystack']",,[],[],1.0,,,intellabs/fastrag,fastRAG,583,49,9,Python,,Efficient Retrieval Augmentation and Generation Framework,intellabs,2024-01-14,2023-01-23,53,10.970430107526882,https://avatars.githubusercontent.com/u/1492758?v=4,Efficient Retrieval Augmentation and Generation Framework,"['benchmark', 'colbert', 'diffusion', 'generative-ai', 'information-retrieval', 'knowledge-graph', 'llm', 'multi-modal', 'nlp', 'question-answering', 'semantic-search', 'sentence-transformers', 'summarization', 'transformers']","['benchmark', 'colbert', 'diffusion', 'generative-ai', 'haystack', 'information-retrieval', 'knowledge-graph', 'llm', 'multi-modal', 'nlp', 'question-answering', 'retrieval-augmentation', 'semantic-search', 'sentence-transformers', 'summarization', 'transformers']",2024-01-11,"[('llmware-ai/llmware', 0.669150710105896, 'llm', 6), ('ai21labs/in-context-ralm', 0.6507914066314697, 'llm', 1), ('paddlepaddle/rocketqa', 0.6380553841590881, 'nlp', 3), ('paddlepaddle/paddlenlp', 0.6250779628753662, 'llm', 4), ('neuml/txtai', 0.6202690005302429, 'nlp', 5), ('muennighoff/sgpt', 0.6058968305587769, 'llm', 2), ('deepset-ai/haystack', 0.5783014297485352, 'llm', 8), ('thilinarajapakse/simpletransformers', 0.5657897591590881, 'nlp', 2), ('rcgai/simplyretrieve', 0.5627588629722595, 'llm', 2), ('jina-ai/finetuner', 0.5626152157783508, 'ml', 0), ('jina-ai/clip-as-service', 0.561098039150238, 'nlp', 0), ('srush/minichain', 0.5593423843383789, 'llm', 2), ('facebookresearch/dpr-scale', 0.5437898635864258, 'nlp', 0), ('eugeneyan/obsidian-copilot', 0.5370295643806458, 'llm', 2), ('huggingface/text-generation-inference', 0.5351554155349731, 'llm', 1), ('deepset-ai/farm', 0.5346118211746216, 'nlp', 2), ('ddangelov/top2vec', 0.527250349521637, 'nlp', 2), ('infinitylogesh/mutate', 0.526527464389801, 'nlp', 0), ('alibaba/easynlp', 0.5131124258041382, 'nlp', 2), ('luohongyin/sail', 0.5123240947723389, 'llm', 0), ('makcedward/nlpaug', 0.5120058655738831, 'nlp', 1), ('extreme-bert/extreme-bert', 0.5088297128677368, 'llm', 1), ('qdrant/fastembed', 0.5029743909835815, 'ml', 0), ('docarray/docarray', 0.501563549041748, 'data', 2), ('ukplab/sentence-transformers', 0.5005632042884827, 'nlp', 2)]",6,4.0,,0.62,11,10,12,0,4,6,4,11.0,17.0,90.0,1.5,42
1807,data,https://github.com/kagisearch/vectordb,['vectordb'],,[],[],,,,kagisearch/vectordb,vectordb,479,21,3,Python,https://vectordb.com,"A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.",kagisearch,2024-01-11,2023-04-25,40,11.975,https://avatars.githubusercontent.com/u/92134518?v=4,"A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search.","['ai', 'artificial-intelligence', 'llm', 'llms', 'machine-learning']","['ai', 'artificial-intelligence', 'llm', 'llms', 'machine-learning', 'vectordb']",2024-01-11,"[('jina-ai/vectordb', 0.6794906258583069, 'data', 1), ('qdrant/fastembed', 0.6594275832176208, 'ml', 1), ('chroma-core/chroma', 0.6572227478027344, 'data', 2), ('pytables/pytables', 0.5746976137161255, 'data', 0), ('activeloopai/deeplake', 0.5621834397315979, 'ml-ops', 3), ('plasticityai/magnitude', 0.5367014408111572, 'nlp', 1), ('openeventdata/mordecai', 0.5354642868041992, 'gis', 0), ('neuml/txtai', 0.529229998588562, 'nlp', 2), ('castorini/pyserini', 0.5248305201530457, 'ml', 0), ('lancedb/lancedb', 0.5210687518119812, 'data', 1), ('weaviate/demo-text2vec-openai', 0.5153085589408875, 'util', 0), ('pemistahl/lingua-py', 0.509565532207489, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5087656378746033, 'llm', 1), ('explosion/spacy', 0.506615936756134, 'nlp', 3), ('llmware-ai/llmware', 0.5052314400672913, 'llm', 2), ('minimaxir/aitextgen', 0.5047615766525269, 'llm', 0), ('minimaxir/textgenrnn', 0.5044152140617371, 'nlp', 0)]",7,4.0,,1.6,13,12,9,0,0,0,0,13.0,26.0,90.0,2.0,42
1271,util,https://github.com/qdrant/qdrant-client,[],,[],[],,,,qdrant/qdrant-client,qdrant-client,477,79,6,Python,https://qdrant.tech,Python client for Qdrant vector search engine,qdrant,2024-01-14,2021-02-09,155,3.07741935483871,https://avatars.githubusercontent.com/u/73504361?v=4,Python client for Qdrant vector search engine,"['qdrant', 'vector-database', 'vector-search', 'vector-search-engine']","['qdrant', 'vector-database', 'vector-search', 'vector-search-engine']",2024-01-11,"[('qdrant/qdrant-haystack', 0.6654171943664551, 'data', 0), ('pinecone-io/pinecone-python-client', 0.6546476483345032, 'data', 1), ('weaviate/weaviate-python-client', 0.5938428640365601, 'util', 1), ('qdrant/vector-db-benchmark', 0.5792778134346008, 'perf', 3), ('typesense/typesense-python', 0.5577874779701233, 'data', 0), ('accenture/cymple', 0.5485904812812805, 'data', 0), ('jina-ai/vectordb', 0.5459500551223755, 'data', 2), ('goldmansachs/gs-quant', 0.5404823422431946, 'finance', 0), ('meilisearch/meilisearch-python', 0.538914680480957, 'data', 0), ('googleapis/google-api-python-client', 0.5371494293212891, 'util', 0), ('qdrant/fastembed', 0.5288180708885193, 'ml', 1), ('gbeced/pyalgotrade', 0.5113593935966492, 'finance', 0), ('qdrant/qdrant', 0.5091877579689026, 'data', 3), ('castorini/pyserini', 0.5034437775611877, 'ml', 0), ('hydrosquall/tiingo-python', 0.501908540725708, 'finance', 0), ('nv7-github/googlesearch', 0.5012821555137634, 'util', 0)]",18,3.0,,3.02,119,92,36,0,11,25,11,113.0,275.0,90.0,2.4,42
1656,llm,https://github.com/langchain-ai/langsmith-cookbook,"['cookbook', 'evaluation', 'language-model']",LangSmith is a platform for building production-grade LLM applications.,[],[],,,,langchain-ai/langsmith-cookbook,langsmith-cookbook,436,62,7,Jupyter Notebook,https://langsmith-cookbook.vercel.app,,langchain-ai,2024-01-13,2023-08-01,26,16.76923076923077,https://avatars.githubusercontent.com/u/126733545?v=4,LangSmith is a platform for building production-grade LLM applications.,[],"['cookbook', 'evaluation', 'language-model']",2023-12-20,"[('langchain-ai/langsmith-sdk', 0.6326169371604919, 'llm', 2), ('hwchase17/langchain', 0.5514335632324219, 'llm', 1), ('citadel-ai/langcheck', 0.5509293079376221, 'llm', 2), ('eugeneyan/open-llms', 0.5495509505271912, 'study', 0), ('hiyouga/llama-factory', 0.5422530174255371, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5422529578208923, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.5349544286727905, 'llm', 0), ('agenta-ai/agenta', 0.5301238894462585, 'llm', 0), ('nat/openplayground', 0.5300737619400024, 'llm', 1), ('langchain-ai/langgraph', 0.5267937183380127, 'llm', 0), ('confident-ai/deepeval', 0.5237811803817749, 'testing', 2), ('lianjiatech/belle', 0.5184667110443115, 'llm', 0), ('ray-project/llm-applications', 0.515418529510498, 'llm', 0), ('alphasecio/langchain-examples', 0.5074201822280884, 'llm', 0)]",10,4.0,,1.9,45,29,6,1,0,0,0,45.0,39.0,90.0,0.9,42
961,study,https://github.com/openai/spinningup,[],,[],[],,,,openai/spinningup,spinningup,9334,2120,229,Python,https://spinningup.openai.com/,An educational resource to help anyone learn deep reinforcement learning.,openai,2024-01-14,2018-11-07,272,34.20837696335079,https://avatars.githubusercontent.com/u/14957082?v=4,An educational resource to help anyone learn deep reinforcement learning.,[],[],2020-02-07,"[('thu-ml/tianshou', 0.5972070097923279, 'ml-rl', 0), ('keras-rl/keras-rl', 0.5871951580047607, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5850274562835693, 'ml-rl', 0), ('mrdbourke/pytorch-deep-learning', 0.580998420715332, 'study', 0), ('huggingface/deep-rl-class', 0.5750541090965271, 'study', 0), ('tensorflow/tensor2tensor', 0.5718406438827515, 'ml', 0), ('tensorlayer/tensorlayer', 0.5584976673126221, 'ml-rl', 0), ('google/dopamine', 0.5561296939849854, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.5327202081680298, 'ml-rl', 0), ('d2l-ai/d2l-en', 0.5276309251785278, 'study', 0), ('udlbook/udlbook', 0.5261555314064026, 'study', 0), ('denys88/rl_games', 0.5233257412910461, 'ml-rl', 0), ('salesforce/warp-drive', 0.5154350399971008, 'ml-rl', 0), ('openai/baselines', 0.5069130063056946, 'ml-rl', 0), ('facebookresearch/habitat-lab', 0.5063529014587402, 'sim', 0), ('pettingzoo-team/pettingzoo', 0.5047026872634888, 'ml-rl', 0)]",25,2.0,,0.0,48,3,63,48,0,1,1,48.0,4.0,90.0,0.1,41
197,llm,https://github.com/eleutherai/gpt-neo,[],,[],[],,,,eleutherai/gpt-neo,gpt-neo,8070,934,179,Python,https://www.eleuther.ai,An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.,eleutherai,2024-01-13,2020-07-05,186,43.32055214723926,https://avatars.githubusercontent.com/u/68924597?v=4,An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.,"['gpt', 'gpt-2', 'gpt-3', 'language-model', 'transformers']","['gpt', 'gpt-2', 'gpt-3', 'language-model', 'transformers']",2022-02-25,"[('tensorflow/mesh', 0.665690541267395, 'ml-dl', 0), ('eleutherai/gpt-neox', 0.6265007257461548, 'llm', 3), ('karpathy/mingpt', 0.5706537961959839, 'llm', 0), ('huggingface/accelerate', 0.5675502419471741, 'ml', 0), ('bigscience-workshop/megatron-deepspeed', 0.5555706024169922, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5555706024169922, 'llm', 0), ('nvidia/tensorrt-llm', 0.5397517681121826, 'viz', 1), ('microsoft/pycodegpt', 0.5271508097648621, 'llm', 0), ('farizrahman4u/loopgpt', 0.5264042019844055, 'llm', 1), ('xtekky/gpt4free', 0.5255731344223022, 'llm', 3), ('marella/ctransformers', 0.5227762460708618, 'nlp', 1), ('hannibal046/awesome-llm', 0.5118930339813232, 'study', 2), ('next-gpt/next-gpt', 0.5107851624488831, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5072481632232666, 'llm', 0), ('karpathy/nanogpt', 0.5039609670639038, 'llm', 0), ('huggingface/exporters', 0.5027332901954651, 'ml', 0), ('nvidia/warp', 0.5011614561080933, 'sim', 0), ('rafiqhasan/auto-tensorflow', 0.5005360841751099, 'ml-dl', 0)]",29,2.0,,0.0,0,0,43,23,0,1,1,0.0,0.0,90.0,0.0,41
655,study,https://github.com/udacity/deep-learning-v2-pytorch,[],,[],[],,,,udacity/deep-learning-v2-pytorch,deep-learning-v2-pytorch,5106,5293,175,Jupyter Notebook,,Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101,udacity,2024-01-14,2018-09-04,282,18.106382978723403,https://avatars.githubusercontent.com/u/1916665?v=4,Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101,"['convolutional-networks', 'deep-learning', 'neural-network', 'pytorch', 'recurrent-networks', 'sentiment-analysis', 'style-transfer']","['convolutional-networks', 'deep-learning', 'neural-network', 'pytorch', 'recurrent-networks', 'sentiment-analysis', 'style-transfer']",2022-12-24,"[('mrdbourke/pytorch-deep-learning', 0.6218847632408142, 'study', 2), ('amanchadha/coursera-deep-learning-specialization', 0.6206496357917786, 'study', 2), ('graykode/nlp-tutorial', 0.5692509412765503, 'study', 1), ('mrdbourke/tensorflow-deep-learning', 0.5660417079925537, 'study', 1), ('d2l-ai/d2l-en', 0.5573995113372803, 'study', 2), ('ageron/handson-ml2', 0.5552393198013306, 'ml', 0), ('tensorlayer/tensorlayer', 0.550219714641571, 'ml-rl', 2), ('explosion/thinc', 0.5484919548034668, 'ml-dl', 2), ('mrdbourke/zero-to-mastery-ml', 0.5445974469184875, 'study', 1), ('christoschristofidis/awesome-deep-learning', 0.5436199903488159, 'study', 3), ('udlbook/udlbook', 0.5336586236953735, 'study', 1), ('atcold/nyu-dlsp21', 0.5313364267349243, 'study', 1), ('rasbt/machine-learning-book', 0.517561674118042, 'study', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5173661708831787, 'study', 0), ('whitead/dmol-book', 0.5150943994522095, 'ml-dl', 1), ('rasbt/stat453-deep-learning-ss20', 0.5104526281356812, 'study', 0), ('tensorflow/tensor2tensor', 0.5104150176048279, 'ml', 1), ('deepmodeling/deepmd-kit', 0.5088664889335632, 'sim', 1), ('keras-team/keras', 0.5079450011253357, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.505573570728302, 'ml-dl', 2), ('pytorch/ignite', 0.5055270195007324, 'ml-dl', 3), ('jindongwang/transferlearning', 0.501124918460846, 'ml', 2)]",98,4.0,,0.0,1,0,65,13,0,0,0,1.0,1.0,90.0,1.0,41
196,viz,https://github.com/lux-org/lux,[],,[],[],1.0,,,lux-org/lux,lux,4829,361,90,Python,,Automatically visualize your pandas dataframe via a single print! 📊 💡,lux-org,2024-01-12,2020-01-08,211,22.793661496965612,https://avatars.githubusercontent.com/u/59636588?v=4,Automatically visualize your pandas dataframe via a single print! 📊 💡,"['data-science', 'exploratory-data-analysis', 'jupyter', 'pandas', 'visualization', 'visualization-tools']","['data-science', 'exploratory-data-analysis', 'jupyter', 'pandas', 'visualization', 'visualization-tools']",2023-07-04,"[('kanaries/pygwalker', 0.7356677055358887, 'pandas', 2), ('man-group/dtale', 0.7073760032653809, 'viz', 3), ('adamerose/pandasgui', 0.6828799843788147, 'pandas', 1), ('tkrabel/bamboolib', 0.6735276579856873, 'pandas', 1), ('mwaskom/seaborn', 0.6733652949333191, 'viz', 2), ('holoviz/panel', 0.6041948795318604, 'viz', 1), ('jakevdp/pythondatasciencehandbook', 0.5983377695083618, 'study', 1), ('scitools/iris', 0.5947362184524536, 'gis', 0), ('twopirllc/pandas-ta', 0.5814751386642456, 'finance', 1), ('bokeh/bokeh', 0.5685267448425293, 'viz', 2), ('vizzuhq/ipyvizzu', 0.5684981346130371, 'jupyter', 1), ('zsailer/pandas_flavor', 0.5670690536499023, 'pandas', 1), ('enthought/mayavi', 0.5663363337516785, 'viz', 1), ('modin-project/modin', 0.5601602792739868, 'perf', 2), ('holoviz/hvplot', 0.5583831071853638, 'pandas', 0), ('holoviz/holoviz', 0.5574216246604919, 'viz', 0), ('residentmario/geoplot', 0.5569419860839844, 'gis', 0), ('hazyresearch/meerkat', 0.5568585991859436, 'viz', 2), ('jmcarpenter2/swifter', 0.5564085841178894, 'pandas', 1), ('pandas-dev/pandas', 0.5501386523246765, 'pandas', 2), ('cmudig/autoprofiler', 0.5497778058052063, 'jupyter', 2), ('plotly/plotly.py', 0.5476740598678589, 'viz', 1), ('quantopian/qgrid', 0.5451768040657043, 'jupyter', 0), ('pyqtgraph/pyqtgraph', 0.5438263416290283, 'viz', 1), ('datapane/datapane', 0.5429391860961914, 'viz', 0), ('ydataai/ydata-profiling', 0.542782723903656, 'pandas', 4), ('nalepae/pandarallel', 0.5424959063529968, 'pandas', 1), ('altair-viz/altair', 0.5405464172363281, 'viz', 1), ('blaze/blaze', 0.5378813743591309, 'pandas', 0), ('mementum/bta-lib', 0.5323396921157837, 'finance', 0), ('rapidsai/cudf', 0.5294429063796997, 'pandas', 2), ('eleutherai/pyfra', 0.5287709832191467, 'ml', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5287317037582397, 'pandas', 0), ('wesm/pydata-book', 0.5268304944038391, 'study', 0), ('contextlab/hypertools', 0.5267577767372131, 'ml', 1), ('plotly/dash', 0.5251437425613403, 'viz', 2), ('cuemacro/chartpy', 0.5205578804016113, 'viz', 0), ('districtdatalabs/yellowbrick', 0.5121427178382874, 'ml', 1), ('mito-ds/monorepo', 0.509769856929779, 'jupyter', 3), ('pydata/pandas-datareader', 0.5096178650856018, 'pandas', 1), ('vaexio/vaex', 0.5093144178390503, 'perf', 2), ('federicoceratto/dashing', 0.5034831762313843, 'term', 0)]",21,3.0,,0.04,2,2,49,6,0,3,3,2.0,0.0,90.0,0.0,41
1781,web,https://github.com/stephenmcd/mezzanine,"['django', 'cms']",,[],[],,,,stephenmcd/mezzanine,mezzanine,4693,1645,247,Python,http://mezzanine.jupo.org,CMS framework for Django,stephenmcd,2024-01-13,2010-05-29,713,6.578093712454946,,CMS framework for Django,[],"['cms', 'django']",2022-11-02,"[('feincms/feincms', 0.8132669925689697, 'web', 1), ('wagtail/wagtail', 0.7118169665336609, 'web', 2), ('pallets/flask', 0.5501194596290588, 'web', 0), ('django/django', 0.5459080934524536, 'web', 1), ('bottlepy/bottle', 0.5408180952072144, 'web', 0)]",327,5.0,,0.0,5,1,166,15,0,17,17,5.0,4.0,90.0,0.8,41
367,nlp,https://github.com/layout-parser/layout-parser,[],,[],[],,,,layout-parser/layout-parser,layout-parser,4198,415,69,Python,https://layout-parser.github.io/,A Unified Toolkit for Deep Learning Based Document Image Analysis,layout-parser,2024-01-12,2020-06-10,189,22.111361926260347,https://avatars.githubusercontent.com/u/66751426?v=4,A Unified Toolkit for Deep Learning Based Document Image Analysis,"['computer-vision', 'deep-learning', 'detectron2', 'document-image-processing', 'document-layout-analysis', 'layout-analysis', 'layout-detection', 'layout-parser', 'object-detection', 'ocr']","['computer-vision', 'deep-learning', 'detectron2', 'document-image-processing', 'document-layout-analysis', 'layout-analysis', 'layout-detection', 'layout-parser', 'object-detection', 'ocr']",2022-08-06,[],8,4.0,,0.0,8,0,44,18,0,3,3,8.0,4.0,90.0,0.5,41
1217,ml,https://github.com/cmusphinx/pocketsphinx,[],,[],[],,,,cmusphinx/pocketsphinx,pocketsphinx,3636,684,158,C,,A small speech recognizer,cmusphinx,2024-01-12,2014-04-07,512,7.099581589958159,https://avatars.githubusercontent.com/u/7203378?v=4,A small speech recognizer,"['c', 'speech-recognition']","['c', 'speech-recognition']",2023-12-28,"[('m-bain/whisperx', 0.58738112449646, 'nlp', 1), ('openai/whisper', 0.5778233408927917, 'ml-dl', 1), ('speechbrain/speechbrain', 0.5464542508125305, 'nlp', 1), ('uberi/speech_recognition', 0.5127110481262207, 'ml', 1)]",28,4.0,,0.88,11,6,119,1,3,1,3,11.0,5.0,90.0,0.5,41
318,gui,https://github.com/dddomodossola/remi,[],,[],[],,,,dddomodossola/remi,remi,3419,408,121,Python,,"Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.",dddomodossola,2024-01-13,2014-03-20,514,6.642520122120455,https://avatars.githubusercontent.com/u/59974634?v=4,"Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.","['gui', 'gui-library', 'platform-independent', 'remi', 'ui']","['gui', 'gui-library', 'platform-independent', 'remi', 'ui']",2023-06-26,"[('beeware/toga', 0.6270981431007385, 'gui', 1), ('hoffstadt/dearpygui', 0.5960089564323425, 'gui', 2), ('kivy/kivy', 0.5648102164268494, 'util', 1), ('willmcgugan/textual', 0.5620313882827759, 'term', 0), ('urwid/urwid', 0.5620279312133789, 'term', 0), ('micropython/micropython', 0.5408753752708435, 'util', 0), ('pytables/pytables', 0.5401478409767151, 'data', 0), ('pyinfra-dev/pyinfra', 0.5348232984542847, 'util', 0), ('bottlepy/bottle', 0.5286058187484741, 'web', 0), ('replicate/replicate-python', 0.5246701836585999, 'ml', 0), ('eventlet/eventlet', 0.5185775756835938, 'perf', 0), ('pypy/pypy', 0.5135564804077148, 'util', 0), ('r0x0r/pywebview', 0.5024312138557434, 'gui', 1)]",57,4.0,,0.08,4,1,120,7,0,2,2,4.0,12.0,90.0,3.0,41
703,pandas,https://github.com/nalepae/pandarallel,[],,[],[],1.0,,,nalepae/pandarallel,pandarallel,3358,198,29,Python,https://nalepae.github.io/pandarallel,A simple and efficient tool to parallelize Pandas operations on all available CPUs,nalepae,2024-01-12,2019-03-10,255,13.153889199776161,,A simple and efficient tool to parallelize Pandas operations on all available CPUs,"['pandas', 'parallel']","['pandas', 'parallel']",2023-05-02,"[('jmcarpenter2/swifter', 0.7605847716331482, 'pandas', 1), ('dask/dask', 0.7318655252456665, 'perf', 1), ('ddelange/mapply', 0.6456267833709717, 'pandas', 0), ('blaze/blaze', 0.6345915794372559, 'pandas', 0), ('modin-project/modin', 0.6011874675750732, 'perf', 1), ('scikit-learn-contrib/sklearn-pandas', 0.5978479981422424, 'pandas', 0), ('ipython/ipyparallel', 0.585684597492218, 'perf', 1), ('holoviz/spatialpandas', 0.5524942874908447, 'pandas', 1), ('joblib/joblib', 0.5519195199012756, 'util', 0), ('lux-org/lux', 0.5424959063529968, 'viz', 1), ('tkrabel/bamboolib', 0.5356969237327576, 'pandas', 1), ('mementum/bta-lib', 0.5342207551002502, 'finance', 0), ('adamerose/pandasgui', 0.5294705629348755, 'pandas', 1), ('eventual-inc/daft', 0.5181572437286377, 'pandas', 0), ('twopirllc/pandas-ta', 0.517140805721283, 'finance', 1), ('numpy/numpy', 0.5160078406333923, 'math', 0), ('rapidsai/cudf', 0.5124971866607666, 'pandas', 1), ('vaexio/vaex', 0.5113261938095093, 'perf', 0), ('geopandas/dask-geopandas', 0.5014110207557678, 'gis', 0)]",25,6.0,,0.08,14,0,59,9,2,8,2,14.0,8.0,90.0,0.6,41
208,crypto,https://github.com/cyberpunkmetalhead/binance-volatility-trading-bot,[],,[],[],,,,cyberpunkmetalhead/binance-volatility-trading-bot,Binance-volatility-trading-bot,3288,758,144,Python,,This is a fully functioning Binance trading bot that measures the volatility of every coin on Binance and places trades with the highest gaining coins If you like this project consider donating though the Brave browser to allow me to continuously improve the script.,cyberpunkmetalhead,2024-01-14,2021-05-08,142,23.085255767301906,,This is a fully functioning Binance trading bot that measures the volatility of every coin on Binance and places trades with the highest gaining coins If you like this project consider donating though the Brave browser to allow me to continuously improve the script.,[],[],2023-08-06,"[('ccxt/ccxt', 0.5664846301078796, 'crypto', 0), ('gbeced/basana', 0.518947184085846, 'finance', 0)]",19,3.0,,0.06,5,2,33,5,0,0,0,5.0,3.0,90.0,0.6,41
635,testing,https://github.com/behave/behave,[],,[],[],,,,behave/behave,behave,3009,686,120,Python,https://behave.readthedocs.io/en/latest/,"BDD, Python style.",behave,2024-01-12,2011-10-25,640,4.7015625,https://avatars.githubusercontent.com/u/3344102?v=4,"BDD, Python style.","['bdd', 'bdd-framework', 'behave', 'behavior-driven-development', 'cucumber-like', 'gherkin']","['bdd', 'bdd-framework', 'behave', 'behavior-driven-development', 'cucumber-like', 'gherkin']",2023-11-09,"[('pytest-dev/pytest-bdd', 0.6536642909049988, 'testing', 0)]",88,6.0,,0.92,29,8,149,2,2,2,2,29.0,33.0,90.0,1.1,41
605,debug,https://github.com/inducer/pudb,[],,[],[],,,,inducer/pudb,pudb,2811,228,49,Python,https://documen.tician.de/pudb/,Full-screen console debugger for Python,inducer,2024-01-14,2011-05-13,663,4.236167922497309,,Full-screen console debugger for Python,"['bpython', 'debug', 'debugger', 'ipython', 'pdb', 'pytest', 'pytest-plugin', 'urwid']","['bpython', 'debug', 'debugger', 'ipython', 'pdb', 'pytest', 'pytest-plugin', 'urwid']",2024-01-05,"[('gotcha/ipdb', 0.6826649904251099, 'debug', 2), ('alexmojaki/snoop', 0.61795973777771, 'debug', 1), ('samuelcolvin/python-devtools', 0.6053194999694824, 'debug', 1), ('alexmojaki/heartrate', 0.5674254894256592, 'debug', 1), ('urwid/urwid', 0.5516170859336853, 'term', 0), ('samuelcolvin/pytest-pretty', 0.5497497320175171, 'testing', 1), ('p403n1x87/austin', 0.5496501326560974, 'profiling', 0), ('pyglet/pyglet', 0.5417070984840393, 'gamedev', 0), ('rockhopper-technologies/enlighten', 0.5411313772201538, 'term', 0), ('jquast/blessed', 0.5037537217140198, 'term', 0)]",94,4.0,,0.69,15,7,154,0,1,6,1,14.0,23.0,90.0,1.6,41
131,ml-dl,https://github.com/explosion/thinc,[],,[],[],,,,explosion/thinc,thinc,2773,284,80,Python,https://thinc.ai,"🔮 A refreshing functional take on deep learning, compatible with your favorite libraries",explosion,2024-01-12,2014-10-16,484,5.720895962275272,https://avatars.githubusercontent.com/u/20011530?v=4,"🔮 A refreshing functional take on deep learning, compatible with your favorite libraries","['ai', 'artificial-intelligence', 'deep-learning', 'functional-programming', 'jax', 'machine-learning', 'machine-learning-library', 'mxnet', 'natural-language-processing', 'nlp', 'pytorch', 'spacy', 'tensorflow', 'type-checking']","['ai', 'artificial-intelligence', 'deep-learning', 'functional-programming', 'jax', 'machine-learning', 'machine-learning-library', 'mxnet', 'natural-language-processing', 'nlp', 'pytorch', 'spacy', 'tensorflow', 'type-checking']",2023-12-14,"[('keras-team/keras', 0.7295705080032349, 'ml-dl', 5), ('huggingface/transformers', 0.7069814801216125, 'nlp', 7), ('tensorlayer/tensorlayer', 0.6832032203674316, 'ml-rl', 3), ('onnx/onnx', 0.6653851866722107, 'ml', 5), ('google/trax', 0.6631956696510315, 'ml-dl', 3), ('nvidia/deeplearningexamples', 0.6549383401870728, 'ml-dl', 5), ('deepmind/dm-haiku', 0.6371207237243652, 'ml-dl', 3), ('tensorflow/tensorflow', 0.6326338052749634, 'ml-dl', 3), ('mosaicml/composer', 0.6281270980834961, 'ml-dl', 3), ('tensorflow/tensor2tensor', 0.626385509967804, 'ml', 2), ('keras-team/autokeras', 0.6246259808540344, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.6082127094268799, 'ml', 1), ('huggingface/datasets', 0.6036242246627808, 'nlp', 6), ('microsoft/onnxruntime', 0.5988422632217407, 'ml', 4), ('alpa-projects/alpa', 0.5971328020095825, 'ml-dl', 3), ('keras-team/keras-nlp', 0.5930647253990173, 'nlp', 5), ('deeppavlov/deeppavlov', 0.5923045873641968, 'nlp', 6), ('neuralmagic/sparseml', 0.5918089747428894, 'ml-dl', 3), ('bentoml/bentoml', 0.5913470387458801, 'ml-ops', 3), ('tensorly/tensorly', 0.5860464572906494, 'ml-dl', 5), ('arogozhnikov/einops', 0.5852126479148865, 'ml-dl', 4), ('explosion/spacy', 0.5825142860412598, 'nlp', 7), ('lucidrains/toolformer-pytorch', 0.5822931528091431, 'llm', 2), ('karpathy/micrograd', 0.581674337387085, 'study', 0), ('thilinarajapakse/simpletransformers', 0.5814999938011169, 'nlp', 0), ('ml-tooling/opyrator', 0.5795862674713135, 'viz', 1), ('d2l-ai/d2l-en', 0.5795356035232544, 'study', 7), ('pytorch/ignite', 0.5792794823646545, 'ml-dl', 3), ('ludwig-ai/ludwig', 0.5764778256416321, 'ml-ops', 4), ('intel/intel-extension-for-pytorch', 0.5743740200996399, 'perf', 3), ('ray-project/ray', 0.5737190246582031, 'ml-ops', 4), ('microsoft/nni', 0.5728657841682434, 'ml', 4), ('koaning/human-learn', 0.5714577436447144, 'data', 1), ('pytorch/rl', 0.5706343650817871, 'ml-rl', 3), ('horovod/horovod', 0.569107711315155, 'ml-ops', 5), ('paddlepaddle/paddlenlp', 0.5689855217933655, 'llm', 1), ('adap/flower', 0.5687860250473022, 'ml-ops', 6), ('amanchadha/coursera-deep-learning-specialization', 0.5682108998298645, 'study', 1), ('lutzroeder/netron', 0.5670014023780823, 'ml', 6), ('mlflow/mlflow', 0.5667772889137268, 'ml-ops', 2), ('activeloopai/deeplake', 0.5618358254432678, 'ml-ops', 5), ('allenai/allennlp', 0.5593048930168152, 'nlp', 4), ('merantix-momentum/squirrel-core', 0.5592201352119446, 'ml', 8), ('alirezadir/machine-learning-interview-enlightener', 0.5558744668960571, 'study', 3), ('tensorflow/addons', 0.5554784536361694, 'ml', 3), ('dylanhogg/awesome-python', 0.5546610951423645, 'study', 4), ('hpcaitech/colossalai', 0.5544447898864746, 'llm', 2), ('gradio-app/gradio', 0.5544411540031433, 'viz', 2), ('llmware-ai/llmware', 0.5531507730484009, 'llm', 4), ('rasbt/machine-learning-book', 0.5526840686798096, 'study', 3), ('aiqc/aiqc', 0.5513455867767334, 'ml-ops', 0), ('udacity/deep-learning-v2-pytorch', 0.5484919548034668, 'study', 2), ('jina-ai/finetuner', 0.5483794808387756, 'ml', 0), ('deepfakes/faceswap', 0.5478455424308777, 'ml-dl', 2), ('microsoft/deepspeed', 0.5471163392066956, 'ml-dl', 3), ('ai4finance-foundation/finrl', 0.5454742312431335, 'finance', 0), ('christoschristofidis/awesome-deep-learning', 0.5450423359870911, 'study', 2), ('lukaszahradnik/pyneuralogic', 0.5442025065422058, 'math', 3), ('google/tf-quant-finance', 0.5434539318084717, 'finance', 1), ('polyaxon/polyaxon', 0.5428985357284546, 'ml-ops', 6), ('facebookresearch/habitat-lab', 0.5424818396568298, 'sim', 2), ('neuralmagic/deepsparse', 0.5401971936225891, 'nlp', 1), ('nccr-itmo/fedot', 0.5396167635917664, 'ml-ops', 1), ('modularml/mojo', 0.5388814806938171, 'util', 2), ('opentensor/bittensor', 0.5385252833366394, 'ml', 4), ('rafiqhasan/auto-tensorflow', 0.5384374856948853, 'ml-dl', 2), ('fastai/fastcore', 0.5361101627349854, 'util', 1), ('interpretml/interpret', 0.5354365706443787, 'ml-interpretability', 3), ('docarray/docarray', 0.5351216793060303, 'data', 3), ('makcedward/nlpaug', 0.5350939631462097, 'nlp', 5), 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3), ('pyg-team/pytorch_geometric', 0.516636073589325, 'ml-dl', 2), ('uber/petastorm', 0.5144953727722168, 'data', 4), ('apache/incubator-mxnet', 0.5136386156082153, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5136130452156067, 'ml-dl', 5), ('deepchecks/deepchecks', 0.5133498311042786, 'data', 3), ('blackhc/toma', 0.512549638748169, 'ml-dl', 2), ('deepmodeling/deepmd-kit', 0.5123194456100464, 'sim', 2), ('determined-ai/determined', 0.5116865038871765, 'ml-ops', 4), ('ageron/handson-ml2', 0.5114274621009827, 'ml', 0), ('alibaba/easynlp', 0.5110622048377991, 'nlp', 4), ('explosion/spacy-models', 0.5104645490646362, 'nlp', 4), ('nvidia/nemo', 0.5100991129875183, 'nlp', 2), ('polyaxon/datatile', 0.5097380876541138, 'pandas', 2), ('xplainable/xplainable', 0.5094534754753113, 'ml-interpretability', 1), ('google/mediapipe', 0.5080121159553528, 'ml', 2), ('nevronai/metisfl', 0.5079528093338013, 'ml', 3), ('pytoolz/toolz', 0.507887601852417, 'util', 0), ('roboflow/supervision', 0.5064694881439209, 'ml', 4), ('rwightman/pytorch-image-models', 0.5064119696617126, 'ml-dl', 1), ('avaiga/taipy', 0.5057910680770874, 'data', 0), ('cheshire-cat-ai/core', 0.5046352744102478, 'llm', 1), ('tensorflow/similarity', 0.5028195381164551, 'ml-dl', 3), ('microsoft/semi-supervised-learning', 0.5021923780441284, 'ml', 4), ('feast-dev/feast', 0.5021685361862183, 'ml-ops', 1), ('google/dopamine', 0.5020156502723694, 'ml-rl', 2), ('bigscience-workshop/petals', 0.5014582276344299, 'data', 4), ('ourownstory/neural_prophet', 0.501133382320404, 'ml', 4), ('nltk/nltk', 0.5009826421737671, 'nlp', 3)]",63,3.0,,0.9,11,10,113,1,8,11,8,11.0,6.0,90.0,0.5,41
410,util,https://github.com/yaml/pyyaml,[],,[],[],,,,yaml/pyyaml,pyyaml,2358,512,53,Python,,Canonical source repository for PyYAML,yaml,2024-01-13,2011-11-03,638,3.6917915455155446,https://avatars.githubusercontent.com/u/69535?v=4,Canonical source repository for PyYAML,['yaml'],['yaml'],2023-11-14,"[('mozillazg/pypy', 0.5088357329368591, 'util', 0)]",40,8.0,,0.1,40,14,148,2,0,4,4,40.0,66.0,90.0,1.6,41
222,data,https://github.com/pynamodb/pynamodb,[],,[],[],1.0,,,pynamodb/pynamodb,PynamoDB,2338,430,40,Python,http://pynamodb.readthedocs.io,A pythonic interface to Amazon's DynamoDB,pynamodb,2024-01-13,2014-01-20,523,4.469142545057346,https://avatars.githubusercontent.com/u/27022537?v=4,A pythonic interface to Amazon's DynamoDB,"['aws', 'dynamodb']","['aws', 'dynamodb']",2024-01-05,"[('amzn/ion-python', 0.6932819485664368, 'data', 0), ('awslabs/python-deequ', 0.6457101702690125, 'ml', 1), ('aws/aws-sdk-pandas', 0.6329684257507324, 'pandas', 1), ('geeogi/async-python-lambda-template', 0.6284517049789429, 'template', 0), ('nasdaq/data-link-python', 0.5948277711868286, 'finance', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.5816658735275269, 'util', 0), ('boto/boto3', 0.5776386857032776, 'util', 1), ('nficano/python-lambda', 0.573145866394043, 'util', 1), ('primal100/pybitcointools', 0.5435667634010315, 'crypto', 0), ('aws/chalice', 0.5429194569587708, 'web', 1), ('ethereum/web3.py', 0.536690890789032, 'crypto', 0), ('airbnb/omniduct', 0.5225140452384949, 'data', 0), ('samuelcolvin/aioaws', 0.521031379699707, 'data', 1), ('prefecthq/prefect-aws', 0.5191414952278137, 'data', 1), ('falconry/falcon', 0.5108199119567871, 'web', 0), ('pytables/pytables', 0.5032399892807007, 'data', 0), ('pyeve/eve', 0.5015949010848999, 'web', 0)]",107,3.0,,0.38,26,9,122,0,6,9,6,26.0,22.0,90.0,0.8,41
611,testing,https://github.com/pytest-dev/pytest-testinfra,[],,[],[],,,,pytest-dev/pytest-testinfra,pytest-testinfra,2282,345,78,Python,https://testinfra.readthedocs.io,Testinfra test your infrastructures,pytest-dev,2024-01-12,2015-03-15,463,4.925686093123651,https://avatars.githubusercontent.com/u/8897583?v=4,Testinfra test your infrastructures,"['ansible', 'chef', 'devops', 'devops-tools', 'docker', 'infrastructure-as-code', 'infrastructure-testing', 'kubernetes', 'nagios', 'puppet', 'pytest-plugin', 'saltstack', 'tdd', 'tdd-utilities', 'testing', 'testing-tools']","['ansible', 'chef', 'devops', 'devops-tools', 'docker', 'infrastructure-as-code', 'infrastructure-testing', 'kubernetes', 'nagios', 'puppet', 'pytest-plugin', 'saltstack', 'tdd', 'tdd-utilities', 'testing', 'testing-tools']",2023-11-13,"[('pytest-dev/pytest-xdist', 0.5474545359611511, 'testing', 1), ('pytest-dev/pytest', 0.5438085794448853, 'testing', 1), ('aquasecurity/trivy', 0.538640558719635, 'security', 3), ('eugeneyan/python-collab-template', 0.5332326292991638, 'template', 0), ('orchest/orchest', 0.5304024815559387, 'ml-ops', 2), ('vedro-universe/vedro', 0.526130199432373, 'testing', 2), ('tox-dev/tox', 0.5243973731994629, 'testing', 1), ('taverntesting/tavern', 0.5087285041809082, 'testing', 1), ('seleniumbase/seleniumbase', 0.5065727233886719, 'testing', 1), ('chaostoolkit/chaostoolkit', 0.5015678405761719, 'util', 2)]",136,5.0,,1.1,24,11,108,2,4,10,4,24.0,15.0,90.0,0.6,41
1029,finance,https://github.com/robcarver17/pysystemtrade,[],,[],[],,,,robcarver17/pysystemtrade,pysystemtrade,2255,779,167,Python,,Systematic Trading in python,robcarver17,2024-01-13,2015-11-27,426,5.286336235766912,,Systematic Trading in python,[],[],2024-01-08,"[('gbeced/pyalgotrade', 0.756932258605957, 'finance', 0), ('quantopian/zipline', 0.6582158803939819, 'finance', 0), ('gbeced/basana', 0.6477593779563904, 'finance', 0), ('cuemacro/finmarketpy', 0.6369601488113403, 'finance', 0), ('goldmansachs/gs-quant', 0.6150317788124084, 'finance', 0), ('mementum/backtrader', 0.6113889813423157, 'finance', 0), ('quantconnect/lean', 0.6064723134040833, 'finance', 0), ('quantecon/quantecon.py', 0.5519441962242126, 'sim', 0), ('quantopian/pyfolio', 0.5509036779403687, 'finance', 0), ('kernc/backtesting.py', 0.5506398677825928, 'finance', 0), ('eleutherai/pyfra', 0.5468427538871765, 'ml', 0), ('pmorissette/ffn', 0.5440186262130737, 'finance', 0), ('ethereum/web3.py', 0.5425991415977478, 'crypto', 0), ('ranaroussi/quantstats', 0.5409232378005981, 'finance', 0), ('firmai/atspy', 0.5296515226364136, 'time-series', 0), ('google/pyglove', 0.5276491045951843, 'util', 0), ('hydrosquall/tiingo-python', 0.5165610313415527, 'finance', 0), ('domokane/financepy', 0.5038099884986877, 'finance', 0)]",64,2.0,,11.71,35,32,99,0,0,0,0,35.0,30.0,90.0,0.9,41
1296,llm,https://github.com/ofa-sys/ofa,[],,[],[],,,,ofa-sys/ofa,OFA,2235,242,22,Python,,"Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework",ofa-sys,2024-01-14,2022-01-29,104,21.40218878248974,https://avatars.githubusercontent.com/u/98636793?v=4,"Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework","['chinese', 'image-captioning', 'multimodal', 'pretrained-models', 'pretraining', 'prompt', 'prompt-tuning', 'referring-expression-comprehension', 'text-to-image-synthesis', 'vision-language', 'visual-question-answering']","['chinese', 'image-captioning', 'multimodal', 'pretrained-models', 'pretraining', 'prompt', 'prompt-tuning', 'referring-expression-comprehension', 'text-to-image-synthesis', 'vision-language', 'visual-question-answering']",2023-08-31,"[('nvlabs/prismer', 0.664030134677887, 'diffusion', 1), ('salesforce/blip', 0.6362955570220947, 'diffusion', 3), ('reasoning-machines/pal', 0.5922104120254517, 'llm', 0), ('srush/minichain', 0.5822784304618835, 'llm', 0), ('microsoft/unilm', 0.5649746060371399, 'nlp', 1), ('bytedance/lightseq', 0.5597769021987915, 'nlp', 0), ('alibaba/easynlp', 0.5513916611671448, 'nlp', 2), ('next-gpt/next-gpt', 0.538471519947052, 'llm', 1), ('microsoft/generative-ai-for-beginners', 0.5347688794136047, 'study', 0), ('eleutherai/lm-evaluation-harness', 0.5323944091796875, 'llm', 0), ('huggingface/setfit', 0.5320513844490051, 'nlp', 0), ('promptslab/awesome-prompt-engineering', 0.5315465927124023, 'study', 2), ('tatsu-lab/stanford_alpaca', 0.5313153266906738, 'llm', 0), ('pan-ml/panml', 0.5308709740638733, 'llm', 0), ('openai/clip', 0.5293337106704712, 'ml-dl', 0), ('cg123/mergekit', 0.5244383811950684, 'llm', 0), ('thudm/cogvideo', 0.5225783586502075, 'ml', 0), ('lm-sys/fastchat', 0.5206315517425537, 'llm', 0), ('thudm/glm-130b', 0.5175337195396423, 'llm', 0), ('eugeneyan/obsidian-copilot', 0.5171679854393005, 'llm', 0), ('infinitylogesh/mutate', 0.514673113822937, 'nlp', 0), ('deepset-ai/farm', 0.5126766562461853, 'nlp', 1), ('yueyu1030/attrprompt', 0.5103440284729004, 'llm', 0), ('luodian/otter', 0.5090562701225281, 'llm', 0), ('thudm/p-tuning-v2', 0.5087725520133972, 'nlp', 1), ('thilinarajapakse/simpletransformers', 0.5072410702705383, 'nlp', 0), ('huggingface/transformers', 0.5054358243942261, 'nlp', 1), ('night-chen/toolqa', 0.5051456093788147, 'llm', 0), ('openlmlab/moss', 0.5047501921653748, 'llm', 0), ('facebookresearch/mmf', 0.5046265721321106, 'ml-dl', 2), ('llmware-ai/llmware', 0.5022109150886536, 'llm', 0), ('jina-ai/clip-as-service', 0.5020403265953064, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.500640332698822, 'llm', 1)]",12,6.0,,0.15,12,3,24,5,0,0,0,12.0,5.0,90.0,0.4,41
233,crypto,https://github.com/ethereum/py-evm,[],,[],[],,,,ethereum/py-evm,py-evm,2138,625,96,Python,https://py-evm.readthedocs.io/en/latest/,A Python implementation of the Ethereum Virtual Machine,ethereum,2024-01-12,2016-12-27,370,5.778378378378378,https://avatars.githubusercontent.com/u/6250754?v=4,A Python implementation of the Ethereum Virtual Machine,"['ethereum', 'ethereum-virtual-machine', 'evm']","['ethereum', 'ethereum-virtual-machine', 'evm']",2024-01-12,"[('ethereum/web3.py', 0.6437891721725464, 'crypto', 0), ('pyston/pyston', 0.5943373441696167, 'util', 0), ('primal100/pybitcointools', 0.5760893821716309, 'crypto', 0), ('exaloop/codon', 0.5648453235626221, 'perf', 0), ('micropython/micropython', 0.5545228719711304, 'util', 0), ('1200wd/bitcoinlib', 0.546512246131897, 'crypto', 0), ('pypy/pypy', 0.5445558428764343, 'util', 0), ('oracle/graalpython', 0.54329913854599, 'util', 0), ('paramiko/paramiko', 0.5358531475067139, 'util', 0), ('amzn/ion-python', 0.5310432314872742, 'data', 0), ('replicate/replicate-python', 0.5225535035133362, 'ml', 0), ('pytransitions/transitions', 0.5112031698226929, 'util', 0), ('ipython/ipyparallel', 0.5111911296844482, 'perf', 0), ('backtick-se/cowait', 0.5091575384140015, 'util', 0), ('gbeced/basana', 0.5067870616912842, 'finance', 0), ('fastai/fastcore', 0.5062366724014282, 'util', 0), ('pypa/hatch', 0.5026881694793701, 'util', 0)]",91,2.0,,2.46,29,6,86,0,0,15,15,29.0,3.0,90.0,0.1,41
322,gui,https://github.com/wxwidgets/phoenix,[],,[],[],,,,wxwidgets/phoenix,Phoenix,2120,522,107,Python,http://wxpython.org/,"wxPython's Project Phoenix.  A new implementation of wxPython, better, stronger, faster than he was before.",wxwidgets,2024-01-14,2012-07-17,602,3.521594684385382,https://avatars.githubusercontent.com/u/791023?v=4,"wxPython's Project Phoenix.  A new implementation of wxPython, better, stronger, faster than he was before.","['awesome', 'cross-platform', 'gui', 'gui-framework', 'gui-toolkit', 'linux', 'macosx', 'windows', 'wxpython', 'wxwidgets']","['awesome', 'cross-platform', 'gui', 'gui-framework', 'gui-toolkit', 'linux', 'macosx', 'windows', 'wxpython', 'wxwidgets']",2024-01-11,"[('pysimplegui/pysimplegui', 0.703073263168335, 'gui', 3), ('beeware/toga', 0.6360959410667419, 'gui', 1), ('hoffstadt/dearpygui', 0.6336509585380554, 'gui', 4), ('pypy/pypy', 0.6231735944747925, 'util', 0), ('pyglet/pyglet', 0.5862021446228027, 'gamedev', 0), ('parthjadhav/tkinter-designer', 0.58327716588974, 'gui', 1), ('holoviz/panel', 0.5708284378051758, 'viz', 1), ('kivy/kivy', 0.5668678879737854, 'util', 2), ('r0x0r/pywebview', 0.5664918422698975, 'gui', 3), ('pyston/pyston', 0.5643559694290161, 'util', 0), ('faster-cpython/ideas', 0.563103973865509, 'perf', 0), ('willmcgugan/textual', 0.5400227308273315, 'term', 0), ('ipython/ipyparallel', 0.5365442633628845, 'perf', 0), ('python/cpython', 0.5291924476623535, 'util', 0), ('erotemic/ubelt', 0.5278312563896179, 'util', 1), ('giswqs/mapwidget', 0.5239470601081848, 'gis', 0), ('jupyter-widgets/ipywidgets', 0.5223656296730042, 'jupyter', 0), ('micropython/micropython', 0.5213609933853149, 'util', 0), ('matplotlib/matplotlib', 0.5209715366363525, 'viz', 0), ('faster-cpython/tools', 0.5171552300453186, 'perf', 0), ('fastai/fastcore', 0.5171084403991699, 'util', 0), ('plotly/plotly.py', 0.5154394507408142, 'viz', 0), ('holoviz/holoviz', 0.5125094652175903, 'viz', 0), ('maartenbreddels/ipyvolume', 0.5121052861213684, 'jupyter', 0), ('timofurrer/awesome-asyncio', 0.510340690612793, 'study', 1), ('pyqtgraph/pyqtgraph', 0.509281575679779, 'viz', 0), ('klen/muffin', 0.5061802864074707, 'web', 0), ('cython/cython', 0.5055117011070251, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5054412484169006, 'perf', 0), ('bokeh/bokeh', 0.5054352879524231, 'viz', 0), ('cohere-ai/notebooks', 0.5029077529907227, 'llm', 0), ('tqdm/tqdm', 0.5025215744972229, 'term', 1)]",135,3.0,,1.25,85,42,140,0,1,2,1,85.0,110.0,90.0,1.3,41
1688,util,https://github.com/pypa/flit,"['pypi', 'package-manager', 'packaging']",,[],[],1.0,,,pypa/flit,flit,2058,128,33,Python,https://flit.pypa.io/,Simplified packaging of Python modules,pypa,2024-01-13,2015-03-13,463,4.43944530046225,https://avatars.githubusercontent.com/u/647025?v=4,Simplified packaging of Python modules,[],"['package-manager', 'packaging']",2023-12-09,"[('indygreg/pyoxidizer', 0.8746299147605896, 'util', 2), ('mitsuhiko/rye', 0.825670599937439, 'util', 2), ('python-poetry/poetry', 0.8031641244888306, 'util', 2), ('regebro/pyroma', 0.7084984183311462, 'util', 1), ('pomponchik/instld', 0.6446828842163086, 'util', 1), ('pdm-project/pdm', 0.6415485143661499, 'util', 2), ('pypa/hatch', 0.6373385787010193, 'util', 2), ('pypi/warehouse', 0.6236358284950256, 'util', 0), ('pyodide/micropip', 0.6164460778236389, 'util', 0), ('mamba-org/mamba', 0.5987750291824341, 'util', 2), ('tezromach/python-package-template', 0.5902042984962463, 'template', 0), ('mamba-org/gator', 0.5534806847572327, 'jupyter', 0), ('ofek/pyapp', 0.5471957921981812, 'util', 1), ('jazzband/pip-tools', 0.5470367670059204, 'util', 1), ('pypa/pipenv', 0.5302018523216248, 'util', 1), ('tiangolo/poetry-version-plugin', 0.519473671913147, 'util', 1), ('pyscaffold/pyscaffold', 0.5155603885650635, 'template', 0), ('beeware/briefcase', 0.5033259987831116, 'util', 0), ('grahamdumpleton/wrapt', 0.5029957890510559, 'util', 0)]",70,5.0,,0.56,17,9,108,1,0,5,5,17.0,21.0,90.0,1.2,41
642,util,https://github.com/grahamdumpleton/wrapt,[],,[],[],,,,grahamdumpleton/wrapt,wrapt,1917,218,44,Python,,"A Python module for decorators, wrappers and monkey patching.",grahamdumpleton,2024-01-12,2013-05-29,556,3.4425346331452027,,"A Python module for decorators, wrappers and monkey patching.",[],[],2023-11-10,"[('clarete/forbiddenfruit', 0.6830537915229797, 'util', 0), ('python-rope/rope', 0.5636062026023865, 'util', 0), ('instagram/fixit', 0.5495676398277283, 'util', 0), ('faif/python-patterns', 0.5319310426712036, 'util', 0), ('hhatto/autopep8', 0.5304956436157227, 'util', 0), ('psf/black', 0.527543306350708, 'util', 0), ('indygreg/pyoxidizer', 0.5274959206581116, 'util', 0), ('facebookincubator/bowler', 0.5219171047210693, 'util', 0), ('eugeneyan/python-collab-template', 0.5126464366912842, 'template', 0), ('pdm-project/pdm', 0.5066951513290405, 'util', 0), ('pypa/hatch', 0.5048109292984009, 'util', 0), ('python-poetry/poetry', 0.5032442212104797, 'util', 0), ('pypa/flit', 0.5029957890510559, 'util', 0), ('allrod5/injectable', 0.5006911754608154, 'util', 0)]",27,6.0,,0.69,13,5,129,2,1,5,1,13.0,59.0,90.0,4.5,41
592,time-series,https://github.com/uber/orbit,[],,[],[],,,,uber/orbit,orbit,1770,130,32,Python,https://orbit-ml.readthedocs.io/en/stable/,A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.,uber,2024-01-14,2020-01-07,212,8.349056603773585,https://avatars.githubusercontent.com/u/538264?v=4,A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.,"['arima', 'bayesian', 'bayesian-methods', 'bayesian-statistics', 'changepoint', 'exponential-smoothing', 'forecast', 'forecasting', 'machine-learning', 'orbit', 'probabilistic', 'probabilistic-programming', 'pyro', 'pystan', 'pytorch', 'regression', 'regression-models', 'stan', 'time-series']","['arima', 'bayesian', 'bayesian-methods', 'bayesian-statistics', 'changepoint', 'exponential-smoothing', 'forecast', 'forecasting', 'machine-learning', 'orbit', 'probabilistic', 'probabilistic-programming', 'pyro', 'pystan', 'pytorch', 'regression', 'regression-models', 'stan', 'time-series']",2024-01-12,"[('awslabs/gluonts', 0.6446792483329773, 'time-series', 4), ('pymc-devs/pymc3', 0.6276130080223083, 'ml', 1), ('alkaline-ml/pmdarima', 0.5944992899894714, 'time-series', 4), ('firmai/atspy', 0.5873233675956726, 'time-series', 2), ('ourownstory/neural_prophet', 0.5688930153846741, 'ml', 5), ('statsmodels/statsmodels', 0.5589537024497986, 'ml', 2), ('probml/pyprobml', 0.5333145260810852, 'ml', 4), ('nixtla/statsforecast', 0.5311532020568848, 'time-series', 5), ('unit8co/darts', 0.5234378576278687, 'time-series', 3), ('stan-dev/pystan', 0.5150203704833984, 'ml', 0), ('scikit-optimize/scikit-optimize', 0.5112178325653076, 'ml', 1), ('winedarksea/autots', 0.5088127255439758, 'time-series', 3), ('pyro-ppl/pyro', 0.5006424188613892, 'ml-dl', 4), ('crflynn/stochastic', 0.5002565979957581, 'sim', 0)]",19,3.0,,0.44,25,21,49,0,3,6,3,25.0,10.0,90.0,0.4,41
877,time-series,https://github.com/aistream-peelout/flow-forecast,[],,[],[],,,,aistream-peelout/flow-forecast,flow-forecast,1748,272,28,Python,https://flow-forecast.atlassian.net/wiki/spaces/FF/overview,"Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).",aistream-peelout,2024-01-13,2019-08-15,232,7.511356660527931,https://avatars.githubusercontent.com/u/45472534?v=4,"Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).","['anomaly-detection', 'deep-learning', 'deep-neural-networks', 'forecasting', 'lstm', 'pytorch', 'state-of-the-art-models', 'time-series', 'time-series-analysis', 'time-series-forecasting', 'time-series-regression', 'transfer-learning', 'transformer']","['anomaly-detection', 'deep-learning', 'deep-neural-networks', 'forecasting', 'lstm', 'pytorch', 'state-of-the-art-models', 'time-series', 'time-series-analysis', 'time-series-forecasting', 'time-series-regression', 'transfer-learning', 'transformer']",2024-01-11,"[('unit8co/darts', 0.7890238165855408, 'time-series', 4), ('salesforce/deeptime', 0.7200793027877808, 'time-series', 5), ('yzhao062/pyod', 0.6631090044975281, 'data', 2), ('salesforce/merlion', 0.6415663361549377, 'time-series', 3), ('pycaret/pycaret', 0.6154711246490479, 'ml', 2), ('huggingface/transformers', 0.6060823798179626, 'nlp', 3), ('ourownstory/neural_prophet', 0.5880416035652161, 'ml', 4), ('opengeos/earthformer', 0.5866661071777344, 'gis', 3), ('awslabs/gluonts', 0.5800959467887878, 'time-series', 5), ('tdameritrade/stumpy', 0.567234992980957, 'time-series', 2), ('rasbt/machine-learning-book', 0.5672064423561096, 'study', 2), ('alkaline-ml/pmdarima', 0.5597772002220154, 'time-series', 2), ('pytorch/ignite', 0.5596166253089905, 'ml-dl', 2), ('winedarksea/autots', 0.557109534740448, 'time-series', 3), ('sktime/sktime', 0.5518553256988525, 'time-series', 4), ('keras-team/keras', 0.5506109595298767, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.549731969833374, 'ml-dl', 3), ('ashleve/lightning-hydra-template', 0.5330670475959778, 'util', 2), ('tensorlayer/tensorlayer', 0.5312919020652771, 'ml-rl', 1), ('nyandwi/modernconvnets', 0.5294846296310425, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5282737016677856, 'ml-dl', 2), ('uber/petastorm', 0.5273684859275818, 'data', 2), ('skorch-dev/skorch', 0.5259057879447937, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5234374403953552, 'study', 2), ('explosion/thinc', 0.5212835669517517, 'ml-dl', 2), ('tensorflow/tensorflow', 0.5199403166770935, 'ml-dl', 2), ('intellabs/bayesian-torch', 0.518251359462738, 'ml', 3), ('tensorflow/tensor2tensor', 0.5155697464942932, 'ml', 1), ('neuralmagic/sparseml', 0.5107213854789734, 'ml-dl', 2), ('keras-team/autokeras', 0.5103901624679565, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5097944140434265, 'ml', 1), ('linkedin/greykite', 0.5091543197631836, 'ml', 0), ('nixtla/statsforecast', 0.506076991558075, 'time-series', 2), ('alignmentresearch/tuned-lens', 0.5054931640625, 'ml-interpretability', 1), ('datasystemslab/geotorch', 0.5054202675819397, 'gis', 2), ('christoschristofidis/awesome-deep-learning', 0.5039076209068298, 'study', 1), ('awslabs/autogluon', 0.5033537149429321, 'ml', 5), ('arogozhnikov/einops', 0.5023390650749207, 'ml-dl', 2), ('ray-project/ray-educational-materials', 0.5015262365341187, 'study', 1)]",14,2.0,,5.9,21,15,54,0,1,9,1,21.0,18.0,90.0,0.9,41
511,data,https://github.com/agronholm/sqlacodegen,[],,[],[],,,,agronholm/sqlacodegen,sqlacodegen,1605,228,25,Python,,Automatic model code generator for SQLAlchemy,agronholm,2024-01-13,2016-12-28,369,4.339513325608343,,Automatic model code generator for SQLAlchemy,[],[],2024-01-09,"[('sqlalchemy/sqlalchemy', 0.7499924302101135, 'data', 0), ('sqlalchemy/alembic', 0.6634976267814636, 'data', 0), ('tiangolo/sqlmodel', 0.542255163192749, 'data', 0), ('mause/duckdb_engine', 0.5388209819793701, 'data', 0), ('brokenloop/jsontopydantic', 0.5330995321273804, 'util', 0), ('mcfunley/pugsql', 0.5109015703201294, 'data', 0), ('aminalaee/sqladmin', 0.5044848918914795, 'data', 0), ('microsoft/pycodegpt', 0.5004090666770935, 'llm', 0)]",19,2.0,,1.04,24,12,86,0,0,3,3,24.0,67.0,90.0,2.8,41
1778,perf,https://github.com/python-greenlet/greenlet,['coroutine'],,[],[],,,,python-greenlet/greenlet,greenlet,1559,232,53,C++,,Lightweight in-process concurrent programming,python-greenlet,2024-01-12,2011-12-17,632,2.4651005195391913,https://avatars.githubusercontent.com/u/1270171?v=4,Lightweight in-process concurrent programming,[],['coroutine'],2023-12-21,"[('noxdafox/pebble', 0.6010660529136658, 'perf', 0), ('python-trio/trio', 0.5789716839790344, 'perf', 0), ('sumerc/yappi', 0.5529376268386841, 'profiling', 1), ('agronholm/anyio', 0.517914354801178, 'perf', 0), ('fluentpython/example-code-2e', 0.514401912689209, 'study', 0)]",68,4.0,,1.83,24,17,147,1,0,4,4,24.0,83.0,90.0,3.5,41
1545,web,https://github.com/jordaneremieff/mangum,"['aws', 'lambda', 'asgi']",,[],[],,,,jordaneremieff/mangum,mangum,1518,94,16,Python,https://mangum.io/,AWS Lambda support for ASGI applications,jordaneremieff,2024-01-12,2019-01-14,263,5.768729641693811,,AWS Lambda support for ASGI applications,"['api-gateway', 'asgi', 'asyncio', 'aws', 'aws-lambda', 'django', 'fastapi', 'lambda', 'quart', 'sanic', 'serverless', 'starlette']","['api-gateway', 'asgi', 'asyncio', 'aws', 'aws-lambda', 'django', 'fastapi', 'lambda', 'quart', 'sanic', 'serverless', 'starlette']",2023-11-03,"[('samuelcolvin/aioaws', 0.7040007710456848, 'data', 2), ('geeogi/async-python-lambda-template', 0.6858481168746948, 'template', 0), ('aws/chalice', 0.677313506603241, 'web', 4), ('nficano/python-lambda', 0.6575080752372742, 'util', 3), ('neoteroi/blacksheep', 0.6414300203323364, 'web', 2), ('aws/aws-lambda-python-runtime-interface-client', 0.6282299160957336, 'util', 0), ('pallets/quart', 0.6127312183380127, 'web', 3), ('starlite-api/starlite', 0.5975977182388306, 'web', 2), ('rpgreen/apilogs', 0.595600962638855, 'util', 4), ('boto/boto3', 0.589402973651886, 'util', 1), ('falconry/falcon', 0.5848598480224609, 'web', 1), ('encode/uvicorn', 0.5822369456291199, 'web', 2), ('developmentseed/geolambda', 0.5730166435241699, 'gis', 0), ('aio-libs/aiobotocore', 0.546834409236908, 'util', 2), ('aws/aws-sdk-pandas', 0.5317604541778564, 'pandas', 3), ('alirn76/panther', 0.5160053372383118, 'web', 0), ('encode/starlette', 0.5116714239120483, 'web', 0), ('huge-success/sanic', 0.511349081993103, 'web', 3), ('aio-libs/aiohttp', 0.5083812475204468, 'web', 1), ('developmentseed/titiler', 0.5069487690925598, 'gis', 2)]",30,6.0,,0.02,15,4,61,2,0,12,12,15.0,16.0,90.0,1.1,41
1719,util,https://github.com/sourcery-ai/sourcery,['code-quality'],,[],[],,,,sourcery-ai/sourcery,sourcery,1446,53,17,,https://sourcery.ai,Automatically review and improve your Python code. ⭐ this repo and Sourcery Starbot will send you a PR. Or install our CLI to improve your code locally,sourcery-ai,2024-01-14,2019-07-15,237,6.097590361445783,https://avatars.githubusercontent.com/u/36609879?v=4,Automatically review and improve your Python code. ⭐ this repo and Sourcery Starbot will send you a PR. Or install our CLI to improve your code locally,"['ai', 'code-quality', 'refactoring', 'software-development', 'software-tools']","['ai', 'code-quality', 'refactoring', 'software-development', 'software-tools']",2023-12-21,"[('rubik/radon', 0.6180241703987122, 'util', 0), ('nedbat/coveragepy', 0.5949026942253113, 'testing', 0), ('reloadware/reloadium', 0.5745614767074585, 'profiling', 1), ('amaargiru/pyroad', 0.5690412521362305, 'study', 0), ('featurelabs/featuretools', 0.554305911064148, 'ml', 0), ('willmcgugan/textual', 0.5534403920173645, 'term', 0), ('dosisod/refurb', 0.5449427366256714, 'util', 0), ('eugeneyan/python-collab-template', 0.5378775596618652, 'template', 0), ('prompt-toolkit/ptpython', 0.5356951355934143, 'util', 0), ('jendrikseipp/vulture', 0.5342533588409424, 'util', 1), ('pypy/pypy', 0.5338239073753357, 'util', 0), ('python/cpython', 0.5331263542175293, 'util', 0), ('landscapeio/prospector', 0.5299766659736633, 'util', 0), ('eleutherai/pyfra', 0.5296950340270996, 'ml', 0), ('google/pyglove', 0.5295018553733826, 'util', 0), ('psf/black', 0.5155060291290283, 'util', 1), ('hhatto/autopep8', 0.5095126628875732, 'util', 0), ('samuelcolvin/python-devtools', 0.506219744682312, 'debug', 0), ('pypa/hatch', 0.5025754570960999, 'util', 0), ('gradio-app/gradio', 0.5021675825119019, 'viz', 0), ('norvig/pytudes', 0.5019119381904602, 'util', 0), ('gaogaotiantian/viztracer', 0.5000627636909485, 'profiling', 0)]",9,3.0,,0.5,26,13,55,1,211,100,211,26.0,42.0,90.0,1.6,41
1118,web,https://github.com/wtforms/wtforms,[],,[],[],,,,wtforms/wtforms,wtforms,1443,392,48,Python,https://wtforms.readthedocs.io,A flexible forms validation and rendering library for Python.,wtforms,2024-01-06,2013-09-11,541,2.6630635380964933,https://avatars.githubusercontent.com/u/4740084?v=4,A flexible forms validation and rendering library for Python.,"['forms', 'html', 'validation', 'wtforms']","['forms', 'html', 'validation', 'wtforms']",2024-01-11,"[('pyeve/cerberus', 0.657631516456604, 'data', 0), ('connorferster/handcalcs', 0.5505093336105347, 'jupyter', 0), ('pydantic/pydantic', 0.5329594612121582, 'util', 1), ('pytoolz/toolz', 0.5101136565208435, 'util', 0)]",156,5.0,,1.48,24,19,126,0,3,3,3,24.0,32.0,90.0,1.3,41
932,data,https://github.com/datastax/python-driver,[],,[],[],,,,datastax/python-driver,python-driver,1363,577,78,Python,,DataStax Python Driver for Apache Cassandra,datastax,2024-01-08,2013-07-08,551,2.473043027475376,https://avatars.githubusercontent.com/u/573369?v=4,DataStax Python Driver for Apache Cassandra,[],[],2023-12-21,"[('scylladb/python-driver', 0.8284926414489746, 'data', 0), ('neo4j/neo4j-python-driver', 0.5756858587265015, 'data', 0), ('awslabs/python-deequ', 0.5413904190063477, 'ml', 0), ('nasdaq/data-link-python', 0.5312590599060059, 'finance', 0)]",196,6.0,,1.25,19,14,128,1,0,8,8,19.0,27.0,90.0,1.4,41
983,sim,https://github.com/deepmodeling/deepmd-kit,[],,[],[],,,,deepmodeling/deepmd-kit,deepmd-kit,1296,462,46,C++,https://docs.deepmodeling.com/projects/deepmd/,A deep learning package for many-body potential energy representation and molecular dynamics,deepmodeling,2024-01-13,2017-12-12,320,4.05,https://avatars.githubusercontent.com/u/32671488?v=4,A deep learning package for many-body potential energy representation and molecular dynamics,"['ase', 'c', 'computational-chemistry', 'cpp', 'cuda', 'deep-learning', 'deepmd', 'ipi', 'lammps', 'materials-science', 'molecular-dynamics', 'nodejs', 'potential-energy', 'rocm', 'tensorflow']","['ase', 'c', 'computational-chemistry', 'cpp', 'cuda', 'deep-learning', 'deepmd', 'ipi', 'lammps', 'materials-science', 'molecular-dynamics', 'nodejs', 'potential-energy', 'rocm', 'tensorflow']",2023-10-27,"[('whitead/dmol-book', 0.702418863773346, 'ml-dl', 1), ('tensorlayer/tensorlayer', 0.5839037299156189, 'ml-rl', 2), ('espressomd/espresso', 0.5804603695869446, 'sim', 1), ('nvidia/deeplearningexamples', 0.558430016040802, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5467190146446228, 'study', 2), ('tensorflow/tensor2tensor', 0.5421066880226135, 'ml', 1), ('uber/petastorm', 0.5317656397819519, 'data', 2), ('google/trax', 0.5293905735015869, 'ml-dl', 1), ('denys88/rl_games', 0.5176900625228882, 'ml-rl', 1), ('microsoft/deepspeed', 0.513860821723938, 'ml-dl', 1), ('explosion/thinc', 0.5123194456100464, 'ml-dl', 2), ('keras-team/keras', 0.5097646713256836, 'ml-dl', 2), ('udacity/deep-learning-v2-pytorch', 0.5088664889335632, 'study', 1), ('ashleve/lightning-hydra-template', 0.5063115358352661, 'util', 1), ('dmlc/dgl', 0.5051169991493225, 'ml-dl', 1), ('skorch-dev/skorch', 0.5047734379768372, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5035223960876465, 'study', 1), ('deepmind/dm_control', 0.5011904239654541, 'ml-rl', 1)]",64,1.0,,6.83,228,196,74,3,8,11,8,223.0,296.0,90.0,1.3,41
603,testing,https://github.com/pytest-dev/pytest-xdist,[],,[],[],,,,pytest-dev/pytest-xdist,pytest-xdist,1287,216,49,Python,https://pytest-xdist.readthedocs.io,pytest plugin for distributed testing and loop-on-failures testing modes. ,pytest-dev,2024-01-11,2015-09-01,439,2.931662870159453,https://avatars.githubusercontent.com/u/8897583?v=4,pytest plugin for distributed testing and loop-on-failures testing modes. ,"['pytest', 'pytest-plugin']","['pytest', 'pytest-plugin']",2024-01-10,"[('pytest-dev/pytest-cov', 0.7077765464782715, 'testing', 1), ('pytest-dev/pytest', 0.6530086994171143, 'testing', 0), ('teemu/pytest-sugar', 0.6500891447067261, 'testing', 2), ('pytest-dev/pytest-asyncio', 0.6232805252075195, 'testing', 1), ('ionelmc/pytest-benchmark', 0.613385796546936, 'testing', 1), ('pytest-dev/pytest-mock', 0.6102861762046814, 'testing', 1), ('taverntesting/tavern', 0.6020364165306091, 'testing', 1), ('kiwicom/pytest-recording', 0.5940394401550293, 'testing', 1), ('computationalmodelling/nbval', 0.5855597257614136, 'jupyter', 2), ('samuelcolvin/pytest-pretty', 0.5537928342819214, 'testing', 1), ('wolever/parameterized', 0.5527563095092773, 'testing', 0), ('pytest-dev/pytest-testinfra', 0.5474545359611511, 'testing', 1), ('samuelcolvin/dirty-equals', 0.546781599521637, 'util', 1), ('nedbat/coveragepy', 0.5098601579666138, 'testing', 0), ('buildbot/buildbot', 0.5035778880119324, 'util', 0)]",93,5.0,,1.17,60,36,102,0,1,8,1,60.0,98.0,90.0,1.6,41
895,util,https://github.com/ossf/criticality_score,[],,[],[],,,,ossf/criticality_score,criticality_score,1255,109,34,Go,,Gives criticality score for an open source project,ossf,2024-01-10,2020-11-17,167,7.514970059880239,https://avatars.githubusercontent.com/u/67707773?v=4,Gives criticality score for an open source project,[],[],2023-12-14,[],20,3.0,,1.4,55,43,38,1,4,3,4,55.0,40.0,90.0,0.7,41
576,util,https://github.com/lidatong/dataclasses-json,[],,[],[],,,,lidatong/dataclasses-json,dataclasses-json,1248,142,8,Python,,Easily serialize Data Classes to and from JSON,lidatong,2024-01-13,2018-04-21,301,4.140284360189574,,Easily serialize Data Classes to and from JSON,"['dataclasses', 'json']","['dataclasses', 'json']",2023-11-27,"[('yukinarit/pyserde', 0.686412513256073, 'util', 2), ('konradhalas/dacite', 0.630731999874115, 'util', 1), ('marshmallow-code/marshmallow', 0.5334739089012146, 'util', 0), ('python-attrs/cattrs', 0.5296611189842224, 'typing', 0), ('1rgs/jsonformer', 0.529080331325531, 'llm', 1), ('pylons/colander', 0.5289405584335327, 'util', 0), ('jsonpickle/jsonpickle', 0.5186536908149719, 'data', 1)]",67,5.0,,1.15,24,13,70,2,12,14,12,24.0,30.0,90.0,1.2,41
1035,diffusion,https://github.com/nvlabs/prismer,[],,[],[],,,,nvlabs/prismer,prismer,1242,70,15,Python,https://shikun.io/projects/prismer,"The implementation of ""Prismer: A Vision-Language Model with An Ensemble of Experts"".",nvlabs,2024-01-13,2023-03-02,47,26.02994011976048,https://avatars.githubusercontent.com/u/2695301?v=4,"The implementation of ""Prismer: A Vision-Language Model with An Ensemble of Experts"".","['image-captioning', 'language-model', 'multi-modal-learning', 'multi-task-learning', 'vision-and-language', 'vision-language-model', 'vqa']","['image-captioning', 'language-model', 'multi-modal-learning', 'multi-task-learning', 'vision-and-language', 'vision-language-model', 'vqa']",2023-04-29,"[('salesforce/blip', 0.6734409928321838, 'diffusion', 1), ('ofa-sys/ofa', 0.664030134677887, 'llm', 1), ('next-gpt/next-gpt', 0.5736103057861328, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5629435777664185, 'llm', 1), ('freedomintelligence/llmzoo', 0.5624253153800964, 'llm', 1), ('jerryyli/valhalla-nmt', 0.5498813390731812, 'ml-dl', 0), ('luodian/otter', 0.5447686314582825, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.5408389568328857, 'nlp', 0), ('reasoning-machines/pal', 0.5364494323730469, 'llm', 1), ('lm-sys/fastchat', 0.5359172821044922, 'llm', 1), ('facebookresearch/mmf', 0.5333836674690247, 'ml-dl', 1), ('lucidrains/toolformer-pytorch', 0.5290699005126953, 'llm', 1), ('nvlabs/gcvit', 0.5274479389190674, 'diffusion', 0), ('hannibal046/awesome-llm', 0.5241163969039917, 'study', 1), ('huggingface/transformers', 0.5211392045021057, 'nlp', 1), ('jina-ai/clip-as-service', 0.5206122994422913, 'nlp', 0), ('openai/gpt-2', 0.5184028744697571, 'llm', 0), ('microsoft/lora', 0.5161336064338684, 'llm', 1), ('ai21labs/lm-evaluation', 0.5158585906028748, 'llm', 1), ('openai/clip', 0.5122661590576172, 'ml-dl', 0), ('extreme-bert/extreme-bert', 0.5107273459434509, 'llm', 1), ('lvwerra/trl', 0.509940505027771, 'llm', 0), ('lucidrains/imagen-pytorch', 0.5019423961639404, 'ml-dl', 0)]",3,3.0,,0.58,1,1,11,9,0,0,0,1.0,3.0,90.0,3.0,41
854,jupyter,https://github.com/jupyter/nbgrader,[],,[],[],,,,jupyter/nbgrader,nbgrader,1241,319,44,Python,https://nbgrader.readthedocs.io/,A system for assigning and grading notebooks,jupyter,2024-01-11,2014-09-13,489,2.5356100408639812,https://avatars.githubusercontent.com/u/7388996?v=4,A system for assigning and grading notebooks,"['grading', 'jupyter', 'jupyter-notebook', 'jupyterhub', 'nbgrader', 'teaching']","['grading', 'jupyter', 'jupyter-notebook', 'jupyterhub', 'nbgrader', 'teaching']",2023-12-04,"[('jupyter/nbformat', 0.6341890692710876, 'jupyter', 0), ('jupyter/nbconvert', 0.6126564145088196, 'jupyter', 0), ('cohere-ai/notebooks', 0.5908788442611694, 'llm', 0), ('mwouts/jupytext', 0.5783310532569885, 'jupyter', 1), ('jupyter/notebook', 0.5747985243797302, 'jupyter', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5345233678817749, 'study', 0), ('nteract/papermill', 0.532752513885498, 'jupyter', 1), ('quantopian/qgrid', 0.5307871103286743, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.5242305397987366, 'jupyter', 0), ('jupyter/nbdime', 0.5187388062477112, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.5142012238502502, 'study', 1), ('ageron/handson-ml2', 0.5094665884971619, 'ml', 0), ('koaning/calm-notebooks', 0.5042494535446167, 'study', 0)]",105,4.0,,1.1,27,11,114,1,8,4,8,27.0,56.0,90.0,2.1,41
401,perf,https://github.com/tiangolo/asyncer,[],,[],[],,,,tiangolo/asyncer,asyncer,1235,47,18,Python,https://asyncer.tiangolo.com/,"Asyncer, async and await, focused on developer experience.",tiangolo,2024-01-12,2022-01-04,108,11.435185185185185,,"Asyncer, async and await, focused on developer experience.","['anyio', 'async', 'asyncio', 'trio']","['anyio', 'async', 'asyncio', 'trio']",2023-12-10,"[('agronholm/anyio', 0.7173997759819031, 'perf', 2), ('magicstack/uvloop', 0.6868960857391357, 'util', 2), ('python-trio/trio', 0.6240719556808472, 'perf', 2), ('encode/starlette', 0.5958313345909119, 'web', 1), ('aio-libs/aiohttp', 0.5814606547355652, 'web', 2), ('alirn76/panther', 0.5792798399925232, 'web', 0), ('pallets/quart', 0.5767269134521484, 'web', 1), ('huge-success/sanic', 0.5628356337547302, 'web', 1), ('timofurrer/awesome-asyncio', 0.5529595017433167, 'study', 1), ('alex-sherman/unsync', 0.5449034571647644, 'util', 0), ('sumerc/yappi', 0.5410847067832947, 'profiling', 1), ('tiangolo/fastapi', 0.525113582611084, 'web', 2), ('noxdafox/pebble', 0.5147314667701721, 'perf', 1), ('samuelcolvin/arq', 0.5030202865600586, 'data', 2)]",10,5.0,,0.62,26,12,25,1,0,1,1,26.0,15.0,90.0,0.6,41
1599,llm,https://github.com/srush/minichain,"['prompt-engineering', 'question-answering', 'retrieval-augmentation']",,[],[],,,,srush/minichain,MiniChain,1148,72,15,Python,https://srush-minichain.hf.space/,A tiny library for coding with large language models.,srush,2024-01-13,2023-02-10,50,22.70056497175141,,A tiny library for coding with large language models.,[],"['prompt-engineering', 'question-answering', 'retrieval-augmentation']",2023-12-07,"[('keirp/automatic_prompt_engineer', 0.6470367908477783, 'llm', 1), ('openai/finetune-transformer-lm', 0.6227275133132935, 'llm', 0), ('stanfordnlp/dspy', 0.6080040335655212, 'llm', 0), ('ai21labs/in-context-ralm', 0.5984602570533752, 'llm', 1), ('ofa-sys/ofa', 0.5822784304618835, 'llm', 0), ('neulab/prompt2model', 0.5800988674163818, 'llm', 0), ('hazyresearch/ama_prompting', 0.5800570845603943, 'llm', 1), ('hannibal046/awesome-llm', 0.5760703086853027, 'study', 0), ('reasoning-machines/pal', 0.5743135809898376, 'llm', 0), ('freedomintelligence/llmzoo', 0.5740912556648254, 'llm', 0), ('ai21labs/lm-evaluation', 0.5739299654960632, 'llm', 0), ('lm-sys/fastchat', 0.572079062461853, 'llm', 0), ('juncongmoo/pyllama', 0.5712380409240723, 'llm', 0), ('bigscience-workshop/promptsource', 0.5696084499359131, 'nlp', 0), ('1rgs/jsonformer', 0.5634762644767761, 'llm', 1), ('paddlepaddle/rocketqa', 0.5608699321746826, 'nlp', 1), ('intellabs/fastrag', 0.5593423843383789, 'nlp', 2), ('guidance-ai/guidance', 0.5577107667922974, 'llm', 1), ('deepset-ai/farm', 0.5554335713386536, 'nlp', 1), ('lupantech/chameleon-llm', 0.5546031594276428, 'llm', 0), ('infinitylogesh/mutate', 0.5535112023353577, 'nlp', 0), ('kyegomez/tree-of-thoughts', 0.5509519577026367, 'llm', 1), ('jonasgeiping/cramming', 0.5501981973648071, 'nlp', 0), ('mit-han-lab/streaming-llm', 0.5470554232597351, 'llm', 0), ('salesforce/blip', 0.5436856746673584, 'diffusion', 0), ('eleutherai/lm-evaluation-harness', 0.542423665523529, 'llm', 0), ('llmware-ai/llmware', 0.5373873114585876, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5372619032859802, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5370650291442871, 'nlp', 0), ('yizhongw/self-instruct', 0.5332199931144714, 'llm', 0), ('openai/gpt-2', 0.5330092310905457, 'llm', 0), ('thudm/chatglm-6b', 0.5271021127700806, 'llm', 0), ('thudm/p-tuning-v2', 0.5268715620040894, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5253647565841675, 'llm', 0), ('togethercomputer/redpajama-data', 0.5224243998527527, 'llm', 0), ('openlmlab/moss', 0.5213707089424133, 'llm', 0), ('lianjiatech/belle', 0.519889771938324, 'llm', 0), ('night-chen/toolqa', 0.5163832306861877, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5138395428657532, 'llm', 0), ('databrickslabs/dolly', 0.5138243436813354, 'llm', 0), ('defog-ai/sqlcoder', 0.512403130531311, 'llm', 0), ('bigscience-workshop/biomedical', 0.5102963447570801, 'data', 0), ('next-gpt/next-gpt', 0.5092249512672424, 'llm', 0), ('huggingface/text-generation-inference', 0.5085004568099976, 'llm', 0), ('luohongyin/sail', 0.5050071477890015, 'llm', 0), ('bytedance/lightseq', 0.5048473477363586, 'nlp', 0), ('cg123/mergekit', 0.5045192837715149, 'llm', 0)]",7,4.0,,2.67,0,0,11,1,2,2,2,0.0,0.0,90.0,0.0,41
733,ml,https://github.com/koaning/scikit-lego,[],,[],[],,,,koaning/scikit-lego,scikit-lego,1097,107,23,Python,https://koaning.github.io/scikit-lego/,Extra blocks for scikit-learn pipelines.,koaning,2024-01-09,2019-01-21,262,4.184741144414169,,Extra blocks for scikit-learn pipelines.,"['common-sense', 'machine-learning', 'scikit-learn']","['common-sense', 'machine-learning', 'scikit-learn']",2024-01-06,"[('koaning/scikit-partial', 0.6257511377334595, 'data', 0), ('skops-dev/skops', 0.6168782711029053, 'ml-ops', 2), ('automl/auto-sklearn', 0.6153001189231873, 'ml', 1), ('iryna-kondr/scikit-llm', 0.614821195602417, 'llm', 2), ('koaning/human-learn', 0.5959609150886536, 'data', 2), ('rasbt/machine-learning-book', 0.5857502818107605, 'study', 2), ('skorch-dev/skorch', 0.5642397999763489, 'ml-dl', 2), ('intel/scikit-learn-intelex', 0.5534452199935913, 'perf', 2), ('kubeflow/pipelines', 0.539598822593689, 'ml-ops', 1), ('featurelabs/featuretools', 0.5262479782104492, 'ml', 2), ('csinva/imodels', 0.5232925415039062, 'ml', 2), ('teamhg-memex/eli5', 0.5188755989074707, 'ml', 2), ('ageron/handson-ml2', 0.5152604579925537, 'ml', 0), ('linealabs/lineapy', 0.5148707032203674, 'jupyter', 0), ('kubeflow-kale/kale', 0.5133776664733887, 'ml-ops', 1), ('epistasislab/tpot', 0.5125021934509277, 'ml', 2), ('dask/dask-ml', 0.5104072093963623, 'ml', 0), ('optimalscale/lmflow', 0.5066888928413391, 'llm', 0), ('districtdatalabs/yellowbrick', 0.5034389495849609, 'ml', 2), ('scikit-learn-contrib/metric-learn', 0.5020371675491333, 'ml', 2)]",62,5.0,,0.69,36,28,61,0,3,7,3,36.0,40.0,90.0,1.1,41
502,gis,https://github.com/anitagraser/movingpandas,[],,[],[],,,,anitagraser/movingpandas,movingpandas,1084,185,38,Python,http://movingpandas.org,Movement trajectory classes and functions built on top of GeoPandas,anitagraser,2024-01-13,2018-12-16,267,4.055585248530198,https://avatars.githubusercontent.com/u/123823419?v=4,Movement trajectory classes and functions built on top of GeoPandas,"['geopandas', 'movement-data', 'spatial-data-analysis', 'trajectory', 'trajectory-analysis']","['geopandas', 'movement-data', 'spatial-data-analysis', 'trajectory', 'trajectory-analysis']",2023-12-30,"[('geopandas/geopandas', 0.5975525379180908, 'gis', 1), ('holoviz/spatialpandas', 0.5773379802703857, 'pandas', 1), ('residentmario/geoplot', 0.5739251375198364, 'gis', 1), ('scikit-mobility/scikit-mobility', 0.5389496684074402, 'gis', 0), ('pandas-dev/pandas', 0.5086138248443604, 'pandas', 0)]",33,4.0,,1.23,13,8,62,0,6,4,6,13.0,26.0,90.0,2.0,41
1149,data,https://github.com/aio-libs/aiokafka,[],,[],[],,,,aio-libs/aiokafka,aiokafka,988,212,29,Python,http://aiokafka.readthedocs.io/,asyncio client for kafka,aio-libs,2024-01-12,2014-11-01,482,2.04797157240154,https://avatars.githubusercontent.com/u/7049303?v=4,asyncio client for kafka,"['asyncio', 'kafka', 'kafka-client']","['asyncio', 'kafka', 'kafka-client']",2024-01-13,"[('aio-libs/aiohttp', 0.5572373270988464, 'web', 1), ('aio-libs/aiobotocore', 0.5398765802383423, 'util', 1), ('samuelcolvin/aioaws', 0.5397475957870483, 'data', 1), ('alex-sherman/unsync', 0.5082889199256897, 'util', 0)]",72,6.0,,1.15,42,28,112,0,6,4,6,42.0,77.0,90.0,1.8,41
967,sim,https://github.com/a-r-j/graphein,[],,[],[],,,,a-r-j/graphein,graphein,937,119,20,Jupyter Notebook,https://graphein.ai/,Protein Graph Library,a-r-j,2024-01-12,2019-08-28,230,4.058787128712871,,Protein Graph Library,"['bioinformatics', 'computational-biology', 'deep-learning', 'dgl', 'drug-discovery', 'gene-regulatory-networks', 'geometric-deep-learning', 'graph-neural-networks', 'interactome', 'interactomics', 'ppi-networks', 'protein', 'protein-data-bank', 'protein-design', 'protein-structure', 'pytorch', 'pytorch-geometric', 'rna', 'structural-biology']","['bioinformatics', 'computational-biology', 'deep-learning', 'dgl', 'drug-discovery', 'gene-regulatory-networks', 'geometric-deep-learning', 'graph-neural-networks', 'interactome', 'interactomics', 'ppi-networks', 'protein', 'protein-data-bank', 'protein-design', 'protein-structure', 'pytorch', 'pytorch-geometric', 'rna', 'structural-biology']",2023-12-26,"[('pyg-team/pytorch_geometric', 0.6886248588562012, 'ml-dl', 4), ('benedekrozemberczki/tigerlily', 0.6609140634536743, 'ml-dl', 1), ('graphistry/pygraphistry', 0.6311023235321045, 'data', 0), ('stellargraph/stellargraph', 0.6164317727088928, 'graph', 3), ('dmlc/dgl', 0.5949839949607849, 'ml-dl', 2), ('accenture/ampligraph', 0.5756738185882568, 'data', 0), ('danielegrattarola/spektral', 0.551216721534729, 'ml-dl', 2), ('networkx/networkx', 0.5483938455581665, 'graph', 0), ('tensorlayer/tensorlayer', 0.538101315498352, 'ml-rl', 1), ('h4kor/graph-force', 0.5339735150337219, 'graph', 0), ('rucaibox/recbole', 0.5301325917243958, 'ml', 3), ('pandas-dev/pandas', 0.5266823768615723, 'pandas', 0), ('plotly/plotly.py', 0.5239959955215454, 'viz', 0), ('pytorch/torchrec', 0.5193598866462708, 'ml-dl', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.5173624753952026, 'study', 2), ('docarray/docarray', 0.5024240612983704, 'data', 2)]",25,6.0,,1.31,18,15,53,1,7,4,7,18.0,25.0,90.0,1.4,41
1516,llm,https://github.com/microsoft/llama-2-onnx,"['llama', 'language-model']","A Microsoft optimized version of the Llama 2 model, available from Meta",[],[],,,,microsoft/llama-2-onnx,Llama-2-Onnx,924,74,342,Python,,,microsoft,2024-01-13,2023-07-17,28,32.83248730964467,https://avatars.githubusercontent.com/u/6154722?v=4,"A Microsoft optimized version of the Llama 2 model, available from Meta",[],"['language-model', 'llama']",2023-10-17,"[('facebookresearch/llama-recipes', 0.820441722869873, 'llm', 2), ('tloen/alpaca-lora', 0.7532107830047607, 'llm', 2), ('jzhang38/tinyllama', 0.7340604066848755, 'llm', 2), ('facebookresearch/llama', 0.7213297486305237, 'llm', 2), ('run-llama/llama-lab', 0.7098461985588074, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.6853927969932556, 'llm', 0), ('karpathy/llama2.c', 0.6777373552322388, 'llm', 2), ('zrrskywalker/llama-adapter', 0.6588950157165527, 'llm', 2), ('ggerganov/llama.cpp', 0.6485655903816223, 'llm', 2), ('lightning-ai/lit-llama', 0.643981397151947, 'llm', 2), ('facebookresearch/codellama', 0.6238771080970764, 'llm', 2), ('abetlen/llama-cpp-python', 0.6152366399765015, 'llm', 2), ('tairov/llama2.mojo', 0.6118836998939514, 'llm', 1), ('openlm-research/open_llama', 0.6107590794563293, 'llm', 2), ('oobabooga/text-generation-webui', 0.5708426833152771, 'llm', 1), ('young-geng/easylm', 0.5490400791168213, 'llm', 2), ('cg123/mergekit', 0.5430543422698975, 'llm', 1), ('jerryjliu/llama_index', 0.542698860168457, 'llm', 2), ('nat/openplayground', 0.5403831005096436, 'llm', 1), ('bigscience-workshop/petals', 0.5293133854866028, 'data', 1), ('bentoml/openllm', 0.5186194181442261, 'ml-ops', 1), ('run-llama/llama-hub', 0.5180464386940002, 'data', 0), ('juncongmoo/pyllama', 0.5146937966346741, 'llm', 0), ('salesforce/xgen', 0.5118889212608337, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5092772841453552, 'llm', 2), ('hiyouga/llama-factory', 0.5092772841453552, 'llm', 2)]",8,1.0,,0.44,25,9,6,3,0,0,0,25.0,25.0,90.0,1.0,41
1443,util,https://github.com/pypa/gh-action-pypi-publish,[],,[],[],,,,pypa/gh-action-pypi-publish,gh-action-pypi-publish,780,78,9,Python,https://packaging.python.org/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/,"The blessed :octocat: GitHub Action, for publishing your :package: distribution files to PyPI: https://github.com/marketplace/actions/pypi-publish",pypa,2024-01-13,2019-03-27,252,3.0847457627118646,https://avatars.githubusercontent.com/u/647025?v=4,"The blessed :octocat: GitHub Action, for publishing your 📦 distribution files to PyPI: https://github.com/marketplace/actions/pypi-publish","['actions', 'github-action', 'github-actions', 'github-actions-python', 'oidc', 'pypa-guide', 'python-packaging', 'release', 'release-automation', 'release-helper', 'secrets', 'testpypi', 'twine', 'upload', 'workflow', 'workflow-automation', 'workflows']","['actions', 'github-action', 'github-actions', 'github-actions-python', 'oidc', 'pypa-guide', 'python-packaging', 'release', 'release-automation', 'release-helper', 'secrets', 'testpypi', 'twine', 'upload', 'workflow', 'workflow-automation', 'workflows']",2023-12-20,"[('mozillazg/pypy', 0.6144118309020996, 'util', 0), ('pypi/warehouse', 0.6143344044685364, 'util', 0), ('indygreg/pyoxidizer', 0.5328114628791809, 'util', 0), ('pypa/hatch', 0.5319401025772095, 'util', 0), ('pypa/setuptools_scm', 0.5103867650032043, 'util', 0), ('hugovk/pypistats', 0.5029650926589966, 'util', 0)]",23,7.0,,1.17,25,15,58,1,15,7,15,25.0,49.0,90.0,2.0,41
588,gis,https://github.com/matplotlib/basemap,[],,[],[],,,,matplotlib/basemap,basemap,755,397,61,Python,,Plot on map projections (with coastlines and political boundaries) using matplotlib,matplotlib,2024-01-10,2011-02-19,675,1.117808798646362,https://avatars.githubusercontent.com/u/215947?v=4,Plot on map projections (with coastlines and political boundaries) using matplotlib,"['gis', 'maps', 'plots']","['gis', 'maps', 'plots']",2024-01-11,"[('scitools/cartopy', 0.6154806613922119, 'gis', 1), ('csurfer/pyheat', 0.5544516444206238, 'profiling', 0), ('matplotlib/matplotlib', 0.505860447883606, 'viz', 0), ('mwaskom/seaborn', 0.500274658203125, 'viz', 0)]",71,7.0,,3.71,33,26,157,0,4,3,4,33.0,112.0,90.0,3.4,41
819,diffusion,https://github.com/thereforegames/unprompted,[],,[],[],,,,thereforegames/unprompted,unprompted,712,62,16,Python,,Templating language written for Stable Diffusion workflows. Available as an extension for the Automatic1111 WebUI.,thereforegames,2024-01-07,2022-10-31,65,10.929824561403509,,Templating language written for Stable Diffusion workflows. Available as an extension for the Automatic1111 WebUI.,"['a1111-stable-diffusion-webui', 'ai-art', 'deep-learning', 'gpt', 'gradio', 'img2img', 'shortcode', 'stable-diffusion', 'template-engine', 'text2image', 'txt2img', 'wildcards']","['a1111-stable-diffusion-webui', 'ai-art', 'deep-learning', 'gpt', 'gradio', 'img2img', 'shortcode', 'stable-diffusion', 'template-engine', 'text2image', 'txt2img', 'wildcards']",2023-12-01,"[('automatic1111/stable-diffusion-webui', 0.7207518219947815, 'diffusion', 7), ('mlc-ai/web-stable-diffusion', 0.6838393807411194, 'diffusion', 2), ('civitai/sd_civitai_extension', 0.6360564827919006, 'llm', 0), ('saharmor/dalle-playground', 0.5805418491363525, 'diffusion', 1), ('carson-katri/dream-textures', 0.5799620747566223, 'diffusion', 1), ('invoke-ai/invokeai', 0.553229808807373, 'diffusion', 4), ('pallets/jinja', 0.5376675128936768, 'util', 1), ('comfyanonymous/comfyui', 0.5250580906867981, 'diffusion', 1), ('stability-ai/stability-sdk', 0.5244125127792358, 'diffusion', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5091450810432434, 'web', 0), ('nateraw/stable-diffusion-videos', 0.5079180002212524, 'diffusion', 2), ('bentoml/onediffusion', 0.5029755234718323, 'diffusion', 1), ('ashawkey/stable-dreamfusion', 0.5019211769104004, 'diffusion', 1)]",8,2.0,,3.21,19,9,15,1,0,0,0,19.0,53.0,90.0,2.8,41
1764,data,https://github.com/duckdb/dbt-duckdb,[],,[],[],,,,duckdb/dbt-duckdb,dbt-duckdb,636,56,17,Python,,dbt (http://getdbt.com) adapter for DuckDB (http://duckdb.org),duckdb,2024-01-13,2020-09-25,174,3.6432078559738135,https://avatars.githubusercontent.com/u/82039556?v=4,dbt (http://getdbt.com) adapter for DuckDB (http://duckdb.org),"['dbt', 'duckdb']","['dbt', 'duckdb']",2024-01-13,"[('duckdb/duckdb', 0.6090986132621765, 'pandas', 0), ('databricks/dbt-databricks', 0.5661155581474304, 'data', 1), ('airbnb/omniduct', 0.5301816463470459, 'data', 0)]",23,3.0,,6.12,46,43,40,0,11,4,11,46.0,52.0,90.0,1.1,41
1703,data,https://github.com/dgarnitz/vectorflow,[],,[],[],,,,dgarnitz/vectorflow,vectorflow,592,37,10,Python,https://www.getvectorflow.com/,"VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice. ",dgarnitz,2024-01-14,2023-07-25,27,21.925925925925927,,"VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice. ","['ai', 'data-engineering', 'embeddings', 'machine-learning', 'nlp', 'vectors']","['ai', 'data-engineering', 'embeddings', 'machine-learning', 'nlp', 'vectors']",2023-12-07,"[('activeloopai/deeplake', 0.6199436187744141, 'ml-ops', 2), ('mage-ai/mage-ai', 0.6055392622947693, 'ml-ops', 2), ('jina-ai/vectordb', 0.5692445039749146, 'data', 0), ('featureform/embeddinghub', 0.5690073370933533, 'nlp', 2), ('lancedb/lancedb', 0.5619788765907288, 'data', 0), ('kubeflow/pipelines', 0.5347921252250671, 'ml-ops', 1), ('superduperdb/superduperdb', 0.5114015340805054, 'data', 1), ('towhee-io/towhee', 0.5035332441329956, 'ml-ops', 2)]",10,1.0,,1.87,39,29,6,1,0,0,0,39.0,34.0,90.0,0.9,41
1439,ml,https://github.com/replicate/replicate-python,[],,[],[],,,,replicate/replicate-python,replicate-python,512,195,30,Python,https://replicate.com,Python client for Replicate,replicate,2024-01-13,2022-05-11,89,5.697933227344992,https://avatars.githubusercontent.com/u/60410876?v=4,Python client for Replicate,[],[],2024-01-04,"[('steamship-core/python-client', 0.6438118815422058, 'util', 0), ('encode/httpx', 0.5832346677780151, 'web', 0), ('eleutherai/pyfra', 0.5586503148078918, 'ml', 0), ('simple-salesforce/simple-salesforce', 0.5586280226707458, 'data', 0), ('ethereum/web3.py', 0.5461574792861938, 'crypto', 0), ('dddomodossola/remi', 0.5246701836585999, 'gui', 0), ('ethereum/py-evm', 0.5225535035133362, 'crypto', 0), ('masoniteframework/masonite', 0.5155618190765381, 'web', 0), ('pypy/pypy', 0.5141051411628723, 'util', 0), ('uqfoundation/dill', 0.5080131888389587, 'data', 0), ('aio-libs/aiohttp', 0.5043667554855347, 'web', 0), ('willmcgugan/textual', 0.5011894106864929, 'term', 0)]",13,2.0,,2.17,79,68,20,0,33,32,33,79.0,92.0,90.0,1.2,41
1062,sim,https://github.com/netket/netket,[],,[],[],,,,netket/netket,netket,473,164,24,Python,https://www.netket.org,Machine learning algorithms for many-body quantum systems ,netket,2024-01-13,2018-04-23,301,1.5706831119544593,https://avatars.githubusercontent.com/u/38641916?v=4,Machine learning algorithms for many-body quantum systems ,"['complex-neural-network', 'deep-learning', 'exact-diagonalization', 'hamiltonian', 'jax', 'machine-learning', 'machine-learning-algorithms', 'markov-chain-monte-carlo', 'monte-carlo-methods', 'neural-networks', 'physics-simulation', 'quantum', 'quantum-state-tomography', 'unitaryhack', 'variational-method', 'variational-monte-carlo']","['complex-neural-network', 'deep-learning', 'exact-diagonalization', 'hamiltonian', 'jax', 'machine-learning', 'machine-learning-algorithms', 'markov-chain-monte-carlo', 'monte-carlo-methods', 'neural-networks', 'physics-simulation', 'quantum', 'quantum-state-tomography', 'unitaryhack', 'variational-method', 'variational-monte-carlo']",2024-01-12,"[('jackhidary/quantumcomputingbook', 0.626657247543335, 'study', 1), ('quantumlib/cirq', 0.5286350250244141, 'sim', 0), ('qiskit/qiskit', 0.5100851058959961, 'sim', 1)]",63,5.0,,3.75,121,80,70,0,8,10,8,121.0,226.0,90.0,1.9,41
1753,ml,https://github.com/deepgraphlearning/ultra,"['reasoning', 'knowledge-graph']",,[],[],,,,deepgraphlearning/ultra,ULTRA,238,31,5,Python,,A foundation model for knowledge graph reasoning,deepgraphlearning,2024-01-12,2023-10-23,14,16.828282828282827,https://avatars.githubusercontent.com/u/38018154?v=4,A foundation model for knowledge graph reasoning,[],"['knowledge-graph', 'reasoning']",2024-01-13,"[('awslabs/dgl-ke', 0.5505498647689819, 'ml', 1), ('dylanhogg/llmgraph', 0.5485401749610901, 'ml', 1), ('accenture/ampligraph', 0.5169753432273865, 'data', 1), ('zjunlp/deepke', 0.5096688270568848, 'ml', 1)]",4,3.0,,0.21,11,10,3,0,0,0,0,11.0,25.0,90.0,2.3,41
1655,llm,https://github.com/langchain-ai/langsmith-sdk,[],,[],[],,,,langchain-ai/langsmith-sdk,langsmith-sdk,224,25,5,Python,https://smith.langchain.com/,LangSmith Client SDK Implementations,langchain-ai,2024-01-11,2023-05-30,35,6.4,https://avatars.githubusercontent.com/u/126733545?v=4,LangSmith Client SDK Implementations,"['evaluation', 'language-model', 'observability']","['evaluation', 'language-model', 'observability']",2024-01-13,"[('langchain-ai/langsmith-cookbook', 0.6326169371604919, 'llm', 2), ('anthropics/anthropic-sdk-python', 0.5629613995552063, 'util', 1), ('gkamradt/langchain-tutorials', 0.5348765254020691, 'study', 0), ('langchain-ai/langgraph', 0.5179738998413086, 'llm', 0), ('alphasecio/langchain-examples', 0.5165998935699463, 'llm', 0), ('hwchase17/langchain', 0.5111579298973083, 'llm', 1), ('openai/tiktoken', 0.5109971761703491, 'nlp', 0), ('prefecthq/langchain-prefect', 0.5083007216453552, 'llm', 0)]",15,2.0,,5.94,116,108,8,0,70,126,70,116.0,92.0,90.0,0.8,41
1059,study,https://github.com/shangtongzhang/reinforcement-learning-an-introduction,[],,[],[],,,,shangtongzhang/reinforcement-learning-an-introduction,reinforcement-learning-an-introduction,12960,4791,565,Python,,Python Implementation of Reinforcement Learning: An Introduction,shangtongzhang,2024-01-13,2016-09-13,385,33.66233766233766,,Python Implementation of Reinforcement Learning: An Introduction,"['artificial-intelligence', 'reinforcement-learning']","['artificial-intelligence', 'reinforcement-learning']",2022-05-10,"[('deepmind/acme', 0.6519054770469666, 'ml-rl', 1), ('pytorch/rl', 0.6356885433197021, 'ml-rl', 1), ('openai/gym', 0.6223573088645935, 'ml-rl', 1), ('artemyk/dynpy', 0.583624005317688, 'sim', 0), ('thu-ml/tianshou', 0.5635957717895508, 'ml-rl', 0), ('scikit-learn/scikit-learn', 0.5567522644996643, 'ml', 0), ('humancompatibleai/imitation', 0.5537171363830566, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.5474168062210083, 'ml-rl', 1), ('pymc-devs/pymc3', 0.5400987863540649, 'ml', 0), ('infer-actively/pymdp', 0.5399729013442993, 'ml', 0), ('pettingzoo-team/pettingzoo', 0.5307071805000305, 'ml-rl', 1), ('nvidia-omniverse/omniisaacgymenvs', 0.5251854062080383, 'sim', 0), ('arise-initiative/robosuite', 0.5221055746078491, 'ml-rl', 1), ('facebookresearch/reagent', 0.5210154056549072, 'ml-rl', 0), ('google/dopamine', 0.5188839435577393, 'ml-rl', 0), ('denys88/rl_games', 0.5131837725639343, 'ml-rl', 1), ('probml/pyprobml', 0.5084415078163147, 'ml', 0), ('gbeced/pyalgotrade', 0.5082697868347168, 'finance', 0), ('sympy/sympy', 0.5073643326759338, 'math', 0)]",33,3.0,,0.0,0,0,89,20,0,0,0,0.0,0.0,90.0,0.0,40
246,util,https://github.com/jorgebastida/awslogs,[],,[],[],,,,jorgebastida/awslogs,awslogs,4714,336,57,Python,,AWS CloudWatch logs for Humans™,jorgebastida,2024-01-12,2015-01-21,470,10.011529126213592,,AWS CloudWatch logs for Humans™,[],[],2020-07-10,"[('rpgreen/apilogs', 0.5721848607063293, 'util', 0), ('nccgroup/scoutsuite', 0.5164755582809448, 'security', 0)]",39,5.0,,0.0,2,1,109,43,0,1,1,2.0,6.0,90.0,3.0,40
847,profiling,https://github.com/pythonprofilers/memory_profiler,[],,[],[],,,,pythonprofilers/memory_profiler,memory_profiler,4110,403,80,Python,http://pypi.python.org/pypi/memory_profiler,Monitor Memory usage of Python code,pythonprofilers,2024-01-14,2011-10-14,641,6.406145624582498,https://avatars.githubusercontent.com/u/32906038?v=4,Monitor Memory usage of Python code,[],[],2023-10-23,"[('pympler/pympler', 0.8423640131950378, 'perf', 0), ('pythonspeed/filprofiler', 0.664732813835144, 'profiling', 0), ('pyutils/line_profiler', 0.577487051486969, 'profiling', 0), ('nedbat/coveragepy', 0.5676537156105042, 'testing', 0), ('gaogaotiantian/viztracer', 0.5663729906082153, 'profiling', 0), ('rubik/radon', 0.5579990148544312, 'util', 0), ('dgilland/cacheout', 0.555606484413147, 'perf', 0), ('joblib/joblib', 0.5550679564476013, 'util', 0), ('bloomberg/memray', 0.5463239550590515, 'profiling', 0), ('alexmojaki/heartrate', 0.5310880541801453, 'debug', 0), ('landscapeio/prospector', 0.5307861566543579, 'util', 0), ('benfred/py-spy', 0.5219977498054504, 'profiling', 0), ('jendrikseipp/vulture', 0.5172761678695679, 'util', 0), ('pytables/pytables', 0.5096424221992493, 'data', 0), ('cython/cython', 0.5089790225028992, 'util', 0), ('erotemic/ubelt', 0.5078474879264832, 'util', 0)]",103,7.0,,0.06,3,1,149,3,0,4,4,3.0,1.0,90.0,0.3,40
1179,diffusion,https://github.com/salesforce/blip,[],,[],[],,,,salesforce/blip,BLIP,3885,530,33,Jupyter Notebook,,PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation  ,salesforce,2024-01-13,2022-01-25,105,37.0,https://avatars.githubusercontent.com/u/453694?v=4,PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation  ,"['image-captioning', 'image-text-retrieval', 'vision-and-language-pre-training', 'vision-language', 'vision-language-transformer', 'visual-question-answering', 'visual-reasoning']","['image-captioning', 'image-text-retrieval', 'vision-and-language-pre-training', 'vision-language', 'vision-language-transformer', 'visual-question-answering', 'visual-reasoning']",2022-09-20,"[('nvlabs/prismer', 0.6734409928321838, 'diffusion', 1), ('ofa-sys/ofa', 0.6362955570220947, 'llm', 3), ('nvlabs/gcvit', 0.5959486365318298, 'diffusion', 0), ('pytorch/ignite', 0.5812243223190308, 'ml-dl', 0), ('lucidrains/imagen-pytorch', 0.579346776008606, 'ml-dl', 0), ('jerryyli/valhalla-nmt', 0.5753607749938965, 'ml-dl', 0), ('graykode/nlp-tutorial', 0.5725541710853577, 'study', 0), ('allenai/allennlp', 0.5719388127326965, 'nlp', 0), ('openai/finetune-transformer-lm', 0.5705159902572632, 'llm', 0), ('huggingface/transformers', 0.5568798184394836, 'nlp', 0), ('deci-ai/super-gradients', 0.5560441613197327, 'ml-dl', 0), ('alibaba/easynlp', 0.553688108921051, 'nlp', 0), ('openai/clip', 0.5517219305038452, 'ml-dl', 0), ('next-gpt/next-gpt', 0.5469942092895508, 'llm', 0), ('lightly-ai/lightly', 0.5466323494911194, 'ml', 0), ('srush/minichain', 0.5436856746673584, 'llm', 0), ('hysts/pytorch_image_classification', 0.5436573624610901, 'ml-dl', 0), ('lucidrains/dalle2-pytorch', 0.5417680740356445, 'diffusion', 0), ('intel/intel-extension-for-pytorch', 0.5368949770927429, 'perf', 0), ('mrdbourke/pytorch-deep-learning', 0.5360457897186279, 'study', 0), ('thudm/glm-130b', 0.5314812064170837, 'llm', 0), ('openai/image-gpt', 0.5261111855506897, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5254507064819336, 'llm', 0), ('microsoft/lora', 0.52529376745224, 'llm', 0), ('skorch-dev/skorch', 0.5246773362159729, 'ml-dl', 0), ('roboflow/notebooks', 0.523171603679657, 'study', 0), ('google-research/electra', 0.5222206115722656, 'ml-dl', 0), ('nvidia/apex', 0.5196071267127991, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5187211036682129, 'study', 0), ('microsoft/unilm', 0.5181792378425598, 'nlp', 0), ('databrickslabs/dolly', 0.516700029373169, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5148594379425049, 'llm', 0), ('ibm/transition-amr-parser', 0.5134626626968384, 'nlp', 0), ('reasoning-machines/pal', 0.512751042842865, 'llm', 0), ('pytorch/captum', 0.5107000470161438, 'ml-interpretability', 0), ('bigscience-workshop/megatron-deepspeed', 0.5101778507232666, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5101778507232666, 'llm', 0), ('deepset-ai/farm', 0.5072547197341919, 'nlp', 0), ('timdettmers/bitsandbytes', 0.5067077875137329, 'util', 0), ('optimalscale/lmflow', 0.5060290694236755, 'llm', 0), ('lm-sys/fastchat', 0.5055869817733765, 'llm', 0), ('microsoft/semi-supervised-learning', 0.5032647848129272, 'ml', 0), ('huggingface/autotrain-advanced', 0.5011047124862671, 'ml', 0), ('nvidia/deeplearningexamples', 0.5009772181510925, 'ml-dl', 0), ('bytedance/lightseq', 0.5009039044380188, 'nlp', 0), ('lucidrains/vit-pytorch', 0.500484049320221, 'ml-dl', 0), ('rwightman/pytorch-image-models', 0.5003149509429932, 'ml-dl', 0), ('facebookresearch/mmf', 0.5001189112663269, 'ml-dl', 0)]",4,1.0,,0.0,19,3,24,16,0,0,0,19.0,17.0,90.0,0.9,40
1026,finance,https://github.com/polakowo/vectorbt,[],,[],[],,,,polakowo/vectorbt,vectorbt,3466,537,116,Python,https://vectorbt.dev,"Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research. ",polakowo,2024-01-14,2017-11-14,324,10.697530864197532,,"Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research. ","['algorithmic-trading', 'algorithmic-traiding', 'backtesting', 'cryptocurrency', 'data-science', 'data-visualization', 'finance', 'machine-learning', 'portfolio-optimization', 'quantitative-analysis', 'quantitative-finance', 'time-series', 'trading', 'trading-strategies']","['algorithmic-trading', 'algorithmic-traiding', 'backtesting', 'cryptocurrency', 'data-science', 'data-visualization', 'finance', 'machine-learning', 'portfolio-optimization', 'quantitative-analysis', 'quantitative-finance', 'time-series', 'trading', 'trading-strategies']",2023-09-30,"[('idanya/algo-trader', 0.6762666702270508, 'finance', 3), ('openbb-finance/openbbterminal', 0.6711627244949341, 'finance', 4), ('quantconnect/lean', 0.6572080254554749, 'finance', 3), ('kernc/backtesting.py', 0.6390688419342041, 'finance', 5), ('ranaroussi/quantstats', 0.6319853663444519, 'finance', 4), ('zvtvz/zvt', 0.6255056858062744, 'finance', 6), ('ai4finance-foundation/finrl', 0.6179881691932678, 'finance', 2), ('freqtrade/freqtrade', 0.606457531452179, 'crypto', 2), ('numerai/example-scripts', 0.5881688594818115, 'finance', 2), ('gbeced/basana', 0.5863175392150879, 'finance', 3), ('cuemacro/finmarketpy', 0.5674868822097778, 'finance', 1), ('polyaxon/datatile', 0.5571960806846619, 'pandas', 2), ('stefmolin/stock-analysis', 0.5499805212020874, 'finance', 0), ('xplainable/xplainable', 0.532191812992096, 'ml-interpretability', 2), ('gbeced/pyalgotrade', 0.5312750935554504, 'finance', 0), ('quantopian/zipline', 0.519047737121582, 'finance', 1), ('google/tf-quant-finance', 0.5182610154151917, 'finance', 2), ('ccxt/ccxt', 0.5177646279335022, 'crypto', 2), ('mementum/backtrader', 0.5124863982200623, 'finance', 2), ('goldmansachs/gs-quant', 0.5025342106819153, 'finance', 1)]",11,4.0,,0.17,22,5,75,4,0,0,0,22.0,35.0,90.0,1.6,40
1235,llm,https://github.com/yizhongw/self-instruct,[],,[],[],,,,yizhongw/self-instruct,self-instruct,3459,400,52,Python,,Aligning pretrained language models with instruction data generated by themselves.,yizhongw,2024-01-14,2022-12-20,58,59.63793103448276,,Aligning pretrained language models with instruction data generated by themselves.,"['general-purpose-model', 'instruction-tuning', 'language-model']","['general-purpose-model', 'instruction-tuning', 'language-model']",2023-03-27,"[('cg123/mergekit', 0.6453080177307129, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.6405363082885742, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5977901816368103, 'llm', 1), ('tiger-ai-lab/mammoth', 0.5941129922866821, 'llm', 1), ('openai/finetune-transformer-lm', 0.5861937999725342, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5800374150276184, 'llm', 1), ('neulab/prompt2model', 0.5685259699821472, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5658340454101562, 'llm', 1), ('guidance-ai/guidance', 0.5607982873916626, 'llm', 1), ('juncongmoo/pyllama', 0.5553812384605408, 'llm', 0), ('hannibal046/awesome-llm', 0.5532945394515991, 'study', 1), ('declare-lab/instruct-eval', 0.550851047039032, 'llm', 0), ('freedomintelligence/llmzoo', 0.5435721278190613, 'llm', 1), ('openbmb/toolbench', 0.542241096496582, 'llm', 1), ('thudm/glm-130b', 0.5371110439300537, 'llm', 0), ('hazyresearch/h3', 0.534803569316864, 'llm', 0), ('ai21labs/lm-evaluation', 0.5341971516609192, 'llm', 1), ('srush/minichain', 0.5332199931144714, 'llm', 0), ('hiyouga/llama-factory', 0.5308533310890198, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.530853271484375, 'llm', 2), ('jonasgeiping/cramming', 0.5202336311340332, 'nlp', 1), ('optimalscale/lmflow', 0.5196613073348999, 'llm', 1), ('lianjiatech/belle', 0.5185281038284302, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.513164758682251, 'llm', 1), ('infinitylogesh/mutate', 0.5120193362236023, 'nlp', 1), ('luohongyin/sail', 0.5114395022392273, 'llm', 1), ('bigscience-workshop/biomedical', 0.5062916278839111, 'data', 0), ('thudm/codegeex', 0.5008969902992249, 'llm', 0)]",2,1.0,,0.08,2,0,13,10,0,0,0,2.0,1.0,90.0,0.5,40
957,ml-dl,https://github.com/facebookresearch/pytorch-biggraph,[],,[],[],,,,facebookresearch/pytorch-biggraph,PyTorch-BigGraph,3329,449,91,Python,https://torchbiggraph.readthedocs.io/,Generate embeddings from large-scale graph-structured data.,facebookresearch,2024-01-11,2018-10-01,278,11.968669748330765,https://avatars.githubusercontent.com/u/16943930?v=4,Generate embeddings from large-scale graph-structured data.,[],[],2024-01-06,"[('h4kor/graph-force', 0.6328504085540771, 'graph', 0), ('awslabs/dgl-ke', 0.6120842099189758, 'ml', 0), ('koaning/embetter', 0.5848337411880493, 'data', 0), ('vhranger/nodevectors', 0.5276350975036621, 'viz', 0), ('huggingface/text-embeddings-inference', 0.5272315144538879, 'llm', 0)]",31,5.0,,0.12,0,0,64,0,0,1,1,0.0,0.0,90.0,0.0,40
42,nlp,https://github.com/life4/textdistance,[],,[],[],,,,life4/textdistance,textdistance,3248,247,65,Python,,"📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.",life4,2024-01-12,2017-05-05,351,9.238520926452662,https://avatars.githubusercontent.com/u/48201596?v=4,"📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.","['algorithm', 'algorithms', 'damerau-levenshtein', 'damerau-levenshtein-distance', 'diff', 'distance', 'distance-calculation', 'hamming-distance', 'jellyfish', 'levenshtein', 'levenshtein-distance', 'textdistance']","['algorithm', 'algorithms', 'damerau-levenshtein', 'damerau-levenshtein-distance', 'diff', 'distance', 'distance-calculation', 'hamming-distance', 'jellyfish', 'levenshtein', 'levenshtein-distance', 'textdistance']",2023-12-29,"[('jamesturk/jellyfish', 0.6177361011505127, 'nlp', 1), ('scipy/scipy', 0.5068709850311279, 'math', 1), ('spotify/annoy', 0.5062219500541687, 'ml', 0)]",14,5.0,,0.31,2,2,82,1,2,2,2,2.0,1.0,90.0,0.5,40
780,study,https://github.com/cosmicpython/book,[],,[],[],,,,cosmicpython/book,book,3162,520,95,Python,https://www.cosmicpython.com,"A Book about Pythonic Application Architecture Patterns for Managing Complexity.  Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it ""Cosmic Python"" tho.",cosmicpython,2024-01-13,2019-02-05,260,12.161538461538461,https://avatars.githubusercontent.com/u/47350834?v=4,"A Book about Pythonic Application Architecture Patterns for Managing Complexity.  Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it ""Cosmic Python"" tho.",[],[],2023-09-11,"[('roban/cosmolopy', 0.5579319000244141, 'sim', 0), ('faif/python-patterns', 0.532017707824707, 'util', 0), ('google/gin-config', 0.5146122574806213, 'util', 0), ('timofurrer/awesome-asyncio', 0.5105115175247192, 'study', 0), ('eleutherai/pyfra', 0.5096278190612793, 'ml', 0), ('python/cpython', 0.5018055438995361, 'util', 0)]",46,2.0,,0.1,1,1,60,4,0,0,0,1.0,3.0,90.0,3.0,40
155,pandas,https://github.com/adamerose/pandasgui,[],,[],[],,,,adamerose/pandasgui,PandasGUI,3079,223,54,Python,,A GUI for Pandas DataFrames,adamerose,2024-01-11,2019-06-12,241,12.730655640874188,,A GUI for Pandas DataFrames,"['dataframe', 'gui', 'pandas', 'viewer']","['dataframe', 'gui', 'pandas', 'viewer']",2023-12-07,"[('tkrabel/bamboolib', 0.8184458017349243, 'pandas', 1), ('lux-org/lux', 0.6828799843788147, 'viz', 1), ('kanaries/pygwalker', 0.6812300682067871, 'pandas', 2), ('holoviz/panel', 0.6248031854629517, 'viz', 1), ('man-group/dtale', 0.6185036897659302, 'viz', 1), ('twopirllc/pandas-ta', 0.5920196771621704, 'finance', 2), ('beeware/toga', 0.590601921081543, 'gui', 1), ('mwaskom/seaborn', 0.5795894861221313, 'viz', 1), ('quantopian/qgrid', 0.5642687678337097, 'jupyter', 0), ('rsheftel/pandas_market_calendars', 0.5483811497688293, 'finance', 1), ('jmcarpenter2/swifter', 0.5478309988975525, 'pandas', 1), ('parthjadhav/tkinter-designer', 0.5451450943946838, 'gui', 1), ('pola-rs/polars', 0.5391742587089539, 'pandas', 1), ('blaze/blaze', 0.5380495190620422, 'pandas', 0), ('nalepae/pandarallel', 0.5294705629348755, 'pandas', 1), ('eleutherai/pyfra', 0.5291572213172913, 'ml', 0), ('zsailer/pandas_flavor', 0.5256001949310303, 'pandas', 1), ('federicoceratto/dashing', 0.5236207246780396, 'term', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5230746865272522, 'pandas', 0), ('modin-project/modin', 0.5174034237861633, 'perf', 2), ('geopandas/geopandas', 0.51722252368927, 'gis', 1), ('hazyresearch/meerkat', 0.5161508321762085, 'viz', 1), ('cmudig/autoprofiler', 0.5127207040786743, 'jupyter', 1), ('pandas-dev/pandas', 0.5116315484046936, 'pandas', 2), ('rapidsai/cudf', 0.5097667574882507, 'pandas', 2), ('holoviz/hvplot', 0.5072020888328552, 'pandas', 0), ('bokeh/bokeh', 0.5031503438949585, 'viz', 0), ('holoviz/spatialpandas', 0.5028727054595947, 'pandas', 1), ('mementum/bta-lib', 0.5000632405281067, 'finance', 0)]",15,1.0,,0.06,9,3,56,1,0,9,9,9.0,4.0,90.0,0.4,40
698,data,https://github.com/pyeve/cerberus,[],,[],[],,,,pyeve/cerberus,cerberus,3071,238,50,Python,http://python-cerberus.org,"Lightweight, extensible data validation library for Python",pyeve,2024-01-12,2012-10-10,589,5.206345362073141,https://avatars.githubusercontent.com/u/26229868?v=4,"Lightweight, extensible data validation library for Python",['data-validation'],['data-validation'],2023-10-23,"[('pydantic/pydantic', 0.7001333832740784, 'util', 0), ('wtforms/wtforms', 0.657631516456604, 'web', 0), ('marshmallow-code/marshmallow', 0.6555448174476624, 'util', 0), ('tensorflow/data-validation', 0.6110429167747498, 'ml-ops', 0), ('unionai-oss/pandera', 0.6107540726661682, 'pandas', 1), ('python-odin/odin', 0.6078689098358154, 'util', 0), ('pytoolz/toolz', 0.5941500067710876, 'util', 0), ('pandas-dev/pandas', 0.5807719230651855, 'pandas', 0), ('rasbt/mlxtend', 0.5806938409805298, 'ml', 0), ('pylons/colander', 0.5786774754524231, 'util', 0), ('legrandin/pycryptodome', 0.5516589283943176, 'util', 0), ('andialbrecht/sqlparse', 0.5441608428955078, 'data', 0), ('wolever/parameterized', 0.5334105491638184, 'testing', 0), ('collerek/ormar', 0.5298268795013428, 'data', 0), ('snyk/faker-security', 0.5294828414916992, 'security', 0), ('pycaret/pycaret', 0.5282540321350098, 'ml', 0), ('lk-geimfari/mimesis', 0.5154099464416504, 'data', 0), ('pypy/pypy', 0.5118589997291565, 'util', 0), ('imageio/imageio', 0.5100802183151245, 'util', 0), ('facebook/pyre-check', 0.5100794434547424, 'typing', 0), ('pyston/pyston', 0.5083901882171631, 'util', 0), ('pmorissette/bt', 0.507025420665741, 'finance', 0), ('pytables/pytables', 0.5037830471992493, 'data', 0), ('featurelabs/featuretools', 0.503200888633728, 'ml', 0)]",66,4.0,,0.88,8,6,137,3,0,2,2,8.0,11.0,90.0,1.4,40
1841,finance,https://github.com/zvtvz/zvt,[],,[],[],,,,zvtvz/zvt,zvt,2790,811,131,Python,https://zvt.readthedocs.io/en/latest/,modular quant framework.,zvtvz,2024-01-12,2019-04-04,251,11.08399545970488,https://avatars.githubusercontent.com/u/49115722?v=4,modular quant framework.,"['algorithmic-trading', 'backtesting', 'cryptocurrency', 'fintech', 'fundamental-analysis', 'machine-learning', 'ml', 'quant', 'quantitative-finance', 'quantitative-trading', 'stock', 'stock-market', 'technical-analysis', 'trading-bot', 'trading-platform', 'trading-strategies', 'zvt']","['algorithmic-trading', 'backtesting', 'cryptocurrency', 'fintech', 'fundamental-analysis', 'machine-learning', 'ml', 'quant', 'quantitative-finance', 'quantitative-trading', 'stock', 'stock-market', 'technical-analysis', 'trading-bot', 'trading-platform', 'trading-strategies', 'zvt']",2023-11-09,"[('ranaroussi/quantstats', 0.6442165970802307, 'finance', 4), ('quantconnect/lean', 0.6376045942306519, 'finance', 3), ('polakowo/vectorbt', 0.6255056858062744, 'finance', 6), ('goldmansachs/gs-quant', 0.6025742888450623, 'finance', 1), ('numerai/example-scripts', 0.5974596738815308, 'finance', 2), ('microsoft/qlib', 0.5502883791923523, 'finance', 6), ('google/tf-quant-finance', 0.5456732511520386, 'finance', 1), ('kernc/backtesting.py', 0.5392759442329407, 'finance', 3), ('idanya/algo-trader', 0.5369071364402771, 'finance', 5), ('ai4finance-foundation/finrl', 0.5343793034553528, 'finance', 2), ('openbb-finance/openbbterminal', 0.5273526310920715, 'finance', 3), ('quantopian/zipline', 0.5200475454330444, 'finance', 2), ('stefmolin/stock-analysis', 0.5058038830757141, 'finance', 2), ('gbeced/basana', 0.5045093297958374, 'finance', 4)]",64,4.0,,0.25,3,0,58,2,1,15,1,3.0,2.0,90.0,0.7,40
1438,testing,https://github.com/cobrateam/splinter,[],,[],[],,,,cobrateam/splinter,splinter,2672,532,95,Python,http://splinter.readthedocs.org/en/stable/index.html,splinter - python test framework for web applications ,cobrateam,2024-01-14,2010-09-18,697,3.8312167144612865,https://avatars.githubusercontent.com/u/403905?v=4,splinter - python test framework for web applications ,"['automation', 'selenium', 'webdriver']","['automation', 'selenium', 'webdriver']",2024-01-09,"[('seleniumbase/seleniumbase', 0.7703961730003357, 'testing', 2), ('microsoft/playwright-python', 0.6916419267654419, 'testing', 1), ('roniemartinez/dude', 0.5549781918525696, 'util', 1), ('masoniteframework/masonite', 0.5544414520263672, 'web', 0), ('wolever/parameterized', 0.5428141355514526, 'testing', 0), ('buildbot/buildbot', 0.5403817296028137, 'util', 0), ('scrapy/scrapy', 0.5401042699813843, 'data', 0), ('alirezamika/autoscraper', 0.5292969346046448, 'data', 1), ('webpy/webpy', 0.5233193635940552, 'web', 0), ('pallets/flask', 0.5134128332138062, 'web', 0), ('nedbat/coveragepy', 0.512545645236969, 'testing', 0), ('r0x0r/pywebview', 0.5121274590492249, 'gui', 0), ('eleutherai/pyfra', 0.5119724273681641, 'ml', 0), ('reflex-dev/reflex', 0.5109301805496216, 'web', 0)]",179,3.0,,1.35,30,26,162,0,1,4,1,30.0,23.0,90.0,0.8,40
1127,ml,https://github.com/scikit-learn-contrib/hdbscan,[],,[],[],,,,scikit-learn-contrib/hdbscan,hdbscan,2600,479,57,Jupyter Notebook,http://hdbscan.readthedocs.io/en/latest/,A high performance implementation of HDBSCAN clustering.,scikit-learn-contrib,2024-01-12,2015-04-22,457,5.6786271450858035,https://avatars.githubusercontent.com/u/17349883?v=4,A high performance implementation of HDBSCAN clustering.,"['cluster-analysis', 'clustering', 'clustering-algorithm', 'clustering-evaluation', 'machine-learning', 'machine-learning-algorithms']","['cluster-analysis', 'clustering', 'clustering-algorithm', 'clustering-evaluation', 'machine-learning', 'machine-learning-algorithms']",2023-11-20,[],86,4.0,,0.48,12,3,106,2,5,5,5,12.0,8.0,90.0,0.7,40
337,perf,https://github.com/tlkh/asitop,[],,[],[],,,,tlkh/asitop,asitop,2346,125,27,Python,https://tlkh.github.io/asitop/,Perf monitoring CLI tool for Apple Silicon,tlkh,2024-01-14,2021-10-27,117,19.905454545454546,,Perf monitoring CLI tool for Apple Silicon,"['apple-silicon', 'cli', 'cpu', 'gpu', 'm1', 'macos']","['apple-silicon', 'cli', 'cpu', 'gpu', 'm1', 'macos']",2023-01-24,"[('ml-explore/mlx', 0.5542373061180115, 'ml', 1), ('tlkh/tf-metal-experiments', 0.540374219417572, 'perf', 2), ('mrdbourke/m1-machine-learning-test', 0.5042293071746826, 'ml', 0)]",8,2.0,,0.02,14,0,27,12,0,0,0,14.0,36.0,90.0,2.6,40
352,ml-interpretability,https://github.com/seldonio/alibi,[],,[],[],,,,seldonio/alibi,alibi,2246,279,47,Python,https://docs.seldon.io/projects/alibi/en/stable/,Algorithms for explaining machine learning models,seldonio,2024-01-13,2019-02-26,257,8.73929961089494,https://avatars.githubusercontent.com/u/10297834?v=4,Algorithms for explaining machine learning models,"['counterfactual', 'explanations', 'interpretability', 'machine-learning', 'xai']","['counterfactual', 'explanations', 'interpretability', 'machine-learning', 'xai']",2023-11-13,"[('marcotcr/lime', 0.7131057977676392, 'ml-interpretability', 0), ('maif/shapash', 0.6979689002037048, 'ml', 2), ('carla-recourse/carla', 0.6956292986869812, 'ml', 2), ('slundberg/shap', 0.6683449745178223, 'ml-interpretability', 2), ('interpretml/interpret', 0.6674531102180481, 'ml-interpretability', 3), ('pair-code/lit', 0.6469577550888062, 'ml-interpretability', 1), ('teamhg-memex/eli5', 0.6379401683807373, 'ml', 1), ('xplainable/xplainable', 0.6222488880157471, 'ml-interpretability', 2), ('oegedijk/explainerdashboard', 0.6191368699073792, 'ml-interpretability', 1), ('csinva/imodels', 0.6186242699623108, 'ml', 2), ('tensorflow/lucid', 0.5702253580093384, 'ml-interpretability', 2), ('tensorflow/data-validation', 0.5581661462783813, 'ml-ops', 0), ('rafiqhasan/auto-tensorflow', 0.5504202842712402, 'ml-dl', 1), ('huggingface/evaluate', 0.5494807958602905, 'ml', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.545021116733551, 'study', 1), ('patchy631/machine-learning', 0.5448355078697205, 'ml', 0), ('eleutherai/pythia', 0.5343512296676636, 'ml-interpretability', 1), ('eugeneyan/testing-ml', 0.5242266654968262, 'testing', 1), ('selfexplainml/piml-toolbox', 0.5072975158691406, 'ml-interpretability', 0), ('pytorch/captum', 0.5067712664604187, 'ml-interpretability', 1), ('microsoft/robustlearn', 0.5054094791412354, 'time-series', 0), ('google-research/google-research', 0.5054081678390503, 'ml', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5047518014907837, 'study', 1), ('linkedin/fasttreeshap', 0.5031470060348511, 'ml', 2)]",19,2.0,,1.77,18,7,59,2,4,6,4,18.0,16.0,90.0,0.9,40
1224,util,https://github.com/dateutil/dateutil,[],,[],[],,,,dateutil/dateutil,dateutil,2193,470,45,Python,,Useful extensions to the standard Python datetime features,dateutil,2024-01-13,2014-11-19,479,4.570110151830902,https://avatars.githubusercontent.com/u/9849410?v=4,Useful extensions to the standard Python datetime features,"['datetime', 'parsing', 'time', 'timezones']","['datetime', 'parsing', 'time', 'timezones']",2023-11-13,"[('sdispater/pendulum', 0.7966391444206238, 'util', 3), ('scrapinghub/dateparser', 0.7123557329177856, 'util', 2), ('arrow-py/arrow', 0.7106708884239197, 'util', 3), ('stub42/pytz', 0.621961236000061, 'util', 0), ('rjt1990/pyflux', 0.5882995128631592, 'time-series', 0), ('google/temporian', 0.5748814344406128, 'time-series', 0), ('alkaline-ml/pmdarima', 0.536246657371521, 'time-series', 0), ('tdameritrade/stumpy', 0.530729353427887, 'time-series', 0), ('rasbt/watermark', 0.5191126465797424, 'util', 0), ('firmai/atspy', 0.5173526406288147, 'time-series', 0), ('pytoolz/toolz', 0.5082383155822754, 'util', 0), ('pastas/pastas', 0.502344012260437, 'time-series', 0)]",131,5.0,,0.1,41,11,111,2,0,2,2,41.0,39.0,90.0,1.0,40
1480,web,https://github.com/masoniteframework/masonite,[],,[],[],,,,masoniteframework/masonite,masonite,2109,130,63,Python,http://docs.masoniteproject.com,The Modern And Developer Centric Python Web Framework. Be sure to read the documentation and join the Discord channel for questions: https://discord.gg/TwKeFahmPZ,masoniteframework,2024-01-13,2017-12-06,320,6.5730186999109526,https://avatars.githubusercontent.com/u/35498523?v=4,The Modern And Developer Centric Python Web Framework. Be sure to read the documentation and join the Discord channel for questions: https://discord.gg/TwKeFahmPZ,"['framework', 'masonite', 'web', 'webframework']","['framework', 'masonite', 'web', 'webframework']",2024-01-01,"[('pallets/flask', 0.7340575456619263, 'web', 0), ('klen/muffin', 0.7306077480316162, 'web', 1), ('webpy/webpy', 0.7172554731369019, 'web', 0), ('bottlepy/bottle', 0.6854233741760254, 'web', 0), ('pylons/pyramid', 0.6714824438095093, 'web', 0), ('eleutherai/pyfra', 0.6563796401023865, 'ml', 0), ('pyscript/pyscript', 0.6563617587089539, 'web', 0), ('willmcgugan/textual', 0.6535159349441528, 'term', 1), ('falconry/falcon', 0.6416914463043213, 'web', 2), ('pallets/werkzeug', 0.6412118673324585, 'web', 0), ('r0x0r/pywebview', 0.6317577362060547, 'gui', 0), ('cherrypy/cherrypy', 0.6259151697158813, 'web', 0), ('reflex-dev/reflex', 0.6244274973869324, 'web', 1), ('clips/pattern', 0.6094305515289307, 'nlp', 0), ('pallets/quart', 0.607140302658081, 'web', 0), ('timofurrer/awesome-asyncio', 0.6026535034179688, 'study', 0), ('scrapy/scrapy', 0.6013022065162659, 'data', 1), ('neoteroi/blacksheep', 0.5978954434394836, 'web', 2), ('encode/httpx', 0.5954501628875732, 'web', 0), ('pypy/pypy', 0.5918770432472229, 'util', 0), ('python/cpython', 0.5917008519172668, 'util', 0), ('holoviz/panel', 0.5894260406494141, 'viz', 0), ('requests/toolbelt', 0.5709607005119324, 'util', 0), ('dylanhogg/awesome-python', 0.5680248141288757, 'study', 0), ('ethereum/web3.py', 0.5657337307929993, 'crypto', 0), ('indico/indico', 0.5639700293540955, 'web', 0), ('encode/uvicorn', 0.5623204708099365, 'web', 0), ('roniemartinez/dude', 0.560670018196106, 'util', 1), ('bokeh/bokeh', 0.5602609515190125, 'viz', 0), ('hugapi/hug', 0.5562730431556702, 'util', 0), ('emmett-framework/emmett', 0.5554807186126709, 'web', 0), ('cobrateam/splinter', 0.5544414520263672, 'testing', 0), ('pyodide/pyodide', 0.5539262294769287, 'util', 0), ('seleniumbase/seleniumbase', 0.5500764846801758, 'testing', 0), ('buildbot/buildbot', 0.549967885017395, 'util', 0), ('microsoft/playwright-python', 0.549170970916748, 'testing', 0), ('pytoolz/toolz', 0.5471121668815613, 'util', 0), ('plotly/dash', 0.5463806390762329, 'viz', 0), ('1200wd/bitcoinlib', 0.5462782979011536, 'crypto', 0), ('alirn76/panther', 0.5462374091148376, 'web', 1), ('hoffstadt/dearpygui', 0.5427703261375427, 'gui', 0), ('urwid/urwid', 0.5398597121238708, 'term', 0), ('plotly/plotly.py', 0.5396803021430969, 'viz', 0), ('backtick-se/cowait', 0.5356951951980591, 'util', 0), ('amaargiru/pyroad', 0.5333690643310547, 'study', 0), ('pywebio/pywebio', 0.5325572490692139, 'web', 0), ('flet-dev/flet', 0.5303350687026978, 'web', 1), ('adafruit/circuitpython', 0.5271365642547607, 'util', 0), ('minimaxir/simpleaichat', 0.5257116556167603, 'llm', 0), ('simple-salesforce/simple-salesforce', 0.5242258310317993, 'data', 0), ('primal100/pybitcointools', 0.5237247347831726, 'crypto', 0), ('pyston/pyston', 0.523208498954773, 'util', 0), ('cohere-ai/notebooks', 0.5226303935050964, 'llm', 0), ('tornadoweb/tornado', 0.5226184725761414, 'web', 0), ('voila-dashboards/voila', 0.5220005512237549, 'jupyter', 0), ('man-c/pycoingecko', 0.5191899538040161, 'crypto', 0), ('pyinfra-dev/pyinfra', 0.5186633467674255, 'util', 0), ('python-restx/flask-restx', 0.5184113383293152, 'web', 0), ('eventual-inc/daft', 0.5179644227027893, 'pandas', 0), ('psf/requests', 0.5165953040122986, 'web', 0), ('replicate/replicate-python', 0.5155618190765381, 'ml', 0), ('pysimplegui/pysimplegui', 0.5136278867721558, 'gui', 0), ('ta-lib/ta-lib-python', 0.512328028678894, 'finance', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5106345415115356, 'study', 0), ('websocket-client/websocket-client', 0.5101653933525085, 'web', 0), ('sqlalchemy/mako', 0.5073601603507996, 'template', 0), ('alirezamika/autoscraper', 0.5066377520561218, 'data', 0), ('nficano/python-lambda', 0.5054439306259155, 'util', 0), ('pylons/waitress', 0.5041007399559021, 'web', 0), ('goldmansachs/gs-quant', 0.5026025176048279, 'finance', 0), ('kivy/kivy', 0.5023773312568665, 'util', 0), ('maartenbreddels/ipyvolume', 0.5016263127326965, 'jupyter', 0), ('tkrabel/bamboolib', 0.5014203190803528, 'pandas', 0)]",87,3.0,,0.48,25,11,74,0,3,19,3,25.0,3.0,90.0,0.1,40
1578,data,https://github.com/accenture/ampligraph,['knowledge-graph'],,[],[],,,,accenture/ampligraph,AmpliGraph,2045,247,67,Python,,Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org,accenture,2024-01-13,2019-01-09,263,7.750406063887385,https://avatars.githubusercontent.com/u/10454368?v=4,Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org,"['graph-embeddings', 'graph-representation-learning', 'knowledge-graph', 'knowledge-graph-embeddings', 'machine-learning', 'relational-learning', 'representation-learning']","['graph-embeddings', 'graph-representation-learning', 'knowledge-graph', 'knowledge-graph-embeddings', 'machine-learning', 'relational-learning', 'representation-learning']",2023-07-12,"[('awslabs/dgl-ke', 0.7346105575561523, 'ml', 2), ('dmlc/dgl', 0.6121450066566467, 'ml-dl', 0), ('zjunlp/deepke', 0.593433678150177, 'ml', 1), ('dylanhogg/llmgraph', 0.5810795426368713, 'ml', 1), ('pyg-team/pytorch_geometric', 0.58009934425354, 'ml-dl', 0), ('a-r-j/graphein', 0.5756738185882568, 'sim', 0), ('strawberry-graphql/strawberry', 0.5520169734954834, 'web', 0), ('stellargraph/stellargraph', 0.5474543571472168, 'graph', 1), ('chandlerbang/awesome-self-supervised-gnn', 0.5335105061531067, 'study', 1), ('graphistry/pygraphistry', 0.532095193862915, 'data', 0), ('benedekrozemberczki/tigerlily', 0.5214887857437134, 'ml-dl', 2), ('deepgraphlearning/ultra', 0.5169753432273865, 'ml', 1), ('jina-ai/vectordb', 0.5142897367477417, 'data', 0), ('qdrant/fastembed', 0.5070711970329285, 'ml', 0), ('danielegrattarola/spektral', 0.5038337707519531, 'ml-dl', 0)]",20,4.0,,6.08,1,1,61,6,2,3,2,1.0,2.0,90.0,2.0,40
376,ml-interpretability,https://github.com/jalammar/ecco,[],,[],[],,,,jalammar/ecco,ecco,1849,153,24,Jupyter Notebook,https://ecco.readthedocs.io,"Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).",jalammar,2024-01-12,2020-11-07,168,10.977947413061917,,"Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).","['explorables', 'language-models', 'natural-language-processing', 'nlp', 'pytorch', 'visualization']","['explorables', 'language-models', 'natural-language-processing', 'nlp', 'pytorch', 'visualization']",2023-08-10,"[('allenai/allennlp', 0.5496200323104858, 'nlp', 3), ('alibaba/easynlp', 0.549338698387146, 'nlp', 2), ('koaning/whatlies', 0.5461040735244751, 'nlp', 1), ('brandtbucher/specialist', 0.5420230031013489, 'perf', 0), ('hannibal046/awesome-llm', 0.5404885411262512, 'study', 0), ('lianjiatech/belle', 0.5401182174682617, 'llm', 0), ('guidance-ai/guidance', 0.5360292196273804, 'llm', 0), ('vizzuhq/ipyvizzu', 0.5353596806526184, 'jupyter', 0), ('freedomintelligence/llmzoo', 0.5306503772735596, 'llm', 0), ('jbesomi/texthero', 0.5305410623550415, 'nlp', 1), ('ai21labs/lm-evaluation', 0.5280724167823792, 'llm', 0), ('opengeos/leafmap', 0.5254456996917725, 'gis', 0), ('bigscience-workshop/megatron-deepspeed', 0.5253652930259705, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5253652930259705, 'llm', 0), ('lm-sys/fastchat', 0.5213225483894348, 'llm', 0), ('bokeh/bokeh', 0.5211085081100464, 'viz', 1), ('maartengr/bertopic', 0.5192667841911316, 'nlp', 1), ('explosion/spacy', 0.5182880163192749, 'nlp', 2), ('flairnlp/flair', 0.517105758190155, 'nlp', 3), ('ctlllll/llm-toolmaker', 0.5147423148155212, 'llm', 0), ('explosion/spacy-models', 0.513950765132904, 'nlp', 2), ('conceptofmind/toolformer', 0.5121808648109436, 'llm', 0), ('openlmlab/moss', 0.5079621076583862, 'llm', 1), ('graykode/nlp-tutorial', 0.5071130990982056, 'study', 3), ('killianlucas/open-interpreter', 0.5064843893051147, 'llm', 0), ('holoviz/holoviz', 0.5038740634918213, 'viz', 0), ('plotly/plotly.py', 0.5030951499938965, 'viz', 1)]",11,7.0,,0.06,5,0,39,5,0,4,4,5.0,4.0,90.0,0.8,40
656,util,https://github.com/numba/llvmlite,[],,[],[],,,,numba/llvmlite,llvmlite,1760,317,56,Python,http://llvmlite.pydata.org/,A lightweight LLVM python binding for writing JIT compilers,numba,2024-01-13,2014-08-07,494,3.55760900952931,https://avatars.githubusercontent.com/u/1628082?v=4,A lightweight LLVM python binding for writing JIT compilers,[],[],2023-12-13,"[('exaloop/codon', 0.6857039332389832, 'perf', 0), ('rustpython/rustpython', 0.638462483882904, 'util', 0), ('numba/numba', 0.5931792855262756, 'perf', 0), ('oracle/graalpython', 0.586859405040741, 'util', 0), ('cqcl/tket', 0.5800346732139587, 'util', 0), ('pyston/pyston', 0.5717125535011292, 'util', 0), ('citadel-ai/langcheck', 0.5582582950592041, 'llm', 0), ('pypy/pypy', 0.5528749227523804, 'util', 0), ('alpha-vllm/llama2-accessory', 0.539250910282135, 'llm', 0), ('psf/black', 0.5366169810295105, 'util', 0), ('google/jax', 0.534612774848938, 'ml', 0), ('pytoolz/toolz', 0.5343791842460632, 'util', 0), ('nomic-ai/pygpt4all', 0.5271598100662231, 'llm', 0), ('google/gin-config', 0.5255877375602722, 'util', 0), ('cython/cython', 0.5224786996841431, 'util', 0), ('astral-sh/ruff', 0.5217279195785522, 'util', 0), ('lcompilers/lpython', 0.5191760063171387, 'util', 0), ('instagram/monkeytype', 0.5182361602783203, 'typing', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5170167088508606, 'study', 0), ('py4j/py4j', 0.5133116245269775, 'util', 0), ('salesforce/codet5', 0.5081905126571655, 'nlp', 0), ('google/latexify_py', 0.5081674456596375, 'util', 0), ('python/cpython', 0.5075445175170898, 'util', 0), ('nvidia/cuda-python', 0.5029944181442261, 'ml', 0), ('micropython/micropython', 0.5028732419013977, 'util', 0)]",88,3.0,,2.87,37,22,115,1,2,12,2,37.0,61.0,90.0,1.6,40
1027,finance,https://github.com/domokane/financepy,[],,[],[],,,,domokane/financepy,FinancePy,1743,271,60,Jupyter Notebook,https://financepy.com/,"A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives. ",domokane,2024-01-13,2019-10-27,222,7.841259640102828,,"A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives. ","['asset-allocation', 'bonds', 'credit', 'currency', 'derivatives', 'derivatives-pricing', 'finance', 'fixed-income', 'investment', 'numba', 'pricing', 'risk', 'risk-management', 'students', 'valuation']","['asset-allocation', 'bonds', 'credit', 'currency', 'derivatives', 'derivatives-pricing', 'finance', 'fixed-income', 'investment', 'numba', 'pricing', 'risk', 'risk-management', 'students', 'valuation']",2023-12-10,"[('pmorissette/ffn', 0.6907992959022522, 'finance', 0), ('goldmansachs/gs-quant', 0.6860893964767456, 'finance', 2), ('cuemacro/finmarketpy', 0.6049692630767822, 'finance', 0), ('quantopian/pyfolio', 0.6018176078796387, 'finance', 0), ('ta-lib/ta-lib-python', 0.5799912810325623, 'finance', 1), ('quantecon/quantecon.py', 0.5794845819473267, 'sim', 0), ('gbeced/pyalgotrade', 0.5683234333992004, 'finance', 0), ('mementum/backtrader', 0.5463677048683167, 'finance', 0), ('1200wd/bitcoinlib', 0.5450164675712585, 'crypto', 0), ('pytoolz/toolz', 0.5421152114868164, 'util', 0), ('ranaroussi/quantstats', 0.526004433631897, 'finance', 1), ('pandas-dev/pandas', 0.5133991837501526, 'pandas', 0), ('cuemacro/findatapy', 0.5130403637886047, 'finance', 0), ('bashtage/arch', 0.5076860785484314, 'time-series', 2), ('eleutherai/pyfra', 0.5048255920410156, 'ml', 0), ('robcarver17/pysystemtrade', 0.5038099884986877, 'finance', 0), ('krzjoa/awesome-python-data-science', 0.5010226368904114, 'study', 0)]",29,3.0,,4.52,12,5,51,1,0,0,0,12.0,17.0,90.0,1.4,40
606,testing,https://github.com/pytest-dev/pytest-mock,[],,[],[],,,,pytest-dev/pytest-mock,pytest-mock,1705,135,36,Python,https://pytest-mock.readthedocs.io/en/latest/,Thin-wrapper around the mock package for easier use with pytest,pytest-dev,2024-01-12,2014-07-17,497,3.4256601607347874,https://avatars.githubusercontent.com/u/8897583?v=4,Thin-wrapper around the mock package for easier use with pytest,"['mock', 'pytest']","['mock', 'pytest']",2023-12-20,"[('pytest-dev/pytest', 0.6347528100013733, 'testing', 0), ('pytest-dev/pytest-cov', 0.6337581872940063, 'testing', 1), ('ionelmc/pytest-benchmark', 0.6237248182296753, 'testing', 1), ('pytest-dev/pytest-asyncio', 0.6129404306411743, 'testing', 0), ('samuelcolvin/dirty-equals', 0.6112805008888245, 'util', 1), ('pytest-dev/pytest-xdist', 0.6102861762046814, 'testing', 1), ('lundberg/respx', 0.5953378081321716, 'testing', 2), ('getsentry/responses', 0.5724997520446777, 'testing', 0), ('samuelcolvin/pytest-pretty', 0.5683526992797852, 'testing', 1), ('computationalmodelling/nbval', 0.5595971345901489, 'jupyter', 1), ('teemu/pytest-sugar', 0.5402851700782776, 'testing', 1), ('nteract/testbook', 0.5319708585739136, 'jupyter', 1), ('wolever/parameterized', 0.5089722275733948, 'testing', 0), ('taverntesting/tavern', 0.5072631239891052, 'testing', 1)]",68,7.0,,0.92,17,16,116,1,2,7,2,17.0,18.0,90.0,1.1,40
1040,llm,https://github.com/openai/gpt-discord-bot,[],,[],[],,,,openai/gpt-discord-bot,gpt-discord-bot,1636,633,35,Python,,"Example Discord bot written in Python that uses the completions API to have conversations with the `text-davinci-003` model, and the moderations API to filter the messages.",openai,2024-01-12,2022-12-21,57,28.276543209876543,https://avatars.githubusercontent.com/u/14957082?v=4,"Example Discord bot written in Python that uses the completions API to have conversations with the `text-davinci-003` model, and the moderations API to filter the messages.",[],[],2024-01-09,"[('nomic-ai/gpt4all', 0.5539908409118652, 'llm', 0), ('minimaxir/simpleaichat', 0.552017331123352, 'llm', 0), ('rasahq/rasa', 0.5486682057380676, 'llm', 0), ('togethercomputer/openchatkit', 0.5345660448074341, 'nlp', 0), ('eternnoir/pytelegrambotapi', 0.5340373516082764, 'util', 0), ('gunthercox/chatterbot', 0.5279523730278015, 'nlp', 0), ('microsoft/autogen', 0.5150191783905029, 'llm', 0), ('embedchain/embedchain', 0.5135484933853149, 'llm', 0), ('run-llama/rags', 0.5125691890716553, 'llm', 0), ('rcgai/simplyretrieve', 0.5008484125137329, 'llm', 0)]",3,0.0,,0.19,24,22,13,0,0,0,0,24.0,21.0,90.0,0.9,40
448,gis,https://github.com/jupyter-widgets/ipyleaflet,[],,[],[],,,,jupyter-widgets/ipyleaflet,ipyleaflet,1435,363,66,TypeScript,https://ipyleaflet.readthedocs.io,A Jupyter - Leaflet.js bridge,jupyter-widgets,2024-01-12,2014-05-07,507,2.8255977496483826,https://avatars.githubusercontent.com/u/25869250?v=4,A Jupyter - Leaflet.js bridge,"['jupyter', 'jupyterlab-extension', 'leaflet', 'visualization']","['jupyter', 'jupyterlab-extension', 'leaflet', 'visualization']",2024-01-12,"[('giswqs/mapwidget', 0.6565911769866943, 'gis', 2), ('jupyter-widgets/ipywidgets', 0.6435815095901489, 'jupyter', 1), ('python-visualization/folium', 0.6334434151649475, 'gis', 0), ('vizzuhq/ipyvizzu', 0.6106564402580261, 'jupyter', 1), ('voila-dashboards/voila', 0.5810590386390686, 'jupyter', 2), ('jupyterlab/jupyterlab-desktop', 0.5790235996246338, 'jupyter', 1), ('jupyter/notebook', 0.5543774366378784, 'jupyter', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5437476634979248, 'jupyter', 2), ('maartenbreddels/ipyvolume', 0.540547251701355, 'jupyter', 1), ('aws/graph-notebook', 0.5392546653747559, 'jupyter', 1), ('jupyterlite/jupyterlite', 0.539129376411438, 'jupyter', 2), ('opengeos/leafmap', 0.5388780832290649, 'gis', 1), ('jupyterlab/jupyterlab', 0.523621678352356, 'jupyter', 1), ('quantopian/qgrid', 0.5120397806167603, 'jupyter', 0), ('bloomberg/ipydatagrid', 0.5112629532814026, 'jupyter', 1), ('ipython/ipykernel', 0.504456102848053, 'util', 1), ('jakevdp/pythondatasciencehandbook', 0.5016043186187744, 'study', 0), ('jupyter/nbviewer', 0.5015178918838501, 'jupyter', 1)]",87,4.0,,0.46,41,26,118,0,3,8,3,41.0,68.0,90.0,1.7,40
1859,sim,https://github.com/nvidia-omniverse/isaacgymenvs,['gym'],,[],[],,,,nvidia-omniverse/isaacgymenvs,IsaacGymEnvs,1357,309,36,Python,,Isaac Gym Reinforcement Learning Environments,nvidia-omniverse,2024-01-14,2021-08-27,126,10.721218961625283,https://avatars.githubusercontent.com/u/57824658?v=4,Isaac Gym Reinforcement Learning Environments,[],['gym'],2023-10-18,"[('nvidia-omniverse/omniisaacgymenvs', 0.8064512610435486, 'sim', 0), ('farama-foundation/gymnasium', 0.6873985528945923, 'ml-rl', 1), ('pettingzoo-team/pettingzoo', 0.6462188959121704, 'ml-rl', 1), ('humancompatibleai/imitation', 0.617064356803894, 'ml-rl', 0), ('kzl/decision-transformer', 0.5959394574165344, 'ml-rl', 1), ('inspirai/timechamber', 0.56003338098526, 'sim', 0), ('thu-ml/tianshou', 0.5374286770820618, 'ml-rl', 0), ('huggingface/deep-rl-class', 0.534843921661377, 'study', 0), ('openai/baselines', 0.5117769837379456, 'ml-rl', 0), ('google/dopamine', 0.5014722943305969, 'ml-rl', 0)]",13,3.0,,0.29,46,14,29,3,0,2,2,46.0,56.0,90.0,1.2,40
946,diffusion,https://github.com/coyote-a/ultimate-upscale-for-automatic1111,[],,[],[],,,,coyote-a/ultimate-upscale-for-automatic1111,ultimate-upscale-for-automatic1111,1331,136,15,Python,,,coyote-a,2024-01-14,2023-01-02,56,23.70737913486005,,coyote-a/ultimate-upscale-for-automatic1111,[],[],2023-09-09,[],8,1.0,,0.29,6,2,13,4,0,0,0,6.0,12.0,90.0,2.0,40
487,gis,https://github.com/scitools/cartopy,[],,[],[],,,,scitools/cartopy,cartopy,1318,354,55,Python,https://scitools.org.uk/cartopy/docs/latest,Cartopy - a cartographic python library with matplotlib support,scitools,2024-01-12,2012-08-03,599,2.1982368358351203,https://avatars.githubusercontent.com/u/1391487?v=4,Cartopy - a cartographic python library with matplotlib support,"['cartopy', 'geometry', 'maps', 'matplotlib', 'projections', 'spatial']","['cartopy', 'geometry', 'maps', 'matplotlib', 'projections', 'spatial']",2024-01-10,"[('pyproj4/pyproj', 0.7539182305335999, 'gis', 0), ('holoviz/geoviews', 0.7182220220565796, 'gis', 1), ('raphaelquast/eomaps', 0.6839972138404846, 'gis', 2), ('residentmario/geoplot', 0.6602010726928711, 'gis', 1), ('dfki-ric/pytransform3d', 0.6194444298744202, 'math', 1), ('matplotlib/basemap', 0.6154806613922119, 'gis', 1), ('altair-viz/altair', 0.6086525917053223, 'viz', 0), ('earthlab/earthpy', 0.6048831343650818, 'gis', 0), ('marceloprates/prettymaps', 0.5919488072395325, 'viz', 2), ('mwaskom/seaborn', 0.5874788761138916, 'viz', 1), ('pysal/pysal', 0.5811754465103149, 'gis', 0), ('has2k1/plotnine', 0.5810568332672119, 'viz', 0), ('cuemacro/chartpy', 0.5699009299278259, 'viz', 1), ('matplotlib/matplotlib', 0.566702663898468, 'viz', 1), ('plotly/plotly.py', 0.5586792826652527, 'viz', 0), ('imageio/imageio', 0.5579615831375122, 'util', 0), ('opengeos/leafmap', 0.5553449988365173, 'gis', 0), ('albahnsen/pycircular', 0.5550414323806763, 'math', 0), ('holoviz/hvplot', 0.5497469305992126, 'pandas', 0), ('csurfer/pyheat', 0.542796790599823, 'profiling', 1), ('artelys/geonetworkx', 0.5421754121780396, 'gis', 0), ('holoviz/holoviz', 0.5334916114807129, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5279793739318848, 'study', 1), ('gregorhd/mapcompare', 0.5211345553398132, 'gis', 0), ('kanaries/pygwalker', 0.5204256772994995, 'pandas', 1), ('man-group/dtale', 0.519442617893219, 'viz', 0), ('python-pillow/pillow', 0.5182700157165527, 'util', 0), ('enthought/mayavi', 0.5173063278198242, 'viz', 0), ('pypa/installer', 0.5148464441299438, 'util', 0), ('pyglet/pyglet', 0.5082891583442688, 'gamedev', 0), ('graphistry/pygraphistry', 0.5080820918083191, 'data', 0), ('geopandas/geopandas', 0.5073442459106445, 'gis', 1), ('vispy/vispy', 0.5065507888793945, 'viz', 0), ('bokeh/bokeh', 0.5045799612998962, 'viz', 0)]",124,4.0,,2.65,72,47,139,0,1,4,1,72.0,137.0,90.0,1.9,40
1871,ml,https://github.com/eric-mitchell/direct-preference-optimization,['dpo'],,[],[],,,,eric-mitchell/direct-preference-optimization,direct-preference-optimization,1147,82,13,Python,,Reference implementation for DPO (Direct Preference Optimization),eric-mitchell,2024-01-13,2023-06-22,31,36.166666666666664,,Reference implementation for DPO (Direct Preference Optimization),[],['dpo'],2023-12-14,[],2,0.0,,0.25,27,13,7,1,0,0,0,27.0,32.0,90.0,1.2,40
1068,llm,https://github.com/bigscience-workshop/megatron-deepspeed,[],,[],[],,,,bigscience-workshop/megatron-deepspeed,Megatron-DeepSpeed,1144,199,24,Python,,"Ongoing research training transformer language models at scale, including: BERT & GPT-2",bigscience-workshop,2024-01-13,2021-07-02,134,8.501061571125266,https://avatars.githubusercontent.com/u/82455566?v=4,"Ongoing research training transformer language models at scale, including: BERT & GPT-2",[],[],2023-12-08,"[('microsoft/megatron-deepspeed', 1.0000001192092896, 'llm', 0), ('nvidia/megatron-lm', 0.6671424508094788, 'llm', 0), ('lvwerra/trl', 0.6662755608558655, 'llm', 0), ('jonasgeiping/cramming', 0.6582860946655273, 'nlp', 0), ('huggingface/transformers', 0.6457441449165344, 'nlp', 0), ('explosion/spacy-transformers', 0.6363678574562073, 'llm', 0), ('hannibal046/awesome-llm', 0.6277967095375061, 'study', 0), ('extreme-bert/extreme-bert', 0.6164913773536682, 'llm', 0), ('graykode/nlp-tutorial', 0.6075314879417419, 'study', 0), ('karpathy/mingpt', 0.6039530634880066, 'llm', 0), ('lianjiatech/belle', 0.5846147537231445, 'llm', 0), ('huggingface/text-generation-inference', 0.5798518061637878, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.5679675936698914, 'study', 0), ('next-gpt/next-gpt', 0.5671409964561462, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5593957901000977, 'llm', 0), ('eleutherai/gpt-neo', 0.5555706024169922, 'llm', 0), ('eleutherai/knowledge-neurons', 0.548306405544281, 'ml-interpretability', 0), ('ai21labs/lm-evaluation', 0.5460460186004639, 'llm', 0), ('microsoft/lora', 0.5398515462875366, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5385422110557556, 'llm', 0), ('deepset-ai/farm', 0.5374853014945984, 'nlp', 0), ('jalammar/ecco', 0.5253652930259705, 'ml-interpretability', 0), ('promptslab/awesome-prompt-engineering', 0.5253517031669617, 'study', 0), ('bigscience-workshop/biomedical', 0.5243796706199646, 'data', 0), ('xtekky/gpt4free', 0.5237749814987183, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5230602622032166, 'llm', 0), ('ist-daslab/gptq', 0.5179560780525208, 'llm', 0), ('jina-ai/finetuner', 0.5171146988868713, 'ml', 0), ('bytedance/lightseq', 0.515959620475769, 'nlp', 0), ('lm-sys/fastchat', 0.51581871509552, 'llm', 0), ('alignmentresearch/tuned-lens', 0.515650749206543, 'ml-interpretability', 0), ('freedomintelligence/llmzoo', 0.5145018100738525, 'llm', 0), ('cdpierse/transformers-interpret', 0.5142018795013428, 'ml-interpretability', 0), ('bobazooba/xllm', 0.5139332413673401, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5127219557762146, 'llm', 0), ('llmware-ai/llmware', 0.5115044116973877, 'llm', 0), ('microsoft/autogen', 0.5109838843345642, 'llm', 0), ('salesforce/blip', 0.5101778507232666, 'diffusion', 0), ('thilinarajapakse/simpletransformers', 0.5100935697555542, 'nlp', 0), ('openai/finetune-transformer-lm', 0.5082074999809265, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5077611804008484, 'llm', 0), ('openai/gpt-2', 0.5042035579681396, 'llm', 0), ('alibaba/easynlp', 0.5024697184562683, 'nlp', 0), ('muennighoff/sgpt', 0.5024375915527344, 'llm', 0)]",49,4.0,,0.04,4,2,31,1,0,4,4,4.0,3.0,90.0,0.8,40
622,data,https://github.com/intake/intake,[],,[],[],,,,intake/intake,intake,954,136,42,Python,https://intake.readthedocs.io/,"Intake is a lightweight package for finding, investigating, loading and disseminating data.",intake,2024-01-11,2017-08-14,337,2.8296610169491525,https://avatars.githubusercontent.com/u/1469464?v=4,"Intake is a lightweight package for finding, investigating, loading and disseminating data.","['data-access', 'data-catalog']","['data-access', 'data-catalog']",2023-10-10,"[('hyperqueryhq/whale', 0.5861302614212036, 'data', 1), ('airbnb/omniduct', 0.567048192024231, 'data', 0), ('lean-dojo/leandojo', 0.5443071722984314, 'math', 0), ('simonw/datasette', 0.5237489938735962, 'data', 0), ('linealabs/lineapy', 0.5228672623634338, 'jupyter', 0), ('saulpw/visidata', 0.5221759080886841, 'term', 0), ('airbytehq/airbyte', 0.5141798853874207, 'data', 0), ('google/ml-metadata', 0.506055474281311, 'ml-ops', 0), ('dlt-hub/dlt', 0.5047716498374939, 'data', 0), ('kubeflow-kale/kale', 0.5041489005088806, 'ml-ops', 0), ('jovianml/opendatasets', 0.5019022226333618, 'data', 0)]",86,5.0,,2.1,7,1,78,3,0,7,7,7.0,26.0,90.0,3.7,40
443,gis,https://github.com/pyproj4/pyproj,[],,[],[],,,,pyproj4/pyproj,pyproj,951,211,33,Python,https://pyproj4.github.io/pyproj,Python interface to PROJ (cartographic projections and coordinate transformations library),pyproj4,2024-01-10,2014-12-29,474,2.005724615848147,https://avatars.githubusercontent.com/u/48302803?v=4,Python interface to PROJ (cartographic projections and coordinate transformations library),"['cartographic-projection', 'coordinate-systems', 'coordinate-transformation', 'geodesic', 'geospatial']","['cartographic-projection', 'coordinate-systems', 'coordinate-transformation', 'geodesic', 'geospatial']",2023-11-08,"[('scitools/cartopy', 0.7539182305335999, 'gis', 0), ('holoviz/geoviews', 0.6277405023574829, 'gis', 0), ('residentmario/geoplot', 0.5774697065353394, 'gis', 0), ('artelys/geonetworkx', 0.5713775753974915, 'gis', 0), ('raphaelquast/eomaps', 0.5670640468597412, 'gis', 1), ('dfki-ric/pytransform3d', 0.5527838468551636, 'math', 0), ('geopandas/geopandas', 0.5460281372070312, 'gis', 1), ('opengeos/leafmap', 0.5364670157432556, 'gis', 1), ('pysal/pysal', 0.5152866244316101, 'gis', 0), ('has2k1/plotnine', 0.5081332921981812, 'viz', 0), ('pytoolz/toolz', 0.5079793334007263, 'util', 0)]",65,5.0,,1.37,21,15,110,2,6,7,6,20.0,62.0,90.0,3.1,40
1845,ml-dl,https://github.com/jeshraghian/snntorch,[],,[],[],,,,jeshraghian/snntorch,snntorch,924,166,25,Python,https://snntorch.readthedocs.io/en/latest/,Deep and online learning with spiking neural networks in Python,jeshraghian,2024-01-12,2020-09-28,174,5.305988515176374,,Deep and online learning with spiking neural networks in Python,"['machine-learning', 'neural-networks', 'neuron-models', 'neuroscience', 'pytorch', 'snn', 'spike', 'spiking', 'spiking-neural-networks']","['machine-learning', 'neural-networks', 'neuron-models', 'neuroscience', 'pytorch', 'snn', 'spike', 'spiking', 'spiking-neural-networks']",2023-12-14,"[('online-ml/river', 0.615203320980072, 'ml', 1), ('ageron/handson-ml2', 0.5824640989303589, 'ml', 0), ('pytorch/pytorch', 0.5666177272796631, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5650865435600281, 'ml', 2), ('scikit-learn/scikit-learn', 0.5448082089424133, 'ml', 1), ('gradio-app/gradio', 0.5395351648330688, 'viz', 1), ('adafruit/circuitpython', 0.5297858119010925, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5287100076675415, 'study', 0), ('rasbt/machine-learning-book', 0.5273086428642273, 'study', 3), ('skorch-dev/skorch', 0.5167255401611328, 'ml-dl', 2), ('awslabs/gluonts', 0.5149070620536804, 'time-series', 3), ('intel/intel-extension-for-pytorch', 0.5118179321289062, 'perf', 2), ('pycaret/pycaret', 0.5082858800888062, 'ml', 1), ('lightly-ai/lightly', 0.5078932046890259, 'ml', 2), ('yzhao062/pyod', 0.5056750178337097, 'data', 2), ('pytorch/ignite', 0.5029792189598083, 'ml-dl', 2), ('joblib/joblib', 0.5017874836921692, 'util', 0)]",28,5.0,,3.54,31,12,40,1,2,10,2,31.0,38.0,90.0,1.2,40
1207,ml,https://github.com/hazyresearch/safari,[],,[],[],,,,hazyresearch/safari,safari,802,73,36,Assembly,,Convolutions for Sequence Modeling,hazyresearch,2024-01-12,2023-02-14,50,16.04,https://avatars.githubusercontent.com/u/2165246?v=4,Convolutions for Sequence Modeling,[],[],2023-09-29,"[('amazon-science/dq-bart', 0.5790391564369202, 'nlp', 0), ('bytedance/lightseq', 0.5279725790023804, 'nlp', 0)]",6,3.0,,0.48,6,4,11,4,0,0,0,6.0,20.0,90.0,3.3,40
1618,util,https://github.com/samuelcolvin/dirty-equals,[],,[],[],,,,samuelcolvin/dirty-equals,dirty-equals,744,35,12,Python,https://dirty-equals.helpmanual.io,Doing dirty (but extremely useful) things with equals.,samuelcolvin,2024-01-07,2022-01-26,104,7.0953678474114446,,Doing dirty (but extremely useful) things with equals.,"['pytest', 'testing-tools', 'unit-testing']","['pytest', 'testing-tools', 'unit-testing']",2023-11-15,"[('pytest-dev/pytest', 0.6580618023872375, 'testing', 1), ('ionelmc/pytest-benchmark', 0.6507035493850708, 'testing', 1), ('pytest-dev/pytest-mock', 0.6112805008888245, 'testing', 1), ('pytest-dev/pytest-cov', 0.5618858933448792, 'testing', 1), ('nteract/testbook', 0.5593006014823914, 'jupyter', 2), ('pytest-dev/pytest-xdist', 0.546781599521637, 'testing', 1), ('nedbat/coveragepy', 0.5362508296966553, 'testing', 0), ('teemu/pytest-sugar', 0.5335487723350525, 'testing', 1), ('computationalmodelling/nbval', 0.5271663665771484, 'jupyter', 1), ('wolever/parameterized', 0.524022102355957, 'testing', 0), ('samuelcolvin/pytest-pretty', 0.5239970088005066, 'testing', 1), ('pmorissette/bt', 0.5151998996734619, 'finance', 0), ('eugeneyan/python-collab-template', 0.514961302280426, 'template', 1)]",16,4.0,,0.62,16,13,24,2,4,8,4,16.0,24.0,90.0,1.5,40
1543,util,https://github.com/yukinarit/pyserde,"['serialization', 'dataclasses']",,[],[],,,,yukinarit/pyserde,pyserde,611,32,8,Python,https://yukinarit.github.io/pyserde/guide/en,"Yet another serialization library on top of dataclasses, inspired by serde-rs.",yukinarit,2024-01-13,2018-12-05,268,2.2725823591923486,,"Yet another serialization library on top of dataclasses, inspired by serde-rs.","['dataclasses', 'json', 'msgpack', 'serde', 'serialization', 'toml', 'typing', 'yaml']","['dataclasses', 'json', 'msgpack', 'serde', 'serialization', 'toml', 'typing', 'yaml']",2024-01-13,"[('lidatong/dataclasses-json', 0.686412513256073, 'util', 2), ('pylons/colander', 0.6523554921150208, 'util', 1), ('marshmallow-code/marshmallow', 0.6370522379875183, 'util', 2), ('google/flatbuffers', 0.6145598292350769, 'perf', 1), ('python-odin/odin', 0.5727947354316711, 'util', 3), ('jsonpickle/jsonpickle', 0.5483598113059998, 'data', 2), ('samuelcolvin/rtoml', 0.5448687076568604, 'data', 1), ('fabiocaccamo/python-benedict', 0.5148563981056213, 'util', 3)]",27,7.0,,2.04,36,28,62,0,20,9,20,35.0,42.0,90.0,1.2,40
866,util,https://github.com/ipython/ipykernel,[],,[],[],,,,ipython/ipykernel,ipykernel,596,361,37,Python,https://ipykernel.readthedocs.io/en/stable/,IPython Kernel for Jupyter,ipython,2024-01-12,2015-04-09,459,1.2964574269732754,https://avatars.githubusercontent.com/u/230453?v=4,IPython Kernel for Jupyter,"['ipython', 'ipython-kernel', 'jupyter', 'jupyter-notebook', 'kernel']","['ipython', 'ipython-kernel', 'jupyter', 'jupyter-notebook', 'kernel']",2024-01-13,"[('jupyter/notebook', 0.6881211400032043, 'jupyter', 2), ('ipython/ipyparallel', 0.6646348237991333, 'perf', 1), ('jupyterlab/jupyterlab', 0.662561297416687, 'jupyter', 1), ('jupyter/nbformat', 0.6578472852706909, 'jupyter', 0), ('ipython/ipython', 0.6446179747581482, 'util', 2), ('computationalmodelling/nbval', 0.6354666948318481, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.6094779968261719, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.6043174862861633, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5854012966156006, 'jupyter', 2), ('jupyter/nbconvert', 0.5823147892951965, 'jupyter', 0), ('jakevdp/pythondatasciencehandbook', 0.5796281099319458, 'study', 1), ('chaoleili/jupyterlab_tensorboard', 0.5732330679893494, 'jupyter', 0), ('aws/graph-notebook', 0.568101167678833, 'jupyter', 2), ('mwouts/jupytext', 0.5664380788803101, 'jupyter', 1), ('ageron/handson-ml2', 0.55137699842453, 'ml', 0), ('cohere-ai/notebooks', 0.5502215623855591, 'llm', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5428215265274048, 'study', 0), ('rasbt/watermark', 0.541002631187439, 'util', 2), ('nteract/testbook', 0.5405094623565674, 'jupyter', 1), ('gotcha/ipdb', 0.5379695892333984, 'debug', 1), ('voila-dashboards/voila', 0.5356465578079224, 'jupyter', 2), ('jupyterlite/jupyterlite', 0.5333779454231262, 'jupyter', 1), ('jupyter-lsp/jupyterlab-lsp', 0.5254985690116882, 'jupyter', 3), ('wesm/pydata-book', 0.5184985995292664, 'study', 0), ('koaning/drawdata', 0.5152633190155029, 'jupyter', 1), ('quantopian/qgrid', 0.514000654220581, 'jupyter', 0), ('faster-cpython/tools', 0.5114536285400391, 'perf', 0), ('mamba-org/gator', 0.5081201791763306, 'jupyter', 1), ('vizzuhq/ipyvizzu', 0.5079095363616943, 'jupyter', 3), ('tkrabel/bamboolib', 0.5052734017372131, 'pandas', 1), ('jupyterlab/jupyter-ai', 0.5044593811035156, 'jupyter', 1), ('jupyter-widgets/ipyleaflet', 0.504456102848053, 'gis', 1), ('python/cpython', 0.5019452571868896, 'util', 0)]",176,7.0,,1.52,45,28,107,0,18,15,18,45.0,43.0,90.0,1.0,40
1086,ml,https://github.com/opentensor/bittensor,[],,[],[],,,,opentensor/bittensor,bittensor,575,177,28,Python,https://www.bittensor.com/,Internet-scale Neural Networks,opentensor,2024-01-14,2020-07-28,183,3.1420765027322406,https://avatars.githubusercontent.com/u/61063461?v=4,Internet-scale Neural Networks,"['ai', 'blockchain', 'cryptocurrency', 'deep-learning', 'machine-learning', 'neural-networks', 'p2p', 'p2p-network', 'polkadot', 'pytorch', 'substrate', 'torch']","['ai', 'blockchain', 'cryptocurrency', 'deep-learning', 'machine-learning', 'neural-networks', 'p2p', 'p2p-network', 'polkadot', 'pytorch', 'substrate', 'torch']",2024-01-09,"[('alpa-projects/alpa', 0.556601345539093, 'ml-dl', 2), ('hpcaitech/colossalai', 0.552403450012207, 'llm', 2), ('ai4finance-foundation/finrl', 0.5417373776435852, 'finance', 0), ('explosion/thinc', 0.5385252833366394, 'ml-dl', 4), ('ddbourgin/numpy-ml', 0.5296874642372131, 'ml', 2), ('mosaicml/composer', 0.5280351042747498, 'ml-dl', 4), ('microsoft/onnxruntime', 0.5280240774154663, 'ml', 4), ('lutzroeder/netron', 0.5261327028274536, 'ml', 5), ('onnx/onnx', 0.5157052874565125, 'ml', 3), ('keras-team/keras', 0.5132687091827393, 'ml-dl', 4), ('adap/flower', 0.5103121399879456, 'ml-ops', 4), ('numerai/example-scripts', 0.5083948969841003, 'finance', 2), ('tensorflow/tensorflow', 0.5074020028114319, 'ml-dl', 2), ('keras-rl/keras-rl', 0.5059829950332642, 'ml-rl', 2), ('automatic1111/stable-diffusion-webui', 0.5035480856895447, 'diffusion', 4), ('freqtrade/freqtrade', 0.5012444853782654, 'crypto', 1)]",48,2.0,,13.79,172,162,42,0,24,12,24,172.0,49.0,90.0,0.3,40
1808,data,https://github.com/jina-ai/vectordb,['vectordb'],,[],[],,,,jina-ai/vectordb,vectordb,415,29,8,Python,,"A Python vector database you just need - no more, no less.",jina-ai,2024-01-12,2023-05-02,39,10.64102564102564,https://avatars.githubusercontent.com/u/60539444?v=4,"A Python vector database you just need - no more, no less.","['embedding-similarity', 'neural-search', 'sentence-embeddings', 'vector-database', 'vector-database-embedding', 'vector-search']","['embedding-similarity', 'neural-search', 'sentence-embeddings', 'vector-database', 'vector-database-embedding', 'vector-search', 'vectordb']",2023-10-23,"[('qdrant/fastembed', 0.7195001244544983, 'ml', 2), ('kagisearch/vectordb', 0.6794906258583069, 'data', 1), ('neuml/txtai', 0.6555339097976685, 'nlp', 4), ('chroma-core/chroma', 0.6463486552238464, 'data', 1), ('activeloopai/deeplake', 0.639483630657196, 'ml-ops', 2), ('milvus-io/bootcamp', 0.6278480887413025, 'data', 1), ('lancedb/lancedb', 0.612372100353241, 'data', 2), ('koaning/embetter', 0.5935912728309631, 'data', 0), ('qdrant/qdrant', 0.5856242775917053, 'data', 3), ('plasticityai/magnitude', 0.5780693888664246, 'nlp', 0), ('dgarnitz/vectorflow', 0.5692445039749146, 'data', 0), ('nomic-ai/nomic', 0.5595861077308655, 'nlp', 0), ('featureform/embeddinghub', 0.549279510974884, 'nlp', 1), ('qdrant/qdrant-client', 0.5459500551223755, 'util', 2), ('ddangelov/top2vec', 0.5428557395935059, 'nlp', 0), ('llmware-ai/llmware', 0.5389397740364075, 'llm', 0), ('jina-ai/clip-as-service', 0.5312561988830566, 'nlp', 1), ('ibis-project/ibis', 0.5219907760620117, 'data', 0), ('qdrant/vector-db-benchmark', 0.5212831497192383, 'perf', 2), ('tiangolo/sqlmodel', 0.52092444896698, 'data', 0), ('accenture/ampligraph', 0.5142897367477417, 'data', 0), ('amansrivastava17/embedding-as-service', 0.5125251412391663, 'nlp', 0), ('qdrant/qdrant-haystack', 0.5077526569366455, 'data', 0), ('docarray/docarray', 0.5065739154815674, 'data', 1), ('koaning/whatlies', 0.5039339661598206, 'nlp', 0), ('mcfunley/pugsql', 0.5005654096603394, 'data', 0)]",6,2.0,,1.79,4,2,9,3,10,24,10,4.0,9.0,90.0,2.2,40
730,ml-ops,https://github.com/skops-dev/skops,[],,[],[],,,,skops-dev/skops,skops,385,49,10,Python,https://skops.readthedocs.io/en/stable/,skops is a Python library helping you share your scikit-learn based models and put them in production,skops-dev,2024-01-05,2022-05-04,90,4.237421383647798,https://avatars.githubusercontent.com/u/104910083?v=4,skops is a Python library helping you share your scikit-learn based models and put them in production,"['huggingface', 'machine-learning', 'mlops', 'scikit-learn']","['huggingface', 'machine-learning', 'mlops', 'scikit-learn']",2024-01-05,"[('fmind/mlops-python-package', 0.6394261121749878, 'template', 1), ('kubeflow/fairing', 0.6278438568115234, 'ml-ops', 0), ('koaning/scikit-lego', 0.6168782711029053, 'ml', 2), ('automl/auto-sklearn', 0.5991626381874084, 'ml', 1), ('intel/scikit-learn-intelex', 0.5795713067054749, 'perf', 2), ('polyaxon/polyaxon', 0.5779671669006348, 'ml-ops', 2), ('iryna-kondr/scikit-llm', 0.5726227760314941, 'llm', 2), ('featurelabs/featuretools', 0.5711256265640259, 'ml', 2), ('huggingface/huggingface_hub', 0.5664080381393433, 'ml', 1), ('gradio-app/gradio', 0.5616341233253479, 'viz', 1), ('rasbt/machine-learning-book', 0.5548707246780396, 'study', 2), ('csinva/imodels', 0.5491555333137512, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5375784635543823, 'ml', 2), ('kubeflow-kale/kale', 0.5353480577468872, 'ml-ops', 1), ('wandb/client', 0.5322269201278687, 'ml', 2), ('scikit-learn-contrib/sklearn-pandas', 0.5230203866958618, 'pandas', 0), ('ageron/handson-ml2', 0.5202804207801819, 'ml', 0), ('jovianml/opendatasets', 0.5186297297477722, 'data', 1), ('dylanhogg/awesome-python', 0.5161980986595154, 'study', 1), ('fatiando/verde', 0.513488233089447, 'gis', 1), ('selfexplainml/piml-toolbox', 0.5126495957374573, 'ml-interpretability', 0), ('firmai/atspy', 0.5083118081092834, 'time-series', 0), ('pycaret/pycaret', 0.5067681074142456, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.504301130771637, 'ml', 2), ('microsoft/nni', 0.5024822950363159, 'ml', 2), ('scikit-learn/scikit-learn', 0.5013717412948608, 'ml', 1)]",16,5.0,,1.77,15,13,21,0,5,6,5,15.0,32.0,90.0,2.1,40
1902,data,https://github.com/meilisearch/meilisearch-python,"['search-engine', 'sdk', 'rust', 'api']",Meilisearch is an open-source search engine,[],[],,,,meilisearch/meilisearch-python,meilisearch-python,372,119,7,Python,https://www.meilisearch.com/,Python wrapper for the Meilisearch API,meilisearch,2024-01-16,2019-12-04,216,1.715415019762846,https://avatars.githubusercontent.com/u/43250847?v=4,Python wrapper for the Meilisearch API,"['client', 'meilisearch', 'sdk']","['api', 'client', 'meilisearch', 'rust', 'sdk', 'search-engine']",2024-01-16,"[('typesense/typesense-python', 0.5989935994148254, 'data', 3), ('dmarx/psaw', 0.5636150240898132, 'data', 0), ('googleapis/google-api-python-client', 0.5505169034004211, 'util', 0), ('qdrant/qdrant-client', 0.538914680480957, 'util', 0), ('man-c/pycoingecko', 0.5315601229667664, 'crypto', 1), ('nv7-github/googlesearch', 0.5313740968704224, 'util', 0), ('goldsmith/wikipedia', 0.5276321768760681, 'data', 0), ('snyk-labs/pysnyk', 0.5068243741989136, 'security', 1)]",55,3.0,,5.19,60,57,50,0,10,11,10,60.0,160.0,90.0,2.7,40
1897,llm,https://github.com/langchain-ai/langgraph,"['langchain', 'multi-actor', 'agents']","LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.",[],[],,,,langchain-ai/langgraph,langgraph,367,22,11,Python,,,langchain-ai,2024-01-14,2023-08-09,24,14.764367816091953,https://avatars.githubusercontent.com/u/126733545?v=4,"LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.",[],"['agents', 'langchain', 'multi-actor']",2024-01-09,"[('alphasecio/langchain-examples', 0.6478251814842224, 'llm', 1), ('gkamradt/langchain-tutorials', 0.6401094794273376, 'study', 0), ('hwchase17/langchain', 0.6307724118232727, 'llm', 1), ('prefecthq/langchain-prefect', 0.6234740018844604, 'llm', 1), ('logspace-ai/langflow', 0.6137604713439941, 'llm', 1), ('microsoft/autogen', 0.6005686521530151, 'llm', 0), ('dylanhogg/llmgraph', 0.5856835842132568, 'ml', 0), ('nat/openplayground', 0.5708506107330322, 'llm', 0), ('zilliztech/gptcache', 0.5597312450408936, 'llm', 1), ('chatarena/chatarena', 0.5562337636947632, 'llm', 0), ('aiwaves-cn/agents', 0.5516238808631897, 'nlp', 0), ('young-geng/easylm', 0.5411015748977661, 'llm', 0), ('jina-ai/thinkgpt', 0.5343421697616577, 'llm', 0), ('nomic-ai/gpt4all', 0.533517599105835, 'llm', 0), ('tigerlab-ai/tiger', 0.5296244025230408, 'llm', 0), ('spcl/graph-of-thoughts', 0.5278857350349426, 'llm', 0), ('langchain-ai/langsmith-cookbook', 0.5267937183380127, 'llm', 0), ('mlc-ai/web-llm', 0.5244644284248352, 'llm', 0), ('lm-sys/fastchat', 0.5237873792648315, 'llm', 0), ('thudm/chatglm2-6b', 0.5230793356895447, 'llm', 0), ('deepset-ai/haystack', 0.5211980938911438, 'llm', 0), ('deep-diver/pingpong', 0.5210304260253906, 'llm', 0), ('langchain-ai/langsmith-sdk', 0.5179738998413086, 'llm', 0), ('hannibal046/awesome-llm', 0.5179132223129272, 'study', 0), ('hiyouga/llama-factory', 0.5161830186843872, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5161828994750977, 'llm', 0), ('operand/agency', 0.5159871578216553, 'llm', 1), ('geekan/metagpt', 0.5153509974479675, 'llm', 0), ('embedchain/embedchain', 0.5151159763336182, 'llm', 0), ('run-llama/rags', 0.5150389671325684, 'llm', 0), ('agenta-ai/agenta', 0.5138852000236511, 'llm', 1), ('pathwaycom/llm-app', 0.5122884511947632, 'llm', 0), ('next-gpt/next-gpt', 0.5100759863853455, 'llm', 0), ('oobabooga/text-generation-webui', 0.5098942518234253, 'llm', 0), ('guardrails-ai/guardrails', 0.5052242875099182, 'llm', 0), ('eugeneyan/open-llms', 0.5050806403160095, 'study', 0), ('lianjiatech/belle', 0.5042188763618469, 'llm', 0), ('lupantech/chameleon-llm', 0.5040978193283081, 'llm', 0), ('mnotgod96/appagent', 0.5023365020751953, 'llm', 0), ('eth-sri/lmql', 0.5018105506896973, 'llm', 0), ('bobazooba/xllm', 0.5015073418617249, 'llm', 0), ('ctlllll/llm-toolmaker', 0.501153290271759, 'llm', 0), ('salesforce/xgen', 0.5001147389411926, 'llm', 0)]",3,1.0,,3.21,30,24,5,0,0,15,15,30.0,6.0,90.0,0.2,40
1698,util,https://github.com/mkdocstrings/griffe,[],,[],[],,,,mkdocstrings/griffe,griffe,232,35,6,Python,https://mkdocstrings.github.io/griffe,"Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API.",mkdocstrings,2024-01-13,2021-09-09,124,1.8602520045819015,https://avatars.githubusercontent.com/u/75664361?v=4,"Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API.","['api', 'breaking-changes', 'docs', 'mkdocstrings-collector', 'parser', 'signature']","['api', 'breaking-changes', 'docs', 'mkdocstrings-collector', 'parser', 'signature']",2023-12-06,"[('mitmproxy/pdoc', 0.6451767683029175, 'util', 2), ('landscapeio/prospector', 0.5961143970489502, 'util', 0), ('omry/omegaconf', 0.5919175744056702, 'util', 0), ('pdoc3/pdoc', 0.5867151618003845, 'util', 1), ('python-odin/odin', 0.5799145102500916, 'util', 0), ('eugeneyan/python-collab-template', 0.5616798400878906, 'template', 0), ('amaargiru/pyroad', 0.5457209944725037, 'study', 0), ('mkdocstrings/python', 0.5423215627670288, 'util', 0), ('mgedmin/check-manifest', 0.537682056427002, 'util', 0), ('pypi/warehouse', 0.5345215797424316, 'util', 0), ('pytoolz/toolz', 0.5321711301803589, 'util', 0), ('erotemic/ubelt', 0.5309567451477051, 'util', 0), ('jazzband/pip-tools', 0.5269049406051636, 'util', 0), ('pypa/hatch', 0.5250239968299866, 'util', 0), ('pydantic/pydantic', 0.5223087072372437, 'util', 0), ('pympler/pympler', 0.5151218771934509, 'perf', 0), ('legrandin/pycryptodome', 0.5096718668937683, 'util', 0), ('dosisod/refurb', 0.5076294541358948, 'util', 0), ('python-rope/rope', 0.5066107511520386, 'util', 0), ('uqfoundation/dill', 0.506209135055542, 'data', 0), ('pypy/pypy', 0.5051857829093933, 'util', 0), ('samuelcolvin/python-devtools', 0.5050040483474731, 'debug', 0), ('pyca/cryptography', 0.5040481090545654, 'util', 0), ('getsentry/responses', 0.5013567209243774, 'testing', 0), ('instagram/libcst', 0.5002207159996033, 'util', 0)]",26,6.0,,5.1,15,10,29,0,22,38,22,16.0,43.0,90.0,2.7,40
277,data,https://github.com/airbnb/knowledge-repo,[],,[],[],,,,airbnb/knowledge-repo,knowledge-repo,5406,709,175,Python,,A next-generation curated knowledge sharing platform for data scientists and other technical professions.,airbnb,2024-01-12,2016-08-17,388,13.90227773695812,https://avatars.githubusercontent.com/u/698437?v=4,A next-generation curated knowledge sharing platform for data scientists and other technical professions.,"['data', 'data-analysis', 'data-science', 'knowledge']","['data', 'data-analysis', 'data-science', 'knowledge']",2023-04-17,"[('krzjoa/awesome-python-data-science', 0.5916482210159302, 'study', 2), ('drivendata/cookiecutter-data-science', 0.5590470433235168, 'template', 1), ('zenodo/zenodo', 0.5470719933509827, 'util', 0), ('saulpw/visidata', 0.5398018956184387, 'term', 0), ('airbytehq/airbyte', 0.5395446419715881, 'data', 2), ('merantix-momentum/squirrel-core', 0.537761390209198, 'ml', 1), ('firmai/industry-machine-learning', 0.5338939428329468, 'study', 1), ('google/ml-metadata', 0.5292649865150452, 'ml-ops', 0), ('hyperqueryhq/whale', 0.520332932472229, 'data', 0), ('netflix/metaflow', 0.5116251707077026, 'ml-ops', 1), ('simonw/datasette', 0.5094085335731506, 'data', 0), ('brettkromkamp/contextualise', 0.5030013918876648, 'data', 0)]",73,4.0,,1.48,0,0,90,9,1,4,1,0.0,0.0,90.0,0.0,39
992,finance,https://github.com/quantopian/pyfolio,[],,[],[],,,,quantopian/pyfolio,pyfolio,5308,1719,304,Jupyter Notebook,https://quantopian.github.io/pyfolio,Portfolio and risk analytics in Python,quantopian,2024-01-13,2015-06-01,452,11.739652448657187,https://avatars.githubusercontent.com/u/1393215?v=4,Portfolio and risk analytics in Python,[],[],2020-07-15,"[('ranaroussi/quantstats', 0.6542462706565857, 'finance', 0), ('goldmansachs/gs-quant', 0.6110407114028931, 'finance', 0), ('quantopian/empyrical', 0.6045350432395935, 'finance', 0), ('domokane/financepy', 0.6018176078796387, 'finance', 0), ('eleutherai/pyfra', 0.5698432922363281, 'ml', 0), ('gbeced/pyalgotrade', 0.5584282875061035, 'finance', 0), ('scikit-learn/scikit-learn', 0.5517749786376953, 'ml', 0), ('robcarver17/pysystemtrade', 0.5509036779403687, 'finance', 0), ('cuemacro/finmarketpy', 0.5475354194641113, 'finance', 0), ('pymc-devs/pymc3', 0.5452370047569275, 'ml', 0), ('pmorissette/ffn', 0.5423455834388733, 'finance', 0), ('quantecon/quantecon.py', 0.5116593837738037, 'sim', 0), ('firmai/atspy', 0.5053930282592773, 'time-series', 0)]",59,4.0,,0.0,13,5,105,47,0,2,2,13.0,10.0,90.0,0.8,39
387,nlp,https://github.com/makcedward/nlpaug,[],,[],[],,,,makcedward/nlpaug,nlpaug,4222,454,42,Jupyter Notebook,https://makcedward.github.io/,Data augmentation for NLP ,makcedward,2024-01-13,2019-03-21,253,16.640765765765767,,Data augmentation for NLP ,"['adversarial-attacks', 'adversarial-example', 'ai', 'artificial-intelligence', 'augmentation', 'data-science', 'machine-learning', 'ml', 'natural-language-processing', 'nlp']","['adversarial-attacks', 'adversarial-example', 'ai', 'artificial-intelligence', 'augmentation', 'data-science', 'machine-learning', 'ml', 'natural-language-processing', 'nlp']",2022-07-07,"[('explosion/spacy', 0.5835755467414856, 'nlp', 6), ('nltk/nltk', 0.5745749473571777, 'nlp', 3), ('aleju/imgaug', 0.5599479079246521, 'ml', 2), ('infinitylogesh/mutate', 0.5548282265663147, 'nlp', 0), ('explosion/spacy-llm', 0.5450884103775024, 'llm', 3), ('alibaba/easynlp', 0.5402962565422058, 'nlp', 2), ('explosion/thinc', 0.5350939631462097, 'ml-dl', 5), ('thilinarajapakse/simpletransformers', 0.5320999026298523, 'nlp', 0), ('explosion/spacy-models', 0.5314129590988159, 'nlp', 3), ('huggingface/autotrain-advanced', 0.5261876583099365, 'ml', 2), ('rasahq/rasa', 0.5259739756584167, 'llm', 3), ('allenai/allennlp', 0.5257618427276611, 'nlp', 3), ('keras-team/keras-nlp', 0.5205011367797852, 'nlp', 3), ('norskregnesentral/skweak', 0.5184004902839661, 'nlp', 2), ('sdv-dev/sdv', 0.517977237701416, 'data', 1), ('jbesomi/texthero', 0.5141093134880066, 'nlp', 2), ('huggingface/datasets', 0.5126201510429382, 'nlp', 3), ('intellabs/fastrag', 0.5120058655738831, 'nlp', 1), ('interpretml/interpret', 0.5114248394966125, 'ml-interpretability', 3), ('bentoml/bentoml', 0.5093832612037659, 'ml-ops', 2), ('cleanlab/cleanlab', 0.505994439125061, 'ml', 1), ('llmware-ai/llmware', 0.5014007091522217, 'llm', 3)]",33,6.0,,0.0,0,0,59,19,0,5,5,0.0,0.0,90.0,0.0,39
13,ml,https://github.com/districtdatalabs/yellowbrick,[],,[],[],,,,districtdatalabs/yellowbrick,yellowbrick,4142,554,103,Python,http://www.scikit-yb.org/,Visual analysis and diagnostic tools to facilitate machine learning model selection.,districtdatalabs,2024-01-12,2016-05-18,401,10.307145396373977,https://avatars.githubusercontent.com/u/7107115?v=4,Visual analysis and diagnostic tools to facilitate machine learning model selection.,"['anaconda', 'estimator', 'machine-learning', 'matplotlib', 'model-selection', 'scikit-learn', 'visual-analysis', 'visualization', 'visualizer']","['anaconda', 'estimator', 'machine-learning', 'matplotlib', 'model-selection', 'scikit-learn', 'visual-analysis', 'visualization', 'visualizer']",2023-07-05,"[('automl/auto-sklearn', 0.6410435438156128, 'ml', 1), ('huggingface/evaluate', 0.6335552334785461, 'ml', 1), ('teamhg-memex/eli5', 0.6274762749671936, 'ml', 2), ('tensorflow/data-validation', 0.6272578239440918, 'ml-ops', 0), ('huggingface/datasets', 0.621246874332428, 'nlp', 1), ('wandb/client', 0.6167464256286621, 'ml', 1), ('nccr-itmo/fedot', 0.6075289249420166, 'ml-ops', 1), ('selfexplainml/piml-toolbox', 0.6020028591156006, 'ml-interpretability', 0), ('scikit-learn/scikit-learn', 0.5833300948143005, 'ml', 1), ('microsoft/nni', 0.5821363925933838, 'ml', 1), ('pyvista/pyvista', 0.578895092010498, 'viz', 1), ('polyaxon/datatile', 0.5754048228263855, 'pandas', 1), ('featurelabs/featuretools', 0.5721520185470581, 'ml', 2), ('lutzroeder/netron', 0.570451557636261, 'ml', 2), ('gradio-app/gradio', 0.5597376227378845, 'viz', 1), ('rasbt/mlxtend', 0.5595728754997253, 'ml', 1), ('epistasislab/tpot', 0.5539801120758057, 'ml', 3), ('apple/coremltools', 0.5530053377151489, 'ml', 1), ('pair-code/lit', 0.5486710071563721, 'ml-interpretability', 2), ('firmai/industry-machine-learning', 0.5426095128059387, 'study', 1), ('csinva/imodels', 0.5383384227752686, 'ml', 2), ('skops-dev/skops', 0.5375784635543823, 'ml-ops', 2), ('scikit-optimize/scikit-optimize', 0.5361714959144592, 'ml', 3), ('evidentlyai/evidently', 0.531039834022522, 'ml-ops', 1), ('hazyresearch/meerkat', 0.5290077924728394, 'viz', 1), ('ddbourgin/numpy-ml', 0.5262637138366699, 'ml', 1), ('polyaxon/polyaxon', 0.5260629653930664, 'ml-ops', 1), ('oegedijk/explainerdashboard', 0.5237336158752441, 'ml-interpretability', 0), ('patchy631/machine-learning', 0.5236752033233643, 'ml', 0), ('pyqtgraph/pyqtgraph', 0.5196901559829712, 'viz', 1), ('eugeneyan/testing-ml', 0.5194308757781982, 'testing', 1), ('man-group/dtale', 0.5179511308670044, 'viz', 1), ('microsoft/flaml', 0.5168565511703491, 'ml', 2), ('scikit-learn-contrib/metric-learn', 0.516703188419342, 'ml', 2), ('mlflow/mlflow', 0.5155874490737915, 'ml-ops', 1), ('lux-org/lux', 0.5121427178382874, 'viz', 1), ('doccano/doccano', 0.5116096138954163, 'nlp', 1), ('sktime/sktime', 0.5111024975776672, 'time-series', 2), ('determined-ai/determined', 0.5109866857528687, 'ml-ops', 1), ('mosaicml/composer', 0.5096165537834167, 'ml-dl', 1), ('intel/scikit-learn-intelex', 0.5062663555145264, 'perf', 2), ('koaning/scikit-lego', 0.5034389495849609, 'ml', 2), ('firmai/atspy', 0.5014930367469788, 'time-series', 0), ('roboflow/supervision', 0.5006172060966492, 'ml', 1), ('tensorflow/lucid', 0.5002766251564026, 'ml-interpretability', 2)]",113,2.0,,0.08,1,0,93,6,0,3,3,1.0,1.0,90.0,1.0,39
0,data,https://github.com/andialbrecht/sqlparse,[],,[],[],,,,andialbrecht/sqlparse,sqlparse,3494,663,96,Python,,A non-validating SQL parser module for Python,andialbrecht,2024-01-14,2012-04-18,614,5.682620817843866,,A non-validating SQL parser module for Python,[],[],2023-10-12,"[('tiangolo/sqlmodel', 0.6739121079444885, 'data', 0), ('sqlalchemy/sqlalchemy', 0.6582309007644653, 'data', 0), ('tobymao/sqlglot', 0.6332684755325317, 'data', 0), ('ibis-project/ibis', 0.6058615446090698, 'data', 0), ('macbre/sql-metadata', 0.5947306156158447, 'data', 0), ('collerek/ormar', 0.5768142938613892, 'data', 0), ('mcfunley/pugsql', 0.5752284526824951, 'data', 0), ('kayak/pypika', 0.5649843811988831, 'data', 0), ('machow/siuba', 0.5550650358200073, 'pandas', 0), ('pyparsing/pyparsing', 0.5442314147949219, 'util', 0), ('pyeve/cerberus', 0.5441608428955078, 'data', 0), ('pydantic/pydantic', 0.534013569355011, 'util', 0), ('instagram/libcst', 0.5294908285140991, 'util', 0), ('simonw/sqlite-utils', 0.5041010975837708, 'data', 0)]",103,3.0,,0.71,22,4,143,3,0,3,3,22.0,13.0,90.0,0.6,39
383,llm,https://github.com/minimaxir/gpt-2-simple,[],,[],[],,,,minimaxir/gpt-2-simple,gpt-2-simple,3358,681,77,Python,,Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts,minimaxir,2024-01-13,2019-04-13,250,13.409013120365088,,Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts,"['openai', 'tensorflow', 'text-generation', 'textgenrnn']","['openai', 'tensorflow', 'text-generation', 'textgenrnn']",2022-05-22,"[('minimaxir/aitextgen', 0.7194006443023682, 'llm', 0), ('huggingface/text-generation-inference', 0.6420865058898926, 'llm', 0), ('microsoft/pycodegpt', 0.629753589630127, 'llm', 0), ('karpathy/mingpt', 0.6132301688194275, 'llm', 0), ('xtekky/gpt4free', 0.604681670665741, 'llm', 1), ('infinitylogesh/mutate', 0.5839410424232483, 'nlp', 1), ('minimaxir/textgenrnn', 0.5805365443229675, 'nlp', 2), ('langchain-ai/opengpts', 0.5654781460762024, 'llm', 0), ('google-research/electra', 0.5602125525474548, 'ml-dl', 1), ('sharonzhou/long_stable_diffusion', 0.5567746758460999, 'diffusion', 0), ('weaviate/demo-text2vec-openai', 0.555767834186554, 'util', 1), ('openlmlab/moss', 0.5483125448226929, 'llm', 1), ('bytedance/lightseq', 0.5469551682472229, 'nlp', 0), ('nateshmbhat/pyttsx3', 0.5467420220375061, 'util', 0), ('lianjiatech/belle', 0.5449079871177673, 'llm', 0), ('lucidrains/dalle2-pytorch', 0.5398170948028564, 'diffusion', 0), ('openai/openai-cookbook', 0.5388956665992737, 'ml', 1), ('bigscience-workshop/megatron-deepspeed', 0.5385422110557556, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5385422110557556, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5367690920829773, 'llm', 0), ('hannibal046/awesome-llm', 0.5346177220344543, 'study', 0), ('guardrails-ai/guardrails', 0.5327882766723633, 'llm', 1), ('lucidrains/deep-daze', 0.5265099406242371, 'ml', 0), ('run-llama/rags', 0.5192126035690308, 'llm', 1), ('nvidia/tensorrt-llm', 0.5171143412590027, 'viz', 0), ('microsoft/autogen', 0.5105615258216858, 'llm', 0), ('allenai/allennlp', 0.510489821434021, 'nlp', 0), ('promptslab/promptify', 0.5102137327194214, 'nlp', 1), ('openai/tiktoken', 0.5088499784469604, 'nlp', 0), ('explosion/spacy-transformers', 0.5079010128974915, 'llm', 1), ('eleutherai/gpt-neo', 0.5072481632232666, 'llm', 0), ('pndurette/gtts', 0.5039756298065186, 'util', 0)]",21,5.0,,0.0,1,0,58,20,0,4,4,1.0,1.0,90.0,1.0,39
617,util,https://github.com/suor/funcy,[],,[],[],,,,suor/funcy,funcy,3206,140,71,Python,,A fancy and practical functional tools,suor,2024-01-13,2012-10-13,589,5.439166262724188,,A fancy and practical functional tools,"['functional-programming', 'utilities']","['functional-programming', 'utilities']",2023-12-17,"[('evhub/coconut', 0.6461945176124573, 'util', 1), ('pytoolz/toolz', 0.6413887143135071, 'util', 0), ('gondolav/pyfuncol', 0.5316035747528076, 'util', 0), ('pytoolz/cytoolz', 0.5295758247375488, 'util', 0), ('ethereum/eth-utils', 0.5216156244277954, 'crypto', 0)]",33,2.0,,0.56,17,13,137,1,0,5,5,17.0,16.0,90.0,0.9,39
1412,viz,https://github.com/netflix/flamescope,['data-visualization'],,[],[],,,,netflix/flamescope,flamescope,2951,181,342,Python,,FlameScope is a visualization tool for exploring different time ranges as Flame Graphs.,netflix,2024-01-13,2018-03-30,304,9.689024390243903,https://avatars.githubusercontent.com/u/913567?v=4,FlameScope is a visualization tool for exploring different time ranges as Flame Graphs.,[],['data-visualization'],2022-04-21,"[('mwaskom/seaborn', 0.5240253806114197, 'viz', 1), ('matplotlib/mplfinance', 0.5054373741149902, 'finance', 0)]",26,6.0,,0.0,1,0,71,21,0,0,0,1.0,4.0,90.0,4.0,39
1475,util,https://github.com/pexpect/pexpect,['automation'],,[],[],,,,pexpect/pexpect,pexpect,2476,475,91,Python,http://pexpect.readthedocs.io/,A Python module for controlling interactive programs in a pseudo-terminal,pexpect,2024-01-12,2013-09-17,541,4.576709796672828,https://avatars.githubusercontent.com/u/5480175?v=4,A Python module for controlling interactive programs in a pseudo-terminal,[],['automation'],2023-11-25,"[('tmbo/questionary', 0.6019250750541687, 'term', 0), ('google/python-fire', 0.5853911638259888, 'term', 0), ('python/cpython', 0.5793547630310059, 'util', 0), ('pallets/click', 0.5701817870140076, 'term', 0), ('jquast/blessed', 0.5613400340080261, 'term', 0), ('google/pyglove', 0.557771623134613, 'util', 0), ('urwid/urwid', 0.553383469581604, 'term', 0), ('pyscript/pyscript-cli', 0.5519907474517822, 'web', 0), ('hoffstadt/dearpygui', 0.5491853952407837, 'gui', 0), ('pyston/pyston', 0.5372031331062317, 'util', 0), ('microsoft/playwright-python', 0.5341052412986755, 'testing', 1), ('tiangolo/typer', 0.5308032035827637, 'term', 0), ('textualize/trogon', 0.5282593965530396, 'term', 0), ('stanfordnlp/dspy', 0.5249006152153015, 'llm', 0), ('pypy/pypy', 0.5242745280265808, 'util', 0), ('eleutherai/pyfra', 0.5216565728187561, 'ml', 0), ('pytoolz/toolz', 0.5134918093681335, 'util', 0), ('ianmiell/shutit', 0.5116491317749023, 'util', 0), ('willmcgugan/textual', 0.5012505054473877, 'term', 0)]",108,5.0,,0.56,13,5,126,2,1,2,1,13.0,14.0,90.0,1.1,39
892,ml,https://github.com/shankarpandala/lazypredict,[],,[],[],,,,shankarpandala/lazypredict,lazypredict,2347,276,27,Python,,Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning,shankarpandala,2024-01-13,2019-11-16,219,10.695963541666666,,Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning,"['automl', 'classification', 'machine-learning', 'regression']","['automl', 'classification', 'machine-learning', 'regression']",2022-09-28,"[('microsoft/flaml', 0.6851475834846497, 'ml', 4), ('microsoft/nni', 0.5795509815216064, 'ml', 2), ('winedarksea/autots', 0.5729676485061646, 'time-series', 2), ('automl/auto-sklearn', 0.5726633667945862, 'ml', 1), ('rafiqhasan/auto-tensorflow', 0.5576849579811096, 'ml-dl', 2), ('nccr-itmo/fedot', 0.5503329038619995, 'ml-ops', 2), ('mljar/mljar-supervised', 0.5433629155158997, 'ml', 2), ('awslabs/autogluon', 0.5431965589523315, 'ml', 2), ('mosaicml/composer', 0.5417070984840393, 'ml-dl', 1), ('keras-team/autokeras', 0.5269849896430969, 'ml-dl', 2), ('xplainable/xplainable', 0.5252819061279297, 'ml-interpretability', 1), ('eugeneyan/testing-ml', 0.5170032382011414, 'testing', 1), ('firmai/atspy', 0.5108960866928101, 'time-series', 0), ('huggingface/evaluate', 0.5062326788902283, 'ml', 1), ('patchy631/machine-learning', 0.5044746398925781, 'ml', 0), ('teamhg-memex/eli5', 0.5024835467338562, 'ml', 1), ('selfexplainml/piml-toolbox', 0.500099241733551, 'ml-interpretability', 0)]",17,7.0,,0.0,10,2,51,16,0,3,3,10.0,6.0,90.0,0.6,39
1843,util,https://github.com/pndurette/gtts,['tts'],,[],[],,,,pndurette/gtts,gTTS,2078,347,66,Python,http://gtts.readthedocs.org/,Python library and CLI tool to interface with Google Translate's text-to-speech API,pndurette,2024-01-14,2014-05-15,506,4.100930363687623,,Python library and CLI tool to interface with Google Translate's text-to-speech API,"['cli', 'gtts', 'speech', 'speech-api', 'text-to-speech', 'tts']","['cli', 'gtts', 'speech', 'speech-api', 'text-to-speech', 'tts']",2024-01-07,"[('googleapis/python-speech', 0.7090864181518555, 'ml', 0), ('uberi/speech_recognition', 0.7022308707237244, 'ml', 0), ('nateshmbhat/pyttsx3', 0.6920114159584045, 'util', 1), ('facebookresearch/seamless_communication', 0.5938905477523804, 'nlp', 1), ('espnet/espnet', 0.5670027732849121, 'nlp', 0), ('pemistahl/lingua-py', 0.5668443441390991, 'nlp', 0), ('irmen/pyminiaudio', 0.5513820648193359, 'util', 0), ('dialogflow/dialogflow-python-client-v2', 0.5459315776824951, 'nlp', 0), ('spotify/pedalboard', 0.5376996994018555, 'util', 0), ('speechbrain/speechbrain', 0.5305692553520203, 'nlp', 0), ('dsdanielpark/bard-api', 0.5274245738983154, 'llm', 0), ('minimaxir/simpleaichat', 0.526296854019165, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5201086401939392, 'nlp', 0), ('googleapis/google-api-python-client', 0.5190337300300598, 'util', 0), ('killianlucas/open-interpreter', 0.5171147584915161, 'llm', 0), ('m-bain/whisperx', 0.509675920009613, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5039756298065186, 'llm', 0), ('plachtaa/vall-e-x', 0.5036769509315491, 'llm', 2)]",37,2.0,,0.73,15,15,118,0,4,4,4,15.0,16.0,90.0,1.1,39
816,data,https://github.com/uqfoundation/dill,[],,[],[],1.0,,,uqfoundation/dill,dill,2062,208,23,Python,http://dill.rtfd.io,serialize all of Python,uqfoundation,2024-01-13,2013-06-28,552,3.731644260599793,https://avatars.githubusercontent.com/u/2855931?v=4,serialize all of Python,[],[],2024-01-01,"[('jsonpickle/jsonpickle', 0.601097047328949, 'data', 0), ('marshmallow-code/marshmallow', 0.5810075402259827, 'util', 0), ('instagram/libcst', 0.5361902713775635, 'util', 0), ('python-odin/odin', 0.5188406705856323, 'util', 0), ('replicate/replicate-python', 0.5080131888389587, 'ml', 0), ('mkdocstrings/griffe', 0.506209135055542, 'util', 0)]",43,5.0,,0.65,32,12,128,0,1,2,1,33.0,31.0,90.0,0.9,39
693,util,https://github.com/grantjenks/python-diskcache,[],,[],[],,,,grantjenks/python-diskcache,python-diskcache,1953,151,22,Python,http://www.grantjenks.com/docs/diskcache/,Python disk-backed cache (Django-compatible). Faster than Redis and Memcached. Pure-Python.,grantjenks,2024-01-14,2016-02-03,416,4.685058259081563,,Python disk-backed cache (Django-compatible). Faster than Redis and Memcached. Pure-Python.,"['cache', 'filesystem', 'key-value-store', 'persistence']","['cache', 'filesystem', 'key-value-store', 'persistence']",2023-08-31,"[('python-cachier/cachier', 0.6893970966339111, 'perf', 1), ('dgilland/cacheout', 0.6435301899909973, 'perf', 0), ('aio-libs/aiocache', 0.6346278786659241, 'data', 1), ('long2ice/fastapi-cache', 0.6009683609008789, 'web', 1), ('erotemic/ubelt', 0.5623111724853516, 'util', 0), ('klen/py-frameworks-bench', 0.5420731902122498, 'perf', 0), ('pytables/pytables', 0.5363883376121521, 'data', 0), ('joblib/joblib', 0.5340135097503662, 'util', 0), ('fsspec/filesystem_spec', 0.5200872421264648, 'util', 0), ('spotify/annoy', 0.5176307559013367, 'ml', 0), ('samuelcolvin/arq', 0.5104993581771851, 'data', 0), ('samuelcolvin/watchfiles', 0.5041387677192688, 'util', 1), ('neoteroi/blacksheep', 0.5029258728027344, 'web', 0)]",24,3.0,,0.56,12,3,97,5,0,11,11,12.0,28.0,90.0,2.3,39
32,nlp,https://github.com/jamesturk/jellyfish,[],,[],[],,,,jamesturk/jellyfish,jellyfish,1944,160,44,Jupyter Notebook,https://jamesturk.github.io/jellyfish/,🪼 a python library for doing approximate and phonetic matching of strings.,jamesturk,2024-01-12,2010-07-09,707,2.747425802543913,,🪼 a python library for doing approximate and phonetic matching of strings.,"['fuzzy-search', 'hamming', 'jaro-winkler', 'levenshtein', 'metaphone', 'soundex']","['fuzzy-search', 'hamming', 'jaro-winkler', 'levenshtein', 'metaphone', 'soundex']",2023-11-17,"[('life4/textdistance', 0.6177361011505127, 'nlp', 1), ('uberi/speech_recognition', 0.545852541923523, 'ml', 0), ('pytoolz/toolz', 0.5310186147689819, 'util', 0), ('spotify/pedalboard', 0.5257704854011536, 'util', 0)]",31,7.0,,1.67,13,10,165,2,0,4,4,13.0,22.0,90.0,1.7,39
1333,util,https://github.com/carpedm20/emoji,[],,[],[],,,,carpedm20/emoji,emoji,1776,299,26,Python,,emoji terminal output for Python,carpedm20,2024-01-13,2014-08-18,493,3.601390498261877,,emoji terminal output for Python,['emoji'],['emoji'],2023-12-05,"[('trananhkma/fucking-awesome-python', 0.5481189489364624, 'study', 0), ('tartley/colorama', 0.5459503531455994, 'util', 0), ('willmcgugan/rich', 0.5216153264045715, 'term', 1), ('jquast/blessed', 0.5172060132026672, 'term', 0)]",65,2.0,,0.77,6,3,115,1,8,3,8,6.0,11.0,90.0,1.8,39
1855,template,https://github.com/cjolowicz/cookiecutter-hypermodern-python,['hypermodern'],Cookiecutter template for a Python package based on the Hypermodern Python article series.,[],[],,,,cjolowicz/cookiecutter-hypermodern-python,cookiecutter-hypermodern-python,1665,243,19,Python,http://cookiecutter-hypermodern-python.readthedocs.io/,Hypermodern Python Cookiecutter,cjolowicz,2024-01-11,2020-02-07,207,8.021335168616655,,Hypermodern Python Cookiecutter,[],['hypermodern'],2023-07-08,"[('ionelmc/cookiecutter-pylibrary', 0.6513864994049072, 'template', 0), ('lyz-code/cookiecutter-python-project', 0.6242104768753052, 'template', 0), ('tedivm/robs_awesome_python_template', 0.5808916687965393, 'template', 0), ('giswqs/pypackage', 0.5635570883750916, 'template', 0)]",21,6.0,,1.04,8,1,48,6,0,6,6,8.0,4.0,90.0,0.5,39
1303,sim,https://github.com/microsoft/promptcraft-robotics,['prompt-engineering'],,[],[],,,,microsoft/promptcraft-robotics,PromptCraft-Robotics,1587,167,40,Python,https://aka.ms/ChatGPT-Robotics,Community for applying LLMs to robotics and a robot simulator with ChatGPT integration,microsoft,2024-01-13,2023-02-08,50,31.20505617977528,https://avatars.githubusercontent.com/u/6154722?v=4,Community for applying LLMs to robotics and a robot simulator with ChatGPT integration,"['airsim', 'chatgpt', 'llm', 'prompt-engineering', 'robotics', 'simulation']","['airsim', 'chatgpt', 'llm', 'prompt-engineering', 'robotics', 'simulation']",2023-04-19,"[('nomic-ai/gpt4all', 0.6439793109893799, 'llm', 0), ('hwchase17/langchain', 0.6214913129806519, 'llm', 0), ('microsoft/promptflow', 0.6192827224731445, 'llm', 3), ('deep-diver/llm-as-chatbot', 0.6085308194160461, 'llm', 0), ('embedchain/embedchain', 0.596112847328186, 'llm', 2), ('microsoft/chatgpt-robot-manipulation-prompts', 0.580747663974762, 'llm', 0), ('chatarena/chatarena', 0.5778838992118835, 'llm', 1), ('mmabrouk/chatgpt-wrapper', 0.5717716217041016, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5677699446678162, 'perf', 0), ('pathwaycom/llm-app', 0.5630580186843872, 'llm', 1), ('microsoft/lmops', 0.5502687096595764, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5414144992828369, 'llm', 2), ('iryna-kondr/scikit-llm', 0.5381367206573486, 'llm', 2), ('microsoft/autogen', 0.5330193638801575, 'llm', 1), ('confident-ai/deepeval', 0.5318647623062134, 'testing', 2), ('agenta-ai/agenta', 0.5304033756256104, 'llm', 2), ('microsoft/semantic-kernel', 0.5297889113426208, 'llm', 1), ('shishirpatil/gorilla', 0.5253349542617798, 'llm', 2), ('run-llama/rags', 0.5236408710479736, 'llm', 2), ('prefecthq/marvin', 0.5221617817878723, 'nlp', 1), ('chainlit/chainlit', 0.5212441086769104, 'llm', 2), ('dylanhogg/llmgraph', 0.5207331776618958, 'ml', 2), ('cheshire-cat-ai/core', 0.513721764087677, 'llm', 1), ('humanoidagents/humanoidagents', 0.51203453540802, 'sim', 2), ('mnotgod96/appagent', 0.5117555260658264, 'llm', 2)]",4,1.0,,0.1,3,2,11,9,1,1,1,3.0,3.0,90.0,1.0,39
350,ml,https://github.com/jina-ai/finetuner,[],,[],[],,,,jina-ai/finetuner,finetuner,1373,64,25,Python,https://finetuner.jina.ai,":dart: Task-oriented embedding tuning for BERT, CLIP, etc.",jina-ai,2024-01-13,2021-08-11,128,10.65521064301552,https://avatars.githubusercontent.com/u/60539444?v=4,":dart: Task-oriented embedding tuning for BERT, CLIP, etc.","['bert', 'few-shot-learning', 'fine-tuning', 'finetuning', 'jina', 'metric-learning', 'negative-sampling', 'neural-search', 'openai-clip', 'pretrained-models', 'siamese-network', 'similarity-learning', 'transfer-learning', 'triplet-loss']","['bert', 'few-shot-learning', 'fine-tuning', 'finetuning', 'jina', 'metric-learning', 'negative-sampling', 'neural-search', 'openai-clip', 'pretrained-models', 'siamese-network', 'similarity-learning', 'transfer-learning', 'triplet-loss']",2023-07-26,"[('jina-ai/clip-as-service', 0.7554095387458801, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.6416314840316772, 'llm', 3), ('llmware-ai/llmware', 0.6378600597381592, 'llm', 1), ('extreme-bert/extreme-bert', 0.6179499626159668, 'llm', 1), ('alibaba/easynlp', 0.6169459819793701, 'nlp', 3), ('ukplab/sentence-transformers', 0.5992518663406372, 'nlp', 0), ('huggingface/transformers', 0.5934631824493408, 'nlp', 2), ('ddangelov/top2vec', 0.587577760219574, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5800686478614807, 'nlp', 1), ('whu-zqh/chatgpt-vs.-bert', 0.577040433883667, 'llm', 1), ('explosion/spacy-transformers', 0.5754354596138, 'llm', 2), ('neuml/txtai', 0.56916743516922, 'nlp', 1), ('deepset-ai/farm', 0.5661262273788452, 'nlp', 3), ('jonasgeiping/cramming', 0.5652620792388916, 'nlp', 0), ('intellabs/fastrag', 0.5626152157783508, 'nlp', 0), ('qdrant/quaterion', 0.5520226359367371, 'ml', 2), ('bigscience-workshop/petals', 0.5490538477897644, 'data', 1), ('graykode/nlp-tutorial', 0.5485501885414124, 'study', 1), ('explosion/thinc', 0.5483794808387756, 'ml-dl', 0), ('qdrant/fastembed', 0.5480974912643433, 'ml', 0), ('plasticityai/magnitude', 0.5438824892044067, 'nlp', 0), ('nvidia/deeplearningexamples', 0.5358811616897583, 'ml-dl', 0), ('bytedance/lightseq', 0.5338277220726013, 'nlp', 1), ('luodian/otter', 0.5296557545661926, 'llm', 0), ('docarray/docarray', 0.5270631313323975, 'data', 1), ('koaning/embetter', 0.526446521282196, 'data', 0), ('bigscience-workshop/megatron-deepspeed', 0.5171146988868713, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5171146988868713, 'llm', 0), ('huggingface/neuralcoref', 0.5169621706008911, 'nlp', 0), ('muennighoff/sgpt', 0.512883186340332, 'llm', 1), ('qanastek/drbert', 0.5117612481117249, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5112559199333191, 'llm', 0), ('openai/clip', 0.505680501461029, 'ml-dl', 0), ('maartengr/bertopic', 0.5031505227088928, 'nlp', 1), ('lm-sys/fastchat', 0.5029986500740051, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.5025129914283752, 'nlp', 0), ('freedomintelligence/llmzoo', 0.5019564628601074, 'llm', 0)]",35,3.0,,1.06,8,6,30,6,10,17,10,8.0,1.0,90.0,0.1,39
509,typing,https://github.com/agronholm/typeguard,"['typechecker', 'code-quality']",,[],[],,,,agronholm/typeguard,typeguard,1372,101,22,Python,,Run-time type checker for Python,agronholm,2024-01-13,2015-12-27,422,3.2489851150202975,,Run-time type checker for Python,[],"['code-quality', 'typechecker']",2024-01-09,"[('microsoft/pyright', 0.9137517213821411, 'typing', 2), ('facebook/pyre-check', 0.8064729571342468, 'typing', 2), ('google/pytype', 0.7289842367172241, 'typing', 2), ('python/mypy', 0.7131239771842957, 'typing', 2), ('instagram/monkeytype', 0.6599376201629639, 'typing', 1), ('pycqa/mccabe', 0.5824611186981201, 'util', 0), ('patrick-kidger/torchtyping', 0.5700576305389404, 'typing', 0), ('python/typeshed', 0.5519527792930603, 'typing', 1), ('jendrikseipp/vulture', 0.5461918711662292, 'util', 1), ('grantjenks/blue', 0.5374644994735718, 'util', 1), ('rubik/radon', 0.5345003604888916, 'util', 0), ('pydantic/pydantic', 0.5333467721939087, 'util', 0), ('psf/black', 0.5326973795890808, 'util', 1), ('google/yapf', 0.5313608050346375, 'util', 1), ('landscapeio/prospector', 0.528834879398346, 'util', 0), ('tiangolo/typer', 0.5242935419082642, 'term', 0), ('nedbat/coveragepy', 0.5188262462615967, 'testing', 0), ('pympler/pympler', 0.5068668127059937, 'perf', 0), ('pycqa/pycodestyle', 0.5046524405479431, 'util', 0)]",33,4.0,,3.96,19,11,98,0,2,8,2,19.0,21.0,90.0,1.1,39
900,viz,https://github.com/datapane/datapane,[],,[],[],,,,datapane/datapane,datapane,1330,96,19,Python,https://datapane.com,Build and share data reports in 100% Python,datapane,2024-01-13,2020-04-23,196,6.761074800290486,https://avatars.githubusercontent.com/u/55440415?v=4,Build and share data reports in 100% Python,"['dashboard', 'data-visualization', 'reporting']","['dashboard', 'data-visualization', 'reporting']",2023-09-07,"[('federicoceratto/dashing', 0.6180770993232727, 'term', 1), ('mwaskom/seaborn', 0.5832452774047852, 'viz', 1), ('plotly/dash', 0.5667321681976318, 'viz', 1), ('holoviz/panel', 0.5654339790344238, 'viz', 0), ('altair-viz/altair', 0.556462287902832, 'viz', 0), ('pytables/pytables', 0.5492084622383118, 'data', 0), ('lux-org/lux', 0.5429391860961914, 'viz', 0), ('man-group/dtale', 0.5396667718887329, 'viz', 1), ('kanaries/pygwalker', 0.5378132462501526, 'pandas', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5317434668540955, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5247305035591125, 'jupyter', 1), ('enthought/mayavi', 0.5168454051017761, 'viz', 0)]",13,4.0,,2.87,1,1,45,4,0,15,15,1.0,1.0,90.0,1.0,39
163,llm,https://github.com/explosion/spacy-transformers,[],,[],[],,,,explosion/spacy-transformers,spacy-transformers,1306,162,32,Python,https://spacy.io/usage/embeddings-transformers,"🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy",explosion,2024-01-13,2019-07-26,235,5.543966040024257,https://avatars.githubusercontent.com/u/20011530?v=4,"🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy","['bert', 'google', 'gpt-2', 'huggingface', 'language-model', 'machine-learning', 'natural-language-processing', 'natural-language-understanding', 'nlp', 'openai', 'pytorch', 'pytorch-model', 'spacy', 'spacy-extension', 'spacy-pipeline', 'transfer-learning', 'xlnet']","['bert', 'google', 'gpt-2', 'huggingface', 'language-model', 'machine-learning', 'natural-language-processing', 'natural-language-understanding', 'nlp', 'openai', 'pytorch', 'pytorch-model', 'spacy', 'spacy-extension', 'spacy-pipeline', 'transfer-learning', 'xlnet']",2023-12-19,"[('explosion/spacy-models', 0.6662450432777405, 'nlp', 4), ('huggingface/transformers', 0.6527365446090698, 'nlp', 6), ('bigscience-workshop/megatron-deepspeed', 0.6363678574562073, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6363678574562073, 'llm', 0), ('extreme-bert/extreme-bert', 0.6331859230995178, 'llm', 6), ('huggingface/neuralcoref', 0.6309342980384827, 'nlp', 6), ('explosion/spacy-stanza', 0.6275186538696289, 'nlp', 5), ('explosion/spacy-streamlit', 0.5857328772544861, 'nlp', 4), ('jina-ai/finetuner', 0.5754354596138, 'ml', 2), ('alibaba/easynlp', 0.5717461109161377, 'nlp', 5), ('jonasgeiping/cramming', 0.5527690649032593, 'nlp', 2), ('karpathy/mingpt', 0.5479704141616821, 'llm', 0), ('deepset-ai/farm', 0.5454752445220947, 'nlp', 4), ('lucidrains/toolformer-pytorch', 0.5443832278251648, 'llm', 1), ('paddlepaddle/paddlenlp', 0.539587140083313, 'llm', 2), ('bobazooba/xllm', 0.529644787311554, 'llm', 2), ('lianjiatech/belle', 0.5292598009109497, 'llm', 0), ('llmware-ai/llmware', 0.5283253192901611, 'llm', 4), ('neuralmagic/sparseml', 0.525894284248352, 'ml-dl', 3), ('thilinarajapakse/simpletransformers', 0.5258124470710754, 'nlp', 0), ('explosion/spacy-llm', 0.524055004119873, 'llm', 5), ('explosion/thinc', 0.5206478238105774, 'ml-dl', 5), ('explosion/spacy', 0.5188299417495728, 'nlp', 4), ('qanastek/drbert', 0.5139819979667664, 'llm', 3), ('huggingface/optimum', 0.5108177661895752, 'ml', 1), ('google-research/electra', 0.5100114941596985, 'ml-dl', 1), ('microsoft/autogen', 0.5083666443824768, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5079010128974915, 'llm', 1), ('lm-sys/fastchat', 0.5027205348014832, 'llm', 1)]",22,6.0,,0.87,6,6,54,1,10,11,10,6.0,1.0,90.0,0.2,39
446,gis,https://github.com/pysal/pysal,[],,[],[],1.0,,,pysal/pysal,pysal,1236,303,84,Jupyter Notebook,http://pysal.org/pysal,PySAL: Python Spatial Analysis Library Meta-Package,pysal,2024-01-14,2013-02-19,571,2.1646234676007006,https://avatars.githubusercontent.com/u/3769919?v=4,PySAL: Python Spatial Analysis Library Meta-Package,[],[],2023-12-11,"[('makepath/xarray-spatial', 0.6753366589546204, 'gis', 0), ('earthlab/earthpy', 0.6416592597961426, 'gis', 0), ('artelys/geonetworkx', 0.625034749507904, 'gis', 0), ('toblerity/rtree', 0.6110436320304871, 'gis', 0), ('scikit-geometry/scikit-geometry', 0.5866171717643738, 'gis', 0), ('albahnsen/pycircular', 0.581803023815155, 'math', 0), ('scitools/cartopy', 0.5811754465103149, 'gis', 0), ('altair-viz/altair', 0.5805773735046387, 'viz', 0), ('residentmario/geoplot', 0.579075276851654, 'gis', 0), ('geopandas/geopandas', 0.5672765374183655, 'gis', 0), ('opengeos/leafmap', 0.5648621320724487, 'gis', 0), ('pytoolz/toolz', 0.5646018385887146, 'util', 0), ('marcomusy/vedo', 0.560834527015686, 'viz', 0), ('scipy/scipy', 0.5544923543930054, 'math', 0), ('has2k1/plotnine', 0.5488908290863037, 'viz', 0), ('eleutherai/pyfra', 0.547865092754364, 'ml', 0), ('numpy/numpy', 0.5434750318527222, 'math', 0), ('gboeing/pynamical', 0.5425774455070496, 'sim', 0), ('pyutils/line_profiler', 0.5387760400772095, 'profiling', 0), ('contextlab/hypertools', 0.535285472869873, 'ml', 0), ('holoviz/geoviews', 0.535015881061554, 'gis', 0), ('stan-dev/pystan', 0.5347137451171875, 'ml', 0), ('pandas-dev/pandas', 0.5282700061798096, 'pandas', 0), ('scikit-mobility/scikit-mobility', 0.5261431336402893, 'gis', 0), ('scitools/iris', 0.5261117815971375, 'gis', 0), ('enthought/mayavi', 0.5210456848144531, 'viz', 0), ('rasbt/mlxtend', 0.5194495320320129, 'ml', 0), ('pycaret/pycaret', 0.5162516236305237, 'ml', 0), ('pyproj4/pyproj', 0.5152866244316101, 'gis', 0), ('csurfer/pyheat', 0.5142292380332947, 'profiling', 0), ('wesm/pydata-book', 0.5121508836746216, 'study', 0), ('scikit-learn-contrib/metric-learn', 0.5091261267662048, 'ml', 0), ('mwaskom/seaborn', 0.5034690499305725, 'viz', 0), ('alkaline-ml/pmdarima', 0.5029148459434509, 'time-series', 0)]",78,6.0,,0.15,7,5,133,1,3,3,3,7.0,22.0,90.0,3.1,39
1447,ml-rl,https://github.com/humancompatibleai/imitation,[],,[],[],,,,humancompatibleai/imitation,imitation,1050,198,17,Python,https://imitation.readthedocs.io/,Clean PyTorch implementations of imitation and reward learning algorithms,humancompatibleai,2024-01-14,2018-12-08,268,3.9116551357104843,https://avatars.githubusercontent.com/u/33107497?v=4,Clean PyTorch implementations of imitation and reward learning algorithms,"['gymnasium', 'imitation-learning', 'inverse-reinforcement-learning', 'reward-learning']","['gymnasium', 'imitation-learning', 'inverse-reinforcement-learning', 'reward-learning']",2023-12-15,"[('thu-ml/tianshou', 0.7333576679229736, 'ml-rl', 1), ('pytorch/rl', 0.6811489462852478, 'ml-rl', 0), ('denys88/rl_games', 0.6343870759010315, 'ml-rl', 0), ('nvidia-omniverse/isaacgymenvs', 0.617064356803894, 'sim', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.6052762866020203, 'sim', 0), ('openai/baselines', 0.5837662816047668, 'ml-rl', 0), ('google/dopamine', 0.576420783996582, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.5637737512588501, 'ml-rl', 0), ('deepmind/acme', 0.5565671920776367, 'ml-rl', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5537171363830566, 'study', 0), ('pettingzoo-team/pettingzoo', 0.5525628924369812, 'ml-rl', 1), ('openai/gym', 0.5389503836631775, 'ml-rl', 0), ('mrdbourke/pytorch-deep-learning', 0.5296970009803772, 'study', 0), ('kzl/decision-transformer', 0.5276908874511719, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5231532454490662, 'ml-rl', 0), ('pytorch/ignite', 0.5217827558517456, 'ml-dl', 0), ('keras-rl/keras-rl', 0.5172331929206848, 'ml-rl', 0), ('arise-initiative/robosuite', 0.5085115432739258, 'ml-rl', 0), ('inspirai/timechamber', 0.50629061460495, 'sim', 0)]",34,4.0,,1.12,40,27,62,1,2,2,2,40.0,65.0,90.0,1.6,39
813,viz,https://github.com/facultyai/dash-bootstrap-components,[],,[],[],,,,facultyai/dash-bootstrap-components,dash-bootstrap-components,1036,220,23,JavaScript,https://dash-bootstrap-components.opensource.faculty.ai/,Bootstrap components for Plotly Dash,facultyai,2024-01-12,2018-09-21,279,3.705671946857435,https://avatars.githubusercontent.com/u/10586141?v=4,Bootstrap components for Plotly Dash,"['bootstrap', 'dashboards', 'julia', 'plotly-dash', 'r']","['bootstrap', 'dashboards', 'julia', 'plotly-dash', 'r']",2024-01-06,"[('plotly/plotly.py', 0.5394929051399231, 'viz', 1)]",31,2.0,,1.06,16,10,65,0,14,31,14,16.0,20.0,90.0,1.2,39
1312,llm,https://github.com/nomic-ai/pygpt4all,[],,[],[],,,,nomic-ai/pygpt4all,pygpt4all,1019,162,13,C++,https://nomic-ai.github.io/pygpt4all/,Official supported Python bindings for llama.cpp + gpt4all,nomic-ai,2024-01-12,2023-04-03,43,23.619205298013245,https://avatars.githubusercontent.com/u/102670180?v=4,Official supported Python bindings for llama.cpp + gpt4all,[],[],2023-05-12,"[('abetlen/llama-cpp-python', 0.7292018532752991, 'llm', 0), ('numba/llvmlite', 0.5271598100662231, 'util', 0)]",12,3.0,,1.48,0,0,10,8,5,6,5,0.0,0.0,90.0,0.0,39
614,jupyter,https://github.com/nbqa-dev/nbqa,[],,[],[],,,,nbqa-dev/nbqa,nbQA,924,36,8,Python,https://nbqa.readthedocs.io/en/latest/index.html,"Run ruff, isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks",nbqa-dev,2024-01-12,2020-07-11,185,4.983050847457627,https://avatars.githubusercontent.com/u/69012749?v=4,"Run ruff, isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks","['black', 'codequality', 'doctest', 'flake8', 'isort', 'jupyter-notebook', 'lint', 'mypy', 'pre-commit', 'pre-commit-hook', 'pylint', 'pyupgrade', 'ruff', 'yapf']","['black', 'codequality', 'doctest', 'flake8', 'isort', 'jupyter-notebook', 'lint', 'mypy', 'pre-commit', 'pre-commit-hook', 'pylint', 'pyupgrade', 'ruff', 'yapf']",2023-11-27,"[('mwouts/jupytext', 0.5719586610794067, 'jupyter', 1), ('psf/black', 0.5564059019088745, 'util', 2), ('cohere-ai/notebooks', 0.550182044506073, 'llm', 0), ('jupyter/nbformat', 0.5436616539955139, 'jupyter', 0), ('jupyter/nbdime', 0.5352687239646912, 'jupyter', 1), ('grantjenks/blue', 0.5135296583175659, 'util', 2), ('computationalmodelling/nbval', 0.5108411312103271, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5034378170967102, 'study', 0)]",25,6.0,,0.6,6,5,43,2,0,25,25,6.0,6.0,90.0,1.0,39
475,pandas,https://github.com/holoviz/hvplot,[],,[],[],,,,holoviz/hvplot,hvplot,874,94,23,Python,https://hvplot.holoviz.org,"A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews",holoviz,2024-01-12,2018-03-19,306,2.8548763415772282,https://avatars.githubusercontent.com/u/51678735?v=4,"A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews","['datashader', 'holoviews', 'holoviz', 'plotting']","['datashader', 'holoviews', 'holoviz', 'plotting']",2023-12-22,"[('matplotlib/matplotlib', 0.7096381187438965, 'viz', 1), ('cuemacro/chartpy', 0.6870236992835999, 'viz', 1), ('holoviz/holoviews', 0.682068407535553, 'viz', 3), ('man-group/dtale', 0.6696478724479675, 'viz', 0), ('holoviz/holoviz', 0.6662247776985168, 'viz', 3), ('holoviz/panel', 0.6510308980941772, 'viz', 2), ('plotly/plotly.py', 0.6457611918449402, 'viz', 0), ('mwaskom/seaborn', 0.636111855506897, 'viz', 0), ('kanaries/pygwalker', 0.6253153085708618, 'pandas', 0), ('residentmario/geoplot', 0.6143922805786133, 'gis', 0), ('bokeh/bokeh', 0.6104094982147217, 'viz', 1), ('graphistry/pygraphistry', 0.6067784428596497, 'data', 0), ('holoviz/geoviews', 0.6014821529388428, 'gis', 3), ('westhealth/pyvis', 0.5998131036758423, 'graph', 0), ('pyqtgraph/pyqtgraph', 0.5959815979003906, 'viz', 0), ('altair-viz/altair', 0.5933358669281006, 'viz', 0), ('holoviz/datashader', 0.5903522372245789, 'gis', 2), ('contextlab/hypertools', 0.5875470042228699, 'ml', 0), ('scitools/iris', 0.5860223770141602, 'gis', 0), ('has2k1/plotnine', 0.5800595879554749, 'viz', 1), ('pyvista/pyvista', 0.5786033868789673, 'viz', 1), ('enthought/mayavi', 0.5784933567047119, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5669666528701782, 'study', 0), ('maartenbreddels/ipyvolume', 0.5658844113349915, 'jupyter', 1), ('lux-org/lux', 0.5583831071853638, 'viz', 0), ('scitools/cartopy', 0.5497469305992126, 'gis', 0), ('matplotlib/mplfinance', 0.5378217101097107, 'finance', 0), ('pygraphviz/pygraphviz', 0.5362616181373596, 'viz', 0), ('makepath/xarray-spatial', 0.5334243178367615, 'gis', 1), ('pydata/xarray', 0.5328260064125061, 'util', 0), ('vizzuhq/ipyvizzu', 0.5226277112960815, 'jupyter', 1), ('artelys/geonetworkx', 0.5222033858299255, 'gis', 0), ('blaze/blaze', 0.5221161246299744, 'pandas', 0), ('facebookresearch/hiplot', 0.5185054540634155, 'viz', 0), ('dfki-ric/pytransform3d', 0.5157265663146973, 'math', 0), ('plotly/dash', 0.5148411393165588, 'viz', 0), ('marcomusy/vedo', 0.5138098001480103, 'viz', 0), ('rapidsai/cudf', 0.5121793746948242, 'pandas', 0), ('nomic-ai/deepscatter', 0.511212944984436, 'viz', 0), ('vaexio/vaex', 0.5080651640892029, 'perf', 0), ('adamerose/pandasgui', 0.5072020888328552, 'pandas', 0), ('federicoceratto/dashing', 0.5051881670951843, 'term', 0), ('nschloe/tikzplotlib', 0.5042620897293091, 'util', 0), ('jmcnamara/xlsxwriter', 0.5030190348625183, 'data', 0), ('holoviz/spatialpandas', 0.5009598135948181, 'pandas', 1), ('raphaelquast/eomaps', 0.5002750754356384, 'gis', 1)]",45,3.0,,1.81,95,37,71,1,3,21,3,95.0,193.0,90.0,2.0,39
481,gis,https://github.com/sentinel-hub/sentinelhub-py,[],,[],[],,,,sentinel-hub/sentinelhub-py,sentinelhub-py,753,237,49,Python,http://sentinelhub-py.readthedocs.io/en/latest/,Download and process satellite imagery in Python using Sentinel Hub services.,sentinel-hub,2024-01-12,2017-05-17,349,2.152307064107799,https://avatars.githubusercontent.com/u/31830596?v=4,Download and process satellite imagery in Python using Sentinel Hub services.,"['aws', 'ogc-services', 'satellite-imagery', 'sentinel-hub']","['aws', 'ogc-services', 'satellite-imagery', 'sentinel-hub']",2024-01-10,"[('radiantearth/radiant-mlhub', 0.6164925694465637, 'gis', 1), ('pytroll/satpy', 0.6114635467529297, 'gis', 0), ('sentinelsat/sentinelsat', 0.5519406199455261, 'gis', 1), ('boto/boto3', 0.5275383591651917, 'util', 1), ('cuemacro/findatapy', 0.5211452841758728, 'finance', 0)]",46,3.0,,2.13,38,34,81,0,12,9,12,38.0,45.0,90.0,1.2,39
1182,nlp,https://github.com/pemistahl/lingua-py,[],,[],[],,,,pemistahl/lingua-py,lingua-py,747,37,11,Python,,"The most accurate natural language detection library for Python, suitable for short text and mixed-language text",pemistahl,2024-01-14,2021-07-13,133,5.616541353383458,,"The most accurate natural language detection library for Python, suitable for short text and mixed-language text","['language-classification', 'language-detection', 'language-identification', 'language-recognition', 'natural-language-processing', 'nlp']","['language-classification', 'language-detection', 'language-identification', 'language-recognition', 'natural-language-processing', 'nlp']",2023-12-12,"[('uberi/speech_recognition', 0.6296498775482178, 'ml', 0), ('allenai/allennlp', 0.6268466711044312, 'nlp', 2), ('explosion/spacy', 0.5869116187095642, 'nlp', 2), ('sloria/textblob', 0.5679237246513367, 'nlp', 2), ('pndurette/gtts', 0.5668443441390991, 'util', 0), ('pypy/pypy', 0.5571848154067993, 'util', 0), ('flairnlp/flair', 0.5448145270347595, 'nlp', 2), ('rasbt/mlxtend', 0.5422750115394592, 'ml', 0), ('gunthercox/chatterbot-corpus', 0.5412878394126892, 'nlp', 0), ('pytoolz/toolz', 0.5412816405296326, 'util', 0), ('nipunsadvilkar/pysbd', 0.5244992971420288, 'nlp', 0), ('clips/pattern', 0.5218809247016907, 'nlp', 1), ('openeventdata/mordecai', 0.5192466974258423, 'gis', 1), ('pyston/pyston', 0.5171454548835754, 'util', 0), ('pandas-dev/pandas', 0.5153101682662964, 'pandas', 0), ('kagisearch/vectordb', 0.509565532207489, 'data', 0), ('explosion/spacy-models', 0.5069782137870789, 'nlp', 2), ('pycaret/pycaret', 0.5025511980056763, 'ml', 0), ('rasahq/rasa', 0.5019610524177551, 'llm', 2), ('dylanhogg/awesome-python', 0.5016226172447205, 'study', 2)]",5,2.0,,0.73,41,30,30,1,6,6,6,42.0,88.0,90.0,2.1,39
634,data,https://github.com/dask/fastparquet,[],,[],[],,,,dask/fastparquet,fastparquet,707,173,20,Python,,python implementation of the parquet columnar file format.,dask,2024-01-10,2015-11-06,429,1.6458264050548719,https://avatars.githubusercontent.com/u/17131925?v=4,python implementation of the parquet columnar file format.,[],[],2023-12-22,"[('ktrueda/parquet-tools', 0.5958738327026367, 'data', 0), ('crunch-io/lazycsv', 0.5394817590713501, 'perf', 0), ('jupyter/nbformat', 0.5203571915626526, 'jupyter', 0), ('google/yapf', 0.5155162811279297, 'util', 0), ('wireservice/csvkit', 0.5032108426094055, 'util', 0)]",93,5.0,,0.85,30,25,100,1,0,6,6,30.0,69.0,90.0,2.3,39
1584,util,https://github.com/barracuda-fsh/pyobd,['diagnostics'],,[],[],,,,barracuda-fsh/pyobd,pyobd,678,20,16,Python,,open source obd2 car diagnostics program - reuploaded ,barracuda-fsh,2024-01-13,2023-08-18,23,28.763636363636362,,open source obd2 car diagnostics program - reuploaded ,[],['diagnostics'],2023-09-17,[],2,0.0,,0.29,1,1,5,4,2,5,2,1.0,3.0,90.0,3.0,39
1803,ml,https://github.com/awslabs/python-deequ,"['aws', 'data-quality', 'spark']","Python API for Deequ, a library built on Spark for defining ""unit tests for data"", which measure data quality in large datasets",[],[],,,,awslabs/python-deequ,python-deequ,618,169,19,Python,,Python API for Deequ,awslabs,2024-01-12,2020-11-09,168,3.675446049277825,https://avatars.githubusercontent.com/u/3299148?v=4,Python API for Deequ,[],"['aws', 'data-quality', 'spark']",2024-01-08,"[('pynamodb/pynamodb', 0.6457101702690125, 'data', 1), ('boto/boto3', 0.5848604440689087, 'util', 1), ('geeogi/async-python-lambda-template', 0.5767794251441956, 'template', 0), ('aws/aws-sdk-pandas', 0.5529321432113647, 'pandas', 1), ('nficano/python-lambda', 0.542289137840271, 'util', 1), ('datastax/python-driver', 0.5413904190063477, 'data', 0), ('nasdaq/data-link-python', 0.5292037725448608, 'finance', 0), ('aws/chalice', 0.5238412618637085, 'web', 1), ('aws/aws-lambda-python-runtime-interface-client', 0.5195323824882507, 'util', 0), ('amzn/ion-python', 0.5171683430671692, 'data', 0)]",18,5.0,,0.35,37,7,39,0,4,2,4,37.0,61.0,90.0,1.6,39
1444,util,https://github.com/pypa/build,['build'],,[],[],,,,pypa/build,build,605,116,25,Python,https://build.pypa.io,"A simple, correct Python build frontend",pypa,2024-01-13,2020-05-10,194,3.113970588235294,https://avatars.githubusercontent.com/u/647025?v=4,"A simple, correct Python build frontend",[],['build'],2023-12-21,"[('pypa/hatch', 0.5941884517669678, 'util', 1), ('r0x0r/pywebview', 0.5934130549430847, 'gui', 0), ('tezromach/python-package-template', 0.5865764021873474, 'template', 0), ('pypa/pipenv', 0.5441533327102661, 'util', 0), ('pallets/flask', 0.5296503901481628, 'web', 0), ('ofek/pyapp', 0.5296021103858948, 'util', 1), ('eugeneyan/python-collab-template', 0.523838460445404, 'template', 0), ('amaargiru/pyroad', 0.5195523500442505, 'study', 0), ('willmcgugan/textual', 0.5193080306053162, 'term', 0), ('pyodide/micropip', 0.5155296325683594, 'util', 0), ('tedivm/robs_awesome_python_template', 0.5029564499855042, 'template', 0)]",48,6.0,,2.04,42,26,45,1,2,6,2,42.0,77.0,90.0,1.8,39
779,util,https://github.com/gefyrahq/gefyra,[],,[],[],,,,gefyrahq/gefyra,gefyra,597,27,9,Python,https://gefyra.dev,"Blazingly-fast :rocket:, rock-solid, local application development :arrow_right: with Kubernetes.",gefyrahq,2024-01-09,2021-11-18,114,5.204234122042341,https://avatars.githubusercontent.com/u/101178654?v=4,"Blazingly-fast 🚀, rock-solid, local application development :arrow_right: with Kubernetes.","['coding', 'container', 'containers', 'developer-tool', 'development', 'docker', 'k8s', 'kubernetes', 'tunnel']","['coding', 'container', 'containers', 'developer-tool', 'development', 'docker', 'k8s', 'kubernetes', 'tunnel']",2024-01-02,"[('aquasecurity/trivy', 0.6041578054428101, 'security', 3), ('bodywork-ml/bodywork-core', 0.5484521389007568, 'ml-ops', 1), ('orchest/orchest', 0.5443967580795288, 'ml-ops', 2), ('astronomer/astronomer', 0.5323020815849304, 'ml-ops', 2), ('flyteorg/flyte', 0.5291821956634521, 'ml-ops', 1), ('backtick-se/cowait', 0.5228185653686523, 'util', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.5177972912788391, 'template', 1), ('kubeflow/pipelines', 0.5056382417678833, 'ml-ops', 1), ('kubeflow-kale/kale', 0.5049176216125488, 'ml-ops', 0)]",13,1.0,,9.48,56,33,26,0,14,30,14,56.0,34.0,90.0,0.6,39
1702,util,https://github.com/platformdirs/platformdirs,[],,[],[],,,,platformdirs/platformdirs,platformdirs,425,42,9,Python,https://platformdirs.readthedocs.io,"A small Python module for determining appropriate platform-specific dirs, e.g. a ""user data dir"".",platformdirs,2024-01-12,2021-05-13,141,2.998991935483871,https://avatars.githubusercontent.com/u/84131773?v=4,"A small Python module for determining appropriate platform-specific dirs, e.g. a ""user data dir"".","['appdirs', 'configuration', 'cross-platform', 'xdg', 'xdg-user-dirs']","['appdirs', 'configuration', 'cross-platform', 'xdg', 'xdg-user-dirs']",2024-01-10,"[('fsspec/filesystem_spec', 0.5413647294044495, 'util', 0), ('erotemic/ubelt', 0.5185686945915222, 'util', 1)]",66,5.0,,1.67,23,21,33,0,21,17,21,23.0,20.0,90.0,0.9,39
1784,llm,https://github.com/tigerlab-ai/tiger,[],,[],[],,,,tigerlab-ai/tiger,tiger,356,24,10,Jupyter Notebook,https://www.tigerlab.ai,"Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)",tigerlab-ai,2024-01-12,2023-10-23,14,25.171717171717173,,"Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)","['ai-safety', 'aisafety', 'classification', 'data-augmentation', 'fine-tuning', 'large-language-models', 'llm', 'llm-training', 'rag']","['ai-safety', 'aisafety', 'classification', 'data-augmentation', 'fine-tuning', 'large-language-models', 'llm', 'llm-training', 'rag']",2023-12-02,"[('alpha-vllm/llama2-accessory', 0.7141748666763306, 'llm', 1), ('argilla-io/argilla', 0.6661051511764526, 'nlp', 1), ('hiyouga/llama-factory', 0.6649466753005981, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.6649465560913086, 'llm', 3), ('hegelai/prompttools', 0.6615456342697144, 'llm', 1), ('h2oai/h2o-llmstudio', 0.6549601554870605, 'llm', 3), ('bentoml/openllm', 0.6545884013175964, 'ml-ops', 2), ('microsoft/semantic-kernel', 0.6522761583328247, 'llm', 1), ('pathwaycom/llm-app', 0.6506632566452026, 'llm', 2), ('microsoft/promptflow', 0.6455790996551514, 'llm', 1), ('bobazooba/xllm', 0.6268063187599182, 'llm', 2), ('iryna-kondr/scikit-llm', 0.6261765956878662, 'llm', 1), ('nebuly-ai/nebullvm', 0.6134838461875916, 'perf', 2), ('ludwig-ai/ludwig', 0.6120554804801941, 'ml-ops', 3), ('bigscience-workshop/petals', 0.6084714531898499, 'data', 1), ('ray-project/llm-applications', 0.6013271808624268, 'llm', 2), ('young-geng/easylm', 0.6003850698471069, 'llm', 1), ('nomic-ai/gpt4all', 0.5997213125228882, 'llm', 0), ('alphasecio/langchain-examples', 0.5991637706756592, 'llm', 1), ('salesforce/xgen', 0.5921275615692139, 'llm', 2), ('mlc-ai/mlc-llm', 0.5859246850013733, 'llm', 1), ('embedchain/embedchain', 0.5847614407539368, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5830023884773254, 'llm', 1), ('salesforce/codet5', 0.5818438529968262, 'nlp', 1), ('llmware-ai/llmware', 0.5777447819709778, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5748347640037537, 'perf', 0), ('eugeneyan/open-llms', 0.5710621476173401, 'study', 2), ('deepset-ai/haystack', 0.5681328773498535, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5661339163780212, 'study', 1), ('nat/openplayground', 0.5651535987854004, 'llm', 0), ('microsoft/torchscale', 0.5640652179718018, 'llm', 0), ('doccano/doccano', 0.5629643797874451, 'nlp', 0), ('agenta-ai/agenta', 0.5620597004890442, 'llm', 3), ('aiwaves-cn/agents', 0.5588130950927734, 'nlp', 1), ('guardrails-ai/guardrails', 0.5576193928718567, 'llm', 1), ('nvidia/tensorrt-llm', 0.5560727119445801, 'viz', 0), ('microsoft/nni', 0.5559183359146118, 'ml', 0), ('citadel-ai/langcheck', 0.5546644926071167, 'llm', 0), ('microsoft/jarvis', 0.552547812461853, 'llm', 0), ('microsoft/lmops', 0.5503789186477661, 'llm', 1), ('mlflow/mlflow', 0.5502049922943115, 'ml-ops', 0), ('vllm-project/vllm', 0.5497113466262817, 'llm', 1), ('arize-ai/phoenix', 0.5459373593330383, 'ml-interpretability', 0), ('lucidrains/toolformer-pytorch', 0.5445385575294495, 'llm', 0), ('night-chen/toolqa', 0.5423557758331299, 'llm', 1), ('infinitylogesh/mutate', 0.5381948947906494, 'nlp', 1), ('conceptofmind/toolformer', 0.5376387238502502, 'llm', 0), ('shishirpatil/gorilla', 0.5373891592025757, 'llm', 1), ('hwchase17/langchain', 0.5362697243690491, 'llm', 0), ('explosion/spacy-llm', 0.5356959104537964, 'llm', 2), ('determined-ai/determined', 0.5315389633178711, 'ml-ops', 0), ('lancedb/lancedb', 0.531322181224823, 'data', 0), ('cheshire-cat-ai/core', 0.5307193398475647, 'llm', 1), ('langchain-ai/langgraph', 0.5296244025230408, 'llm', 0), ('confident-ai/deepeval', 0.5287721157073975, 'testing', 1), ('nvidia/nemo-guardrails', 0.527998149394989, 'llm', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5272374749183655, 'study', 0), ('lastmile-ai/aiconfig', 0.5260499715805054, 'util', 1), ('jerryjliu/llama_index', 0.5246336460113525, 'llm', 3), ('zilliztech/gptcache', 0.5229496955871582, 'llm', 1), ('lm-sys/fastchat', 0.5211068987846375, 'llm', 0), ('truera/trulens', 0.5201952457427979, 'llm', 1), ('microsoft/flaml', 0.5200607180595398, 'ml', 1), ('lightning-ai/lit-gpt', 0.5195765495300293, 'llm', 1), ('cg123/mergekit', 0.5194407105445862, 'llm', 1), ('microsoft/autogen', 0.5187461376190186, 'llm', 0), ('rasahq/rasa', 0.5164182186126709, 'llm', 0), ('giskard-ai/giskard', 0.5133479237556458, 'data', 1), ('nvidia/deeplearningexamples', 0.5106365084648132, 'ml-dl', 1), ('ibm/dromedary', 0.5087694525718689, 'llm', 0), ('lianjiatech/belle', 0.5057373642921448, 'llm', 0), ('openlm-research/open_llama', 0.5050448179244995, 'llm', 0), ('tensorflow/tensorflow', 0.5049441456794739, 'ml-dl', 0), ('huggingface/datasets', 0.5033867955207825, 'nlp', 0), ('titanml/takeoff', 0.5030698180198669, 'llm', 1), ('berriai/litellm', 0.5017346143722534, 'llm', 1), ('openbmb/toolbench', 0.5014435052871704, 'llm', 0), ('epfllm/meditron', 0.501189649105072, 'llm', 0), ('eleutherai/the-pile', 0.5005179643630981, 'data', 1)]",8,1.0,,2.25,19,12,3,1,0,0,0,19.0,8.0,90.0,0.4,39
1539,llm,https://github.com/tsinghuadatabasegroup/db-gpt,"['language-model', 'dba']",LLM As Database Administrator,[],[],,,,tsinghuadatabasegroup/db-gpt,DB-GPT,327,45,8,Python,http://dbgpt.dbmind.cn/,An LLM Based Diagnosis System  (https://arxiv.org/pdf/2312.01454.pdf),tsinghuadatabasegroup,2024-01-14,2023-04-02,43,7.554455445544554,,An LLM Based Diagnosis System  (https://arxiv.org/pdf/2312.01454.pdf),"['database', 'dba', 'diagnosis', 'tuning']","['database', 'dba', 'diagnosis', 'language-model', 'tuning']",2024-01-13,"[('epfllm/meditron', 0.5455986261367798, 'llm', 1), ('hiyouga/llama-factory', 0.5159657001495361, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5159655809402466, 'llm', 1), ('microsoft/torchscale', 0.5131277441978455, 'llm', 0), ('young-geng/easylm', 0.5007253289222717, 'llm', 1)]",6,3.0,,7.5,53,45,10,0,0,0,0,53.0,72.0,90.0,1.4,39
1657,data,https://github.com/unstructured-io/unstructured-api,"['unstructured', 'api']",API for Open-Source Pre-Processing Tools for Unstructured Data,[],[],,,,unstructured-io/unstructured-api,unstructured-api,231,50,17,Python,,,unstructured-io,2024-01-09,2022-12-09,59,3.8776978417266186,https://avatars.githubusercontent.com/u/108372208?v=4,API for Open-Source Pre-Processing Tools for Unstructured Data,[],"['api', 'unstructured']",2024-01-12,"[('unstructured-io/pipeline-sec-filings', 0.5717188715934753, 'data', 1), ('simonw/datasette', 0.5543832778930664, 'data', 0), ('saulpw/visidata', 0.536859393119812, 'term', 0)]",23,3.0,,3.85,61,55,13,0,29,27,29,61.0,57.0,90.0,0.9,39
723,ml,https://github.com/cleverhans-lab/cleverhans,[],,[],[],,,,cleverhans-lab/cleverhans,cleverhans,6000,1394,190,Jupyter Notebook,,"An adversarial example library for constructing attacks, building defenses, and benchmarking both",cleverhans-lab,2024-01-13,2016-09-15,384,15.595989602673598,https://avatars.githubusercontent.com/u/51966688?v=4,"An adversarial example library for constructing attacks, building defenses, and benchmarking both","['benchmarking', 'machine-learning', 'security']","['benchmarking', 'machine-learning', 'security']",2023-01-31,"[('borealisai/advertorch', 0.7116900086402893, 'ml', 3), ('huggingface/evaluate', 0.5058858394622803, 'ml', 1), ('zorzi-s/projectregularization', 0.5036975741386414, 'gis', 0)]",131,3.0,,0.02,1,0,89,12,0,1,1,1.0,0.0,90.0,0.0,38
565,ml,https://github.com/mdbloice/augmentor,[],,[],[],,,,mdbloice/augmentor,Augmentor,4997,870,124,Python,https://augmentor.readthedocs.io/en/stable,Image augmentation library in Python for machine learning.,mdbloice,2024-01-13,2016-03-01,413,12.099273607748184,,Image augmentation library in Python for machine learning.,"['augmentation', 'deep-learning', 'machine-learning', 'neural-networks']","['augmentation', 'deep-learning', 'machine-learning', 'neural-networks']",2023-03-29,"[('aleju/imgaug', 0.7141932845115662, 'ml', 3), ('lightly-ai/lightly', 0.7019970417022705, 'ml', 2), ('albumentations-team/albumentations', 0.6705105900764465, 'ml-dl', 3), ('facebookresearch/augly', 0.6478663086891174, 'data', 0), ('pytorch/ignite', 0.5899521708488464, 'ml-dl', 2), ('fepegar/torchio', 0.5896494388580322, 'ml-dl', 3), ('featurelabs/featuretools', 0.5716681480407715, 'ml', 1), ('rasbt/mlxtend', 0.5698684453964233, 'ml', 1), ('weecology/deepforest', 0.5662134885787964, 'gis', 0), ('google-research/deeplab2', 0.5629528760910034, 'ml', 0), ('luispedro/mahotas', 0.5612508654594421, 'viz', 0), ('deci-ai/super-gradients', 0.5568101406097412, 'ml-dl', 1), ('dmlc/dgl', 0.5562937259674072, 'ml-dl', 1), ('gradio-app/gradio', 0.5561718344688416, 'viz', 2), ('intel/intel-extension-for-pytorch', 0.5521341562271118, 'perf', 2), ('ageron/handson-ml2', 0.5503374338150024, 'ml', 0), ('skorch-dev/skorch', 0.5499297976493835, 'ml-dl', 1), ('python-pillow/pillow', 0.5397363305091858, 'util', 0), ('imageio/imageio', 0.5311002135276794, 'util', 0), ('pycaret/pycaret', 0.5300421118736267, 'ml', 1), ('rasbt/machine-learning-book', 0.5249707102775574, 'study', 3), ('tensorflow/addons', 0.5146579742431641, 'ml', 2), ('yzhao062/pyod', 0.5144423842430115, 'data', 3), ('nvlabs/gcvit', 0.5139862895011902, 'diffusion', 1), ('neuralmagic/sparseml', 0.5088579654693604, 'ml-dl', 0), ('scikit-learn/scikit-learn', 0.5044978260993958, 'ml', 1), ('facebookresearch/pytorch3d', 0.5032729506492615, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5032522678375244, 'ml-dl', 1), ('huggingface/huggingface_hub', 0.5017703175544739, 'ml', 2)]",23,3.0,,0.1,2,1,96,10,0,3,3,2.0,1.0,90.0,0.5,38
1213,ml-interpretability,https://github.com/tensorflow/lucid,[],,[],[],,,,tensorflow/lucid,lucid,4592,659,159,Jupyter Notebook,,A collection of infrastructure and tools for research in neural network interpretability.,tensorflow,2024-01-12,2018-01-25,313,14.63752276867031,https://avatars.githubusercontent.com/u/15658638?v=4,A collection of infrastructure and tools for research in neural network interpretability.,"['colab', 'interpretability', 'jupyter-notebook', 'machine-learning', 'tensorflow', 'visualization']","['colab', 'interpretability', 'jupyter-notebook', 'machine-learning', 'tensorflow', 'visualization']",2021-03-19,"[('pytorch/captum', 0.6784272193908691, 'ml-interpretability', 1), ('pair-code/lit', 0.6758688688278198, 'ml-interpretability', 2), ('csinva/imodels', 0.6663603782653809, 'ml', 2), ('interpretml/interpret', 0.626980721950531, 'ml-interpretability', 2), ('marcotcr/lime', 0.61471027135849, 'ml-interpretability', 0), ('lutzroeder/netron', 0.5944969654083252, 'ml', 2), ('pytorch/ignite', 0.5938147902488708, 'ml-dl', 1), ('eleutherai/pythia', 0.5809341669082642, 'ml-interpretability', 1), ('seldonio/alibi', 0.5702253580093384, 'ml-interpretability', 2), ('rafiqhasan/auto-tensorflow', 0.5636606216430664, 'ml-dl', 2), ('teamhg-memex/eli5', 0.5620574355125427, 'ml', 1), ('maif/shapash', 0.5549944639205933, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5517100095748901, 'ml-interpretability', 0), ('oegedijk/explainerdashboard', 0.5506213903427124, 'ml-interpretability', 0), ('ageron/handson-ml2', 0.5488042235374451, 'ml', 0), ('nvidia/deeplearningexamples', 0.5472056269645691, 'ml-dl', 1), ('tensorflow/tensorflow', 0.5469701290130615, 'ml-dl', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5438190698623657, 'study', 1), ('onnx/onnx', 0.5405921936035156, 'ml', 2), ('carla-recourse/carla', 0.5387822389602661, 'ml', 2), ('ddbourgin/numpy-ml', 0.5334033370018005, 'ml', 1), ('wandb/client', 0.5249351263046265, 'ml', 2), ('huggingface/evaluate', 0.5247843861579895, 'ml', 1), ('skorch-dev/skorch', 0.5241331458091736, 'ml-dl', 1), ('explosion/thinc', 0.522412121295929, 'ml-dl', 2), ('huggingface/datasets', 0.5218112468719482, 'nlp', 2), ('tensorflow/data-validation', 0.5206327438354492, 'ml-ops', 0), ('tensorly/tensorly', 0.5121048092842102, 'ml-dl', 2), ('mlflow/mlflow', 0.5101231336593628, 'ml-ops', 1), ('rasbt/machine-learning-book', 0.5070700645446777, 'study', 1), ('arogozhnikov/einops', 0.5045328140258789, 'ml-dl', 1), ('xl0/lovely-tensors', 0.5038846731185913, 'ml-dl', 1), ('slundberg/shap', 0.5016194581985474, 'ml-interpretability', 2), ('districtdatalabs/yellowbrick', 0.5002766251564026, 'ml', 2)]",40,3.0,,0.0,2,1,73,34,0,4,4,2.0,1.0,90.0,0.5,38
292,util,https://github.com/pytoolz/toolz,[],,[],[],,,,pytoolz/toolz,toolz,4431,305,83,Python,http://toolz.readthedocs.org/,A functional standard library for Python.,pytoolz,2024-01-13,2013-09-13,541,8.181746241097336,https://avatars.githubusercontent.com/u/5448828?v=4,A functional standard library for Python.,[],[],2022-11-03,"[('pyston/pyston', 0.719200074672699, 'util', 0), ('pypy/pypy', 0.7066237330436707, 'util', 0), ('pmorissette/ffn', 0.6924606561660767, 'finance', 0), ('google/latexify_py', 0.6760282516479492, 'util', 0), ('eleutherai/pyfra', 0.6730476021766663, 'ml', 0), ('python/cpython', 0.6690644025802612, 'util', 0), ('ta-lib/ta-lib-python', 0.653429388999939, 'finance', 0), ('pandas-dev/pandas', 0.6519975066184998, 'pandas', 0), ('python-rope/rope', 0.649603009223938, 'util', 0), ('evhub/coconut', 0.6476520895957947, 'util', 0), ('suor/funcy', 0.6413887143135071, 'util', 0), ('fastai/fastcore', 0.6410788297653198, 'util', 0), ('urwid/urwid', 0.6324057579040527, 'term', 0), ('erotemic/ubelt', 0.6275792717933655, 'util', 0), ('connorferster/handcalcs', 0.6247959733009338, 'jupyter', 0), ('artemyk/dynpy', 0.6178411841392517, 'sim', 0), ('gondolav/pyfuncol', 0.6170833706855774, 'util', 0), ('pyparsing/pyparsing', 0.6166950464248657, 'util', 0), ('agronholm/apscheduler', 0.6112861037254333, 'util', 0), ('dgilland/cacheout', 0.6084659695625305, 'perf', 0), ('rasbt/mlxtend', 0.6010292172431946, 'ml', 0), ('instagram/libcst', 0.6008638739585876, 'util', 0), ('wesm/pydata-book', 0.599173903465271, 'study', 0), ('dylanhogg/awesome-python', 0.5974959135055542, 'study', 0), ('goldmansachs/gs-quant', 0.5948769450187683, 'finance', 0), ('pyeve/cerberus', 0.5941500067710876, 'data', 0), ('pyscript/pyscript-cli', 0.5934836864471436, 'web', 0), ('pdm-project/pdm', 0.592634379863739, 'util', 0), ('sympy/sympy', 0.5918540358543396, 'math', 0), ('instagram/monkeytype', 0.5914798974990845, 'typing', 0), ('marshmallow-code/marshmallow', 0.5878369808197021, 'util', 0), ('landscapeio/prospector', 0.5871560573577881, 'util', 0), ('cython/cython', 0.5866791605949402, 'util', 0), ('imageio/imageio', 0.5862778425216675, 'util', 0), ('google/pytype', 0.5861064195632935, 'typing', 0), ('facebook/pyre-check', 0.5858352184295654, 'typing', 0), ('gbeced/pyalgotrade', 0.5849488377571106, 'finance', 0), ('astral-sh/ruff', 0.5842194557189941, 'util', 0), ('google/python-fire', 0.5839331150054932, 'term', 0), ('julienpalard/pipe', 0.5835241079330444, 'util', 0), ('1200wd/bitcoinlib', 0.5831727385520935, 'crypto', 0), ('legrandin/pycryptodome', 0.5827405452728271, 'util', 0), ('primal100/pybitcointools', 0.5817379951477051, 'crypto', 0), ('hoffstadt/dearpygui', 0.5808964371681213, 'gui', 0), ('fredrik-johansson/mpmath', 0.580827534198761, 'math', 0), ('ethtx/ethtx', 0.5787340402603149, 'crypto', 0), ('pypa/installer', 0.5778533220291138, 'util', 0), ('klen/py-frameworks-bench', 0.5774570107460022, 'perf', 0), ('xrudelis/pytrait', 0.5742577314376831, 'util', 0), ('timofurrer/awesome-asyncio', 0.5735023021697998, 'study', 0), ('featurelabs/featuretools', 0.5733933448791504, 'ml', 0), ('lk-geimfari/mimesis', 0.5705004334449768, 'data', 0), ('numpy/numpy', 0.5658218860626221, 'math', 0), ('requests/toolbelt', 0.5654045939445496, 'util', 0), ('pysal/pysal', 0.5646018385887146, 'gis', 0), ('has2k1/plotnine', 0.5640243887901306, 'viz', 0), ('python-odin/odin', 0.563791811466217, 'util', 0), ('tiangolo/typer', 0.5614187121391296, 'term', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5609208941459656, 'study', 0), ('pyscf/pyscf', 0.5605663657188416, 'sim', 0), ('mementum/backtrader', 0.5593503713607788, 'finance', 0), ('python/typeshed', 0.5580030083656311, 'typing', 0), ('nedbat/coveragepy', 0.5574312210083008, 'testing', 0), ('google/gin-config', 0.555968701839447, 'util', 0), ('python-cachier/cachier', 0.5554683208465576, 'perf', 0), ('stanfordnlp/dspy', 0.5550901889801025, 'llm', 0), ('pytorch/data', 0.5517788529396057, 'data', 0), 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0.5407304763793945, 'util', 0), ('pytables/pytables', 0.5390780568122864, 'data', 0), ('mynameisfiber/high_performance_python_2e', 0.5383166670799255, 'study', 0), ('libtcod/python-tcod', 0.5379080176353455, 'gamedev', 0), ('irmen/pyminiaudio', 0.5377234816551208, 'util', 0), ('viblo/pymunk', 0.5366160869598389, 'sim', 0), ('beeware/toga', 0.5362921953201294, 'gui', 0), ('willmcgugan/textual', 0.5360779166221619, 'term', 0), ('opengeos/leafmap', 0.5356908440589905, 'gis', 0), ('google/yapf', 0.5353810787200928, 'util', 0), ('strawberry-graphql/strawberry', 0.5352164506912231, 'web', 0), ('numba/llvmlite', 0.5343791842460632, 'util', 0), ('google/jax', 0.5340706706047058, 'ml', 0), ('stan-dev/pystan', 0.5338031053543091, 'ml', 0), ('wolever/parameterized', 0.5330343842506409, 'testing', 0), ('dagworks-inc/hamilton', 0.5323624014854431, 'ml-ops', 0), ('mkdocstrings/griffe', 0.5321711301803589, 'util', 0), ('spotify/pedalboard', 0.5321218371391296, 'util', 0), ('rustpython/rustpython', 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935,ml,https://github.com/thudm/cogvideo,[],,[],[],,,,thudm/cogvideo,CogVideo,3339,352,102,Python,,"Text-to-video generation. The repo for ICLR2023 paper ""CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers""",thudm,2024-01-13,2022-05-29,87,38.253682487725044,https://avatars.githubusercontent.com/u/48590610?v=4,"Text-to-video generation. The repo for ICLR2023 paper ""CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers""",[],[],2023-06-09,"[('sharonzhou/long_stable_diffusion', 0.6085971593856812, 'diffusion', 0), ('chenyangqiqi/fatezero', 0.6052513718605042, 'diffusion', 0), ('lucidrains/deep-daze', 0.5634084939956665, 'ml', 0), ('openai/image-gpt', 0.558464765548706, 'llm', 0), ('williamyang1991/vtoonify', 0.5548344850540161, 'ml-dl', 0), ('openai/glide-text2im', 0.5404991507530212, 'diffusion', 0), ('saharmor/dalle-playground', 0.529645562171936, 'diffusion', 0), ('ofa-sys/ofa', 0.5225783586502075, 'llm', 0), ('open-mmlab/mmediting', 0.5184540748596191, 'ml', 0), ('borisdayma/dalle-mini', 0.5106388926506042, 'diffusion', 0), ('nateraw/stable-diffusion-videos', 0.5062693357467651, 'diffusion', 0), ('huggingface/text-generation-inference', 0.5058658719062805, 'llm', 0)]",4,3.0,,0.04,0,0,20,7,0,0,0,0.0,0.0,90.0,0.0,38
34,nlp,https://github.com/jbesomi/texthero,[],,[],[],,,,jbesomi/texthero,texthero,2841,240,43,Python,https://texthero.org,"Text preprocessing, representation and visualization from zero to hero.",jbesomi,2024-01-14,2020-04-06,199,14.266140602582496,,"Text preprocessing, representation and visualization from zero to hero.","['machine-learning', 'nlp', 'nlp-pipeline', 'text-clustering', 'text-mining', 'text-preprocessing', 'text-representation', 'text-visualization', 'texthero', 'word-embeddings']","['machine-learning', 'nlp', 'nlp-pipeline', 'text-clustering', 'text-mining', 'text-preprocessing', 'text-representation', 'text-visualization', 'texthero', 'word-embeddings']",2023-08-29,"[('alibaba/easynlp', 0.5708900690078735, 'nlp', 2), ('nltk/nltk', 0.5517333149909973, 'nlp', 2), ('sloria/textblob', 0.5416422486305237, 'nlp', 1), ('explosion/spacy-streamlit', 0.5377371907234192, 'nlp', 2), ('rasahq/rasa', 0.5357376337051392, 'llm', 2), ('jalammar/ecco', 0.5305410623550415, 'ml-interpretability', 1), ('makcedward/nlpaug', 0.5141093134880066, 'nlp', 2), ('infinitylogesh/mutate', 0.5125301480293274, 'nlp', 0), ('allenai/allennlp', 0.5118191242218018, 'nlp', 1), ('koaning/whatlies', 0.5086743831634521, 'nlp', 1), ('explosion/spacy-llm', 0.5079323053359985, 'llm', 2), ('microsoft/unilm', 0.5031306147575378, 'nlp', 1)]",21,6.0,,0.1,0,0,46,5,0,2,2,0.0,0.0,90.0,0.0,38
431,pandas,https://github.com/pydata/pandas-datareader,[],,[],[],,,,pydata/pandas-datareader,pandas-datareader,2761,675,141,Python,https://pydata.github.io/pandas-datareader/stable/index.html,Extract data from a wide range of Internet sources into a pandas DataFrame.,pydata,2024-01-12,2015-01-15,471,5.853119321623258,https://avatars.githubusercontent.com/u/1284191?v=4,Extract data from a wide range of Internet sources into a pandas DataFrame.,"['data', 'data-analysis', 'dataset', 'econdb', 'economic-data', 'fama-french', 'finance', 'financial-data', 'fred', 'html', 'pandas', 'pydata', 'stock-data']","['data', 'data-analysis', 'dataset', 'econdb', 'economic-data', 'fama-french', 'finance', 'financial-data', 'fred', 'html', 'pandas', 'pydata', 'stock-data']",2023-10-24,"[('ranaroussi/yfinance', 0.6126816868782043, 'finance', 3), ('twopirllc/pandas-ta', 0.5820935368537903, 'finance', 2), ('cuemacro/findatapy', 0.5381442308425903, 'finance', 1), ('lux-org/lux', 0.5096178650856018, 'viz', 1)]",91,1.0,,0.38,27,16,110,3,0,3,3,27.0,30.0,90.0,1.1,38
1280,ml,https://github.com/scikit-optimize/scikit-optimize,[],,[],[],,,,scikit-optimize/scikit-optimize,scikit-optimize,2700,535,64,Python,https://scikit-optimize.github.io,Sequential model-based optimization with a  `scipy.optimize` interface,scikit-optimize,2024-01-12,2016-03-20,410,6.580779944289693,https://avatars.githubusercontent.com/u/18578550?v=4,Sequential model-based optimization with a  `scipy.optimize` interface,"['bayesian-optimization', 'bayesopt', 'binder', 'hyperparameter', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'machine-learning', 'optimization', 'scientific-computing', 'scientific-visualization', 'scikit-learn', 'sequential-recommendation', 'visualization']","['bayesian-optimization', 'bayesopt', 'binder', 'hyperparameter', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'machine-learning', 'optimization', 'scientific-computing', 'scientific-visualization', 'scikit-learn', 'sequential-recommendation', 'visualization']",2021-10-12,"[('google/vizier', 0.6410875916481018, 'ml', 5), ('automl/auto-sklearn', 0.6117300391197205, 'ml', 5), ('pymc-devs/pymc3', 0.5821582674980164, 'ml', 0), ('ray-project/tune-sklearn', 0.550593912601471, 'ml', 3), ('hyperopt/hyperopt', 0.5412236452102661, 'ml', 0), ('pytorch/botorch', 0.5391286611557007, 'ml-dl', 0), ('scipy/scipy', 0.5384607911109924, 'math', 1), ('districtdatalabs/yellowbrick', 0.5361714959144592, 'ml', 3), ('microsoft/flaml', 0.5336490869522095, 'ml', 3), ('pyomo/pyomo', 0.5328642129898071, 'math', 1), ('kubeflow/katib', 0.5325116515159607, 'ml', 0), ('cma-es/pycma', 0.5281330943107605, 'math', 0), ('epistasislab/tpot', 0.5264012217521667, 'ml', 3), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5192668437957764, 'study', 0), ('pyro-ppl/pyro', 0.5141356587409973, 'ml-dl', 1), ('uber/orbit', 0.5112178325653076, 'time-series', 1), ('optuna/optuna', 0.5056672096252441, 'ml', 2)]",76,5.0,,0.0,23,4,95,27,0,3,3,23.0,33.0,90.0,1.4,38
173,viz,https://github.com/facebookresearch/hiplot,[],,[],[],,,,facebookresearch/hiplot,hiplot,2641,135,29,TypeScript,https://facebookresearch.github.io/hiplot/,HiPlot makes understanding high dimensional data easy,facebookresearch,2024-01-13,2019-11-08,220,11.973445595854923,https://avatars.githubusercontent.com/u/16943930?v=4,HiPlot makes understanding high dimensional data easy,[],[],2023-07-19,"[('contextlab/hypertools', 0.5843124985694885, 'ml', 0), ('holoviz/holoviews', 0.5553148984909058, 'viz', 0), ('holoviz/hvplot', 0.5185054540634155, 'pandas', 0), ('holoviz/datashader', 0.5003904104232788, 'gis', 0)]",9,1.0,,0.17,9,1,51,6,0,10,10,9.0,7.0,90.0,0.8,38
1028,finance,https://github.com/goldmansachs/gs-quant,[],,[],[],,,,goldmansachs/gs-quant,gs-quant,2255,477,91,Jupyter Notebook,https://developer.gs.com/discover/products/gs-quant/,Python toolkit for quantitative finance,goldmansachs,2024-01-14,2018-12-14,267,8.427656166577682,https://avatars.githubusercontent.com/u/1268489?v=4,Python toolkit for quantitative finance,"['derivatives', 'goldman-sachs', 'gs-quant', 'risk-management', 'trading-strategies']","['derivatives', 'goldman-sachs', 'gs-quant', 'risk-management', 'trading-strategies']",2024-01-09,"[('ranaroussi/quantstats', 0.7339354157447815, 'finance', 0), ('cuemacro/finmarketpy', 0.6932242512702942, 'finance', 1), ('ta-lib/ta-lib-python', 0.6905857920646667, 'finance', 0), ('domokane/financepy', 0.6860893964767456, 'finance', 2), ('gbeced/pyalgotrade', 0.6751449108123779, 'finance', 0), ('quantconnect/lean', 0.6400971412658691, 'finance', 1), ('google/tf-quant-finance', 0.6332827806472778, 'finance', 0), ('pmorissette/ffn', 0.6325653195381165, 'finance', 0), ('quantecon/quantecon.py', 0.6292877197265625, 'sim', 0), ('eleutherai/pyfra', 0.6204242706298828, 'ml', 0), ('robcarver17/pysystemtrade', 0.6150317788124084, 'finance', 0), ('quantopian/pyfolio', 0.6110407114028931, 'finance', 0), ('firmai/atspy', 0.6063892841339111, 'time-series', 0), ('zvtvz/zvt', 0.6025742888450623, 'finance', 1), ('plotly/dash', 0.5997943878173828, 'viz', 0), ('pytoolz/toolz', 0.5948769450187683, 'util', 0), ('mementum/backtrader', 0.5926412343978882, 'finance', 0), ('krzjoa/awesome-python-data-science', 0.5910742282867432, 'study', 0), ('quantopian/zipline', 0.5875362753868103, 'finance', 0), ('scikit-mobility/scikit-mobility', 0.5793623328208923, 'gis', 0), ('pandas-dev/pandas', 0.5788717865943909, 'pandas', 0), ('wesm/pydata-book', 0.5769721269607544, 'study', 0), ('dylanhogg/awesome-python', 0.5735217928886414, 'study', 0), ('statsmodels/statsmodels', 0.5688977241516113, 'ml', 0), ('gradio-app/gradio', 0.5611568689346313, 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0.5158860087394714, 'nlp', 0), ('probml/pyprobml', 0.5155179500579834, 'ml', 0), ('bottlepy/bottle', 0.5141693949699402, 'web', 0), ('federicoceratto/dashing', 0.5140957832336426, 'term', 0), ('pymc-devs/pymc3', 0.5136345028877258, 'ml', 0), ('pyscf/pyscf', 0.5135458707809448, 'sim', 0), ('fredrik-johansson/mpmath', 0.5134932398796082, 'math', 0), ('kubeflow/fairing', 0.5122407078742981, 'ml-ops', 0), ('hoffstadt/dearpygui', 0.5079163908958435, 'gui', 0), ('numpy/numpy', 0.5077769160270691, 'math', 0), ('r0x0r/pywebview', 0.5072776079177856, 'gui', 0), ('lballabio/quantlib-swig', 0.5069200396537781, 'finance', 0), ('fastai/fastcore', 0.5064576268196106, 'util', 0), ('samuelcolvin/python-devtools', 0.5059173107147217, 'debug', 0), ('ai4finance-foundation/fingpt', 0.5048463344573975, 'finance', 0), ('google/gin-config', 0.5047725439071655, 'util', 0), ('cython/cython', 0.5038447380065918, 'util', 0), ('masoniteframework/masonite', 0.5026025176048279, 'web', 0), ('polakowo/vectorbt', 0.5025342106819153, 'finance', 1), ('tensorly/tensorly', 0.5013471841812134, 'ml-dl', 0), ('crflynn/stochastic', 0.5013076663017273, 'sim', 0)]",27,2.0,,1.0,1,0,62,0,51,37,51,1.0,0.0,90.0,0.0,38
1315,study,https://github.com/krzjoa/awesome-python-data-science,['awesome'],,[],[],,,,krzjoa/awesome-python-data-science,awesome-python-data-science,2179,353,56,,https://krzjoa.github.io/awesome-python-data-science,Probably the best curated list of data science software in Python.,krzjoa,2024-01-10,2017-12-21,318,6.836844464365755,,Probably the best curated list of data science software in Python.,"['awesome', 'awesome-list', 'awesome-python', 'data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'machine-learning', 'scikit-learn', 'statistics']","['awesome', 'awesome-list', 'awesome-python', 'data-analysis', 'data-science', 'data-visualization', 'deep-learning', 'machine-learning', 'scikit-learn', 'statistics']",2023-10-30,"[('dylanhogg/awesome-python', 0.7601116299629211, 'study', 6), ('plotly/dash', 0.6923890113830566, 'viz', 2), ('pandas-dev/pandas', 0.6641040444374084, 'pandas', 2), ('polyaxon/datatile', 0.6403499245643616, 'pandas', 3), ('firmai/industry-machine-learning', 0.6359909772872925, 'study', 2), ('gradio-app/gradio', 0.620049238204956, 'viz', 5), ('rasbt/mlxtend', 0.610371470451355, 'ml', 2), ('thealgorithms/python', 0.5975322127342224, 'study', 0), ('dagworks-inc/hamilton', 0.5971183776855469, 'ml-ops', 3), ('holoviz/panel', 0.5961334705352783, 'viz', 0), ('airbnb/knowledge-repo', 0.5916482210159302, 'data', 2), ('goldmansachs/gs-quant', 0.5910742282867432, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5890659093856812, 'study', 2), ('featurelabs/featuretools', 0.580768883228302, 'ml', 3), ('scikit-learn/scikit-learn', 0.578446090221405, 'ml', 4), ('ranaroussi/quantstats', 0.5782219767570496, 'finance', 0), ('wesm/pydata-book', 0.5766783952713013, 'study', 0), ('ibis-project/ibis', 0.5760530233383179, 'data', 0), ('man-group/dtale', 0.5704981088638306, 'viz', 3), ('jovianml/opendatasets', 0.5699965357780457, 'data', 2), ('malloydata/malloy-py', 0.5664848685264587, 'data', 0), ('tiangolo/sqlmodel', 0.5662049651145935, 'data', 0), ('merantix-momentum/squirrel-core', 0.5624656081199646, 'ml', 3), ('fatiando/verde', 0.559866726398468, 'gis', 1), ('scitools/iris', 0.5579238533973694, 'gis', 1), ('pycaret/pycaret', 0.554579496383667, 'ml', 2), ('cython/cython', 0.5530298352241516, 'util', 0), ('keon/algorithms', 0.5512800812721252, 'util', 0), ('ta-lib/ta-lib-python', 0.5507447123527527, 'finance', 0), ('joowani/binarytree', 0.5454331636428833, 'util', 0), ('eleutherai/pyfra', 0.5438522100448608, 'ml', 0), ('eventual-inc/daft', 0.5433785915374756, 'pandas', 3), ('1200wd/bitcoinlib', 0.5427032113075256, 'crypto', 0), ('unionai-oss/pandera', 0.5424999594688416, 'pandas', 0), ('fastai/fastcore', 0.5385904908180237, 'util', 0), ('python-odin/odin', 0.5375770330429077, 'util', 0), ('scikit-learn-contrib/imbalanced-learn', 0.535544753074646, 'ml', 4), ('saulpw/visidata', 0.5329089760780334, 'term', 0), ('scikit-mobility/scikit-mobility', 0.5322821736335754, 'gis', 3), ('zenodo/zenodo', 0.5312089323997498, 'util', 0), ('ydataai/ydata-profiling', 0.5310283303260803, 'pandas', 5), ('statsmodels/statsmodels', 0.5274232625961304, 'ml', 3), ('imageio/imageio', 0.5262821316719055, 'util', 0), ('earthlab/earthpy', 0.5236297249794006, 'gis', 0), ('pypy/pypy', 0.5223199725151062, 'util', 0), ('geopandas/geopandas', 0.5221031308174133, 'gis', 0), ('clips/pattern', 0.5186637043952942, 'nlp', 1), ('pytoolz/toolz', 0.5155652761459351, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5150753259658813, 'study', 1), ('residentmario/geoplot', 0.5134770274162292, 'gis', 0), ('mito-ds/monorepo', 0.5128975510597229, 'jupyter', 3), ('great-expectations/great_expectations', 0.5083687901496887, 'ml-ops', 1), ('google/pyglove', 0.5056788325309753, 'util', 1), ('ploomber/ploomber', 0.5054386258125305, 'ml-ops', 2), ('feast-dev/feast', 0.5033879280090332, 'ml-ops', 2), ('scipy/scipy', 0.5031641721725464, 'math', 0), ('domokane/financepy', 0.5010226368904114, 'finance', 0), ('wandb/client', 0.5009933114051819, 'ml', 3)]",31,9.0,,1.04,1,1,74,3,0,0,0,1.0,1.0,90.0,1.0,38
294,util,https://github.com/pyfilesystem/pyfilesystem2,[],,[],[],,,,pyfilesystem/pyfilesystem2,pyfilesystem2,1921,180,43,Python,https://www.pyfilesystem.org,Python's Filesystem abstraction layer,pyfilesystem,2024-01-13,2016-10-14,380,5.047672672672673,https://avatars.githubusercontent.com/u/11898830?v=4,Python's Filesystem abstraction layer,"['filesystem', 'filesystem-library', 'ftp', 'pyfilesystem', 'pyfilesystem2', 'tar', 'zip']","['filesystem', 'filesystem-library', 'ftp', 'pyfilesystem', 'pyfilesystem2', 'tar', 'zip']",2022-10-18,"[('fsspec/filesystem_spec', 0.7090234160423279, 'util', 0), ('drivendataorg/cloudpathlib', 0.5201523900032043, 'data', 0)]",47,5.0,,0.0,9,2,88,15,0,7,7,9.0,18.0,90.0,2.0,38
46,jupyter,https://github.com/maartenbreddels/ipyvolume,[],,[],[],,,,maartenbreddels/ipyvolume,ipyvolume,1896,239,52,TypeScript,,3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL,maartenbreddels,2024-01-09,2016-12-21,370,5.112480739599384,https://avatars.githubusercontent.com/u/99180851?v=4,3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL,"['dataviz', 'ipython-widget', 'jupyter', 'jupyter-notebook', 'plotting', 'quiver', 'rendering-3d-volumes', 'scientific-visualization', 'threejs', 'virtual-reality', 'visualisation', 'volume-rendering', 'webgl']","['dataviz', 'ipython-widget', 'jupyter', 'jupyter-notebook', 'plotting', 'quiver', 'rendering-3d-volumes', 'scientific-visualization', 'threejs', 'virtual-reality', 'visualisation', 'volume-rendering', 'webgl']",2023-07-07,"[('vizzuhq/ipyvizzu', 0.7133920788764954, 'jupyter', 4), ('plotly/plotly.py', 0.6658064723014832, 'viz', 2), ('bokeh/bokeh', 0.6592708230018616, 'viz', 3), ('jupyter-widgets/ipywidgets', 0.6541346311569214, 'jupyter', 0), ('giswqs/mapwidget', 0.6517302989959717, 'gis', 1), ('voila-dashboards/voila', 0.6421133279800415, 'jupyter', 2), ('ipython/ipyparallel', 0.6384920477867126, 'perf', 1), ('jupyterlab/jupyterlab-desktop', 0.6347945928573608, 'jupyter', 2), ('matplotlib/matplotlib', 0.6306770443916321, 'viz', 1), ('jupyter/notebook', 0.6165135502815247, 'jupyter', 2), ('holoviz/holoviz', 0.6008582711219788, 'viz', 0), ('holoviz/panel', 0.597214937210083, 'viz', 2), ('vispy/vispy', 0.5861509442329407, 'viz', 0), ('ipython/ipykernel', 0.5854012966156006, 'util', 2), ('pyglet/pyglet', 0.580999493598938, 'gamedev', 1), ('opengeos/leafmap', 0.5771999359130859, 'gis', 3), ('man-group/dtale', 0.5735721588134766, 'viz', 1), ('marcomusy/vedo', 0.5702053308486938, 'viz', 1), ('jakevdp/pythondatasciencehandbook', 0.5677738189697266, 'study', 1), ('holoviz/hvplot', 0.5658844113349915, 'pandas', 1), ('jupyter/nbformat', 0.5637709498405457, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.5636622309684753, 'jupyter', 1), ('cuemacro/chartpy', 0.5550907254219055, 'viz', 1), ('dfki-ric/pytransform3d', 0.5542986392974854, 'math', 0), ('koaning/drawdata', 0.5521408915519714, 'jupyter', 1), ('jupyterlite/jupyterlite', 0.5511241555213928, 'jupyter', 1), ('giswqs/geemap', 0.5499334931373596, 'gis', 3), ('enthought/mayavi', 0.5471249222755432, 'viz', 1), ('plotly/dash', 0.5407821536064148, 'viz', 1), ('jupyter-widgets/ipyleaflet', 0.540547251701355, 'gis', 1), ('aws/graph-notebook', 0.539257287979126, 'jupyter', 2), ('holoviz/geoviews', 0.5372338891029358, 'gis', 1), ('kanaries/pygwalker', 0.5359721779823303, 'pandas', 0), ('wesm/pydata-book', 0.5356112122535706, 'study', 0), ('residentmario/geoplot', 0.5270797610282898, 'gis', 0), ('brandtbucher/specialist', 0.5239526033401489, 'perf', 0), ('altair-viz/altair', 0.5231117010116577, 'viz', 0), ('connorferster/handcalcs', 0.5224195122718811, 'jupyter', 0), ('cohere-ai/notebooks', 0.5213759541511536, 'llm', 0), ('mwouts/jupytext', 0.520946741104126, 'jupyter', 1), ('r0x0r/pywebview', 0.5197725296020508, 'gui', 0), ('fchollet/deep-learning-with-python-notebooks', 0.516755223274231, 'study', 0), ('computationalmodelling/nbval', 0.5154886841773987, 'jupyter', 1), ('quantopian/qgrid', 0.5143440961837769, 'jupyter', 0), ('wxwidgets/phoenix', 0.5121052861213684, 'gui', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5118504762649536, 'jupyter', 0), ('pysimplegui/pysimplegui', 0.5105063319206238, 'gui', 0), ('webpy/webpy', 0.5092483162879944, 'web', 0), ('ipython/ipython', 0.5083285570144653, 'util', 1), ('jupyter/nbconvert', 0.5078595280647278, 'jupyter', 0), ('klen/muffin', 0.50477135181427, 'web', 0), ('pyvista/pyvista', 0.5043342709541321, 'viz', 2), ('masoniteframework/masonite', 0.5016263127326965, 'web', 0), ('python/cpython', 0.5007892847061157, 'util', 0), ('bloomberg/ipydatagrid', 0.5007188320159912, 'jupyter', 0)]",45,7.0,,0.44,2,0,86,6,0,7,7,2.0,2.0,90.0,1.0,38
1211,util,https://github.com/astanin/python-tabulate,[],,[],[],,,,astanin/python-tabulate,python-tabulate,1881,194,21,Python,https://pypi.org/project/tabulate/,"Pretty-print tabular data in Python, a library and a command-line utility. Repository migrated from bitbucket.org/astanin/python-tabulate.",astanin,2024-01-12,2019-09-02,230,8.1731843575419,,"Pretty-print tabular data in Python, a library and a command-line utility. Repository migrated from bitbucket.org/astanin/python-tabulate.",[],[],2023-04-30,"[('jazzband/prettytable', 0.6471048593521118, 'term', 0), ('jazzband/tablib', 0.6442596316337585, 'data', 0), ('camelot-dev/camelot', 0.6331050395965576, 'util', 0), ('wireservice/csvkit', 0.5541922450065613, 'util', 0), ('vaexio/vaex', 0.5106703042984009, 'perf', 0), ('saulpw/visidata', 0.5104148387908936, 'term', 0), ('mljar/mljar-supervised', 0.5009972453117371, 'ml', 0)]",84,3.0,,0.0,19,2,53,9,0,6,6,19.0,9.0,90.0,0.5,38
262,sim,https://github.com/quantecon/quantecon.py,[],,[],[],,,,quantecon/quantecon.py,QuantEcon.py,1802,2287,150,Python,https://quantecon.org/quantecon-py/,A community based Python library for quantitative economics,quantecon,2024-01-12,2013-03-22,566,3.1805345436207766,https://avatars.githubusercontent.com/u/8703060?v=4,A community based Python library for quantitative economics,[],[],2023-08-09,"[('gbeced/pyalgotrade', 0.6401932835578918, 'finance', 0), ('goldmansachs/gs-quant', 0.6292877197265625, 'finance', 0), ('domokane/financepy', 0.5794845819473267, 'finance', 0), ('eleutherai/pyfra', 0.5649722218513489, 'ml', 0), ('cuemacro/finmarketpy', 0.5637820959091187, 'finance', 0), ('pmorissette/ffn', 0.559691309928894, 'finance', 0), ('robcarver17/pysystemtrade', 0.5519441962242126, 'finance', 0), ('statsmodels/statsmodels', 0.5498467087745667, 'ml', 0), ('wesm/pydata-book', 0.5465633273124695, 'study', 0), ('quantopian/zipline', 0.5450539588928223, 'finance', 0), ('ta-lib/ta-lib-python', 0.54204261302948, 'finance', 0), ('rasbt/mlxtend', 0.5213393568992615, 'ml', 0), ('ranaroussi/quantstats', 0.513916015625, 'finance', 0), ('py-why/dowhy', 0.513481080532074, 'ml', 0), ('quantopian/pyfolio', 0.5116593837738037, 'finance', 0), ('alkaline-ml/pmdarima', 0.5083404779434204, 'time-series', 0), ('dit/dit', 0.5037375688552856, 'math', 0), ('pytoolz/toolz', 0.502926766872406, 'util', 0), ('microsoft/qlib', 0.5021764636039734, 'finance', 0), ('scikit-mobility/scikit-mobility', 0.5013092756271362, 'gis', 0)]",43,7.0,,0.42,5,0,132,5,4,4,4,5.0,12.0,90.0,2.4,38
1616,data,https://github.com/samuelcolvin/arq,[],,[],[],,,,samuelcolvin/arq,arq,1766,147,32,Python,https://arq-docs.helpmanual.io/,Fast job queuing and RPC in python with asyncio and redis.,samuelcolvin,2024-01-13,2016-07-21,392,4.496907966533285,,Fast job queuing and RPC in python with asyncio and redis.,"['async', 'asyncio', 'concurrency', 'concurrent', 'distributed', 'msgpack', 'queue', 'redis', 'tasks', 'worker']","['async', 'asyncio', 'concurrency', 'concurrent', 'distributed', 'msgpack', 'queue', 'redis', 'tasks', 'worker']",2023-10-30,"[('python-trio/trio', 0.6487094759941101, 'perf', 1), ('agronholm/anyio', 0.6350996494293213, 'perf', 1), ('magicstack/uvloop', 0.6330302953720093, 'util', 2), ('airtai/faststream', 0.6273122429847717, 'perf', 2), ('aio-libs/aiohttp', 0.6253662705421448, 'web', 2), ('geeogi/async-python-lambda-template', 0.6250224113464355, 'template', 0), ('noxdafox/pebble', 0.5885294675827026, 'perf', 1), ('sumerc/yappi', 0.5881884098052979, 'profiling', 1), ('bogdanp/dramatiq', 0.5857503414154053, 'util', 1), ('pallets/quart', 0.5751336216926575, 'web', 1), ('alirn76/panther', 0.5734840035438538, 'web', 0), ('joblib/loky', 0.5648357272148132, 'perf', 0), ('hyperopt/hyperopt', 0.5642699599266052, 'ml', 0), ('aio-libs/aiocache', 0.554317057132721, 'data', 2), ('eventlet/eventlet', 0.5532472729682922, 'perf', 1), ('joblib/joblib', 0.5469869375228882, 'util', 0), ('dask/dask', 0.5444415211677551, 'perf', 0), ('samuelcolvin/aioaws', 0.5388724207878113, 'data', 1), ('celery/celery', 0.5360067486763, 'perf', 1), ('neoteroi/blacksheep', 0.5355557799339294, 'web', 1), ('encode/httpx', 0.525906503200531, 'web', 1), ('timofurrer/awesome-asyncio', 0.5215858817100525, 'study', 1), ('alex-sherman/unsync', 0.5158486366271973, 'util', 0), ('fastai/fastcore', 0.5146878957748413, 'util', 0), ('mher/flower', 0.5143932104110718, 'perf', 1), ('pytest-dev/pytest-asyncio', 0.5120292901992798, 'testing', 1), ('agronholm/apscheduler', 0.5115315914154053, 'util', 0), ('grantjenks/python-diskcache', 0.5104993581771851, 'util', 0), ('aio-libs/aiobotocore', 0.5054838061332703, 'util', 1), ('tiangolo/asyncer', 0.5030202865600586, 'perf', 2), ('samuelcolvin/watchfiles', 0.5021094679832458, 'util', 1)]",55,3.0,,0.06,19,8,91,2,0,8,8,19.0,17.0,90.0,0.9,38
323,security,https://github.com/pyupio/safety,[],,[],[],,,,pyupio/safety,safety,1571,138,32,Python,https://pyup.io/safety/,Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected.,pyupio,2024-01-12,2016-10-19,379,4.135765325310267,https://avatars.githubusercontent.com/u/16113910?v=4,Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected.,"['security', 'security-vulnerability', 'travis', 'vulnerability-detection', 'vulnerability-scanners']","['security', 'security-vulnerability', 'travis', 'vulnerability-detection', 'vulnerability-scanners']",2023-11-15,"[('trailofbits/pip-audit', 0.7114713788032532, 'security', 1), ('aswinnnn/pyscan', 0.6276425123214722, 'security', 2), ('sonatype-nexus-community/jake', 0.607435405254364, 'security', 1), ('jazzband/pip-tools', 0.5792787671089172, 'util', 0), ('facebookincubator/bowler', 0.5631865859031677, 'util', 0), ('facebook/pyre-check', 0.5607929229736328, 'typing', 1), ('legrandin/pycryptodome', 0.5586642622947693, 'util', 1), ('pdm-project/pdm', 0.5419641733169556, 'util', 0), ('pyca/cryptography', 0.5316013097763062, 'util', 0), ('fsspec/filesystem_spec', 0.5129398107528687, 'util', 0)]",41,4.0,,0.44,12,1,88,2,0,8,8,12.0,11.0,90.0,0.9,38
1261,llm,https://github.com/ist-daslab/gptq,[],,[],[],,,,ist-daslab/gptq,gptq,1533,118,28,Python,https://arxiv.org/abs/2210.17323,"Code for the ICLR 2023 paper ""GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers"".",ist-daslab,2024-01-13,2022-10-19,66,22.92948717948718,https://avatars.githubusercontent.com/u/35098403?v=4,"Code for the ICLR 2023 paper ""GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers"".",[],[],2023-07-11,"[('karpathy/mingpt', 0.7072771787643433, 'llm', 0), ('huggingface/optimum', 0.6178866624832153, 'ml', 0), ('openai/image-gpt', 0.6169224381446838, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5439256429672241, 'ml-interpretability', 0), ('eleutherai/knowledge-neurons', 0.53592449426651, 'ml-interpretability', 0), ('nielsrogge/transformers-tutorials', 0.5349066257476807, 'study', 0), ('huggingface/transformers', 0.5263904929161072, 'nlp', 0), ('bigscience-workshop/megatron-deepspeed', 0.5179560780525208, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5179560780525208, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5007389187812805, 'study', 0)]",5,3.0,,0.29,8,2,15,6,0,0,0,8.0,5.0,90.0,0.6,38
1330,llm,https://github.com/jina-ai/thinkgpt,"['chain-of-thought', 'language-model']",,[],[],,,,jina-ai/thinkgpt,thinkgpt,1420,119,25,Python,,Agent techniques to augment your LLM and push it beyong its limits,jina-ai,2024-01-13,2023-04-14,41,34.15807560137457,https://avatars.githubusercontent.com/u/60539444?v=4,Agent techniques to augment your LLM and push it beyong its limits,[],"['chain-of-thought', 'language-model']",2023-05-16,"[('aiwaves-cn/agents', 0.6464569568634033, 'nlp', 1), ('ibm/dromedary', 0.6295039057731628, 'llm', 1), ('microsoft/autogen', 0.5731350779533386, 'llm', 0), ('minedojo/voyager', 0.5710037350654602, 'llm', 0), ('hwchase17/langchain', 0.5536667704582214, 'llm', 1), ('nomic-ai/gpt4all', 0.551749587059021, 'llm', 1), ('young-geng/easylm', 0.5500134825706482, 'llm', 1), ('langchain-ai/langgraph', 0.5343421697616577, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5296755433082581, 'study', 0), ('operand/agency', 0.5273327827453613, 'llm', 0), ('deepset-ai/haystack', 0.527265191078186, 'llm', 1), ('nebuly-ai/nebullvm', 0.5268137454986572, 'perf', 0), ('explosion/spacy-llm', 0.5256912708282471, 'llm', 0), ('noahshinn/reflexion', 0.5254539847373962, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5156129002571106, 'llm', 1), ('mlc-ai/mlc-llm', 0.5101083517074585, 'llm', 1), ('microsoft/lmops', 0.5094317197799683, 'llm', 1), ('ray-project/ray-llm', 0.5078703165054321, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5059940814971924, 'llm', 0), ('geekan/metagpt', 0.5057692527770996, 'llm', 0), ('oliveirabruno01/babyagi-asi', 0.5025618076324463, 'llm', 1), ('deep-diver/pingpong', 0.5024981498718262, 'llm', 0)]",3,2.0,,1.19,2,0,9,8,0,0,0,2.0,1.0,90.0,0.5,38
1678,util,https://github.com/pycqa/pyflakes,[],,[],[],,,,pycqa/pyflakes,pyflakes,1317,182,29,Python,https://pypi.org/project/pyflakes,A simple program which checks Python source files for errors,pycqa,2024-01-12,2014-04-07,512,2.5715481171548116,https://avatars.githubusercontent.com/u/8749848?v=4,A simple program which checks Python source files for errors,['linter'],['linter'],2024-01-05,"[('klen/pylama', 0.6928360462188721, 'util', 1), ('nedbat/coveragepy', 0.5710163116455078, 'testing', 0), ('instagram/fixit', 0.5664411783218384, 'util', 1), ('landscapeio/prospector', 0.5659628510475159, 'util', 0), ('pycqa/pycodestyle', 0.5580477118492126, 'util', 0), ('google/yapf', 0.5379751920700073, 'util', 0), ('microsoft/pyright', 0.511178195476532, 'typing', 0), ('google/pytype', 0.5093093514442444, 'typing', 1), ('python/mypy', 0.5037577748298645, 'typing', 1)]",84,4.0,,0.19,11,10,119,0,0,3,3,11.0,14.0,90.0,1.3,38
445,ml,https://github.com/csinva/imodels,[],,[],[],,,,csinva/imodels,imodels,1237,110,26,Jupyter Notebook,https://csinva.io/imodels,"Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).",csinva,2024-01-11,2019-07-04,238,5.181926989826451,,"Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).","['ai', 'artificial-intelligence', 'bayesian-rule-list', 'data-science', 'explainable-ai', 'explainable-ml', 'imodels', 'interpretability', 'machine-learning', 'ml', 'optimal-classification-tree', 'rule-learning', 'rulefit', 'rules', 'scikit-learn', 'statistics', 'supervised-learning']","['ai', 'artificial-intelligence', 'bayesian-rule-list', 'data-science', 'explainable-ai', 'explainable-ml', 'imodels', 'interpretability', 'machine-learning', 'ml', 'optimal-classification-tree', 'rule-learning', 'rulefit', 'rules', 'scikit-learn', 'statistics', 'supervised-learning']",2023-12-30,"[('interpretml/interpret', 0.7508851289749146, 'ml-interpretability', 7), ('pair-code/lit', 0.6702791452407837, 'ml-interpretability', 1), ('tensorflow/lucid', 0.6663603782653809, 'ml-interpretability', 2), ('maif/shapash', 0.6528401374816895, 'ml', 3), ('selfexplainml/piml-toolbox', 0.6522828936576843, 'ml-interpretability', 0), ('marcotcr/lime', 0.6466513276100159, 'ml-interpretability', 0), ('pytorch/captum', 0.6309248208999634, 'ml-interpretability', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.6214880347251892, 'study', 2), ('xplainable/xplainable', 0.6195234656333923, 'ml-interpretability', 5), ('seldonio/alibi', 0.6186242699623108, 'ml-interpretability', 2), ('teamhg-memex/eli5', 0.6143868565559387, 'ml', 3), ('mlflow/mlflow', 0.5987911224365234, 'ml-ops', 3), ('huggingface/evaluate', 0.5934129357337952, 'ml', 1), ('eleutherai/pythia', 0.5875741243362427, 'ml-interpretability', 1), ('polyaxon/datatile', 0.5763207674026489, 'pandas', 3), ('huggingface/datasets', 0.5633199214935303, 'nlp', 1), ('tensorflow/tensorflow', 0.5597484707832336, 'ml-dl', 2), ('oegedijk/explainerdashboard', 0.5566130876541138, 'ml-interpretability', 0), ('cleanlab/cleanlab', 0.5562312602996826, 'ml', 1), ('tensorflow/data-validation', 0.5509118437767029, 'ml-ops', 0), ('skops-dev/skops', 0.5491555333137512, 'ml-ops', 2), ('ddbourgin/numpy-ml', 0.5491467118263245, 'ml', 1), ('giskard-ai/giskard', 0.5474556088447571, 'data', 3), ('firmai/industry-machine-learning', 0.5446041226387024, 'study', 2), ('slundberg/shap', 0.5444343090057373, 'ml-interpretability', 2), ('whylabs/whylogs', 0.5419551730155945, 'util', 2), ('linkedin/fasttreeshap', 0.5407923460006714, 'ml', 3), ('wandb/client', 0.5401941537857056, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5383384227752686, 'ml', 2), ('automl/auto-sklearn', 0.5375404953956604, 'ml', 1), ('nccr-itmo/fedot', 0.5283494591712952, 'ml-ops', 1), ('mosaicml/composer', 0.5268099904060364, 'ml-dl', 1), ('scikit-learn/scikit-learn', 0.525899350643158, 'ml', 3), ('microsoft/nni', 0.524517297744751, 'ml', 2), ('explosion/thinc', 0.5241812467575073, 'ml-dl', 3), ('rasbt/machine-learning-book', 0.5234281420707703, 'study', 2), ('patchy631/machine-learning', 0.5233355760574341, 'ml', 0), ('koaning/scikit-lego', 0.5232925415039062, 'ml', 2), ('rafiqhasan/auto-tensorflow', 0.5232248902320862, 'ml-dl', 1), ('carla-recourse/carla', 0.5205970406532288, 'ml', 4), ('alirezadir/machine-learning-interview-enlightener', 0.5202349424362183, 'study', 2), ('onnx/onnx', 0.5179754495620728, 'ml', 3), ('bentoml/bentoml', 0.5169817209243774, 'ml-ops', 2), ('rasbt/mlxtend', 0.5165061354637146, 'ml', 3), ('determined-ai/determined', 0.5160141587257385, 'ml-ops', 2), ('aimhubio/aim', 0.5152265429496765, 'ml-ops', 4), ('polyaxon/polyaxon', 0.5118154883384705, 'ml-ops', 4), ('gradio-app/gradio', 0.5105863809585571, 'viz', 2), ('featurelabs/featuretools', 0.5102759599685669, 'ml', 3), ('kubeflow/fairing', 0.5098203420639038, 'ml-ops', 0), ('tensorflow/tensor2tensor', 0.5070091485977173, 'ml', 1), ('ml-tooling/opyrator', 0.5064631104469299, 'viz', 1), ('arize-ai/phoenix', 0.5046104192733765, 'ml-interpretability', 0), ('cdpierse/transformers-interpret', 0.500318169593811, 'ml-interpretability', 3), ('tensorlayer/tensorlayer', 0.5001160502433777, 'ml-rl', 1)]",22,5.0,,2.12,8,4,55,0,5,8,5,8.0,1.0,90.0,0.1,38
1010,time-series,https://github.com/bashtage/arch,[],,[],[],,,,bashtage/arch,arch,1215,279,44,Python,,ARCH models in Python,bashtage,2024-01-13,2014-08-29,491,2.4716652136006974,,ARCH models in Python,"['adf', 'arch', 'bootstrap', 'df-gls', 'dickey-fuller', 'finance', 'financial-econometrics', 'forecasting', 'model-confidence-set', 'multiple-comparison-procedures', 'phillips-perron', 'reality-check', 'risk', 'spa', 'time-series', 'unit-root', 'variance', 'volatility']","['adf', 'arch', 'bootstrap', 'df-gls', 'dickey-fuller', 'finance', 'financial-econometrics', 'forecasting', 'model-confidence-set', 'multiple-comparison-procedures', 'phillips-perron', 'reality-check', 'risk', 'spa', 'time-series', 'unit-root', 'variance', 'volatility']",2024-01-05,"[('firmai/atspy', 0.6273349523544312, 'time-series', 3), ('alkaline-ml/pmdarima', 0.6099317669868469, 'time-series', 2), ('statsmodels/statsmodels', 0.5926198363304138, 'ml', 1), ('goldmansachs/gs-quant', 0.5530153512954712, 'finance', 0), ('kernc/backtesting.py', 0.5312038064002991, 'finance', 1), ('awslabs/gluonts', 0.5267676115036011, 'time-series', 2), ('domokane/financepy', 0.5076860785484314, 'finance', 2), ('pmorissette/ffn', 0.5025431513786316, 'finance', 0), ('ranaroussi/quantstats', 0.5018336772918701, 'finance', 1), ('cuemacro/finmarketpy', 0.5008066892623901, 'finance', 0), ('crflynn/stochastic', 0.5002689957618713, 'sim', 0)]",35,3.0,,1.29,25,24,114,0,8,5,8,25.0,25.0,90.0,1.0,38
1043,data,https://github.com/ydataai/ydata-synthetic,[],,[],[],,,,ydataai/ydata-synthetic,ydata-synthetic,1195,233,29,Jupyter Notebook,https://docs.synthetic.ydata.ai,Synthetic data generators for tabular and time-series data,ydataai,2024-01-13,2020-05-04,195,6.123718887262079,https://avatars.githubusercontent.com/u/57689451?v=4,Synthetic data generators for tabular and time-series data,"['datageneration', 'datagenerator', 'deep-learning', 'gan', 'gan-architectures', 'gans', 'generative-adversarial-network', 'machine-learning', 'pytorch', 'synthetic-data', 'tensorflow2', 'time-series', 'timeseries', 'training-data']","['datageneration', 'datagenerator', 'deep-learning', 'gan', 'gan-architectures', 'gans', 'generative-adversarial-network', 'machine-learning', 'pytorch', 'synthetic-data', 'tensorflow2', 'time-series', 'timeseries', 'training-data']",2024-01-02,"[('sdv-dev/sdv', 0.9112098217010498, 'data', 7), ('awslabs/autogluon', 0.5872030258178711, 'ml', 4), ('borisbanushev/stockpredictionai', 0.5105303525924683, 'finance', 0), ('vaexio/vaex', 0.5099949836730957, 'perf', 1), ('winedarksea/autots', 0.5098974704742432, 'time-series', 3), ('nicolas-hbt/pygraft', 0.5071893334388733, 'ml', 2), ('mljar/mljar-supervised', 0.5016704797744751, 'ml', 1)]",22,1.0,,1.23,34,17,45,0,9,9,9,34.0,16.0,90.0,0.5,38
966,gis,https://github.com/microsoft/globalmlbuildingfootprints,[],,[],[],,,,microsoft/globalmlbuildingfootprints,GlobalMLBuildingFootprints,1186,175,60,Python,,Worldwide building footprints derived from satellite imagery ,microsoft,2024-01-12,2022-04-22,92,12.811728395061728,https://avatars.githubusercontent.com/u/6154722?v=4,Worldwide building footprints derived from satellite imagery ,[],[],2024-01-03,"[('zorzi-s/polyworldpretrainednetwork', 0.600134015083313, 'gis', 0), ('lydorn/polygonization-by-frame-field-learning', 0.5449085831642151, 'gis', 0)]",7,2.0,,0.35,17,8,21,0,0,0,0,17.0,11.0,90.0,0.6,38
625,util,https://github.com/pyca/bcrypt,[],,[],[],,,,pyca/bcrypt,bcrypt,1083,195,28,Python,,Modern(-ish) password hashing for your software and your servers,pyca,2024-01-13,2013-05-11,559,1.9359039836567926,https://avatars.githubusercontent.com/u/5615737?v=4,Modern(-ish) password hashing for your software and your servers,[],[],2024-01-12,[],32,5.0,,3.48,86,81,130,0,0,2,2,86.0,90.0,90.0,1.0,38
811,ml,https://github.com/automl/tabpfn,[],,[],[],,,,automl/tabpfn,TabPFN,1028,85,14,Python,http://priorlabs.ai,Official implementation of the TabPFN paper (https://arxiv.org/abs/2207.01848) and the tabpfn package.,automl,2024-01-13,2022-07-01,82,12.449826989619377,https://avatars.githubusercontent.com/u/6469053?v=4,Official implementation of the TabPFN paper (https://arxiv.org/abs/2207.01848) and the tabpfn package.,[],[],2023-10-22,[],7,3.0,,0.67,26,14,19,3,0,0,0,26.0,23.0,90.0,0.9,38
1150,data,https://github.com/aio-libs/aiocache,[],,[],[],,,,aio-libs/aiocache,aiocache,971,139,22,Python,http://aiocache.readthedocs.io,"Asyncio cache manager for redis, memcached and memory",aio-libs,2024-01-11,2016-09-30,382,2.5380881254667664,https://avatars.githubusercontent.com/u/7049303?v=4,"Asyncio cache manager for redis, memcached and memory","['asyncio', 'cache', 'cachemanager', 'memcached', 'redis']","['asyncio', 'cache', 'cachemanager', 'memcached', 'redis']",2024-01-10,"[('long2ice/fastapi-cache', 0.6432105898857117, 'web', 3), ('grantjenks/python-diskcache', 0.6346278786659241, 'util', 1), ('samuelcolvin/arq', 0.554317057132721, 'data', 2), ('dgilland/cacheout', 0.5477664470672607, 'perf', 0), ('python-cachier/cachier', 0.5451957583427429, 'perf', 2)]",43,5.0,,2.15,36,33,89,0,3,3,3,36.0,28.0,90.0,0.8,38
89,testing,https://github.com/taverntesting/tavern,[],,[],[],,,,taverntesting/tavern,tavern,969,186,27,Python,https://taverntesting.github.io/,"A command-line tool and Python library and Pytest plugin for automated testing of RESTful APIs, with a simple, concise and flexible YAML-based syntax",taverntesting,2024-01-12,2017-11-01,325,2.9736957474791756,https://avatars.githubusercontent.com/u/33286481?v=4,"A command-line tool and Python library and Pytest plugin for automated testing of RESTful APIs, with a simple, concise and flexible YAML-based syntax","['http', 'mqtt', 'pytest', 'test-automation', 'testing']","['http', 'mqtt', 'pytest', 'test-automation', 'testing']",2023-12-26,"[('pytest-dev/pytest-xdist', 0.6020364165306091, 'testing', 1), ('simple-salesforce/simple-salesforce', 0.6006324291229248, 'data', 0), ('ionelmc/pytest-benchmark', 0.5957930684089661, 'testing', 1), ('getsentry/responses', 0.5727768540382385, 'testing', 0), ('wolever/parameterized', 0.5629963874816895, 'testing', 0), ('lundberg/respx', 0.5600868463516235, 'testing', 2), ('seleniumbase/seleniumbase', 0.5573855042457581, 'testing', 1), ('hugapi/hug', 0.5526415705680847, 'util', 1), ('falconry/falcon', 0.5523936152458191, 'web', 1), ('flipkart-incubator/astra', 0.552314817905426, 'web', 0), ('requests/toolbelt', 0.545853853225708, 'util', 1), ('python-restx/flask-restx', 0.5409488081932068, 'web', 0), ('nedbat/coveragepy', 0.5402073264122009, 'testing', 0), ('pytest-dev/pytest', 0.5295860767364502, 'testing', 1), ('buildbot/buildbot', 0.5253430604934692, 'util', 0), ('computationalmodelling/nbval', 0.5216328501701355, 'jupyter', 2), ('pmorissette/bt', 0.5144594311714172, 'finance', 0), ('pytest-dev/pytest-cov', 0.511971116065979, 'testing', 1), ('samuelcolvin/pytest-pretty', 0.5094279050827026, 'testing', 1), ('pytest-dev/pytest-testinfra', 0.5087285041809082, 'testing', 1), ('pytest-dev/pytest-mock', 0.5072631239891052, 'testing', 1), ('teemu/pytest-sugar', 0.5043500661849976, 'testing', 2), ('nasdaq/data-link-python', 0.5025106072425842, 'finance', 0)]",61,4.0,,1.06,27,19,76,1,0,33,33,27.0,16.0,90.0,0.6,38
147,util,https://github.com/zenodo/zenodo,[],,[],[],,,,zenodo/zenodo,zenodo,862,252,45,Python,https://zenodo.org,Research. Shared.,zenodo,2024-01-06,2013-02-11,572,1.5066167290886392,https://avatars.githubusercontent.com/u/2675345?v=4,Research. Shared.,"['digital-library', 'elasticsearch', 'flask', 'invenio', 'inveniosoftware', 'library-management', 'open-access', 'open-science', 'postgresql', 'research-data-management', 'research-data-repository', 'scientific-publications', 'zenodo']","['digital-library', 'elasticsearch', 'flask', 'invenio', 'inveniosoftware', 'library-management', 'open-access', 'open-science', 'postgresql', 'research-data-management', 'research-data-repository', 'scientific-publications', 'zenodo']",2023-12-11,"[('simonw/datasette', 0.6021542549133301, 'data', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5640177726745605, 'template', 1), ('piccolo-orm/piccolo_admin', 0.5565163493156433, 'data', 1), ('airbytehq/airbyte', 0.5533841252326965, 'data', 1), ('airbnb/knowledge-repo', 0.5470719933509827, 'data', 0), ('cerlymarco/medium_notebook', 0.5398790240287781, 'study', 0), ('firmai/industry-machine-learning', 0.5377620458602905, 'study', 0), ('plotly/dash', 0.5337997078895569, 'viz', 1), ('krzjoa/awesome-python-data-science', 0.5312089323997498, 'study', 0), ('aws/aws-sdk-pandas', 0.5311623811721802, 'pandas', 0), ('saulpw/visidata', 0.5197573900222778, 'term', 0), ('netflix/metaflow', 0.513927161693573, 'ml-ops', 0), ('eleutherai/pyfra', 0.5133705139160156, 'ml', 0), ('brettkromkamp/contextualise', 0.5124539136886597, 'data', 0), ('alphasecio/langchain-examples', 0.511971652507782, 'llm', 0), ('coleifer/peewee', 0.5084249377250671, 'data', 0), ('github/innovationgraph', 0.5064103007316589, 'data', 0), ('polyaxon/datatile', 0.5025171041488647, 'pandas', 0)]",67,5.0,,0.48,65,12,133,1,0,39,39,64.0,111.0,90.0,1.7,38
803,data,https://github.com/neo4j/neo4j-python-driver,[],,[],[],,,,neo4j/neo4j-python-driver,neo4j-python-driver,850,213,98,Python,https://neo4j.com/docs/api/python-driver/current/,Neo4j Bolt driver for Python,neo4j,2024-01-08,2015-05-05,456,1.8640350877192982,https://avatars.githubusercontent.com/u/201120?v=4,Neo4j Bolt driver for Python,"['binary-protocol', 'cypher', 'database-driver', 'driver', 'graph-database', 'neo4j', 'protocol', 'query-language']","['binary-protocol', 'cypher', 'database-driver', 'driver', 'graph-database', 'neo4j', 'protocol', 'query-language']",2024-01-09,"[('accenture/cymple', 0.6172598600387573, 'data', 2), ('datastax/python-driver', 0.5756858587265015, 'data', 0), ('scylladb/python-driver', 0.5569538474082947, 'data', 0), ('pydot/pydot', 0.5061096549034119, 'viz', 0)]",43,4.0,,1.9,36,36,106,0,15,14,15,36.0,25.0,90.0,0.7,38
790,ml-interpretability,https://github.com/selfexplainml/piml-toolbox,[],,[],[],,,,selfexplainml/piml-toolbox,PiML-Toolbox,791,96,21,Jupyter Notebook,https://selfexplainml.github.io/PiML-Toolbox,PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics,selfexplainml,2024-01-13,2022-04-29,91,8.638065522620904,https://avatars.githubusercontent.com/u/74489521?v=4,PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics,"['interpretable-machine-learning', 'low-code', 'ml-workflow', 'model-diagnostics']","['interpretable-machine-learning', 'low-code', 'ml-workflow', 'model-diagnostics']",2024-01-08,"[('csinva/imodels', 0.6522828936576843, 'ml', 0), ('kubeflow/fairing', 0.6489830613136292, 'ml-ops', 0), ('pair-code/lit', 0.6214944124221802, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.6020028591156006, 'ml', 0), ('huggingface/evaluate', 0.5961371660232544, 'ml', 0), ('wandb/client', 0.5873650908470154, 'ml', 0), ('teamhg-memex/eli5', 0.5864848494529724, 'ml', 0), ('pan-ml/panml', 0.5762995481491089, 'llm', 0), ('featurelabs/featuretools', 0.5691878795623779, 'ml', 0), ('ml-tooling/opyrator', 0.5633277297019958, 'viz', 0), ('stan-dev/pystan', 0.5616912245750427, 'ml', 0), ('microsoft/nni', 0.5612987279891968, 'ml', 0), ('apple/coremltools', 0.558883786201477, 'ml', 0), ('gradio-app/gradio', 0.5576595664024353, 'viz', 0), ('evidentlyai/evidently', 0.5544941425323486, 'ml-ops', 0), ('linkedin/fasttreeshap', 0.5518995523452759, 'ml', 0), ('tensorflow/lucid', 0.5517100095748901, 'ml-interpretability', 0), ('huggingface/datasets', 0.5501903891563416, 'nlp', 0), ('mlflow/mlflow', 0.5481675863265991, 'ml-ops', 0), ('scikit-learn/scikit-learn', 0.5468170642852783, 'ml', 0), ('rafiqhasan/auto-tensorflow', 0.5389483571052551, 'ml-dl', 0), ('polyaxon/datatile', 0.5384085178375244, 'pandas', 0), ('rasbt/mlxtend', 0.5372369885444641, 'ml', 0), ('epistasislab/tpot', 0.5343623161315918, 'ml', 0), ('tensorflow/data-validation', 0.5335401892662048, 'ml-ops', 0), ('nccr-itmo/fedot', 0.53252112865448, 'ml-ops', 0), ('pytorch/captum', 0.5324576497077942, 'ml-interpretability', 0), ('goldmansachs/gs-quant', 0.5287847518920898, 'finance', 0), ('pycaret/pycaret', 0.5286346077919006, 'ml', 0), ('maif/shapash', 0.527449905872345, 'ml', 0), ('zenml-io/zenml', 0.5244457125663757, 'ml-ops', 0), ('dagworks-inc/hamilton', 0.5237611532211304, 'ml-ops', 0), ('interpretml/interpret', 0.5234452486038208, 'ml-interpretability', 1), ('eleutherai/pyfra', 0.5199788808822632, 'ml', 0), ('huggingface/huggingface_hub', 0.5193759202957153, 'ml', 0), ('brokenloop/jsontopydantic', 0.515333354473114, 'util', 0), ('skops-dev/skops', 0.5126495957374573, 'ml-ops', 0), ('probml/pyprobml', 0.512611985206604, 'ml', 0), ('pytoolz/toolz', 0.5094278454780579, 'util', 0), ('polyaxon/polyaxon', 0.5091049075126648, 'ml-ops', 0), ('whylabs/whylogs', 0.5081930756568909, 'util', 0), ('huggingface/transformers', 0.5078108310699463, 'nlp', 0), ('titanml/takeoff', 0.5073431134223938, 'llm', 0), ('seldonio/alibi', 0.5072975158691406, 'ml-interpretability', 0), ('mljar/mljar-supervised', 0.5052536129951477, 'ml', 0), ('microsoft/flaml', 0.5052233338356018, 'ml', 0), ('lucidrains/toolformer-pytorch', 0.5051028728485107, 'llm', 0), ('pymc-devs/pymc3', 0.5050045847892761, 'ml', 0), ('amaargiru/pyroad', 0.5047166347503662, 'study', 0), ('firmai/atspy', 0.5030118823051453, 'time-series', 0), ('conceptofmind/toolformer', 0.5030021071434021, 'llm', 0), ('fmind/mlops-python-package', 0.5029227137565613, 'template', 0), ('anthropics/evals', 0.5009229183197021, 'llm', 0), ('shankarpandala/lazypredict', 0.500099241733551, 'ml', 0)]",6,2.0,,2.46,4,1,21,0,3,2,3,4.0,3.0,90.0,0.8,38
824,typing,https://github.com/python-attrs/cattrs,[],,[],[],,,,python-attrs/cattrs,cattrs,725,101,20,Python,https://catt.rs,Composable custom class converters for attrs.,python-attrs,2024-01-13,2016-08-28,387,1.8720029509406124,https://avatars.githubusercontent.com/u/25880274?v=4,Composable custom class converters for attrs.,"['attrs', 'deserialization', 'serialization']","['attrs', 'deserialization', 'serialization']",2024-01-13,"[('lidatong/dataclasses-json', 0.5296611189842224, 'util', 0)]",62,3.0,,2.48,78,63,90,0,4,4,4,78.0,121.0,90.0,1.6,38
1754,ml,https://github.com/criteo/autofaiss,"['knn', 'similarity', 'embeddings', 'vector-search']",,[],[],1.0,,,criteo/autofaiss,autofaiss,684,65,18,Python,https://criteo.github.io/autofaiss/,Automatically create Faiss knn indices with the most optimal similarity search parameters.,criteo,2024-01-13,2021-04-28,143,4.754716981132075,https://avatars.githubusercontent.com/u/1713646?v=4,Automatically create Faiss knn indices with the most optimal similarity search parameters.,[],"['embeddings', 'knn', 'similarity', 'vector-search']",2024-01-13,"[('facebookresearch/faiss', 0.629601776599884, 'ml', 3), ('qdrant/quaterion', 0.6242879629135132, 'ml', 1), ('lmcinnes/pynndescent', 0.5459467172622681, 'ml', 0), ('qdrant/qdrant', 0.5368368625640869, 'data', 1)]",15,4.0,,0.25,14,7,33,0,4,25,4,14.0,15.0,90.0,1.1,38
1752,jupyter,https://github.com/aws/graph-notebook,[],,[],[],,,,aws/graph-notebook,graph-notebook,652,157,35,Jupyter Notebook,https://github.com/aws/graph-notebook,"Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.",aws,2024-01-13,2020-10-01,173,3.7532894736842106,https://avatars.githubusercontent.com/u/2232217?v=4,"Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.","['apache', 'cypher', 'graph', 'gremlin', 'jupyter', 'jupyter-notebook', 'jupyter-widgets', 'neptune', 'opencypher', 'rdf', 'sparql', 'tinkerpop']","['apache', 'cypher', 'graph', 'gremlin', 'jupyter', 'jupyter-notebook', 'jupyter-widgets', 'neptune', 'opencypher', 'rdf', 'sparql', 'tinkerpop']",2023-12-21,"[('jupyter-widgets/ipywidgets', 0.687077522277832, 'jupyter', 0), ('voila-dashboards/voila', 0.6807038187980652, 'jupyter', 2), ('cohere-ai/notebooks', 0.6492716073989868, 'llm', 0), ('mwouts/jupytext', 0.6454940438270569, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.6357448697090149, 'jupyter', 2), ('jupyter/nbformat', 0.6213883757591248, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5957169532775879, 'jupyter', 2), ('jupyter/notebook', 0.5949639081954956, 'jupyter', 2), ('bloomberg/ipydatagrid', 0.5870513916015625, 'jupyter', 0), ('quantopian/qgrid', 0.571941614151001, 'jupyter', 0), ('ipython/ipykernel', 0.568101167678833, 'util', 2), ('jupyter-lsp/jupyterlab-lsp', 0.5679754018783569, 'jupyter', 2), ('jakevdp/pythondatasciencehandbook', 0.559874951839447, 'study', 1), ('jupyter/nbdime', 0.5575152039527893, 'jupyter', 2), ('holoviz/panel', 0.5436458587646484, 'viz', 1), ('nteract/papermill', 0.5402319431304932, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.539257287979126, 'jupyter', 2), ('jupyter-widgets/ipyleaflet', 0.5392546653747559, 'gis', 1), ('opengeos/leafmap', 0.5389112830162048, 'gis', 2), ('mamba-org/gator', 0.5385718941688538, 'jupyter', 1), ('tkrabel/bamboolib', 0.5372664928436279, 'pandas', 1), ('alphasecio/langchain-examples', 0.5361274480819702, 'llm', 1), ('ipython/ipyparallel', 0.5359687805175781, 'perf', 1), ('plotly/plotly.py', 0.5306247472763062, 'viz', 1), ('fchollet/deep-learning-with-python-notebooks', 0.525762140750885, 'study', 0), ('jupyterlab/jupyterlab', 0.5233699083328247, 'jupyter', 1), ('jupyter/nbconvert', 0.522909939289093, 'jupyter', 0), ('giswqs/mapwidget', 0.5196599364280701, 'gis', 1), ('ageron/handson-ml2', 0.519343912601471, 'ml', 0), ('accenture/cymple', 0.5142837166786194, 'data', 1), ('strawberry-graphql/strawberry', 0.5056154131889343, 'web', 0), ('aws/aws-sdk-pandas', 0.5009594559669495, 'pandas', 0)]",30,4.0,,1.63,20,13,40,1,10,13,10,20.0,18.0,90.0,0.9,38
619,gis,https://github.com/scitools/iris,[],,[],[],,,,scitools/iris,iris,587,279,45,Python,https://scitools-iris.readthedocs.io/en/stable/,"A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data",scitools,2024-01-05,2012-08-06,599,0.9797329518359561,https://avatars.githubusercontent.com/u/1391487?v=4,"A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data","['data-analysis', 'earth-science', 'grib', 'iris', 'meteorology', 'netcdf', 'oceanography', 'spaceweather', 'visualisation']","['data-analysis', 'earth-science', 'grib', 'iris', 'meteorology', 'netcdf', 'oceanography', 'spaceweather', 'visualisation']",2024-01-12,"[('enthought/mayavi', 0.7232843041419983, 'viz', 0), ('giswqs/geemap', 0.6783716082572937, 'gis', 0), ('residentmario/geoplot', 0.6692157983779907, 'gis', 0), ('holoviz/holoviz', 0.6550614833831787, 'viz', 0), ('mwaskom/seaborn', 0.6527572870254517, 'viz', 0), ('contextlab/hypertools', 0.6321009993553162, 'ml', 0), ('pyqtgraph/pyqtgraph', 0.6289077401161194, 'viz', 0), ('altair-viz/altair', 0.6287659406661987, 'viz', 0), ('sentinel-hub/eo-learn', 0.623789370059967, 'gis', 0), ('marcomusy/vedo', 0.6136019825935364, 'viz', 0), ('pytroll/satpy', 0.6135034561157227, 'gis', 0), ('cloudsen12/easystac', 0.6042935252189636, 'gis', 0), ('gregorhd/mapcompare', 0.6005646586418152, 'gis', 0), ('roban/cosmolopy', 0.5999904274940491, 'sim', 0), ('lux-org/lux', 0.5947362184524536, 'viz', 0), ('holoviz/hvplot', 0.5860223770141602, 'pandas', 0), ('man-group/dtale', 0.5857634544372559, 'viz', 1), ('holoviz/panel', 0.5823258757591248, 'viz', 0), ('opengeos/leafmap', 0.5820508003234863, 'gis', 0), ('earthlab/earthpy', 0.5802125930786133, 'gis', 0), ('has2k1/plotnine', 0.5705260634422302, 'viz', 1), ('holoviz/geoviews', 0.5698901414871216, 'gis', 0), ('matplotlib/matplotlib', 0.5653036236763, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5579238533973694, 'study', 1), ('numpy/numpy', 0.5556809306144714, 'math', 0), ('artelys/geonetworkx', 0.5512532591819763, 'gis', 0), ('raphaelquast/eomaps', 0.5512466430664062, 'gis', 0), ('kanaries/pygwalker', 0.5480643510818481, 'pandas', 1), ('geopandas/geopandas', 0.5416747331619263, 'gis', 0), ('pandas-dev/pandas', 0.5412265062332153, 'pandas', 1), ('graphistry/pygraphistry', 0.5314244627952576, 'data', 0), ('pysal/pysal', 0.5261117815971375, 'gis', 0), ('makepath/xarray-spatial', 0.523902952671051, 'gis', 0), ('plotly/plotly.py', 0.5217393040657043, 'viz', 0), ('fatiando/verde', 0.5199081301689148, 'gis', 1), ('imageio/imageio', 0.5107640027999878, 'util', 0), ('albahnsen/pycircular', 0.5095266103744507, 'math', 0), ('bokeh/bokeh', 0.5092189908027649, 'viz', 1), ('bmabey/pyldavis', 0.5086809992790222, 'ml', 0), ('plotly/dash', 0.5075518488883972, 'viz', 0), ('mito-ds/monorepo', 0.5061976909637451, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5051771402359009, 'study', 0), ('westhealth/pyvis', 0.503166139125824, 'graph', 0), ('rasbt/mlxtend', 0.5022916793823242, 'ml', 0)]",107,2.0,,5.21,276,209,139,0,8,7,8,276.0,410.0,90.0,1.5,38
216,ml-ops,https://github.com/nccr-itmo/fedot,[],,[],[],,,,nccr-itmo/fedot,FEDOT,579,80,9,Python,https://fedot.readthedocs.io,Automated modeling and machine learning framework FEDOT,nccr-itmo,2024-01-13,2020-01-13,211,2.7422192151556155,https://avatars.githubusercontent.com/u/65946329?v=4,Automated modeling and machine learning framework FEDOT,"['automated-machine-learning', 'automation', 'automl', 'evolutionary-algorithms', 'fedot', 'genetic-programming', 'hyperparameter-optimization', 'machine-learning', 'multimodality', 'parameter-tuning', 'structural-learning']","['automated-machine-learning', 'automation', 'automl', 'evolutionary-algorithms', 'fedot', 'genetic-programming', 'hyperparameter-optimization', 'machine-learning', 'multimodality', 'parameter-tuning', 'structural-learning']",2024-01-10,"[('automl/auto-sklearn', 0.7373944520950317, 'ml', 3), ('microsoft/nni', 0.6999444365501404, 'ml', 4), ('winedarksea/autots', 0.6496773958206177, 'time-series', 2), ('microsoft/flaml', 0.6372272372245789, 'ml', 4), ('keras-team/autokeras', 0.6287457346916199, 'ml-dl', 3), ('awslabs/autogluon', 0.6268003582954407, 'ml', 4), ('adap/flower', 0.618629515171051, 'ml-ops', 1), ('districtdatalabs/yellowbrick', 0.6075289249420166, 'ml', 1), ('epistasislab/tpot', 0.6070546507835388, 'ml', 6), ('huggingface/autotrain-advanced', 0.5916618704795837, 'ml', 1), ('xplainable/xplainable', 0.5892693996429443, 'ml-interpretability', 1), ('nevronai/metisfl', 0.5833522081375122, 'ml', 1), ('mlflow/mlflow', 0.5817329287528992, 'ml-ops', 1), ('featurelabs/featuretools', 0.5784884095191956, 'ml', 3), ('mosaicml/composer', 0.5770966410636902, 'ml-dl', 1), ('ml-tooling/opyrator', 0.5759302377700806, 'viz', 1), ('mljar/mljar-supervised', 0.5752649307250977, 'ml', 4), ('alpa-projects/alpa', 0.5670198202133179, 'ml-dl', 1), ('operand/agency', 0.5655133128166199, 'llm', 1), ('tensorflow/tensorflow', 0.5654616355895996, 'ml-dl', 1), ('bentoml/bentoml', 0.5645349621772766, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5636411905288696, 'ml-ops', 2), ('huggingface/datasets', 0.5636368989944458, 'nlp', 1), ('google/pyglove', 0.5598992109298706, 'util', 2), ('determined-ai/determined', 0.5581679940223694, 'ml-ops', 2), ('shankarpandala/lazypredict', 0.5503329038619995, 'ml', 2), ('ludwig-ai/ludwig', 0.5483391284942627, 'ml-ops', 1), ('rafiqhasan/auto-tensorflow', 0.5481418371200562, 'ml-dl', 2), ('giskard-ai/giskard', 0.5448036789894104, 'data', 1), ('onnx/onnx', 0.5438551306724548, 'ml', 1), ('ai4finance-foundation/finrl', 0.5424655675888062, 'finance', 0), ('ray-project/ray', 0.541328489780426, 'ml-ops', 3), ('explosion/thinc', 0.5396167635917664, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5393419861793518, 'ml', 1), ('microsoft/lmops', 0.5380227565765381, 'llm', 0), ('ourownstory/neural_prophet', 0.5363118648529053, 'ml', 1), ('firmai/industry-machine-learning', 0.5340647101402283, 'study', 1), ('apple/coremltools', 0.5334883332252502, 'ml', 1), ('firmai/atspy', 0.5334661602973938, 'time-series', 0), ('selfexplainml/piml-toolbox', 0.53252112865448, 'ml-interpretability', 0), ('lucidrains/toolformer-pytorch', 0.5307614803314209, 'llm', 0), ('csinva/imodels', 0.5283494591712952, 'ml', 1), ('scikit-learn/scikit-learn', 0.5258017778396606, 'ml', 1), ('gradio-app/gradio', 0.5231483578681946, 'viz', 1), ('polyaxon/datatile', 0.521264910697937, 'pandas', 0), ('sktime/sktime', 0.520081639289856, 'time-series', 1), ('mlc-ai/mlc-llm', 0.5192214250564575, 'llm', 0), ('patchy631/machine-learning', 0.517076313495636, 'ml', 0), ('online-ml/river', 0.5161853432655334, 'ml', 1), ('eugeneyan/testing-ml', 0.5155410766601562, 'testing', 1), ('horovod/horovod', 0.5143430829048157, 'ml-ops', 1), ('ggerganov/ggml', 0.5140013098716736, 'ml', 1), ('hpcaitech/colossalai', 0.5125412344932556, 'llm', 0), ('interpretml/interpret', 0.5118159651756287, 'ml-interpretability', 1), ('huggingface/transformers', 0.5109488368034363, 'nlp', 1), ('neuralmagic/sparseml', 0.508357048034668, 'ml-dl', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5080487728118896, 'study', 1), ('kubeflow/pipelines', 0.5040023922920227, 'ml-ops', 1), ('tensorly/tensorly', 0.5016763806343079, 'ml-dl', 1), ('feast-dev/feast', 0.5015140175819397, 'ml-ops', 1)]",32,3.0,,2.06,102,58,49,0,4,3,4,102.0,146.0,90.0,1.4,38
905,util,https://github.com/methexis-inc/terminal-copilot,[],,[],[],,,,methexis-inc/terminal-copilot,terminal-copilot,539,41,7,Python,,A smart terminal assistant that helps you find the right command.,methexis-inc,2024-01-10,2022-12-11,59,9.09156626506024,https://avatars.githubusercontent.com/u/110575158?v=4,A smart terminal assistant that helps you find the right command.,[],[],2023-12-09,"[('tiangolo/typer', 0.5327745079994202, 'term', 0), ('tconbeer/harlequin', 0.5151594877243042, 'term', 0)]",10,3.0,,0.35,4,4,13,1,3,5,3,4.0,6.0,90.0,1.5,38
1533,data,https://github.com/dbt-labs/dbt-spark,"['spark', 'dbt']",,[],[],,,,dbt-labs/dbt-spark,dbt-spark,339,199,18,Python,https://getdbt.com,dbt-spark contains all of the code enabling dbt to work with Apache Spark and Databricks,dbt-labs,2024-01-06,2019-03-21,253,1.3361486486486487,https://avatars.githubusercontent.com/u/18339788?v=4,dbt-spark contains all of the code enabling dbt to work with Apache Spark and Databricks,[],"['dbt', 'spark']",2024-01-11,"[('databricks/dbt-databricks', 0.6950157284736633, 'data', 1)]",73,4.0,,2.98,94,63,59,0,28,18,28,94.0,103.0,90.0,1.1,38
1837,llm,https://github.com/bobazooba/xllm,[],,[],[],,,,bobazooba/xllm,xllm,308,17,3,Python,https://t.me/talequestbot,🦖 X—LLM: Cutting Edge & Easy LLM Finetuning,bobazooba,2024-01-14,2023-11-10,11,26.617283950617285,,🦖 X—LLM: Cutting Edge & Easy LLM Finetuning,"['alpaca', 'bitsandbytes', 'cerebras', 'chatgpt', 'deep-learning', 'deep-neural-networks', 'gpt', 'gpt-4', 'gptq', 'large-language-models', 'llama', 'llama2', 'llm', 'mistral', 'openai', 'pytorch', 'torch', 'vicuna', 'zephyr']","['alpaca', 'bitsandbytes', 'cerebras', 'chatgpt', 'deep-learning', 'deep-neural-networks', 'gpt', 'gpt-4', 'gptq', 'large-language-models', 'llama', 'llama2', 'llm', 'mistral', 'openai', 'pytorch', 'torch', 'vicuna', 'zephyr']",2023-12-07,"[('hiyouga/llama-efficient-tuning', 0.7037465572357178, 'llm', 5), ('hiyouga/llama-factory', 0.7037465572357178, 'llm', 5), ('lianjiatech/belle', 0.6800654530525208, 'llm', 1), ('bigscience-workshop/petals', 0.667163610458374, 'data', 6), ('intel/intel-extension-for-transformers', 0.6433131694793701, 'perf', 0), ('tigerlab-ai/tiger', 0.6268063187599182, 'llm', 2), ('hannibal046/awesome-llm', 0.6244999766349792, 'study', 1), ('artidoro/qlora', 0.6219983100891113, 'llm', 0), ('vllm-project/vllm', 0.6193138957023621, 'llm', 4), ('explosion/spacy-llm', 0.6183709502220154, 'llm', 5), ('microsoft/autogen', 0.6154872179031372, 'llm', 3), ('next-gpt/next-gpt', 0.61397784948349, 'llm', 4), ('lightning-ai/lit-llama', 0.6132877469062805, 'llm', 1), ('salesforce/xgen', 0.607836902141571, 'llm', 2), ('xtekky/gpt4free', 0.6042348742485046, 'llm', 4), ('paddlepaddle/paddlenlp', 0.6009507775306702, 'llm', 2), ('ray-project/ray-llm', 0.5996918082237244, 'llm', 2), ('young-geng/easylm', 0.5975850224494934, 'llm', 3), ('squeezeailab/squeezellm', 0.596723735332489, 'llm', 3), ('ludwig-ai/ludwig', 0.5947787761688232, 'ml-ops', 6), ('zilliztech/gptcache', 0.5947780013084412, 'llm', 5), ('opengvlab/omniquant', 0.5902096033096313, 'llm', 2), ('eth-sri/lmql', 0.5887289047241211, 'llm', 1), ('nvidia/tensorrt-llm', 0.5863513350486755, 'viz', 0), ('bentoml/openllm', 0.5858603119850159, 'ml-ops', 5), ('dylanhogg/llmgraph', 0.5852743983268738, 'ml', 3), ('microsoft/lora', 0.5789094567298889, 'llm', 2), ('li-plus/chatglm.cpp', 0.5759797692298889, 'llm', 1), ('juncongmoo/pyllama', 0.5698232054710388, 'llm', 0), ('thudm/chatglm2-6b', 0.5669134855270386, 'llm', 2), ('cg123/mergekit', 0.5655726194381714, 'llm', 2), ('iryna-kondr/scikit-llm', 0.5620294213294983, 'llm', 3), ('h2oai/h2o-llmstudio', 0.5602803230285645, 'llm', 5), ('jerryjliu/llama_index', 0.5593804121017456, 'llm', 2), ('microsoft/jarvis', 0.5591229200363159, 'llm', 2), ('sjtu-ipads/powerinfer', 0.5589494705200195, 'llm', 3), ('infinitylogesh/mutate', 0.5542972087860107, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.5522361397743225, 'study', 1), ('titanml/takeoff', 0.551180899143219, 'llm', 2), ('microsoft/torchscale', 0.5502038598060608, 'llm', 0), ('huggingface/text-generation-inference', 0.5498690605163574, 'llm', 3), ('argilla-io/argilla', 0.5486025214195251, 'nlp', 2), ('lupantech/chameleon-llm', 0.5471572875976562, 'llm', 4), ('eleutherai/the-pile', 0.5469703674316406, 'data', 1), ('huggingface/transformers', 0.5439950227737427, 'nlp', 2), ('guardrails-ai/guardrails', 0.5430307984352112, 'llm', 2), ('salesforce/codet5', 0.5426592826843262, 'nlp', 1), ('alphasecio/langchain-examples', 0.5409016609191895, 'llm', 2), ('mlc-ai/web-llm', 0.5402325987815857, 'llm', 3), ('predibase/lorax', 0.5401611924171448, 'llm', 4), ('nomic-ai/gpt4all', 0.5367876887321472, 'llm', 0), ('run-llama/rags', 0.5356626510620117, 'llm', 3), ('nebuly-ai/nebullvm', 0.5337145328521729, 'perf', 2), ('eugeneyan/open-llms', 0.5326270461082458, 'study', 2), ('epfllm/meditron', 0.5308298468589783, 'llm', 0), ('jzhang38/tinyllama', 0.5300989151000977, 'llm', 1), ('explosion/spacy-transformers', 0.529644787311554, 'llm', 2), ('shishirpatil/gorilla', 0.5277217626571655, 'llm', 2), ('optimalscale/lmflow', 0.5273640751838684, 'llm', 3), ('openlm-research/open_llama', 0.5272755026817322, 'llm', 1), ('huawei-noah/pretrained-language-model', 0.5261392593383789, 'nlp', 0), ('microsoft/promptflow', 0.52497398853302, 'llm', 3), ('baichuan-inc/baichuan-13b', 0.5246362090110779, 'llm', 3), ('night-chen/toolqa', 0.5220770835876465, 'llm', 1), ('pathwaycom/llm-app', 0.5216497778892517, 'llm', 1), ('tairov/llama2.mojo', 0.5215964913368225, 'llm', 2), ('haotian-liu/llava', 0.5188300609588623, 'llm', 4), ('lightning-ai/lit-gpt', 0.5175870656967163, 'llm', 0), ('confident-ai/deepeval', 0.5172545909881592, 'testing', 2), ('bigscience-workshop/megatron-deepspeed', 0.5139332413673401, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5139332413673401, 'llm', 0), ('freedomintelligence/llmzoo', 0.5108060240745544, 'llm', 0), ('ashleve/lightning-hydra-template', 0.5093933939933777, 'util', 2), ('ai4finance-foundation/fingpt', 0.5081082582473755, 'finance', 4), ('pytorch/glow', 0.5073339343070984, 'ml', 0), ('bytedance/lightseq', 0.5065972208976746, 'nlp', 1), ('deepset-ai/haystack', 0.5065814852714539, 'llm', 3), ('databrickslabs/dolly', 0.5060972571372986, 'llm', 1), ('llmware-ai/llmware', 0.503739595413208, 'llm', 2), ('microsoft/semantic-kernel', 0.5029643774032593, 'llm', 2), ('hegelai/prompttools', 0.5022645592689514, 'llm', 2), ('lm-sys/fastchat', 0.5017418265342712, 'llm', 0), ('langchain-ai/langgraph', 0.5015073418617249, 'llm', 0), ('alpa-projects/alpa', 0.5013929605484009, 'ml-dl', 2), ('oobabooga/text-generation-webui', 0.501327633857727, 'llm', 0)]",1,0.0,,1.15,16,11,2,1,5,85,5,16.0,9.0,90.0,0.6,38
1575,data,https://github.com/scikit-hep/uproot5,['science'],,[],[],,,,scikit-hep/uproot5,uproot5,208,63,19,Python,https://uproot.readthedocs.io,ROOT I/O in pure Python and NumPy.,scikit-hep,2024-01-09,2020-05-08,194,1.0690161527165933,https://avatars.githubusercontent.com/u/23454624?v=4,ROOT I/O in pure Python and NumPy.,"['analysis', 'big-data', 'bigdata', 'file-format', 'hep', 'hep-ex', 'hep-py', 'numpy', 'root', 'root-cern', 'scikit-hep']","['analysis', 'big-data', 'bigdata', 'file-format', 'hep', 'hep-ex', 'hep-py', 'numpy', 'root', 'root-cern', 'science', 'scikit-hep']",2024-01-12,"[('numpy/numpy', 0.5980868935585022, 'math', 1), ('scipy/scipy', 0.5634987354278564, 'math', 0), ('blaze/blaze', 0.5468524098396301, 'pandas', 0), ('cython/cython', 0.5226044058799744, 'util', 1), ('fredrik-johansson/mpmath', 0.516451895236969, 'math', 0), ('pypy/pypy', 0.5114945769309998, 'util', 0), ('fsspec/filesystem_spec', 0.5076752305030823, 'util', 0)]",44,4.0,,3.35,87,72,45,0,26,29,26,87.0,180.0,90.0,2.1,38
1532,data,https://github.com/databricks/dbt-databricks,"['databricks', 'dbt']",,[],[],,,,databricks/dbt-databricks,dbt-databricks,165,91,19,Python,https://databricks.com,A dbt adapter for Databricks.,databricks,2024-01-06,2021-10-19,119,1.3865546218487395,https://avatars.githubusercontent.com/u/4998052?v=4,A dbt adapter for Databricks.,"['databricks', 'dbt', 'etl', 'sql']","['databricks', 'dbt', 'etl', 'sql']",2024-01-12,"[('dbt-labs/dbt-spark', 0.6950157284736633, 'data', 1), ('databrickslabs/dbx', 0.6466124057769775, 'data', 1), ('duckdb/dbt-duckdb', 0.5661155581474304, 'data', 1), ('airbnb/omniduct', 0.5590947866439819, 'data', 0), ('airbytehq/airbyte', 0.5521032214164734, 'data', 1), ('dbt-labs/dbt-core', 0.5302814841270447, 'ml-ops', 0), ('dlt-hub/dlt', 0.5166671276092529, 'data', 0), ('tobymao/sqlglot', 0.5069236755371094, 'data', 2), ('tconbeer/sqlfmt', 0.5048282146453857, 'data', 2)]",69,3.0,,5.67,107,88,27,0,29,38,29,107.0,168.0,90.0,1.6,38
1746,util,https://github.com/callowayproject/bump-my-version,['code-quality'],,[],[],,,,callowayproject/bump-my-version,bump-my-version,115,14,7,Python,https://callowayproject.github.io/bump-my-version/,A small command line tool to simplify releasing software by updating all version strings in your source code by the correct increment and optionally commit and tag the changes.,callowayproject,2024-01-11,2023-04-12,41,2.7474402730375425,https://avatars.githubusercontent.com/u/305772?v=4,A small command line tool to simplify releasing software by updating all version strings in your source code by the correct increment and optionally commit and tag the changes.,"['bumpversion', 'version', 'versioning']","['bumpversion', 'code-quality', 'version', 'versioning']",2024-01-13,"[('c4urself/bump2version', 0.7459608316421509, 'util', 1), ('pypa/setuptools_scm', 0.6197443604469299, 'util', 1), ('mtkennerly/dunamai', 0.6012407541275024, 'util', 1), ('asottile/pyupgrade', 0.5788130760192871, 'util', 1), ('mtkennerly/poetry-dynamic-versioning', 0.5595990419387817, 'util', 1), ('python-versioneer/python-versioneer', 0.5228790640830994, 'util', 0)]",12,5.0,,4.5,58,50,9,0,24,38,24,58.0,92.0,90.0,1.6,38
747,study,https://github.com/karpathy/micrograd,[],,[],[],,,,karpathy/micrograd,micrograd,7103,917,131,Jupyter Notebook,,A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API,karpathy,2024-01-14,2020-04-13,198,35.847873107426096,,A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API,[],[],2020-04-18,"[('pytorch/ignite', 0.7160765528678894, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.6794243454933167, 'perf', 0), ('skorch-dev/skorch', 0.644400417804718, 'ml-dl', 0), ('nvidia/apex', 0.6441670060157776, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.634564220905304, 'ml-dl', 0), ('denys88/rl_games', 0.6286336779594421, 'ml-rl', 0), ('pytorch/glow', 0.6073175668716431, 'ml', 0), ('ggerganov/ggml', 0.6055065393447876, 'ml', 0), ('huggingface/transformers', 0.6045575737953186, 'nlp', 0), ('mrdbourke/pytorch-deep-learning', 0.602993369102478, 'study', 0), ('microsoft/nni', 0.6010633111000061, 'ml', 0), ('arogozhnikov/einops', 0.600235104560852, 'ml-dl', 0), ('neuralmagic/sparseml', 0.597611129283905, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.597282350063324, 'study', 0), ('pytorch/rl', 0.5971662402153015, 'ml-rl', 0), ('microsoft/flaml', 0.5962915420532227, 'ml', 0), ('thu-ml/tianshou', 0.5894965529441833, 'ml-rl', 0), ('nvidia/deeplearningexamples', 0.583264946937561, 'ml-dl', 0), ('explosion/thinc', 0.581674337387085, 'ml-dl', 0), ('keras-team/autokeras', 0.5800349712371826, 'ml-dl', 0), ('pytorch/pytorch', 0.5792595744132996, 'ml-dl', 0), ('pytorch/data', 0.5738345980644226, 'data', 0), ('ray-project/ray', 0.5609050393104553, 'ml-ops', 0), ('alpa-projects/alpa', 0.5604699850082397, 'ml-dl', 0), ('ashleve/lightning-hydra-template', 0.5562566518783569, 'util', 0), ('uber/petastorm', 0.5534528493881226, 'data', 0), ('horovod/horovod', 0.552807092666626, 'ml-ops', 0), ('microsoft/onnxruntime', 0.5451004505157471, 'ml', 0), ('rentruewang/koila', 0.5448654890060425, 'ml', 0), ('tensorlayer/tensorlayer', 0.544183075428009, 'ml-rl', 0), ('lucidrains/imagen-pytorch', 0.5438899993896484, 'ml-dl', 0), ('nicolas-chaulet/torch-points3d', 0.5409857034683228, 'ml', 0), ('deepmind/dm-haiku', 0.5368869304656982, 'ml-dl', 0), ('xl0/lovely-tensors', 0.5362251400947571, 'ml-dl', 0), ('determined-ai/determined', 0.5347241163253784, 'ml-ops', 0), ('pyg-team/pytorch_geometric', 0.5318362712860107, 'ml-dl', 0), ('huggingface/optimum', 0.5316488146781921, 'ml', 0), ('google/trax', 0.5311139822006226, 'ml-dl', 0), ('aws/sagemaker-python-sdk', 0.5261572003364563, 'ml', 0), ('facebookresearch/pytorch3d', 0.5227355360984802, 'ml-dl', 0), ('lightly-ai/lightly', 0.5221474766731262, 'ml', 0), ('aiqc/aiqc', 0.5214496850967407, 'ml-ops', 0), ('intellabs/bayesian-torch', 0.5212419629096985, 'ml', 0), ('huggingface/accelerate', 0.5209835767745972, 'ml', 0), ('mosaicml/composer', 0.517072856426239, 'ml-dl', 0), ('allenai/allennlp', 0.5140243768692017, 'nlp', 0), ('google/automl', 0.5122846364974976, 'ml', 0), ('ludwig-ai/ludwig', 0.5106154680252075, 'ml-ops', 0), ('koaning/human-learn', 0.509779691696167, 'data', 0), ('pyro-ppl/pyro', 0.5077245831489563, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5075839757919312, 'ml', 0), ('activeloopai/deeplake', 0.5072367191314697, 'ml-ops', 0), ('epistasislab/tpot', 0.5062547922134399, 'ml', 0), ('plasma-umass/scalene', 0.5055549740791321, 'profiling', 0), ('rasbt/deeplearning-models', 0.5046243667602539, 'ml-dl', 0), ('huggingface/peft', 0.5045297145843506, 'llm', 0), ('kubeflow/fairing', 0.5037677884101868, 'ml-ops', 0), ('microsoft/deepspeed', 0.5035680532455444, 'ml-dl', 0), ('lucidrains/dalle2-pytorch', 0.5032515525817871, 'diffusion', 0), ('salesforce/deeptime', 0.5016177892684937, 'time-series', 0)]",2,1.0,,0.0,7,4,46,46,0,0,0,7.0,3.0,90.0,0.4,37
817,study,https://github.com/firmai/industry-machine-learning,[],,[],[],,,,firmai/industry-machine-learning,industry-machine-learning,6946,1151,389,Jupyter Notebook,https://www.linkedin.com/company/firmai,A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai),firmai,2024-01-13,2019-05-03,247,28.05654933641085,,A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai),"['data-science', 'datascience', 'example', 'firmai', 'jupyter-notebook', 'machine-learning', 'practical-machine-learning']","['data-science', 'datascience', 'example', 'firmai', 'jupyter-notebook', 'machine-learning', 'practical-machine-learning']",2021-12-18,"[('cerlymarco/medium_notebook', 0.6431946158409119, 'study', 2), ('ageron/handson-ml2', 0.6424956321716309, 'ml', 0), ('feast-dev/feast', 0.6406149864196777, 'ml-ops', 2), ('krzjoa/awesome-python-data-science', 0.6359909772872925, 'study', 2), ('tensorflow/data-validation', 0.6276463866233826, 'ml-ops', 0), ('gradio-app/gradio', 0.6118634939193726, 'viz', 2), ('mlflow/mlflow', 0.6037585735321045, 'ml-ops', 1), ('tensorflow/tensorflow', 0.6019365191459656, 'ml-dl', 1), ('kubeflow-kale/kale', 0.5946695804595947, 'ml-ops', 2), ('scikit-learn/scikit-learn', 0.5884523987770081, 'ml', 2), ('huggingface/datasets', 0.5870203375816345, 'nlp', 1), ('mrdbourke/zero-to-mastery-ml', 0.5863468647003174, 'study', 2), ('onnx/onnx', 0.5830479264259338, 'ml', 1), ('tensorflow/tensor2tensor', 0.5822129249572754, 'ml', 1), ('polyaxon/polyaxon', 0.5819257497787476, 'ml-ops', 2), ('patchy631/machine-learning', 0.5763393044471741, 'ml', 0), ('rasbt/mlxtend', 0.5761663913726807, 'ml', 2), ('determined-ai/determined', 0.574253261089325, 'ml-ops', 2), ('polyaxon/datatile', 0.5717259049415588, 'pandas', 1), ('googlecloudplatform/vertex-ai-samples', 0.5715494751930237, 'ml', 1), ('dylanhogg/awesome-python', 0.5685208439826965, 'study', 2), ('jovianml/opendatasets', 0.5650241374969482, 'data', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5614524483680725, 'study', 1), ('rasbt/machine-learning-book', 0.5610123872756958, 'study', 1), ('huggingface/evaluate', 0.5610095858573914, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5604248642921448, 'ml', 2), ('xplainable/xplainable', 0.5596107840538025, 'ml-interpretability', 2), ('tensorlayer/tensorlayer', 0.5479680299758911, 'ml-rl', 0), ('automl/auto-sklearn', 0.5466862320899963, 'ml', 0), ('csinva/imodels', 0.5446041226387024, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5426095128059387, 'ml', 1), ('aws/sagemaker-python-sdk', 0.5424572229385376, 'ml', 1), ('sktime/sktime', 0.541723370552063, 'time-series', 2), ('fchollet/deep-learning-with-python-notebooks', 0.5408152937889099, 'study', 0), ('teamhg-memex/eli5', 0.5391788482666016, 'ml', 2), ('zenodo/zenodo', 0.5377620458602905, 'util', 0), ('uber/petastorm', 0.5362139940261841, 'data', 1), ('nccr-itmo/fedot', 0.5340647101402283, 'ml-ops', 1), ('airbnb/knowledge-repo', 0.5338939428329468, 'data', 1), ('google-research/google-research', 0.5328598618507385, 'ml', 1), ('ddbourgin/numpy-ml', 0.5301154255867004, 'ml', 1), ('microsoft/nni', 0.528578519821167, 'ml', 2), ('explosion/thinc', 0.5279266834259033, 'ml-dl', 1), ('online-ml/river', 0.5276996493339539, 'ml', 2), ('rasbt/stat451-machine-learning-fs20', 0.5267770886421204, 'study', 0), ('wandb/client', 0.5243285894393921, 'ml', 2), ('dagworks-inc/hamilton', 0.5236888527870178, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5232796669006348, 'study', 1), ('probml/pyprobml', 0.5219587087631226, 'ml', 1), ('keras-team/keras', 0.5215654969215393, 'ml-dl', 2), ('kubeflow/pipelines', 0.5197041034698486, 'ml-ops', 2), ('fatiando/verde', 0.5189895629882812, 'gis', 1), ('d2l-ai/d2l-en', 0.5169808268547058, 'study', 2), ('scikit-learn-contrib/imbalanced-learn', 0.5154035687446594, 'ml', 2), ('google/tf-quant-finance', 0.5136438012123108, 'finance', 0), ('hazyresearch/meerkat', 0.5096346735954285, 'viz', 2), ('drivendata/cookiecutter-data-science', 0.5075307488441467, 'template', 2), ('wesm/pydata-book', 0.5071250200271606, 'study', 0), ('netflix/metaflow', 0.5058495402336121, 'ml-ops', 3), ('adap/flower', 0.5053890347480774, 'ml-ops', 1), ('doccano/doccano', 0.50450199842453, 'nlp', 1), ('milvus-io/bootcamp', 0.5041669011116028, 'data', 0), ('pycaret/pycaret', 0.5026911497116089, 'ml', 2), ('ploomber/ploomber', 0.5007092952728271, 'ml-ops', 2)]",6,4.0,,0.0,0,0,57,25,0,0,0,0.0,0.0,90.0,0.0,37
1022,finance,https://github.com/google/tf-quant-finance,[],,[],[],,,,google/tf-quant-finance,tf-quant-finance,4160,545,168,Python,,High-performance TensorFlow library for quantitative finance.,google,2024-01-14,2019-07-24,235,17.637795275590552,https://avatars.githubusercontent.com/u/1342004?v=4,High-performance TensorFlow library for quantitative finance.,"['finance', 'gpu', 'gpu-computing', 'high-performance', 'high-performance-computing', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'quantitative-finance', 'quantlib', 'tensorflow']","['finance', 'gpu', 'gpu-computing', 'high-performance', 'high-performance-computing', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'quantitative-finance', 'quantlib', 'tensorflow']",2023-08-15,"[('tensorly/tensorly', 0.636419951915741, 'ml-dl', 1), ('pytorch/pytorch', 0.6339718103408813, 'ml-dl', 1), ('goldmansachs/gs-quant', 0.6332827806472778, 'finance', 0), ('intel/intel-extension-for-pytorch', 0.6181202530860901, 'perf', 0), ('ggerganov/ggml', 0.6140791773796082, 'ml', 0), ('arogozhnikov/einops', 0.6102033853530884, 'ml-dl', 1), ('nvidia/tensorrt-llm', 0.6080841422080994, 'viz', 1), ('horovod/horovod', 0.6027436852455139, 'ml-ops', 1), ('tensorlayer/tensorlayer', 0.5923408269882202, 'ml-rl', 1), ('xl0/lovely-tensors', 0.5893839597702026, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5870956182479858, 'ml', 1), ('tlkh/tf-metal-experiments', 0.5859279036521912, 'perf', 2), ('tensorflow/tensorflow', 0.5797778367996216, 'ml-dl', 1), ('catboost/catboost', 0.5715440511703491, 'ml', 2), ('ray-project/ray', 0.5673449039459229, 'ml-ops', 1), ('tensorflow/addons', 0.5670149326324463, 'ml', 1), ('microsoft/deepspeed', 0.5623762011528015, 'ml-dl', 1), ('google/gin-config', 0.5610681772232056, 'util', 1), ('rafiqhasan/auto-tensorflow', 0.5538135766983032, 'ml-dl', 1), ('tensorflow/similarity', 0.552481472492218, 'ml-dl', 1), ('ranaroussi/quantstats', 0.5515581369400024, 'finance', 2), ('pytorch/ignite', 0.5492528676986694, 'ml-dl', 0), ('ta-lib/ta-lib-python', 0.5487057566642761, 'finance', 2), ('huggingface/datasets', 0.5474328994750977, 'nlp', 1), ('determined-ai/determined', 0.5471197366714478, 'ml-ops', 1), ('aws/sagemaker-python-sdk', 0.5465176105499268, 'ml', 1), ('zvtvz/zvt', 0.5456732511520386, 'finance', 1), ('polyaxon/datatile', 0.5451685190200806, 'pandas', 1), ('microsoft/qlib', 0.5447477698326111, 'finance', 2), ('ai4finance-foundation/finrl', 0.54345703125, 'finance', 1), ('explosion/thinc', 0.5434539318084717, 'ml-dl', 1), ('keras-team/keras', 0.5417336821556091, 'ml-dl', 1), ('blackhc/toma', 0.5406495928764343, 'ml-dl', 1), ('nvidia/warp', 0.5373433828353882, 'sim', 1), ('pytorchlightning/pytorch-lightning', 0.534396767616272, 'ml-dl', 0), ('fastai/fastcore', 0.5327866077423096, 'util', 0), ('eventual-inc/daft', 0.5302774906158447, 'pandas', 0), ('dmlc/xgboost', 0.5295057892799377, 'ml', 0), ('d2l-ai/d2l-en', 0.5287189483642578, 'study', 1), ('mrdbourke/m1-machine-learning-test', 0.5282031893730164, 'ml', 1), ('intel/scikit-learn-intelex', 0.5270321369171143, 'perf', 1), ('activeloopai/deeplake', 0.5263845324516296, 'ml-ops', 1), ('rapidsai/cudf', 0.5263254046440125, 'pandas', 1), ('pytorch/torchrec', 0.5250528454780579, 'ml-dl', 1), ('cupy/cupy', 0.5244703888893127, 'math', 1), ('quantconnect/lean', 0.5243059992790222, 'finance', 1), ('rasbt/machine-learning-book', 0.523298442363739, 'study', 0), ('cython/cython', 0.5225537419319153, 'util', 0), ('isl-org/open3d', 0.5206282138824463, 'sim', 2), ('huggingface/transformers', 0.5193233489990234, 'nlp', 1), ('polakowo/vectorbt', 0.5182610154151917, 'finance', 2), ('googlecloudplatform/vertex-ai-samples', 0.5174439549446106, 'ml', 0), ('plasma-umass/scalene', 0.5155574083328247, 'profiling', 1), ('gradio-app/gradio', 0.5152886509895325, 'viz', 0), ('dylanhogg/awesome-python', 0.5142082571983337, 'study', 0), ('firmai/industry-machine-learning', 0.5136438012123108, 'study', 0), ('ashleve/lightning-hydra-template', 0.5111380219459534, 'util', 0), ('gbeced/pyalgotrade', 0.5105053186416626, 'finance', 0), ('ddbourgin/numpy-ml', 0.509579598903656, 'ml', 0), ('huggingface/accelerate', 0.5086351633071899, 'ml', 0), ('exaloop/codon', 0.5077102780342102, 'perf', 1), ('pytorch/glow', 0.5075867772102356, 'ml', 0), ('keras-team/autokeras', 0.5068373680114746, 'ml-dl', 1), ('merantix-momentum/squirrel-core', 0.5065999031066895, 'ml', 1), ('pytorch/rl', 0.5055859088897705, 'ml-rl', 0), ('pycaret/pycaret', 0.5044597387313843, 'ml', 1), ('pypy/pypy', 0.5041675567626953, 'util', 0), ('salesforce/warp-drive', 0.5037093162536621, 'ml-rl', 1), ('mrdbourke/tensorflow-deep-learning', 0.5032038688659668, 'study', 1), ('uber/petastorm', 0.5018066167831421, 'data', 1), ('nyandwi/modernconvnets', 0.501559853553772, 'ml-dl', 1)]",47,2.0,,0.29,0,0,54,5,0,1,1,0.0,0.0,90.0,0.0,37
460,util,https://github.com/rspeer/python-ftfy,[],,[],[],,,,rspeer/python-ftfy,python-ftfy,3647,153,76,Python,http://ftfy.readthedocs.org,"Fixes mojibake and other glitches in Unicode text, after the fact.",rspeer,2024-01-12,2012-08-24,596,6.113266283524904,,"Fixes mojibake and other glitches in Unicode text, after the fact.",[],[],2023-11-21,[],18,6.0,,0.1,1,0,139,2,0,12,12,1.0,0.0,90.0,0.0,37
86,graph,https://github.com/stellargraph/stellargraph,[],,[],[],,,,stellargraph/stellargraph,stellargraph,2836,418,64,Python,https://stellargraph.readthedocs.io/,StellarGraph - Machine Learning on Graphs,stellargraph,2024-01-13,2018-04-13,302,9.372993389990556,https://avatars.githubusercontent.com/u/36725857?v=4,StellarGraph - Machine Learning on Graphs,"['data-science', 'deep-learning', 'gcn', 'geometric-deep-learning', 'graph-analysis', 'graph-convolutional-networks', 'graph-data', 'graph-machine-learning', 'graph-neural-networks', 'graphs', 'heterogeneous-networks', 'interpretability', 'link-prediction', 'machine-learning', 'machine-learning-algorithms', 'networkx', 'saliency-map', 'stellargraph-library']","['data-science', 'deep-learning', 'gcn', 'geometric-deep-learning', 'graph-analysis', 'graph-convolutional-networks', 'graph-data', 'graph-machine-learning', 'graph-neural-networks', 'graphs', 'heterogeneous-networks', 'interpretability', 'link-prediction', 'machine-learning', 'machine-learning-algorithms', 'networkx', 'saliency-map', 'stellargraph-library']",2021-10-29,"[('chandlerbang/awesome-self-supervised-gnn', 0.6943688988685608, 'study', 3), ('danielegrattarola/spektral', 0.6824637055397034, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.6726529002189636, 'ml-dl', 4), ('dmlc/dgl', 0.6574554443359375, 'ml-dl', 2), ('google-deepmind/materials_discovery', 0.6555060148239136, 'sim', 0), ('benedekrozemberczki/tigerlily', 0.6446079015731812, 'ml-dl', 3), ('a-r-j/graphein', 0.6164317727088928, 'sim', 3), ('graphistry/pygraphistry', 0.5959926247596741, 'data', 1), ('rampasek/graphgps', 0.5865841507911682, 'graph', 0), ('accenture/ampligraph', 0.5474543571472168, 'data', 1), ('networkx/networkx', 0.5425050258636475, 'graph', 1), ('googlecloudplatform/vertex-ai-samples', 0.5413510799407959, 'ml', 1), ('ddbourgin/numpy-ml', 0.5346408486366272, 'ml', 1), ('onnx/onnx', 0.5222296714782715, 'ml', 2), ('awslabs/dgl-ke', 0.5202558040618896, 'ml', 1), ('lutzroeder/netron', 0.514754056930542, 'ml', 2), ('tensorflow/tensorflow', 0.5133888125419617, 'ml-dl', 2), ('pygraphviz/pygraphviz', 0.5024316310882568, 'viz', 0), ('hazyresearch/hgcn', 0.500386118888855, 'ml', 0)]",36,6.0,,0.0,5,1,70,27,0,5,5,5.0,2.0,90.0,0.4,37
818,study,https://github.com/alirezadir/machine-learning-interview-enlightener,[],,[],[],,,,alirezadir/machine-learning-interview-enlightener,Machine-Learning-Interviews,2645,494,53,Jupyter Notebook,,This repo is meant to serve as a guide for Machine Learning/AI technical interviews. ,alirezadir,2024-01-14,2021-01-31,156,16.924131627056674,,This repo is meant to serve as a guide for Machine Learning/AI technical interviews. ,"['ai', 'deep-learning', 'interview', 'interview-practice', 'interview-preparation', 'interviews', 'machine-learning', 'machine-learning-algorithms', 'scalable-applications', 'system-design']","['ai', 'deep-learning', 'interview', 'interview-practice', 'interview-preparation', 'interviews', 'machine-learning', 'machine-learning-algorithms', 'scalable-applications', 'system-design']",2023-10-26,"[('bentoml/bentoml', 0.657772958278656, 'ml-ops', 3), ('google-research/google-research', 0.6368386745452881, 'ml', 2), ('google-research/language', 0.6070800423622131, 'nlp', 1), ('amanchadha/coursera-deep-learning-specialization', 0.6021793484687805, 'study', 1), ('patchy631/machine-learning', 0.5960847735404968, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5835177302360535, 'ml', 1), ('oegedijk/explainerdashboard', 0.5830463171005249, 'ml-interpretability', 0), ('xplainable/xplainable', 0.5782299637794495, 'ml-interpretability', 2), ('tensorflow/tensorflow', 0.5763082504272461, 'ml-dl', 2), ('microsoft/nni', 0.5761905312538147, 'ml', 3), ('onnx/onnx', 0.5712395906448364, 'ml', 2), ('tensorlayer/tensorlayer', 0.5669152140617371, 'ml-rl', 1), ('mlflow/mlflow', 0.5668816566467285, 'ml-ops', 2), ('tensorflow/tensor2tensor', 0.5649195909500122, 'ml', 2), ('polyaxon/polyaxon', 0.564633309841156, 'ml-ops', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5615020394325256, 'study', 2), ('firmai/industry-machine-learning', 0.5614524483680725, 'study', 1), ('wandb/client', 0.5583623051643372, 'ml', 2), ('explosion/thinc', 0.5558744668960571, 'ml-dl', 3), ('mindsdb/mindsdb', 0.5548688173294067, 'data', 2), ('feast-dev/feast', 0.5510007739067078, 'ml-ops', 1), ('netflix/metaflow', 0.5488802194595337, 'ml-ops', 2), ('aimhubio/aim', 0.5477184653282166, 'ml-ops', 2), ('doccano/doccano', 0.5449758768081665, 'nlp', 1), ('deepmind/dm_control', 0.5414921641349792, 'ml-rl', 2), ('nvidia/nemo', 0.5406086444854736, 'nlp', 1), ('lastmile-ai/aiconfig', 0.5404054522514343, 'util', 1), ('cheshire-cat-ai/core', 0.539746105670929, 'llm', 1), ('activeloopai/deeplake', 0.5346347689628601, 'ml-ops', 3), ('keras-team/keras', 0.5340282917022705, 'ml-dl', 2), ('winedarksea/autots', 0.5339345335960388, 'time-series', 2), ('cleanlab/cleanlab', 0.5334048867225647, 'ml', 0), ('determined-ai/determined', 0.5306956768035889, 'ml-ops', 2), ('ml-tooling/opyrator', 0.5304664969444275, 'viz', 1), ('antonosika/gpt-engineer', 0.527275800704956, 'llm', 1), ('avaiga/taipy', 0.5267717242240906, 'data', 0), ('gradio-app/gradio', 0.5261391401290894, 'viz', 2), ('keras-rl/keras-rl', 0.5244634747505188, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.5230525732040405, 'ml-rl', 2), ('cerlymarco/medium_notebook', 0.5219733119010925, 'study', 2), ('polyaxon/datatile', 0.521611213684082, 'pandas', 0), ('csinva/imodels', 0.5202349424362183, 'ml', 2), ('sweepai/sweep', 0.5161364078521729, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5155435800552368, 'nlp', 0), ('iterative/dvc', 0.5138351917266846, 'ml-ops', 2), ('hpcaitech/colossalai', 0.5127544403076172, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5122131109237671, 'ml-dl', 3), ('qdrant/qdrant', 0.5117506384849548, 'data', 1), ('automl/auto-sklearn', 0.5101978778839111, 'ml', 0), ('interpretml/interpret', 0.5096079707145691, 'ml-interpretability', 2), ('salesforce/logai', 0.5083866119384766, 'util', 2), ('nccr-itmo/fedot', 0.5080487728118896, 'ml-ops', 1), ('seldonio/alibi', 0.5047518014907837, 'ml-interpretability', 1), ('microsoft/onnxruntime', 0.5041807293891907, 'ml', 2), ('ourownstory/neural_prophet', 0.503267228603363, 'ml', 2), ('ddbourgin/numpy-ml', 0.5029667019844055, 'ml', 1), ('microsoft/generative-ai-for-beginners', 0.5020521283149719, 'study', 1), ('jindongwang/transferlearning', 0.5019001364707947, 'ml', 2), ('marqo-ai/marqo', 0.5009713768959045, 'ml', 2), ('oneil512/insight', 0.5009233355522156, 'ml', 1), ('kubeflow/pipelines', 0.5008544921875, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5003179907798767, 'ml-dl', 1)]",7,4.0,,1.33,1,0,36,3,0,0,0,1.0,0.0,90.0,0.0,37
704,pandas,https://github.com/jmcarpenter2/swifter,[],,[],[],,,,jmcarpenter2/swifter,swifter,2402,101,31,Python,,A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner,jmcarpenter2,2024-01-12,2018-04-07,303,7.916195856873823,,A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner,"['dask', 'modin', 'pandas', 'pandas-dataframe', 'parallel-computing', 'parallelization']","['dask', 'modin', 'pandas', 'pandas-dataframe', 'parallel-computing', 'parallelization']",2023-07-31,"[('nalepae/pandarallel', 0.7605847716331482, 'pandas', 1), ('ddelange/mapply', 0.6554047465324402, 'pandas', 0), ('modin-project/modin', 0.6068665385246277, 'perf', 2), ('dask/dask', 0.60169517993927, 'perf', 2), ('rapidsai/cudf', 0.5688180923461914, 'pandas', 2), ('mementum/bta-lib', 0.5637902021408081, 'finance', 0), ('blaze/blaze', 0.5631571412086487, 'pandas', 0), ('holoviz/spatialpandas', 0.562759518623352, 'pandas', 1), ('lux-org/lux', 0.5564085841178894, 'viz', 1), ('joblib/joblib', 0.5563712120056152, 'util', 1), ('eventual-inc/daft', 0.55370032787323, 'pandas', 0), ('adamerose/pandasgui', 0.5478309988975525, 'pandas', 1), ('vaexio/vaex', 0.5449737906455994, 'perf', 0), ('pandas-dev/pandas', 0.5424719452857971, 'pandas', 1), ('tkrabel/bamboolib', 0.5419985055923462, 'pandas', 1), ('twopirllc/pandas-ta', 0.539636492729187, 'finance', 1), ('fugue-project/fugue', 0.5381412506103516, 'pandas', 2), ('sfu-db/connector-x', 0.5345582962036133, 'data', 0), ('pola-rs/polars', 0.5324315428733826, 'pandas', 0), ('pytoolz/toolz', 0.5265946388244629, 'util', 0), ('klen/py-frameworks-bench', 0.519839346408844, 'perf', 0), ('pytables/pytables', 0.5137337446212769, 'data', 0), ('fastai/fastcore', 0.5132153034210205, 'util', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5050438046455383, 'pandas', 0)]",17,4.0,,0.33,5,0,70,6,0,14,14,5.0,2.0,90.0,0.4,37
956,ml-dl,https://github.com/danielegrattarola/spektral,[],,['2006.12138'],[],,,,danielegrattarola/spektral,spektral,2314,337,44,Python,https://graphneural.network,Graph Neural Networks with Keras and Tensorflow 2.,danielegrattarola,2024-01-13,2019-01-17,262,8.808047852093528,,Graph Neural Networks with Keras and Tensorflow 2.,"['deep-learning', 'graph-deep-learning', 'graph-neural-networks', 'keras', 'tensorflow', 'tensorflow2']","['deep-learning', 'graph-deep-learning', 'graph-neural-networks', 'keras', 'tensorflow', 'tensorflow2']",2023-06-01,"[('pyg-team/pytorch_geometric', 0.7439136505126953, 'ml-dl', 2), ('dmlc/dgl', 0.6934017539024353, 'ml-dl', 2), ('stellargraph/stellargraph', 0.6824637055397034, 'graph', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.6770707964897156, 'study', 2), ('nyandwi/modernconvnets', 0.6024564504623413, 'ml-dl', 2), ('rampasek/graphgps', 0.5940183997154236, 'graph', 0), ('keras-rl/keras-rl', 0.5715480446815491, 'ml-rl', 2), ('keras-team/keras', 0.5592799186706543, 'ml-dl', 2), ('benedekrozemberczki/tigerlily', 0.5574583411216736, 'ml-dl', 1), ('tensorflow/addons', 0.5564872026443481, 'ml', 2), ('hazyresearch/hgcn', 0.5537418723106384, 'ml', 0), ('a-r-j/graphein', 0.551216721534729, 'sim', 2), ('googlecloudplatform/vertex-ai-samples', 0.545731782913208, 'ml', 0), ('lutzroeder/netron', 0.5387458205223083, 'ml', 3), ('google-deepmind/materials_discovery', 0.5381258130073547, 'sim', 0), ('graphistry/pygraphistry', 0.5304725170135498, 'data', 0), ('onnx/onnx', 0.5261642932891846, 'ml', 3), ('tensorflow/tensorflow', 0.5244050025939941, 'ml-dl', 2), ('cvxgrp/pymde', 0.520326554775238, 'ml', 0), ('tensorlayer/tensorlayer', 0.5144057869911194, 'ml-rl', 2), ('keras-team/keras-nlp', 0.5112001299858093, 'nlp', 3), ('horovod/horovod', 0.5111513733863831, 'ml-ops', 3), ('ddbourgin/numpy-ml', 0.5083205699920654, 'ml', 0), ('xl0/lovely-tensors', 0.5056509375572205, 'ml-dl', 1), ('accenture/ampligraph', 0.5038337707519531, 'data', 0), ('tensorly/tensorly', 0.5032951831817627, 'ml-dl', 1)]",27,3.0,,0.33,3,1,61,8,1,1,1,3.0,5.0,90.0,1.7,37
255,crypto,https://github.com/bmoscon/cryptofeed,[],,[],[],,,,bmoscon/cryptofeed,cryptofeed,1973,712,79,Python,,Cryptocurrency Exchange Websocket Data Feed Handler,bmoscon,2024-01-13,2017-12-16,319,6.176654740608229,,Cryptocurrency Exchange Websocket Data Feed Handler,"['asyncio', 'binance', 'bitcoin', 'btc', 'coinbase', 'coinbase-api', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'ethereum', 'exchange', 'ftx-exchange', 'influxdb', 'market-data', 'trading', 'trading-platform', 'websocket', 'websockets']","['asyncio', 'binance', 'bitcoin', 'btc', 'coinbase', 'coinbase-api', 'crypto', 'cryptocurrencies', 'cryptocurrency', 'ethereum', 'exchange', 'ftx-exchange', 'influxdb', 'market-data', 'trading', 'trading-platform', 'websocket', 'websockets']",2024-01-07,"[('ccxt/ccxt', 0.6092362999916077, 'crypto', 9), ('miguelgrinberg/python-socketio', 0.595906138420105, 'util', 2), ('freqtrade/freqtrade', 0.5561859011650085, 'crypto', 3), ('websocket-client/websocket-client', 0.5356486439704895, 'web', 2), ('pmaji/crypto-whale-watching-app', 0.5131617188453674, 'crypto', 3), ('gbeced/basana', 0.5091555118560791, 'finance', 3)]",112,0.0,,0.71,16,11,74,0,2,12,2,16.0,13.0,90.0,0.8,37
1684,util,https://github.com/landscapeio/prospector,"['linting', 'styling']",,[],[],,,,landscapeio/prospector,prospector,1882,176,35,Python,,"Inspects Python source files and provides information about type and location of classes, methods etc",landscapeio,2024-01-12,2013-08-05,547,3.439686684073107,https://avatars.githubusercontent.com/u/4759094?v=4,"Inspects Python source files and provides information about type and location of classes, methods etc",[],"['linting', 'styling']",2023-10-18,"[('eugeneyan/python-collab-template', 0.6496816873550415, 'template', 1), ('google/pytype', 0.6452606916427612, 'typing', 0), ('pympler/pympler', 0.6098625659942627, 'perf', 0), ('hadialqattan/pycln', 0.6040316224098206, 'util', 0), ('nedbat/coveragepy', 0.6040084362030029, 'testing', 0), ('gaogaotiantian/viztracer', 0.6018545627593994, 'profiling', 0), ('pyutils/line_profiler', 0.5992322564125061, 'profiling', 0), ('mkdocstrings/griffe', 0.5961143970489502, 'util', 0), ('facebook/pyre-check', 0.5935577154159546, 'typing', 0), ('klen/pylama', 0.5915239453315735, 'util', 0), ('pytoolz/toolz', 0.5871560573577881, 'util', 0), ('urwid/urwid', 0.586963951587677, 'term', 0), ('python/cpython', 0.5828319787979126, 'util', 0), ('jiffyclub/snakeviz', 0.582079291343689, 'profiling', 0), ('hhatto/autopep8', 0.5784581303596497, 'util', 0), ('astral-sh/ruff', 0.5776445269584656, 'util', 0), ('eleutherai/pyfra', 0.5767074227333069, 'ml', 0), ('brandon-rhodes/python-patterns', 0.5751134157180786, 'util', 0), ('pycqa/isort', 0.574863851070404, 'util', 0), ('instagram/monkeytype', 0.5743587017059326, 'typing', 0), ('rubik/radon', 0.5695351362228394, 'util', 0), ('google/yapf', 0.5676681995391846, 'util', 0), ('python-rope/rope', 0.5660980939865112, 'util', 0), ('pycqa/pyflakes', 0.5659628510475159, 'util', 0), ('mitmproxy/pdoc', 0.5606246590614319, 'util', 0), ('tiangolo/typer', 0.558975100517273, 'term', 0), ('xrudelis/pytrait', 0.5579221248626709, 'util', 0), ('wesm/pydata-book', 0.5570473670959473, 'study', 0), ('grantjenks/blue', 0.5565185546875, 'util', 0), ('requests/toolbelt', 0.5488244891166687, 'util', 0), ('amaargiru/pyroad', 0.5477283596992493, 'study', 0), ('alexmojaki/snoop', 0.5469942688941956, 'debug', 0), ('pypi/warehouse', 0.545502245426178, 'util', 0), ('psf/black', 0.5413801670074463, 'util', 0), ('hoffstadt/dearpygui', 0.5395547747612, 'gui', 0), ('ta-lib/ta-lib-python', 0.5358104109764099, 'finance', 0), ('python/mypy', 0.5345390439033508, 'typing', 0), ('pypa/hatch', 0.5343112945556641, 'util', 0), ('samuelcolvin/python-devtools', 0.5334382057189941, 'debug', 0), ('microsoft/pyright', 0.5327200293540955, 'typing', 0), ('pypy/pypy', 0.5321429371833801, 'util', 0), ('pycqa/pycodestyle', 0.531697690486908, 'util', 0), ('pycqa/eradicate', 0.5313110947608948, 'util', 2), ('pythonprofilers/memory_profiler', 0.5307861566543579, 'profiling', 0), ('sourcery-ai/sourcery', 0.5299766659736633, 'util', 0), ('agronholm/typeguard', 0.528834879398346, 'typing', 0), ('pygments/pygments', 0.5254445672035217, 'util', 0), ('pycqa/flake8', 0.5223195552825928, 'util', 0), ('instagram/fixit', 0.5204662084579468, 'util', 0), ('erotemic/ubelt', 0.518781840801239, 'util', 0), ('beeware/toga', 0.517236590385437, 'gui', 0), ('google/python-fire', 0.5145845413208008, 'term', 0), ('googleapis/google-api-python-client', 0.5145336389541626, 'util', 0), ('python-attrs/attrs', 0.5136226415634155, 'typing', 0), ('facebookincubator/bowler', 0.5116630792617798, 'util', 0), ('python/typeshed', 0.509270191192627, 'typing', 0), ('dosisod/refurb', 0.5087876915931702, 'util', 0), ('pycqa/pylint-django', 0.5079518556594849, 'util', 0), ('roniemartinez/dude', 0.5066967606544495, 'util', 0), ('willmcgugan/textual', 0.5064331889152527, 'term', 0), ('pyglet/pyglet', 0.5031312108039856, 'gamedev', 0)]",90,3.0,,0.88,15,2,127,3,4,10,4,15.0,23.0,90.0,1.5,37
298,util,https://github.com/julienpalard/pipe,[],,[],[],,,,julienpalard/pipe,Pipe,1797,111,26,Python,,A Python library to use infix notation in Python,julienpalard,2024-01-13,2010-04-08,720,2.4933597621407335,,A Python library to use infix notation in Python,[],[],2024-01-07,"[('google/latexify_py', 0.5925378799438477, 'util', 0), ('pytoolz/toolz', 0.5835241079330444, 'util', 0), ('connorferster/handcalcs', 0.5392968058586121, 'jupyter', 0), ('pyston/pyston', 0.5197369456291199, 'util', 0), ('sympy/sympy', 0.5134634971618652, 'math', 0), ('pmorissette/ffn', 0.5104817748069763, 'finance', 0), ('geospatialpython/pyshp', 0.5082536935806274, 'gis', 0)]",29,4.0,,0.17,9,6,168,0,0,1,1,9.0,22.0,90.0,2.4,37
591,data,https://github.com/uber/petastorm,[],,[],[],,,,uber/petastorm,petastorm,1711,280,41,Python,,"Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.",uber,2024-01-12,2018-06-15,293,5.828223844282238,https://avatars.githubusercontent.com/u/538264?v=4,"Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.","['deep-learning', 'machine-learning', 'parquet', 'parquet-files', 'pyarrow', 'pyspark', 'pytorch', 'sysml', 'tensorflow']","['deep-learning', 'machine-learning', 'parquet', 'parquet-files', 'pyarrow', 'pyspark', 'pytorch', 'sysml', 'tensorflow']",2023-12-02,"[('microsoft/deepspeed', 0.6462931632995605, 'ml-dl', 3), ('horovod/horovod', 0.6362690329551697, 'ml-ops', 4), ('determined-ai/determined', 0.6062763929367065, 'ml-ops', 4), ('ageron/handson-ml2', 0.5999022126197815, 'ml', 0), ('gradio-app/gradio', 0.5904715061187744, 'viz', 2), ('pytorch/ignite', 0.5855341553688049, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5829108953475952, 'ml-dl', 3), ('merantix-momentum/squirrel-core', 0.5779027938842773, 'ml', 4), ('tensorflow/tensor2tensor', 0.5750295519828796, 'ml', 2), ('ashleve/lightning-hydra-template', 0.5747993588447571, 'util', 2), ('aws/sagemaker-python-sdk', 0.5683594346046448, 'ml', 3), ('rasbt/machine-learning-book', 0.5682684183120728, 'study', 3), ('paddlepaddle/paddle', 0.5659868717193604, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5632723569869995, 'perf', 3), ('fchollet/deep-learning-with-python-notebooks', 0.5607595443725586, 'study', 0), ('mlflow/mlflow', 0.559465765953064, 'ml-ops', 1), ('huggingface/huggingface_hub', 0.5579898357391357, 'ml', 3), ('nvidia/deeplearningexamples', 0.5578290820121765, 'ml-dl', 3), ('eventual-inc/daft', 0.557521641254425, 'pandas', 2), ('deepmind/dm-haiku', 0.557473361492157, 'ml-dl', 2), ('neuralmagic/sparseml', 0.5541288256645203, 'ml-dl', 2), ('karpathy/micrograd', 0.5534528493881226, 'study', 0), ('huggingface/transformers', 0.5520153641700745, 'nlp', 4), ('oml-team/open-metric-learning', 0.5485543012619019, 'ml', 2), ('skorch-dev/skorch', 0.5473853945732117, 'ml-dl', 2), ('ggerganov/ggml', 0.5469512939453125, 'ml', 1), ('aiqc/aiqc', 0.5461896061897278, 'ml-ops', 0), ('dmlc/xgboost', 0.5380932688713074, 'ml', 1), ('firmai/industry-machine-learning', 0.5362139940261841, 'study', 1), ('microsoft/flaml', 0.5358170866966248, 'ml', 2), ('huggingface/datasets', 0.5324203372001648, 'nlp', 4), ('keras-team/autokeras', 0.5320855975151062, 'ml-dl', 3), ('deepmodeling/deepmd-kit', 0.5317656397819519, 'sim', 2), ('alpa-projects/alpa', 0.5310642719268799, 'ml-dl', 2), ('ray-project/ray', 0.5304347276687622, 'ml-ops', 4), ('kubeflow/fairing', 0.529427170753479, 'ml-ops', 0), ('aistream-peelout/flow-forecast', 0.5273684859275818, 'time-series', 2), ('microsoft/jarvis', 0.5265276432037354, 'llm', 2), ('apache/incubator-mxnet', 0.5250005722045898, 'ml-dl', 0), ('adap/flower', 0.5249413847923279, 'ml-ops', 4), ('towhee-io/towhee', 0.5247065424919128, 'ml-ops', 1), ('mrdbourke/pytorch-deep-learning', 0.5223989486694336, 'study', 3), ('lightly-ai/lightly', 0.5194175243377686, 'ml', 3), ('keras-team/keras', 0.5165746808052063, 'ml-dl', 4), ('explosion/thinc', 0.5144953727722168, 'ml-dl', 4), ('facebookresearch/pytorch3d', 0.5134731531143188, 'ml-dl', 0), ('deepchecks/deepchecks', 0.5133116245269775, 'data', 3), ('pytorch/torchrec', 0.5117344260215759, 'ml-dl', 2), ('google/trax', 0.5106483101844788, 'ml-dl', 2), ('ml-tooling/opyrator', 0.5089090466499329, 'viz', 1), ('dmlc/dgl', 0.5088360905647278, 'ml-dl', 1), ('microsoft/nni', 0.508219301700592, 'ml', 4), ('pytorch/data', 0.5076653361320496, 'data', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5072482824325562, 'ml', 3), ('tensorlayer/tensorlayer', 0.5072025060653687, 'ml-rl', 2), ('arogozhnikov/einops', 0.5071843862533569, 'ml-dl', 3), ('dylanhogg/awesome-python', 0.5067077875137329, 'study', 2), ('titanml/takeoff', 0.5053594708442688, 'llm', 0), ('nvidia/apex', 0.5052030086517334, 'ml-dl', 0), ('mosaicml/composer', 0.5039240121841431, 'ml-dl', 3), ('fastai/fastcore', 0.5022857189178467, 'util', 0), ('google/tf-quant-finance', 0.5018066167831421, 'finance', 1), ('pyg-team/pytorch_geometric', 0.5014254450798035, 'ml-dl', 2), ('catboost/catboost', 0.5008516311645508, 'ml', 1), ('vaexio/vaex', 0.500319242477417, 'perf', 2), ('pycaret/pycaret', 0.5002906322479248, 'ml', 1)]",50,4.0,,0.06,4,2,68,1,1,20,1,4.0,4.0,90.0,1.0,37
1004,finance,https://github.com/pmorissette/ffn,[],,[],[],,,,pmorissette/ffn,ffn,1697,283,59,Python,pmorissette.github.io/ffn,ffn - a financial function library for Python,pmorissette,2024-01-13,2014-06-19,501,3.3824031890660593,,ffn - a financial function library for Python,[],[],2023-12-31,"[('pytoolz/toolz', 0.6924606561660767, 'util', 0), ('domokane/financepy', 0.6907992959022522, 'finance', 0), ('goldmansachs/gs-quant', 0.6325653195381165, 'finance', 0), ('gbeced/pyalgotrade', 0.628166913986206, 'finance', 0), ('ta-lib/ta-lib-python', 0.5941312909126282, 'finance', 0), ('fredrik-johansson/mpmath', 0.58493971824646, 'math', 0), ('daxm/fmpsdk', 0.5664402842521667, 'finance', 0), ('hydrosquall/tiingo-python', 0.5629584789276123, 'finance', 0), ('quantecon/quantecon.py', 0.559691309928894, 'sim', 0), ('ethtx/ethtx', 0.5535359978675842, 'crypto', 0), ('cuemacro/finmarketpy', 0.5505915284156799, 'finance', 0), ('firmai/atspy', 0.5481270551681519, 'time-series', 0), ('robcarver17/pysystemtrade', 0.5440186262130737, 'finance', 0), ('quantopian/pyfolio', 0.5423455834388733, 'finance', 0), ('connorferster/handcalcs', 0.5378669500350952, 'jupyter', 0), ('eleutherai/pyfra', 0.5368715524673462, 'ml', 0), ('alkaline-ml/pmdarima', 0.5328418612480164, 'time-series', 0), ('pandas-dev/pandas', 0.5297597050666809, 'pandas', 0), ('numpy/numpy', 0.5275481343269348, 'math', 0), ('stan-dev/pystan', 0.5250624418258667, 'ml', 0), ('quantopian/zipline', 0.5246074199676514, 'finance', 0), ('mementum/backtrader', 0.5245431661605835, 'finance', 0), ('primal100/pybitcointools', 0.5242424607276917, 'crypto', 0), ('1200wd/bitcoinlib', 0.5135668516159058, 'crypto', 0), ('cuemacro/findatapy', 0.5108827948570251, 'finance', 0), ('julienpalard/pipe', 0.5104817748069763, 'util', 0), ('google/latexify_py', 0.5060634016990662, 'util', 0), ('scipy/scipy', 0.502606987953186, 'math', 0), ('bashtage/arch', 0.5025431513786316, 'time-series', 0), ('pypy/pypy', 0.5018722414970398, 'util', 0), ('rjt1990/pyflux', 0.5010930895805359, 'time-series', 0)]",32,3.0,,0.44,22,18,117,0,4,1,4,22.0,15.0,90.0,0.7,37
629,debug,https://github.com/alexmojaki/birdseye,[],,[],[],,,,alexmojaki/birdseye,birdseye,1593,75,42,JavaScript,https://birdseye.readthedocs.io,Graphical Python debugger which lets you easily view the values of all evaluated expressions,alexmojaki,2024-01-13,2017-07-22,340,4.679395719681074,,Graphical Python debugger which lets you easily view the values of all evaluated expressions,"['ast', 'birdseye', 'debugger', 'debugging', 'python-debugger']","['ast', 'birdseye', 'debugger', 'debugging', 'python-debugger']",2023-10-16,"[('alexmojaki/snoop', 0.6003603935241699, 'debug', 2), ('alexmojaki/heartrate', 0.5575025081634521, 'debug', 1), ('gaogaotiantian/viztracer', 0.5461402535438538, 'profiling', 1), ('samuelcolvin/python-devtools', 0.5242282748222351, 'debug', 0), ('google/pytype', 0.5039346218109131, 'typing', 0)]",10,4.0,,0.02,2,2,79,3,0,1,1,2.0,9.0,90.0,4.5,37
179,nlp,https://github.com/explosion/spacy-models,[],,[],[],,,,explosion/spacy-models,spacy-models,1465,301,47,Python,https://spacy.io,💫  Models for the spaCy Natural Language Processing (NLP) library,explosion,2024-01-12,2017-03-14,359,4.080779944289693,https://avatars.githubusercontent.com/u/20011530?v=4,💫  Models for the spaCy Natural Language Processing (NLP) library,"['machine-learning', 'machine-learning-models', 'models', 'natural-language-processing', 'nlp', 'spacy', 'spacy-models', 'statistical-models']","['machine-learning', 'machine-learning-models', 'models', 'natural-language-processing', 'nlp', 'spacy', 'spacy-models', 'statistical-models']",2023-11-22,"[('explosion/spacy-stanza', 0.7454922199249268, 'nlp', 4), ('nltk/nltk', 0.6720556020736694, 'nlp', 3), ('huggingface/neuralcoref', 0.6682751774787903, 'nlp', 3), ('explosion/spacy-transformers', 0.6662450432777405, 'llm', 4), ('explosion/spacy-streamlit', 0.6529020071029663, 'nlp', 4), ('flairnlp/flair', 0.6428545117378235, 'nlp', 3), ('explosion/spacy', 0.6409233212471008, 'nlp', 4), ('allenai/allennlp', 0.6203604340553284, 'nlp', 2), ('iclrandd/blackstone', 0.612856388092041, 'nlp', 2), ('norskregnesentral/skweak', 0.608527660369873, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.5910124182701111, 'llm', 1), ('explosion/spacy-llm', 0.5904589295387268, 'llm', 4), ('sloria/textblob', 0.5832897424697876, 'nlp', 2), ('freedomintelligence/llmzoo', 0.5769272446632385, 'llm', 0), ('lm-sys/fastchat', 0.5767900943756104, 'llm', 0), ('rasahq/rasa', 0.5728164315223694, 'llm', 4), ('lianjiatech/belle', 0.5633299946784973, 'llm', 0), ('llmware-ai/llmware', 0.5522136092185974, 'llm', 2), ('hannibal046/awesome-llm', 0.5515271425247192, 'study', 0), ('deepset-ai/farm', 0.5511075854301453, 'nlp', 1), ('infinitylogesh/mutate', 0.5492627620697021, 'nlp', 0), ('yueyu1030/attrprompt', 0.5483715534210205, 'llm', 1), ('alibaba/easynlp', 0.5481346845626831, 'nlp', 2), ('qanastek/drbert', 0.5431317090988159, 'llm', 2), ('mooler0410/llmspracticalguide', 0.5413009524345398, 'study', 2), ('lexpredict/lexpredict-lexnlp', 0.5394803285598755, 'nlp', 1), ('huggingface/transformers', 0.5389872193336487, 'nlp', 3), ('keras-team/keras-nlp', 0.5345762372016907, 'nlp', 3), ('makcedward/nlpaug', 0.5314129590988159, 'nlp', 3), ('jonasgeiping/cramming', 0.5299116373062134, 'nlp', 1), ('juncongmoo/pyllama', 0.5298112630844116, 'llm', 0), ('graykode/nlp-tutorial', 0.5290482044219971, 'study', 2), ('thilinarajapakse/simpletransformers', 0.5240768790245056, 'nlp', 0), ('eleutherai/lm-evaluation-harness', 0.5173574686050415, 'llm', 0), ('koaning/whatlies', 0.5144206881523132, 'nlp', 1), ('jalammar/ecco', 0.513950765132904, 'ml-interpretability', 2), ('neuralmagic/sparseml', 0.5111375451087952, 'ml-dl', 1), ('explosion/thinc', 0.5104645490646362, 'ml-dl', 4), ('ai21labs/lm-evaluation', 0.5086509585380554, 'llm', 0), ('pemistahl/lingua-py', 0.5069782137870789, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5058228969573975, 'llm', 3), ('gunthercox/chatterbot-corpus', 0.5032789707183838, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5031586289405823, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5027161836624146, 'llm', 1), ('paddlepaddle/rocketqa', 0.5023024678230286, 'nlp', 1), ('openai/gpt-2', 0.5017908215522766, 'llm', 0), ('reasoning-machines/pal', 0.5003161430358887, 'llm', 0)]",14,7.0,,4.5,3,3,83,2,199,149,199,3.0,0.0,90.0,0.0,37
1617,util,https://github.com/samuelcolvin/watchfiles,[],,[],[],,,,samuelcolvin/watchfiles,watchfiles,1435,99,18,Python,https://watchfiles.helpmanual.io,"Simple, modern and fast file watching and code reload in python.",samuelcolvin,2024-01-14,2017-10-13,328,4.3673913043478265,,"Simple, modern and fast file watching and code reload in python.","['asyncio', 'filesystem', 'inotify', 'inotifywatch', 'notify', 'uvicorn']","['asyncio', 'filesystem', 'inotify', 'inotifywatch', 'notify', 'uvicorn']",2023-11-25,"[('airtai/faststream', 0.5354728698730469, 'perf', 1), ('tox-dev/py-filelock', 0.5285630822181702, 'util', 0), ('magicstack/uvloop', 0.5178032517433167, 'util', 1), ('timofurrer/awesome-asyncio', 0.5153577327728271, 'study', 1), ('erotemic/ubelt', 0.5087971091270447, 'util', 0), ('sumerc/yappi', 0.5043572187423706, 'profiling', 1), ('python-trio/trio', 0.5042653679847717, 'perf', 0), ('grantjenks/python-diskcache', 0.5041387677192688, 'util', 1), ('samuelcolvin/arq', 0.5021094679832458, 'data', 1)]",40,3.0,,0.25,10,4,76,2,3,5,3,10.0,15.0,90.0,1.5,37
645,profiling,https://github.com/p403n1x87/austin,[],,[],[],,,,p403n1x87/austin,austin,1311,40,17,C,https://pypi.org/project/austin-dist/,Python frame stack sampler for CPython,p403n1x87,2024-01-12,2018-09-20,279,4.6869254341164455,,Python frame stack sampler for CPython,"['debugging-tools', 'performance', 'profiling']","['debugging-tools', 'performance', 'profiling']",2023-10-04,"[('faster-cpython/tools', 0.6291638612747192, 'perf', 0), ('faster-cpython/ideas', 0.6115438938140869, 'perf', 0), ('benfred/py-spy', 0.5970548987388611, 'profiling', 1), ('brandtbucher/specialist', 0.5802419185638428, 'perf', 0), ('gotcha/ipdb', 0.5680873394012451, 'debug', 0), ('pympler/pympler', 0.5645143389701843, 'perf', 0), ('klen/py-frameworks-bench', 0.5635471343994141, 'perf', 0), ('python/cpython', 0.5600637197494507, 'util', 0), ('markshannon/faster-cpython', 0.5521705150604248, 'perf', 0), ('pyutils/line_profiler', 0.5507018566131592, 'profiling', 0), ('inducer/pudb', 0.5496501326560974, 'debug', 0), ('ipython/ipyparallel', 0.5462062954902649, 'perf', 0), ('alexmojaki/snoop', 0.5450026392936707, 'debug', 1), ('alexmojaki/heartrate', 0.5422681570053101, 'debug', 0), ('ionelmc/pytest-benchmark', 0.540304958820343, 'testing', 1), ('pytorch/data', 0.5269789695739746, 'data', 0), ('samuelcolvin/python-devtools', 0.523003876209259, 'debug', 0), ('sumerc/yappi', 0.5229708552360535, 'profiling', 1), ('pypy/pypy', 0.5207717418670654, 'util', 0), ('cython/cython', 0.5145069360733032, 'util', 1), ('lcompilers/lpython', 0.5144882798194885, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5097584128379822, 'study', 0), ('pythonspeed/filprofiler', 0.5042270421981812, 'profiling', 0), ('facebookincubator/cinder', 0.5041605830192566, 'perf', 0), ('asweigart/pyperclip', 0.5013977885246277, 'util', 0)]",7,4.0,,1.19,2,0,65,3,2,5,2,2.0,7.0,90.0,3.5,37
612,testing,https://github.com/pytest-dev/pytest-bdd,[],,[],[],,,,pytest-dev/pytest-bdd,pytest-bdd,1239,210,57,Python,https://pytest-bdd.readthedocs.io/en/latest/,BDD library for the py.test runner,pytest-dev,2024-01-13,2013-03-29,565,2.1907047234150037,https://avatars.githubusercontent.com/u/8897583?v=4,BDD library for the py.test runner,[],[],2023-12-02,"[('behave/behave', 0.6536642909049988, 'testing', 0), ('nedbat/coveragepy', 0.6206257939338684, 'testing', 0), ('pmorissette/bt', 0.5979208946228027, 'finance', 0), ('ionelmc/pytest-benchmark', 0.5788668394088745, 'testing', 0), ('wolever/parameterized', 0.5707379579544067, 'testing', 0), ('libtcod/python-tcod', 0.5265241265296936, 'gamedev', 0), ('microsoft/playwright-python', 0.5180904865264893, 'testing', 0), ('pytoolz/toolz', 0.5150437355041504, 'util', 0), ('teemu/pytest-sugar', 0.5115352869033813, 'testing', 0), ('pyodide/pyodide', 0.5067077279090881, 'util', 0), ('alexmojaki/snoop', 0.5047500133514404, 'debug', 0)]",60,3.0,,1.1,33,14,131,1,0,9,9,33.0,37.0,90.0,1.1,37
791,data,https://github.com/jsonpickle/jsonpickle,[],,[],[],,,,jsonpickle/jsonpickle,jsonpickle,1180,163,34,Python,https://jsonpickle.readthedocs.io/en/latest/,"Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python.",jsonpickle,2024-01-13,2009-12-10,737,1.5995352439969017,https://avatars.githubusercontent.com/u/165337?v=4,"Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python.","['bsd-3-clause', 'deserialization', 'json', 'objectstorage', 'pickle', 'serialization']","['bsd-3-clause', 'deserialization', 'json', 'objectstorage', 'pickle', 'serialization']",2023-12-03,"[('marshmallow-code/marshmallow', 0.6203814744949341, 'util', 2), ('python-odin/odin', 0.6082868576049805, 'util', 1), ('uqfoundation/dill', 0.601097047328949, 'data', 0), ('brokenloop/jsontopydantic', 0.5869114398956299, 'util', 0), ('yukinarit/pyserde', 0.5483598113059998, 'util', 2), ('strawberry-graphql/strawberry', 0.5452963709831238, 'web', 0), ('graphistry/pygraphistry', 0.535290539264679, 'data', 0), ('tiangolo/sqlmodel', 0.5255077481269836, 'data', 1), ('lidatong/dataclasses-json', 0.5186536908149719, 'util', 1), ('plotly/plotly.py', 0.5167441964149475, 'viz', 0), ('lk-geimfari/mimesis', 0.5048171281814575, 'data', 1), ('scikit-hep/awkward-1.0', 0.500355064868927, 'data', 1)]",73,6.0,,0.67,11,5,172,1,0,3,3,11.0,24.0,90.0,2.2,37
1222,testing,https://github.com/ionelmc/pytest-benchmark,[],,[],[],,,,ionelmc/pytest-benchmark,pytest-benchmark,1158,115,19,Python,,py.test fixture for benchmarking code,ionelmc,2024-01-12,2014-10-10,485,2.384819064430715,,py.test fixture for benchmarking code,"['benchmark', 'benchmarking', 'performance', 'pytest']","['benchmark', 'benchmarking', 'performance', 'pytest']",2023-12-15,"[('klen/py-frameworks-bench', 0.6951150298118591, 'perf', 1), ('pytest-dev/pytest', 0.6786613464355469, 'testing', 0), ('samuelcolvin/dirty-equals', 0.6507035493850708, 'util', 1), ('locustio/locust', 0.6405083537101746, 'testing', 2), ('teemu/pytest-sugar', 0.6360356211662292, 'testing', 1), ('pytest-dev/pytest-mock', 0.6237248182296753, 'testing', 1), ('nedbat/coveragepy', 0.6183704733848572, 'testing', 0), ('pytest-dev/pytest-xdist', 0.613385796546936, 'testing', 1), ('pmorissette/bt', 0.6115341186523438, 'finance', 0), ('computationalmodelling/nbval', 0.6038401126861572, 'jupyter', 1), ('wolever/parameterized', 0.6030191779136658, 'testing', 0), ('taverntesting/tavern', 0.5957930684089661, 'testing', 1), ('pytest-dev/pytest-bdd', 0.5788668394088745, 'testing', 0), ('pytest-dev/pytest-asyncio', 0.5777239203453064, 'testing', 0), ('pympler/pympler', 0.5586925148963928, 'perf', 0), ('rubik/radon', 0.554404079914093, 'util', 0), ('pyutils/line_profiler', 0.5505008101463318, 'profiling', 0), ('nteract/testbook', 0.5495516061782837, 'jupyter', 1), ('pypy/pypy', 0.5463877320289612, 'util', 0), ('samuelcolvin/pytest-pretty', 0.5456304550170898, 'testing', 1), ('spulec/freezegun', 0.5428466200828552, 'testing', 0), ('p403n1x87/austin', 0.540304958820343, 'profiling', 1), ('mrdbourke/m1-machine-learning-test', 0.5336135625839233, 'ml', 0), ('kiwicom/pytest-recording', 0.5216888785362244, 'testing', 1), ('alexmojaki/snoop', 0.5151593685150146, 'debug', 0), ('eugeneyan/python-collab-template', 0.5136798024177551, 'template', 0), ('getsentry/responses', 0.5085520148277283, 'testing', 0), ('pytest-dev/pytest-cov', 0.5074380040168762, 'testing', 1), ('benfred/py-spy', 0.5037754774093628, 'profiling', 0)]",41,6.0,,0.29,11,6,113,1,0,2,2,11.0,20.0,90.0,1.8,37
442,gis,https://github.com/toblerity/fiona,[],,[],[],,,,toblerity/fiona,Fiona,1096,209,47,Python,https://fiona.readthedocs.io/,Fiona reads and writes geographic data files,toblerity,2024-01-10,2011-12-31,630,1.7384998866983912,https://avatars.githubusercontent.com/u/859968?v=4,Fiona reads and writes geographic data files,"['cli', 'cython', 'gdal', 'gis', 'ogr', 'vector']","['cli', 'cython', 'gdal', 'gis', 'ogr', 'vector']",2023-12-17,"[('rasterio/rasterio', 0.5375838279724121, 'gis', 4)]",72,3.0,,2.04,23,18,147,1,8,10,8,23.0,33.0,90.0,1.4,37
1467,term,https://github.com/1j01/textual-paint,[],,[],[],,,,1j01/textual-paint,textual-paint,861,10,4,Python,https://pypi.org/project/textual-paint/,:art: MS Paint in your terminal.,1j01,2024-01-12,2023-04-10,42,20.43050847457627,,:art: MS Paint in your terminal.,"['ansi-art', 'ansi-editor', 'artscene', 'ascii-art', 'bbs', 'drawing', 'image', 'image-editor', 'irc', 'mirc', 'mspaint', 'paint', 'pixel-art', 'pixel-editor', 'terminal', 'text-art', 'textual', 'tui']","['ansi-art', 'ansi-editor', 'artscene', 'ascii-art', 'bbs', 'drawing', 'image', 'image-editor', 'irc', 'mirc', 'mspaint', 'paint', 'pixel-art', 'pixel-editor', 'terminal', 'text-art', 'textual', 'tui']",2024-01-12,"[('borisdayma/dalle-mini', 0.510983943939209, 'diffusion', 0)]",1,0.0,,28.81,0,0,9,0,0,5,5,0.0,0.0,90.0,0.0,37
1735,viz,https://github.com/pygraphviz/pygraphviz,[],,[],[],,,,pygraphviz/pygraphviz,pygraphviz,717,200,36,C,https://pygraphviz.github.io/,Python interface to Graphviz graph drawing package,pygraphviz,2024-01-08,2013-08-02,547,1.3094182102791547,https://avatars.githubusercontent.com/u/5148488?v=4,Python interface to Graphviz graph drawing package,"['complex-networks', 'graph-visualization', 'spec-0']","['complex-networks', 'graph-visualization', 'spec-0']",2024-01-08,"[('westhealth/pyvis', 0.7577512264251709, 'graph', 0), ('pydot/pydot', 0.7296152114868164, 'viz', 0), ('networkx/networkx', 0.6983128786087036, 'graph', 3), ('graphistry/pygraphistry', 0.678774356842041, 'data', 1), ('plotly/plotly.py', 0.6489458680152893, 'viz', 0), ('artelys/geonetworkx', 0.5921590328216553, 'gis', 0), ('dmlc/dgl', 0.5841652154922485, 'ml-dl', 0), ('h4kor/graph-force', 0.571456253528595, 'graph', 0), ('graphql-python/graphene', 0.5481510162353516, 'web', 0), ('matplotlib/matplotlib', 0.5422382354736328, 'viz', 0), ('holoviz/hvplot', 0.5362616181373596, 'pandas', 0), ('vizzuhq/ipyvizzu', 0.5348131060600281, 'jupyter', 0), ('holoviz/holoviz', 0.5206543207168579, 'viz', 0), ('has2k1/plotnine', 0.5177363753318787, 'viz', 0), ('holoviz/panel', 0.5157747268676758, 'viz', 0), ('cuemacro/chartpy', 0.5139192342758179, 'viz', 0), ('bokeh/bokeh', 0.5085844993591309, 'viz', 0), ('altair-viz/altair', 0.5070845484733582, 'viz', 0), ('pyg-team/pytorch_geometric', 0.502930760383606, 'ml-dl', 0), ('stellargraph/stellargraph', 0.5024316310882568, 'graph', 0), ('kuanb/peartree', 0.5017895698547363, 'gis', 0), ('pyvista/pyvista', 0.5015963315963745, 'viz', 0), ('vmiklos/ged2dot', 0.5002699494361877, 'data', 0)]",53,5.0,,1.04,34,23,127,0,3,2,3,34.0,59.0,90.0,1.7,37
1704,util,https://github.com/mtkennerly/poetry-dynamic-versioning,['poetry'],,[],[],,,,mtkennerly/poetry-dynamic-versioning,poetry-dynamic-versioning,530,33,4,Python,,Plugin for Poetry to enable dynamic versioning based on VCS tags,mtkennerly,2024-01-13,2019-06-06,242,2.183637433784579,,Plugin for Poetry to enable dynamic versioning based on VCS tags,"['bazaar', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'plugin', 'poetry', 'semantic-versioning', 'subversion', 'versioning']","['bazaar', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'plugin', 'poetry', 'semantic-versioning', 'subversion', 'versioning']",2024-01-03,"[('mtkennerly/dunamai', 0.7415024042129517, 'util', 11), ('tiangolo/poetry-version-plugin', 0.6403163075447083, 'util', 0), ('pypa/setuptools_scm', 0.6003091931343079, 'util', 2), ('python-versioneer/python-versioneer', 0.5653854012489319, 'util', 0), ('callowayproject/bump-my-version', 0.5595990419387817, 'util', 1), ('python-poetry/install.python-poetry.org', 0.5113168358802795, 'util', 1)]",13,3.0,,1.1,13,12,56,0,11,13,11,13.0,34.0,90.0,2.6,37
1861,sim,https://github.com/nvidia-omniverse/omniisaacgymenvs,['robot-learning'],,[],[],,,,nvidia-omniverse/omniisaacgymenvs,OmniIsaacGymEnvs,518,141,16,Python,,Reinforcement Learning Environments for Omniverse Isaac Gym,nvidia-omniverse,2024-01-14,2022-06-01,86,5.963815789473684,https://avatars.githubusercontent.com/u/57824658?v=4,Reinforcement Learning Environments for Omniverse Isaac Gym,[],['robot-learning'],2023-12-08,"[('nvidia-omniverse/isaacgymenvs', 0.8064512610435486, 'sim', 0), ('nvidia-omniverse/orbit', 0.6811214685440063, 'sim', 1), ('humancompatibleai/imitation', 0.6052762866020203, 'ml-rl', 0), ('farama-foundation/gymnasium', 0.6047082543373108, 'ml-rl', 0), ('arise-initiative/robosuite', 0.5800082087516785, 'ml-rl', 1), ('openai/baselines', 0.5605931282043457, 'ml-rl', 0), ('inspirai/timechamber', 0.5575793981552124, 'sim', 0), ('pettingzoo-team/pettingzoo', 0.5554696917533875, 'ml-rl', 0), ('pytorch/rl', 0.5446157455444336, 'ml-rl', 0), ('unity-technologies/ml-agents', 0.5333084464073181, 'ml-rl', 0), ('kzl/decision-transformer', 0.5326739549636841, 'ml-rl', 0), ('thu-ml/tianshou', 0.5271867513656616, 'ml-rl', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5251854062080383, 'study', 0), ('facebookresearch/habitat-lab', 0.5088497400283813, 'sim', 0), ('google/dopamine', 0.5081252455711365, 'ml-rl', 0)]",6,1.0,,1.4,59,17,20,1,0,4,4,59.0,151.0,90.0,2.6,37
1527,llm,https://github.com/vahe1994/spqr,"['falcon', 'llama', 'quantization', 'compression']",Quantization algorithm and the model evaluation code for SpQR method for LLM compression,[],[],,,,vahe1994/spqr,SpQR,475,39,22,Python,,,vahe1994,2024-01-12,2023-06-05,34,13.912133891213388,,Quantization algorithm and the model evaluation code for SpQR method for LLM compression,[],"['compression', 'falcon', 'llama', 'quantization']",2023-11-13,"[('opengvlab/omniquant', 0.6055932641029358, 'llm', 1), ('artidoro/qlora', 0.5260251760482788, 'llm', 0)]",8,5.0,,0.52,8,4,7,2,0,0,0,8.0,5.0,90.0,0.6,37
556,gis,https://github.com/corteva/rioxarray,[],,[],[],,,,corteva/rioxarray,rioxarray,455,69,16,Python,https://corteva.github.io/rioxarray,geospatial xarray extension powered by rasterio,corteva,2024-01-09,2019-04-16,250,1.82,https://avatars.githubusercontent.com/u/39543515?v=4,geospatial xarray extension powered by rasterio,"['gdal', 'geospatial', 'gis', 'netcdf', 'raster', 'rasterio', 'xarray']","['gdal', 'geospatial', 'gis', 'netcdf', 'raster', 'rasterio', 'xarray']",2023-12-29,"[('osgeo/gdal', 0.5278557538986206, 'gis', 1), ('rasterio/rasterio', 0.5225644707679749, 'gis', 3), ('makepath/xarray-spatial', 0.511622428894043, 'gis', 1), ('cogeotiff/rio-tiler', 0.5002418756484985, 'gis', 3)]",33,7.0,,1.0,30,12,58,0,4,14,4,30.0,37.0,90.0,1.2,37
1700,template,https://github.com/asacristani/fastapi-rocket-boilerplate,[],,[],[],,,,asacristani/fastapi-rocket-boilerplate,fastapi-rocket-boilerplate,370,56,6,Python,,🐍💨 FastAPI Rocket Boilerplate to build an API based in Python with its most modern technologies!,asacristani,2024-01-10,2023-09-20,18,19.62121212121212,,🐍💨 FastAPI Rocket Boilerplate to build an API based in Python with its most modern technologies!,"['boilerplate', 'boilerplate-backend', 'fastapi']","['boilerplate', 'boilerplate-backend', 'fastapi']",2023-10-16,"[('tiangolo/fastapi', 0.7025260925292969, 'web', 1), ('fastai/fastcore', 0.6752527952194214, 'util', 0), ('rawheel/fastapi-boilerplate', 0.6740272045135498, 'web', 2), ('s3rius/fastapi-template', 0.6634681224822998, 'web', 1), ('vitalik/django-ninja', 0.6605289578437805, 'web', 0), ('dmontagu/fastapi_client', 0.6545476913452148, 'web', 0), ('koxudaxi/fastapi-code-generator', 0.607068657875061, 'web', 1), ('starlite-api/starlite', 0.5981463193893433, 'web', 0), ('hugapi/hug', 0.5935749411582947, 'util', 0), ('python-restx/flask-restx', 0.5854663848876953, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5566263198852539, 'template', 1), ('falconry/falcon', 0.5512214303016663, 'web', 0), ('lk-geimfari/mimesis', 0.5495461225509644, 'data', 0), ('janetech-inc/fast-api-admin-template', 0.5484818816184998, 'template', 0), ('awtkns/fastapi-crudrouter', 0.5432131886482239, 'web', 1), ('fastapi-users/fastapi-users', 0.5379810333251953, 'web', 1), ('willmcgugan/textual', 0.5363441109657288, 'term', 0), ('pyston/pyston', 0.5267325639724731, 'util', 0), ('kubeflow/fairing', 0.5263985991477966, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5223199725151062, 'viz', 1), ('zhanymkanov/fastapi-best-practices', 0.5217467546463013, 'study', 1), ('alirn76/panther', 0.5177244544029236, 'web', 0), ('pypy/pypy', 0.508145809173584, 'util', 0), ('airtai/faststream', 0.5075085759162903, 'perf', 0), ('urwid/urwid', 0.5037996172904968, 'term', 0), ('pdoc3/pdoc', 0.502884566783905, 'util', 0), ('pytoolz/toolz', 0.5019211769104004, 'util', 0), ('martinheinz/python-project-blueprint', 0.5014850497245789, 'template', 1)]",5,2.0,,0.83,4,1,4,3,1,3,1,4.0,3.0,90.0,0.8,37
1455,util,https://github.com/conda/conda-build,['conda'],,[],[],,,,conda/conda-build,conda-build,356,403,51,Python,https://docs.conda.io/projects/conda-build/,Commands and tools for building conda packages,conda,2024-01-14,2014-01-17,523,0.6799454297407913,https://avatars.githubusercontent.com/u/6392739?v=4,Commands and tools for building conda packages,"['conda', 'conda-build', 'package-management']","['conda', 'conda-build', 'package-management']",2024-01-10,"[('conda/constructor', 0.8005626201629639, 'util', 1), ('mamba-org/boa', 0.7872036695480347, 'util', 1), ('mamba-org/quetz', 0.7619379758834839, 'util', 1), ('conda/conda-pack', 0.7299324870109558, 'util', 1), ('mamba-org/mamba', 0.6777679920196533, 'util', 1), ('mamba-org/gator', 0.6426661014556885, 'jupyter', 1), ('pypa/hatch', 0.5962553024291992, 'util', 0), ('conda/conda', 0.5913727283477783, 'util', 2), ('pomponchik/instld', 0.5880274772644043, 'util', 0), ('mamba-org/micromamba-docker', 0.5653933882713318, 'util', 1), ('conda-forge/feedstocks', 0.5539048314094543, 'util', 1), ('conda-forge/conda-smithy', 0.5523984432220459, 'util', 0), ('indygreg/pyoxidizer', 0.5000386834144592, 'util', 0)]",244,3.0,,4.15,391,335,122,0,9,25,9,390.0,208.0,90.0,0.5,37
1761,data,https://github.com/tconbeer/sqlfmt,['code-quality'],,[],[],1.0,,,tconbeer/sqlfmt,sqlfmt,307,11,3,Python,https://sqlfmt.com,sqlfmt formats your dbt SQL files so you don't have to,tconbeer,2024-01-11,2021-07-19,132,2.323243243243243,,sqlfmt formats your dbt SQL files so you don't have to,"['dbt', 'formatter', 'sql']","['code-quality', 'dbt', 'formatter', 'sql']",2024-01-12,"[('tconbeer/harlequin', 0.569164514541626, 'term', 1), ('databricks/dbt-databricks', 0.5048282146453857, 'data', 2)]",12,5.0,,2.1,56,41,30,0,16,19,16,56.0,49.0,90.0,0.9,37
1357,gis,https://github.com/raphaelquast/eomaps,[],,[],[],,,,raphaelquast/eomaps,EOmaps,284,20,5,Python,https://eomaps.readthedocs.io/,A library to create interactive maps of geographical datasets,raphaelquast,2024-01-13,2021-09-27,122,2.325146198830409,,A library to create interactive maps of geographical datasets,"['cartopy', 'earth-observation', 'geospatial', 'gis', 'interactive-maps', 'interactive-visualization', 'mapping', 'matplotlib', 'plotting', 'visualization']","['cartopy', 'earth-observation', 'geospatial', 'gis', 'interactive-maps', 'interactive-visualization', 'mapping', 'matplotlib', 'plotting', 'visualization']",2023-12-20,"[('residentmario/geoplot', 0.707546055316925, 'gis', 1), ('scitools/cartopy', 0.6839972138404846, 'gis', 2), ('opengeos/leafmap', 0.6778762936592102, 'gis', 3), ('holoviz/geoviews', 0.6664366126060486, 'gis', 2), ('giswqs/geemap', 0.6554696559906006, 'gis', 3), ('gregorhd/mapcompare', 0.625028669834137, 'gis', 0), ('geopandas/geopandas', 0.6084570288658142, 'gis', 2), ('marceloprates/prettymaps', 0.6013832688331604, 'viz', 1), ('visgl/deck.gl', 0.6003251075744629, 'viz', 1), ('artelys/geonetworkx', 0.585459291934967, 'gis', 0), ('bokeh/bokeh', 0.5836126804351807, 'viz', 2), ('plotly/plotly.py', 0.581186056137085, 'viz', 1), ('earthlab/earthpy', 0.5780810713768005, 'gis', 0), ('pyproj4/pyproj', 0.5670640468597412, 'gis', 1), ('domlysz/blendergis', 0.5599436163902283, 'gis', 2), ('scitools/iris', 0.5512466430664062, 'gis', 0), ('hazyresearch/meerkat', 0.543424129486084, 'viz', 0), ('python-visualization/folium', 0.5424359440803528, 'gis', 0), ('holoviz/holoviz', 0.5355204343795776, 'viz', 0), ('mwaskom/seaborn', 0.531478226184845, 'viz', 1), ('altair-viz/altair', 0.5242673754692078, 'viz', 1), ('nomic-ai/deepscatter', 0.5207083225250244, 'viz', 1), ('man-group/dtale', 0.5189169049263, 'viz', 1), ('imageio/imageio', 0.514751672744751, 'util', 0), ('pandas-dev/pandas', 0.5139701962471008, 'pandas', 0), ('isl-org/open3d', 0.5115019679069519, 'sim', 1), ('holoviz/panel', 0.5107569098472595, 'viz', 1), ('matplotlib/matplotlib', 0.5100708603858948, 'viz', 2), ('darribas/gds_env', 0.5032002329826355, 'gis', 0), ('fatiando/verde', 0.5024335384368896, 'gis', 1), ('holoviz/hvplot', 0.5002750754356384, 'pandas', 1)]",6,3.0,,22.73,37,27,28,1,25,33,25,37.0,61.0,90.0,1.6,37
582,ml,https://github.com/merantix-momentum/squirrel-core,[],,[],[],,,,merantix-momentum/squirrel-core,squirrel-core,271,8,14,Python,https://squirrel-core.readthedocs.io/,"A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:",merantix-momentum,2024-01-05,2022-02-11,102,2.6420612813370474,https://avatars.githubusercontent.com/u/98414099?v=4,"A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way 🌰","['ai', 'cloud-computing', 'collaboration', 'computer-vision', 'cv', 'data-ingestion', 'data-mesh', 'data-science', 'dataops', 'datasets', 'deep-learning', 'distributed', 'jax', 'machine-learning', 'ml', 'natural-language-processing', 'nlp', 'pytorch', 'tensorflow']","['ai', 'cloud-computing', 'collaboration', 'computer-vision', 'cv', 'data-ingestion', 'data-mesh', 'data-science', 'dataops', 'datasets', 'deep-learning', 'distributed', 'jax', 'machine-learning', 'ml', 'natural-language-processing', 'nlp', 'pytorch', 'tensorflow']",2024-01-04,"[('eventual-inc/daft', 0.6807416081428528, 'pandas', 3), ('huggingface/datasets', 0.6653859615325928, 'nlp', 8), ('gradio-app/gradio', 0.6419711709022522, 'viz', 3), ('tensorflow/tensorflow', 0.6386204957962036, 'ml-dl', 5), ('mlflow/mlflow', 0.63669753074646, 'ml-ops', 3), ('kubeflow/fairing', 0.6223782300949097, 'ml-ops', 0), ('dylanhogg/awesome-python', 0.6165292263031006, 'study', 5), ('ray-project/ray', 0.6162523627281189, 'ml-ops', 6), ('polyaxon/polyaxon', 0.6144108772277832, 'ml-ops', 6), ('horovod/horovod', 0.6060230135917664, 'ml-ops', 4), ('wandb/client', 0.5987028479576111, 'ml', 7), ('featurelabs/featuretools', 0.5986401438713074, 'ml', 2), ('determined-ai/determined', 0.5961888432502747, 'ml-ops', 5), ('aws/sagemaker-python-sdk', 0.5961750149726868, 'ml', 3), ('polyaxon/datatile', 0.5911334156990051, 'pandas', 4), ('dagworks-inc/hamilton', 0.5907508730888367, 'ml-ops', 2), ('fmind/mlops-python-package', 0.5865539312362671, 'template', 2), ('huggingface/transformers', 0.5804790258407593, 'nlp', 7), ('uber/petastorm', 0.5779027938842773, 'data', 4), ('backtick-se/cowait', 0.5777786374092102, 'util', 1), ('fastai/fastcore', 0.572002649307251, 'util', 0), ('pycaret/pycaret', 0.5700594782829285, 'ml', 3), ('huggingface/huggingface_hub', 0.5627287030220032, 'ml', 4), ('krzjoa/awesome-python-data-science', 0.5624656081199646, 'study', 3), ('tensorlayer/tensorlayer', 0.5620189905166626, 'ml-rl', 2), ('onnx/onnx', 0.5616537928581238, 'ml', 5), ('googlecloudplatform/vertex-ai-samples', 0.5609938502311707, 'ml', 3), ('nevronai/metisfl', 0.560804009437561, 'ml', 2), ('firmai/industry-machine-learning', 0.5604248642921448, 'study', 2), ('netflix/metaflow', 0.560415506362915, 'ml-ops', 4), ('explosion/thinc', 0.5592201352119446, 'ml-dl', 8), ('ml-tooling/opyrator', 0.5560594797134399, 'viz', 1), ('rasbt/mlxtend', 0.5547300577163696, 'ml', 2), ('activeloopai/deeplake', 0.5520520210266113, 'ml-ops', 10), ('microsoft/nni', 0.5516149401664734, 'ml', 6), ('online-ml/river', 0.5505257248878479, 'ml', 2), ('adap/flower', 0.5503032803535461, 'ml-ops', 5), ('uber/fiber', 0.5499705672264099, 'data', 1), ('tensorflow/tensor2tensor', 0.5476986765861511, 'ml', 2), ('orchest/orchest', 0.5455144643783569, 'ml-ops', 2), ('bentoml/bentoml', 0.5453324913978577, 'ml-ops', 3), ('whylabs/whylogs', 0.5451236367225647, 'util', 3), ('microsoft/onnxruntime', 0.5414808392524719, 'ml', 4), ('keras-team/keras', 0.5407350659370422, 'ml-dl', 6), ('fugue-project/fugue', 0.5387210845947266, 'pandas', 2), ('eleutherai/pyfra', 0.53780198097229, 'ml', 0), ('airbnb/knowledge-repo', 0.537761390209198, 'data', 1), ('nvidia/deeplearningexamples', 0.5371223092079163, 'ml-dl', 5), ('epistasislab/tpot', 0.5355274081230164, 'ml', 2), ('ploomber/ploomber', 0.5347124338150024, 'ml-ops', 2), ('mage-ai/mage-ai', 0.5338592529296875, 'ml-ops', 2), ('falconry/falcon', 0.5313084721565247, 'web', 0), ('explosion/spacy', 0.5305957198143005, 'nlp', 6), ('pandas-dev/pandas', 0.5296874046325684, 'pandas', 1), ('flyteorg/flyte', 0.5294408798217773, 'ml-ops', 3), ('pytorch/rl', 0.527829647064209, 'ml-rl', 3), ('intel/intel-extension-for-pytorch', 0.5278235077857971, 'perf', 3), ('avaiga/taipy', 0.5268411636352539, 'data', 0), ('kestra-io/kestra', 0.5266260504722595, 'ml-ops', 0), ('lightly-ai/lightly', 0.5261020660400391, 'ml', 4), ('google-research/language', 0.5260434150695801, 'nlp', 2), ('jina-ai/jina', 0.5257314443588257, 'ml', 2), ('streamlit/streamlit', 0.5240601897239685, 'viz', 3), ('rasbt/machine-learning-book', 0.5238132476806641, 'study', 3), ('ashleve/lightning-hydra-template', 0.5204800367355347, 'util', 2), ('rasahq/rasa', 0.5163630247116089, 'llm', 3), ('kubeflow-kale/kale', 0.5156881809234619, 'ml-ops', 1), ('aimhubio/aim', 0.5153231024742126, 'ml-ops', 6), ('airbytehq/airbyte', 0.5148383975028992, 'data', 0), ('keras-team/autokeras', 0.5147408246994019, 'ml-dl', 3), ('dagster-io/dagster', 0.5139255523681641, 'ml-ops', 1), ('scikit-learn-contrib/imbalanced-learn', 0.5136831998825073, 'ml', 2), ('scikit-learn/scikit-learn', 0.5131182670593262, 'ml', 2), ('superduperdb/superduperdb', 0.5102357268333435, 'data', 3), ('keras-team/keras-nlp', 0.5084515810012817, 'nlp', 5), ('google/mediapipe', 0.5076141953468323, 'ml', 3), ('databrickslabs/dolly', 0.5068414807319641, 'llm', 0), ('google/tf-quant-finance', 0.5065999031066895, 'finance', 1), ('meltano/meltano', 0.506161093711853, 'ml-ops', 1), ('drivendata/cookiecutter-data-science', 0.5045228004455566, 'template', 3), ('willmcgugan/textual', 0.50379478931427, 'term', 0), ('agronholm/apscheduler', 0.5034119486808777, 'util', 0), ('tensorly/tensorly', 0.5030725002288818, 'ml-dl', 4), ('jovianml/opendatasets', 0.5021243095397949, 'data', 3), ('microsoft/deepspeed', 0.5016716122627258, 'ml-dl', 3), ('pytables/pytables', 0.5015002489089966, 'data', 0), ('apache/incubator-mxnet', 0.500947892665863, 'ml-dl', 0), ('reloadware/reloadium', 0.5004510283470154, 'profiling', 1), ('microsoft/flaml', 0.5003235340118408, 'ml', 4)]",16,6.0,,0.4,33,30,23,0,7,10,7,33.0,42.0,90.0,1.3,37
1458,util,https://github.com/mamba-org/micromamba-docker,[],,[],[],,,,mamba-org/micromamba-docker,micromamba-docker,232,41,11,Shell,,Rapid builds of small Conda-based containers using micromamba.,mamba-org,2024-01-13,2021-01-22,157,1.4723481414324568,https://avatars.githubusercontent.com/u/66118895?v=4,Rapid builds of small Conda-based containers using micromamba.,"['build', 'ci', 'conda', 'container', 'docker', 'dockerfile', 'environment', 'mamba', 'micromamba']","['build', 'ci', 'conda', 'container', 'docker', 'dockerfile', 'environment', 'mamba', 'micromamba']",2024-01-11,"[('mamba-org/boa', 0.6690220236778259, 'util', 2), ('conda/conda-build', 0.5653933882713318, 'util', 1), ('darribas/gds_env', 0.5356658697128296, 'gis', 1), ('mamba-org/quetz', 0.5259016752243042, 'util', 1)]",18,7.0,,2.67,34,33,36,0,18,11,18,34.0,42.0,90.0,1.2,37
1614,llm,https://github.com/tiger-ai-lab/mammoth,['instruction-tuning'],,[],[],,,,tiger-ai-lab/mammoth,MAmmoTH,221,23,11,Jupyter Notebook,,"This repo contains the code and data for ""MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning""",tiger-ai-lab,2024-01-12,2023-09-06,20,10.595890410958905,https://avatars.githubusercontent.com/u/144196744?v=4,"This repo contains the code and data for ""MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning""",[],['instruction-tuning'],2024-01-13,"[('declare-lab/instruct-eval', 0.6364500522613525, 'llm', 0), ('yizhongw/self-instruct', 0.5941129922866821, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5743323564529419, 'llm', 0), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5739008784294128, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5284292697906494, 'llm', 1), ('hiyouga/llama-factory', 0.5284292697906494, 'llm', 1)]",4,3.0,,0.98,18,13,4,0,0,0,0,18.0,25.0,90.0,1.4,37
1453,util,https://github.com/conda-forge/conda-smithy,[],,[],[],,,,conda-forge/conda-smithy,conda-smithy,140,173,25,Python,https://conda-forge.org/,The tool for managing conda-forge feedstocks.,conda-forge,2024-01-05,2015-04-11,459,0.30472636815920395,https://avatars.githubusercontent.com/u/11897326?v=4,The tool for managing conda-forge feedstocks.,['continuous-integration'],['continuous-integration'],2024-01-11,"[('conda-forge/feedstocks', 0.8073468208312988, 'util', 0), ('conda/conda-build', 0.5523984432220459, 'util', 0), ('conda/conda-pack', 0.5372809767723083, 'util', 0), ('mamba-org/quetz', 0.5191918015480042, 'util', 0), ('mamba-org/mamba', 0.5094754695892334, 'util', 0)]",112,3.0,,6.65,61,48,107,0,19,24,19,61.0,174.0,90.0,2.9,37
101,nlp,https://github.com/clips/pattern,[],,[],[],,,,clips/pattern,pattern,8609,1600,544,Python,https://github.com/clips/pattern/wiki,"Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.",clips,2024-01-13,2011-05-03,665,12.945864661654136,https://avatars.githubusercontent.com/u/765924?v=4,"Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.","['machine-learning', 'natural-language-processing', 'network-analysis', 'sentiment-analysis', 'web-mining', 'wordnet']","['machine-learning', 'natural-language-processing', 'network-analysis', 'sentiment-analysis', 'web-mining', 'wordnet']",2020-04-25,"[('alirezamika/autoscraper', 0.7112637758255005, 'data', 1), ('scrapy/scrapy', 0.667265772819519, 'data', 0), ('webpy/webpy', 0.6514905095100403, 'web', 0), ('rasbt/mlxtend', 0.6350256204605103, 'ml', 1), ('roniemartinez/dude', 0.6342862844467163, 'util', 0), ('sloria/textblob', 0.6183449029922485, 'nlp', 1), ('masoniteframework/masonite', 0.6094305515289307, 'web', 0), ('plotly/dash', 0.5705082416534424, 'viz', 0), ('nv7-github/googlesearch', 0.5700188279151917, 'util', 0), ('holoviz/panel', 0.5649107098579407, 'viz', 0), ('gradio-app/gradio', 0.5603600144386292, 'viz', 1), ('pallets/flask', 0.5583645105361938, 'web', 0), ('requests/toolbelt', 0.5543271899223328, 'util', 0), ('eleutherai/pyfra', 0.5541388988494873, 'ml', 0), ('explosion/spacy', 0.5512140989303589, 'nlp', 2), ('dylanhogg/awesome-python', 0.5490561723709106, 'study', 2), ('reflex-dev/reflex', 0.547705352306366, 'web', 0), ('1200wd/bitcoinlib', 0.5464682579040527, 'crypto', 0), ('ranaroussi/quantstats', 0.5454891324043274, 'finance', 0), ('binux/pyspider', 0.5396731495857239, 'data', 0), ('falconry/falcon', 0.5375832915306091, 'web', 0), ('googleapis/google-api-python-client', 0.5339103937149048, 'util', 0), ('online-ml/river', 0.5316668152809143, 'ml', 1), ('seleniumbase/seleniumbase', 0.5308951735496521, 'testing', 0), ('bottlepy/bottle', 0.5306470990180969, 'web', 0), ('eliasdabbas/advertools', 0.5270535349845886, 'data', 0), ('willmcgugan/textual', 0.5270101428031921, 'term', 0), ('scikit-learn/scikit-learn', 0.524055540561676, 'ml', 1), ('pemistahl/lingua-py', 0.5218809247016907, 'nlp', 1), ('krzjoa/awesome-python-data-science', 0.5186637043952942, 'study', 1), ('polyaxon/datatile', 0.5179307460784912, 'pandas', 0), ('gbeced/pyalgotrade', 0.5164464712142944, 'finance', 0), ('uberi/speech_recognition', 0.5162601470947266, 'ml', 0), ('goldmansachs/gs-quant', 0.5158860087394714, 'finance', 0), ('pylons/pyramid', 0.5145288705825806, 'web', 0), ('wesm/pydata-book', 0.5136392116546631, 'study', 0), ('klen/muffin', 0.5102404356002808, 'web', 0), ('quantconnect/lean', 0.5098506808280945, 'finance', 0), ('pytoolz/toolz', 0.5079754590988159, 'util', 0), ('pyodide/pyodide', 0.5079214572906494, 'util', 0), ('ta-lib/ta-lib-python', 0.5062140226364136, 'finance', 0), ('r0x0r/pywebview', 0.5047861933708191, 'gui', 0), ('pallets/werkzeug', 0.5018793344497681, 'web', 0), ('pandas-dev/pandas', 0.5012263655662537, 'pandas', 0), ('cherrypy/cherrypy', 0.500634491443634, 'web', 0), ('probml/pyprobml', 0.5005698204040527, 'ml', 1)]",30,6.0,,0.0,2,0,155,45,0,0,0,2.0,1.0,90.0,0.5,36
137,nlp,https://github.com/ddangelov/top2vec,[],,[],[],,,,ddangelov/top2vec,Top2Vec,2768,363,40,Python,,"Top2Vec learns jointly embedded topic, document and word vectors.",ddangelov,2024-01-13,2020-03-20,201,13.73210489014883,,"Top2Vec learns jointly embedded topic, document and word vectors.","['bert', 'document-embedding', 'pre-trained-language-models', 'semantic-search', 'sentence-encoder', 'sentence-transformers', 'text-search', 'text-semantic-similarity', 'top2vec', 'topic-modeling', 'topic-modelling', 'topic-search', 'topic-vector', 'word-embeddings']","['bert', 'document-embedding', 'pre-trained-language-models', 'semantic-search', 'sentence-encoder', 'sentence-transformers', 'text-search', 'text-semantic-similarity', 'top2vec', 'topic-modeling', 'topic-modelling', 'topic-search', 'topic-vector', 'word-embeddings']",2023-11-16,"[('sebischair/lbl2vec', 0.8003798723220825, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.6133404970169067, 'llm', 1), ('neuml/txtai', 0.608359694480896, 'nlp', 1), ('maartengr/bertopic', 0.6046782732009888, 'nlp', 3), ('rare-technologies/gensim', 0.59908527135849, 'nlp', 2), ('muennighoff/sgpt', 0.5922024846076965, 'llm', 1), ('llmware-ai/llmware', 0.5913721323013306, 'llm', 2), ('jina-ai/clip-as-service', 0.5902553796768188, 'nlp', 1), ('jina-ai/finetuner', 0.587577760219574, 'ml', 1), ('plasticityai/magnitude', 0.584179162979126, 'nlp', 1), ('koaning/whatlies', 0.5833550095558167, 'nlp', 0), ('ukplab/sentence-transformers', 0.5732832551002502, 'nlp', 1), ('alibaba/easynlp', 0.5686218738555908, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5621562600135803, 'nlp', 1), ('graykode/nlp-tutorial', 0.5482358932495117, 'study', 1), ('jonasgeiping/cramming', 0.5433996915817261, 'nlp', 0), ('extreme-bert/extreme-bert', 0.5432024598121643, 'llm', 1), ('jina-ai/vectordb', 0.5428557395935059, 'data', 0), ('deepset-ai/farm', 0.5399625897407532, 'nlp', 1), ('chroma-core/chroma', 0.5397540330886841, 'data', 0), ('intellabs/fastrag', 0.527250349521637, 'nlp', 2), ('ai21labs/in-context-ralm', 0.5187664031982422, 'llm', 0), ('qdrant/fastembed', 0.5031333565711975, 'ml', 0)]",2,0.0,,0.54,11,3,46,2,7,7,7,11.0,7.0,90.0,0.6,36
685,ml-dl,https://github.com/nerdyrodent/vqgan-clip,[],,[],[],,,,nerdyrodent/vqgan-clip,VQGAN-CLIP,2537,423,53,Python,,"Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.",nerdyrodent,2024-01-12,2021-07-02,134,18.85244161358811,,"Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.","['text-to-image', 'text2image']","['text-to-image', 'text2image']",2022-10-02,[],7,2.0,,0.0,5,2,31,16,0,0,0,5.0,4.0,90.0,0.8,36
725,study,https://github.com/amanchadha/coursera-deep-learning-specialization,[],,[],[],,,,amanchadha/coursera-deep-learning-specialization,coursera-deep-learning-specialization,2459,1925,25,Jupyter Notebook,,"Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models",amanchadha,2024-01-13,2020-06-24,187,13.089733840304183,,"Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models","['andrew-ng', 'andrew-ng-course', 'cnns', 'convolutional-neural-network', 'convolutional-neural-networks', 'coursera', 'coursera-assignment', 'coursera-machine-learning', 'coursera-specialization', 'deep-learning', 'hyperparameter-optimization', 'hyperparameter-tuning', 'neural-machine-translation', 'neural-network', 'neural-networks', 'neural-style-transfer', 'recurrent-neural-network', 'recurrent-neural-networks', 'regularization', 'rnns']","['andrew-ng', 'andrew-ng-course', 'cnns', 'convolutional-neural-network', 'convolutional-neural-networks', 'coursera', 'coursera-assignment', 'coursera-machine-learning', 'coursera-specialization', 'deep-learning', 'hyperparameter-optimization', 'hyperparameter-tuning', 'neural-machine-translation', 'neural-network', 'neural-networks', 'neural-style-transfer', 'recurrent-neural-network', 'recurrent-neural-networks', 'regularization', 'rnns']",2024-01-12,"[('udacity/deep-learning-v2-pytorch', 0.6206496357917786, 'study', 2), ('alirezadir/machine-learning-interview-enlightener', 0.6021793484687805, 'study', 1), ('mrdbourke/tensorflow-deep-learning', 0.592336118221283, 'study', 1), ('mosaicml/composer', 0.5713127255439758, 'ml-dl', 3), ('explosion/thinc', 0.5682108998298645, 'ml-dl', 1), ('onnx/onnx', 0.5587916970252991, 'ml', 2), ('mrdbourke/zero-to-mastery-ml', 0.5553426742553711, 'study', 1), ('jindongwang/transferlearning', 0.5513812899589539, 'ml', 1), ('keras-team/keras', 0.5479432940483093, 'ml-dl', 2), ('rwightman/pytorch-image-models', 0.5459620952606201, 'ml-dl', 0), ('ddbourgin/numpy-ml', 0.5457329154014587, 'ml', 1), ('bentoml/bentoml', 0.540047824382782, 'ml-ops', 1), ('mrdbourke/pytorch-deep-learning', 0.5357545614242554, 'study', 1), ('nyandwi/modernconvnets', 0.5350536704063416, 'ml-dl', 3), ('christoschristofidis/awesome-deep-learning', 0.5327649116516113, 'study', 2), ('tensorlayer/tensorlayer', 0.5279957056045532, 'ml-rl', 2), ('keras-rl/keras-rl', 0.5269455909729004, 'ml-rl', 1), ('nvidia/deeplearningexamples', 0.5228648781776428, 'ml-dl', 1), ('alpa-projects/alpa', 0.5197420120239258, 'ml-dl', 1), ('thilinarajapakse/simpletransformers', 0.5146978497505188, 'nlp', 0), ('milvus-io/bootcamp', 0.5138573050498962, 'data', 1), ('lutzroeder/netron', 0.5117756128311157, 'ml', 2), ('tensorflow/tensorflow', 0.5084401369094849, 'ml-dl', 2), ('tensorflow/tensor2tensor', 0.5060564875602722, 'ml', 1), ('graykode/nlp-tutorial', 0.5051466226577759, 'study', 0), ('keras-team/autokeras', 0.5036728382110596, 'ml-dl', 1), ('huggingface/autotrain-advanced', 0.500561535358429, 'ml', 1)]",8,2.0,,0.04,4,1,43,0,0,0,0,4.0,0.0,90.0,0.0,36
235,nlp,https://github.com/salesforce/codet5,[],,[],[],,,,salesforce/codet5,CodeT5,2438,381,40,Python,https://arxiv.org/abs/2305.07922,Home of CodeT5: Open Code LLMs for Code Understanding and Generation,salesforce,2024-01-14,2021-08-16,128,19.025641025641026,https://avatars.githubusercontent.com/u/453694?v=4,Home of CodeT5: Open Code LLMs for Code Understanding and Generation,"['code-generation', 'code-intelligence', 'code-understanding', 'language-model', 'large-language-models']","['code-generation', 'code-intelligence', 'code-understanding', 'language-model', 'large-language-models']",2023-07-21,"[('thudm/codegeex', 0.6897627115249634, 'llm', 1), ('salesforce/codegen', 0.6593608856201172, 'nlp', 0), ('ludwig-ai/ludwig', 0.6258037686347961, 'ml-ops', 0), ('alpha-vllm/llama2-accessory', 0.6213086843490601, 'llm', 0), ('salesforce/xgen', 0.6172817945480347, 'llm', 2), ('eugeneyan/open-llms', 0.6007983088493347, 'study', 1), ('nomic-ai/gpt4all', 0.5974037051200867, 'llm', 1), ('young-geng/easylm', 0.591428816318512, 'llm', 2), ('bigcode-project/starcoder', 0.5859589576721191, 'llm', 1), ('conceptofmind/toolformer', 0.5832077264785767, 'llm', 1), ('tigerlab-ai/tiger', 0.5818438529968262, 'llm', 1), ('hegelai/prompttools', 0.580586850643158, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5782813429832458, 'study', 1), ('h2oai/h2o-llmstudio', 0.5726329684257507, 'llm', 0), ('argilla-io/argilla', 0.5648234486579895, 'nlp', 0), ('dylanhogg/llmgraph', 0.563266396522522, 'ml', 0), ('hwchase17/langchain', 0.5549836158752441, 'llm', 1), ('hiyouga/llama-factory', 0.5533753633499146, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5533753037452698, 'llm', 2), ('lupantech/chameleon-llm', 0.551190972328186, 'llm', 1), ('citadel-ai/langcheck', 0.5499178171157837, 'llm', 1), ('eth-sri/lmql', 0.5481631755828857, 'llm', 1), ('nat/openplayground', 0.5432443022727966, 'llm', 1), ('microsoft/promptflow', 0.5427641868591309, 'llm', 0), ('bobazooba/xllm', 0.5426592826843262, 'llm', 1), ('agenta-ai/agenta', 0.5426530241966248, 'llm', 1), ('nebuly-ai/nebullvm', 0.5400938987731934, 'perf', 1), ('eleutherai/the-pile', 0.5400211811065674, 'data', 0), ('llmware-ai/llmware', 0.5333616733551025, 'llm', 1), ('shishirpatil/gorilla', 0.5298489332199097, 'llm', 0), ('juncongmoo/pyllama', 0.5293552875518799, 'llm', 0), ('bentoml/openllm', 0.5279268622398376, 'ml-ops', 0), ('next-gpt/next-gpt', 0.5271520614624023, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5260124802589417, 'perf', 0), ('night-chen/toolqa', 0.525848388671875, 'llm', 1), ('thudm/chatglm2-6b', 0.524960994720459, 'llm', 1), ('facebookresearch/codellama', 0.5207399725914001, 'llm', 1), ('lianjiatech/belle', 0.5200687050819397, 'llm', 0), ('ibm/dromedary', 0.5191128253936768, 'llm', 1), ('modularml/mojo', 0.518414318561554, 'util', 0), ('confident-ai/deepeval', 0.5176749229431152, 'testing', 1), ('bigscience-workshop/petals', 0.5150795578956604, 'data', 1), ('embedchain/embedchain', 0.5125962495803833, 'llm', 0), ('ravenscroftj/turbopilot', 0.5119407773017883, 'llm', 1), ('numba/llvmlite', 0.5081905126571655, 'util', 0), ('openbmb/toolbench', 0.5070496201515198, 'llm', 0), ('openai/evals', 0.503447949886322, 'llm', 1), ('run-llama/llama-lab', 0.5031118392944336, 'llm', 1), ('li-plus/chatglm.cpp', 0.5017570853233337, 'llm', 1), ('lastmile-ai/aiconfig', 0.500541627407074, 'util', 0)]",3,1.0,,0.44,14,3,29,6,0,0,0,14.0,9.0,90.0,0.6,36
1326,util,https://github.com/scrapinghub/dateparser,"['date', 'datetime', 'parsing']",,[],[],,,,scrapinghub/dateparser,dateparser,2408,462,134,Python,,python parser for human readable dates,scrapinghub,2024-01-13,2014-11-24,479,5.0256410256410255,https://avatars.githubusercontent.com/u/699596?v=4,python parser for human readable dates,[],"['date', 'datetime', 'parsing']",2023-12-21,"[('dateutil/dateutil', 0.7123557329177856, 'util', 2), ('sdispater/pendulum', 0.6702179908752441, 'util', 2), ('arrow-py/arrow', 0.5817139744758606, 'util', 2)]",133,0.0,,0.62,30,15,111,1,3,3,3,30.0,33.0,90.0,1.1,36
1815,study,https://github.com/mrdbourke/zero-to-mastery-ml,[],,[],[],,,,mrdbourke/zero-to-mastery-ml,zero-to-mastery-ml,2378,3146,124,Jupyter Notebook,https://dbourke.link/ZTMmlcourse,All course materials for the Zero to Mastery Machine Learning and Data Science course.,mrdbourke,2024-01-13,2019-09-23,227,10.469182389937107,,All course materials for the Zero to Mastery Machine Learning and Data Science course.,"['data-science', 'deep-learning', 'machine-learning']","['data-science', 'deep-learning', 'machine-learning']",2023-11-16,"[('mrdbourke/tensorflow-deep-learning', 0.7862590551376343, 'study', 1), ('mrdbourke/pytorch-deep-learning', 0.676668107509613, 'study', 2), ('firmai/industry-machine-learning', 0.5863468647003174, 'study', 2), ('patchy631/machine-learning', 0.5848323106765747, 'ml', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5553426742553711, 'study', 1), ('udacity/deep-learning-v2-pytorch', 0.5445974469184875, 'study', 1), ('tensorlayer/tensorlayer', 0.5104413628578186, 'ml-rl', 1), ('d2l-ai/d2l-en', 0.5092582702636719, 'study', 3), ('tensorflow/tensorflow', 0.5074943900108337, 'ml-dl', 2), ('onnx/onnx', 0.5012384057044983, 'ml', 2)]",25,1.0,,1.21,6,2,52,2,0,0,0,6.0,5.0,90.0,0.8,36
1627,math,https://github.com/mckinsey/causalnex,['causation'],,[],[],,,,mckinsey/causalnex,causalnex,2070,242,46,Python,http://causalnex.readthedocs.io/,A Python library that helps data scientists to infer causation rather than observing correlation.,mckinsey,2024-01-12,2019-12-12,215,9.596026490066226,https://avatars.githubusercontent.com/u/4265350?v=4,A Python library that helps data scientists to infer causation rather than observing correlation.,"['bayesian-inference', 'bayesian-networks', 'causal-inference', 'causal-models', 'causal-networks', 'causalnex', 'data-science', 'machine-learning']","['bayesian-inference', 'bayesian-networks', 'causal-inference', 'causal-models', 'causal-networks', 'causalnex', 'causation', 'data-science', 'machine-learning']",2023-07-11,"[('py-why/dowhy', 0.7358757853507996, 'ml', 5), ('willianfuks/tfcausalimpact', 0.6074860692024231, 'math', 1), ('py-why/econml', 0.5422161221504211, 'ml', 2), ('rasbt/mlxtend', 0.5194756984710693, 'ml', 2), ('carla-recourse/carla', 0.5116320252418518, 'ml', 1), ('teamhg-memex/eli5', 0.5101348161697388, 'ml', 2)]",35,3.0,,0.37,7,0,50,6,4,5,4,7.0,1.0,90.0,0.1,36
1505,study,https://github.com/cgpotts/cs224u,"['nlp', 'nlu']",Code for CS224u: Natural Language Understanding,[],[],,,,cgpotts/cs224u,cs224u,2020,861,85,Jupyter Notebook,,Code for Stanford CS224u,cgpotts,2024-01-12,2015-01-30,469,4.301794949802251,,Code for Stanford CS224u,[],"['nlp', 'nlu']",2023-12-14,"[('tatsu-lab/stanford_alpaca', 0.5506641268730164, 'llm', 0), ('lexpredict/lexpredict-lexnlp', 0.5473800897598267, 'nlp', 1), ('allenai/allennlp', 0.5052735805511475, 'nlp', 1)]",30,6.0,,0.44,2,2,109,1,0,0,0,2.0,2.0,90.0,1.0,36
301,template,https://github.com/pyscaffold/pyscaffold,[],,[],[],,,,pyscaffold/pyscaffold,pyscaffold,1941,177,39,Python,https://pyscaffold.org,🛠 Python project template generator with batteries included,pyscaffold,2024-01-13,2014-04-02,512,3.7846796657381616,https://avatars.githubusercontent.com/u/34571116?v=4,🛠 Python project template generator with batteries included,"['distribution', 'git', 'package', 'package-creation', 'project-template', 'release-automation', 'template-project']","['distribution', 'git', 'package', 'package-creation', 'project-template', 'release-automation', 'template-project']",2023-06-20,"[('eugeneyan/python-collab-template', 0.6021620631217957, 'template', 0), ('martinheinz/python-project-blueprint', 0.5791087746620178, 'template', 0), ('sqlalchemy/mako', 0.5707492828369141, 'template', 0), ('pypa/hatch', 0.5699118971824646, 'util', 0), ('tezromach/python-package-template', 0.553695797920227, 'template', 0), ('pdoc3/pdoc', 0.5431029796600342, 'util', 0), ('python-poetry/poetry', 0.5353856682777405, 'util', 0), ('pdm-project/pdm', 0.531360924243927, 'util', 0), ('pypa/flit', 0.5155603885650635, 'util', 0), ('indygreg/pyoxidizer', 0.5098890662193298, 'util', 0)]",58,6.0,,1.12,6,0,119,7,3,20,3,6.0,6.0,90.0,1.0,36
382,llm,https://github.com/minimaxir/aitextgen,[],,[],[],,,,minimaxir/aitextgen,aitextgen,1824,220,42,Python,https://docs.aitextgen.io,A robust Python tool for text-based AI training and generation using GPT-2.,minimaxir,2024-01-14,2019-12-29,213,8.551908908238445,,A robust Python tool for text-based AI training and generation using GPT-2.,[],[],2023-05-17,"[('minimaxir/gpt-2-simple', 0.7194006443023682, 'llm', 0), ('microsoft/pycodegpt', 0.6111297011375427, 'llm', 0), ('facebookresearch/parlai', 0.5751350522041321, 'nlp', 0), ('minimaxir/textgenrnn', 0.57369065284729, 'nlp', 0), ('huggingface/text-generation-inference', 0.563347339630127, 'llm', 0), ('databrickslabs/dolly', 0.5475108623504639, 'llm', 0), ('nvidia/nemo', 0.5452271699905396, 'nlp', 0), ('google/sentencepiece', 0.5358452796936035, 'nlp', 0), ('microsoft/generative-ai-for-beginners', 0.5282863974571228, 'study', 0), ('explosion/spacy', 0.5253174901008606, 'nlp', 0), ('nateshmbhat/pyttsx3', 0.5253090858459473, 'util', 0), ('sharonzhou/long_stable_diffusion', 0.5227047801017761, 'diffusion', 0), ('prefecthq/marvin', 0.5176252126693726, 'nlp', 0), ('bytedance/lightseq', 0.5134293437004089, 'nlp', 0), ('openlmlab/moss', 0.5120126008987427, 'llm', 0), ('torantulino/auto-gpt', 0.5113155841827393, 'llm', 0), ('minimaxir/simpleaichat', 0.507713258266449, 'llm', 0), ('google-research/electra', 0.5072848796844482, 'ml-dl', 0), ('killianlucas/open-interpreter', 0.5057147741317749, 'llm', 0), ('kagisearch/vectordb', 0.5047615766525269, 'data', 0), ('krohling/bondai', 0.5016161799430847, 'llm', 0), ('norskregnesentral/skweak', 0.5007104873657227, 'nlp', 0)]",11,4.0,,0.04,3,0,49,8,0,3,3,3.0,3.0,90.0,1.0,36
364,ml,https://github.com/rentruewang/koila,[],,[],[],,,,rentruewang/koila,koila,1804,64,11,Python,https://rentruewang.github.io/koila/,Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.,rentruewang,2024-01-12,2021-11-17,114,15.706467661691542,,Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.,"['deep-learning', 'gradient-accumulation', 'lazy-evaluation', 'machine-learning', 'memory-management', 'neural-network', 'out-of-memory', 'pytorch']","['deep-learning', 'gradient-accumulation', 'lazy-evaluation', 'machine-learning', 'memory-management', 'neural-network', 'out-of-memory', 'pytorch']",2024-01-10,"[('blackhc/toma', 0.6982141137123108, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.6254847645759583, 'perf', 4), ('pytorch/ignite', 0.6201809644699097, 'ml-dl', 4), ('nvidia/apex', 0.6033901572227478, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5876379609107971, 'study', 3), ('arogozhnikov/einops', 0.5856212973594666, 'ml-dl', 2), ('skorch-dev/skorch', 0.5766161680221558, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.5745282769203186, 'study', 3), ('pytorch/data', 0.5698388814926147, 'data', 0), ('cvxgrp/pymde', 0.5673205852508545, 'ml', 2), ('karpathy/micrograd', 0.5448654890060425, 'study', 0), ('huggingface/accelerate', 0.5446726083755493, 'ml', 0), ('timdettmers/bitsandbytes', 0.5362823009490967, 'util', 0), ('pyg-team/pytorch_geometric', 0.5282607674598694, 'ml-dl', 2), ('allenai/allennlp', 0.5262885689735413, 'nlp', 2), ('denys88/rl_games', 0.5238789916038513, 'ml-rl', 2), ('ashleve/lightning-hydra-template', 0.5227289199829102, 'util', 2), ('pytorch/pytorch', 0.5214821696281433, 'ml-dl', 3), ('pytorch/torchrec', 0.5190505385398865, 'ml-dl', 2), ('cupy/cupy', 0.5168486833572388, 'math', 0), ('pytorch/captum', 0.5131102800369263, 'ml-interpretability', 0), ('nicolas-chaulet/torch-points3d', 0.5067731738090515, 'ml', 0), ('nvidia/cuda-python', 0.5058760046958923, 'ml', 0)]",4,2.0,,0.46,1,0,26,0,0,1,1,1.0,0.0,90.0,0.0,36
908,math,https://github.com/google-research/torchsde,[],,[],[],,,,google-research/torchsde,torchsde,1415,178,34,Python,,Differentiable SDE solvers with GPU support and efficient sensitivity analysis. ,google-research,2024-01-12,2020-07-06,186,7.601688411358404,https://avatars.githubusercontent.com/u/43830688?v=4,Differentiable SDE solvers with GPU support and efficient sensitivity analysis. ,"['deep-learning', 'deep-neural-networks', 'differential-equations', 'dynamical-systems', 'neural-differential-equations', 'pytorch', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-volatility-models']","['deep-learning', 'deep-neural-networks', 'differential-equations', 'dynamical-systems', 'neural-differential-equations', 'pytorch', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-volatility-models']",2023-09-26,"[('denys88/rl_games', 0.5168188810348511, 'ml-rl', 2), ('stability-ai/stability-sdk', 0.5064745545387268, 'diffusion', 0)]",8,4.0,,0.12,5,4,43,4,1,2,1,5.0,6.0,90.0,1.2,36
1214,crypto,https://github.com/binance/binance-public-data,[],,[],[],,,,binance/binance-public-data,binance-public-data,1231,411,32,Python,,Details on how to get Binance public data,binance,2024-01-13,2020-08-24,179,6.871610845295056,https://avatars.githubusercontent.com/u/69836600?v=4,Details on how to get Binance public data,[],[],2023-11-01,[],20,3.0,,0.15,51,38,41,2,0,0,0,51.0,48.0,90.0,0.9,36
949,web,https://github.com/long2ice/fastapi-cache,[],,[],[],,,,long2ice/fastapi-cache,fastapi-cache,974,119,9,Python,https://github.com/long2ice/fastapi-cache,"fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached.",long2ice,2024-01-12,2020-08-25,179,5.441340782122905,,"fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached.","['cache', 'fastapi', 'memcached', 'redis']","['cache', 'fastapi', 'memcached', 'redis']",2023-12-07,"[('aio-libs/aiocache', 0.6432105898857117, 'data', 3), ('grantjenks/python-diskcache', 0.6009683609008789, 'util', 1), ('dgilland/cacheout', 0.5193830728530884, 'perf', 0), ('zilliztech/gptcache', 0.506112813949585, 'llm', 1), ('python-cachier/cachier', 0.5056164860725403, 'perf', 1), ('dmontagu/fastapi_client', 0.5032126307487488, 'web', 0)]",26,1.0,,2.75,65,38,41,1,1,3,1,65.0,43.0,90.0,0.7,36
482,gis,https://github.com/sentinelsat/sentinelsat,[],,[],[],,,,sentinelsat/sentinelsat,sentinelsat,943,239,62,Python,https://sentinelsat.readthedocs.io,Search and download Copernicus Sentinel satellite images,sentinelsat,2024-01-10,2015-05-22,453,2.079055118110236,https://avatars.githubusercontent.com/u/29057552?v=4,Search and download Copernicus Sentinel satellite images,"['copernicus', 'esa', 'geographic-data', 'open-data', 'remote-sensing', 'satellite-imagery', 'sentinel']","['copernicus', 'esa', 'geographic-data', 'open-data', 'remote-sensing', 'satellite-imagery', 'sentinel']",2023-11-08,"[('plant99/felicette', 0.6691089272499084, 'gis', 1), ('giswqs/aws-open-data-geo', 0.6044768691062927, 'gis', 2), ('sentinel-hub/sentinelhub-py', 0.5519406199455261, 'gis', 1), ('developmentseed/label-maker', 0.5269395112991333, 'gis', 2), ('azavea/raster-vision', 0.5150529742240906, 'gis', 1), ('developmentseed/landsat-util', 0.5123438239097595, 'gis', 0)]",43,5.0,,0.23,11,9,105,2,1,3,1,11.0,37.0,90.0,3.4,36
1140,viz,https://github.com/nomic-ai/deepscatter,[],,[],[],,,,nomic-ai/deepscatter,deepscatter,928,42,16,TypeScript,,"Zoomable, animated scatterplots in the browser that scales over a billion points",nomic-ai,2024-01-13,2018-10-30,274,3.386861313868613,https://avatars.githubusercontent.com/u/102670180?v=4,"Zoomable, animated scatterplots in the browser that scales over a billion points","['data-visualization', 'visualization', 'webgl']","['data-visualization', 'visualization', 'webgl']",2024-01-10,"[('visgl/deck.gl', 0.6989966630935669, 'viz', 3), ('holoviz/datashader', 0.6156784296035767, 'gis', 0), ('bokeh/bokeh', 0.594667911529541, 'viz', 1), ('mckinsey/vizro', 0.5346618294715881, 'viz', 2), ('altair-viz/altair', 0.5337156653404236, 'viz', 1), ('plotly/plotly.py', 0.5329233407974243, 'viz', 2), ('residentmario/geoplot', 0.5221992135047913, 'gis', 0), ('raphaelquast/eomaps', 0.5207083225250244, 'gis', 1), ('holoviz/holoviz', 0.5163466930389404, 'viz', 0), ('holoviz/hvplot', 0.511212944984436, 'pandas', 0), ('pyqtgraph/pyqtgraph', 0.5000237822532654, 'viz', 1)]",16,4.0,,2.17,11,8,63,0,3,1,3,11.0,4.0,90.0,0.4,36
874,time-series,https://github.com/winedarksea/autots,[],,[],[],,,,winedarksea/autots,AutoTS,925,87,18,Python,,Automated Time Series Forecasting,winedarksea,2024-01-13,2019-11-26,218,4.243119266055046,,Automated Time Series Forecasting,"['automl', 'autots', 'deep-learning', 'feature-engineering', 'forecasting', 'machine-learning', 'preprocessing', 'time-series']","['automl', 'autots', 'deep-learning', 'feature-engineering', 'forecasting', 'machine-learning', 'preprocessing', 'time-series']",2024-01-03,"[('awslabs/autogluon', 0.7558371424674988, 'ml', 5), ('sktime/sktime', 0.7021391987800598, 'time-series', 3), ('ourownstory/neural_prophet', 0.6846452355384827, 'ml', 4), ('salesforce/merlion', 0.682404637336731, 'time-series', 4), ('microsoft/nni', 0.6789240837097168, 'ml', 4), ('automl/auto-sklearn', 0.6762388944625854, 'ml', 1), ('firmai/atspy', 0.6719325184822083, 'time-series', 2), ('keras-team/autokeras', 0.6600058078765869, 'ml-dl', 3), ('microsoft/flaml', 0.6591876745223999, 'ml', 3), ('nccr-itmo/fedot', 0.6496773958206177, 'ml-ops', 2), ('xplainable/xplainable', 0.6385115385055542, 'ml-interpretability', 1), ('nixtla/statsforecast', 0.6360719799995422, 'time-series', 4), ('huggingface/autotrain-advanced', 0.6144011616706848, 'ml', 2), ('alkaline-ml/pmdarima', 0.6125960350036621, 'time-series', 3), ('mljar/mljar-supervised', 0.578948438167572, 'ml', 3), ('alpa-projects/alpa', 0.5752678513526917, 'ml-dl', 2), ('shankarpandala/lazypredict', 0.5729676485061646, 'ml', 2), ('salesforce/deeptime', 0.5623159408569336, 'time-series', 3), ('awslabs/gluonts', 0.5618434548377991, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.557109534740448, 'time-series', 3), ('bentoml/bentoml', 0.5495694875717163, 'ml-ops', 2), ('torantulino/auto-gpt', 0.5468161702156067, 'llm', 0), ('autoviml/auto_ts', 0.5450201034545898, 'time-series', 2), ('featurelabs/featuretools', 0.5383171439170837, 'ml', 3), ('feast-dev/feast', 0.5365046262741089, 'ml-ops', 1), ('unit8co/darts', 0.5341631770133972, 'time-series', 4), ('alirezadir/machine-learning-interview-enlightener', 0.5339345335960388, 'study', 2), ('mosaicml/composer', 0.5297396183013916, 'ml-dl', 2), ('mindsdb/mindsdb', 0.5278527736663818, 'data', 2), ('facebook/prophet', 0.5207078456878662, 'time-series', 2), ('blue-yonder/tsfresh', 0.5182939171791077, 'time-series', 1), ('onnx/onnx', 0.516512930393219, 'ml', 2), ('google/temporian', 0.5130333304405212, 'time-series', 2), ('google/pyglove', 0.5115013122558594, 'util', 2), ('ydataai/ydata-synthetic', 0.5098974704742432, 'data', 3), ('uber/orbit', 0.5088127255439758, 'time-series', 3), ('huggingface/datasets', 0.5051737427711487, 'nlp', 2)]",1,0.0,,5.08,21,15,50,0,13,12,13,21.0,50.0,90.0,2.4,36
929,web,https://github.com/koxudaxi/fastapi-code-generator,[],,[],[],,,,koxudaxi/fastapi-code-generator,fastapi-code-generator,862,92,20,Python,,This code generator creates FastAPI app from an openapi file.,koxudaxi,2024-01-12,2020-06-14,189,4.553962264150943,,This code generator creates FastAPI app from an openapi file.,"['fastapi', 'generator', 'openapi', 'pydantic']","['fastapi', 'generator', 'openapi', 'pydantic']",2023-09-07,"[('dmontagu/fastapi_client', 0.6843157410621643, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.607068657875061, 'template', 1), ('kuimono/openapi-schema-pydantic', 0.6038326025009155, 'util', 1)]",24,3.0,,1.21,16,8,44,4,5,11,5,16.0,18.0,90.0,1.1,36
842,util,https://github.com/wolph/python-progressbar,[],,[],[],,,,wolph/python-progressbar,python-progressbar,831,141,22,Python,http://progressbar-2.readthedocs.org/en/latest/,"Progressbar 2 - A progress bar for Python 2 and Python 3 - ""pip install progressbar2""",wolph,2024-01-10,2012-02-20,623,1.3335625859697386,,"Progressbar 2 - A progress bar for Python 2 and Python 3 - ""pip install progressbar2""","['bar', 'cli', 'console', 'eta', 'gui', 'percentage', 'progress', 'progress-bar', 'progressbar', 'rate', 'terminal', 'time']","['bar', 'cli', 'console', 'eta', 'gui', 'percentage', 'progress', 'progress-bar', 'progressbar', 'rate', 'terminal', 'time']",2024-01-02,"[('tqdm/tqdm', 0.7864949107170105, 'term', 9), ('rockhopper-technologies/enlighten', 0.7673426270484924, 'term', 0), ('rsalmei/alive-progress', 0.6114795207977295, 'util', 7), ('hugovk/pypistats', 0.5268552303314209, 'util', 1)]",46,3.0,,0.96,13,9,145,0,3,9,3,13.0,38.0,90.0,2.9,36
1500,ml-dl,https://github.com/deepmind/chex,"['numpy', 'testing', 'autograd', 'jax']",Chex is a library of utilities for helping to write reliable JAX code,[],[],,,,deepmind/chex,chex,667,40,18,Python,https://chex.readthedocs.io,,deepmind,2024-01-13,2020-08-06,181,3.6705974842767297,https://avatars.githubusercontent.com/u/8596759?v=4,Chex is a library of utilities for helping to write reliable JAX code,[],"['autograd', 'jax', 'numpy', 'testing']",2023-12-09,"[('google/flax', 0.5962191820144653, 'ml-dl', 1), ('deepmind/dm-haiku', 0.5875384211540222, 'ml-dl', 1), ('deepmind/synjax', 0.5291113257408142, 'math', 1), ('samuelcolvin/rtoml', 0.5042668581008911, 'data', 0)]",40,4.0,,1.33,18,11,42,1,7,6,7,18.0,8.0,90.0,0.4,36
1497,util,https://github.com/instagram/fixit,['linter'],,[],[],,,,instagram/fixit,Fixit,633,57,26,Python,https://fixit.rtfd.io/en/latest/,Advanced Python linting framework with auto-fixes and hierarchical configuration that makes it easy to write custom in-repo lint rules.,instagram,2024-01-14,2020-02-20,205,3.0770833333333334,https://avatars.githubusercontent.com/u/549085?v=4,Advanced Python linting framework with auto-fixes and hierarchical configuration that makes it easy to write custom in-repo lint rules.,[],['linter'],2023-12-21,"[('python-rope/rope', 0.5731984972953796, 'util', 0), ('pycqa/pyflakes', 0.5664411783218384, 'util', 1), ('python/mypy', 0.5660220384597778, 'typing', 1), ('grahamdumpleton/wrapt', 0.5495676398277283, 'util', 0), ('klen/pylama', 0.5459169745445251, 'util', 1), ('landscapeio/prospector', 0.5204662084579468, 'util', 0), ('eugeneyan/python-collab-template', 0.5144226551055908, 'template', 0), ('pytoolz/toolz', 0.5103968977928162, 'util', 0), ('asottile/reorder-python-imports', 0.5014389753341675, 'util', 1)]",41,4.0,,2.12,37,22,47,1,0,3,3,37.0,28.0,90.0,0.8,36
1144,util,https://github.com/terrycain/aioboto3,[],,[],[],,,,terrycain/aioboto3,aioboto3,608,63,8,Python,,Wrapper to use boto3 resources with the aiobotocore async backend,terrycain,2024-01-12,2017-09-25,331,1.8360655737704918,,Wrapper to use boto3 resources with the aiobotocore async backend,"['async', 'aws', 'boto3']","['async', 'aws', 'boto3']",2023-12-08,"[('aio-libs/aiobotocore', 0.6956399083137512, 'util', 1), ('geeogi/async-python-lambda-template', 0.5498723387718201, 'template', 0), ('samuelcolvin/aioaws', 0.5090085864067078, 'data', 1)]",27,4.0,,0.37,14,10,77,1,0,10,10,14.0,29.0,90.0,2.1,36
358,ml-ops,https://github.com/google/ml-metadata,[],,[],[],,,,google/ml-metadata,ml-metadata,577,134,29,C++,https://www.tensorflow.org/tfx/guide/mlmd,For recording and retrieving metadata associated with ML developer and data scientist workflows.,google,2024-01-10,2019-01-15,263,2.193916349809886,https://avatars.githubusercontent.com/u/1342004?v=4,For recording and retrieving metadata associated with ML developer and data scientist workflows.,[],[],2024-01-12,"[('astronomer/astro-sdk', 0.5482959747314453, 'ml-ops', 0), ('whylabs/whylogs', 0.5445284247398376, 'util', 0), ('ploomber/ploomber', 0.5324745774269104, 'ml-ops', 0), ('airbnb/knowledge-repo', 0.5292649865150452, 'data', 0), ('hyperqueryhq/whale', 0.5290652513504028, 'data', 0), ('dagworks-inc/hamilton', 0.5270819664001465, 'ml-ops', 0), ('mage-ai/mage-ai', 0.524271547794342, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5165597200393677, 'ml-ops', 0), ('simonw/datasette', 0.5161576271057129, 'data', 0), ('netflix/metaflow', 0.5132206082344055, 'ml-ops', 0), ('intake/intake', 0.506055474281311, 'data', 0), ('linealabs/lineapy', 0.5038784146308899, 'jupyter', 0), ('dbt-labs/dbt-core', 0.5018987655639648, 'ml-ops', 0), ('iterative/dvc', 0.5009445548057556, 'ml-ops', 0)]",19,2.0,,1.1,12,1,61,0,3,7,3,12.0,53.0,90.0,4.4,36
1569,ml,https://github.com/nicolas-hbt/pygraft,"['knowledge-graph', 'ontology-generation']",,[],[],1.0,,,nicolas-hbt/pygraft,pygraft,551,36,12,Python,https://pygraft.readthedocs.io/en/latest/,Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips,nicolas-hbt,2024-01-12,2023-09-07,20,26.6,,Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips,"['artificial-intelligence', 'benchmarking', 'contributions-welcome', 'data-generator', 'graph-generator', 'knowledge-base', 'knowledge-graph', 'linked-data', 'machine-learning', 'ontology', 'ontology-generation', 'owl', 'rdf', 'rdfs', 'schema', 'semantic-web', 'semantics', 'synthetic-data', 'synthetic-dataset-generation']","['artificial-intelligence', 'benchmarking', 'contributions-welcome', 'data-generator', 'graph-generator', 'knowledge-base', 'knowledge-graph', 'linked-data', 'machine-learning', 'ontology', 'ontology-generation', 'owl', 'rdf', 'rdfs', 'schema', 'semantic-web', 'semantics', 'synthetic-data', 'synthetic-dataset-generation']",2023-12-01,"[('sdv-dev/sdv', 0.6006342172622681, 'data', 2), ('mindsdb/mindsdb', 0.5319455862045288, 'data', 2), ('dylanhogg/llmgraph', 0.5217655897140503, 'ml', 1), ('ydataai/ydata-synthetic', 0.5071893334388733, 'data', 2), ('strawberry-graphql/strawberry', 0.5039038062095642, 'web', 0)]",1,1.0,,0.75,1,0,4,1,0,0,0,1.0,1.0,90.0,1.0,36
1247,util,https://github.com/steamship-core/steamship-langchain,[],,[],[],,,,steamship-core/steamship-langchain,steamship-langchain,499,98,12,Python,,steamship-langchain,steamship-core,2024-01-12,2023-02-04,51,9.702777777777778,https://avatars.githubusercontent.com/u/99272373?v=4,steamship-langchain,[],[],2023-09-12,"[('steamship-core/python-client', 0.5675639510154724, 'util', 0)]",7,2.0,,2.23,1,0,11,4,17,42,17,1.0,1.0,90.0,1.0,36
1706,util,https://github.com/snok/install-poetry,"['github', 'action']",,[],[],,,,snok/install-poetry,install-poetry,491,46,6,Shell,,Github action for installing and configuring Poetry,snok,2024-01-05,2020-10-25,170,2.8833892617449663,https://avatars.githubusercontent.com/u/64945977?v=4,Github action for installing and configuring Poetry,[],"['action', 'github']",2024-01-11,"[('python-poetry/install.python-poetry.org', 0.6302388310432434, 'util', 0)]",22,5.0,,0.44,15,11,39,0,1,8,1,15.0,27.0,90.0,1.8,36
1612,llm,https://github.com/lupantech/scienceqa,['thought-chain'],,[],[],,,,lupantech/scienceqa,ScienceQA,487,62,9,Python,,"Data and code for NeurIPS 2022 Paper ""Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering"".",lupantech,2024-01-13,2022-10-17,67,7.253191489361702,,"Data and code for NeurIPS 2022 Paper ""Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering"".",[],['thought-chain'],2023-12-30,"[('kyegomez/tree-of-thoughts', 0.5537173748016357, 'llm', 0), ('noahshinn/reflexion', 0.5062936544418335, 'llm', 0)]",4,2.0,,0.67,5,5,15,0,0,1,1,5.0,10.0,90.0,2.0,36
490,gis,https://github.com/cogeotiff/rio-tiler,[],,[],[],,,,cogeotiff/rio-tiler,rio-tiler,453,96,65,Python,https://cogeotiff.github.io/rio-tiler/,User friendly Rasterio plugin to read raster datasets.,cogeotiff,2024-01-11,2017-10-06,329,1.374512353706112,https://avatars.githubusercontent.com/u/40065466?v=4,User friendly Rasterio plugin to read raster datasets.,"['cog', 'cogeotiff', 'gdal', 'maptile', 'mercator', 'raster', 'raster-processing', 'rasterio', 'satellite', 'slippy-map', 'tile']","['cog', 'cogeotiff', 'gdal', 'maptile', 'mercator', 'raster', 'raster-processing', 'rasterio', 'satellite', 'slippy-map', 'tile']",2024-01-12,"[('rasterio/rasterio', 0.7036706209182739, 'gis', 2), ('cogeotiff/rio-cogeo', 0.6072856783866882, 'gis', 4), ('osgeo/gdal', 0.5156446099281311, 'gis', 1), ('corteva/rioxarray', 0.5002418756484985, 'gis', 3)]",26,4.0,,1.38,20,15,76,0,0,24,24,20.0,29.0,90.0,1.4,36
855,ml-ops,https://github.com/astronomer/astronomer,[],,[],[],,,,astronomer/astronomer,astronomer,453,83,45,Python,https://www.astronomer.io,"Helm Charts for the Astronomer Platform, Apache Airflow as a Service on Kubernetes",astronomer,2024-01-11,2018-01-15,315,1.4374433363553945,https://avatars.githubusercontent.com/u/12449437?v=4,"Helm Charts for the Astronomer Platform, Apache Airflow as a Service on Kubernetes","['apache-airflow', 'astronomer-platform', 'docker', 'kubernetes']","['apache-airflow', 'astronomer-platform', 'docker', 'kubernetes']",2024-01-09,"[('astronomer/airflow-chart', 0.8572540283203125, 'ml-ops', 2), ('anyscale/airflow-provider-ray', 0.5748955011367798, 'ml-ops', 0), ('apache/airflow', 0.5602481961250305, 'ml-ops', 1), ('gefyrahq/gefyra', 0.5323020815849304, 'util', 2)]",75,4.0,,5.15,56,53,73,0,15,60,15,56.0,17.0,90.0,0.3,36
1457,util,https://github.com/conda/constructor,['conda'],,[],[],,,,conda/constructor,constructor,430,166,33,Python,https://conda.github.io/constructor/,tool for creating installers from conda packages,conda,2024-01-04,2016-02-12,415,1.0347198349948437,https://avatars.githubusercontent.com/u/6392739?v=4,tool for creating installers from conda packages,[],['conda'],2024-01-13,"[('conda/conda-build', 0.8005626201629639, 'util', 1), ('conda/conda-pack', 0.7213409543037415, 'util', 1), ('mamba-org/boa', 0.7149392366409302, 'util', 1), ('mamba-org/quetz', 0.7079612612724304, 'util', 1), ('mamba-org/gator', 0.5621293783187866, 'jupyter', 1), ('mamba-org/mamba', 0.5558754801750183, 'util', 1), ('pyodide/micropip', 0.5295533537864685, 'util', 0), ('ofek/pyapp', 0.5201569199562073, 'util', 0), ('conda-forge/feedstocks', 0.507718563079834, 'util', 1)]",70,6.0,,1.56,57,47,96,0,8,6,8,57.0,33.0,90.0,0.6,36
713,gis,https://github.com/weecology/deepforest,[],,[],[],,,,weecology/deepforest,DeepForest,411,157,15,Python,https://deepforest.readthedocs.io/,Python Package for Airborne RGB machine learning,weecology,2024-01-10,2018-03-07,307,1.3350348027842227,https://avatars.githubusercontent.com/u/1156696?v=4,Python Package for Airborne RGB machine learning,[],[],2023-12-27,"[('sentinel-hub/eo-learn', 0.5667561888694763, 'gis', 0), ('mdbloice/augmentor', 0.5662134885787964, 'ml', 0), ('lightly-ai/lightly', 0.5572022199630737, 'ml', 0), ('pycaret/pycaret', 0.5376171469688416, 'ml', 0), ('earthlab/earthpy', 0.5284902453422546, 'gis', 0), ('radiantearth/radiant-mlhub', 0.5279979705810547, 'gis', 0), ('gradio-app/gradio', 0.525834858417511, 'viz', 0), ('azavea/raster-vision', 0.5204096436500549, 'gis', 0), ('facebookresearch/pytorch3d', 0.5166642069816589, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5097160339355469, 'study', 0), ('featurelabs/featuretools', 0.5066676139831543, 'ml', 0), ('rasbt/mlxtend', 0.5020350217819214, 'ml', 0)]",14,6.0,,2.17,115,86,71,1,0,12,12,115.0,104.0,90.0,0.9,36
1526,llm,https://github.com/operand/agency,[],,[],[],,,,operand/agency,agency,351,18,10,Python,https://createwith.agency,A fast and minimal framework for building agent-integrated systems,operand,2024-01-12,2023-05-23,36,9.75,,A fast and minimal framework for building agent-integrated systems,"['actor', 'actor-model', 'agent', 'agents', 'agi', 'ai', 'api', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agent', 'autonomous-agents', 'framework', 'llm', 'llmops', 'llms', 'machine-learning', 'minimal']","['actor', 'actor-model', 'agent', 'agents', 'agi', 'ai', 'api', 'artificial-general-intelligence', 'artificial-intelligence', 'autonomous-agent', 'autonomous-agents', 'framework', 'llm', 'llmops', 'llms', 'machine-learning', 'minimal']",2024-01-12,"[('prefecthq/marvin', 0.6231884956359863, 'nlp', 3), ('transformeroptimus/superagi', 0.6139060258865356, 'llm', 8), ('microsoft/lmops', 0.6131894588470459, 'llm', 2), ('geekan/metagpt', 0.6091906428337097, 'llm', 2), ('mlc-ai/mlc-llm', 0.6070036888122559, 'llm', 1), ('unity-technologies/ml-agents', 0.596383810043335, 'ml-rl', 1), ('mindsdb/mindsdb', 0.5936707854270935, 'data', 4), ('ludwig-ai/ludwig', 0.5899900197982788, 'ml-ops', 2), ('zacwellmer/worldmodels', 0.5862823128700256, 'ml-rl', 0), ('aiwaves-cn/agents', 0.5783196687698364, 'nlp', 2), ('antonosika/gpt-engineer', 0.5778323411941528, 'llm', 2), ('cheshire-cat-ai/core', 0.5766038298606873, 'llm', 2), ('projectmesa/mesa', 0.5751315355300903, 'sim', 0), ('bentoml/bentoml', 0.5735385417938232, 'ml-ops', 3), ('nccr-itmo/fedot', 0.5655133128166199, 'ml-ops', 1), ('microsoft/semantic-kernel', 0.5540736317634583, 'llm', 3), ('lastmile-ai/aiconfig', 0.5538642406463623, 'util', 2), ('lucidrains/toolformer-pytorch', 0.5433422923088074, 'llm', 1), ('facebookresearch/habitat-lab', 0.5416178107261658, 'sim', 1), ('pathwaycom/llm-app', 0.5409510135650635, 'llm', 3), ('hpcaitech/colossalai', 0.5398745536804199, 'llm', 1), ('facebookresearch/droidlet', 0.5389538407325745, 'sim', 0), ('deepset-ai/haystack', 0.5344579815864563, 'llm', 2), ('microsoft/promptflow', 0.5344325304031372, 'llm', 2), ('ml-tooling/opyrator', 0.5331600308418274, 'viz', 1), ('krohling/bondai', 0.5317063331604004, 'llm', 2), ('yoheinakajima/babyagi', 0.53130042552948, 'llm', 2), ('pettingzoo-team/pettingzoo', 0.5311383605003357, 'ml-rl', 1), ('smol-ai/developer', 0.5302104949951172, 'llm', 2), ('jina-ai/thinkgpt', 0.5273327827453613, 'llm', 0), ('pytorchlightning/pytorch-lightning', 0.526900053024292, 'ml-dl', 3), ('assafelovic/gpt-researcher', 0.5227841734886169, 'llm', 1), ('chatarena/chatarena', 0.5225850343704224, 'llm', 2), ('google/dopamine', 0.5216368436813354, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5203875303268433, 'ml-rl', 1), ('oliveirabruno01/babyagi-asi', 0.519800066947937, 'llm', 3), ('microsoft/autogen', 0.5193168520927429, 'llm', 2), ('minedojo/voyager', 0.516743540763855, 'llm', 0), ('langchain-ai/langgraph', 0.5159871578216553, 'llm', 1), ('mnotgod96/appagent', 0.51549232006073, 'llm', 2), ('googlecloudplatform/vertex-ai-samples', 0.5143194198608398, 'ml', 1), ('adap/flower', 0.5125753283500671, 'ml-ops', 4), ('linksoul-ai/autoagents', 0.5123705267906189, 'llm', 1), ('uber/fiber', 0.5117772817611694, 'data', 1), ('nebuly-ai/nebullvm', 0.5101591348648071, 'perf', 3), ('inspirai/timechamber', 0.5097473859786987, 'sim', 0), ('microsoft/generative-ai-for-beginners', 0.5075168013572693, 'study', 2), ('pytorch/rl', 0.5073025822639465, 'ml-rl', 2), ('torantulino/auto-gpt', 0.5023353099822998, 'llm', 3), ('modularml/mojo', 0.5005974769592285, 'util', 2), ('onnx/onnx', 0.5004202127456665, 'ml', 1)]",3,1.0,,5.54,11,9,8,0,15,23,15,11.0,0.0,90.0,0.0,36
857,ml-ops,https://github.com/astronomer/astro-sdk,[],,[],[],,,,astronomer/astro-sdk,astro-sdk,299,35,13,Python,https://astro-sdk-python.rtfd.io/,"Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.",astronomer,2024-01-12,2021-12-06,112,2.6662420382165606,https://avatars.githubusercontent.com/u/12449437?v=4,"Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.","['airflow', 'apache-airflow', 'bigquery', 'dags', 'data-analysis', 'data-science', 'elt', 'etl', 'gcs', 'pandas', 'postgres', 's3', 'snowflake', 'sql', 'sqlite', 'workflows']","['airflow', 'apache-airflow', 'bigquery', 'dags', 'data-analysis', 'data-science', 'elt', 'etl', 'gcs', 'pandas', 'postgres', 's3', 'snowflake', 'sql', 'sqlite', 'workflows']",2024-01-09,"[('mage-ai/mage-ai', 0.6467173099517822, 'ml-ops', 4), ('apache/airflow', 0.6314418911933899, 'ml-ops', 5), ('kestra-io/kestra', 0.6130484342575073, 'ml-ops', 2), ('flyteorg/flyte', 0.5711103081703186, 'ml-ops', 2), ('hi-primus/optimus', 0.5675498843193054, 'ml-ops', 2), ('ploomber/ploomber', 0.5675156116485596, 'ml-ops', 1), ('google/ml-metadata', 0.5482959747314453, 'ml-ops', 0), ('prefecthq/server', 0.5464804768562317, 'util', 0), ('orchest/orchest', 0.5454973578453064, 'ml-ops', 3), ('getindata/kedro-kubeflow', 0.541987419128418, 'ml-ops', 0), ('dagster-io/dagster', 0.5415989756584167, 'ml-ops', 2), ('kubeflow-kale/kale', 0.5413955450057983, 'ml-ops', 0), ('prefecthq/prefect', 0.5360534191131592, 'ml-ops', 1), ('linealabs/lineapy', 0.5296847224235535, 'jupyter', 0), ('aws/aws-sdk-pandas', 0.525879442691803, 'pandas', 3), ('fugue-project/fugue', 0.5199980735778809, 'pandas', 2), ('tobymao/sqlglot', 0.5131558179855347, 'data', 5), ('fastai/fastcore', 0.5107561945915222, 'util', 0), ('meltano/meltano', 0.5098263621330261, 'ml-ops', 1), ('airbytehq/airbyte', 0.5056763887405396, 'data', 6), ('kubeflow/fairing', 0.5054138898849487, 'ml-ops', 0)]",39,2.0,,3.85,66,57,26,0,14,40,14,66.0,29.0,90.0,0.4,36
1520,ml-ops,https://github.com/lithops-cloud/lithops,[],,[],[],,,,lithops-cloud/lithops,lithops,293,92,13,Python,http://lithops.cloud,"A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀",lithops-cloud,2024-01-12,2018-04-23,301,0.9729601518026565,https://avatars.githubusercontent.com/u/71205470?v=4,"A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀","['big-data', 'big-data-analytics', 'cloud-computing', 'data-processing', 'distributed', 'kubernetes', 'multicloud', 'multiprocessing', 'object-storage', 'parallel', 'serverless', 'serverless-computing', 'serverless-functions']","['big-data', 'big-data-analytics', 'cloud-computing', 'data-processing', 'distributed', 'kubernetes', 'multicloud', 'multiprocessing', 'object-storage', 'parallel', 'serverless', 'serverless-computing', 'serverless-functions']",2024-01-12,"[('skypilot-org/skypilot', 0.6185680031776428, 'llm', 2), ('apache/spark', 0.5891563296318054, 'data', 1), ('flyteorg/flyte', 0.5794029235839844, 'ml-ops', 1), ('backtick-se/cowait', 0.5676038861274719, 'util', 1), ('jina-ai/jina', 0.5622606873512268, 'ml', 1), ('eventual-inc/daft', 0.5597764849662781, 'pandas', 0), ('airbytehq/airbyte', 0.5396174788475037, 'data', 0), ('fugue-project/fugue', 0.53661048412323, 'pandas', 1), ('aws/chalice', 0.5222293138504028, 'web', 1), ('netflix/metaflow', 0.5198798179626465, 'ml-ops', 1), ('localstack/localstack', 0.514224648475647, 'util', 0), ('dagster-io/dagster', 0.5072967410087585, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.5040023922920227, 'ml', 0)]",45,2.0,,5.6,59,58,70,0,5,13,5,59.0,152.0,90.0,2.6,36
950,util,https://github.com/cqcl/tket,[],,[],[],,,,cqcl/tket,tket,220,45,17,C++,https://tket.quantinuum.com/,"Source code for the TKET quantum compiler, Python bindings and utilities",cqcl,2024-01-11,2021-09-13,124,1.7721518987341771,https://avatars.githubusercontent.com/u/15688781?v=4,"Source code for the TKET quantum compiler, Python bindings and utilities","['compiler', 'quantum-computing']","['compiler', 'quantum-computing']",2024-01-12,"[('pyscf/pyscf', 0.677206814289093, 'sim', 0), ('cqcl/lambeq', 0.6623311638832092, 'nlp', 0), ('quantumlib/cirq', 0.6193458437919617, 'sim', 1), ('jackhidary/quantumcomputingbook', 0.6081982851028442, 'study', 1), ('numba/llvmlite', 0.5800346732139587, 'util', 0), ('qiskit/qiskit', 0.5771373510360718, 'sim', 1)]",29,2.0,,6.54,155,134,28,0,50,58,50,155.0,102.0,90.0,0.7,36
1867,util,https://github.com/pypdfium2-team/pypdfium2,[],,[],[],,,,pypdfium2-team/pypdfium2,pypdfium2,216,12,6,Python,https://pypdfium2.readthedocs.io/,Python bindings to PDFium,pypdfium2-team,2024-01-13,2021-10-23,118,1.8238841978287093,https://avatars.githubusercontent.com/u/93039761?v=4,Python bindings to PDFium,"['pdf', 'pdf-documents', 'pdf-to-image', 'pdfium', 'rasterisation']","['pdf', 'pdf-documents', 'pdf-to-image', 'pdfium', 'rasterisation']",2024-01-10,"[('py-pdf/pypdf2', 0.6358337998390198, 'util', 2), ('pyfpdf/fpdf2', 0.6115421652793884, 'util', 1), ('camelot-dev/camelot', 0.5336915850639343, 'util', 0), ('jorisschellekens/borb', 0.5246109366416931, 'util', 1)]",4,2.0,,8.15,45,44,27,0,37,42,37,45.0,93.0,90.0,2.1,36
1768,data,https://github.com/meltano/sdk,['data-engineering'],,[],[],,,,meltano/sdk,sdk,74,53,7,Python,https://sdk.meltano.com,Write 70% less code by using the SDK to build custom extractors and loaders that adhere to the Singer standard: https://sdk.meltano.com,meltano,2024-01-12,2021-06-21,136,0.5435466946484785,https://avatars.githubusercontent.com/u/43816713?v=4,Write 70% less code by using the SDK to build custom extractors and loaders that adhere to the Singer standard: https://sdk.meltano.com,['sdk'],"['data-engineering', 'sdk']",2024-01-11,[],70,3.0,,10.56,159,134,31,0,28,31,28,157.0,284.0,90.0,1.8,36
931,data,https://github.com/scylladb/python-driver,[],,[],[],,,,scylladb/python-driver,python-driver,57,35,9,Python,https://python-driver.docs.scylladb.com,"ScyllaDB Python Driver, originally DataStax Python Driver for Apache Cassandra",scylladb,2024-01-11,2018-11-20,271,0.21033210332103322,https://avatars.githubusercontent.com/u/14364730?v=4,"ScyllaDB Python Driver, originally DataStax Python Driver for Apache Cassandra",['scylladb'],['scylladb'],2024-01-11,"[('datastax/python-driver', 0.8284926414489746, 'data', 0), ('neo4j/neo4j-python-driver', 0.5569538474082947, 'data', 0)]",211,6.0,,2.17,41,16,63,0,0,23,23,41.0,105.0,90.0,2.6,36
1306,study,https://github.com/timofurrer/awesome-asyncio,['awesome'],,[],[],,,,timofurrer/awesome-asyncio,awesome-asyncio,4297,321,122,,,"A curated list of awesome Python asyncio frameworks, libraries, software and resources",timofurrer,2024-01-13,2016-11-01,378,11.367724867724867,,"A curated list of awesome Python asyncio frameworks, libraries, software and resources","['asyncio', 'awesome', 'awesome-list', 'coroutines', 'python-asyncio']","['asyncio', 'awesome', 'awesome-list', 'coroutines', 'python-asyncio']",2023-01-08,"[('dylanhogg/awesome-python', 0.7224661111831665, 'study', 2), ('pallets/quart', 0.6550799608230591, 'web', 1), ('aio-libs/aiohttp', 0.6443581581115723, 'web', 1), ('encode/httpx', 0.640356183052063, 'web', 1), ('klen/muffin', 0.6332191228866577, 'web', 1), ('magicstack/uvloop', 0.6274605989456177, 'util', 1), ('encode/uvicorn', 0.618058979511261, 'web', 1), ('fastai/fastcore', 0.6136905550956726, 'util', 0), ('neoteroi/blacksheep', 0.6109622716903687, 'web', 1), ('masoniteframework/masonite', 0.6026535034179688, 'web', 0), ('sumerc/yappi', 0.6000396013259888, 'profiling', 1), ('pypy/pypy', 0.5946156978607178, 'util', 0), ('krzjoa/awesome-python-data-science', 0.5890659093856812, 'study', 2), ('alirn76/panther', 0.5865374207496643, 'web', 0), ('aio-libs/aiobotocore', 0.5803163647651672, 'util', 1), ('samuelcolvin/aioaws', 0.5785270929336548, 'data', 1), ('pytest-dev/pytest-asyncio', 0.5779024958610535, 'testing', 1), ('willmcgugan/textual', 0.575556218624115, 'term', 0), ('pytoolz/toolz', 0.5735023021697998, 'util', 0), ('ets-labs/python-dependency-injector', 0.5701932311058044, 'util', 1), ('trananhkma/fucking-awesome-python', 0.5646189451217651, 'study', 1), ('starlite-api/starlite', 0.5622597932815552, 'web', 1), ('python-trio/trio', 0.5602687001228333, 'perf', 0), ('pylons/pyramid', 0.5598770380020142, 'web', 0), ('alex-sherman/unsync', 0.5594347715377808, 'util', 0), ('falconry/falcon', 0.5593904852867126, 'web', 0), ('ta-lib/ta-lib-python', 0.5574406981468201, 'finance', 0), ('tiangolo/asyncer', 0.5529595017433167, 'perf', 1), ('python/cpython', 0.55228590965271, 'util', 0), ('geeogi/async-python-lambda-template', 0.5508684515953064, 'template', 0), ('pallets/flask', 0.5479661822319031, 'web', 0), ('tornadoweb/tornado', 0.5469016432762146, 'web', 0), ('eleutherai/pyfra', 0.5461949110031128, 'ml', 0), ('christoschristofidis/awesome-deep-learning', 0.5383524298667908, 'study', 2), ('1200wd/bitcoinlib', 0.5357022285461426, 'crypto', 0), ('python-restx/flask-restx', 0.5342033505439758, 'web', 0), ('bottlepy/bottle', 0.5336396098136902, 'web', 0), ('agronholm/anyio', 0.5322125554084778, 'perf', 1), ('openai/openai-python', 0.524164617061615, 'util', 0), ('gradio-app/gradio', 0.5227011442184448, 'viz', 0), ('aio-libs/aiomysql', 0.522577166557312, 'data', 1), ('samuelcolvin/arq', 0.5215858817100525, 'data', 1), ('huggingface/huggingface_hub', 0.5210409164428711, 'ml', 0), ('plotly/dash', 0.5202446579933167, 'viz', 0), ('cherrypy/cherrypy', 0.5200496912002563, 'web', 0), ('wesm/pydata-book', 0.5189331769943237, 'study', 0), ('cython/cython', 0.5178491473197937, 'util', 0), ('samuelcolvin/watchfiles', 0.5153577327728271, 'util', 1), ('r0x0r/pywebview', 0.5146098136901855, 'gui', 0), ('flet-dev/flet', 0.5139799118041992, 'web', 0), ('tiangolo/fastapi', 0.5137189030647278, 'web', 1), ('cosmicpython/book', 0.5105115175247192, 'study', 0), ('wxwidgets/phoenix', 0.510340690612793, 'gui', 1), ('scrapy/scrapy', 0.5095760226249695, 'data', 0), ('webpy/webpy', 0.5086891651153564, 'web', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5076543092727661, 'study', 0), ('emmett-framework/emmett', 0.5075742602348328, 'web', 1), ('pyston/pyston', 0.5064808130264282, 'util', 0), ('klen/py-frameworks-bench', 0.5063012838363647, 'perf', 0), ('erotemic/ubelt', 0.5056865811347961, 'util', 0), ('hoffstadt/dearpygui', 0.5044320225715637, 'gui', 0), ('cuemacro/finmarketpy', 0.5032345056533813, 'finance', 0), ('cohere-ai/notebooks', 0.5014423131942749, 'llm', 0), ('plotly/plotly.py', 0.5011566877365112, 'viz', 0), ('faster-cpython/ideas', 0.500760018825531, 'perf', 0), ('python-rope/rope', 0.5005401968955994, 'util', 0), ('holoviz/panel', 0.5001717805862427, 'viz', 0)]",57,5.0,,0.0,0,0,88,12,0,0,0,0.0,0.0,90.0,0.0,35
203,viz,https://github.com/ml-tooling/opyrator,[],,[],[],1.0,,,ml-tooling/opyrator,opyrator,2974,157,50,Python,https://opyrator-playground.mltooling.org,"🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.",ml-tooling,2024-01-14,2021-04-06,147,20.231292517006803,https://avatars.githubusercontent.com/u/45942048?v=4,"🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.","['deployment', 'faas', 'fastapi', 'functions', 'machine-learning', 'microservices', 'pydantic', 'python-functions', 'serverless', 'streamlit', 'type-hints']","['deployment', 'faas', 'fastapi', 'functions', 'machine-learning', 'microservices', 'pydantic', 'python-functions', 'serverless', 'streamlit', 'type-hints']",2021-05-06,"[('unionai-oss/unionml', 0.6805552840232849, 'ml-ops', 1), ('titanml/takeoff', 0.6359823942184448, 'llm', 1), ('gradio-app/gradio', 0.6309301853179932, 'viz', 1), ('wandb/client', 0.6187072992324829, 'ml', 1), ('avaiga/taipy', 0.617566704750061, 'data', 0), ('polyaxon/polyaxon', 0.6038118600845337, 'ml-ops', 1), ('kubeflow/fairing', 0.5929511189460754, 'ml-ops', 0), ('bentoml/bentoml', 0.5898632407188416, 'ml-ops', 2), ('microsoft/nni', 0.5824997425079346, 'ml', 1), ('hugapi/hug', 0.5820281505584717, 'util', 0), ('mlflow/mlflow', 0.58051598072052, 'ml-ops', 1), ('explosion/thinc', 0.5795862674713135, 'ml-dl', 1), ('tiangolo/fastapi', 0.5786353349685669, 'web', 2), ('lucidrains/toolformer-pytorch', 0.5760772228240967, 'llm', 0), ('nccr-itmo/fedot', 0.5759302377700806, 'ml-ops', 1), ('huggingface/transformers', 0.5750659108161926, 'nlp', 1), ('online-ml/river', 0.574630618095398, 'ml', 1), ('googlecloudplatform/vertex-ai-samples', 0.5720667243003845, 'ml', 0), ('ludwig-ai/ludwig', 0.570716142654419, 'ml-ops', 1), ('falconry/falcon', 0.5693379044532776, 'web', 1), ('selfexplainml/piml-toolbox', 0.5633277297019958, 'ml-interpretability', 0), ('onnx/onnx', 0.5625835061073303, 'ml', 1), ('alpa-projects/alpa', 0.5594131350517273, 'ml-dl', 1), ('merantix-momentum/squirrel-core', 0.5560594797134399, 'ml', 1), ('adap/flower', 0.5547651648521423, 'ml-ops', 1), ('automl/auto-sklearn', 0.546398937702179, 'ml', 0), ('xplainable/xplainable', 0.5456689596176147, 'ml-interpretability', 1), ('google/trax', 0.5450164079666138, 'ml-dl', 1), ('ray-project/ray', 0.5436031818389893, 'ml-ops', 2), ('aws/sagemaker-python-sdk', 0.5434074401855469, 'ml', 1), ('deepmind/dm-haiku', 0.5379303097724915, 'ml-dl', 1), ('jina-ai/jina', 0.5350887775421143, 'ml', 2), ('young-geng/easylm', 0.5335082411766052, 'llm', 0), ('operand/agency', 0.5331600308418274, 'llm', 1), ('aiqc/aiqc', 0.532943844795227, 'ml-ops', 0), ('dylanhogg/awesome-python', 0.532485842704773, 'study', 1), ('huggingface/datasets', 0.5313810110092163, 'nlp', 1), ('ajndkr/lanarky', 0.5310590267181396, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5304664969444275, 'study', 1), ('mindsdb/mindsdb', 0.5296940207481384, 'data', 1), ('google/pyglove', 0.5290511846542358, 'util', 1), ('patchy631/machine-learning', 0.5287477970123291, 'ml', 0), ('google/vizier', 0.528394877910614, 'ml', 1), ('mosaicml/composer', 0.5281829833984375, 'ml-dl', 1), ('oegedijk/explainerdashboard', 0.5270505547523499, 'ml-interpretability', 0), ('google/mediapipe', 0.5265260338783264, 'ml', 1), ('epistasislab/tpot', 0.5252538323402405, 'ml', 1), ('eugeneyan/testing-ml', 0.5247636437416077, 'testing', 1), ('determined-ai/determined', 0.5243386626243591, 'ml-ops', 1), ('fastai/fastcore', 0.5239315629005432, 'util', 0), ('nvidia/deeplearningexamples', 0.5223838090896606, 'ml-dl', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5223199725151062, 'template', 1), ('nevronai/metisfl', 0.5207219123840332, 'ml', 1), ('modularml/mojo', 0.5188204050064087, 'util', 1), ('vitalik/django-ninja', 0.5184093713760376, 'web', 1), ('radiantearth/radiant-mlhub', 0.5183095932006836, 'gis', 1), ('mlc-ai/mlc-llm', 0.5173749327659607, 'llm', 0), ('pathwaycom/pathway', 0.5166861414909363, 'data', 0), ('pathwaycom/llm-app', 0.5144424438476562, 'llm', 1), ('microsoft/flaml', 0.5143886804580688, 'ml', 1), ('meltano/meltano', 0.5139985084533691, 'ml-ops', 0), ('polyaxon/datatile', 0.5134544372558594, 'pandas', 0), ('ddbourgin/numpy-ml', 0.5126323699951172, 'ml', 1), ('feast-dev/feast', 0.5118728876113892, 'ml-ops', 1), ('huggingface/huggingface_hub', 0.5117499828338623, 'ml', 1), ('uber/petastorm', 0.5089090466499329, 'data', 1), ('microsoft/lmops', 0.5080411434173584, 'llm', 0), ('lukaszahradnik/pyneuralogic', 0.5073249936103821, 'math', 1), ('fmind/mlops-python-package', 0.50709468126297, 'template', 0), ('csinva/imodels', 0.5064631104469299, 'ml', 1), ('cheshire-cat-ai/core', 0.5056010484695435, 'llm', 0), ('scikit-learn/scikit-learn', 0.5052728056907654, 'ml', 1), ('uber/fiber', 0.5049328804016113, 'data', 1), ('fugue-project/fugue', 0.504669725894928, 'pandas', 1), ('microsoft/generative-ai-for-beginners', 0.5041592717170715, 'study', 0), ('kevinmusgrave/pytorch-metric-learning', 0.502253532409668, 'ml', 1), ('lancedb/lancedb', 0.501854658126831, 'data', 0), ('unity-technologies/ml-agents', 0.5017167925834656, 'ml-rl', 1), ('zenml-io/zenml', 0.5011926293373108, 'ml-ops', 1), ('bigscience-workshop/petals', 0.5010274648666382, 'data', 1), ('tensorflow/tensorflow', 0.5004213452339172, 'ml-dl', 1)]",4,0.0,,0.0,12,6,34,33,0,4,4,12.0,11.0,90.0,0.9,35
535,nlp,https://github.com/huawei-noah/pretrained-language-model,[],,[],[],,,,huawei-noah/pretrained-language-model,Pretrained-Language-Model,2910,618,56,Python,,Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.,huawei-noah,2024-01-11,2019-12-02,217,13.401315789473685,https://avatars.githubusercontent.com/u/12619994?v=4,Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.,"['knowledge-distillation', 'large-scale-distributed', 'model-compression', 'pretrained-models', 'quantization']","['knowledge-distillation', 'large-scale-distributed', 'model-compression', 'pretrained-models', 'quantization']",2023-05-21,"[('yizhongw/self-instruct', 0.6405363082885742, 'llm', 0), ('cg123/mergekit', 0.6082916855812073, 'llm', 0), ('openai/finetune-transformer-lm', 0.5976941585540771, 'llm', 0), ('predibase/llm_distillation_playbook', 0.5744475722312927, 'llm', 0), ('freedomintelligence/llmzoo', 0.5728333592414856, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5544146299362183, 'llm', 0), ('young-geng/easylm', 0.5518462657928467, 'llm', 0), ('lianjiatech/belle', 0.5496155023574829, 'llm', 0), ('hiyouga/llama-factory', 0.5485014915466309, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5485014319419861, 'llm', 1), ('titanml/takeoff', 0.5469074249267578, 'llm', 1), ('squeezeailab/squeezellm', 0.5468460917472839, 'llm', 2), ('microsoft/unilm', 0.5461228489875793, 'nlp', 0), ('tatsu-lab/stanford_alpaca', 0.5441707372665405, 'llm', 0), ('bigscience-workshop/biomedical', 0.5387159585952759, 'data', 0), ('jonasgeiping/cramming', 0.5355748534202576, 'nlp', 0), ('togethercomputer/redpajama-data', 0.5328022837638855, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5290946364402771, 'llm', 0), ('bobazooba/xllm', 0.5261392593383789, 'llm', 0), ('extreme-bert/extreme-bert', 0.5199653506278992, 'llm', 0), ('hannibal046/awesome-llm', 0.5183480381965637, 'study', 0), ('artidoro/qlora', 0.5163824558258057, 'llm', 0), ('optimalscale/lmflow', 0.5153259038925171, 'llm', 1), ('juncongmoo/pyllama', 0.5146656036376953, 'llm', 0), ('huggingface/transformers', 0.51437908411026, 'nlp', 1), ('bigscience-workshop/petals', 0.5137228965759277, 'data', 1), ('next-gpt/next-gpt', 0.5096499919891357, 'llm', 0), ('jzhang38/tinyllama', 0.5081942081451416, 'llm', 0)]",20,2.0,,0.04,5,0,50,8,0,0,0,5.0,4.0,90.0,0.8,35
295,sim,https://github.com/openai/mujoco-py,[],,[],[],,,,openai/mujoco-py,mujoco-py,2666,809,197,Cython,,"MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.",openai,2024-01-12,2016-04-24,405,6.578075431794149,https://avatars.githubusercontent.com/u/14957082?v=4,"MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.",[],[],2022-11-17,"[('deepmind/dm_control', 0.5940796732902527, 'ml-rl', 0), ('viblo/pymunk', 0.5651075839996338, 'sim', 0)]",33,3.0,,0.0,24,4,94,14,0,1,1,24.0,24.0,90.0,1.0,35
501,gis,https://github.com/google/earthengine-api,[],,[],[],,,,google/earthengine-api,earthengine-api,2445,980,203,JavaScript,,Python and JavaScript bindings for calling the Earth Engine API.,google,2024-01-13,2015-04-22,457,5.34009360374415,https://avatars.githubusercontent.com/u/1342004?v=4,Python and JavaScript bindings for calling the Earth Engine API.,[],[],2024-01-10,"[('giswqs/geemap', 0.5498977899551392, 'gis', 0)]",37,1.0,,0.46,3,1,106,0,0,34,34,3.0,0.0,90.0,0.0,35
1158,util,https://github.com/nateshmbhat/pyttsx3,[],,[],[],,,,nateshmbhat/pyttsx3,pyttsx3,1816,296,37,Python,,Offline Text To Speech synthesis for python,nateshmbhat,2024-01-14,2017-06-24,344,5.272501036914144,,Offline Text To Speech synthesis for python,"['pyttsx', 'pyttsx3', 'text-to-speech', 'text-to-speech-python3']","['pyttsx', 'pyttsx3', 'text-to-speech', 'text-to-speech-python3']",2021-07-14,"[('uberi/speech_recognition', 0.6923384070396423, 'ml', 0), ('pndurette/gtts', 0.6920114159584045, 'util', 1), ('espnet/espnet', 0.6220706105232239, 'nlp', 0), ('speechbrain/speechbrain', 0.5789477229118347, 'nlp', 0), ('facebookresearch/seamless_communication', 0.5521857142448425, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5467420220375061, 'llm', 0), ('spotify/pedalboard', 0.5451530814170837, 'util', 0), ('pytorch-labs/gpt-fast', 0.537563145160675, 'llm', 0), ('irmen/pyminiaudio', 0.536530077457428, 'util', 0), ('minimaxir/aitextgen', 0.5253090858459473, 'llm', 0), ('googleapis/python-speech', 0.5236392617225647, 'ml', 0), ('pypy/pypy', 0.5199990272521973, 'util', 0), ('m-bain/whisperx', 0.5181179046630859, 'nlp', 0), ('bastibe/python-soundfile', 0.5068590044975281, 'util', 0)]",15,5.0,,0.0,20,3,80,30,0,3,3,20.0,31.0,90.0,1.6,35
415,ml,https://github.com/bmabey/pyldavis,[],,[],[],,,,bmabey/pyldavis,pyLDAvis,1756,355,53,Jupyter Notebook,,Python library for interactive topic model visualization. Port of the R LDAvis package.,bmabey,2024-01-13,2015-04-09,459,3.819763828464885,,Python library for interactive topic model visualization. Port of the R LDAvis package.,[],[],2023-12-14,"[('contextlab/hypertools', 0.5628495812416077, 'ml', 0), ('holoviz/holoviz', 0.518200159072876, 'viz', 0), ('enthought/mayavi', 0.5115525722503662, 'viz', 0), ('scitools/iris', 0.5086809992790222, 'gis', 0), ('altair-viz/altair', 0.5086315274238586, 'viz', 0)]",41,3.0,,0.35,14,13,107,1,2,3,2,14.0,6.0,90.0,0.4,35
1585,util,https://github.com/pydoit/doit,[],,[],[],,,,pydoit/doit,doit,1741,174,48,Python,http://pydoit.org,task management & automation tool,pydoit,2024-01-13,2014-02-14,519,3.350838603244432,https://avatars.githubusercontent.com/u/6440864?v=4,task management & automation tool,"['build-automation', 'build-system', 'build-tool', 'data-pipeline', 'data-science', 'task-runner', 'workflow', 'workflow-automation', 'workflow-management']","['build-automation', 'build-system', 'build-tool', 'data-pipeline', 'data-science', 'task-runner', 'workflow', 'workflow-automation', 'workflow-management']",2023-01-16,"[('dagster-io/dagster', 0.5810200572013855, 'ml-ops', 3), ('avaiga/taipy', 0.5572507977485657, 'data', 1), ('ploomber/ploomber', 0.5523402094841003, 'ml-ops', 2), ('mage-ai/mage-ai', 0.551956057548523, 'ml-ops', 1), ('apache/airflow', 0.5482216477394104, 'ml-ops', 2), ('tox-dev/tox', 0.5430145859718323, 'testing', 0), ('chaostoolkit/chaostoolkit', 0.5246021747589111, 'util', 0), ('orchest/orchest', 0.522449254989624, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5158570408821106, 'ml-ops', 2), ('netflix/metaflow', 0.5121597051620483, 'ml-ops', 1), ('kestra-io/kestra', 0.5050574541091919, 'ml-ops', 2)]",65,7.0,,0.0,8,2,121,12,0,5,5,8.0,6.0,90.0,0.8,35
1146,data,https://github.com/aio-libs/aiomysql,[],,[],[],,,,aio-libs/aiomysql,aiomysql,1675,253,47,Python,https://aiomysql.rtfd.io,aiomysql is a library for accessing a MySQL database from the asyncio,aio-libs,2024-01-11,2015-01-07,472,3.5422960725075527,https://avatars.githubusercontent.com/u/7049303?v=4,aiomysql is a library for accessing a MySQL database from the asyncio,"['aiomysql', 'async', 'asyncio', 'mariadb', 'mysql', 'sqlalchemy']","['aiomysql', 'async', 'asyncio', 'mariadb', 'mysql', 'sqlalchemy']",2023-06-11,"[('aio-libs/aiopg', 0.7151634693145752, 'data', 2), ('tiangolo/sqlmodel', 0.5758809447288513, 'data', 1), ('sqlalchemy/sqlalchemy', 0.5567764043807983, 'data', 1), ('mcfunley/pugsql', 0.5428056716918945, 'data', 0), ('timofurrer/awesome-asyncio', 0.522577166557312, 'study', 1), ('ibis-project/ibis', 0.5113950967788696, 'data', 2)]",98,6.0,,0.5,7,3,110,7,1,3,1,7.0,6.0,90.0,0.9,35
607,testing,https://github.com/pytest-dev/pytest-cov,[],,[],[],,,,pytest-dev/pytest-cov,pytest-cov,1601,209,41,Python,,Coverage plugin for pytest.,pytest-dev,2024-01-11,2014-04-17,510,3.134825174825175,https://avatars.githubusercontent.com/u/8897583?v=4,Coverage plugin for pytest.,['pytest'],['pytest'],2023-05-24,"[('pytest-dev/pytest-xdist', 0.7077765464782715, 'testing', 1), ('pytest-dev/pytest-mock', 0.6337581872940063, 'testing', 1), ('samuelcolvin/pytest-pretty', 0.6255528926849365, 'testing', 1), ('pytest-dev/pytest-asyncio', 0.6025922894477844, 'testing', 0), ('nedbat/coveragepy', 0.5966586470603943, 'testing', 0), ('teemu/pytest-sugar', 0.5729233622550964, 'testing', 1), ('samuelcolvin/dirty-equals', 0.5618858933448792, 'util', 1), ('computationalmodelling/nbval', 0.5596536993980408, 'jupyter', 1), ('pytest-dev/pytest', 0.5345095992088318, 'testing', 0), ('kiwicom/pytest-recording', 0.52159583568573, 'testing', 1), ('taverntesting/tavern', 0.511971116065979, 'testing', 1), ('ionelmc/pytest-benchmark', 0.5074380040168762, 'testing', 1)]",86,4.0,,0.17,25,6,119,8,0,4,4,26.0,22.0,90.0,0.8,35
869,time-series,https://github.com/alkaline-ml/pmdarima,[],,[],[],,,,alkaline-ml/pmdarima,pmdarima,1470,229,34,Python,https://www.alkaline-ml.com/pmdarima,"A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.",alkaline-ml,2024-01-13,2017-03-30,356,4.120945134160993,https://avatars.githubusercontent.com/u/58331763?v=4,"A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.","['arima', 'econometrics', 'forecasting', 'forecasting-models', 'machine-learning', 'pmdarima', 'sarimax', 'time-series']","['arima', 'econometrics', 'forecasting', 'forecasting-models', 'machine-learning', 'pmdarima', 'sarimax', 'time-series']",2023-10-23,"[('firmai/atspy', 0.7777550220489502, 'time-series', 3), ('rjt1990/pyflux', 0.7352306842803955, 'time-series', 1), ('unit8co/darts', 0.7097618579864502, 'time-series', 3), ('awslabs/gluonts', 0.6824057102203369, 'time-series', 3), ('tdameritrade/stumpy', 0.6753336787223816, 'time-series', 0), ('rasbt/mlxtend', 0.6218876838684082, 'ml', 1), ('statsmodels/statsmodels', 0.6218242645263672, 'ml', 2), ('ta-lib/ta-lib-python', 0.6164563894271851, 'finance', 0), ('pycaret/pycaret', 0.6138346791267395, 'ml', 2), ('winedarksea/autots', 0.6125960350036621, 'time-series', 3), ('bashtage/arch', 0.6099317669868469, 'time-series', 2), ('pandas-dev/pandas', 0.5989955067634583, 'pandas', 0), ('uber/orbit', 0.5944992899894714, 'time-series', 4), ('linkedin/greykite', 0.5944582223892212, 'ml', 0), ('sktime/sktime', 0.5908213257789612, 'time-series', 3), ('salesforce/merlion', 0.589963436126709, 'time-series', 3), ('google/temporian', 0.5879765152931213, 'time-series', 1), ('scikit-learn/scikit-learn', 0.5828933715820312, 'ml', 1), ('facebook/prophet', 0.581493079662323, 'time-series', 2), ('stan-dev/pystan', 0.5813616514205933, 'ml', 0), ('nixtla/statsforecast', 0.5763822197914124, 'time-series', 5), ('aistream-peelout/flow-forecast', 0.5597772002220154, 'time-series', 2), ('wilsonrljr/sysidentpy', 0.5545415878295898, 'time-series', 2), ('microsoft/flaml', 0.5537622570991516, 'ml', 1), ('pytoolz/toolz', 0.5435473918914795, 'util', 0), ('microprediction/microprediction', 0.5424190759658813, 'time-series', 2), ('ourownstory/neural_prophet', 0.5404297709465027, 'ml', 3), ('gbeced/pyalgotrade', 0.5389692783355713, 'finance', 0), ('dateutil/dateutil', 0.536246657371521, 'util', 0), ('wesm/pydata-book', 0.5350579619407654, 'study', 0), ('blue-yonder/tsfresh', 0.5346757173538208, 'time-series', 1), ('salesforce/deeptime', 0.533848762512207, 'time-series', 2), ('cuemacro/finmarketpy', 0.5333058834075928, 'finance', 0), ('pmorissette/ffn', 0.5328418612480164, 'finance', 0), ('featurelabs/featuretools', 0.5302847623825073, 'ml', 1), ('goldmansachs/gs-quant', 0.5274640917778015, 'finance', 0), ('mementum/bta-lib', 0.526504635810852, 'finance', 0), ('autoviml/auto_ts', 0.5241935849189758, 'time-series', 2), ('ranaroussi/quantstats', 0.522229790687561, 'finance', 0), ('pastas/pastas', 0.518909215927124, 'time-series', 0), ('facebookresearch/kats', 0.5154160261154175, 'time-series', 1), ('polyaxon/datatile', 0.5126562118530273, 'pandas', 0), ('awslabs/autogluon', 0.5124308466911316, 'ml', 3), ('mljar/mljar-supervised', 0.509310781955719, 'ml', 1), ('plotly/dash', 0.5089923143386841, 'viz', 0), ('quantecon/quantecon.py', 0.5083404779434204, 'sim', 0), ('yzhao062/pyod', 0.5062800049781799, 'data', 1), ('twopirllc/pandas-ta', 0.5053380131721497, 'finance', 0), ('gradio-app/gradio', 0.5039731860160828, 'viz', 1), ('pysal/pysal', 0.5029148459434509, 'gis', 0), ('scikit-mobility/scikit-mobility', 0.5019405484199524, 'gis', 0)]",22,5.0,,0.17,10,7,83,3,2,7,2,10.0,7.0,90.0,0.7,35
1077,ml-ops,https://github.com/kubeflow/examples,[],,[],[],,,,kubeflow/examples,examples,1356,746,45,Jsonnet,,A repository to host extended examples and tutorials,kubeflow,2024-01-13,2018-02-01,312,4.3362265874828685,https://avatars.githubusercontent.com/u/33164907?v=4,A repository to host extended examples and tutorials,[],[],2023-10-19,"[('conceptofmind/toolformer', 0.5328210592269897, 'llm', 0)]",99,4.0,,0.12,20,4,72,3,0,1,1,20.0,16.0,90.0,0.8,35
1147,data,https://github.com/aio-libs/aiopg,[],,[],[],,,,aio-libs/aiopg,aiopg,1356,156,41,Python,http://aiopg.readthedocs.io,aiopg is a library for accessing a PostgreSQL database from the asyncio,aio-libs,2024-01-11,2014-04-03,512,2.644747840624129,https://avatars.githubusercontent.com/u/7049303?v=4,aiopg is a library for accessing a PostgreSQL database from the asyncio,"['asyncio', 'postgresql', 'sqlalchemy']","['asyncio', 'postgresql', 'sqlalchemy']",2023-08-30,"[('aio-libs/aiomysql', 0.7151634693145752, 'data', 2), ('sqlalchemy/sqlalchemy', 0.5661270618438721, 'data', 1), ('coleifer/peewee', 0.5507586002349854, 'data', 0), ('tiangolo/sqlmodel', 0.546112596988678, 'data', 1), ('mcfunley/pugsql', 0.5243105888366699, 'data', 0), ('ibis-project/ibis', 0.5114596486091614, 'data', 2)]",62,7.0,,0.13,3,2,119,5,0,6,6,3.0,3.0,90.0,1.0,35
810,ml-dl,https://github.com/calculatedcontent/weightwatcher,[],,[],[],,,,calculatedcontent/weightwatcher,WeightWatcher,1354,119,32,Python,,The WeightWatcher tool for predicting the accuracy of   Deep Neural Networks,calculatedcontent,2024-01-13,2018-11-28,269,5.017469560614082,https://avatars.githubusercontent.com/u/4150431?v=4,The WeightWatcher tool for predicting the accuracy of   Deep Neural Networks,[],[],2024-01-13,"[('rasbt/deeplearning-models', 0.5426604747772217, 'ml-dl', 0), ('facebookresearch/ppuda', 0.5242863297462463, 'ml-dl', 0), ('intellabs/bayesian-torch', 0.51715487241745, 'ml', 0), ('neuralmagic/deepsparse', 0.5087005496025085, 'nlp', 0), ('rasbt/stat453-deep-learning-ss20', 0.505051851272583, 'study', 0), ('pytorch/ignite', 0.5007023811340332, 'ml-dl', 0)]",8,2.0,,7.0,6,1,62,0,1,2,1,6.0,3.0,90.0,0.5,35
648,profiling,https://github.com/sumerc/yappi,[],,[],[],,,,sumerc/yappi,yappi,1317,72,15,Python,,"Yet Another Python Profiler, but this time multithreading, asyncio and gevent aware.",sumerc,2024-01-13,2009-10-07,746,1.763389441469013,,"Yet Another Python Profiler, but this time multithreading, asyncio and gevent aware.","['asgi', 'asynchronous', 'asyncio', 'coroutine', 'cpu', 'gevent', 'greenlet', 'multi-threaded-applications', 'multithreading', 'performance', 'profile', 'profilers', 'thread']","['asgi', 'asynchronous', 'asyncio', 'coroutine', 'cpu', 'gevent', 'greenlet', 'multi-threaded-applications', 'multithreading', 'performance', 'profile', 'profilers', 'thread']",2023-12-18,"[('noxdafox/pebble', 0.7094334959983826, 'perf', 1), ('pythonspeed/filprofiler', 0.6349960565567017, 'profiling', 0), ('python-trio/trio', 0.6272547245025635, 'perf', 0), ('joblib/joblib', 0.6218010783195496, 'util', 0), ('joerick/pyinstrument', 0.6088473200798035, 'profiling', 2), ('magicstack/uvloop', 0.6066219806671143, 'util', 1), ('benfred/py-spy', 0.6047118902206421, 'profiling', 0), ('timofurrer/awesome-asyncio', 0.6000396013259888, 'study', 1), ('agronholm/anyio', 0.593978226184845, 'perf', 1), ('klen/muffin', 0.5936639904975891, 'web', 2), ('samuelcolvin/arq', 0.5881884098052979, 'data', 1), ('pallets/quart', 0.5867199897766113, 'web', 2), ('neoteroi/blacksheep', 0.577142596244812, 'web', 2), ('klen/py-frameworks-bench', 0.5642154216766357, 'perf', 0), ('fastai/fastcore', 0.5639339685440063, 'util', 0), ('pympler/pympler', 0.559504508972168, 'perf', 0), ('pypy/pypy', 0.5568121671676636, 'util', 0), ('python-greenlet/greenlet', 0.5529376268386841, 'perf', 1), ('miguelgrinberg/python-socketio', 0.552412211894989, 'util', 2), ('tqdm/tqdm', 0.549595832824707, 'term', 0), ('plasma-umass/scalene', 0.549071192741394, 'profiling', 1), ('tiangolo/asyncer', 0.5410847067832947, 'perf', 1), ('locustio/locust', 0.5397162437438965, 'testing', 1), ('aio-libs/aiohttp', 0.5396091341972351, 'web', 1), ('airtai/faststream', 0.5390495657920837, 'perf', 1), ('jiffyclub/snakeviz', 0.5380180478096008, 'profiling', 0), ('alirn76/panther', 0.5367398858070374, 'web', 0), ('agronholm/apscheduler', 0.5361764430999756, 'util', 0), ('pyutils/line_profiler', 0.5341673493385315, 'profiling', 0), ('tornadoweb/tornado', 0.5336647629737854, 'web', 1), ('facebookincubator/cinder', 0.5309968590736389, 'perf', 0), ('reloadware/reloadium', 0.5243247151374817, 'profiling', 0), ('p403n1x87/austin', 0.5229708552360535, 'profiling', 1), ('faster-cpython/ideas', 0.5191786289215088, 'perf', 0), ('bogdanp/dramatiq', 0.517719566822052, 'util', 0), ('python-cachier/cachier', 0.5168151259422302, 'perf', 0), ('xrudelis/pytrait', 0.5159697532653809, 'util', 0), ('joblib/loky', 0.5158117413520813, 'perf', 0), ('ets-labs/python-dependency-injector', 0.5105165839195251, 'util', 1), ('ipython/ipyparallel', 0.5100932121276855, 'perf', 0), ('cython/cython', 0.5085721611976624, 'util', 1), ('google/gin-config', 0.5081563591957092, 'util', 0), ('starlite-api/starlite', 0.5070291757583618, 'web', 2), ('fluentpython/example-code-2e', 0.5051793456077576, 'study', 0), ('willmcgugan/textual', 0.5049981474876404, 'term', 0), ('eventlet/eventlet', 0.5047085285186768, 'perf', 1), ('mher/flower', 0.5045316219329834, 'perf', 1), ('samuelcolvin/watchfiles', 0.5043572187423706, 'util', 1), ('eleutherai/pyfra', 0.5033401250839233, 'ml', 0), ('encode/uvicorn', 0.5030171871185303, 'web', 2), ('dgilland/cacheout', 0.5023199915885925, 'perf', 0), ('s3rius/fastapi-template', 0.5003270506858826, 'web', 2)]",31,7.0,,0.63,26,24,174,1,2,0,2,26.0,21.0,90.0,0.8,35
941,web,https://github.com/awtkns/fastapi-crudrouter,[],,[],[],,,,awtkns/fastapi-crudrouter,fastapi-crudrouter,1258,145,15,Python,https://fastapi-crudrouter.awtkns.com,A dynamic FastAPI router that automatically creates CRUD routes for your models,awtkns,2024-01-12,2020-12-19,162,7.744942832014072,,A dynamic FastAPI router that automatically creates CRUD routes for your models,"['api', 'async', 'asyncio', 'code-generation', 'crud', 'crud-routes', 'fastapi', 'fastapi-crudrouter', 'framework', 'openapi', 'openapi-route', 'redoc', 'rest', 'sql', 'swagger-ui', 'web']","['api', 'async', 'asyncio', 'code-generation', 'crud', 'crud-routes', 'fastapi', 'fastapi-crudrouter', 'framework', 'openapi', 'openapi-route', 'redoc', 'rest', 'sql', 'swagger-ui', 'web']",2023-01-28,"[('vitalik/django-ninja', 0.6941371560096741, 'web', 2), ('tiangolo/fastapi', 0.6808283925056458, 'web', 10), ('tiangolo/full-stack-fastapi-postgresql', 0.5604096055030823, 'template', 2), ('starlite-api/starlite', 0.550251841545105, 'web', 5), ('asacristani/fastapi-rocket-boilerplate', 0.5432131886482239, 'template', 1), ('dmontagu/fastapi_client', 0.5253075361251831, 'web', 0), ('s3rius/fastapi-template', 0.514696478843689, 'web', 2), ('python-restx/flask-restx', 0.5144665837287903, 'web', 2), ('fastapi-users/fastapi-users', 0.5089220404624939, 'web', 3)]",21,4.0,,0.15,8,2,37,12,1,3,1,8.0,11.0,90.0,1.4,35
620,viz,https://github.com/enthought/mayavi,"['visualization', 'scientific-visualization']",,[],[],,,,enthought/mayavi,mayavi,1192,276,96,Python,http://docs.enthought.com/mayavi/mayavi/,3D visualization of scientific data in Python,enthought,2024-01-12,2011-01-24,679,1.755153554901136,https://avatars.githubusercontent.com/u/539651?v=4,3D visualization of scientific data in Python,[],"['scientific-visualization', 'visualization']",2023-12-09,"[('mwaskom/seaborn', 0.7422676086425781, 'viz', 0), ('marcomusy/vedo', 0.742211639881134, 'viz', 2), ('pyqtgraph/pyqtgraph', 0.734231173992157, 'viz', 2), ('scitools/iris', 0.7232843041419983, 'gis', 0), ('altair-viz/altair', 0.7132139801979065, 'viz', 1), ('contextlab/hypertools', 0.6949652433395386, 'ml', 1), ('dfki-ric/pytransform3d', 0.6477158665657043, 'math', 1), ('holoviz/holoviz', 0.6358242630958557, 'viz', 0), ('man-group/dtale', 0.6186628937721252, 'viz', 1), ('residentmario/geoplot', 0.6169453859329224, 'gis', 0), ('matplotlib/matplotlib', 0.6048174500465393, 'viz', 0), ('numpy/numpy', 0.5977712869644165, 'math', 0), ('holoviz/geoviews', 0.5856287479400635, 'gis', 0), ('pyvista/pyvista', 0.5794352293014526, 'viz', 2), ('holoviz/hvplot', 0.5784933567047119, 'pandas', 0), ('lux-org/lux', 0.5663363337516785, 'viz', 1), ('kanaries/pygwalker', 0.5563317537307739, 'pandas', 1), ('roban/cosmolopy', 0.5520604252815247, 'sim', 0), ('bokeh/bokeh', 0.550900936126709, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5471249222755432, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5390788912773132, 'study', 0), ('gregorhd/mapcompare', 0.5386293530464172, 'gis', 0), ('westhealth/pyvis', 0.5338135361671448, 'graph', 0), ('holoviz/panel', 0.5323789715766907, 'viz', 0), ('has2k1/plotnine', 0.5297136902809143, 'viz', 0), ('earthlab/earthpy', 0.5246884822845459, 'gis', 0), ('pysal/pysal', 0.5210456848144531, 'gis', 0), ('scitools/cartopy', 0.5173063278198242, 'gis', 0), ('plotly/plotly.py', 0.5172713398933411, 'viz', 1), ('vispy/vispy', 0.5171870589256287, 'viz', 1), ('datapane/datapane', 0.5168454051017761, 'viz', 0), ('holoviz/holoviews', 0.5158518552780151, 'viz', 0), ('bmabey/pyldavis', 0.5115525722503662, 'ml', 0), ('artelys/geonetworkx', 0.5101572275161743, 'gis', 0), ('holoviz/datashader', 0.504320502281189, 'gis', 0), ('eleutherai/pyfra', 0.5028614401817322, 'ml', 0), ('cuemacro/chartpy', 0.5027343034744263, 'viz', 0), ('albahnsen/pycircular', 0.5026959776878357, 'math', 0), ('graphistry/pygraphistry', 0.5026683807373047, 'data', 1), ('vizzuhq/ipyvizzu', 0.501654863357544, 'jupyter', 0)]",95,6.0,,0.44,14,4,158,1,0,3,3,14.0,13.0,90.0,0.9,35
1017,term,https://github.com/jazzband/prettytable,[],,[],[],,,,jazzband/prettytable,prettytable,1186,137,22,Python,https://pypi.org/project/PrettyTable/,Display tabular data in a visually appealing ASCII table format,jazzband,2024-01-13,2016-01-15,419,2.8266939053455906,https://avatars.githubusercontent.com/u/15129049?v=4,Display tabular data in a visually appealing ASCII table format,"['package', 'utility-library']","['package', 'utility-library']",2024-01-01,"[('astanin/python-tabulate', 0.6471048593521118, 'util', 0), ('wireservice/csvkit', 0.5902390480041504, 'util', 0), ('camelot-dev/camelot', 0.5252398252487183, 'util', 0), ('jazzband/tablib', 0.5020918250083923, 'data', 0)]",55,2.0,,0.46,16,10,97,0,3,3,3,15.0,14.0,90.0,0.9,35
537,util,https://github.com/pdoc3/pdoc,[],,[],[],,,,pdoc3/pdoc,pdoc,1051,147,8,Python,https://pdoc3.github.io/pdoc/, :snake: :arrow_right: :scroll:  Auto-generate API documentation for Python projects,pdoc3,2024-01-12,2019-01-02,264,3.9681769147788564,https://avatars.githubusercontent.com/u/46306955?v=4, 🐍 :arrow_right: 📜  Auto-generate API documentation for Python projects,"['api-documentation', 'docs', 'docs-generator', 'docstring', 'docstring-documentation', 'docstrings', 'documentation', 'documentation-dumper', 'documentation-generator', 'documentation-tool', 'generator', 'pdoc']","['api-documentation', 'docs', 'docs-generator', 'docstring', 'docstring-documentation', 'docstrings', 'documentation', 'documentation-dumper', 'documentation-generator', 'documentation-tool', 'generator', 'pdoc']",2022-12-21,"[('mitmproxy/pdoc', 0.7915372252464294, 'util', 8), ('sphinx-doc/sphinx', 0.6652024388313293, 'util', 3), ('mkdocstrings/mkdocstrings', 0.6134604215621948, 'util', 1), ('mkdocstrings/griffe', 0.5867151618003845, 'util', 1), ('danielnoord/pydocstringformatter', 0.5444629192352295, 'util', 1), ('pyscaffold/pyscaffold', 0.5431029796600342, 'template', 0), ('mkdocstrings/python', 0.5424914360046387, 'util', 1), ('hhatto/autopep8', 0.5197219252586365, 'util', 0), ('mkdocs/mkdocs', 0.5143741369247437, 'util', 1), ('eugeneyan/python-collab-template', 0.5139718651771545, 'template', 0), ('eternnoir/pytelegrambotapi', 0.5075708031654358, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.502884566783905, 'template', 0)]",58,4.0,,0.0,18,9,61,13,0,10,10,18.0,13.0,90.0,0.7,35
1061,ml-rl,https://github.com/arise-initiative/robosuite,[],,[],[],,,,arise-initiative/robosuite,robosuite,993,329,25,Python,https://robosuite.ai,robosuite: A Modular Simulation Framework and Benchmark for Robot Learning,arise-initiative,2024-01-13,2018-10-25,274,3.6146645865834635,https://avatars.githubusercontent.com/u/66855713?v=4,robosuite: A Modular Simulation Framework and Benchmark for Robot Learning,"['physics-simulation', 'reinforcement-learning', 'robot-learning', 'robot-manipulation', 'robotics']","['physics-simulation', 'reinforcement-learning', 'robot-learning', 'robot-manipulation', 'robotics']",2023-09-29,"[('nvidia-omniverse/orbit', 0.6593456268310547, 'sim', 2), ('pytorch/rl', 0.6132838129997253, 'ml-rl', 2), ('facebookresearch/habitat-lab', 0.596968412399292, 'sim', 2), ('google/brax', 0.5911247134208679, 'sim', 3), ('unity-technologies/ml-agents', 0.58687424659729, 'ml-rl', 1), ('nvidia-omniverse/omniisaacgymenvs', 0.5800082087516785, 'sim', 1), ('thu-ml/tianshou', 0.5250179171562195, 'ml-rl', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5221055746078491, 'study', 1), ('bulletphysics/bullet3', 0.5200599431991577, 'sim', 2), ('tensorlayer/tensorlayer', 0.5120179057121277, 'ml-rl', 1), ('deepmind/dm_control', 0.5112686157226562, 'ml-rl', 2), ('humancompatibleai/imitation', 0.5085115432739258, 'ml-rl', 0), ('openai/gym', 0.500554084777832, 'ml-rl', 1)]",24,3.0,,0.21,36,19,64,4,0,2,2,36.0,53.0,90.0,1.5,35
250,ml-dl,https://github.com/tensorflow/similarity,[],,[],[],,,,tensorflow/similarity,similarity,990,106,29,Python,,TensorFlow Similarity is a python package focused on making similarity learning quick and easy.,tensorflow,2024-01-12,2020-06-15,189,5.234138972809668,https://avatars.githubusercontent.com/u/15658638?v=4,TensorFlow Similarity is a python package focused on making similarity learning quick and easy.,"['barlow-twins', 'clustering', 'contrastive-learning', 'cosine-similarity', 'deep-learning', 'knn', 'machine-learning', 'metric-learning', 'nearest-neighbor-search', 'nearest-neighbors', 'simclr', 'simclr2', 'similarity-learning', 'similarity-search', 'simsiam', 'tensorflow', 'unsupervised-learning']","['barlow-twins', 'clustering', 'contrastive-learning', 'cosine-similarity', 'deep-learning', 'knn', 'machine-learning', 'metric-learning', 'nearest-neighbor-search', 'nearest-neighbors', 'simclr', 'simclr2', 'similarity-learning', 'similarity-search', 'simsiam', 'tensorflow', 'unsupervised-learning']",2023-10-23,"[('qdrant/quaterion', 0.6630039811134338, 'ml', 9), ('tensorly/tensorly', 0.6049955487251282, 'ml-dl', 2), ('arogozhnikov/einops', 0.5858622193336487, 'ml-dl', 2), ('ggerganov/ggml', 0.5696033239364624, 'ml', 1), ('tensorflow/addons', 0.5603650212287903, 'ml', 3), ('google/tf-quant-finance', 0.552481472492218, 'finance', 1), ('horovod/horovod', 0.5510625839233398, 'ml-ops', 3), ('rafiqhasan/auto-tensorflow', 0.5478165745735168, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5410365462303162, 'perf', 2), ('huggingface/transformers', 0.5352794528007507, 'nlp', 3), ('oml-team/open-metric-learning', 0.5347641706466675, 'ml', 3), ('kevinmusgrave/pytorch-metric-learning', 0.5301912426948547, 'ml', 4), ('nyandwi/modernconvnets', 0.5236626863479614, 'ml-dl', 1), ('qdrant/qdrant', 0.522907555103302, 'data', 3), ('xl0/lovely-tensors', 0.5119929313659668, 'ml-dl', 1), ('pytorch/ignite', 0.5106225609779358, 'ml-dl', 2), ('ddbourgin/numpy-ml', 0.510289192199707, 'ml', 2), ('tensorflow/tensorflow', 0.5089390277862549, 'ml-dl', 3), ('explosion/thinc', 0.5028195381164551, 'ml-dl', 3), ('skorch-dev/skorch', 0.5013630986213684, 'ml-dl', 1)]",35,3.0,,1.73,7,3,44,3,2,2,2,7.0,4.0,90.0,0.6,35
845,data,https://github.com/eliasdabbas/advertools,[],,[],[],,,,eliasdabbas/advertools,advertools,979,192,35,Python,https://advertools.readthedocs.io,advertools - online marketing productivity and analysis tools,eliasdabbas,2024-01-13,2017-05-14,350,2.794861337683524,,advertools - online marketing productivity and analysis tools,"['advertising', 'adwords', 'digital-marketing', 'google-ads', 'keywords', 'log-analysis', 'logfile-parser', 'marketing', 'online-marketing', 'robots-txt', 'scrapy', 'search-engine-marketing', 'search-engine-optimization', 'seo', 'seo-crawler', 'serp', 'social-media', 'twitter-api', 'youtube']","['advertising', 'adwords', 'digital-marketing', 'google-ads', 'keywords', 'log-analysis', 'logfile-parser', 'marketing', 'online-marketing', 'robots-txt', 'scrapy', 'search-engine-marketing', 'search-engine-optimization', 'seo', 'seo-crawler', 'serp', 'social-media', 'twitter-api', 'youtube']",2023-11-03,"[('sloria/textblob', 0.5545390248298645, 'nlp', 0), ('clips/pattern', 0.5270535349845886, 'nlp', 0)]",9,4.0,,1.21,16,13,81,2,0,6,6,16.0,14.0,90.0,0.9,35
1576,llm,https://github.com/lupantech/chameleon-llm,"['reasoning', 'language-model']",,[],[],,,,lupantech/chameleon-llm,chameleon-llm,968,84,18,Jupyter Notebook,https://chameleon-llm.github.io,"Codes for ""Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models"".",lupantech,2024-01-13,2023-04-19,40,23.692307692307693,,"Codes for ""Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models"".","['ai', 'chatgpt', 'gpt-4', 'llm', 'openai', 'tool']","['ai', 'chatgpt', 'gpt-4', 'language-model', 'llm', 'openai', 'reasoning', 'tool']",2023-12-23,"[('reasoning-machines/pal', 0.672268271446228, 'llm', 2), ('kyegomez/tree-of-thoughts', 0.6684974431991577, 'llm', 1), ('microsoft/autogen', 0.661708652973175, 'llm', 2), ('hannibal046/awesome-llm', 0.6220220327377319, 'study', 1), ('lianjiatech/belle', 0.6197055578231812, 'llm', 0), ('lm-sys/fastchat', 0.6096516251564026, 'llm', 1), ('next-gpt/next-gpt', 0.6010711193084717, 'llm', 3), ('xtekky/gpt4free', 0.5982718467712402, 'llm', 4), ('openlmlab/moss', 0.5891066789627075, 'llm', 2), ('dylanhogg/llmgraph', 0.574148416519165, 'ml', 2), ('guidance-ai/guidance', 0.5735355019569397, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.5721330046653748, 'study', 4), ('killianlucas/open-interpreter', 0.5703567862510681, 'llm', 2), ('nomic-ai/gpt4all', 0.5625832080841064, 'llm', 1), ('guardrails-ai/guardrails', 0.5615499019622803, 'llm', 3), ('ctlllll/llm-toolmaker', 0.5588576197624207, 'llm', 1), ('run-llama/rags', 0.5582309365272522, 'llm', 3), ('prefecthq/marvin', 0.5581182837486267, 'nlp', 3), ('yueyu1030/attrprompt', 0.5579221248626709, 'llm', 0), ('srush/minichain', 0.5546031594276428, 'llm', 0), ('freedomintelligence/llmzoo', 0.5542495250701904, 'llm', 1), ('salesforce/codet5', 0.551190972328186, 'nlp', 1), ('embedchain/embedchain', 0.5511299967765808, 'llm', 3), ('eugeneyan/obsidian-copilot', 0.5486295819282532, 'llm', 1), ('bobazooba/xllm', 0.5471572875976562, 'llm', 4), ('eth-sri/lmql', 0.5429258942604065, 'llm', 2), ('aiwaves-cn/agents', 0.5420612692832947, 'nlp', 2), ('oobabooga/text-generation-webui', 0.5410828590393066, 'llm', 1), ('mindsdb/mindsdb', 0.5369391441345215, 'data', 2), ('databrickslabs/dolly', 0.5342248678207397, 'llm', 0), ('spcl/graph-of-thoughts', 0.5331078171730042, 'llm', 1), ('juncongmoo/pyllama', 0.5328553915023804, 'llm', 0), ('chatarena/chatarena', 0.5320706963539124, 'llm', 3), ('huggingface/text-generation-inference', 0.531190812587738, 'llm', 0), ('davidadsp/generative_deep_learning_2nd_edition', 0.5299697518348694, 'study', 1), ('minedojo/voyager', 0.5282621383666992, 'llm', 0), ('llmware-ai/llmware', 0.5269300937652588, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5233466029167175, 'llm', 1), ('microsoft/lora', 0.5212894082069397, 'llm', 1), ('togethercomputer/redpajama-data', 0.5191816687583923, 'llm', 0), ('stanfordnlp/dspy', 0.5166156888008118, 'llm', 1), ('conceptofmind/toolformer', 0.5162728428840637, 'llm', 1), ('openlm-research/open_llama', 0.5159003138542175, 'llm', 1), ('modularml/mojo', 0.5143639445304871, 'util', 1), ('salesforce/codegen', 0.5142695307731628, 'nlp', 1), ('eureka-research/eureka', 0.5140032768249512, 'ml-rl', 0), ('ai21labs/lm-evaluation', 0.5124858617782593, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5114999413490295, 'llm', 2), ('openai/finetune-transformer-lm', 0.5111390352249146, 'llm', 0), ('optimalscale/lmflow', 0.509954571723938, 'llm', 2), ('argilla-io/argilla', 0.5085508823394775, 'nlp', 3), ('deeppavlov/deeppavlov', 0.5067449808120728, 'nlp', 1), ('deepset-ai/haystack', 0.505305290222168, 'llm', 3), ('thudm/codegeex', 0.5048568844795227, 'llm', 0), ('langchain-ai/langgraph', 0.5040978193283081, 'llm', 0), ('thudm/chatglm2-6b', 0.5024386644363403, 'llm', 1), ('young-geng/easylm', 0.5002449154853821, 'llm', 1)]",2,1.0,,0.73,1,1,9,1,1,1,1,1.0,0.0,90.0,0.0,35
1692,util,https://github.com/pyo3/rust-numpy,['rust'],,[],[],,,,pyo3/rust-numpy,rust-numpy,941,90,23,Rust,,PyO3-based Rust bindings of the NumPy C-API,pyo3,2024-01-07,2017-04-21,353,2.6614141414141415,https://avatars.githubusercontent.com/u/28156855?v=4,PyO3-based Rust bindings of the NumPy C-API,"['ndarray', 'numpy', 'numpy-capi', 'rust-bindings', 'rust-ndarray', 'rust-numpy']","['ndarray', 'numpy', 'numpy-capi', 'rust', 'rust-bindings', 'rust-ndarray', 'rust-numpy']",2023-11-11,"[('pyo3/pyo3', 0.6885672211647034, 'util', 1), ('pyo3/maturin', 0.654156506061554, 'util', 1), ('rustpython/rustpython', 0.5917856097221375, 'util', 1), ('delta-io/delta-rs', 0.5224049687385559, 'pandas', 1), ('aswinnnn/pyscan', 0.5087146759033203, 'security', 1), ('cupy/cupy', 0.5001054406166077, 'math', 1)]",42,3.0,,0.6,6,3,82,2,3,5,3,6.0,15.0,90.0,2.5,35
1501,ml-dl,https://github.com/deepmind/android_env,[],,[],[],,,,deepmind/android_env,android_env,923,62,27,Python,,RL research on Android devices.,deepmind,2024-01-12,2021-04-21,144,6.371794871794871,https://avatars.githubusercontent.com/u/8596759?v=4,RL research on Android devices.,"['android', 'reinforcement-learning']","['android', 'reinforcement-learning']",2024-01-12,"[('denys88/rl_games', 0.577807605266571, 'ml-rl', 1), ('lucidrains/palm-rlhf-pytorch', 0.5164604187011719, 'ml-rl', 1)]",8,3.0,,1.23,30,30,33,0,1,1,1,30.0,0.0,90.0,0.0,35
23,math,https://github.com/fredrik-johansson/mpmath,[],,[],[],,,,fredrik-johansson/mpmath,mpmath,863,170,39,Python,http://mpmath.org,Python library for arbitrary-precision floating-point arithmetic,fredrik-johansson,2024-01-12,2011-12-07,633,1.3615055217489294,https://avatars.githubusercontent.com/u/118537268?v=4,Python library for arbitrary-precision floating-point arithmetic,"['arbitrary-precision', 'complex-numbers', 'floating-point', 'multiprecision', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'ordinary-differential-equations', 'plotting', 'special-functions']","['arbitrary-precision', 'complex-numbers', 'floating-point', 'multiprecision', 'numerical-integration', 'numerical-methods', 'numerical-optimization', 'ordinary-differential-equations', 'plotting', 'special-functions']",2024-01-05,"[('sympy/sympy', 0.6130483150482178, 'math', 0), ('numpy/numpy', 0.5976458191871643, 'math', 0), ('pmorissette/ffn', 0.58493971824646, 'finance', 0), ('pytoolz/toolz', 0.580827534198761, 'util', 0), ('connorferster/handcalcs', 0.5756496787071228, 'jupyter', 0), ('scikit-geometry/scikit-geometry', 0.5644670724868774, 'gis', 0), ('pyston/pyston', 0.5607538819313049, 'util', 0), ('pypy/pypy', 0.5547716021537781, 'util', 0), ('scipy/scipy', 0.5546449422836304, 'math', 0), ('legrandin/pycryptodome', 0.5532434582710266, 'util', 0), ('hgrecco/pint', 0.5482094883918762, 'util', 0), ('cython/cython', 0.527232825756073, 'util', 0), ('gbeced/pyalgotrade', 0.5237154364585876, 'finance', 0), ('scikit-hep/uproot5', 0.516451895236969, 'data', 0), ('goldmansachs/gs-quant', 0.5134932398796082, 'finance', 0), ('1200wd/bitcoinlib', 0.5103440284729004, 'crypto', 0), ('google/jax', 0.5052241086959839, 'ml', 0), ('ta-lib/ta-lib-python', 0.5021244287490845, 'finance', 0)]",56,4.0,,3.75,147,127,147,0,1,1,1,147.0,55.0,90.0,0.4,35
784,sim,https://github.com/viblo/pymunk,[],,[],[],1.0,,,viblo/pymunk,pymunk,843,184,21,Python,http://www.pymunk.org,Pymunk is a easy-to-use pythonic 2d physics library that can be used whenever  you need 2d rigid body physics from Python,viblo,2024-01-12,2013-10-02,538,1.5644220572640508,,Pymunk is a easy-to-use pythonic 2d physics library that can be used whenever  you need 2d rigid body physics from Python,"['physics-2d', 'physics-engine', 'physics-simulation', 'pygame', 'pyglet']","['physics-2d', 'physics-engine', 'physics-simulation', 'pygame', 'pyglet']",2023-12-25,"[('pypy/pypy', 0.5984816551208496, 'util', 0), ('pyglet/pyglet', 0.5925350189208984, 'gamedev', 1), ('pythonarcade/arcade', 0.584841787815094, 'gamedev', 0), ('marcomusy/vedo', 0.5741404294967651, 'viz', 0), ('pygame/pygame', 0.5696349740028381, 'gamedev', 1), ('openai/mujoco-py', 0.5651075839996338, 'sim', 0), ('bulletphysics/bullet3', 0.5624234080314636, 'sim', 0), ('gboeing/pynamical', 0.5553194880485535, 'sim', 0), ('pytoolz/toolz', 0.5366160869598389, 'util', 0), ('cyrus2d/pyrus2d', 0.5240238904953003, 'sim', 0), ('pandas-dev/pandas', 0.5168660283088684, 'pandas', 0)]",18,3.0,,1.15,5,3,125,1,3,3,3,5.0,20.0,90.0,4.0,35
1794,viz,https://github.com/hazyresearch/meerkat,[],,[],[],1.0,,,hazyresearch/meerkat,meerkat,793,45,15,Python,,Creative interactive views of any dataset. ,hazyresearch,2024-01-10,2021-05-07,142,5.562124248496994,https://avatars.githubusercontent.com/u/2165246?v=4,Creative interactive views of any dataset. ,"['data-science', 'foundation-models', 'machine-learning', 'ml', 'pandas']","['data-science', 'foundation-models', 'machine-learning', 'ml', 'pandas']",2023-06-27,"[('huggingface/datasets', 0.5792464017868042, 'nlp', 2), ('lux-org/lux', 0.5568585991859436, 'viz', 2), ('man-group/dtale', 0.5547817945480347, 'viz', 2), ('polyaxon/datatile', 0.5525878071784973, 'pandas', 2), ('raphaelquast/eomaps', 0.543424129486084, 'gis', 0), ('pair-code/lit', 0.5387915968894958, 'ml-interpretability', 1), ('districtdatalabs/yellowbrick', 0.5290077924728394, 'ml', 1), ('bokeh/bokeh', 0.5242655277252197, 'viz', 0), ('holoviz/panel', 0.5195465683937073, 'viz', 0), ('tensorflow/data-validation', 0.5165846943855286, 'ml-ops', 0), ('adamerose/pandasgui', 0.5161508321762085, 'pandas', 1), ('huggingface/evaluate', 0.5127301216125488, 'ml', 1), ('koaning/embetter', 0.5117366313934326, 'data', 0), ('firmai/industry-machine-learning', 0.5096346735954285, 'study', 2), ('holoviz/datashader', 0.5083599090576172, 'gis', 0), ('jovianml/opendatasets', 0.5027737021446228, 'data', 2), ('mwaskom/seaborn', 0.5017697215080261, 'viz', 2)]",20,2.0,,14.56,2,0,33,7,7,15,7,2.0,1.0,90.0,0.5,35
1276,llm,https://github.com/oliveirabruno01/babyagi-asi,[],,[],[],,,,oliveirabruno01/babyagi-asi,babyagi-asi,733,82,18,Python,,"BabyAGI: an Autonomous and Self-Improving agent, or BASI",oliveirabruno01,2024-01-14,2023-04-07,42,17.21812080536913,,"BabyAGI: an Autonomous and Self-Improving agent, or BASI","['agi', 'ai', 'autonomous-agents', 'chain-of-thought', 'program-of-thoughts']","['agi', 'ai', 'autonomous-agents', 'chain-of-thought', 'program-of-thoughts']",2023-06-02,"[('transformeroptimus/superagi', 0.5967099666595459, 'llm', 3), ('prefecthq/marvin', 0.5443987846374512, 'nlp', 1), ('operand/agency', 0.519800066947937, 'llm', 3), ('antonosika/gpt-engineer', 0.518890917301178, 'llm', 1), ('jina-ai/thinkgpt', 0.5025618076324463, 'llm', 1), ('noahshinn/reflexion', 0.5022252798080444, 'llm', 1)]",15,5.0,,2.33,0,0,9,8,0,0,0,0.0,0.0,90.0,0.0,35
1032,finance,https://github.com/rsheftel/pandas_market_calendars,[],,[],[],,,,rsheftel/pandas_market_calendars,pandas_market_calendars,695,150,31,Python,,Exchange calendars to use with pandas for trading applications,rsheftel,2024-01-13,2016-12-07,372,1.8639846743295019,,Exchange calendars to use with pandas for trading applications,"['calendar', 'date', 'datetime', 'exchange', 'pandas']","['calendar', 'date', 'datetime', 'exchange', 'pandas']",2023-12-31,"[('gerrymanoim/exchange_calendars', 0.6709998846054077, 'finance', 0), ('adamerose/pandasgui', 0.5483811497688293, 'pandas', 1), ('tkrabel/bamboolib', 0.5060363411903381, 'pandas', 1)]",48,3.0,,0.9,28,24,86,0,7,4,7,28.0,20.0,90.0,0.7,35
1489,llm,https://github.com/opengenerativeai/genossgpt,[],,[],[],,,,opengenerativeai/genossgpt,GenossGPT,682,56,13,Python,https://genoss.ai/,"One API for all LLMs either Private or Public (Anthropic, Llama V2, GPT 3.5/4, Vertex, GPT4ALL, HuggingFace ...) 🌈🐂  Replace OpenAI GPT with any LLMs in your app with one line.",opengenerativeai,2024-01-14,2023-07-16,28,24.11111111111111,https://avatars.githubusercontent.com/u/102953894?v=4,"One API for all LLMs either Private or Public (Anthropic, Llama V2, GPT 3.5/4, Vertex, GPT4ALL, HuggingFace ...) 🌈🐂  Replace OpenAI GPT with any LLMs in your app with one line.","['api', 'gpt', 'gpt4all', 'huggingface', 'inference', 'llama', 'llm', 'openai', 'private', 'public']","['api', 'gpt', 'gpt4all', 'huggingface', 'inference', 'llama', 'llm', 'openai', 'private', 'public']",2023-08-28,"[('berriai/litellm', 0.7308034896850586, 'llm', 2), ('shishirpatil/gorilla', 0.6697072982788086, 'llm', 2), ('mmabrouk/chatgpt-wrapper', 0.56969153881073, 'llm', 2), ('vllm-project/vllm', 0.5517637133598328, 'llm', 4), ('openai/openai-cookbook', 0.5448883175849915, 'ml', 1), ('chainlit/chainlit', 0.5370033979415894, 'llm', 2), ('jerryjliu/llama_index', 0.5268757939338684, 'llm', 2), ('langchain-ai/opengpts', 0.5244327783584595, 'llm', 1), ('microsoft/semantic-kernel', 0.5167599320411682, 'llm', 2), ('predibase/lorax', 0.5129587650299072, 'llm', 3), ('farizrahman4u/loopgpt', 0.5030877590179443, 'llm', 1), ('dylanhogg/llmgraph', 0.5003108382225037, 'ml', 1)]",4,2.0,,2.29,1,0,6,5,1,2,1,1.0,0.0,90.0,0.0,35
1432,util,https://github.com/tox-dev/py-filelock,"['communication', 'processes']",,[],[],,,,tox-dev/py-filelock,filelock,614,92,15,Python,https://py-filelock.readthedocs.io,A platform-independent file lock for Python.,tox-dev,2024-01-12,2014-02-23,518,1.1846747519294376,https://avatars.githubusercontent.com/u/20345659?v=4,A platform-independent file lock for Python.,['filelock'],"['communication', 'filelock', 'processes']",2024-01-10,"[('fsspec/filesystem_spec', 0.6173799633979797, 'util', 0), ('python-trio/trio', 0.5723570585250854, 'perf', 0), ('pantsbuild/pex', 0.5669477581977844, 'util', 0), ('bogdanp/dramatiq', 0.5492278337478638, 'util', 0), ('samuelcolvin/watchfiles', 0.5285630822181702, 'util', 0)]",41,4.0,,1.52,19,17,120,0,17,6,17,19.0,18.0,90.0,0.9,35
787,gis,https://github.com/kvos/coastsat,[],,[],[],,,,kvos/coastsat,CoastSat,589,228,24,Jupyter Notebook,http://coastsat.wrl.unsw.edu.au/,Global shoreline mapping tool from satellite imagery,kvos,2024-01-09,2018-09-28,278,2.1143589743589746,,Global shoreline mapping tool from satellite imagery,"['coastal-engineering', 'earth-engine', 'google-earth-engine', 'remote-sensing', 'satellite-images', 'shoreline-detection']","['coastal-engineering', 'earth-engine', 'google-earth-engine', 'remote-sensing', 'satellite-images', 'shoreline-detection']",2023-12-18,[],16,5.0,,0.75,49,43,64,1,2,3,2,49.0,70.0,90.0,1.4,35
1270,ml,https://github.com/qdrant/quaterion,[],,[],[],,,,qdrant/quaterion,quaterion,586,42,11,Python,https://quaterion.qdrant.tech/,Blazing fast framework for fine-tuning similarity learning models,qdrant,2024-01-11,2021-08-31,126,4.650793650793651,https://avatars.githubusercontent.com/u/73504361?v=4,Blazing fast framework for fine-tuning similarity learning models,"['contrastive-learning', 'cosine-similarity', 'deep-learning', 'knn', 'machine-learning', 'metric-learning', 'nearest-neighbor-search', 'pytorch', 'pytorch-lightning', 'similarity-learning', 'similarity-search']","['contrastive-learning', 'cosine-similarity', 'deep-learning', 'knn', 'machine-learning', 'metric-learning', 'nearest-neighbor-search', 'pytorch', 'pytorch-lightning', 'similarity-learning', 'similarity-search']",2023-12-29,"[('tensorflow/similarity', 0.6630039811134338, 'ml-dl', 9), ('criteo/autofaiss', 0.6242879629135132, 'ml', 1), ('oml-team/open-metric-learning', 0.618319571018219, 'ml', 5), ('kevinmusgrave/pytorch-metric-learning', 0.584888219833374, 'ml', 5), ('lmcinnes/pynndescent', 0.5621644854545593, 'ml', 1), ('jina-ai/finetuner', 0.5520226359367371, 'ml', 2), ('qdrant/qdrant', 0.5457815527915955, 'data', 3), ('facebookresearch/faiss', 0.5449787378311157, 'ml', 0), ('pytorch/ignite', 0.5309311747550964, 'ml-dl', 3), ('scikit-learn-contrib/metric-learn', 0.5228015184402466, 'ml', 2), ('neuralmagic/sparseml', 0.5124584436416626, 'ml-dl', 1), ('huggingface/transformers', 0.5091818571090698, 'nlp', 3), ('skorch-dev/skorch', 0.506058394908905, 'ml-dl', 2), ('koaning/human-learn', 0.5016433000564575, 'data', 1)]",14,3.0,,0.15,9,6,29,1,1,14,1,9.0,19.0,90.0,2.1,35
1691,util,https://github.com/pyo3/setuptools-rust,"['setuptools', 'rust', 'build']",,[],[],,,,pyo3/setuptools-rust,setuptools-rust,533,91,15,Python,https://setuptools-rust.readthedocs.io,Setuptools plugin for Rust support,pyo3,2024-01-11,2017-03-09,359,1.4817315329626688,https://avatars.githubusercontent.com/u/28156855?v=4,Setuptools plugin for Rust support,"['rust', 'setuptools']","['build', 'rust', 'setuptools']",2024-01-02,"[('pypa/setuptools', 0.6672810912132263, 'util', 2), ('pyo3/maturin', 0.5044507384300232, 'util', 1)]",49,5.0,,2.15,34,34,83,0,4,7,4,34.0,18.0,90.0,0.5,35
1229,perf,https://github.com/python-cachier/cachier,[],,[],[],,,,python-cachier/cachier,cachier,482,57,8,Python,,"Persistent, stale-free, local and cross-machine caching for Python functions.",python-cachier,2024-01-12,2016-08-24,387,1.2427255985267034,https://avatars.githubusercontent.com/u/119668473?v=4,"Persistent, stale-free, local and cross-machine caching for Python functions.","['cache', 'cache-storage', 'cachemanager', 'caching', 'decorator', 'decorators', 'memoization', 'memoize', 'mongodb', 'pickle']","['cache', 'cache-storage', 'cachemanager', 'caching', 'decorator', 'decorators', 'memoization', 'memoize', 'mongodb', 'pickle']",2023-12-13,"[('dgilland/cacheout', 0.7924980521202087, 'perf', 2), ('grantjenks/python-diskcache', 0.6893970966339111, 'util', 1), ('joblib/joblib', 0.6731811165809631, 'util', 2), ('pythonspeed/filprofiler', 0.605586588382721, 'profiling', 0), ('erotemic/ubelt', 0.5962907671928406, 'util', 0), ('pympler/pympler', 0.5893210768699646, 'perf', 0), ('pytoolz/toolz', 0.5554683208465576, 'util', 0), ('aio-libs/aiocache', 0.5451957583427429, 'data', 2), ('spotify/annoy', 0.5307378768920898, 'ml', 0), ('python-trio/trio', 0.528437077999115, 'perf', 0), ('pytables/pytables', 0.5254138112068176, 'data', 0), ('fastai/fastcore', 0.5200280547142029, 'util', 0), ('reloadware/reloadium', 0.5175240635871887, 'profiling', 0), ('klen/py-frameworks-bench', 0.5169395208358765, 'perf', 0), ('sumerc/yappi', 0.5168151259422302, 'profiling', 0), ('zilliztech/gptcache', 0.5158444046974182, 'llm', 0), ('collerek/ormar', 0.5154281258583069, 'data', 0), ('ibis-project/ibis', 0.5115165710449219, 'data', 0), ('sqlalchemy/sqlalchemy', 0.5100117921829224, 'data', 0), ('tiangolo/sqlmodel', 0.5081299543380737, 'data', 0), ('locustio/locust', 0.5075251460075378, 'testing', 0), ('long2ice/fastapi-cache', 0.5056164860725403, 'web', 1), ('pypa/hatch', 0.502075731754303, 'util', 0)]",16,5.0,,0.63,12,7,90,1,7,8,7,12.0,40.0,90.0,3.3,35
1517,llm,https://github.com/squeezeailab/squeezellm,[],,[],[],,,,squeezeailab/squeezellm,SqueezeLLM,474,33,15,Python,https://arxiv.org/abs/2306.07629v2,SqueezeLLM: Dense-and-Sparse Quantization,squeezeailab,2024-01-11,2023-06-12,33,14.301724137931034,https://avatars.githubusercontent.com/u/92235103?v=4,SqueezeLLM: Dense-and-Sparse Quantization,"['efficient-inference', 'large-language-models', 'llama', 'llm', 'localllm', 'model-compression', 'natural-language-processing', 'post-training-quantization', 'quantization', 'small-models', 'text-generation', 'transformer']","['efficient-inference', 'large-language-models', 'llama', 'llm', 'localllm', 'model-compression', 'natural-language-processing', 'post-training-quantization', 'quantization', 'small-models', 'text-generation', 'transformer']",2024-01-05,"[('opengvlab/omniquant', 0.6535307168960571, 'llm', 3), ('artidoro/qlora', 0.6456478834152222, 'llm', 0), ('timdettmers/bitsandbytes', 0.6107233166694641, 'util', 0), ('huggingface/text-generation-inference', 0.6000766158103943, 'llm', 1), ('bobazooba/xllm', 0.596723735332489, 'llm', 3), ('neuralmagic/deepsparse', 0.5691953301429749, 'nlp', 1), ('sjtu-ipads/powerinfer', 0.5623429417610168, 'llm', 3), ('vllm-project/vllm', 0.5539712309837341, 'llm', 3), ('huggingface/transformers', 0.5502358078956604, 'nlp', 2), ('young-geng/easylm', 0.5474568605422974, 'llm', 4), ('bigscience-workshop/petals', 0.5468687415122986, 'data', 3), ('huawei-noah/pretrained-language-model', 0.5468460917472839, 'nlp', 2), ('ray-project/ray-llm', 0.5445480942726135, 'llm', 2), ('infinitylogesh/mutate', 0.5389288067817688, 'nlp', 1), ('explosion/spacy-llm', 0.5361400246620178, 'llm', 4), ('hpcaitech/energonai', 0.5279243588447571, 'ml', 0), ('salesforce/xgen', 0.5263859629631042, 'llm', 2), ('titanml/takeoff', 0.5222111940383911, 'llm', 3), ('lianjiatech/belle', 0.5215714573860168, 'llm', 1), ('optimalscale/lmflow', 0.5209859609603882, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5054431557655334, 'llm', 2), ('cg123/mergekit', 0.504544734954834, 'llm', 2), ('google-research/electra', 0.5039941072463989, 'ml-dl', 0)]",6,1.0,,0.75,13,9,7,0,0,0,0,14.0,4.0,90.0,0.3,35
1083,gis,https://github.com/laspy/laspy,[],,[],[],,,,laspy/laspy,laspy,416,114,33,Python,https://laspy.readthedocs.io/en/latest/,Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4. ,laspy,2024-01-10,2012-05-16,610,0.6810102899906455,https://avatars.githubusercontent.com/u/24229635?v=4,Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4. ,"['copc', 'las', 'laz', 'lidar', 'point-cloud', 'pointcloud']","['copc', 'las', 'laz', 'lidar', 'point-cloud', 'pointcloud']",2024-01-04,[],49,6.0,,0.77,13,13,142,0,6,3,6,12.0,24.0,90.0,2.0,35
1832,data,https://github.com/koaning/embetter,"['data-quality', 'sklearn', 'training-data', 'bulk-labelling']",,[],[],,,,koaning/embetter,embetter,381,12,7,Python,https://koaning.github.io/embetter/,just a bunch of useful embeddings,koaning,2024-01-04,2021-10-31,117,3.248477466504263,,just a bunch of useful embeddings,[],"['bulk-labelling', 'data-quality', 'sklearn', 'training-data']",2023-10-31,"[('qdrant/fastembed', 0.6213396787643433, 'ml', 0), ('koaning/bulk', 0.6180914044380188, 'data', 3), ('cvxgrp/pymde', 0.6085571646690369, 'ml', 0), ('koaning/whatlies', 0.6023291945457458, 'nlp', 0), ('milvus-io/bootcamp', 0.6010968089103699, 'data', 0), ('jina-ai/vectordb', 0.5935912728309631, 'data', 0), ('nomic-ai/nomic', 0.5922350287437439, 'nlp', 0), ('facebookresearch/pytorch-biggraph', 0.5848337411880493, 'ml-dl', 0), ('chroma-core/chroma', 0.5779300332069397, 'data', 0), ('neuml/txtai', 0.538155198097229, 'nlp', 0), ('jina-ai/clip-as-service', 0.5349407196044922, 'nlp', 0), ('sebischair/lbl2vec', 0.5337308645248413, 'nlp', 0), ('plasticityai/magnitude', 0.5330700278282166, 'nlp', 0), ('infinitylogesh/mutate', 0.5295093059539795, 'nlp', 0), ('jina-ai/finetuner', 0.526446521282196, 'ml', 0), ('cleanlab/cleanlab', 0.5257184505462646, 'ml', 1), ('awslabs/dgl-ke', 0.5223193764686584, 'ml', 0), ('huggingface/datasets', 0.5214577317237854, 'nlp', 0), ('eleutherai/the-pile', 0.5203182101249695, 'data', 1), ('activeloopai/deeplake', 0.5196226239204407, 'ml-ops', 0), ('flairnlp/flair', 0.5184030532836914, 'nlp', 0), ('huggingface/text-embeddings-inference', 0.5125843286514282, 'llm', 0), ('llmware-ai/llmware', 0.5121687650680542, 'llm', 0), ('hazyresearch/meerkat', 0.5117366313934326, 'viz', 0), ('bigscience-workshop/biomedical', 0.5097063183784485, 'data', 0), ('xl0/lovely-tensors', 0.5069031119346619, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5039408206939697, 'ml-dl', 0)]",8,6.0,,2.62,7,5,27,2,13,9,13,7.0,6.0,90.0,0.9,35
1030,finance,https://github.com/gerrymanoim/exchange_calendars,[],,[],[],,,,gerrymanoim/exchange_calendars,exchange_calendars,352,115,15,Python,,Calendars for various securities exchanges.,gerrymanoim,2024-01-12,2020-10-30,169,2.075821398483572,,Calendars for various securities exchanges.,[],[],2024-01-13,"[('rsheftel/pandas_market_calendars', 0.6709998846054077, 'finance', 0)]",98,2.0,,1.08,16,14,39,0,7,21,7,16.0,18.0,90.0,1.1,35
1218,ml,https://github.com/googleapis/python-speech,[],,[],[],,,,googleapis/python-speech,python-speech,352,261,54,,,This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-speech,googleapis,2024-01-04,2019-12-10,216,1.6296296296296295,https://avatars.githubusercontent.com/u/16785467?v=4,This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-speech,[],[],2023-10-31,"[('dialogflow/dialogflow-python-client-v2', 0.743192732334137, 'nlp', 0), ('pndurette/gtts', 0.7090864181518555, 'util', 0), ('uberi/speech_recognition', 0.6549380421638489, 'ml', 0), ('googleapis/google-api-python-client', 0.5471388101577759, 'util', 0), ('dsdanielpark/bard-api', 0.5438737273216248, 'llm', 0), ('nateshmbhat/pyttsx3', 0.5236392617225647, 'util', 0), ('spotify/pedalboard', 0.5077892541885376, 'util', 0)]",64,7.0,,0.87,8,8,50,2,10,17,10,8.0,11.0,90.0,1.4,35
1748,data,https://github.com/drivendataorg/cloudpathlib,[],,[],[],,,,drivendataorg/cloudpathlib,cloudpathlib,351,43,7,Python,https://cloudpathlib.drivendata.org,"Python pathlib-style classes for cloud storage services such as Amazon S3, Azure Blob Storage, and Google Cloud Storage.",drivendataorg,2024-01-05,2020-07-27,183,1.9165366614664587,https://avatars.githubusercontent.com/u/9515608?v=4,"Python pathlib-style classes for cloud storage services such as Amazon S3, Azure Blob Storage, and Google Cloud Storage.","['azure-blob', 'cloud-storage', 'google-cloud-storage', 'pathlib', 's3']","['azure-blob', 'cloud-storage', 'google-cloud-storage', 'pathlib', 's3']",2023-12-29,"[('boto/boto3', 0.5708515644073486, 'util', 0), ('pyfilesystem/pyfilesystem2', 0.5201523900032043, 'util', 0), ('fsspec/filesystem_spec', 0.5109065175056458, 'util', 0)]",12,3.0,,0.58,28,16,42,0,6,8,6,28.0,71.0,90.0,2.5,35
876,time-series,https://github.com/pastas/pastas,[],,[],[],,,,pastas/pastas,pastas,335,69,16,Python,https://pastas.readthedocs.io,:spaghetti: Pastas is an open-source Python framework for the analysis of groundwater time series.,pastas,2024-01-09,2016-04-15,406,0.8239634574841883,https://avatars.githubusercontent.com/u/18461359?v=4,🍝 Pastas is an open-source Python framework for the analysis of groundwater time series.,"['analysis', 'groundwater', 'hydrology', 'pastas', 'timeseries']","['analysis', 'groundwater', 'hydrology', 'pastas', 'timeseries']",2023-11-30,"[('firmai/atspy', 0.5670745372772217, 'time-series', 0), ('rjt1990/pyflux', 0.5493255257606506, 'time-series', 0), ('alkaline-ml/pmdarima', 0.518909215927124, 'time-series', 0), ('google/temporian', 0.5104494094848633, 'time-series', 0), ('dateutil/dateutil', 0.502344012260437, 'util', 0)]",17,8.0,,3.04,63,32,94,1,7,4,7,63.0,67.0,90.0,1.1,35
1586,llm,https://github.com/princeton-nlp/alce,['citations'],,[],[],,,,princeton-nlp/alce,ALCE,332,21,8,Python,,[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627,princeton-nlp,2024-01-12,2023-05-23,36,9.222222222222221,https://avatars.githubusercontent.com/u/44678448?v=4,[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627,[],['citations'],2023-12-10,"[('huggingface/text-generation-inference', 0.6029461622238159, 'llm', 0), ('paperswithcode/galai', 0.6015400290489197, 'llm', 1), ('whitead/paper-qa', 0.5843133330345154, 'llm', 0), ('yueyu1030/attrprompt', 0.5694684982299805, 'llm', 0), ('infinitylogesh/mutate', 0.5599058866500854, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5408934950828552, 'llm', 0), ('lianjiatech/belle', 0.5311445593833923, 'llm', 0), ('hannibal046/awesome-llm', 0.5235013365745544, 'study', 0), ('togethercomputer/redpajama-data', 0.5159098505973816, 'llm', 0), ('explosion/spacy-llm', 0.5158350467681885, 'llm', 0), ('cg123/mergekit', 0.5112486481666565, 'llm', 0), ('salesforce/xgen', 0.5106900930404663, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5002155900001526, 'llm', 0), ('neuml/txtai', 0.5001633763313293, 'nlp', 0)]",6,3.0,,0.48,4,3,8,1,0,0,0,4.0,6.0,90.0,1.5,35
1530,ml-ops,https://github.com/zenml-io/mlstacks,"['mlops', 'terraform']",,[],[],,,,zenml-io/mlstacks,mlstacks,228,23,5,HCL,https://mlstacks.zenml.io/,A series of Terraform based recipes to provision popular MLOps stacks on the cloud.,zenml-io,2024-01-13,2022-06-29,82,2.7517241379310344,https://avatars.githubusercontent.com/u/88676955?v=4,A series of Terraform based recipes to provision popular MLOps stacks on the cloud.,"['infrastructure-as-code', 'ml', 'mlops']","['infrastructure-as-code', 'ml', 'mlops', 'terraform']",2023-12-22,"[('netflix/metaflow', 0.5789435505867004, 'ml-ops', 2), ('fmind/mlops-python-package', 0.5667558312416077, 'template', 2), ('localstack/localstack', 0.5585765838623047, 'util', 0), ('skypilot-org/skypilot', 0.5491843819618225, 'llm', 0), ('zenml-io/zenml', 0.5486688613891602, 'ml-ops', 2), ('orchest/orchest', 0.5279688239097595, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5243047475814819, 'ml-ops', 1), ('unionai-oss/unionml', 0.5240914225578308, 'ml-ops', 1), ('jina-ai/jina', 0.514028012752533, 'ml', 1), ('polyaxon/polyaxon', 0.5073304176330566, 'ml-ops', 2), ('mingrammer/diagrams', 0.5006417036056519, 'util', 0)]",12,4.0,,2.44,29,23,19,1,16,14,16,29.0,33.0,90.0,1.1,35
1538,llm,https://github.com/openlmlab/leval,"['evaluation', 'language-model']",,[],[],,,,openlmlab/leval,LEval,228,8,4,Python,,"Data and code for L-Eval, a comprehensive long context language models evaluation benchmark",openlmlab,2024-01-13,2023-07-12,28,7.900990099009901,https://avatars.githubusercontent.com/u/127190579?v=4,"Data and code for L-Eval, a comprehensive long context language models evaluation benchmark",[],"['evaluation', 'language-model']",2023-12-20,"[('ai21labs/lm-evaluation', 0.7304577231407166, 'llm', 1), ('openai/evals', 0.6452102661132812, 'llm', 2), ('freedomintelligence/llmzoo', 0.6185758113861084, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.6121481657028198, 'llm', 2), ('juncongmoo/pyllama', 0.5909538269042969, 'llm', 0), ('cstankonrad/long_llama', 0.5901378989219666, 'llm', 1), ('lm-sys/fastchat', 0.5838186740875244, 'llm', 2), ('openbmb/toolbench', 0.5642274022102356, 'llm', 1), ('hannibal046/awesome-llm', 0.540010392665863, 'study', 1), ('fasteval/fasteval', 0.5392605662345886, 'llm', 1), ('anthropics/evals', 0.5348479747772217, 'llm', 0), ('jonasgeiping/cramming', 0.5251019597053528, 'nlp', 1), ('ctlllll/llm-toolmaker', 0.5215489864349365, 'llm', 1), ('lianjiatech/belle', 0.518318772315979, 'llm', 0), ('huggingface/evaluate', 0.5112590193748474, 'ml', 1), ('salesforce/xgen', 0.5019434690475464, 'llm', 1), ('ai21labs/in-context-ralm', 0.5012268424034119, 'llm', 1)]",9,3.0,,2.5,4,4,6,1,0,0,0,4.0,8.0,90.0,2.0,35
1697,util,https://github.com/mkdocstrings/python,[],,[],[],,,,mkdocstrings/python,python,130,54,4,Python,https://mkdocstrings.github.io/python,A Python handler for mkdocstrings.,mkdocstrings,2024-01-10,2021-10-30,117,1.1070559610705597,https://avatars.githubusercontent.com/u/75664361?v=4,A Python handler for mkdocstrings.,"['autodoc', 'documentation', 'mkdocs', 'mkdocstrings', 'mkdocstrings-handler', 'python-documentation']","['autodoc', 'documentation', 'mkdocs', 'mkdocstrings', 'mkdocstrings-handler', 'python-documentation']",2024-01-09,"[('mkdocstrings/mkdocstrings', 0.6996007561683655, 'util', 3), ('danielnoord/pydocstringformatter', 0.563347578048706, 'util', 0), ('pdoc3/pdoc', 0.5424914360046387, 'util', 1), ('mkdocstrings/griffe', 0.5423215627670288, 'util', 0), ('hhatto/autopep8', 0.5049331784248352, 'util', 0), ('mitmproxy/pdoc', 0.5048332214355469, 'util', 1), ('pycqa/docformatter', 0.503516674041748, 'util', 0)]",14,5.0,,2.38,13,8,27,0,18,22,18,13.0,41.0,90.0,3.2,35
1787,llm,https://github.com/citadel-ai/langcheck,"['evaluation', 'language-model']",,[],[],,,,citadel-ai/langcheck,langcheck,88,6,2,Python,https://langcheck.readthedocs.io/en/latest/index.html,"Simple, Pythonic building blocks to evaluate LLM applications.",citadel-ai,2024-01-12,2023-10-10,16,5.5,https://avatars.githubusercontent.com/u/75781367?v=4,"Simple, Pythonic building blocks to evaluate LLM applications.",[],"['evaluation', 'language-model']",2023-12-27,"[('confident-ai/deepeval', 0.6523064374923706, 'testing', 2), ('openai/evals', 0.6390816569328308, 'llm', 2), ('deep-diver/pingpong', 0.6283354759216309, 'llm', 0), ('agenta-ai/agenta', 0.613926351070404, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5963909029960632, 'llm', 0), ('exaloop/codon', 0.5882013440132141, 'perf', 0), ('hwchase17/langchain', 0.5860381722450256, 'llm', 1), ('young-geng/easylm', 0.5656929612159729, 'llm', 1), ('pympler/pympler', 0.5642958283424377, 'perf', 0), ('eugeneyan/open-llms', 0.5613651275634766, 'study', 0), ('numba/llvmlite', 0.5582582950592041, 'util', 0), ('truera/trulens', 0.5569631457328796, 'llm', 1), ('tigerlab-ai/tiger', 0.5546644926071167, 'llm', 0), ('microsoft/promptflow', 0.5546442866325378, 'llm', 0), ('chainlit/chainlit', 0.5534338355064392, 'llm', 0), ('ibm/dromedary', 0.5524911880493164, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5509293079376221, 'llm', 2), ('salesforce/codet5', 0.5499178171157837, 'nlp', 1), ('pathwaycom/llm-app', 0.5472109317779541, 'llm', 0), ('pmorissette/bt', 0.537364661693573, 'finance', 0), ('rlancemartin/auto-evaluator', 0.5368258357048035, 'llm', 1), ('nvidia/tensorrt-llm', 0.5307314395904541, 'viz', 1), ('microsoft/semantic-kernel', 0.5307271480560303, 'llm', 0), ('berriai/litellm', 0.5299030542373657, 'llm', 0), ('bentoml/openllm', 0.5283021926879883, 'ml-ops', 0), ('vllm-project/vllm', 0.5279338359832764, 'llm', 0), ('eth-sri/lmql', 0.5266656279563904, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5261443257331848, 'llm', 1), ('hiyouga/llama-factory', 0.5261441469192505, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5259177088737488, 'study', 0), ('ray-project/ray-llm', 0.524300754070282, 'llm', 0), ('salesforce/xgen', 0.5217398405075073, 'llm', 1), ('pypa/hatch', 0.5197140574455261, 'util', 0), ('ai21labs/lm-evaluation', 0.518677294254303, 'llm', 1), ('nomic-ai/gpt4all', 0.5129213333129883, 'llm', 1), ('night-chen/toolqa', 0.5104190111160278, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5101147890090942, 'llm', 0), ('shishirpatil/gorilla', 0.5088066458702087, 'llm', 0), ('hegelai/prompttools', 0.5087067484855652, 'llm', 0), ('pomponchik/instld', 0.5082795023918152, 'util', 0), ('rubik/radon', 0.505801796913147, 'util', 0), ('deepset-ai/haystack', 0.5050056576728821, 'llm', 1), ('pytransitions/transitions', 0.5025991201400757, 'util', 0)]",7,1.0,,11.92,58,47,3,1,3,12,3,58.0,105.0,90.0,1.8,35
597,testing,https://github.com/newsapps/beeswithmachineguns,[],,[],[],,,,newsapps/beeswithmachineguns,beeswithmachineguns,6372,670,224,Python,http://apps.chicagotribune.com/,A utility for arming (creating) many bees (micro EC2 instances) to attack (load test) targets (web applications).,newsapps,2024-01-12,2010-06-29,709,8.987306064880112,https://avatars.githubusercontent.com/u/317822?v=4,A utility for arming (creating) many bees (micro EC2 instances) to attack (load test) targets (web applications).,[],[],2017-12-20,"[('rhinosecuritylabs/pacu', 0.5492108464241028, 'security', 0)]",49,8.0,,0.0,5,0,165,74,0,0,0,5.0,2.0,90.0,0.4,34
1008,finance,https://github.com/gbeced/pyalgotrade,[],,[],[],,,,gbeced/pyalgotrade,pyalgotrade,4224,1408,350,Python,http://gbeced.github.io/pyalgotrade/,Python Algorithmic Trading Library,gbeced,2024-01-13,2012-03-07,620,6.803497468936953,,Python Algorithmic Trading Library,[],[],2023-03-05,"[('quantopian/zipline', 0.8662933707237244, 'finance', 0), ('robcarver17/pysystemtrade', 0.756932258605957, 'finance', 0), ('cuemacro/finmarketpy', 0.7125941514968872, 'finance', 0), ('quantconnect/lean', 0.7084618806838989, 'finance', 0), ('gbeced/basana', 0.7055126428604126, 'finance', 0), ('goldmansachs/gs-quant', 0.6751449108123779, 'finance', 0), ('mementum/backtrader', 0.6685662269592285, 'finance', 0), ('quantecon/quantecon.py', 0.6401932835578918, 'sim', 0), ('pmorissette/ffn', 0.628166913986206, 'finance', 0), ('ranaroussi/quantstats', 0.6159399747848511, 'finance', 0), ('ta-lib/ta-lib-python', 0.5967674851417542, 'finance', 0), ('primal100/pybitcointools', 0.5933455228805542, 'crypto', 0), ('probml/pyprobml', 0.5891293883323669, 'ml', 0), ('pytoolz/toolz', 0.5849488377571106, 'util', 0), ('sympy/sympy', 0.5751481652259827, 'math', 0), ('cuemacro/findatapy', 0.5724738836288452, 'finance', 0), ('ethereum/web3.py', 0.570094883441925, 'crypto', 0), ('domokane/financepy', 0.5683234333992004, 'finance', 0), ('mynameisfiber/high_performance_python_2e', 0.5649918913841248, 'study', 0), ('hydrosquall/tiingo-python', 0.5620060563087463, 'finance', 0), ('scipy/scipy', 0.5615862011909485, 'math', 0), ('quantopian/pyfolio', 0.5584282875061035, 'finance', 0), ('1200wd/bitcoinlib', 0.5566604733467102, 'crypto', 0), ('firmai/atspy', 0.5548366904258728, 'time-series', 0), ('kernc/backtesting.py', 0.5535092949867249, 'finance', 0), ('keon/algorithms', 0.5470435619354248, 'util', 0), ('eleutherai/pyfra', 0.5397642850875854, 'ml', 0), ('alkaline-ml/pmdarima', 0.5389692783355713, 'time-series', 0), ('ccxt/ccxt', 0.5324677228927612, 'crypto', 0), ('polakowo/vectorbt', 0.5312750935554504, 'finance', 0), ('fredrik-johansson/mpmath', 0.5237154364585876, 'math', 0), ('legrandin/pycryptodome', 0.5236385464668274, 'util', 0), ('wesm/pydata-book', 0.5229116082191467, 'study', 0), ('python/cpython', 0.520494818687439, 'util', 0), ('quantopian/alphalens', 0.5202894806861877, 'finance', 0), ('clips/pattern', 0.5164464712142944, 'nlp', 0), ('pycaret/pycaret', 0.5147980451583862, 'ml', 0), ('connorferster/handcalcs', 0.5120189189910889, 'jupyter', 0), ('pypy/pypy', 0.5119675397872925, 'util', 0), ('qdrant/qdrant-client', 0.5113593935966492, 'util', 0), ('google/tf-quant-finance', 0.5105053186416626, 'finance', 0), ('scikit-learn/scikit-learn', 0.5099478363990784, 'ml', 0), ('numerai/example-scripts', 0.5094029307365417, 'finance', 0), ('thealgorithms/python', 0.5092158317565918, 'study', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5082697868347168, 'study', 0), ('pyston/pyston', 0.507022500038147, 'util', 0), ('google/latexify_py', 0.5051184296607971, 'util', 0), ('freqtrade/freqtrade', 0.5014805197715759, 'crypto', 0)]",11,2.0,,0.04,2,2,144,10,0,2,2,2.0,2.0,90.0,1.0,34
794,data,https://github.com/pallets/itsdangerous,[],,[],[],,,,pallets/itsdangerous,itsdangerous,2781,222,56,Python,https://itsdangerous.palletsprojects.com,Safely pass trusted data to untrusted environments and back.,pallets,2024-01-12,2011-06-24,657,4.229198348902889,https://avatars.githubusercontent.com/u/16748505?v=4,Safely pass trusted data to untrusted environments and back.,"['hmac', 'itsdangerous', 'pallets', 'security', 'serialization']","['hmac', 'itsdangerous', 'pallets', 'security', 'serialization']",2023-09-06,"[('snyk/faker-security', 0.5056630969047546, 'security', 0)]",42,5.0,,0.62,6,2,153,4,0,2,2,6.0,1.0,90.0,0.2,34
114,nlp,https://github.com/huggingface/neuralcoref,[],,[],[],,,,huggingface/neuralcoref,neuralcoref,2775,476,99,C,https://huggingface.co/coref/,✨Fast Coreference Resolution in spaCy with Neural Networks,huggingface,2024-01-13,2017-07-03,343,8.087010824313072,https://avatars.githubusercontent.com/u/25720743?v=4,✨Fast Coreference Resolution in spaCy with Neural Networks,"['coreference', 'coreference-resolution', 'machine-learning', 'neural-networks', 'nlp', 'pytorch', 'spacy', 'spacy-extension', 'spacy-pipeline']","['coreference', 'coreference-resolution', 'machine-learning', 'neural-networks', 'nlp', 'pytorch', 'spacy', 'spacy-extension', 'spacy-pipeline']",2021-06-22,"[('explosion/spacy-models', 0.6682751774787903, 'nlp', 3), ('explosion/spacy-stanza', 0.6504446864128113, 'nlp', 4), ('explosion/spacy-transformers', 0.6309342980384827, 'llm', 6), ('explosion/spacy-streamlit', 0.5396462082862854, 'nlp', 3), ('franck-dernoncourt/neuroner', 0.5318391919136047, 'nlp', 3), ('jina-ai/finetuner', 0.5169621706008911, 'ml', 0), ('google-research/electra', 0.5090615153312683, 'ml-dl', 1), ('neuralmagic/sparseml', 0.5070856213569641, 'ml-dl', 2), ('explosion/spacy', 0.5070719122886658, 'nlp', 4)]",21,5.0,,0.0,5,1,80,31,0,1,1,5.0,4.0,90.0,0.8,34
1470,web,https://github.com/fastapi-admin/fastapi-admin,[],,[],[],,,,fastapi-admin/fastapi-admin,fastapi-admin,2335,318,34,Python,https://fastapi-admin-docs.long2ice.io,"A fast admin dashboard based on FastAPI and TortoiseORM with tabler ui, inspired by Django admin",fastapi-admin,2024-01-13,2020-04-06,199,11.725251076040172,https://avatars.githubusercontent.com/u/83151292?v=4,"A fast admin dashboard based on FastAPI and TortoiseORM with tabler ui, inspired by Django admin","['admin', 'admin-dashboard', 'dashboard', 'fastapi', 'fastapi-admin', 'tabler', 'tortoise-orm']","['admin', 'admin-dashboard', 'dashboard', 'fastapi', 'fastapi-admin', 'tabler', 'tortoise-orm']",2023-07-27,"[('aminalaee/sqladmin', 0.6240462064743042, 'data', 3), ('piccolo-orm/piccolo_admin', 0.6113201975822449, 'data', 3), ('s3rius/fastapi-template', 0.6024838089942932, 'web', 2), ('rawheel/fastapi-boilerplate', 0.5968390107154846, 'web', 1), ('fastapi-users/fastapi-users', 0.5555592775344849, 'web', 1), ('vitalik/django-ninja', 0.5257643461227417, 'web', 0), ('wagtail/wagtail', 0.5146538019180298, 'web', 0), ('fastai/fastcore', 0.5095018148422241, 'util', 0), ('tiangolo/fastapi', 0.5084497928619385, 'web', 1), ('feincms/feincms', 0.5065550804138184, 'web', 0)]",13,1.0,,0.25,4,1,46,6,0,4,4,4.0,1.0,90.0,0.2,34
25,util,https://github.com/google/gin-config,[],,[],[],,,,google/gin-config,gin-config,1946,120,23,Python,,Gin provides a lightweight configuration framework for Python,google,2024-01-08,2018-06-27,291,6.66764561918747,https://avatars.githubusercontent.com/u/1342004?v=4,Gin provides a lightweight configuration framework for Python,"['configuration-management', 'tensorflow', 'tensorflow-experiments']","['configuration-management', 'tensorflow', 'tensorflow-experiments']",2023-10-04,"[('omry/omegaconf', 0.5957804322242737, 'util', 0), ('arogozhnikov/einops', 0.5846500396728516, 'ml-dl', 1), ('micropython/micropython', 0.578527569770813, 'util', 0), ('nvidia/tensorrt-llm', 0.5778404474258423, 'viz', 0), ('eleutherai/pyfra', 0.5706988573074341, 'ml', 0), ('willmcgugan/textual', 0.5691657662391663, 'term', 0), ('google/tf-quant-finance', 0.5610681772232056, 'finance', 1), ('fastai/fastcore', 0.5576726198196411, 'util', 0), ('reloadware/reloadium', 0.5562544465065002, 'profiling', 0), ('pytoolz/toolz', 0.555968701839447, 'util', 0), ('facebookresearch/hydra', 0.5556942224502563, 'util', 0), ('pypy/pypy', 0.5548017621040344, 'util', 0), ('klen/py-frameworks-bench', 0.5510473847389221, 'perf', 0), ('ray-project/ray', 0.5417131185531616, 'ml-ops', 1), ('rafiqhasan/auto-tensorflow', 0.5411630868911743, 'ml-dl', 1), ('google/jax', 0.5384674668312073, 'ml', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5364246964454651, 'study', 0), ('pomponchik/instld', 0.5354675650596619, 'util', 0), ('beeware/toga', 0.5335937738418579, 'gui', 0), ('kubeflow/fairing', 0.5331827402114868, 'ml-ops', 0), ('ashleve/lightning-hydra-template', 0.5326757431030273, 'util', 0), ('pytorch/data', 0.5325197577476501, 'data', 0), ('hoffstadt/dearpygui', 0.5313805341720581, 'gui', 0), ('dosisod/refurb', 0.5311492085456848, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5311468839645386, 'perf', 0), ('pypa/hatch', 0.5304906368255615, 'util', 0), ('pympler/pympler', 0.5286837816238403, 'perf', 0), ('nvidia/warp', 0.5256108641624451, 'sim', 0), ('numba/llvmlite', 0.5255877375602722, 'util', 0), ('gradio-app/gradio', 0.5216200351715088, 'viz', 0), ('determined-ai/determined', 0.520888090133667, 'ml-ops', 1), ('pyston/pyston', 0.5207974910736084, 'util', 0), ('tensorly/tensorly', 0.5206177830696106, 'ml-dl', 1), ('alphasecio/langchain-examples', 0.5197805762290955, 'llm', 0), ('cosmicpython/book', 0.5146122574806213, 'study', 0), ('backtick-se/cowait', 0.5143940448760986, 'util', 0), ('exaloop/codon', 0.5136995911598206, 'perf', 0), ('wandb/client', 0.5134484767913818, 'ml', 1), ('horovod/horovod', 0.5107054710388184, 'ml-ops', 1), ('tensorflow/addons', 0.5091912150382996, 'ml', 1), ('buildbot/buildbot', 0.5084891319274902, 'util', 0), ('ageron/handson-ml2', 0.5084095001220703, 'ml', 0), ('plasma-umass/scalene', 0.5083065032958984, 'profiling', 0), ('sumerc/yappi', 0.5081563591957092, 'profiling', 0), ('pytorch/pytorch', 0.5066419243812561, 'ml-dl', 0), ('eventual-inc/daft', 0.5050129890441895, 'pandas', 0), ('goldmansachs/gs-quant', 0.5047725439071655, 'finance', 0), ('pallets/flask', 0.5042587518692017, 'web', 0), ('google/vizier', 0.5030581951141357, 'ml', 0), ('pyinfra-dev/pyinfra', 0.5022099614143372, 'util', 1), ('klen/muffin', 0.5011733770370483, 'web', 0)]",24,2.0,,0.1,2,1,68,3,0,0,0,2.0,3.0,90.0,1.5,34
1005,finance,https://github.com/pmorissette/bt,[],,[],[],,,,pmorissette/bt,bt,1892,444,88,Python,http://pmorissette.github.io/bt,bt - flexible backtesting for Python,pmorissette,2024-01-12,2014-06-19,501,3.7710706150341684,,bt - flexible backtesting for Python,[],[],2023-12-11,"[('mementum/backtrader', 0.6390605568885803, 'finance', 0), ('wolever/parameterized', 0.620602011680603, 'testing', 0), ('ionelmc/pytest-benchmark', 0.6115341186523438, 'testing', 0), ('nedbat/coveragepy', 0.6093915700912476, 'testing', 0), ('spulec/freezegun', 0.5981432199478149, 'testing', 0), ('pytest-dev/pytest-bdd', 0.5979208946228027, 'testing', 0), ('cuemacro/finmarketpy', 0.5734877586364746, 'finance', 0), ('locustio/locust', 0.5575364828109741, 'testing', 0), ('klen/py-frameworks-bench', 0.5551998615264893, 'perf', 0), ('citadel-ai/langcheck', 0.537364661693573, 'llm', 0), ('pypy/pypy', 0.5179216861724854, 'util', 0), ('getsentry/responses', 0.5153909921646118, 'testing', 0), ('samuelcolvin/dirty-equals', 0.5151998996734619, 'util', 0), ('taverntesting/tavern', 0.5144594311714172, 'testing', 0), ('pytest-dev/pytest', 0.5103425979614258, 'testing', 0), ('pyeve/cerberus', 0.507025420665741, 'data', 0)]",31,2.0,,0.29,21,16,117,1,1,1,1,21.0,14.0,90.0,0.7,34
310,util,https://github.com/linkedin/shiv,[],,[],[],,,,linkedin/shiv,shiv,1662,94,28,Python,,"shiv is a command line utility for building fully self contained Python zipapps as outlined in PEP 441, but with all their dependencies included.",linkedin,2024-01-13,2018-03-13,307,5.413680781758957,https://avatars.githubusercontent.com/u/357098?v=4,"shiv is a command line utility for building fully self contained Python zipapps as outlined in PEP 441, but with all their dependencies included.",[],[],2023-09-07,"[('ofek/pyapp', 0.5412315130233765, 'util', 0), ('indygreg/pyoxidizer', 0.5276411175727844, 'util', 0), ('erotemic/ubelt', 0.5263099670410156, 'util', 0), ('pyodide/micropip', 0.5121049284934998, 'util', 0), ('pypa/pipenv', 0.5103631615638733, 'util', 0), ('beeware/briefcase', 0.5067877173423767, 'util', 0), ('quantopian/zipline', 0.5038162469863892, 'finance', 0), ('pdm-project/pdm', 0.5008074641227722, 'util', 0), ('pypy/pypy', 0.5005409717559814, 'util', 0)]",39,6.0,,0.04,8,0,71,4,1,3,1,8.0,3.0,90.0,0.4,34
759,diffusion,https://github.com/divamgupta/stable-diffusion-tensorflow,[],,[],[],,,,divamgupta/stable-diffusion-tensorflow,stable-diffusion-tensorflow,1546,225,24,Python,,Stable Diffusion in TensorFlow / Keras,divamgupta,2024-01-13,2022-09-15,71,21.55776892430279,,Stable Diffusion in TensorFlow / Keras,[],[],2022-11-22,"[('bentoml/onediffusion', 0.6373262405395508, 'diffusion', 0), ('carson-katri/dream-textures', 0.6051181554794312, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.6014887690544128, 'diffusion', 0), ('lllyasviel/controlnet', 0.5530334115028381, 'diffusion', 0), ('stability-ai/stability-sdk', 0.5413241386413574, 'diffusion', 0), ('openai/improved-diffusion', 0.5380927324295044, 'diffusion', 0), ('comfyanonymous/comfyui', 0.5308429002761841, 'diffusion', 0), ('huggingface/diffusers', 0.5232102274894714, 'diffusion', 0), ('apple/ml-stable-diffusion', 0.5180650949478149, 'diffusion', 0), ('tensorflow/mesh', 0.5049545168876648, 'ml-dl', 0)]",13,2.0,,0.0,3,0,16,14,0,0,0,3.0,1.0,90.0,0.3,34
1742,util,https://github.com/facebookincubator/bowler,['refactoring'],,[],[],,,,facebookincubator/bowler,Bowler,1497,93,36,Python,https://pybowler.io/,Safe code refactoring for modern Python.,facebookincubator,2024-01-13,2018-06-07,294,5.079495879786719,https://avatars.githubusercontent.com/u/19538647?v=4,Safe code refactoring for modern Python.,[],['refactoring'],2023-01-20,"[('python-rope/rope', 0.7546546459197998, 'util', 1), ('dosisod/refurb', 0.5965598225593567, 'util', 0), ('jendrikseipp/vulture', 0.5705008506774902, 'util', 0), ('pyupio/safety', 0.5631865859031677, 'security', 0), ('asottile/reorder-python-imports', 0.5548760890960693, 'util', 1), ('grahamdumpleton/wrapt', 0.5219171047210693, 'util', 0), ('hhatto/autopep8', 0.5189270973205566, 'util', 0), ('landscapeio/prospector', 0.5116630792617798, 'util', 0), ('facebook/pyre-check', 0.5039924383163452, 'typing', 0), ('rubik/radon', 0.5027236938476562, 'util', 0)]",27,6.0,,0.0,1,0,68,12,0,1,1,1.0,2.0,90.0,2.0,34
1113,web,https://github.com/pylons/waitress,[],,[],[],,,,pylons/waitress,waitress,1337,166,37,Python,https://docs.pylonsproject.org/projects/waitress/en/latest/,Waitress - A WSGI server for Python 3,pylons,2024-01-12,2011-12-17,632,2.1140727354867854,https://avatars.githubusercontent.com/u/452227?v=4,Waitress - A WSGI server for Python 3,"['http-server', 'wsgi-server']","['http-server', 'wsgi-server']",2024-01-10,"[('encode/uvicorn', 0.6356403827667236, 'web', 1), ('benoitc/gunicorn', 0.6345184445381165, 'web', 2), ('pallets/werkzeug', 0.6237661838531494, 'web', 0), ('encode/httpx', 0.594509482383728, 'web', 0), ('bottlepy/bottle', 0.5867997407913208, 'web', 0), ('pallets/quart', 0.5782569050788879, 'web', 1), ('pallets/flask', 0.5715507864952087, 'web', 0), ('neoteroi/blacksheep', 0.5685455799102783, 'web', 1), ('falconry/falcon', 0.5612348318099976, 'web', 0), ('pylons/webob', 0.5591251254081726, 'web', 0), ('cherrypy/cherrypy', 0.5498244762420654, 'web', 1), ('pylons/pyramid', 0.5299732089042664, 'web', 0), ('aio-libs/aiohttp', 0.5173540711402893, 'web', 1), ('requests/toolbelt', 0.5072819590568542, 'util', 0), ('aws/chalice', 0.5070593953132629, 'web', 0), ('klen/muffin', 0.5056185126304626, 'web', 0), ('masoniteframework/masonite', 0.5041007399559021, 'web', 0)]",51,6.0,,0.15,10,2,147,0,0,4,4,10.0,2.0,90.0,0.2,34
1265,llm,https://github.com/run-llama/llama-lab,"['llama-index', 'llama', 'language-model']",Llama Lab is a repo dedicated to building cutting-edge projects using LlamaIndex,[],[],,,,run-llama/llama-lab,llama-lab,1149,150,16,Python,,,run-llama,2024-01-14,2023-04-14,41,27.63917525773196,https://avatars.githubusercontent.com/u/130722866?v=4,Llama Lab is a repo dedicated to building cutting-edge projects using LlamaIndex,[],"['language-model', 'llama', 'llama-index']",2023-05-30,"[('facebookresearch/llama-recipes', 0.7252361178398132, 'llm', 2), ('microsoft/llama-2-onnx', 0.7098461985588074, 'llm', 2), ('jerryjliu/llama_index', 0.6514791250228882, 'llm', 3), ('tloen/alpaca-lora', 0.6453814506530762, 'llm', 2), ('jzhang38/tinyllama', 0.6213663816452026, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.6018344759941101, 'llm', 0), ('zrrskywalker/llama-adapter', 0.5806187391281128, 'llm', 2), ('run-llama/llama-hub', 0.5685200095176697, 'data', 0), ('facebookresearch/llama', 0.562117874622345, 'llm', 2), ('abetlen/llama-cpp-python', 0.5617009401321411, 'llm', 2), ('lightning-ai/lit-llama', 0.550618588924408, 'llm', 2), ('ggerganov/llama.cpp', 0.545893132686615, 'llm', 2), ('young-geng/easylm', 0.5406877398490906, 'llm', 2), ('oobabooga/text-generation-webui', 0.5403240919113159, 'llm', 1), ('agenta-ai/agenta', 0.5381309986114502, 'llm', 1), ('bentoml/openllm', 0.5358179211616516, 'ml-ops', 1), ('ajndkr/lanarky', 0.5272144079208374, 'llm', 0), ('karpathy/llama2.c', 0.5159832835197449, 'llm', 2), ('hwchase17/langchain', 0.5104470252990723, 'llm', 1), ('tairov/llama2.mojo', 0.5075839757919312, 'llm', 1), ('eugeneyan/open-llms', 0.5069587826728821, 'study', 0), ('openlm-research/open_llama', 0.5049968957901001, 'llm', 2), ('alpha-vllm/llama2-accessory', 0.5041949152946472, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5041861534118652, 'llm', 1), ('salesforce/codet5', 0.5031118392944336, 'nlp', 1)]",4,2.0,,0.6,2,0,9,8,0,0,0,2.0,0.0,90.0,0.0,34
674,util,https://github.com/lukasschwab/arxiv.py,[],,[],[],,,,lukasschwab/arxiv.py,arxiv.py,887,107,16,Python,,Python wrapper for the arXiv API,lukasschwab,2024-01-13,2015-11-25,426,2.0779785809906293,,Python wrapper for the arXiv API,"['arxiv', 'arxiv-api', 'pdf', 'python-wrapper']","['arxiv', 'arxiv-api', 'pdf', 'python-wrapper']",2023-10-25,"[('man-c/pycoingecko', 0.5484018921852112, 'crypto', 0), ('pyfpdf/fpdf2', 0.5308734774589539, 'util', 1), ('arxiv-vanity/arxiv-vanity', 0.5270992517471313, 'nlp', 1), ('mattbierbaum/arxiv-public-datasets', 0.5195563435554504, 'data', 0), ('requests/toolbelt', 0.5077224969863892, 'util', 0), ('indygreg/pyoxidizer', 0.5008288621902466, 'util', 0)]",14,5.0,,0.52,28,27,99,3,8,4,8,28.0,22.0,90.0,0.8,34
359,ml-ops,https://github.com/tensorflow/data-validation,[],,[],[],,,,tensorflow/data-validation,data-validation,739,162,48,Python,,Library for exploring and validating machine learning data,tensorflow,2024-01-09,2018-07-02,291,2.5382728164867516,https://avatars.githubusercontent.com/u/15658638?v=4,Library for exploring and validating machine learning data,[],[],2024-01-08,"[('huggingface/evaluate', 0.7562239170074463, 'ml', 0), ('teamhg-memex/eli5', 0.6825962066650391, 'ml', 0), ('rasbt/mlxtend', 0.6768656373023987, 'ml', 0), ('firmai/industry-machine-learning', 0.6276463866233826, 'study', 0), ('districtdatalabs/yellowbrick', 0.6272578239440918, 'ml', 0), ('pyeve/cerberus', 0.6110429167747498, 'data', 0), ('patchy631/machine-learning', 0.6105336546897888, 'ml', 0), ('scikit-learn/scikit-learn', 0.5854442715644836, 'ml', 0), ('pycaret/pycaret', 0.584001898765564, 'ml', 0), ('jovianml/opendatasets', 0.5710594654083252, 'data', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5666349530220032, 'ml', 0), ('marcotcr/lime', 0.5638388395309448, 'ml-interpretability', 0), ('mlflow/mlflow', 0.56364506483078, 'ml-ops', 0), ('seldonio/alibi', 0.5581661462783813, 'ml-interpretability', 0), ('csinva/imodels', 0.5509118437767029, 'ml', 0), ('rasbt/machine-learning-book', 0.5481884479522705, 'study', 0), ('pair-code/lit', 0.5432097315788269, 'ml-interpretability', 0), ('featurelabs/featuretools', 0.5417819023132324, 'ml', 0), ('gradio-app/gradio', 0.5404645204544067, 'viz', 0), ('unionai-oss/pandera', 0.5394289493560791, 'pandas', 0), ('eugeneyan/testing-ml', 0.5382910966873169, 'testing', 0), ('tensorflow/tensor2tensor', 0.5354011654853821, 'ml', 0), ('tensorflow/tensorflow', 0.5338260531425476, 'ml-dl', 0), ('selfexplainml/piml-toolbox', 0.5335401892662048, 'ml-interpretability', 0), ('ggerganov/ggml', 0.525627076625824, 'ml', 0), ('microsoft/flaml', 0.5238473415374756, 'ml', 0), ('oml-team/open-metric-learning', 0.5238116979598999, 'ml', 0), ('carla-recourse/carla', 0.5232628583908081, 'ml', 0), ('tensorflow/lucid', 0.5206327438354492, 'ml-interpretability', 0), ('polyaxon/datatile', 0.5198504328727722, 'pandas', 0), ('hazyresearch/meerkat', 0.5165846943855286, 'viz', 0), ('pytorch/ignite', 0.5164223313331604, 'ml-dl', 0), ('deepchecks/deepchecks', 0.512187123298645, 'data', 0), ('dask/dask-ml', 0.5110467076301575, 'ml', 0), ('scikit-learn-contrib/metric-learn', 0.5105538368225098, 'ml', 0), ('huggingface/datasets', 0.5105088353157043, 'nlp', 0), ('catboost/catboost', 0.5083582997322083, 'ml', 0), ('paperswithcode/axcell', 0.5049012899398804, 'util', 0), ('maif/shapash', 0.5037372708320618, 'ml', 0), ('automl/auto-sklearn', 0.5028933882713318, 'ml', 0), ('lightly-ai/lightly', 0.5010249018669128, 'ml', 0)]",26,2.0,,1.48,12,5,67,0,2,8,2,12.0,15.0,90.0,1.2,34
590,gis,https://github.com/uber/h3-py,[],,[],[],,,,uber/h3-py,h3-py,718,127,32,Python,https://uber.github.io/h3-py,"Python bindings for H3, a hierarchical hexagonal geospatial indexing system",uber,2024-01-13,2018-06-12,294,2.442176870748299,https://avatars.githubusercontent.com/u/538264?v=4,"Python bindings for H3, a hierarchical hexagonal geospatial indexing system","['geocoding', 'geospatial', 'gis', 'h3', 'hexagonal-architecture', 'uber']","['geocoding', 'geospatial', 'gis', 'h3', 'hexagonal-architecture', 'uber']",2024-01-01,"[('toblerity/rtree', 0.6043885350227356, 'gis', 0), ('opengeos/leafmap', 0.5105132460594177, 'gis', 2), ('residentmario/geoplot', 0.500649094581604, 'gis', 0)]",16,5.0,,0.12,19,6,68,0,0,4,4,19.0,14.0,90.0,0.7,34
47,data,https://github.com/macbre/sql-metadata,[],,[],[],1.0,,,macbre/sql-metadata,sql-metadata,668,111,15,Python,https://pypi.python.org/pypi/sql-metadata,Uses tokenized query returned by python-sqlparse and generates query metadata,macbre,2024-01-13,2017-06-06,347,1.92507204610951,,Uses tokenized query returned by python-sqlparse and generates query metadata,"['database', 'hive', 'hiveql', 'metadata', 'mysql-query', 'parser', 'python-package', 'python3-library', 'sql', 'sql-parser', 'sqlparse']","['database', 'hive', 'hiveql', 'metadata', 'mysql-query', 'parser', 'python-package', 'python3-library', 'sql', 'sql-parser', 'sqlparse']",2024-01-04,"[('tobymao/sqlglot', 0.6693900227546692, 'data', 3), ('ibis-project/ibis', 0.6164320707321167, 'data', 2), ('tiangolo/sqlmodel', 0.6107516288757324, 'data', 1), ('andialbrecht/sqlparse', 0.5947306156158447, 'data', 0), ('kayak/pypika', 0.5715281367301941, 'data', 1), ('sqlalchemy/sqlalchemy', 0.5708635449409485, 'data', 1), ('malloydata/malloy-py', 0.5103316307067871, 'data', 1), ('python-odin/odin', 0.5047857761383057, 'util', 0)]",20,5.0,,1.21,27,19,80,0,4,5,4,27.0,13.0,90.0,0.5,34
412,util,https://github.com/bastibe/python-soundfile,[],,[],[],,,,bastibe/python-soundfile,python-soundfile,628,135,17,Python,,"SoundFile is an audio library based on libsndfile, CFFI, and NumPy",bastibe,2024-01-12,2013-08-27,544,1.1544117647058822,,"SoundFile is an audio library based on libsndfile, CFFI, and NumPy",[],[],2024-01-05,"[('spotify/pedalboard', 0.7314440608024597, 'util', 0), ('irmen/pyminiaudio', 0.6631749868392944, 'util', 0), ('libaudioflux/audioflux', 0.6440500617027283, 'util', 0), ('taylorsmarks/playsound', 0.6413252949714661, 'util', 0), ('uberi/speech_recognition', 0.575599730014801, 'ml', 0), ('quodlibet/mutagen', 0.5448225736618042, 'util', 0), ('nateshmbhat/pyttsx3', 0.5068590044975281, 'util', 0), ('speechbrain/speechbrain', 0.5037903189659119, 'nlp', 0), ('facebookresearch/audiocraft', 0.5025203227996826, 'util', 0)]",33,4.0,,0.5,23,11,126,0,2,2,2,23.0,44.0,90.0,1.9,34
1729,util,https://github.com/akaihola/darker,['code-quality'],,[],[],,,,akaihola/darker,darker,600,50,10,Python,https://pypi.org/project/darker/,"Apply black reformatting to Python files only in regions changed since a given commit. For a practical usage example, see the blog post at https://dev.to/akaihola/improving-python-code-incrementally-3f7a",akaihola,2024-01-13,2020-02-16,206,2.9085872576177287,,"Apply black reformatting to Python files only in regions changed since a given commit. For a practical usage example, see the blog post at https://dev.to/akaihola/improving-python-code-incrementally-3f7a",[],['code-quality'],2023-08-05,[],28,7.0,,1.23,7,0,48,5,3,6,3,7.0,9.0,90.0,1.3,34
1788,math,https://github.com/dfki-ric/pytransform3d,[],,[],[],,,,dfki-ric/pytransform3d,pytransform3d,536,63,10,Python,https://dfki-ric.github.io/pytransform3d/,3D transformations for Python.,dfki-ric,2024-01-12,2017-05-19,349,1.53330608908868,https://avatars.githubusercontent.com/u/37366708?v=4,3D transformations for Python.,"['mathematics', 'matplotlib', 'transformations', 'visualization']","['mathematics', 'matplotlib', 'transformations', 'visualization']",2023-11-04,"[('enthought/mayavi', 0.6477158665657043, 'viz', 1), ('marcomusy/vedo', 0.6237989664077759, 'viz', 1), ('scitools/cartopy', 0.6194444298744202, 'gis', 1), ('matplotlib/matplotlib', 0.5860082507133484, 'viz', 1), ('mwaskom/seaborn', 0.5858793258666992, 'viz', 1), ('altair-viz/altair', 0.5856305360794067, 'viz', 1), ('holoviz/geoviews', 0.5822384357452393, 'gis', 0), ('residentmario/geoplot', 0.5743772983551025, 'gis', 1), ('google/jax', 0.5666728615760803, 'ml', 0), ('maartenbreddels/ipyvolume', 0.5542986392974854, 'jupyter', 0), ('pyproj4/pyproj', 0.5527838468551636, 'gis', 0), ('holoviz/holoviz', 0.5455291867256165, 'viz', 0), ('contextlab/hypertools', 0.5422584414482117, 'ml', 1), ('artelys/geonetworkx', 0.5281858444213867, 'gis', 0), ('isl-org/open3d', 0.5204105377197266, 'sim', 1), ('luispedro/mahotas', 0.5193596482276917, 'viz', 0), ('holoviz/hvplot', 0.5157265663146973, 'pandas', 0), ('has2k1/plotnine', 0.5153816342353821, 'viz', 0), ('gboeing/pynamical', 0.5125102400779724, 'sim', 2), ('pyqtgraph/pyqtgraph', 0.5104587078094482, 'viz', 1), ('scikit-geometry/scikit-geometry', 0.5100734233856201, 'gis', 0), ('connorferster/handcalcs', 0.5065004229545593, 'jupyter', 0), ('earthlab/earthpy', 0.5059515833854675, 'gis', 0), ('vispy/vispy', 0.5056593418121338, 'viz', 1)]",19,4.0,,10.4,12,5,81,2,8,5,8,12.0,12.0,90.0,1.0,34
1487,crypto,https://github.com/1200wd/bitcoinlib,[],,[],[],,,,1200wd/bitcoinlib,bitcoinlib,531,177,18,Python,http://bitcoinlib.readthedocs.io/,"Bitcoin and other Cryptocurrencies Library for Python. Includes a fully functional  wallet, Mnemonic key generation and management and connection with various service providers to receive and send blockchain and transaction information.",1200wd,2024-01-12,2016-02-17,414,1.2799586776859504,https://avatars.githubusercontent.com/u/944360?v=4,"Bitcoin and other Cryptocurrencies Library for Python. Includes a fully functional  wallet, Mnemonic key generation and management and connection with various service providers to receive and send blockchain and transaction information.","['bitcoin', 'dash', 'litecoin']","['bitcoin', 'dash', 'litecoin']",2023-11-23,"[('primal100/pybitcointools', 0.7157860994338989, 'crypto', 0), ('pyca/cryptography', 0.6551878452301025, 'util', 0), ('legrandin/pycryptodome', 0.6432069540023804, 'util', 0), ('pypy/pypy', 0.6188755631446838, 'util', 0), ('pmaji/crypto-whale-watching-app', 0.6108453869819641, 'crypto', 3), ('ethereum/web3.py', 0.6057431101799011, 'crypto', 0), ('man-c/pycoingecko', 0.6038165092468262, 'crypto', 0), ('pytoolz/toolz', 0.5831727385520935, 'util', 0), ('plotly/dash', 0.5799865126609802, 'viz', 1), ('pyca/pynacl', 0.5711807608604431, 'util', 0), ('pyston/pyston', 0.5686086416244507, 'util', 0), ('ccxt/ccxt', 0.566085934638977, 'crypto', 1), ('willmcgugan/textual', 0.5643294453620911, 'term', 0), ('gbeced/basana', 0.5637730360031128, 'finance', 0), ('dylanhogg/awesome-python', 0.5614901185035706, 'study', 0), ('numerai/example-scripts', 0.5599415898323059, 'finance', 0), ('libtcod/python-tcod', 0.5591480731964111, 'gamedev', 0), ('fastai/fastcore', 0.5587186813354492, 'util', 0), ('erotemic/ubelt', 0.5576600432395935, 'util', 0), ('gbeced/pyalgotrade', 0.5566604733467102, 'finance', 0), ('cherrypy/cherrypy', 0.5514862537384033, 'web', 0), ('ethereum/py-evm', 0.546512246131897, 'crypto', 0), ('clips/pattern', 0.5464682579040527, 'nlp', 0), ('masoniteframework/masonite', 0.5462782979011536, 'web', 0), ('domokane/financepy', 0.5450164675712585, 'finance', 0), ('bottlepy/bottle', 0.5447421669960022, 'web', 0), ('python/cpython', 0.5435817241668701, 'util', 0), ('krzjoa/awesome-python-data-science', 0.5427032113075256, 'study', 0), ('goldmansachs/gs-quant', 0.5417495965957642, 'finance', 0), ('paramiko/paramiko', 0.5394938588142395, 'util', 0), ('ta-lib/ta-lib-python', 0.5371884703636169, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5357022285461426, 'study', 0), ('tiangolo/sqlmodel', 0.5315213799476624, 'data', 0), ('ranaroussi/quantstats', 0.5297749638557434, 'finance', 0), ('webpy/webpy', 0.5290544033050537, 'web', 0), ('eleutherai/pyfra', 0.5283639430999756, 'ml', 0), ('evhub/coconut', 0.5255511403083801, 'util', 0), ('pallets/flask', 0.5223922729492188, 'web', 0), ('python-odin/odin', 0.5217608213424683, 'util', 0), ('cython/cython', 0.5212488770484924, 'util', 0), ('scrapy/scrapy', 0.5212147831916809, 'data', 0), ('adafruit/circuitpython', 0.519145131111145, 'util', 0), ('pygamelib/pygamelib', 0.5186594128608704, 'gamedev', 0), ('imageio/imageio', 0.517402708530426, 'util', 0), ('ofek/bit', 0.5140418410301208, 'crypto', 1), ('pmorissette/ffn', 0.5135668516159058, 'finance', 0), ('pandas-dev/pandas', 0.5127820372581482, 'pandas', 0), ('pyparsing/pyparsing', 0.5124039053916931, 'util', 0), ('r0x0r/pywebview', 0.5121883153915405, 'gui', 0), ('quantopian/zipline', 0.5117334723472595, 'finance', 0), ('fredrik-johansson/mpmath', 0.5103440284729004, 'math', 0), ('dylanhogg/crazy-awesome-crypto', 0.5096782445907593, 'crypto', 1), ('quantconnect/lean', 0.5076516270637512, 'finance', 0), ('falconry/falcon', 0.5075222849845886, 'web', 0), ('wesm/pydata-book', 0.5045524835586548, 'study', 0), ('gradio-app/gradio', 0.5043916702270508, 'viz', 0), ('micropython/micropython', 0.5035257935523987, 'util', 0), ('hoffstadt/dearpygui', 0.5015420913696289, 'gui', 0), ('googleapis/google-api-python-client', 0.5004116892814636, 'util', 0), ('simple-salesforce/simple-salesforce', 0.5001790523529053, 'data', 0)]",26,5.0,,1.85,29,17,96,2,0,6,6,29.0,38.0,90.0,1.3,34
1556,ml,https://github.com/nevronai/metisfl,[],,[],[],,,,nevronai/metisfl,MetisFL,528,49,17,Python,https://metisfl.org,The first open Federated Learning framework implemented in C++ and Python.,nevronai,2024-01-10,2023-06-05,34,15.464435146443515,https://avatars.githubusercontent.com/u/6014092?v=4,The first open Federated Learning framework implemented in C++ and Python.,"['artificial-intelligence', 'collaborative-ai', 'deep-learning', 'federated-analytics', 'federated-learning', 'federated-learning-framework', 'machine-learning']","['artificial-intelligence', 'collaborative-ai', 'deep-learning', 'federated-analytics', 'federated-learning', 'federated-learning-framework', 'machine-learning']",2023-11-06,"[('adap/flower', 0.8411728739738464, 'ml-ops', 6), ('jonasgeiping/breaching', 0.6550554037094116, 'ml', 2), ('tensorflow/tensorflow', 0.6451448798179626, 'ml-dl', 2), ('mlflow/mlflow', 0.5837588310241699, 'ml-ops', 1), ('horovod/horovod', 0.5837339162826538, 'ml-ops', 2), ('nccr-itmo/fedot', 0.5833522081375122, 'ml-ops', 1), ('merantix-momentum/squirrel-core', 0.560804009437561, 'ml', 2), ('determined-ai/determined', 0.5598009824752808, 'ml-ops', 2), ('onnx/onnx', 0.543806254863739, 'ml', 2), ('rasahq/rasa', 0.5226208567619324, 'llm', 1), ('ml-tooling/opyrator', 0.5207219123840332, 'viz', 1), ('uber/fiber', 0.5169682502746582, 'data', 1), ('eventual-inc/daft', 0.515454113483429, 'pandas', 2), ('polyaxon/polyaxon', 0.5108596682548523, 'ml-ops', 3), ('ai4finance-foundation/finrl', 0.5089988112449646, 'finance', 0), ('explosion/thinc', 0.5079528093338013, 'ml-dl', 3), ('paddlepaddle/paddle', 0.5069923996925354, 'ml-dl', 2), ('dylanhogg/awesome-python', 0.5050832033157349, 'study', 2), ('huggingface/huggingface_hub', 0.5000477433204651, 'ml', 2)]",14,2.0,,5.6,0,0,7,2,0,0,0,0.0,0.0,90.0,0.0,34
484,gis,https://github.com/earthlab/earthpy,[],,[],[],,,,earthlab/earthpy,earthpy,472,161,19,Python,https://earthpy.readthedocs.io,A package built to support working with spatial data using open source python,earthlab,2024-01-04,2018-02-20,310,1.5225806451612902,https://avatars.githubusercontent.com/u/19476722?v=4,A package built to support working with spatial data using open source python,"['education', 'raster', 'spatial-data', 'vector']","['education', 'raster', 'spatial-data', 'vector']",2023-08-23,"[('makepath/xarray-spatial', 0.6488336324691772, 'gis', 0), ('pysal/pysal', 0.6416592597961426, 'gis', 0), ('residentmario/geoplot', 0.63930344581604, 'gis', 0), ('gregorhd/mapcompare', 0.6392421722412109, 'gis', 0), ('scitools/cartopy', 0.6048831343650818, 'gis', 0), ('geopandas/geopandas', 0.6019172668457031, 'gis', 0), ('imageio/imageio', 0.5848910808563232, 'util', 0), ('artelys/geonetworkx', 0.5808680057525635, 'gis', 0), ('scitools/iris', 0.5802125930786133, 'gis', 0), ('raphaelquast/eomaps', 0.5780810713768005, 'gis', 0), ('osgeo/gdal', 0.5730497241020203, 'gis', 2), ('opengeos/leafmap', 0.572256863117218, 'gis', 0), ('pandas-dev/pandas', 0.5722144842147827, 'pandas', 0), ('marcomusy/vedo', 0.5666988492012024, 'viz', 0), ('osgeo/grass', 0.5645433068275452, 'gis', 2), ('opengeos/segment-geospatial', 0.5494171977043152, 'gis', 0), ('giswqs/geemap', 0.5480675101280212, 'gis', 0), ('pycaret/pycaret', 0.5419654846191406, 'ml', 0), ('kornia/kornia', 0.5408483743667603, 'ml-dl', 0), ('contextlab/hypertools', 0.5363619923591614, 'ml', 0), ('sentinel-hub/eo-learn', 0.5327595472335815, 'gis', 0), ('isl-org/open3d', 0.5324108004570007, 'sim', 0), ('cloudsen12/easystac', 0.5297847390174866, 'gis', 0), ('weecology/deepforest', 0.5284902453422546, 'gis', 0), ('tebelorg/rpa-python', 0.5264238715171814, 'util', 0), ('toblerity/rtree', 0.5261022448539734, 'gis', 0), ('altair-viz/altair', 0.5252060294151306, 'viz', 0), ('enthought/mayavi', 0.5246884822845459, 'viz', 0), ('krzjoa/awesome-python-data-science', 0.5236297249794006, 'study', 0), ('holoviz/geoviews', 0.5206630229949951, 'gis', 0), ('remotesensinglab/raster4ml', 0.5156380534172058, 'gis', 0), ('fatiando/verde', 0.5133398771286011, 'gis', 0), ('wesm/pydata-book', 0.512826681137085, 'study', 0), ('holoviz/spatialpandas', 0.5123425722122192, 'pandas', 0), ('albahnsen/pycircular', 0.5108852982521057, 'math', 0), ('has2k1/plotnine', 0.5097348093986511, 'viz', 0), ('numpy/numpy', 0.5088415741920471, 'math', 0), ('lightly-ai/lightly', 0.5085079669952393, 'ml', 0), ('holoviz/holoviz', 0.5084066987037659, 'viz', 0), ('featurelabs/featuretools', 0.5079247355461121, 'ml', 0), ('pyqtgraph/pyqtgraph', 0.506048321723938, 'viz', 0), ('dfki-ric/pytransform3d', 0.5059515833854675, 'math', 0), ('mwaskom/seaborn', 0.5044101476669312, 'viz', 0)]",44,8.0,,0.71,17,9,72,5,0,6,6,17.0,21.0,90.0,1.2,34
557,gis,https://github.com/geopandas/contextily,[],,[],[],,,,geopandas/contextily,contextily,456,80,16,Jupyter Notebook,https://contextily.readthedocs.io/en/latest/,Context geo-tiles in Python,geopandas,2024-01-11,2016-09-08,385,1.1822222222222223,https://avatars.githubusercontent.com/u/8130715?v=4,Context geo-tiles in Python,"['cartography', 'geography', 'mapping', 'matplotlib', 'openstreetmap', 'osm', 'stamen', 'stamen-maps', 'tile', 'tiles', 'webtiles']","['cartography', 'geography', 'mapping', 'matplotlib', 'openstreetmap', 'osm', 'stamen', 'stamen-maps', 'tile', 'tiles', 'webtiles']",2023-12-29,"[('bitcraft/pytmx', 0.5879520177841187, 'gamedev', 0), ('opengeos/leafmap', 0.5741623044013977, 'gis', 1), ('holoviz/geoviews', 0.5272794961929321, 'gis', 0), ('marceloprates/prettymaps', 0.5095080137252808, 'viz', 3)]",24,5.0,,0.27,6,5,89,0,3,1,3,6.0,15.0,90.0,2.5,34
1488,ml,https://github.com/scikit-build/scikit-build,[],,[],[],,,,scikit-build/scikit-build,scikit-build,448,160,18,Python,https://scikit-build.readthedocs.io,"Improved build system generator for CPython C, C++, Cython and Fortran extensions",scikit-build,2024-01-07,2014-07-11,498,0.898567335243553,https://avatars.githubusercontent.com/u/8144855?v=4,"Improved build system generator for CPython C, C++, Cython and Fortran extensions","['build-tool', 'c', 'c-extension', 'c-plus-plus', 'cmake', 'cpython', 'cython', 'fortran', 'kitware', 'packaging', 'scikit-build', 'wheels']","['build-tool', 'c', 'c-extension', 'c-plus-plus', 'cmake', 'cpython', 'cython', 'fortran', 'kitware', 'packaging', 'scikit-build', 'wheels']",2023-12-23,"[('cython/cython', 0.6388826966285706, 'util', 3), ('pyo3/maturin', 0.6356386542320251, 'util', 3), ('pypy/pypy', 0.5764753222465515, 'util', 1), ('openai/triton', 0.5731392502784729, 'util', 0), ('pytoolz/cytoolz', 0.5701683759689331, 'util', 0), ('ninja-build/ninja', 0.562438428401947, 'util', 0), ('faster-cpython/tools', 0.5580853819847107, 'perf', 1), ('faster-cpython/ideas', 0.5575029253959656, 'perf', 1), ('python/cpython', 0.5448643565177917, 'util', 1), ('facebookincubator/cinder', 0.5411034226417542, 'perf', 1), ('panda3d/panda3d', 0.5191662907600403, 'gamedev', 1), ('pypa/hatch', 0.509085476398468, 'util', 1), ('spack/spack', 0.5056064128875732, 'util', 0)]",67,3.0,,2.4,24,14,116,1,10,4,10,24.0,31.0,90.0,1.3,34
1831,data,https://github.com/koaning/bulk,"['data-quality', 'training-data', 'bulk-labelling']",Bulk is a quick UI developer tool to apply some bulk labels.,[],[],1.0,,,koaning/bulk,bulk,422,36,9,Python,,A Simple Bulk Labelling Tool,koaning,2024-01-11,2022-05-25,87,4.803252032520326,,A Simple Bulk Labelling Tool,[],"['bulk-labelling', 'data-quality', 'training-data']",2023-11-15,"[('koaning/embetter', 0.6180914044380188, 'data', 3), ('koaning/doubtlab', 0.5618610382080078, 'data', 1), ('cleanlab/cleanlab', 0.5123386383056641, 'ml', 1)]",11,5.0,,1.0,1,1,20,2,0,0,0,1.0,3.0,90.0,3.0,34
951,nlp,https://github.com/cqcl/lambeq,[],,[],[],,,,cqcl/lambeq,lambeq,406,93,27,Python,https://cqcl.github.io/lambeq/,A high-level Python library for Quantum Natural Language Processing,cqcl,2024-01-14,2021-10-10,120,3.375296912114014,https://avatars.githubusercontent.com/u/15688781?v=4,A high-level Python library for Quantum Natural Language Processing,['qnlp'],['qnlp'],2024-01-11,"[('pyscf/pyscf', 0.6749314069747925, 'sim', 0), ('cqcl/tket', 0.6623311638832092, 'util', 0), ('quantumlib/cirq', 0.6558890342712402, 'sim', 0), ('timdettmers/bitsandbytes', 0.5420622229576111, 'util', 0), ('mnooner256/pyqrcode', 0.5321901440620422, 'util', 0), ('jackhidary/quantumcomputingbook', 0.5265811681747437, 'study', 0), ('allenai/allennlp', 0.5252924561500549, 'nlp', 0), ('qiskit/qiskit', 0.5245597958564758, 'sim', 0), ('pytoolz/toolz', 0.5159991979598999, 'util', 0), ('bytedance/lightseq', 0.5084514617919922, 'nlp', 0), ('ferdinandzhong/punctuator', 0.5024779438972473, 'nlp', 0)]",13,2.0,,0.27,7,2,28,0,5,7,5,7.0,14.0,90.0,2.0,34
1817,util,https://github.com/roniemartinez/dude,[],,[],[],,,,roniemartinez/dude,dude,393,22,13,Python,https://roniemartinez.github.io/dude/,dude uncomplicated data extraction: A simple framework for writing web scrapers using Python decorators,roniemartinez,2024-01-10,2022-02-14,102,3.8475524475524474,,dude uncomplicated data extraction: A simple framework for writing web scrapers using Python decorators,"['async', 'beautifulsoup4', 'crawler', 'css', 'framework', 'lxml', 'parsel', 'playwright', 'scraper', 'scraping', 'selenium', 'sync', 'web-scraping', 'webscraping', 'xpath']","['async', 'beautifulsoup4', 'crawler', 'css', 'framework', 'lxml', 'parsel', 'playwright', 'scraper', 'scraping', 'selenium', 'sync', 'web-scraping', 'webscraping', 'xpath']",2024-01-08,"[('alirezamika/autoscraper', 0.781152606010437, 'data', 5), ('scrapy/scrapy', 0.7514920830726624, 'data', 4), ('nv7-github/googlesearch', 0.6479972004890442, 'util', 0), ('clips/pattern', 0.6342862844467163, 'nlp', 0), ('requests/toolbelt', 0.6033374071121216, 'util', 0), ('binux/pyspider', 0.5958371758460999, 'data', 1), ('masoniteframework/masonite', 0.560670018196106, 'web', 1), ('cobrateam/splinter', 0.5549781918525696, 'testing', 1), ('psf/requests', 0.5494452118873596, 'web', 0), ('webpy/webpy', 0.5446486473083496, 'web', 0), ('twintproject/twint', 0.5376254916191101, 'data', 0), ('falconry/falcon', 0.5347031354904175, 'web', 1), ('jiffyclub/snakeviz', 0.5278874635696411, 'profiling', 0), ('python-odin/odin', 0.5273708701133728, 'util', 0), ('python-markdown/markdown', 0.5186324715614319, 'util', 0), ('pallets/werkzeug', 0.5181063413619995, 'web', 0), ('klen/muffin', 0.5161595940589905, 'web', 0), ('eleutherai/pyfra', 0.5149586200714111, 'ml', 0), ('holoviz/panel', 0.5136420726776123, 'viz', 0), ('plotly/dash', 0.512108325958252, 'viz', 0), ('lxml/lxml', 0.5113477110862732, 'util', 0), ('landscapeio/prospector', 0.5066967606544495, 'util', 0), ('pallets/flask', 0.5063819289207458, 'web', 0), ('reflex-dev/reflex', 0.5052448511123657, 'web', 1), ('seleniumbase/seleniumbase', 0.5045518279075623, 'testing', 1), ('bokeh/bokeh', 0.5035302042961121, 'viz', 0), ('jovianml/opendatasets', 0.5030663013458252, 'data', 0)]",3,1.0,,3.5,48,46,23,0,6,23,6,48.0,41.0,90.0,0.9,34
972,util,https://github.com/google/pyglove,[],,[],[],1.0,,,google/pyglove,pyglove,310,17,6,Python,,Manipulating Python Programs,google,2024-01-12,2022-05-12,89,3.4554140127388533,https://avatars.githubusercontent.com/u/1342004?v=4,Manipulating Python Programs,"['automl', 'evolution', 'machine-learning', 'manipulation', 'meta-learning', 'meta-programming', 'symbolic-programming']","['automl', 'evolution', 'machine-learning', 'manipulation', 'meta-learning', 'meta-programming', 'symbolic-programming']",2024-01-09,"[('automl/auto-sklearn', 0.6234724521636963, 'ml', 2), ('epistasislab/tpot', 0.6232022643089294, 'ml', 2), ('featurelabs/featuretools', 0.601290225982666, 'ml', 2), ('norvig/pytudes', 0.5999535322189331, 'util', 0), ('gradio-app/gradio', 0.5854032039642334, 'viz', 1), ('evhub/coconut', 0.583031415939331, 'util', 0), ('eleutherai/pyfra', 0.5793092250823975, 'ml', 0), ('python/cpython', 0.5745415687561035, 'util', 0), ('scikit-learn/scikit-learn', 0.5719713568687439, 'ml', 1), ('mljar/mljar-supervised', 0.5717796683311462, 'ml', 2), ('nccr-itmo/fedot', 0.5598992109298706, 'ml-ops', 2), ('pexpect/pexpect', 0.557771623134613, 'util', 0), ('modularml/mojo', 0.5558887124061584, 'util', 1), ('lukaszahradnik/pyneuralogic', 0.5553054809570312, 'math', 1), ('microsoft/flaml', 0.5550763607025146, 'ml', 2), ('stanfordnlp/dspy', 0.5547299385070801, 'llm', 0), ('fastai/fastcore', 0.5538975596427917, 'util', 0), ('python-rope/rope', 0.5538163781166077, 'util', 0), ('reloadware/reloadium', 0.5510817766189575, 'profiling', 0), ('firmai/atspy', 0.5508860349655151, 'time-series', 0), ('pytoolz/toolz', 0.550031304359436, 'util', 0), ('google/jax', 0.5426806211471558, 'ml', 0), ('pypy/pypy', 0.5365838408470154, 'util', 0), ('pyston/pyston', 0.5362030863761902, 'util', 0), ('dagworks-inc/hamilton', 0.5348206162452698, 'ml-ops', 1), ('pyomo/pyomo', 0.5334599018096924, 'math', 0), ('goldmansachs/gs-quant', 0.5296308994293213, 'finance', 0), ('sourcery-ai/sourcery', 0.5295018553733826, 'util', 0), ('ml-tooling/opyrator', 0.5290511846542358, 'viz', 1), ('robcarver17/pysystemtrade', 0.5276491045951843, 'finance', 0), ('joblib/joblib', 0.526533305644989, 'util', 0), ('amaargiru/pyroad', 0.5239276885986328, 'study', 0), ('microsoft/nni', 0.5230394005775452, 'ml', 2), ('microsoft/pycodegpt', 0.520497739315033, 'llm', 0), ('mementum/backtrader', 0.5199276208877563, 'finance', 0), ('kubeflow/fairing', 0.5187657475471497, 'ml-ops', 0), ('artemyk/dynpy', 0.5151891708374023, 'sim', 0), ('dylanhogg/awesome-python', 0.5149620175361633, 'study', 1), ('sympy/sympy', 0.5143080353736877, 'math', 0), ('lucidrains/toolformer-pytorch', 0.5139380097389221, 'llm', 0), ('winedarksea/autots', 0.5115013122558594, 'time-series', 2), ('allrod5/injectable', 0.5078258514404297, 'util', 0), ('krzjoa/awesome-python-data-science', 0.5056788325309753, 'study', 1), ('awslabs/autogluon', 0.5035163164138794, 'ml', 2)]",7,2.0,,2.62,18,18,20,0,7,5,7,18.0,15.0,90.0,0.8,34
1460,util,https://github.com/mamba-org/quetz,"['conda', 'packages']",,[],[],,,,mamba-org/quetz,quetz,251,66,19,Python,https://quetz.readthedocs.io/en/latest/,The Open-Source Server for Conda Packages,mamba-org,2024-01-04,2020-05-22,192,1.303412462908012,https://avatars.githubusercontent.com/u/66118895?v=4,The Open-Source Server for Conda Packages,[],"['conda', 'packages']",2023-12-04,"[('conda/conda-pack', 0.785363495349884, 'util', 1), ('conda/conda-build', 0.7619379758834839, 'util', 1), ('mamba-org/boa', 0.7533841729164124, 'util', 1), ('conda/constructor', 0.7079612612724304, 'util', 1), ('mamba-org/mamba', 0.699140191078186, 'util', 1), ('conda-forge/feedstocks', 0.6082790493965149, 'util', 1), ('conda/conda', 0.5807616710662842, 'util', 1), ('mamba-org/gator', 0.5806750655174255, 'jupyter', 2), ('conda-forge/miniforge', 0.5642527341842651, 'util', 0), ('mamba-org/micromamba-docker', 0.5259016752243042, 'util', 1), ('conda-forge/conda-smithy', 0.5191918015480042, 'util', 0), ('openai/openai-python', 0.5004561543464661, 'util', 0)]",46,6.0,,1.33,19,9,44,1,14,10,14,19.0,23.0,90.0,1.2,34
1450,util,https://github.com/thoth-station/micropipenv,[],,[],[],,,,thoth-station/micropipenv,micropipenv,224,24,7,Python,https://pypi.org/project/micropipenv/,"A lightweight wrapper for pip to support requirements.txt, Pipenv and Poetry lock files or converting them to pip-tools compatible output. Designed for containerized Python applications but not limited to them.",thoth-station,2024-01-04,2020-02-10,207,1.0813793103448275,https://avatars.githubusercontent.com/u/36954363?v=4,"A lightweight wrapper for pip to support requirements.txt, Pipenv and Poetry lock files or converting them to pip-tools compatible output. Designed for containerized Python applications but not limited to them.","['dependency-management', 'pip', 'pip-tools', 'pip3', 'pipenv', 'poetry', 'thoth']","['dependency-management', 'pip', 'pip-tools', 'pip3', 'pipenv', 'poetry', 'thoth']",2023-11-28,"[('pypa/pipenv', 0.6586019992828369, 'util', 1), ('jazzband/pip-tools', 0.6279821991920471, 'util', 2), ('bndr/pipreqs', 0.625019907951355, 'util', 0), ('pdm-project/pdm', 0.5940836071968079, 'util', 0), ('pomponchik/instld', 0.5940703749656677, 'util', 1), ('pyenv/pyenv', 0.5758981704711914, 'util', 1), ('pypa/hatch', 0.5751578211784363, 'util', 0), ('python-poetry/poetry', 0.5704753994941711, 'util', 1), ('kellyjonbrazil/jc', 0.5520144701004028, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5446652173995972, 'util', 0), ('pypy/pypy', 0.5429415106773376, 'util', 0), ('pytoolz/toolz', 0.5317614078521729, 'util', 0), ('pypa/virtualenv', 0.5239235758781433, 'util', 1), ('dagworks-inc/hamilton', 0.522192656993866, 'ml-ops', 0), ('indygreg/pyoxidizer', 0.513746976852417, 'util', 0), ('trailofbits/pip-audit', 0.5130209922790527, 'security', 1), ('orchest/orchest', 0.5059201121330261, 'ml-ops', 0), ('mitsuhiko/rye', 0.5018079280853271, 'util', 0)]",17,6.0,,0.58,7,7,48,2,0,11,11,7.0,25.0,90.0,3.6,34
1766,data,https://github.com/mause/duckdb_engine,['duckdb'],,[],[],,,,mause/duckdb_engine,duckdb_engine,215,31,4,Python,,SQLAlchemy driver for DuckDB,mause,2024-01-11,2020-09-28,174,1.2346185397867104,,SQLAlchemy driver for DuckDB,"['duckdb', 'duckdb-engine', 'sql', 'sqlalchemy']","['duckdb', 'duckdb-engine', 'sql', 'sqlalchemy']",2024-01-14,"[('sqlalchemy/sqlalchemy', 0.6847227215766907, 'data', 2), ('sqlalchemy/alembic', 0.6483481526374817, 'data', 2), ('aminalaee/sqladmin', 0.5703755021095276, 'data', 1), ('agronholm/sqlacodegen', 0.5388209819793701, 'data', 0), ('tiangolo/sqlmodel', 0.529965877532959, 'data', 2), ('duckdb/duckdb', 0.510148286819458, 'pandas', 1), ('ibis-project/ibis', 0.5086135864257812, 'data', 3), ('mongodb/mongo-python-driver', 0.5003612041473389, 'data', 0)]",22,3.0,,6.37,82,74,40,0,14,16,14,82.0,45.0,90.0,0.5,34
1263,util,https://github.com/weaviate/weaviate-python-client,[],,[],[],,,,weaviate/weaviate-python-client,weaviate-python-client,83,39,24,Python,https://weaviate.io/developers/weaviate/current/client-libraries/python.html,A python native client for easy interaction with a Weaviate instance.,weaviate,2024-01-06,2019-09-10,229,0.3624454148471616,https://avatars.githubusercontent.com/u/37794290?v=4,A python native client for easy interaction with a Weaviate instance.,"['vector-search', 'weaviate']","['vector-search', 'weaviate']",2024-01-02,"[('qdrant/qdrant-client', 0.5938428640365601, 'util', 1), ('pinecone-io/pinecone-python-client', 0.5670716762542725, 'data', 1), ('weaviate/demo-text2vec-openai', 0.5271196365356445, 'util', 2), ('huggingface/huggingface_hub', 0.5164599418640137, 'ml', 0), ('beeware/toga', 0.5031599998474121, 'gui', 0)]",32,3.0,,9.33,184,175,53,0,36,35,36,184.0,218.0,90.0,1.2,34
962,ml-rl,https://github.com/keras-rl/keras-rl,[],,[],[],,,,keras-rl/keras-rl,keras-rl,5463,1368,207,Python,http://keras-rl.readthedocs.io/,Deep Reinforcement Learning for Keras.,keras-rl,2024-01-12,2016-07-02,395,13.815390173410405,https://avatars.githubusercontent.com/u/37139783?v=4,Deep Reinforcement Learning for Keras.,"['keras', 'machine-learning', 'neural-networks', 'reinforcement-learning', 'tensorflow', 'theano']","['keras', 'machine-learning', 'neural-networks', 'reinforcement-learning', 'tensorflow', 'theano']",2019-11-11,"[('tensorlayer/tensorlayer', 0.6922048926353455, 'ml-rl', 2), ('google/trax', 0.6790956258773804, 'ml-dl', 2), ('denys88/rl_games', 0.6609601974487305, 'ml-rl', 1), ('thu-ml/tianshou', 0.6519415378570557, 'ml-rl', 0), ('keras-team/keras', 0.64116370677948, 'ml-dl', 3), ('unity-technologies/ml-agents', 0.6218107342720032, 'ml-rl', 3), ('pytorch/rl', 0.6202392578125, 'ml-rl', 2), ('tensorflow/tensor2tensor', 0.6136747002601624, 'ml', 2), ('salesforce/warp-drive', 0.6099841594696045, 'ml-rl', 1), ('ddbourgin/numpy-ml', 0.6088154911994934, 'ml', 3), ('ai4finance-foundation/finrl', 0.5993512272834778, 'finance', 1), ('google/dopamine', 0.5969773530960083, 'ml-rl', 1), ('deepmind/dm_control', 0.590694010257721, 'ml-rl', 3), ('openai/spinningup', 0.5871951580047607, 'study', 0), ('tensorflow/tensorflow', 0.5854769349098206, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.58319491147995, 'ml-dl', 3), ('d2l-ai/d2l-en', 0.5773378610610962, 'study', 4), ('onnx/onnx', 0.5747138261795044, 'ml', 3), ('danielegrattarola/spektral', 0.5715480446815491, 'ml-dl', 2), ('mosaicml/composer', 0.5626094341278076, 'ml-dl', 2), ('keras-team/keras-nlp', 0.5594122409820557, 'nlp', 3), ('determined-ai/determined', 0.5585846900939941, 'ml-ops', 3), ('microsoft/onnxruntime', 0.5557011961936951, 'ml', 3), ('mrdbourke/pytorch-deep-learning', 0.554979681968689, 'study', 1), ('openai/baselines', 0.5544887781143188, 'ml-rl', 0), ('xl0/lovely-tensors', 0.5526697635650635, 'ml-dl', 0), ('ageron/handson-ml2', 0.5413088202476501, 'ml', 0), ('pytorchlightning/pytorch-lightning', 0.5397281050682068, 'ml-dl', 1), ('kzl/decision-transformer', 0.5381110906600952, 'ml-rl', 0), ('pytorch/pytorch', 0.535692572593689, 'ml-dl', 1), ('googlecloudplatform/vertex-ai-samples', 0.5353646278381348, 'ml', 0), ('tlkh/tf-metal-experiments', 0.5308326482772827, 'perf', 1), ('amanchadha/coursera-deep-learning-specialization', 0.5269455909729004, 'study', 1), ('huggingface/deep-rl-class', 0.5257301926612854, 'study', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5244634747505188, 'study', 1), ('keras-team/autokeras', 0.5215295553207397, 'ml-dl', 3), ('inspirai/timechamber', 0.5203887224197388, 'sim', 1), ('explosion/thinc', 0.5199488997459412, 'ml-dl', 2), ('humancompatibleai/imitation', 0.5172331929206848, 'ml-rl', 0), ('alpa-projects/alpa', 0.5167292356491089, 'ml-dl', 1), ('pyro-ppl/pyro', 0.5166857242584229, 'ml-dl', 1), ('bentoml/bentoml', 0.5166836977005005, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5158306956291199, 'ml-dl', 1), ('deepmind/pysc2', 0.5143329501152039, 'ml-rl', 2), ('horovod/horovod', 0.509486973285675, 'ml-ops', 3), ('ggerganov/ggml', 0.5087230205535889, 'ml', 1), ('pytorch/ignite', 0.5081517696380615, 'ml-dl', 1), ('rasbt/deeplearning-models', 0.5067861080169678, 'ml-dl', 0), ('opentensor/bittensor', 0.5059829950332642, 'ml', 2), ('rasbt/machine-learning-book', 0.5053036212921143, 'study', 2), ('mrdbourke/tensorflow-deep-learning', 0.5023423433303833, 'study', 1), ('polyaxon/polyaxon', 0.5010080337524414, 'ml-ops', 4)]",41,3.0,,0.0,1,0,92,51,0,1,1,1.0,0.0,90.0,0.0,33
170,nlp,https://github.com/minimaxir/textgenrnn,[],,[],[],,,,minimaxir/textgenrnn,textgenrnn,4935,765,138,Python,,Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.,minimaxir,2024-01-13,2017-08-07,338,14.594423320659063,,Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.,"['deep-learning', 'keras', 'tensorflow', 'text-generation']","['deep-learning', 'keras', 'tensorflow', 'text-generation']",2020-07-14,"[('google/sentencepiece', 0.6446799635887146, 'nlp', 0), ('google-research/electra', 0.6392484903335571, 'ml-dl', 2), ('huggingface/text-generation-inference', 0.6332951188087463, 'llm', 1), ('infinitylogesh/mutate', 0.5876234769821167, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5805365443229675, 'llm', 2), ('lucidrains/imagen-pytorch', 0.5763087272644043, 'ml-dl', 1), ('minimaxir/aitextgen', 0.57369065284729, 'llm', 0), ('yueyu1030/attrprompt', 0.545197069644928, 'llm', 0), ('alibaba/easynlp', 0.5449576377868652, 'nlp', 1), ('lucidrains/dalle2-pytorch', 0.5387126207351685, 'diffusion', 1), ('microsoft/unilm', 0.5143858194351196, 'nlp', 0), ('allenai/allennlp', 0.5124790072441101, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5115391612052917, 'ml-dl', 2), ('lucidrains/deep-daze', 0.5096532106399536, 'ml', 1), ('kagisearch/vectordb', 0.5044152140617371, 'data', 0), ('nvidia/nemo', 0.5010759234428406, 'nlp', 1)]",19,3.0,,0.0,0,0,78,43,0,2,2,0.0,0.0,90.0,0.0,33
389,diffusion,https://github.com/openai/glide-text2im,[],,[],[],,,,openai/glide-text2im,glide-text2im,3387,479,158,Python,,GLIDE: a diffusion-based text-conditional image synthesis model,openai,2024-01-14,2021-12-10,111,30.357234314980793,https://avatars.githubusercontent.com/u/14957082?v=4,GLIDE: a diffusion-based text-conditional image synthesis model,[],[],2022-03-21,"[('compvis/stable-diffusion', 0.6833638548851013, 'diffusion', 0), ('compvis/latent-diffusion', 0.6436025500297546, 'diffusion', 0), ('stability-ai/stablediffusion', 0.6436024308204651, 'diffusion', 0), ('sharonzhou/long_stable_diffusion', 0.6345992684364319, 'diffusion', 0), ('saharmor/dalle-playground', 0.6006641387939453, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5790383219718933, 'diffusion', 0), ('huggingface/diffusers', 0.565796971321106, 'diffusion', 0), ('nateraw/stable-diffusion-videos', 0.5577892661094666, 'diffusion', 0), ('chenyangqiqi/fatezero', 0.5413234233856201, 'diffusion', 0), ('thudm/cogvideo', 0.5404991507530212, 'ml', 0), ('albarji/mixture-of-diffusers', 0.5093878507614136, 'diffusion', 0)]",4,1.0,,0.0,2,0,25,22,0,0,0,2.0,0.0,90.0,0.0,33
1811,data,https://github.com/awslabs/amazon-redshift-utils,"['aws', 'redshift']",,[],[],,,,awslabs/amazon-redshift-utils,amazon-redshift-utils,2685,1238,221,Python,,"Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment",awslabs,2024-01-12,2014-12-09,477,5.628930817610063,https://avatars.githubusercontent.com/u/3299148?v=4,"Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment",[],"['aws', 'redshift']",2023-11-03,[],190,2.0,,0.56,6,2,111,2,0,0,0,6.0,0.0,90.0,0.0,33
665,ml,https://github.com/apple/ml-ane-transformers,[],,[],[],,,,apple/ml-ane-transformers,ml-ane-transformers,2416,77,47,Python,,Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE),apple,2024-01-13,2022-06-03,86,27.90759075907591,https://avatars.githubusercontent.com/u/10639145?v=4,Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE),[],[],2022-08-09,"[('huggingface/optimum', 0.5597668886184692, 'ml', 0), ('alignmentresearch/tuned-lens', 0.5322527289390564, 'ml-interpretability', 0), ('eleutherai/knowledge-neurons', 0.5135902762413025, 'ml-interpretability', 0), ('huggingface/transformers', 0.5047073364257812, 'nlp', 0), ('karpathy/mingpt', 0.5006250143051147, 'llm', 0)]",1,1.0,,0.0,0,0,20,17,0,2,2,0.0,0.0,90.0,0.0,33
1376,ml-rl,https://github.com/kzl/decision-transformer,"['gym', 'atari']",,[],[],,,,kzl/decision-transformer,decision-transformer,2001,389,31,Python,,Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.,kzl,2024-01-13,2021-06-02,138,14.410493827160494,,Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.,[],"['atari', 'gym']",2022-02-07,"[('thu-ml/tianshou', 0.6393781900405884, 'ml-rl', 1), ('nvidia-omniverse/isaacgymenvs', 0.5959394574165344, 'sim', 1), ('lvwerra/trl', 0.5943164229393005, 'llm', 0), ('google/trax', 0.5694336891174316, 'ml-dl', 0), ('farama-foundation/gymnasium', 0.5532186031341553, 'ml-rl', 1), ('pytorch/rl', 0.5421603322029114, 'ml-rl', 0), ('keras-rl/keras-rl', 0.5381110906600952, 'ml-rl', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.5326739549636841, 'sim', 0), ('denys88/rl_games', 0.5308516025543213, 'ml-rl', 0), ('humancompatibleai/imitation', 0.5276908874511719, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5139691829681396, 'ml-rl', 1), ('openai/baselines', 0.5097741484642029, 'ml-rl', 0), ('openai/gym', 0.5008352994918823, 'ml-rl', 0)]",5,2.0,,0.0,7,3,32,24,0,0,0,7.0,7.0,90.0,1.0,33
422,debug,https://github.com/gotcha/ipdb,[],,[],[],,,,gotcha/ipdb,ipdb,1788,148,28,Python,,Integration of IPython pdb,gotcha,2024-01-09,2011-01-15,680,2.627755616208272,,Integration of IPython pdb,"['debugger', 'ipython']","['debugger', 'ipython']",2023-08-03,"[('inducer/pudb', 0.6826649904251099, 'debug', 2), ('p403n1x87/austin', 0.5680873394012451, 'profiling', 0), ('python/cpython', 0.5667539238929749, 'util', 0), ('ipython/ipyparallel', 0.5557621717453003, 'perf', 0), ('wesm/pydata-book', 0.5557295083999634, 'study', 0), ('alexmojaki/snoop', 0.5553923845291138, 'debug', 1), ('faster-cpython/tools', 0.549821138381958, 'perf', 0), ('faster-cpython/ideas', 0.5395191311836243, 'perf', 0), ('ipython/ipykernel', 0.5379695892333984, 'util', 1), ('pytorch/data', 0.5220991969108582, 'data', 0), ('brandtbucher/specialist', 0.5149388313293457, 'perf', 0), ('samuelcolvin/python-devtools', 0.5089204907417297, 'debug', 0), ('rasbt/watermark', 0.5067563652992249, 'util', 1)]",58,6.0,,0.13,3,1,158,5,0,3,3,3.0,3.0,90.0,1.0,33
217,ml,https://github.com/linkedin/greykite,[],,[],[],1.0,,,linkedin/greykite,greykite,1765,105,37,Python,,"A flexible, intuitive and fast forecasting library",linkedin,2024-01-11,2021-04-27,144,12.256944444444445,https://avatars.githubusercontent.com/u/357098?v=4,"A flexible, intuitive and fast forecasting library",[],[],2023-06-07,"[('nixtla/statsforecast', 0.5983828902244568, 'time-series', 0), ('alkaline-ml/pmdarima', 0.5944582223892212, 'time-series', 0), ('unit8co/darts', 0.5859647989273071, 'time-series', 0), ('salesforce/merlion', 0.5402796864509583, 'time-series', 0), ('facebook/prophet', 0.5375127196311951, 'time-series', 0), ('sktime/sktime', 0.5227073431015015, 'time-series', 0), ('firmai/atspy', 0.5171492099761963, 'time-series', 0), ('salesforce/deeptime', 0.5127387642860413, 'time-series', 0), ('microsoft/flaml', 0.5093781352043152, 'ml', 0), ('aistream-peelout/flow-forecast', 0.5091543197631836, 'time-series', 0)]",9,3.0,,0.08,2,0,33,7,1,2,1,2.0,0.0,90.0,0.0,33
772,nlp,https://github.com/deepset-ai/farm,['question-answering'],,[],[],,,,deepset-ai/farm,FARM,1710,244,53,Python,https://farm.deepset.ai,:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.,deepset-ai,2024-01-14,2019-07-17,236,7.219541616405308,https://avatars.githubusercontent.com/u/51827949?v=4,🏡 Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.,"['bert', 'deep-learning', 'germanbert', 'language-models', 'ner', 'nlp', 'nlp-framework', 'nlp-library', 'pretrained-models', 'pytorch', 'question-answering', 'roberta', 'transfer-learning', 'xlnet-pytorch']","['bert', 'deep-learning', 'germanbert', 'language-models', 'ner', 'nlp', 'nlp-framework', 'nlp-library', 'pretrained-models', 'pytorch', 'question-answering', 'roberta', 'transfer-learning', 'xlnet-pytorch']",2022-08-31,"[('alibaba/easynlp', 0.6495715975761414, 'nlp', 6), ('paddlepaddle/paddlenlp', 0.6488863229751587, 'llm', 4), ('jonasgeiping/cramming', 0.6375002861022949, 'nlp', 0), ('huggingface/transformers', 0.6317070126533508, 'nlp', 7), ('extreme-bert/extreme-bert', 0.6253734827041626, 'llm', 5), ('graykode/nlp-tutorial', 0.6138063669204712, 'study', 3), ('llmware-ai/llmware', 0.6115893721580505, 'llm', 4), ('explosion/spacy', 0.6052958965301514, 'nlp', 3), ('allenai/allennlp', 0.5969370007514954, 'nlp', 3), ('maartengr/bertopic', 0.5806211233139038, 'nlp', 2), ('huggingface/text-generation-inference', 0.5736597180366516, 'llm', 3), ('jina-ai/finetuner', 0.5661262273788452, 'ml', 3), ('flairnlp/flair', 0.5659800171852112, 'nlp', 2), ('qanastek/drbert', 0.5561661720275879, 'llm', 2), ('srush/minichain', 0.5554335713386536, 'llm', 1), ('explosion/spacy-models', 0.5511075854301453, 'nlp', 1), ('google-research/electra', 0.5499265789985657, 'ml-dl', 2), ('microsoft/unilm', 0.5462345480918884, 'nlp', 1), ('explosion/spacy-transformers', 0.5454752445220947, 'llm', 4), ('jina-ai/clip-as-service', 0.5426168441772461, 'nlp', 3), ('ddangelov/top2vec', 0.5399625897407532, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5379154086112976, 'ml-dl', 3), ('databrickslabs/dolly', 0.5375163555145264, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5374853014945984, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5374853014945984, 'llm', 0), ('intellabs/fastrag', 0.5346118211746216, 'nlp', 2), ('explosion/spacy-llm', 0.5298573970794678, 'llm', 1), ('keras-team/keras-nlp', 0.5295949578285217, 'nlp', 2), ('infinitylogesh/mutate', 0.5289068818092346, 'nlp', 1), ('thilinarajapakse/simpletransformers', 0.5268033742904663, 'nlp', 1), ('norskregnesentral/skweak', 0.5261529088020325, 'nlp', 1), ('paddlepaddle/rocketqa', 0.5212914347648621, 'nlp', 2), ('tatsu-lab/stanford_alpaca', 0.5168743133544922, 'llm', 1), ('ofa-sys/ofa', 0.5126766562461853, 'llm', 1), ('prithivirajdamodaran/styleformer', 0.5097511410713196, 'nlp', 1), ('lvwerra/trl', 0.5094181299209595, 'llm', 0), ('night-chen/toolqa', 0.508597195148468, 'llm', 1), ('salesforce/blip', 0.5072547197341919, 'diffusion', 0), ('amansrivastava17/embedding-as-service', 0.5068530440330505, 'nlp', 4), ('young-geng/easylm', 0.5065217614173889, 'llm', 1), ('rasahq/rasa', 0.505393922328949, 'llm', 1), ('franck-dernoncourt/neuroner', 0.5047802329063416, 'nlp', 2), ('yueyu1030/attrprompt', 0.5037703514099121, 'llm', 0), ('nltk/nltk', 0.5018087029457092, 'nlp', 1), ('bytedance/lightseq', 0.5006200671195984, 'nlp', 1)]",37,3.0,,0.0,3,1,55,17,0,6,6,3.0,1.0,90.0,0.3,33
150,data,https://github.com/simple-salesforce/simple-salesforce,[],,[],[],,,,simple-salesforce/simple-salesforce,simple-salesforce,1540,651,92,Python,,A very simple Salesforce.com REST API client for Python,simple-salesforce,2024-01-13,2013-01-17,575,2.674937965260546,https://avatars.githubusercontent.com/u/19581234?v=4,A very simple Salesforce.com REST API client for Python,"['api', 'api-client', 'salesforce']","['api', 'api-client', 'salesforce']",2023-09-06,"[('snyk-labs/pysnyk', 0.6529852151870728, 'security', 1), ('encode/httpx', 0.6496359705924988, 'web', 0), ('falconry/falcon', 0.6300815939903259, 'web', 1), ('hugapi/hug', 0.6207526922225952, 'util', 0), ('requests/toolbelt', 0.610306441783905, 'util', 0), ('taverntesting/tavern', 0.6006324291229248, 'testing', 0), ('python-restx/flask-restx', 0.5995645523071289, 'web', 1), ('psf/requests', 0.5815314650535583, 'web', 0), ('pyeve/eve', 0.5759877562522888, 'web', 0), ('bottlepy/bottle', 0.5755710601806641, 'web', 0), ('cherrypy/cherrypy', 0.5702207088470459, 'web', 0), ('webpy/webpy', 0.5696903467178345, 'web', 0), ('replicate/replicate-python', 0.5586280226707458, 'ml', 0), ('websocket-client/websocket-client', 0.5527551770210266, 'web', 0), ('ethereum/web3.py', 0.5512309670448303, 'crypto', 0), ('cohere-ai/cohere-python', 0.5442338585853577, 'util', 0), ('nasdaq/data-link-python', 0.5415635704994202, 'finance', 0), ('googleapis/google-api-python-client', 0.541388213634491, 'util', 0), ('hydrosquall/tiingo-python', 0.5396576523780823, 'finance', 0), ('openai/openai-python', 0.5326930284500122, 'util', 0), ('urwid/urwid', 0.5318827629089355, 'term', 0), ('encode/uvicorn', 0.5294420123100281, 'web', 0), ('masoniteframework/masonite', 0.5242258310317993, 'web', 0), ('steamship-core/python-client', 0.5220065116882324, 'util', 0), ('willmcgugan/textual', 0.5137646198272705, 'term', 0), ('fastai/ghapi', 0.5124199986457825, 'util', 1), ('aio-libs/aiohttp', 0.5096431970596313, 'web', 0), ('vitalik/django-ninja', 0.508848249912262, 'web', 0), ('radiantearth/radiant-mlhub', 0.5078471899032593, 'gis', 0), ('man-c/pycoingecko', 0.5065068602561951, 'crypto', 1), ('typesense/typesense-python', 0.503971517086029, 'data', 1), ('mitmproxy/pdoc', 0.5029121041297913, 'util', 1), ('1200wd/bitcoinlib', 0.5001790523529053, 'crypto', 0)]",75,4.0,,0.19,34,10,134,4,3,4,3,34.0,13.0,90.0,0.4,33
1844,util,https://github.com/quodlibet/mutagen,[],,[],[],,,,quodlibet/mutagen,mutagen,1390,152,36,Python,https://mutagen.readthedocs.io,Python module for handling audio metadata,quodlibet,2024-01-13,2016-04-07,407,3.409250175192712,https://avatars.githubusercontent.com/u/11544695?v=4,Python module for handling audio metadata,"['apev2', 'flac', 'id3', 'id3v1', 'id3v2', 'mp3', 'mp4', 'music', 'ogg', 'opus', 'tagging']","['apev2', 'flac', 'id3', 'id3v1', 'id3v2', 'mp3', 'mp4', 'music', 'ogg', 'opus', 'tagging']",2023-10-28,"[('taylorsmarks/playsound', 0.6006749868392944, 'util', 1), ('spotify/pedalboard', 0.5746901035308838, 'util', 0), ('irmen/pyminiaudio', 0.5700762867927551, 'util', 0), ('bastibe/python-soundfile', 0.5448225736618042, 'util', 0), ('uberi/speech_recognition', 0.5264820456504822, 'ml', 0), ('libaudioflux/audioflux', 0.5058978796005249, 'util', 1)]",44,3.0,,0.81,5,4,95,3,1,8,1,5.0,5.0,90.0,1.0,33
719,typing,https://github.com/patrick-kidger/torchtyping,[],,[],[],,,,patrick-kidger/torchtyping,torchtyping,1300,31,16,Python,,"Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.",patrick-kidger,2024-01-13,2021-03-28,148,8.766859344894026,,"Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.","['named-tensors', 'python-typing', 'pytorch', 'shape', 'tensors', 'typing']","['named-tensors', 'python-typing', 'pytorch', 'shape', 'tensors', 'typing']",2023-06-20,"[('arogozhnikov/einops', 0.6377165913581848, 'ml-dl', 1), ('instagram/monkeytype', 0.6300653219223022, 'typing', 0), ('facebook/pyre-check', 0.6189740300178528, 'typing', 0), ('tensorly/tensorly', 0.5934515595436096, 'ml-dl', 1), ('strawberry-graphql/strawberry', 0.5875641703605652, 'web', 0), ('microsoft/pyright', 0.5727251172065735, 'typing', 0), ('agronholm/typeguard', 0.5700576305389404, 'typing', 0), ('ggerganov/ggml', 0.5691167712211609, 'ml', 0), ('python/mypy', 0.5514424443244934, 'typing', 1), ('pydantic/pydantic', 0.5341143012046814, 'util', 1), ('google/pytype', 0.5230032205581665, 'typing', 1), ('rafiqhasan/auto-tensorflow', 0.5187631845474243, 'ml-dl', 0), ('pytorch/pytorch', 0.5150875449180603, 'ml-dl', 0), ('xl0/lovely-tensors', 0.5010529160499573, 'ml-dl', 1)]",7,5.0,,0.04,1,1,34,7,0,0,0,1.0,1.0,90.0,1.0,33
1220,template,https://github.com/ionelmc/cookiecutter-pylibrary,[],,[],[],,,,ionelmc/cookiecutter-pylibrary,cookiecutter-pylibrary,1194,207,23,Python,,Enhanced cookiecutter template for Python libraries.,ionelmc,2024-01-14,2014-05-28,504,2.365025466893039,,Enhanced cookiecutter template for Python libraries.,"['cookiecutter', 'cookiecutter-template', 'template']","['cookiecutter', 'cookiecutter-template', 'template']",2023-12-15,"[('giswqs/pypackage', 0.8761537075042725, 'template', 3), ('lyz-code/cookiecutter-python-project', 0.8550078272819519, 'template', 1), ('tedivm/robs_awesome_python_template', 0.8226386308670044, 'template', 1), ('cookiecutter/cookiecutter', 0.7163523435592651, 'template', 1), ('buuntu/fastapi-react', 0.6523356437683105, 'template', 1), ('cjolowicz/cookiecutter-hypermodern-python', 0.6513864994049072, 'template', 0), ('crmne/cookiecutter-modern-datascience', 0.5333271026611328, 'template', 2)]",50,3.0,,0.27,1,0,117,1,0,2,2,1.0,2.0,90.0,2.0,33
1665,term,https://github.com/python-poetry/cleo,['testing'],,[],[],,,,python-poetry/cleo,cleo,1180,84,23,Python,,Cleo allows you to create beautiful and testable command-line interfaces.,python-poetry,2024-01-13,2013-12-16,528,2.234243981606708,https://avatars.githubusercontent.com/u/48722593?v=4,Cleo allows you to create beautiful and testable command-line interfaces.,"['cli', 'command-line']","['cli', 'command-line', 'testing']",2024-01-11,"[('google/python-fire', 0.6746692657470703, 'term', 1), ('kellyjonbrazil/jc', 0.6012207269668579, 'util', 2), ('pyscript/pyscript-cli', 0.5874193906784058, 'web', 0), ('tiangolo/typer', 0.5780348777770996, 'term', 1), ('textualize/trogon', 0.5179005265235901, 'term', 1), ('tox-dev/tox', 0.515224039554596, 'testing', 2), ('pallets/click', 0.5077859163284302, 'term', 1), ('hoffstadt/dearpygui', 0.5041629672050476, 'gui', 0)]",34,2.0,,1.21,35,30,123,0,1,3,1,35.0,13.0,90.0,0.4,33
378,ml-interpretability,https://github.com/cdpierse/transformers-interpret,[],,[],[],,,,cdpierse/transformers-interpret,transformers-interpret,1163,94,20,Jupyter Notebook,,Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code. ,cdpierse,2024-01-13,2020-05-27,191,6.061801935964259,,Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code. ,"['captum', 'computer-vision', 'deep-learning', 'explainable-ai', 'interpretability', 'machine-learning', 'model-explainability', 'natural-language-processing', 'neural-network', 'nlp', 'transformers', 'transformers-model']","['captum', 'computer-vision', 'deep-learning', 'explainable-ai', 'interpretability', 'machine-learning', 'model-explainability', 'natural-language-processing', 'neural-network', 'nlp', 'transformers', 'transformers-model']",2023-08-30,"[('thilinarajapakse/simpletransformers', 0.6818839311599731, 'nlp', 1), ('huggingface/transformers', 0.6538716554641724, 'nlp', 4), ('alignmentresearch/tuned-lens', 0.6314489245414734, 'ml-interpretability', 2), ('eleutherai/knowledge-neurons', 0.6161298155784607, 'ml-interpretability', 2), ('interpretml/interpret', 0.6050029993057251, 'ml-interpretability', 3), ('mosaicml/composer', 0.5288192629814148, 'ml-dl', 3), ('lucidrains/toolformer-pytorch', 0.5184912085533142, 'llm', 2), ('bigscience-workshop/megatron-deepspeed', 0.5142018795013428, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5142018795013428, 'llm', 0), ('rafiqhasan/auto-tensorflow', 0.5105558633804321, 'ml-dl', 1), ('csinva/imodels', 0.500318169593811, 'ml', 3)]",8,6.0,,0.04,1,0,44,5,1,4,1,1.0,1.0,90.0,1.0,33
691,data,https://github.com/scholarly-python-package/scholarly,[],,[],[],,,,scholarly-python-package/scholarly,scholarly,1145,282,25,Python,https://scholarly.readthedocs.io/,"Retrieve author and publication information from Google Scholar in a friendly, Pythonic way without having to worry about CAPTCHAs!",scholarly-python-package,2024-01-13,2014-12-02,478,2.3953974895397487,https://avatars.githubusercontent.com/u/65581503?v=4,"Retrieve author and publication information from Google Scholar in a friendly, Pythonic way without having to worry about CAPTCHAs!","['citation-analysis', 'citation-index', 'citation-network', 'citations', 'googlescholar', 'publication-data', 'scholar', 'scholarly-articles', 'scholarly-communications']","['citation-analysis', 'citation-index', 'citation-network', 'citations', 'googlescholar', 'publication-data', 'scholar', 'scholarly-articles', 'scholarly-communications']",2023-01-16,"[('nv7-github/googlesearch', 0.6054853796958923, 'util', 0), ('googleapis/google-api-python-client', 0.5367330312728882, 'util', 0), ('urschrei/pyzotero', 0.5180812478065491, 'util', 1), ('goldsmith/wikipedia', 0.5016988515853882, 'data', 0)]",42,6.0,,0.0,13,7,111,12,2,7,2,13.0,21.0,90.0,1.6,33
1631,util,https://github.com/python-versioneer/python-versioneer,[],,[],[],,,,python-versioneer/python-versioneer,python-versioneer,1043,199,19,Python,,version-string management for VCS-controlled trees,python-versioneer,2024-01-12,2011-11-16,636,1.6377299237326155,https://avatars.githubusercontent.com/u/71078182?v=4,version-string management for VCS-controlled trees,[],[],2023-12-31,"[('mtkennerly/dunamai', 0.5904924869537354, 'util', 0), ('mtkennerly/poetry-dynamic-versioning', 0.5653854012489319, 'util', 0), ('pypa/setuptools_scm', 0.5380630493164062, 'util', 0), ('callowayproject/bump-my-version', 0.5228790640830994, 'util', 0)]",70,6.0,,0.85,10,5,148,0,1,2,1,10.0,4.0,90.0,0.4,33
386,nlp,https://github.com/shivam5992/textstat,[],,[],[],,,,shivam5992/textstat,textstat,1041,156,19,Python,https://textstat.org,":memo: python package to calculate readability statistics of a text object - paragraphs, sentences, articles.",shivam5992,2024-01-12,2014-06-18,501,2.0742954739538857,https://avatars.githubusercontent.com/u/88800038?v=4,"📝 python package to calculate readability statistics of a text object - paragraphs, sentences, articles.","['flesch-kincaid-grade', 'flesch-reading-ease', 'readability', 'smog', 'textstat']","['flesch-kincaid-grade', 'flesch-reading-ease', 'readability', 'smog', 'textstat']",2024-01-09,[],47,3.0,,0.15,3,3,117,0,1,2,1,3.0,4.0,90.0,1.3,33
1518,util,https://github.com/c4urself/bump2version,['versioning'],,[],[],,,,c4urself/bump2version,bump2version,1026,135,13,Python,https://pypi.python.org/pypi/bump2version,Version-bump your software with a single command,c4urself,2024-01-11,2017-03-27,357,2.8728,,Version-bump your software with a single command,[],['versioning'],2023-10-11,"[('callowayproject/bump-my-version', 0.7459608316421509, 'util', 1)]",52,5.0,,0.02,5,3,83,3,0,5,5,5.0,2.0,90.0,0.4,33
1514,ml,https://github.com/patchy631/machine-learning,['tutorials'],Machine Learning Tutorials Repository,[],[],,,,patchy631/machine-learning,machine-learning,918,187,30,Jupyter Notebook,,,patchy631,2024-01-11,2022-06-01,86,10.569078947368421,,Machine Learning Tutorials Repository,[],['tutorials'],2024-01-10,"[('rasbt/machine-learning-book', 0.6192141175270081, 'study', 0), ('tensorflow/data-validation', 0.6105336546897888, 'ml-ops', 0), ('automl/auto-sklearn', 0.6001100540161133, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5960847735404968, 'study', 0), ('rasbt/stat451-machine-learning-fs20', 0.5887393355369568, 'study', 0), ('mrdbourke/zero-to-mastery-ml', 0.5848323106765747, 'study', 0), ('microsoft/nni', 0.5835422873497009, 'ml', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5826952457427979, 'study', 0), ('mlflow/mlflow', 0.5804417729377747, 'ml-ops', 0), ('huggingface/evaluate', 0.5767751932144165, 'ml', 0), ('firmai/industry-machine-learning', 0.5763393044471741, 'study', 0), ('scikit-learn/scikit-learn', 0.575724184513092, 'ml', 0), ('teamhg-memex/eli5', 0.5695274472236633, 'ml', 0), ('probml/pyprobml', 0.5563993453979492, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5536148548126221, 'ml', 0), ('eugeneyan/testing-ml', 0.5529058575630188, 'testing', 0), ('ageron/handson-ml2', 0.5471069812774658, 'ml', 0), ('seldonio/alibi', 0.5448355078697205, 'ml-interpretability', 0), ('tensorflow/tensorflow', 0.5448095798492432, 'ml-dl', 0), ('doccano/doccano', 0.5417935848236084, 'nlp', 0), ('ml-tooling/opyrator', 0.5287477970123291, 'viz', 0), ('gradio-app/gradio', 0.5269427299499512, 'viz', 0), ('rasbt/mlxtend', 0.5243169665336609, 'ml', 0), ('districtdatalabs/yellowbrick', 0.5236752033233643, 'ml', 0), ('csinva/imodels', 0.5233355760574341, 'ml', 0), ('nccr-itmo/fedot', 0.517076313495636, 'ml-ops', 0), ('microsoft/flaml', 0.5163537263870239, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.5156992673873901, 'study', 0), ('marcotcr/lime', 0.5150882601737976, 'ml-interpretability', 0), ('google-research/google-research', 0.5150724053382874, 'ml', 0), ('google-research/language', 0.5135775804519653, 'nlp', 0), ('pycaret/pycaret', 0.513546884059906, 'ml', 0), ('sktime/sktime', 0.5128931403160095, 'time-series', 0), ('featurelabs/featuretools', 0.5084584355354309, 'ml', 0), ('keras-team/autokeras', 0.5066499710083008, 'ml-dl', 0), ('jindongwang/transferlearning', 0.5060864090919495, 'ml', 0), ('shankarpandala/lazypredict', 0.5044746398925781, 'ml', 0), ('udlbook/udlbook', 0.5037120580673218, 'study', 0), ('jovianml/opendatasets', 0.5012773275375366, 'data', 0)]",3,1.0,,0.62,1,0,20,0,0,0,0,1.0,1.0,90.0,1.0,33
631,debug,https://github.com/samuelcolvin/python-devtools,"['debug', 'print']",,[],[],,,,samuelcolvin/python-devtools,python-devtools,907,49,11,Python,https://python-devtools.helpmanual.io/,Dev tools for python,samuelcolvin,2024-01-12,2017-08-20,336,2.697111299915038,,Dev tools for python,"['devtools', 'python-devtools']","['debug', 'devtools', 'print', 'python-devtools']",2023-09-03,"[('alexmojaki/snoop', 0.7110832929611206, 'debug', 0), ('inducer/pudb', 0.6053194999694824, 'debug', 1), ('urwid/urwid', 0.5885550379753113, 'term', 0), ('amaargiru/pyroad', 0.5811712741851807, 'study', 0), ('pympler/pympler', 0.5581871271133423, 'perf', 0), ('nedbat/coveragepy', 0.5552131533622742, 'testing', 0), ('gaogaotiantian/viztracer', 0.5530471205711365, 'profiling', 0), ('pypa/hatch', 0.5391005277633667, 'util', 0), ('jquast/blessed', 0.5346581935882568, 'term', 0), ('landscapeio/prospector', 0.5334382057189941, 'util', 0), ('beeware/toga', 0.5332743525505066, 'gui', 0), ('pypa/pipenv', 0.5316519737243652, 'util', 0), ('rubik/radon', 0.5275425314903259, 'util', 0), ('alexmojaki/birdseye', 0.5242282748222351, 'debug', 0), ('p403n1x87/austin', 0.523003876209259, 'profiling', 0), ('kellyjonbrazil/jc', 0.5224640369415283, 'util', 0), ('alexmojaki/heartrate', 0.5222712755203247, 'debug', 0), ('featurelabs/featuretools', 0.5202803015708923, 'ml', 0), ('python/cpython', 0.5196568965911865, 'util', 0), ('cython/cython', 0.5175613164901733, 'util', 0), ('willmcgugan/textual', 0.5147838592529297, 'term', 0), ('google/python-fire', 0.5147319436073303, 'term', 0), ('cool-rr/pysnooper', 0.5116415619850159, 'debug', 1), ('gotcha/ipdb', 0.5089204907417297, 'debug', 0), ('pypy/pypy', 0.5084391236305237, 'util', 0), ('pythagora-io/gpt-pilot', 0.5080005526542664, 'llm', 0), ('dosisod/refurb', 0.5065819621086121, 'util', 0), ('sourcery-ai/sourcery', 0.506219744682312, 'util', 0), ('goldmansachs/gs-quant', 0.5059173107147217, 'finance', 0), ('beeware/briefcase', 0.5053153038024902, 'util', 0), ('mkdocstrings/griffe', 0.5050040483474731, 'util', 0), ('pytoolz/toolz', 0.5032694935798645, 'util', 0)]",12,5.0,,0.37,8,1,78,4,4,3,4,8.0,13.0,90.0,1.6,33
394,web,https://github.com/emmett-framework/emmett,[],,[],[],,,,emmett-framework/emmett,emmett,899,69,30,Python,,The web framework for inventors,emmett-framework,2024-01-10,2014-10-20,484,1.8568899380348185,https://avatars.githubusercontent.com/u/46401492?v=4,The web framework for inventors,"['asgi', 'asyncio', 'emmett', 'web-framework']","['asgi', 'asyncio', 'emmett', 'web-framework']",2023-12-21,"[('django/django', 0.6188867688179016, 'web', 0), ('pallets/flask', 0.6101765632629395, 'web', 1), ('pallets/quart', 0.5764337778091431, 'web', 2), ('neoteroi/blacksheep', 0.5676200985908508, 'web', 2), ('klen/muffin', 0.56635981798172, 'web', 2), ('masoniteframework/masonite', 0.5554807186126709, 'web', 0), ('pylons/pyramid', 0.5466519594192505, 'web', 1), ('reflex-dev/reflex', 0.5462912917137146, 'web', 0), ('huge-success/sanic', 0.5460001230239868, 'web', 3), ('encode/starlette', 0.5443409085273743, 'web', 0), ('pallets/werkzeug', 0.5205287337303162, 'web', 0), ('starlite-api/starlite', 0.5148096084594727, 'web', 2), ('encode/uvicorn', 0.514312207698822, 'web', 2), ('timofurrer/awesome-asyncio', 0.5075742602348328, 'study', 1), ('indico/indico', 0.5001763701438904, 'web', 0)]",24,3.0,,0.77,4,3,112,1,9,13,9,4.0,5.0,90.0,1.2,33
1790,web,https://github.com/feincms/feincms,['cms'],,[],[],,,,feincms/feincms,feincms,878,230,39,Python,http://www.feincms.org/,A Django-based CMS with a focus on extensibility and concise code,feincms,2024-01-13,2009-01-27,783,1.1213282247765006,https://avatars.githubusercontent.com/u/935594?v=4,A Django-based CMS with a focus on extensibility and concise code,[],['cms'],2023-12-22,"[('stephenmcd/mezzanine', 0.8132669925689697, 'web', 1), ('wagtail/wagtail', 0.7559544444084167, 'web', 1), ('django/django', 0.5812693238258362, 'web', 0), ('pylons/pyramid', 0.5519199371337891, 'web', 0), ('pallets/flask', 0.546400785446167, 'web', 0), ('bottlepy/bottle', 0.5126698613166809, 'web', 0), ('fastapi-admin/fastapi-admin', 0.5065550804138184, 'web', 0), ('pycqa/pylint-django', 0.5040256381034851, 'util', 0), ('eleutherai/pyfra', 0.5029860734939575, 'ml', 0), ('python-markdown/markdown', 0.500464141368866, 'util', 0), ('indico/indico', 0.5001288652420044, 'web', 0)]",136,7.0,,0.29,4,4,182,1,0,7,7,4.0,2.0,90.0,0.5,33
1736,viz,https://github.com/pydot/pydot,[],,[],[],,,,pydot/pydot,pydot,830,157,26,Python,https://pypi.python.org/pypi/pydot,Python interface to Graphviz's Dot language,pydot,2024-01-12,2015-04-13,459,1.8077162414436838,https://avatars.githubusercontent.com/u/11192979?v=4,Python interface to Graphviz's Dot language,"['dot-language', 'graphviz']","['dot-language', 'graphviz']",2023-12-30,"[('pygraphviz/pygraphviz', 0.7296152114868164, 'viz', 0), ('vmiklos/ged2dot', 0.5854193568229675, 'data', 0), ('plotly/plotly.py', 0.5558724403381348, 'viz', 0), ('westhealth/pyvis', 0.542072594165802, 'graph', 0), ('graphistry/pygraphistry', 0.5253562331199646, 'data', 0), ('dmlc/dgl', 0.5208896398544312, 'ml-dl', 0), ('neo4j/neo4j-python-driver', 0.5061096549034119, 'data', 0), ('graphql-python/graphene', 0.5014999508857727, 'web', 0)]",26,2.0,,0.23,36,20,107,0,0,2,2,36.0,59.0,90.0,1.6,33
1208,ml,https://github.com/lmcinnes/pynndescent,[],,[],[],,,,lmcinnes/pynndescent,pynndescent,822,101,14,Python,,A Python nearest neighbor descent for approximate nearest neighbors,lmcinnes,2024-01-12,2018-02-07,311,2.635822262940907,,A Python nearest neighbor descent for approximate nearest neighbors,"['approximate-nearest-neighbor-search', 'knn-graphs', 'nearest-neighbor-search']","['approximate-nearest-neighbor-search', 'knn-graphs', 'nearest-neighbor-search']",2024-01-10,"[('spotify/voyager', 0.6540524363517761, 'ml', 1), ('nmslib/hnswlib', 0.6369488835334778, 'ml', 0), ('spotify/annoy', 0.6271777749061584, 'ml', 2), ('scikit-learn-contrib/metric-learn', 0.5760319828987122, 'ml', 0), ('qdrant/quaterion', 0.5621644854545593, 'ml', 1), ('criteo/autofaiss', 0.5459467172622681, 'ml', 0), ('scikit-learn-contrib/lightning', 0.5341041684150696, 'ml', 0)]",27,4.0,,0.5,8,1,72,0,3,4,3,8.0,3.0,90.0,0.4,33
734,data,https://github.com/koaning/human-learn,[],,[],[],,,,koaning/human-learn,human-learn,763,55,15,Jupyter Notebook,https://koaning.github.io/human-learn/,Natural Intelligence is still a pretty good idea.,koaning,2024-01-11,2020-07-11,185,4.114791987673343,,Natural Intelligence is still a pretty good idea.,"['benchmark', 'machine-learning', 'scikit-learn']","['benchmark', 'machine-learning', 'scikit-learn']",2024-01-02,"[('rasbt/machine-learning-book', 0.6072452068328857, 'study', 2), ('koaning/scikit-lego', 0.5959609150886536, 'ml', 2), ('automl/auto-sklearn', 0.5842374563217163, 'ml', 1), ('intel/scikit-learn-intelex', 0.5819746255874634, 'perf', 2), ('aiqc/aiqc', 0.5812904238700867, 'ml-ops', 0), ('tensorflow/tensorflow', 0.5748200416564941, 'ml-dl', 1), ('explosion/thinc', 0.5714577436447144, 'ml-dl', 1), ('skorch-dev/skorch', 0.5661436319351196, 'ml-dl', 2), ('huggingface/datasets', 0.551318883895874, 'nlp', 1), ('mlflow/mlflow', 0.5503108501434326, 'ml-ops', 1), ('intel/intel-extension-for-pytorch', 0.5494535565376282, 'perf', 1), ('keras-team/keras', 0.5424421429634094, 'ml-dl', 1), ('featurelabs/featuretools', 0.5413556694984436, 'ml', 2), ('determined-ai/determined', 0.5347359776496887, 'ml-ops', 1), ('carla-recourse/carla', 0.5305505394935608, 'ml', 2), ('ageron/handson-ml2', 0.5293908715248108, 'ml', 0), ('iryna-kondr/scikit-llm', 0.5255882143974304, 'llm', 2), ('microsoft/flaml', 0.5246617197990417, 'ml', 2), ('onnx/onnx', 0.5241888761520386, 'ml', 2), ('microsoft/onnxruntime', 0.5241006016731262, 'ml', 2), ('tensorflow/tensor2tensor', 0.5240030884742737, 'ml', 1), ('pytorch/ignite', 0.522743284702301, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.520194947719574, 'ml', 1), ('pycaret/pycaret', 0.5194460153579712, 'ml', 1), ('milvus-io/bootcamp', 0.5165793895721436, 'data', 0), ('microsoft/nni', 0.5157192349433899, 'ml', 1), ('google/trax', 0.514390230178833, 'ml-dl', 1), ('tensorlayer/tensorlayer', 0.5132976770401001, 'ml-rl', 0), ('gradio-app/gradio', 0.5125566124916077, 'viz', 1), ('sktime/sktime', 0.5112270712852478, 'time-series', 2), ('huggingface/transformers', 0.5105912089347839, 'nlp', 1), ('teamhg-memex/eli5', 0.5102199912071228, 'ml', 2), ('karpathy/micrograd', 0.509779691696167, 'study', 0), ('unity-technologies/ml-agents', 0.5095399618148804, 'ml-rl', 1), ('adap/flower', 0.5073186755180359, 'ml-ops', 2), ('qdrant/quaterion', 0.5016433000564575, 'ml', 1), ('allenai/allennlp', 0.501263439655304, 'nlp', 0)]",6,4.0,,0.12,7,4,43,0,1,1,1,7.0,4.0,90.0,0.6,33
1720,util,https://github.com/asottile/reorder-python-imports,['code-quality'],,[],[],,,,asottile/reorder-python-imports,reorder-python-imports,692,53,10,Python,,Rewrites source to reorder python imports,asottile,2024-01-13,2015-01-01,473,1.4607961399276237,,Rewrites source to reorder python imports,"['linter', 'pre-commit', 'refactoring']","['code-quality', 'linter', 'pre-commit', 'refactoring']",2024-01-08,"[('pycqa/isort', 0.5951371192932129, 'util', 2), ('python-rope/rope', 0.5853264331817627, 'util', 1), ('facebookincubator/bowler', 0.5548760890960693, 'util', 1), ('tezromach/python-package-template', 0.5504276156425476, 'template', 0), ('dosisod/refurb', 0.5132452249526978, 'util', 0), ('hadialqattan/pycln', 0.5043818354606628, 'util', 0), ('instagram/fixit', 0.5014389753341675, 'util', 1)]",18,5.0,,0.73,8,8,110,0,0,7,7,8.0,3.0,90.0,0.4,33
1862,ml-rl,https://github.com/denys88/rl_games,[],RL Games: High performance RL library,[],[],,,,denys88/rl_games,rl_games,626,107,18,Jupyter Notebook,,RL implementations,denys88,2024-01-14,2019-01-13,263,2.377645143787303,,RL implementations,"['deep-learning', 'pytorch', 'reinforcement-learning']","['deep-learning', 'pytorch', 'reinforcement-learning']",2023-12-01,"[('thu-ml/tianshou', 0.7549825310707092, 'ml-rl', 1), ('pytorch/rl', 0.7474822402000427, 'ml-rl', 2), ('pytorch/ignite', 0.6810092926025391, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.6643456816673279, 'ml-rl', 2), ('keras-rl/keras-rl', 0.6609601974487305, 'ml-rl', 1), ('salesforce/warp-drive', 0.6500195264816284, 'ml-rl', 3), ('google/trax', 0.6481664776802063, 'ml-dl', 2), ('humancompatibleai/imitation', 0.6343870759010315, 'ml-rl', 0), ('mrdbourke/pytorch-deep-learning', 0.6325936913490295, 'study', 2), ('karpathy/micrograd', 0.6286336779594421, 'study', 0), ('pyg-team/pytorch_geometric', 0.618355929851532, 'ml-dl', 2), ('nvidia/apex', 0.6122349500656128, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.6038516163825989, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.6032094359397888, 'perf', 2), ('openai/baselines', 0.5995540618896484, 'ml-rl', 0), ('skorch-dev/skorch', 0.5850237011909485, 'ml-dl', 1), ('huggingface/transformers', 0.5799943208694458, 'nlp', 2), ('deepmind/android_env', 0.577807605266571, 'ml-dl', 1), ('google/dopamine', 0.5697341561317444, 'ml-rl', 0), ('lucidrains/palm-rlhf-pytorch', 0.5653679370880127, 'ml-rl', 2), ('rasbt/machine-learning-book', 0.5648091435432434, 'study', 2), ('ai4finance-foundation/finrl', 0.5618516206741333, 'finance', 1), ('pyro-ppl/pyro', 0.558512806892395, 'ml-dl', 2), ('unity-technologies/ml-agents', 0.5579639077186584, 'ml-rl', 2), ('nicolas-chaulet/torch-points3d', 0.5515884757041931, 'ml', 0), ('ashleve/lightning-hydra-template', 0.550940990447998, 'util', 2), ('intellabs/bayesian-torch', 0.5465734004974365, 'ml', 2), ('huggingface/accelerate', 0.5460307002067566, 'ml', 0), ('allenai/allennlp', 0.5432136654853821, 'nlp', 2), ('determined-ai/determined', 0.5401771664619446, 'ml-ops', 2), ('keras-team/keras', 0.5397940278053284, 'ml-dl', 2), ('ray-project/ray', 0.5337716937065125, 'ml-ops', 3), ('explosion/thinc', 0.5336785316467285, 'ml-dl', 2), ('horovod/horovod', 0.5315641164779663, 'ml-ops', 2), ('deepmind/acme', 0.5312319397926331, 'ml-rl', 1), ('kzl/decision-transformer', 0.5308516025543213, 'ml-rl', 0), ('facebookresearch/pytorch3d', 0.5304609537124634, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5283133387565613, 'ml-dl', 2), ('rentruewang/koila', 0.5238789916038513, 'ml', 2), ('openai/spinningup', 0.5233257412910461, 'study', 0), ('xl0/lovely-tensors', 0.5228703618049622, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5216466188430786, 'study', 3), ('apache/incubator-mxnet', 0.5211288332939148, 'ml-dl', 0), ('mosaicml/composer', 0.5201303958892822, 'ml-dl', 2), ('deepmodeling/deepmd-kit', 0.5176900625228882, 'sim', 1), ('lucidrains/imagen-pytorch', 0.5174486041069031, 'ml-dl', 1), ('google-research/torchsde', 0.5168188810348511, 'math', 2), ('microsoft/deepspeed', 0.5148147940635681, 'ml-dl', 2), ('facebookresearch/habitat-lab', 0.5146901607513428, 'sim', 2), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5131837725639343, 'study', 1), ('lucidrains/dalle2-pytorch', 0.5112625956535339, 'diffusion', 1), ('deepmind/dm_control', 0.5099033117294312, 'ml-rl', 2), ('pytorch/data', 0.5077896118164062, 'data', 0), ('arogozhnikov/einops', 0.5077354311943054, 'ml-dl', 2), ('deepmind/dm-haiku', 0.507714569568634, 'ml-dl', 1), ('pytorch/glow', 0.5074953436851501, 'ml', 0), ('blackhc/toma', 0.5061429738998413, 'ml-dl', 1), ('rasbt/deeplearning-models', 0.5041263103485107, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5028592348098755, 'ml', 2), ('sail-sg/envpool', 0.5019115805625916, 'sim', 1), ('openai/gym', 0.5007779598236084, 'ml-rl', 1), ('pytorch/pytorch', 0.5007427930831909, 'ml-dl', 1)]",14,4.0,,0.42,18,12,61,1,2,2,2,18.0,24.0,90.0,1.3,33
1594,llm,https://github.com/continuum-llms/chatgpt-memory,[],,[],[],1.0,,,continuum-llms/chatgpt-memory,chatgpt-memory,508,65,11,Python,,Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore.,continuum-llms,2024-01-10,2023-03-12,46,10.975308641975309,https://avatars.githubusercontent.com/u/127662766?v=4,Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore.,"['chatgpt', 'chatgpt-api', 'memory', 'redis']","['chatgpt', 'chatgpt-api', 'memory', 'redis']",2023-10-22,"[('run-llama/rags', 0.5546180605888367, 'llm', 1), ('openai/openai-cookbook', 0.5522615909576416, 'ml', 1), ('xtekky/gpt4free', 0.5107817053794861, 'llm', 2), ('zilliztech/gptcache', 0.5097682476043701, 'llm', 3), ('farizrahman4u/loopgpt', 0.5028600096702576, 'llm', 1)]",2,1.0,,2.1,4,4,10,3,0,0,0,4.0,6.0,90.0,1.5,33
253,ml,https://github.com/amzn/pecos,[],,[],[],,,,amzn/pecos,pecos,478,103,20,Python,https://libpecos.org/,PECOS - Prediction for Enormous and Correlated Spaces,amzn,2024-01-08,2020-08-12,180,2.6429699842022116,https://avatars.githubusercontent.com/u/8594673?v=4,PECOS - Prediction for Enormous and Correlated Spaces,"['approximate-nearest-neighbor-search', 'extreme-multi-label-classification', 'extreme-multi-label-ranking', 'machine-learning-algorithms', 'transformers']","['approximate-nearest-neighbor-search', 'extreme-multi-label-classification', 'extreme-multi-label-ranking', 'machine-learning-algorithms', 'transformers']",2024-01-06,"[('scikit-learn-contrib/lightning', 0.5417201519012451, 'ml', 0)]",26,3.0,,0.58,17,13,42,0,5,3,5,17.0,4.0,90.0,0.2,33
1456,util,https://github.com/conda/conda-pack,['conda'],,[],[],,,,conda/conda-pack,conda-pack,459,82,27,Python,https://conda.github.io/conda-pack/,Package conda environments for redistribution,conda,2024-01-12,2017-10-17,328,1.399390243902439,https://avatars.githubusercontent.com/u/6392739?v=4,Package conda environments for redistribution,[],['conda'],2024-01-08,"[('mamba-org/quetz', 0.785363495349884, 'util', 1), ('conda/conda-build', 0.7299324870109558, 'util', 1), ('conda/constructor', 0.7213409543037415, 'util', 1), ('mamba-org/boa', 0.7062498927116394, 'util', 1), ('mamba-org/gator', 0.6601476073265076, 'jupyter', 1), ('mamba-org/mamba', 0.6462101340293884, 'util', 1), ('conda-forge/miniforge', 0.5824256539344788, 'util', 0), ('conda-forge/feedstocks', 0.5661166906356812, 'util', 1), ('conda-forge/conda-smithy', 0.5372809767723083, 'util', 0), ('conda/conda', 0.5275230407714844, 'util', 1), ('indygreg/pyoxidizer', 0.5181369781494141, 'util', 0)]",28,5.0,,0.88,27,19,76,0,1,2,1,27.0,31.0,90.0,1.1,33
330,ml,https://github.com/jacopotagliabue/reclist,[],,[],[],,,,jacopotagliabue/reclist,reclist,433,26,10,Python,https://reclist.io,"Behavioral ""black-box"" testing for recommender systems",jacopotagliabue,2024-01-08,2021-11-08,116,3.7281672816728166,https://avatars.githubusercontent.com/u/105445087?v=4,"Behavioral ""black-box"" testing for recommender systems","['evaluation', 'machine-learning', 'machine-learning-library', 'qa-automation', 'recommender-system']","['evaluation', 'machine-learning', 'machine-learning-library', 'qa-automation', 'recommender-system']",2023-08-09,"[('nicolashug/surprise', 0.5908733010292053, 'ml', 1), ('microsoft/recommenders', 0.5187485218048096, 'study', 1)]",10,2.0,,1.37,1,0,27,5,0,3,3,1.0,4.0,90.0,4.0,33
581,gis,https://github.com/pysal/momepy,[],,[],[],,,,pysal/momepy,momepy,420,54,20,Python,https://docs.momepy.org,Urban Morphology Measuring Toolkit,pysal,2024-01-14,2018-03-30,304,1.3789868667917449,https://avatars.githubusercontent.com/u/3769919?v=4,Urban Morphology Measuring Toolkit,"['morphological-analysis', 'morphology', 'morphometrics', 'urban', 'urban-morphometrics', 'urban-street-networks']","['morphological-analysis', 'morphology', 'morphometrics', 'urban', 'urban-morphometrics', 'urban-street-networks']",2024-01-12,"[('spatialucr/geosnap', 0.5846999287605286, 'gis', 0), ('mcordts/cityscapesscripts', 0.564961314201355, 'gis', 0), ('gboeing/street-network-models', 0.5618022680282593, 'sim', 0), ('udst/urbansim', 0.5606027245521545, 'sim', 0)]",14,4.0,,0.87,19,17,70,0,2,3,2,19.0,36.0,90.0,1.9,33
1796,llm,https://github.com/hazyresearch/manifest,['prompt-engineering'],,[],[],,,,hazyresearch/manifest,manifest,420,45,22,Python,,Prompt programming with FMs.,hazyresearch,2024-01-04,2022-05-21,88,4.749596122778676,https://avatars.githubusercontent.com/u/2165246?v=4,Prompt programming with FMs.,[],['prompt-engineering'],2024-01-12,"[('microsoft/promptbase', 0.629546046257019, 'llm', 1), ('hazyresearch/ama_prompting', 0.5880966782569885, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5553961396217346, 'llm', 1), ('stanfordnlp/dspy', 0.5367062091827393, 'llm', 0), ('promptslab/promptify', 0.5325891971588135, 'nlp', 1)]",9,3.0,,0.83,4,2,20,0,7,6,7,4.0,1.0,90.0,0.2,33
678,util,https://github.com/sciunto-org/python-bibtexparser,[],,[],[],,,,sciunto-org/python-bibtexparser,python-bibtexparser,405,168,21,Python,https://bibtexparser.readthedocs.io,Bibtex parser for Python 3,sciunto-org,2024-01-11,2013-01-05,577,0.7013854527461653,https://avatars.githubusercontent.com/u/8595754?v=4,Bibtex parser for Python 3,"['bibtex', 'bibtex-files', 'latex']","['bibtex', 'bibtex-files', 'latex']",2024-01-04,[],51,5.0,,0.96,28,22,134,0,4,2,4,28.0,37.0,90.0,1.3,33
1420,llm,https://github.com/likenneth/honest_llama,['language-model'],,[],[],,,,likenneth/honest_llama,honest_llama,308,28,7,Python,,Inference-Time Intervention: Eliciting Truthful Answers from a Language Model,likenneth,2024-01-11,2023-05-19,36,8.421875,,Inference-Time Intervention: Eliciting Truthful Answers from a Language Model,[],['language-model'],2023-12-29,"[('keirp/automatic_prompt_engineer', 0.5166599750518799, 'llm', 1), ('reasoning-machines/pal', 0.5100179314613342, 'llm', 1)]",4,0.0,,0.29,10,7,8,0,0,0,0,10.0,39.0,90.0,3.9,33
506,data,https://github.com/facebookresearch/mephisto,[],,[],[],,,,facebookresearch/mephisto,Mephisto,288,72,16,Python,https://mephisto.ai/,A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use. ,facebookresearch,2024-01-09,2019-08-19,232,1.2406153846153847,https://avatars.githubusercontent.com/u/16943930?v=4,A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use. ,[],[],2023-12-27,[],40,3.0,,3.15,31,15,54,1,0,5,5,31.0,40.0,90.0,1.3,33
1679,util,https://github.com/hadialqattan/pycln,[],,[],[],,,,hadialqattan/pycln,pycln,282,17,3,Python,https://hadialqattan.github.io/pycln,A formatter for finding and removing unused import statements.,hadialqattan,2024-01-12,2020-08-14,180,1.5617088607594938,,A formatter for finding and removing unused import statements.,"['cli-app', 'cross-platform', 'formatters', 'linters', 'pycln', 'quality-assurance', 'unused-imports']","['cli-app', 'cross-platform', 'formatters', 'linters', 'pycln', 'quality-assurance', 'unused-imports']",2023-11-14,"[('pycqa/isort', 0.6549221277236938, 'util', 0), ('pycqa/autoflake', 0.6299516558647156, 'util', 0), ('landscapeio/prospector', 0.6040316224098206, 'util', 0), ('google/yapf', 0.5293893218040466, 'util', 0), ('dosisod/refurb', 0.5226925611495972, 'util', 0), ('kellyjonbrazil/jc', 0.5115851163864136, 'util', 0), ('psf/black', 0.5089622735977173, 'util', 0), ('pypi/warehouse', 0.5063982605934143, 'util', 0), ('nedbat/coveragepy', 0.5058234930038452, 'testing', 0), ('grantjenks/blue', 0.5049071311950684, 'util', 0), ('asottile/reorder-python-imports', 0.5043818354606628, 'util', 0)]",13,4.0,,0.62,10,7,42,2,10,12,10,10.0,25.0,90.0,2.5,33
1248,util,https://github.com/steamship-core/python-client,[],,[],[],,,,steamship-core/python-client,python-client,280,52,9,Python,,,steamship-core,2024-01-06,2021-05-11,142,1.971830985915493,https://avatars.githubusercontent.com/u/99272373?v=4,steamship-core/python-client,[],[],2024-01-03,"[('replicate/replicate-python', 0.6438118815422058, 'ml', 0), ('steamship-core/steamship-langchain', 0.5675639510154724, 'util', 0), ('simple-salesforce/simple-salesforce', 0.5220065116882324, 'data', 0)]",12,4.0,,5.48,13,12,33,0,83,79,83,13.0,1.0,90.0,0.1,33
1311,util,https://github.com/rasahq/rasa-sdk,"['sdk', 'nlu']",,[],[],,,,rasahq/rasa-sdk,rasa-sdk,274,225,35,Python,https://rasa.com/docs,SDK for the development of custom actions for Rasa,rasahq,2024-01-12,2018-06-18,293,0.9346978557504874,https://avatars.githubusercontent.com/u/21214473?v=4,SDK for the development of custom actions for Rasa,[],"['nlu', 'sdk']",2024-01-09,[],99,4.0,,2.12,35,30,68,0,23,21,23,35.0,12.0,90.0,0.3,33
444,gis,https://github.com/pysal/spopt,[],,[],[],,,,pysal/spopt,spopt,233,40,13,Python,https://pysal.org/spopt/,Spatial Optimization,pysal,2024-01-10,2019-03-01,256,0.9081291759465479,https://avatars.githubusercontent.com/u/3769919?v=4,Spatial Optimization,"['facility-location', 'location-allocation', 'location-modeling', 'regionalization', 'resource-planning', 'routing', 'spatial-analysis', 'spatial-optimization', 'transportation']","['facility-location', 'location-allocation', 'location-modeling', 'regionalization', 'resource-planning', 'routing', 'spatial-analysis', 'spatial-optimization', 'transportation']",2024-01-02,[],18,5.0,,1.25,38,33,59,0,3,3,3,39.0,63.0,90.0,1.6,33
567,gis,https://github.com/developmentseed/geojson-pydantic,[],,[],[],,,,developmentseed/geojson-pydantic,geojson-pydantic,179,33,14,Python,https://developmentseed.org/geojson-pydantic/,Pydantic data models for the GeoJSON spec,developmentseed,2024-01-09,2020-05-21,192,0.9288361749444033,https://avatars.githubusercontent.com/u/92384?v=4,Pydantic data models for the GeoJSON spec,"['geojson', 'geojson-spec', 'pydantic']","['geojson', 'geojson-spec', 'pydantic']",2023-12-20,"[('brokenloop/jsontopydantic', 0.7540448904037476, 'util', 0)]",22,7.0,,1.54,6,4,44,1,0,6,6,6.0,17.0,90.0,2.8,33
809,util,https://github.com/aws-samples/sagemaker-ssh-helper,[],,[],[],,,,aws-samples/sagemaker-ssh-helper,sagemaker-ssh-helper,156,21,10,Python,,A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell),aws-samples,2024-01-05,2022-10-14,67,2.3086680761099365,https://avatars.githubusercontent.com/u/8931462?v=4,A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell),"['amazon-sagemaker', 'aws', 'aws-systems-manager', 'machine-learning', 'pycharm', 'sagemaker', 'sagemaker-studio', 'ssh', 'vscode']","['amazon-sagemaker', 'aws', 'aws-systems-manager', 'machine-learning', 'pycharm', 'sagemaker', 'sagemaker-studio', 'ssh', 'vscode']",2023-08-28,"[('aws/sagemaker-python-sdk', 0.671806812286377, 'ml', 3), ('boto/boto3', 0.5350908637046814, 'util', 1), ('rhinosecuritylabs/pacu', 0.5007947087287903, 'security', 1)]",6,3.0,,4.25,7,4,15,5,10,11,10,7.0,22.0,90.0,3.1,33
97,data,https://github.com/vi3k6i5/flashtext,[],,[],[],,,,vi3k6i5/flashtext,flashtext,5496,610,140,Python,,Extract Keywords from sentence or Replace keywords in sentences.,vi3k6i5,2024-01-13,2017-08-15,337,16.30860534124629,,Extract Keywords from sentence or Replace keywords in sentences.,"['data-extraction', 'keyword-extraction', 'nlp', 'search-in-text', 'word2vec']","['data-extraction', 'keyword-extraction', 'nlp', 'search-in-text', 'word2vec']",2020-05-03,"[('sloria/textblob', 0.5837533473968506, 'nlp', 1), ('nltk/nltk', 0.5405317544937134, 'nlp', 1), ('maartengr/keybert', 0.5377181768417358, 'nlp', 1)]",7,1.0,,0.0,7,1,78,45,0,0,0,7.0,4.0,90.0,0.6,32
21,nlp,https://github.com/facebookresearch/drqa,['question-answering'],,[],[],,,,facebookresearch/drqa,DrQA,4431,920,160,Python,,Reading Wikipedia to Answer Open-Domain Questions,facebookresearch,2024-01-12,2017-07-07,342,12.934528773978315,https://avatars.githubusercontent.com/u/16943930?v=4,Reading Wikipedia to Answer Open-Domain Questions,[],['question-answering'],2021-05-18,"[('goldsmith/wikipedia', 0.5370073318481445, 'data', 0)]",12,5.0,,0.0,0,0,79,32,0,0,0,0.0,0.0,90.0,0.0,32
1016,debug,https://github.com/shobrook/rebound,[],,[],[],,,,shobrook/rebound,rebound,4047,379,77,Python,,Command-line tool that instantly fetches Stack Overflow results when an exception is thrown,shobrook,2024-01-12,2018-02-28,308,13.103145235892692,,Command-line tool that instantly fetches Stack Overflow results when an exception is thrown,"['command-line-interface', 'command-line-tool', 'error-messages', 'stackoverflow', 'terminal-app']","['command-line-interface', 'command-line-tool', 'error-messages', 'stackoverflow', 'terminal-app']",2022-02-16,[],16,2.0,,0.0,0,0,72,23,0,1,1,0.0,0.0,90.0,0.0,32
999,finance,https://github.com/cuemacro/finmarketpy,[],,[],[],,,,cuemacro/finmarketpy,finmarketpy,3274,497,215,Python,http://www.cuemacro.com,Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians),cuemacro,2024-01-13,2015-02-19,466,7.014998469543924,https://avatars.githubusercontent.com/u/20479975?v=4,Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians),"['backtesting-trading-strategies', 'trading-strategies']","['backtesting-trading-strategies', 'trading-strategies']",2024-01-01,"[('mementum/backtrader', 0.8932955265045166, 'finance', 0), ('gbeced/pyalgotrade', 0.7125941514968872, 'finance', 0), ('goldmansachs/gs-quant', 0.6932242512702942, 'finance', 1), ('kernc/backtesting.py', 0.6904587149620056, 'finance', 2), ('ta-lib/ta-lib-python', 0.6374157071113586, 'finance', 0), ('robcarver17/pysystemtrade', 0.6369601488113403, 'finance', 0), ('ranaroussi/quantstats', 0.6077700853347778, 'finance', 0), ('domokane/financepy', 0.6049692630767822, 'finance', 0), ('wesm/pydata-book', 0.5989004969596863, 'study', 0), ('mementum/bta-lib', 0.5935547947883606, 'finance', 0), ('quantopian/zipline', 0.5911790132522583, 'finance', 0), ('quantconnect/lean', 0.588824987411499, 'finance', 1), ('gbeced/basana', 0.5816241502761841, 'finance', 0), ('pmorissette/bt', 0.5734877586364746, 'finance', 0), ('firmai/atspy', 0.5688939690589905, 'time-series', 0), ('polakowo/vectorbt', 0.5674868822097778, 'finance', 1), ('quantecon/quantecon.py', 0.5637820959091187, 'sim', 0), ('eleutherai/pyfra', 0.5586219429969788, 'ml', 0), ('pmorissette/ffn', 0.5505915284156799, 'finance', 0), ('quantopian/pyfolio', 0.5475354194641113, 'finance', 0), ('python/cpython', 0.5471070408821106, 'util', 0), ('pytoolz/toolz', 0.5427136421203613, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.541059136390686, 'study', 0), ('hydrosquall/tiingo-python', 0.5366682410240173, 'finance', 0), ('alkaline-ml/pmdarima', 0.5333058834075928, 'time-series', 0), ('probml/pyprobml', 0.5201766490936279, 'ml', 0), ('pypy/pypy', 0.5189687013626099, 'util', 0), ('cuemacro/findatapy', 0.5173200368881226, 'finance', 0), ('mynameisfiber/high_performance_python_2e', 0.513410210609436, 'study', 0), ('nedbat/coveragepy', 0.5129390954971313, 'testing', 0), ('statsmodels/statsmodels', 0.5101310610771179, 'ml', 0), ('wolever/parameterized', 0.5073219537734985, 'testing', 0), ('scikit-mobility/scikit-mobility', 0.5044008493423462, 'gis', 0), ('timofurrer/awesome-asyncio', 0.5032345056533813, 'study', 0), ('bashtage/arch', 0.5008066892623901, 'time-series', 0), ('pandas-dev/pandas', 0.5004711747169495, 'pandas', 0)]",14,1.0,,0.08,0,0,108,0,2,2,2,0.0,0.0,90.0,0.0,32
1155,nlp,https://github.com/jsvine/markovify,[],,[],[],,,,jsvine/markovify,markovify,3244,351,70,Python,,"A simple, extensible Markov chain generator.",jsvine,2024-01-13,2015-01-02,473,6.850075414781297,,"A simple, extensible Markov chain generator.",[],[],2023-04-04,[],62,2.0,,0.12,0,0,110,9,0,4,4,0.0,0.0,90.0,0.0,32
1129,pandas,https://github.com/scikit-learn-contrib/sklearn-pandas,[],,[],[],,,,scikit-learn-contrib/sklearn-pandas,sklearn-pandas,2768,420,94,Python,,Pandas integration with sklearn,scikit-learn-contrib,2024-01-12,2013-04-22,562,4.924015247776366,https://avatars.githubusercontent.com/u/17349883?v=4,Pandas integration with sklearn,[],[],2022-07-17,"[('blaze/blaze', 0.626749575138092, 'pandas', 0), ('nalepae/pandarallel', 0.5978479981422424, 'pandas', 0), ('ddelange/mapply', 0.5479068756103516, 'pandas', 0), ('tkrabel/bamboolib', 0.5288722515106201, 'pandas', 0), ('lux-org/lux', 0.5287317037582397, 'viz', 0), ('adamerose/pandasgui', 0.5230746865272522, 'pandas', 0), ('skops-dev/skops', 0.5230203866958618, 'ml-ops', 0), ('holoviz/spatialpandas', 0.5225083231925964, 'pandas', 0), ('jmcarpenter2/swifter', 0.5050438046455383, 'pandas', 0), ('jakevdp/pythondatasciencehandbook', 0.5033451914787292, 'study', 0)]",39,4.0,,0.0,1,1,131,18,0,1,1,1.0,1.0,90.0,1.0,32
1481,util,https://github.com/nschloe/tikzplotlib,[],,[],[],,,,nschloe/tikzplotlib,tikzplotlib,2245,190,44,Python,,:bar_chart: Save matplotlib figures as TikZ/PGFplots for smooth integration into LaTeX.,nschloe,2024-01-13,2010-01-14,732,3.063950087736401,,📊 Save matplotlib figures as TikZ/PGFplots for smooth integration into LaTeX.,"['latex', 'matplotlib', 'pgfplots', 'tikz']","['latex', 'matplotlib', 'pgfplots', 'tikz']",2022-02-28,"[('cuemacro/chartpy', 0.5781739354133606, 'viz', 1), ('matplotlib/matplotlib', 0.5151798725128174, 'viz', 1), ('holoviz/hvplot', 0.5042620897293091, 'pandas', 0)]",61,3.0,,0.0,6,1,170,23,0,6,6,6.0,7.0,90.0,1.2,32
476,ml-dl,https://github.com/tensorflow/mesh,[],,[],[],,,,tensorflow/mesh,mesh,1495,260,50,Python,,Mesh TensorFlow: Model Parallelism Made Easier,tensorflow,2024-01-12,2018-09-20,279,5.344739530132789,https://avatars.githubusercontent.com/u/15658638?v=4,Mesh TensorFlow: Model Parallelism Made Easier,[],[],2023-11-17,"[('eleutherai/gpt-neo', 0.665690541267395, 'llm', 0), ('arogozhnikov/einops', 0.5747230052947998, 'ml-dl', 0), ('huggingface/accelerate', 0.5616391897201538, 'ml', 0), ('zacwellmer/worldmodels', 0.5606265068054199, 'ml-rl', 0), ('xl0/lovely-tensors', 0.5570968389511108, 'ml-dl', 0), ('ggerganov/ggml', 0.5549049973487854, 'ml', 0), ('nicolas-chaulet/torch-points3d', 0.5492365956306458, 'ml', 0), ('rafiqhasan/auto-tensorflow', 0.5327334403991699, 'ml-dl', 0), ('huggingface/exporters', 0.5134114623069763, 'ml', 0), ('horovod/horovod', 0.5112833976745605, 'ml-ops', 0), ('hazyresearch/hgcn', 0.5072011351585388, 'ml', 0), ('pytorch/pytorch', 0.5063491463661194, 'ml-dl', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5049545168876648, 'diffusion', 0)]",50,3.0,,0.06,2,2,65,2,0,1,1,2.0,0.0,90.0,0.0,32
1353,study,https://github.com/atcold/nyu-dlsp21,"['nyu', 'deep-learning']",,[],[],,,,atcold/nyu-dlsp21,NYU-DLSP21,1477,271,51,Jupyter Notebook,https://atcold.github.io/NYU-DLSP21/,NYU Deep Learning Spring 2021,atcold,2024-01-12,2021-04-15,145,10.136274509803922,,NYU Deep Learning Spring 2021,"['deep-learning', 'ebm', 'nyu', 'yann-le-cunn']","['deep-learning', 'ebm', 'nyu', 'yann-le-cunn']",2023-10-04,"[('rasbt/stat453-deep-learning-ss20', 0.6361130475997925, 'study', 0), ('d2l-ai/d2l-en', 0.5323129296302795, 'study', 1), ('udacity/deep-learning-v2-pytorch', 0.5313364267349243, 'study', 1), ('udlbook/udlbook', 0.5171167850494385, 'study', 1)]",22,3.0,,0.15,0,0,33,3,0,0,0,0.0,0.0,90.0,0.0,32
109,util,https://github.com/nficano/python-lambda,[],,[],[],,,,nficano/python-lambda,python-lambda,1463,270,32,Python,, A toolkit for developing and deploying serverless Python code in AWS Lambda. ,nficano,2024-01-12,2016-02-26,413,3.5374784110535407,, A toolkit for developing and deploying serverless Python code in AWS Lambda. ,"['aws', 'aws-lambda', 'microservices', 'serverless']","['aws', 'aws-lambda', 'microservices', 'serverless']",2022-06-03,"[('aws/chalice', 0.9045315980911255, 'web', 3), ('aws/aws-lambda-python-runtime-interface-client', 0.706771194934845, 'util', 0), ('geeogi/async-python-lambda-template', 0.6575234532356262, 'template', 0), ('jordaneremieff/mangum', 0.6575080752372742, 'web', 3), ('boto/boto3', 0.6395252346992493, 'util', 1), ('rpgreen/apilogs', 0.635263204574585, 'util', 2), ('developmentseed/geolambda', 0.6138771176338196, 'gis', 0), ('localstack/localstack', 0.5929733514785767, 'util', 1), ('falconry/falcon', 0.5897996425628662, 'web', 1), ('pallets/quart', 0.5740982890129089, 'web', 0), ('pynamodb/pynamodb', 0.573145866394043, 'data', 1), ('backtick-se/cowait', 0.5466134548187256, 'util', 0), ('awslabs/python-deequ', 0.542289137840271, 'ml', 1), ('amzn/ion-python', 0.5418636798858643, 'data', 0), ('samuelcolvin/aioaws', 0.5388737916946411, 'data', 1), ('aws/serverless-application-model', 0.5357404947280884, 'util', 2), ('aws/aws-sdk-pandas', 0.5259411334991455, 'pandas', 2), ('pyinfra-dev/pyinfra', 0.5205470323562622, 'util', 0), ('pallets/flask', 0.5108464956283569, 'web', 0), ('alirn76/panther', 0.5092719793319702, 'web', 0), ('masoniteframework/masonite', 0.5054439306259155, 'web', 0), ('python-restx/flask-restx', 0.505323588848114, 'web', 0), ('eventual-inc/daft', 0.5019513964653015, 'pandas', 0)]",48,5.0,,0.0,1,0,96,20,0,7,7,1.0,1.0,90.0,1.0,32
890,ml-ops,https://github.com/hi-primus/optimus,[],,[],[],,,,hi-primus/optimus,optimus,1415,237,39,Python,https://hi-optimus.com,":truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark",hi-primus,2024-01-13,2017-07-13,341,4.140886287625418,https://avatars.githubusercontent.com/u/86029697?v=4,":truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark","['big-data-cleaning', 'bigdata', 'cudf', 'dask', 'dask-cudf', 'data-analysis', 'data-cleaner', 'data-cleaning', 'data-cleansing', 'data-exploration', 'data-extraction', 'data-preparation', 'data-profiling', 'data-science', 'data-transformation', 'data-wrangling', 'machine-learning', 'pyspark', 'spark']","['big-data-cleaning', 'bigdata', 'cudf', 'dask', 'dask-cudf', 'data-analysis', 'data-cleaner', 'data-cleaning', 'data-cleansing', 'data-exploration', 'data-extraction', 'data-preparation', 'data-profiling', 'data-science', 'data-transformation', 'data-wrangling', 'machine-learning', 'pyspark', 'spark']",2023-05-19,"[('ydataai/ydata-profiling', 0.6320505142211914, 'pandas', 5), ('rapidsai/cudf', 0.6309337019920349, 'pandas', 4), ('pyjanitor-devs/pyjanitor', 0.6122784614562988, 'pandas', 0), ('great-expectations/great_expectations', 0.6027732491493225, 'ml-ops', 2), ('mage-ai/mage-ai', 0.5950447916984558, 'ml-ops', 3), ('ploomber/ploomber', 0.5924459099769592, 'ml-ops', 2), ('saulpw/visidata', 0.5910075902938843, 'term', 0), ('dagworks-inc/hamilton', 0.5767509341239929, 'ml-ops', 3), ('polyaxon/datatile', 0.5730414986610413, 'pandas', 5), ('astronomer/astro-sdk', 0.5675498843193054, 'ml-ops', 2), ('fugue-project/fugue', 0.5615660548210144, 'pandas', 3), ('linealabs/lineapy', 0.5603882074356079, 'jupyter', 0), ('unionai-oss/pandera', 0.557157576084137, 'pandas', 1), ('orchest/orchest', 0.5557096004486084, 'ml-ops', 2), ('meltano/meltano', 0.5476229190826416, 'ml-ops', 0), ('apache/spark', 0.5423697233200073, 'data', 1), ('airbytehq/airbyte', 0.5406291484832764, 'data', 1), ('kubeflow-kale/kale', 0.5404044389724731, 'ml-ops', 1), ('eventual-inc/daft', 0.5366455316543579, 'pandas', 2), ('aws/aws-sdk-pandas', 0.533242404460907, 'pandas', 1), ('netflix/metaflow', 0.5270899534225464, 'ml-ops', 2), ('modin-project/modin', 0.5256850123405457, 'perf', 1), ('avaiga/taipy', 0.5233602523803711, 'data', 0), ('ibis-project/ibis', 0.5208921432495117, 'data', 2), ('vaexio/vaex', 0.5148595571517944, 'perf', 3), ('backtick-se/cowait', 0.5054327249526978, 'util', 3), ('man-group/dtale', 0.5049717426300049, 'viz', 2), ('pandas-dev/pandas', 0.5029491782188416, 'pandas', 2), ('huggingface/datasets', 0.5028654932975769, 'nlp', 1), ('autoviml/auto_ts', 0.5023893713951111, 'time-series', 0)]",24,5.0,,0.06,8,5,79,8,1,22,1,8.0,5.0,90.0,0.6,32
413,nlp,https://github.com/chrismattmann/tika-python,[],,[],[],,,,chrismattmann/tika-python,tika-python,1373,273,39,Python,,Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.,chrismattmann,2024-01-12,2014-06-26,500,2.742082738944365,,Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.,"['buffer', 'covid-19', 'detection', 'extraction', 'memex', 'mime', 'nlp', 'nlp-library', 'nlp-machine-learning', 'parse', 'parser-interface', 'recognition', 'text-extraction', 'text-recognition', 'tika-python', 'tika-server', 'tika-server-jar', 'translation-interface', 'usc']","['buffer', 'covid-19', 'detection', 'extraction', 'memex', 'mime', 'nlp', 'nlp-library', 'nlp-machine-learning', 'parse', 'parser-interface', 'recognition', 'text-extraction', 'text-recognition', 'tika-python', 'tika-server', 'tika-server-jar', 'translation-interface', 'usc']",2023-08-11,"[('nuitka/nuitka', 0.5718207955360413, 'util', 0)]",68,4.0,,0.06,2,2,116,5,0,2,2,2.0,2.0,90.0,1.0,32
1132,ml,https://github.com/scikit-learn-contrib/metric-learn,[],,[],[],,,,scikit-learn-contrib/metric-learn,metric-learn,1358,278,49,Python,http://contrib.scikit-learn.org/metric-learn/,Metric learning algorithms in Python,scikit-learn-contrib,2024-01-12,2013-11-02,534,2.541031809676557,https://avatars.githubusercontent.com/u/17349883?v=4,Metric learning algorithms in Python,"['machine-learning', 'metric-learning', 'scikit-learn']","['machine-learning', 'metric-learning', 'scikit-learn']",2023-09-29,"[('oml-team/open-metric-learning', 0.6861700415611267, 'ml', 1), ('scikit-learn/scikit-learn', 0.6777765154838562, 'ml', 1), ('scikit-learn-contrib/lightning', 0.6158003807067871, 'ml', 1), ('pycaret/pycaret', 0.6015337109565735, 'ml', 1), ('rasbt/mlxtend', 0.6002252101898193, 'ml', 1), ('kevinmusgrave/pytorch-metric-learning', 0.5882241129875183, 'ml', 2), ('automl/auto-sklearn', 0.576133131980896, 'ml', 1), ('lmcinnes/pynndescent', 0.5760319828987122, 'ml', 0), ('featurelabs/featuretools', 0.5696079730987549, 'ml', 2), ('gradio-app/gradio', 0.5687407851219177, 'viz', 1), ('huggingface/evaluate', 0.556303083896637, 'ml', 1), ('dask/dask-ml', 0.5469420552253723, 'ml', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5348906517028809, 'ml', 1), ('sentinel-hub/eo-learn', 0.5314062237739563, 'gis', 1), ('ageron/handson-ml2', 0.5313714146614075, 'ml', 0), ('goldmansachs/gs-quant', 0.5309397578239441, 'finance', 0), ('nicolashug/surprise', 0.5304577946662903, 'ml', 1), ('rasbt/machine-learning-book', 0.5294020175933838, 'study', 2), ('guyallard/markov_clustering', 0.5289827585220337, 'graph', 0), ('numpy/numpy', 0.5264557003974915, 'math', 0), ('scipy/scipy', 0.5236490368843079, 'math', 0), ('qdrant/quaterion', 0.5228015184402466, 'ml', 2), ('districtdatalabs/yellowbrick', 0.516703188419342, 'ml', 2), ('kubeflow/fairing', 0.5143347382545471, 'ml-ops', 0), ('tensorflow/data-validation', 0.5105538368225098, 'ml-ops', 0), ('pysal/pysal', 0.5091261267662048, 'gis', 0), ('skops-dev/skops', 0.504301130771637, 'ml-ops', 2), ('spotify/voyager', 0.503491997718811, 'ml', 1), ('koaning/scikit-lego', 0.5020371675491333, 'ml', 2), ('roban/cosmolopy', 0.500042200088501, 'sim', 0)]",22,8.0,,0.17,1,1,124,4,1,1,1,1.0,1.0,90.0,1.0,32
1825,util,https://github.com/aws-samples/aws-glue-samples,"['aws', 'glue']",,[],[],,,,aws-samples/aws-glue-samples,aws-glue-samples,1349,771,76,Python,,AWS Glue code samples,aws-samples,2024-01-08,2017-05-21,349,3.8621676891615544,https://avatars.githubusercontent.com/u/8931462?v=4,AWS Glue code samples,[],"['aws', 'glue']",2023-10-18,[],34,2.0,,0.62,2,0,81,3,0,0,0,2.0,4.0,90.0,2.0,32
405,perf,https://github.com/pympler/pympler,[],,[],[],,,,pympler/pympler,pympler,1119,90,10,Python,,"Development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.",pympler,2024-01-13,2012-10-04,590,1.894316807738815,https://avatars.githubusercontent.com/u/2490143?v=4,"Development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.",[],[],2022-07-24,"[('pythonprofilers/memory_profiler', 0.8423640131950378, 'profiling', 0), ('pythonspeed/filprofiler', 0.7303571105003357, 'profiling', 0), ('joblib/joblib', 0.6179777979850769, 'util', 0), ('eleutherai/pyfra', 0.6173771023750305, 'ml', 0), ('gaogaotiantian/viztracer', 0.6162266731262207, 'profiling', 0), ('landscapeio/prospector', 0.6098625659942627, 'util', 0), ('dgilland/cacheout', 0.6028500199317932, 'perf', 0), ('pyston/pyston', 0.5993977189064026, 'util', 0), ('alexmojaki/snoop', 0.5967013835906982, 'debug', 0), ('nedbat/coveragepy', 0.5953695178031921, 'testing', 0), ('pypy/pypy', 0.5909916758537292, 'util', 0), ('python-cachier/cachier', 0.5893210768699646, 'perf', 0), ('pyutils/line_profiler', 0.5873364806175232, 'profiling', 0), ('open-telemetry/opentelemetry-python-contrib', 0.5808881521224976, 'util', 0), ('benfred/py-spy', 0.5802413821220398, 'profiling', 0), ('pytables/pytables', 0.5767551064491272, 'data', 0), ('erotemic/ubelt', 0.574570894241333, 'util', 0), ('alexmojaki/heartrate', 0.57415771484375, 'debug', 0), ('bloomberg/memray', 0.5725077390670776, 'profiling', 0), ('py4j/py4j', 0.5723164677619934, 'util', 0), ('rubik/radon', 0.568540632724762, 'util', 0), ('p403n1x87/austin', 0.5645143389701843, 'profiling', 0), ('citadel-ai/langcheck', 0.5642958283424377, 'llm', 0), ('willmcgugan/textual', 0.5624990463256836, 'term', 0), ('sumerc/yappi', 0.559504508972168, 'profiling', 0), ('ionelmc/pytest-benchmark', 0.5586925148963928, 'testing', 0), ('samuelcolvin/python-devtools', 0.5581871271133423, 'debug', 0), ('klen/py-frameworks-bench', 0.5578858256340027, 'perf', 0), ('malloydata/malloy-py', 0.5531739592552185, 'data', 0), ('exaloop/codon', 0.5520622730255127, 'perf', 0), ('micropython/micropython', 0.5511940121650696, 'util', 0), ('dosisod/refurb', 0.5501025915145874, 'util', 0), ('python/cpython', 0.5499320030212402, 'util', 0), ('facebook/pyre-check', 0.5445180535316467, 'typing', 0), ('agronholm/apscheduler', 0.5432737469673157, 'util', 0), ('cython/cython', 0.5429266691207886, 'util', 0), ('pytoolz/toolz', 0.5427612066268921, 'util', 0), ('pyglet/pyglet', 0.5425298810005188, 'gamedev', 0), ('hoffstadt/dearpygui', 0.5417070388793945, 'gui', 0), ('pypa/hatch', 0.5399362444877625, 'util', 0), ('jiffyclub/snakeviz', 0.5296313762664795, 'profiling', 0), ('google/gin-config', 0.5286837816238403, 'util', 0), ('requests/toolbelt', 0.5279673337936401, 'util', 0), ('indygreg/pyoxidizer', 0.5277453064918518, 'util', 0), ('reloadware/reloadium', 0.5263128876686096, 'profiling', 0), ('google/pytype', 0.524192750453949, 'typing', 0), ('libtcod/python-tcod', 0.52317214012146, 'gamedev', 0), ('locustio/locust', 0.5212385058403015, 'testing', 0), ('fastai/fastcore', 0.5204732418060303, 'util', 0), ('getsentry/responses', 0.520066499710083, 'testing', 0), ('lcompilers/lpython', 0.5168886184692383, 'util', 0), ('google/jax', 0.516033411026001, 'ml', 0), ('mkdocstrings/griffe', 0.5151218771934509, 'util', 0), ('beeware/toga', 0.5115267634391785, 'gui', 0), ('python-rope/rope', 0.5101566910743713, 'util', 0), ('nickreynke/python-gedcom', 0.5089355111122131, 'data', 0), ('agronholm/typeguard', 0.5068668127059937, 'typing', 0), ('wesm/pydata-book', 0.505323588848114, 'study', 0), ('google/python-fire', 0.5053215622901917, 'term', 0), ('wolever/parameterized', 0.5015774965286255, 'testing', 0), ('xrudelis/pytrait', 0.5008642077445984, 'util', 0), ('connorferster/handcalcs', 0.5004320740699768, 'jupyter', 0)]",29,6.0,,0.0,2,1,137,18,0,2,2,2.0,8.0,90.0,4.0,32
1485,util,https://github.com/python-injector/injector,['dependency-injection'],,[],[],,,,python-injector/injector,injector,1092,76,14,Python,,"Python dependency injection framework, inspired by Guice",python-injector,2024-01-14,2010-11-25,687,1.5878687162442875,https://avatars.githubusercontent.com/u/119698538?v=4,"Python dependency injection framework, inspired by Guice","['dependency-injection', 'dependency-injection-framework', 'di', 'injector']","['dependency-injection', 'dependency-injection-framework', 'di', 'injector']",2023-12-13,"[('ivankorobkov/python-inject', 0.7356547713279724, 'util', 1), ('allrod5/injectable', 0.6809417605400085, 'util', 1), ('ets-labs/python-dependency-injector', 0.6611223220825195, 'util', 2), ('proofit404/dependencies', 0.554645836353302, 'util', 1), ('mitsuhiko/rye', 0.5345786213874817, 'util', 0), ('indygreg/pyoxidizer', 0.5219050645828247, 'util', 0), ('python-poetry/poetry', 0.5174149870872498, 'util', 0), ('pdm-project/pdm', 0.5129967927932739, 'util', 0)]",29,3.0,,0.38,9,6,160,1,0,4,4,8.0,10.0,90.0,1.2,32
721,gis,https://github.com/geospatialpython/pyshp,[],,[],[],,,,geospatialpython/pyshp,pyshp,1062,263,66,Python,,This library reads and writes ESRI Shapefiles in pure Python.,geospatialpython,2024-01-09,2014-03-04,517,2.0541586073500966,,This library reads and writes ESRI Shapefiles in pure Python.,[],[],2023-08-18,"[('imageio/imageio', 0.5908835530281067, 'util', 0), ('jorisschellekens/borb', 0.5256045460700989, 'util', 0), ('pytoolz/toolz', 0.5238029360771179, 'util', 0), ('julienpalard/pipe', 0.5082536935806274, 'util', 0)]",42,5.0,,0.04,1,0,120,5,0,2,2,1.0,2.0,90.0,2.0,32
1728,llm,https://github.com/rlancemartin/auto-evaluator,"['evaluation', 'question-answering']",,[],[],,,,rlancemartin/auto-evaluator,auto-evaluator,982,90,8,Python,https://autoevaluator.langchain.com/,Evaluation tool for LLM QA chains,rlancemartin,2024-01-12,2023-04-14,41,23.621993127147768,,Evaluation tool for LLM QA chains,[],"['evaluation', 'question-answering']",2023-05-10,"[('night-chen/toolqa', 0.641534149646759, 'llm', 1), ('whitead/paper-qa', 0.5889334678649902, 'llm', 1), ('citadel-ai/langcheck', 0.5368258357048035, 'llm', 1), ('ibm/dromedary', 0.521746814250946, 'llm', 0), ('confident-ai/deepeval', 0.5153782963752747, 'testing', 1)]",8,1.0,,0.48,0,0,9,8,0,0,0,0.0,0.0,90.0,0.0,32
1877,math,https://github.com/cma-es/pycma,['numerical-optimization'],pycma is a Python implementation of CMA-ES and a few related numerical optimization tools.,[],[],,,,cma-es/pycma,pycma,979,170,17,Python,,Python implementation of CMA-ES,cma-es,2024-01-09,2016-09-22,383,2.5513775130305287,https://avatars.githubusercontent.com/u/9052298?v=4,Python implementation of CMA-ES,[],['numerical-optimization'],2023-12-12,"[('scipy/scipy', 0.5867729187011719, 'math', 0), ('numpy/numpy', 0.5440859198570251, 'math', 0), ('scikit-optimize/scikit-optimize', 0.5281330943107605, 'ml', 0), ('stijnwoestenborghs/gradi-mojo', 0.5154417753219604, 'util', 0), ('pyomo/pyomo', 0.5060227513313293, 'math', 0)]",13,2.0,,1.37,30,20,89,1,1,2,1,30.0,27.0,90.0,0.9,32
1677,util,https://github.com/pycqa/autoflake,[],,[],[],,,,pycqa/autoflake,autoflake,833,79,14,Python,https://pypi.org/project/autoflake/,Removes unused imports and unused variables as reported by pyflakes,pycqa,2024-01-12,2012-12-27,578,1.4393976795852876,https://avatars.githubusercontent.com/u/8749848?v=4,Removes unused imports and unused variables as reported by pyflakes,"['formatter', 'linter', 'pyflakes']","['formatter', 'linter', 'pyflakes']",2024-01-12,"[('hadialqattan/pycln', 0.6299516558647156, 'util', 0), ('pycqa/eradicate', 0.5524262189865112, 'util', 0)]",38,5.0,,1.5,8,8,134,0,6,5,6,8.0,0.0,90.0,0.0,32
1567,llm,https://github.com/cerebras/modelzoo,['training'],,[],[],,,,cerebras/modelzoo,modelzoo,776,110,23,Python,,,cerebras,2024-01-13,2022-04-08,94,8.205438066465257,https://avatars.githubusercontent.com/u/19580083?v=4,cerebras/modelzoo,[],['training'],2023-11-28,[],4,1.0,,0.29,12,8,22,2,0,4,4,12.0,1.0,90.0,0.1,32
1479,testing,https://github.com/nose-devs/nose2,[],,[],[],,,,nose-devs/nose2,nose2,768,137,23,Python,https://nose2.io,"The successor to nose, based on unittest2",nose-devs,2024-01-10,2011-12-14,632,1.2135440180586907,https://avatars.githubusercontent.com/u/1263082?v=4,"The successor to nose, based on unittest2","['nose', 'nose2', 'nosetest', 'nosetests', 'testing', 'testing-framework', 'testing-library', 'testing-tool', 'testing-tools', 'unit-test', 'unittest', 'unittesting']","['nose', 'nose2', 'nosetest', 'nosetests', 'testing', 'testing-framework', 'testing-library', 'testing-tool', 'testing-tools', 'unit-test', 'unittest', 'unittesting']",2023-12-25,[],77,4.0,,0.75,13,8,147,1,0,3,3,13.0,11.0,90.0,0.8,32
623,gis,https://github.com/makepath/xarray-spatial,[],,[],[],,,,makepath/xarray-spatial,xarray-spatial,752,79,23,Python,https://xarray-spatial.org,Raster-based Spatial Analytics for Python,makepath,2024-01-04,2020-02-08,207,3.62534435261708,https://avatars.githubusercontent.com/u/60046102?v=4,Raster-based Spatial Analytics for Python,"['datashader', 'numba', 'raster-analysis', 'spatial-analysis', 'xarray']","['datashader', 'numba', 'raster-analysis', 'spatial-analysis', 'xarray']",2023-07-10,"[('pysal/pysal', 0.6753366589546204, 'gis', 0), ('earthlab/earthpy', 0.6488336324691772, 'gis', 0), ('residentmario/geoplot', 0.59319007396698, 'gis', 1), ('toblerity/rtree', 0.5867227911949158, 'gis', 0), ('contextlab/hypertools', 0.5519405603408813, 'ml', 0), ('geopandas/geopandas', 0.5509017705917358, 'gis', 0), ('pandas-dev/pandas', 0.5457472801208496, 'pandas', 0), ('holoviz/spatialpandas', 0.5339615941047668, 'pandas', 0), ('artelys/geonetworkx', 0.5338029265403748, 'gis', 0), ('holoviz/hvplot', 0.5334243178367615, 'pandas', 1), ('opengeos/leafmap', 0.5318371057510376, 'gis', 0), ('altair-viz/altair', 0.5251989364624023, 'viz', 0), ('scitools/iris', 0.523902952671051, 'gis', 0), ('scikit-mobility/scikit-mobility', 0.5155532956123352, 'gis', 0), ('gregorhd/mapcompare', 0.5138509273529053, 'gis', 0), ('corteva/rioxarray', 0.511622428894043, 'gis', 1), ('holoviz/holoviz', 0.510444700717926, 'viz', 1), ('perrygeo/python-rasterstats', 0.5050948262214661, 'gis', 0), ('pycaret/pycaret', 0.5047616362571716, 'ml', 0), ('pyqtgraph/pyqtgraph', 0.503014862537384, 'viz', 0), ('tdameritrade/stumpy', 0.5024837851524353, 'time-series', 1), ('marcomusy/vedo', 0.5006656646728516, 'viz', 0)]",28,4.0,,0.21,99,95,48,6,2,10,2,99.0,3.0,90.0,0.0,32
1726,util,https://github.com/urschrei/pyzotero,['zotero'],,[],[],,,,urschrei/pyzotero,pyzotero,734,86,17,Python,https://pyzotero.readthedocs.org,Pyzotero: a Python client for the Zotero API,urschrei,2024-01-12,2011-02-28,674,1.088789997880907,,Pyzotero: a Python client for the Zotero API,"['citations', 'digital-humanities', 'zotero']","['citations', 'digital-humanities', 'zotero']",2023-12-04,"[('goldsmith/wikipedia', 0.5380213856697083, 'data', 0), ('eleutherai/pyfra', 0.5244025588035583, 'ml', 0), ('scholarly-python-package/scholarly', 0.5180812478065491, 'data', 1), ('paperswithcode/galai', 0.5094993710517883, 'llm', 1)]",27,8.0,,0.69,6,4,157,1,11,11,11,6.0,3.0,90.0,0.5,32
679,ml,https://github.com/nvidia/cuda-python,[],,[],[],,,,nvidia/cuda-python,cuda-python,689,90,30,Python,https://nvidia.github.io/cuda-python/,CUDA Python Low-level Bindings,nvidia,2024-01-13,2021-06-28,135,5.09830866807611,https://avatars.githubusercontent.com/u/1728152?v=4,CUDA Python Low-level Bindings,[],[],2023-11-29,"[('pybind/pybind11', 0.6058850288391113, 'perf', 0), ('cupy/cupy', 0.5955917835235596, 'math', 0), ('pytorch/data', 0.5482061505317688, 'data', 0), ('numba/numba', 0.5387571454048157, 'perf', 0), ('rapidsai/cudf', 0.5373781323432922, 'pandas', 0), ('pytoolz/toolz', 0.5279120802879333, 'util', 0), ('marella/ctransformers', 0.5207217931747437, 'nlp', 0), ('pachyderm/python-pachyderm', 0.5181779861450195, 'data', 0), ('pyca/pynacl', 0.5136300921440125, 'util', 0), ('google/jax', 0.5065921545028687, 'ml', 0), ('rentruewang/koila', 0.5058760046958923, 'ml', 0), ('arogozhnikov/einops', 0.5039113759994507, 'ml-dl', 0), ('numba/llvmlite', 0.5029944181442261, 'util', 0)]",2,1.0,,0.13,7,5,31,2,6,6,6,7.0,9.0,90.0,1.3,32
1568,ml,https://github.com/huggingface/exporters,['coreml'],,[],[],,,,huggingface/exporters,exporters,484,29,23,Python,,Export Hugging Face models to Core ML and TensorFlow Lite,huggingface,2024-01-10,2022-05-23,88,5.491085899513776,https://avatars.githubusercontent.com/u/25720743?v=4,Export Hugging Face models to Core ML and TensorFlow Lite,"['coreml', 'coremltools', 'deep-learning', 'machine-learning', 'model-converter', 'pytorch', 'tensorflow', 'tflite', 'transformer']","['coreml', 'coremltools', 'deep-learning', 'machine-learning', 'model-converter', 'pytorch', 'tensorflow', 'tflite', 'transformer']",2023-11-22,"[('apple/coremltools', 0.6746289730072021, 'ml', 6), ('huggingface/huggingface_hub', 0.6611513495445251, 'ml', 3), ('huggingface/transformers', 0.618623673915863, 'nlp', 5), ('aws/sagemaker-python-sdk', 0.5837609171867371, 'ml', 3), ('lutzroeder/netron', 0.5719174742698669, 'ml', 5), ('huggingface/datasets', 0.5662267804145813, 'nlp', 4), ('tensorly/tensorly', 0.5597980618476868, 'ml-dl', 3), ('nielsrogge/transformers-tutorials', 0.5573378205299377, 'study', 1), ('rafiqhasan/auto-tensorflow', 0.556242823600769, 'ml-dl', 2), ('deepfakes/faceswap', 0.5523707270622253, 'ml-dl', 2), ('arogozhnikov/einops', 0.5518941283226013, 'ml-dl', 3), ('ggerganov/ggml', 0.5472779870033264, 'ml', 1), ('kubeflow/fairing', 0.5441210269927979, 'ml-ops', 0), ('neuralmagic/sparseml', 0.5338050127029419, 'ml-dl', 2), ('nvlabs/gcvit', 0.5337070822715759, 'diffusion', 1), ('skorch-dev/skorch', 0.532882034778595, 'ml-dl', 2), ('keras-team/autokeras', 0.5266240239143372, 'ml-dl', 3), ('xl0/lovely-tensors', 0.5227699875831604, 'ml-dl', 2), ('huggingface/optimum', 0.5176335573196411, 'ml', 2), ('horovod/horovod', 0.5157522559165955, 'ml-ops', 4), ('tensorflow/mesh', 0.5134114623069763, 'ml-dl', 0), ('microsoft/nni', 0.5089559555053711, 'ml', 4), ('mosaicml/composer', 0.5061023235321045, 'ml-dl', 3), ('ashawkey/stable-dreamfusion', 0.503178060054779, 'diffusion', 0), ('eleutherai/gpt-neo', 0.5027332901954651, 'llm', 0), ('ashleve/lightning-hydra-template', 0.5000954270362854, 'util', 2)]",6,3.0,,0.79,14,8,20,2,0,0,0,14.0,12.0,90.0,0.9,32
1253,llm,https://github.com/hazyresearch/h3,[],,[],[],,,,hazyresearch/h3,H3,472,54,32,Assembly,,Language Modeling with the H3 State Space Model,hazyresearch,2024-01-05,2022-12-28,56,8.301507537688442,https://avatars.githubusercontent.com/u/2165246?v=4,Language Modeling with the H3 State Space Model,[],[],2023-09-29,"[('freedomintelligence/llmzoo', 0.5786004066467285, 'llm', 0), ('juncongmoo/pyllama', 0.567010223865509, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5581068992614746, 'llm', 0), ('lvwerra/trl', 0.5428241491317749, 'llm', 0), ('yizhongw/self-instruct', 0.534803569316864, 'llm', 0), ('hannibal046/awesome-llm', 0.5333759784698486, 'study', 0), ('jonasgeiping/cramming', 0.5188775062561035, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5161812901496887, 'llm', 0), ('facebookresearch/codellama', 0.513182520866394, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5113564729690552, 'llm', 0), ('guidance-ai/guidance', 0.5101184248924255, 'llm', 0), ('openai/gpt-2', 0.5015887022018433, 'llm', 0)]",4,1.0,,0.52,1,0,13,4,0,0,0,1.0,4.0,90.0,4.0,32
716,perf,https://github.com/noxdafox/pebble,[],,[],[],,,,noxdafox/pebble,pebble,465,46,10,Python,,Multi threading and processing eye-candy.,noxdafox,2024-01-13,2013-10-16,536,0.8661522086216072,,Multi threading and processing eye-candy.,"['asyncio', 'decorators', 'multiprocessing', 'pool', 'threading']","['asyncio', 'decorators', 'multiprocessing', 'pool', 'threading']",2023-12-26,"[('sumerc/yappi', 0.7094334959983826, 'profiling', 1), ('joblib/joblib', 0.6440830826759338, 'util', 2), ('agronholm/anyio', 0.6101366281509399, 'perf', 1), ('python-greenlet/greenlet', 0.6010660529136658, 'perf', 0), ('python-trio/trio', 0.5942329168319702, 'perf', 0), ('samuelcolvin/arq', 0.5885294675827026, 'data', 1), ('magicstack/uvloop', 0.5719602108001709, 'util', 1), ('joblib/loky', 0.5490549802780151, 'perf', 0), ('tiangolo/asyncer', 0.5147314667701721, 'perf', 1), ('alex-sherman/unsync', 0.5057518482208252, 'util', 0)]",15,5.0,,0.42,3,3,125,1,3,2,3,3.0,9.0,90.0,3.0,32
49,util,https://github.com/mozillazg/pypy,[],,[],[],,,,mozillazg/pypy,pypy,442,66,13,Python,https://foss.heptapod.net/pypy/pypy,The unofficial GitHub mirror of PyPy (mirrored via https://github.com/mozillazg/job-mirror-hg-repos),mozillazg,2024-01-13,2015-08-03,443,0.9974210186976145,,The unofficial GitHub mirror of PyPy (mirrored via https://github.com/mozillazg/job-mirror-hg-repos),"['github-mirror', 'pypy', 'read-only-repository', 'readonly', 'unofficial', 'unofficial-mirror']","['github-mirror', 'pypy', 'read-only-repository', 'readonly', 'unofficial', 'unofficial-mirror']",2023-12-25,"[('pypa/gh-action-pypi-publish', 0.6144118309020996, 'util', 0), ('pypi/warehouse', 0.5795431733131409, 'util', 0), ('fauxpilot/fauxpilot', 0.5119403004646301, 'llm', 0), ('yaml/pyyaml', 0.5088357329368591, 'util', 0)]",374,2.0,,4.33,5,1,103,1,0,22,22,5.0,2.0,90.0,0.4,32
1205,llm,https://github.com/kbressem/medalpaca,['question-answering'],,[],[],,,,kbressem/medalpaca,medAlpaca,378,39,14,Python,,LLM finetuned for medical question answering,kbressem,2024-01-13,2023-03-28,44,8.590909090909092,,LLM finetuned for medical question answering,[],['question-answering'],2023-09-07,"[('epfllm/meditron', 0.5290087461471558, 'llm', 0)]",6,4.0,,1.46,5,0,10,4,0,0,0,5.0,3.0,90.0,0.6,32
519,gis,https://github.com/pygeos/pygeos,[],,[],[],,,,pygeos/pygeos,pygeos,375,42,14,Python,https://pygeos.readthedocs.io,Wraps GEOS geometry functions in numpy ufuncs.,pygeos,2024-01-10,2019-06-10,242,1.5486725663716814,https://avatars.githubusercontent.com/u/56478268?v=4,Wraps GEOS geometry functions in numpy ufuncs.,[],[],2022-12-14,[],13,9.0,,0.0,1,1,56,13,0,4,4,1.0,5.0,90.0,5.0,32
589,gis,https://github.com/geopython/owslib,[],,[],[],,,,geopython/owslib,OWSLib,356,271,30,Python,https://owslib.readthedocs.io,"OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models.",geopython,2024-01-12,2012-01-13,628,0.5663636363636364,https://avatars.githubusercontent.com/u/1855122?v=4,"OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models.","['ogc', 'ogcapi', 'ows']","['ogc', 'ogcapi', 'ows']",2023-12-18,[],146,5.0,,0.77,14,9,146,1,6,7,6,14.0,12.0,90.0,0.9,32
336,util,https://github.com/mrabarnett/mrab-regex,[],,[],[],,,,mrabarnett/mrab-regex,mrab-regex,321,37,7,C,,,mrabarnett,2024-01-05,2020-11-02,169,1.897804054054054,,mrabarnett/mrab-regex,[],[],2023-12-24,[],9,2.0,,0.31,15,8,39,1,0,9,9,15.0,33.0,90.0,2.2,32
1009,finance,https://github.com/gbeced/basana,[],,[],[],,,,gbeced/basana,basana,292,33,13,Python,,"A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.",gbeced,2024-01-13,2023-03-04,47,6.156626506024097,,"A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.","['algorithmic-trading', 'asyncio', 'backtesting', 'binance', 'cryptocurrency', 'trading-bot']","['algorithmic-trading', 'asyncio', 'backtesting', 'binance', 'cryptocurrency', 'trading-bot']",2024-01-07,"[('gbeced/pyalgotrade', 0.7055126428604126, 'finance', 0), ('quantopian/zipline', 0.6551344394683838, 'finance', 1), ('robcarver17/pysystemtrade', 0.6477593779563904, 'finance', 0), ('quantconnect/lean', 0.6268382668495178, 'finance', 1), ('freqtrade/freqtrade', 0.6232483983039856, 'crypto', 3), ('ccxt/ccxt', 0.6148871183395386, 'crypto', 1), ('idanya/algo-trader', 0.6138817071914673, 'finance', 3), ('ethereum/web3.py', 0.6058024168014526, 'crypto', 0), ('numerai/example-scripts', 0.6021682024002075, 'finance', 1), ('polakowo/vectorbt', 0.5863175392150879, 'finance', 3), ('primal100/pybitcointools', 0.5842757821083069, 'crypto', 0), ('cuemacro/finmarketpy', 0.5816241502761841, 'finance', 0), ('blankly-finance/blankly', 0.5776981711387634, 'finance', 3), ('mementum/backtrader', 0.5649062991142273, 'finance', 1), ('1200wd/bitcoinlib', 0.5637730360031128, 'crypto', 0), ('pmaji/crypto-whale-watching-app', 0.553300142288208, 'crypto', 1), ('hydrosquall/tiingo-python', 0.5487340688705444, 'finance', 0), ('kernc/backtesting.py', 0.5409364700317383, 'finance', 2), ('ranaroussi/quantstats', 0.5287092328071594, 'finance', 1), ('goldmansachs/gs-quant', 0.5250836610794067, 'finance', 0), ('magicstack/uvloop', 0.5239098072052002, 'util', 1), ('cyberpunkmetalhead/binance-volatility-trading-bot', 0.518947184085846, 'crypto', 0), ('bmoscon/cryptofeed', 0.5091555118560791, 'crypto', 3), ('ethereum/py-evm', 0.5067870616912842, 'crypto', 0), ('zvtvz/zvt', 0.5045093297958374, 'finance', 4), ('pallets/quart', 0.5044360160827637, 'web', 1), ('firmai/atspy', 0.5008696913719177, 'time-series', 0)]",5,0.0,,2.46,1,1,11,0,0,12,12,1.0,1.0,90.0,1.0,32
489,gis,https://github.com/cogeotiff/rio-cogeo,[],,[],[],,,,cogeotiff/rio-cogeo,rio-cogeo,275,34,44,Python,https://cogeotiff.github.io/rio-cogeo/,Cloud Optimized GeoTIFF creation and validation plugin for rasterio,cogeotiff,2024-01-11,2018-03-09,307,0.8941012540640966,https://avatars.githubusercontent.com/u/40065466?v=4,Cloud Optimized GeoTIFF creation and validation plugin for rasterio,"['cog', 'cogeotiff', 'geotiff', 'rasterio', 'satellite']","['cog', 'cogeotiff', 'geotiff', 'rasterio', 'satellite']",2024-01-08,"[('cogeotiff/rio-tiler', 0.6072856783866882, 'gis', 4)]",17,5.0,,0.52,2,2,71,0,0,11,11,2.0,4.0,90.0,2.0,32
1615,data,https://github.com/github/innovationgraph,"['github', 'economy']",,[],[],,,,github/innovationgraph,innovationgraph,248,20,88,Python,https://innovationgraph.github.com/,GitHub Innovation Graph,github,2024-01-10,2023-09-14,19,12.579710144927537,https://avatars.githubusercontent.com/u/9919?v=4,GitHub Innovation Graph,"['data', 'github', 'open-data']","['data', 'economy', 'github', 'open-data']",2024-01-11,"[('fastai/ghapi', 0.5455352067947388, 'util', 1), ('zenodo/zenodo', 0.5064103007316589, 'util', 0)]",3,1.0,,0.12,4,3,4,0,2,6,2,4.0,0.0,90.0,0.0,32
851,jupyter,https://github.com/jupyter/nbformat,[],,[],[],,,,jupyter/nbformat,nbformat,231,154,22,Python,http://nbformat.readthedocs.io/,Reference implementation of the Jupyter Notebook format,jupyter,2024-01-09,2015-04-09,459,0.502486016159105,https://avatars.githubusercontent.com/u/7388996?v=4,Reference implementation of the Jupyter Notebook format,[],[],2024-01-02,"[('jupyter/nbconvert', 0.810336709022522, 'jupyter', 0), ('jupyter/notebook', 0.7493022680282593, 'jupyter', 0), ('cohere-ai/notebooks', 0.7138713598251343, 'llm', 0), ('jupyter-widgets/ipywidgets', 0.7056740522384644, 'jupyter', 0), ('jakevdp/pythondatasciencehandbook', 0.6972000598907471, 'study', 0), ('mwouts/jupytext', 0.6959807276725769, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.6620615720748901, 'jupyter', 0), ('ipython/ipykernel', 0.6578472852706909, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.6576974987983704, 'study', 0), ('jupyterlab/jupyterlab', 0.6569828987121582, 'jupyter', 0), ('jupyter/nbgrader', 0.6341890692710876, 'jupyter', 0), ('quantopian/qgrid', 0.6320311427116394, 'jupyter', 0), ('aws/graph-notebook', 0.6213883757591248, 'jupyter', 0), ('nteract/papermill', 0.618989109992981, 'jupyter', 0), ('jupyter/nbdime', 0.6054288148880005, 'jupyter', 0), ('wesm/pydata-book', 0.6051349639892578, 'study', 0), ('nteract/testbook', 0.5974855422973633, 'jupyter', 0), ('voila-dashboards/voila', 0.5947333574295044, 'jupyter', 0), ('ageron/handson-ml2', 0.5934438705444336, 'ml', 0), ('ipython/ipyparallel', 0.5791874527931213, 'perf', 0), ('computationalmodelling/nbval', 0.5732461214065552, 'jupyter', 0), ('vizzuhq/ipyvizzu', 0.5726070404052734, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5637709498405457, 'jupyter', 0), ('rasbt/watermark', 0.5627220869064331, 'util', 0), ('bloomberg/ipydatagrid', 0.5524267554283142, 'jupyter', 0), ('nbqa-dev/nbqa', 0.5436616539955139, 'jupyter', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5423336029052734, 'jupyter', 0), ('xiaohk/stickyland', 0.5371445417404175, 'jupyter', 0), ('koaning/drawdata', 0.5362401008605957, 'jupyter', 0), ('tkrabel/bamboolib', 0.5312812328338623, 'pandas', 0), ('mamba-org/gator', 0.5302620530128479, 'jupyter', 0), ('python/cpython', 0.5262817740440369, 'util', 0), ('jupyterlite/jupyterlite', 0.5237820744514465, 'jupyter', 0), ('dask/fastparquet', 0.5203571915626526, 'data', 0), ('chaoleili/jupyterlab_tensorboard', 0.5145013928413391, 'jupyter', 0)]",79,7.0,,0.71,15,8,107,0,4,4,4,15.0,13.0,90.0,0.9,32
1886,llm,https://github.com/predibase/llm_distillation_playbook,['llm-distillation'],,[],[],,,,predibase/llm_distillation_playbook,llm_distillation_playbook,214,13,5,Jupyter Notebook,,Practical best practices for distilling large language models.,predibase,2024-01-14,2023-12-06,7,27.236363636363638,https://avatars.githubusercontent.com/u/75280641?v=4,Practical best practices for distilling large language models.,[],['llm-distillation'],2024-01-08,"[('cg123/mergekit', 0.622988224029541, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5744475722312927, 'nlp', 0), ('artidoro/qlora', 0.5419549345970154, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5375890731811523, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5229454040527344, 'study', 0), ('salesforce/xgen', 0.5224753022193909, 'llm', 0), ('bigscience-workshop/biomedical', 0.5187903642654419, 'data', 0), ('juncongmoo/pyllama', 0.5181902647018433, 'llm', 0), ('hiyouga/llama-factory', 0.5162743330001831, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5162742733955383, 'llm', 0), ('amazon-science/dq-bart', 0.5063695311546326, 'nlp', 0), ('young-geng/easylm', 0.5040218234062195, 'llm', 0), ('lianjiatech/belle', 0.500493049621582, 'llm', 0)]",2,0.0,,0.71,4,4,1,0,0,0,0,4.0,0.0,90.0,0.0,32
560,gis,https://github.com/geopandas/pyogrio,[],,[],[],,,,geopandas/pyogrio,pyogrio,209,14,10,Python,https://pyogrio.readthedocs.io,Vectorized vector I/O using OGR,geopandas,2024-01-04,2020-03-27,200,1.042022792022792,https://avatars.githubusercontent.com/u/8130715?v=4,Vectorized vector I/O using OGR,[],[],2024-01-11,[],12,3.0,,1.27,29,17,46,0,6,4,6,29.0,58.0,90.0,2.0,32
982,sim,https://github.com/espressomd/espresso,[],,[],[],,,,espressomd/espresso,espresso,206,181,22,C++,https://espressomd.org,The ESPResSo package,espressomd,2024-01-04,2011-03-25,670,0.3072006817213464,https://avatars.githubusercontent.com/u/689837?v=4,The ESPResSo package,"['c-plus-plus', 'lattice-boltzmann', 'molecular-dynamics', 'physics', 'scientific-computing', 'soft-matter']","['c-plus-plus', 'lattice-boltzmann', 'molecular-dynamics', 'physics', 'scientific-computing', 'soft-matter']",2024-01-08,"[('deepmodeling/deepmd-kit', 0.5804603695869446, 'sim', 1), ('rdkit/rdkit', 0.5796418786048889, 'sim', 1)]",184,4.0,,5.9,65,39,156,0,1,4,1,65.0,56.0,90.0,0.9,32
1272,perf,https://github.com/qdrant/vector-db-benchmark,[],,[],[],,,,qdrant/vector-db-benchmark,vector-db-benchmark,170,42,5,Python,https://qdrant.tech/benchmarks/,Framework for benchmarking vector search engines,qdrant,2024-01-09,2022-07-12,81,2.0987654320987654,https://avatars.githubusercontent.com/u/73504361?v=4,Framework for benchmarking vector search engines,"['benchmark', 'vector-database', 'vector-search', 'vector-search-engine']","['benchmark', 'vector-database', 'vector-search', 'vector-search-engine']",2024-01-12,"[('qdrant/qdrant', 0.582831621170044, 'data', 3), ('qdrant/qdrant-client', 0.5792778134346008, 'util', 3), ('qdrant/qdrant-haystack', 0.5743999481201172, 'data', 0), ('klen/py-frameworks-bench', 0.5516537427902222, 'perf', 1), ('lancedb/lancedb', 0.5436363816261292, 'data', 1), ('facebookresearch/faiss', 0.5264250636100769, 'ml', 1), ('pola-rs/polars', 0.5243891477584839, 'pandas', 0), ('jina-ai/vectordb', 0.5212831497192383, 'data', 2), ('qdrant/fastembed', 0.519282341003418, 'ml', 1), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5176312923431396, 'data', 1), ('pinecone-io/pinecone-python-client', 0.512586772441864, 'data', 1), ('milvus-io/bootcamp', 0.5074758529663086, 'data', 1), ('weaviate/demo-text2vec-openai', 0.5073652267456055, 'util', 1), ('chroma-core/chroma', 0.5014863610267639, 'data', 0)]",9,5.0,,2.79,34,25,18,0,0,1,1,34.0,32.0,90.0,0.9,32
825,pandas,https://github.com/blaze/blaze,[],,[],[],,,,blaze/blaze,blaze,3178,393,195,Python,blaze.pydata.org,NumPy and Pandas interface to Big Data,blaze,2024-01-08,2012-10-26,587,5.408704108922928,https://avatars.githubusercontent.com/u/12833921?v=4,NumPy and Pandas interface to Big Data,[],[],2019-08-15,"[('nalepae/pandarallel', 0.6345915794372559, 'pandas', 0), ('scikit-learn-contrib/sklearn-pandas', 0.626749575138092, 'pandas', 0), ('vaexio/vaex', 0.6084710359573364, 'perf', 0), ('numpy/numpy', 0.6074309349060059, 'math', 0), ('pytables/pytables', 0.5941234230995178, 'data', 0), ('mwaskom/seaborn', 0.5715226531028748, 'viz', 0), ('jmcarpenter2/swifter', 0.5631571412086487, 'pandas', 0), ('pyqtgraph/pyqtgraph', 0.5560141205787659, 'viz', 0), ('scikit-hep/uproot5', 0.5468524098396301, 'data', 0), ('jakevdp/pythondatasciencehandbook', 0.5417280793190002, 'study', 0), ('adamerose/pandasgui', 0.5380495190620422, 'pandas', 0), ('lux-org/lux', 0.5378813743591309, 'viz', 0), ('dask/dask', 0.5343106985092163, 'perf', 0), ('xl0/lovely-numpy', 0.5316489934921265, 'util', 0), ('holoviz/hvplot', 0.5221161246299744, 'pandas', 0), ('geopandas/geopandas', 0.5217344164848328, 'gis', 0), ('holoviz/spatialpandas', 0.5175127387046814, 'pandas', 0), ('ddelange/mapply', 0.513414204120636, 'pandas', 0), ('roban/cosmolopy', 0.5100215673446655, 'sim', 0), ('tkrabel/bamboolib', 0.5050551295280457, 'pandas', 0), ('contextlab/hypertools', 0.5032052397727966, 'ml', 0)]",67,6.0,,0.0,1,1,137,54,0,4,4,1.0,0.0,90.0,0.0,31
1441,ml,https://github.com/hrnet/hrnet-semantic-segmentation,[],,[],[],,,,hrnet/hrnet-semantic-segmentation,HRNet-Semantic-Segmentation,3006,681,56,Python,,The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919,hrnet,2024-01-11,2019-04-09,251,11.97609561752988,https://avatars.githubusercontent.com/u/49397099?v=4,The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919,"['cityscapes', 'high-resolution', 'high-resolution-net', 'hrnets', 'lip', 'pascal-context', 'segmentation', 'segmentation-transformer', 'semantic-segmentation', 'transformer']","['cityscapes', 'high-resolution', 'high-resolution-net', 'hrnets', 'lip', 'pascal-context', 'segmentation', 'segmentation-transformer', 'semantic-segmentation', 'transformer']",2021-05-04,"[('rapidai/rapidocr', 0.5680984258651733, 'data', 0), ('microsoft/swin-transformer', 0.5201693177223206, 'ml', 1), ('jaidedai/easyocr', 0.5142496824264526, 'data', 0), ('madmaze/pytesseract', 0.5132443308830261, 'data', 0)]",8,1.0,,0.0,4,0,58,33,0,0,0,4.0,5.0,90.0,1.2,31
993,finance,https://github.com/quantopian/alphalens,[],,[],[],,,,quantopian/alphalens,alphalens,2946,1083,167,Jupyter Notebook,http://quantopian.github.io/alphalens,Performance analysis of predictive (alpha) stock factors,quantopian,2024-01-13,2016-06-03,399,7.372899535216304,https://avatars.githubusercontent.com/u/1393215?v=4,Performance analysis of predictive (alpha) stock factors,"['algorithmic-trading', 'finance', 'jupyter', 'numpy', 'pandas']","['algorithmic-trading', 'finance', 'jupyter', 'numpy', 'pandas']",2020-04-27,"[('gbeced/pyalgotrade', 0.5202894806861877, 'finance', 0)]",25,2.0,,0.0,5,2,93,45,0,2,2,5.0,3.0,90.0,0.6,31
112,ml,https://github.com/teamhg-memex/eli5,[],,[],[],,,,teamhg-memex/eli5,eli5,2705,332,66,Jupyter Notebook,http://eli5.readthedocs.io,A library for debugging/inspecting machine learning classifiers and explaining their predictions,teamhg-memex,2024-01-09,2016-09-15,384,7.031191979205347,https://avatars.githubusercontent.com/u/9270052?v=4,A library for debugging/inspecting machine learning classifiers and explaining their predictions,"['crfsuite', 'data-science', 'explanation', 'inspection', 'lightgbm', 'machine-learning', 'nlp', 'scikit-learn', 'xgboost']","['crfsuite', 'data-science', 'explanation', 'inspection', 'lightgbm', 'machine-learning', 'nlp', 'scikit-learn', 'xgboost']",2020-01-22,"[('tensorflow/data-validation', 0.6825962066650391, 'ml-ops', 0), ('marcotcr/lime', 0.6452601552009583, 'ml-interpretability', 0), ('seldonio/alibi', 0.6379401683807373, 'ml-interpretability', 1), ('huggingface/evaluate', 0.6364562511444092, 'ml', 1), ('carla-recourse/carla', 0.6284937262535095, 'ml', 1), ('districtdatalabs/yellowbrick', 0.6274762749671936, 'ml', 2), ('csinva/imodels', 0.6143868565559387, 'ml', 3), ('rasbt/machine-learning-book', 0.607665479183197, 'study', 2), ('oegedijk/explainerdashboard', 0.602367103099823, 'ml-interpretability', 0), ('featurelabs/featuretools', 0.5933169722557068, 'ml', 3), ('selfexplainml/piml-toolbox', 0.5864848494529724, 'ml-interpretability', 0), ('wandb/client', 0.5802046656608582, 'ml', 2), ('patchy631/machine-learning', 0.5695274472236633, 'ml', 0), ('pair-code/lit', 0.5625553727149963, 'ml-interpretability', 1), ('tensorflow/lucid', 0.5620574355125427, 'ml-interpretability', 1), ('interpretml/interpret', 0.5581210851669312, 'ml-interpretability', 2), ('pycaret/pycaret', 0.5573887825012207, 'ml', 2), ('rasbt/mlxtend', 0.553990364074707, 'ml', 2), ('polyaxon/datatile', 0.5490293502807617, 'pandas', 1), ('gradio-app/gradio', 0.5446726679801941, 'viz', 2), ('linkedin/fasttreeshap', 0.542718231678009, 'ml', 3), ('firmai/industry-machine-learning', 0.5391788482666016, 'study', 2), ('automl/auto-sklearn', 0.5375847220420837, 'ml', 1), ('microsoft/nni', 0.5334489941596985, 'ml', 2), ('eugeneyan/testing-ml', 0.5324820280075073, 'testing', 1), ('rafiqhasan/auto-tensorflow', 0.5304438471794128, 'ml-dl', 1), ('scikit-learn/scikit-learn', 0.5276551842689514, 'ml', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5232833623886108, 'study', 1), ('maif/shapash', 0.5209477543830872, 'ml', 1), ('microsoft/flaml', 0.5194395184516907, 'ml', 3), ('koaning/scikit-lego', 0.5188755989074707, 'ml', 2), ('xplainable/xplainable', 0.517871618270874, 'ml-interpretability', 2), ('whylabs/whylogs', 0.511576235294342, 'util', 2), ('reloadware/reloadium', 0.5111801624298096, 'profiling', 0), ('koaning/human-learn', 0.5102199912071228, 'data', 2), ('mckinsey/causalnex', 0.5101348161697388, 'math', 2), ('alexmojaki/snoop', 0.5058972835540771, 'debug', 0), ('catboost/catboost', 0.5058757066726685, 'ml', 2), ('scikit-learn-contrib/imbalanced-learn', 0.5050359964370728, 'ml', 2), ('shankarpandala/lazypredict', 0.5024835467338562, 'ml', 1), ('ionelmc/python-hunter', 0.5018780827522278, 'debug', 0), ('ashleve/lightning-hydra-template', 0.5004812479019165, 'util', 0)]",14,6.0,,0.0,1,1,89,48,0,4,4,1.0,0.0,90.0,0.0,31
175,util,https://github.com/liiight/notifiers,[],,[],[],,,,liiight/notifiers,notifiers,2564,100,30,Python,http://notifiers.readthedocs.io/,The easy way to send notifications,liiight,2024-01-13,2017-06-01,347,7.373870172555464,,The easy way to send notifications,"['notification-service', 'notifications', 'notifier']","['notification-service', 'notifications', 'notifier']",2022-07-14,"[('caronc/apprise', 0.6810635924339294, 'util', 3)]",19,2.0,,0.0,4,0,81,18,0,4,4,4.0,1.0,90.0,0.2,31
852,jupyter,https://github.com/jupyter/nbviewer,[],,[],[],,,,jupyter/nbviewer,nbviewer,2146,551,93,Python,https://nbviewer.jupyter.org,nbconvert as a web service: Render Jupyter Notebooks as static web pages,jupyter,2024-01-13,2012-08-05,599,3.5809296781883195,https://avatars.githubusercontent.com/u/7388996?v=4,nbconvert as a web service: Render Jupyter Notebooks as static web pages,"['jupyter', 'jupyter-notebook', 'nbconvert']","['jupyter', 'jupyter-notebook', 'nbconvert']",2023-01-27,"[('voila-dashboards/voila', 0.6448253989219666, 'jupyter', 2), ('jupyter-widgets/ipywidgets', 0.5482261180877686, 'jupyter', 0), ('jupyter/notebook', 0.547979474067688, 'jupyter', 2), ('jupyterlite/jupyterlite', 0.5257112979888916, 'jupyter', 1), ('vizzuhq/ipyvizzu', 0.5107673406600952, 'jupyter', 2), ('jupyter-widgets/ipyleaflet', 0.5015178918838501, 'gis', 1), ('jupyterlab/jupyterlab-desktop', 0.5004984736442566, 'jupyter', 2)]",96,5.0,,0.04,6,1,139,12,0,0,0,6.0,4.0,90.0,0.7,31
586,template,https://github.com/buuntu/fastapi-react,[],,[],[],,,,buuntu/fastapi-react,fastapi-react,1945,315,41,Python,,"🚀   Cookiecutter Template for FastAPI + React Projects.  Using PostgreSQL, SQLAlchemy, and Docker",buuntu,2024-01-12,2020-03-21,201,9.656028368794326,,"🚀   Cookiecutter Template for FastAPI + React Projects.  Using PostgreSQL, SQLAlchemy, and Docker","['boilerplate', 'cookiecutter', 'docker', 'fastapi', 'full-stack', 'jwt', 'nginx', 'oauth2', 'postgres', 'react', 'react-admin', 'sqlalchemy', 'typescript']","['boilerplate', 'cookiecutter', 'docker', 'fastapi', 'full-stack', 'jwt', 'nginx', 'oauth2', 'postgres', 'react', 'react-admin', 'sqlalchemy', 'typescript']",2022-02-18,"[('lyz-code/cookiecutter-python-project', 0.6677143573760986, 'template', 1), ('tedivm/robs_awesome_python_template', 0.6574198603630066, 'template', 0), ('ionelmc/cookiecutter-pylibrary', 0.6523356437683105, 'template', 1), ('cookiecutter/cookiecutter', 0.6335521340370178, 'template', 1), ('s3rius/fastapi-template', 0.6214925646781921, 'web', 2), ('giswqs/pypackage', 0.6124856472015381, 'template', 1), ('crmne/cookiecutter-modern-datascience', 0.5824753046035767, 'template', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5327474474906921, 'template', 4), ('rawheel/fastapi-boilerplate', 0.531681478023529, 'web', 4), ('aeternalis-ingenium/fastapi-backend-template', 0.5084453225135803, 'web', 4)]",13,5.0,,0.0,0,0,46,23,0,1,1,0.0,0.0,90.0,0.0,31
987,nlp,https://github.com/thudm/p-tuning-v2,[],,[],[],,,,thudm/p-tuning-v2,P-tuning-v2,1790,173,30,Python,,An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks,thudm,2024-01-13,2021-10-14,119,14.952267303102625,https://avatars.githubusercontent.com/u/48590610?v=4,An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks,"['natural-language-processing', 'p-tuning', 'parameter-efficient-learning', 'pretrained-language-model', 'prompt-tuning']","['natural-language-processing', 'p-tuning', 'parameter-efficient-learning', 'pretrained-language-model', 'prompt-tuning']",2023-10-20,"[('keirp/automatic_prompt_engineer', 0.6158888339996338, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5705534815788269, 'study', 1), ('kyegomez/tree-of-thoughts', 0.5489909052848816, 'llm', 1), ('srush/minichain', 0.5268715620040894, 'llm', 0), ('microsoft/unilm', 0.5134304761886597, 'nlp', 0), ('ofa-sys/ofa', 0.5087725520133972, 'llm', 1)]",3,1.0,,0.04,7,0,27,3,0,0,0,7.0,1.0,90.0,0.1,31
608,testing,https://github.com/teemu/pytest-sugar,[],,[],[],,,,teemu/pytest-sugar,pytest-sugar,1189,70,18,Python,,"a plugin for py.test that changes the default look and feel of py.test (e.g. progressbar, show tests that fail instantly)",teemu,2024-01-13,2013-06-25,553,2.150090415913201,,"a plugin for py.test that changes the default look and feel of py.test (e.g. progressbar, show tests that fail instantly)","['pytest', 'pytest-plugin', 'pytest-sugar', 'testing']","['pytest', 'pytest-plugin', 'pytest-sugar', 'testing']",2023-08-08,"[('pytest-dev/pytest-xdist', 0.6500891447067261, 'testing', 2), ('computationalmodelling/nbval', 0.6413612365722656, 'jupyter', 3), ('ionelmc/pytest-benchmark', 0.6360356211662292, 'testing', 1), ('samuelcolvin/pytest-pretty', 0.5765059590339661, 'testing', 1), ('pytest-dev/pytest-cov', 0.5729233622550964, 'testing', 1), ('pytest-dev/pytest-asyncio', 0.5544842481613159, 'testing', 2), ('pytest-dev/pytest', 0.5432929992675781, 'testing', 1), ('pytest-dev/pytest-mock', 0.5402851700782776, 'testing', 1), ('samuelcolvin/dirty-equals', 0.5335487723350525, 'util', 1), ('kiwicom/pytest-recording', 0.5215980410575867, 'testing', 2), ('pytest-dev/pytest-bdd', 0.5115352869033813, 'testing', 0), ('rockhopper-technologies/enlighten', 0.5103371739387512, 'term', 0), ('taverntesting/tavern', 0.5043500661849976, 'testing', 2)]",50,7.0,,0.27,1,0,128,5,1,1,1,1.0,1.0,90.0,1.0,31
883,ml-dl,https://github.com/xl0/lovely-tensors,[],,[],[],,,,xl0/lovely-tensors,lovely-tensors,1017,14,11,Jupyter Notebook,https://xl0.github.io/lovely-tensors,"Tensors, ready for human consumption",xl0,2024-01-13,2022-10-07,68,14.83125,,"Tensors, ready for human consumption","['deep-learning', 'pytorch', 'statistics', 'visualization']","['deep-learning', 'pytorch', 'statistics', 'visualization']",2023-04-27,"[('xl0/lovely-numpy', 0.7344928979873657, 'util', 3), ('ggerganov/ggml', 0.6742641925811768, 'ml', 0), ('pytorch/ignite', 0.6465728282928467, 'ml-dl', 2), ('keras-team/keras', 0.6338521242141724, 'ml-dl', 2), ('arogozhnikov/einops', 0.6286166310310364, 'ml-dl', 2), ('tensorly/tensorly', 0.61644446849823, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.613914966583252, 'study', 2), ('pytorch/pytorch', 0.6134235858917236, 'ml-dl', 1), ('skorch-dev/skorch', 0.612391471862793, 'ml-dl', 1), ('intel/intel-extension-for-pytorch', 0.610737144947052, 'perf', 2), ('google/tf-quant-finance', 0.5893839597702026, 'finance', 0), ('huggingface/datasets', 0.5892137289047241, 'nlp', 2), ('ageron/handson-ml2', 0.583730936050415, 'ml', 0), ('cvxgrp/pymde', 0.5830146074295044, 'ml', 2), ('facebookresearch/pytorch3d', 0.5774106383323669, 'ml-dl', 0), ('tensorflow/tensorflow', 0.5675086975097656, 'ml-dl', 1), ('pyg-team/pytorch_geometric', 0.5648797154426575, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.5606785416603088, 'ml-dl', 0), ('d2l-ai/d2l-en', 0.5590056777000427, 'study', 2), ('blackhc/toma', 0.5582937002182007, 'ml-dl', 1), ('huggingface/accelerate', 0.557181715965271, 'ml', 0), ('tensorflow/mesh', 0.5570968389511108, 'ml-dl', 0), ('tlkh/tf-metal-experiments', 0.5548020005226135, 'perf', 1), ('keras-rl/keras-rl', 0.5526697635650635, 'ml-rl', 0), ('pytorch/captum', 0.5507166385650635, 'ml-interpretability', 0), ('rasbt/deeplearning-models', 0.5497564673423767, 'ml-dl', 0), ('lutzroeder/netron', 0.549081027507782, 'ml', 2), ('tensorlayer/tensorlayer', 0.5486395359039307, 'ml-rl', 1), ('lightly-ai/lightly', 0.5458189845085144, 'ml', 2), ('huggingface/transformers', 0.5455830097198486, 'nlp', 2), ('tensorflow/addons', 0.5444281697273254, 'ml', 1), ('activeloopai/deeplake', 0.5410858392715454, 'ml-ops', 2), ('ashleve/lightning-hydra-template', 0.539811372756958, 'util', 2), ('rasbt/stat453-deep-learning-ss20', 0.5372262597084045, 'study', 0), ('rafiqhasan/auto-tensorflow', 0.5368985533714294, 'ml-dl', 0), ('karpathy/micrograd', 0.5362251400947571, 'study', 0), ('ddbourgin/numpy-ml', 0.5339087843894958, 'ml', 0), ('mosaicml/composer', 0.5325333476066589, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.5321751236915588, 'study', 2), ('nvidia/deeplearningexamples', 0.5299148559570312, 'ml-dl', 2), ('geomstats/geomstats', 0.5296072959899902, 'math', 2), ('explosion/thinc', 0.5290781855583191, 'ml-dl', 2), ('nvidia/apex', 0.5290429592132568, 'ml-dl', 0), ('denys88/rl_games', 0.5228703618049622, 'ml-rl', 2), ('huggingface/exporters', 0.5227699875831604, 'ml', 2), ('nvidia/tensorrt-llm', 0.5217703580856323, 'viz', 0), ('horovod/horovod', 0.5179717540740967, 'ml-ops', 2), ('pytorch/torchrec', 0.5139912366867065, 'ml-dl', 2), ('pytorch/rl', 0.5124273896217346, 'ml-rl', 1), ('tensorflow/similarity', 0.5119929313659668, 'ml-dl', 1), ('microsoft/onnxruntime', 0.5118368864059448, 'ml', 2), ('pytorch/data', 0.5113422274589539, 'data', 0), ('tensorflow/tensor2tensor', 0.5108433961868286, 'ml', 1), ('roboflow/supervision', 0.5090969800949097, 'ml', 2), ('huggingface/huggingface_hub', 0.5074052214622498, 'ml', 2), ('koaning/embetter', 0.5069031119346619, 'data', 0), ('danielegrattarola/spektral', 0.5056509375572205, 'ml-dl', 1), ('nicolas-chaulet/torch-points3d', 0.5040701031684875, 'ml', 0), ('tensorflow/lucid', 0.5038846731185913, 'ml-interpretability', 1), ('lucidrains/imagen-pytorch', 0.5031198263168335, 'ml-dl', 1), ('allenai/allennlp', 0.5030158162117004, 'nlp', 2), ('patrick-kidger/torchtyping', 0.5010529160499573, 'typing', 1), ('gradio-app/gradio', 0.5005459785461426, 'viz', 1)]",1,1.0,,0.1,0,0,15,9,0,14,14,0.0,0.0,90.0,0.0,31
340,term,https://github.com/jquast/blessed,[],,[],[],,,,jquast/blessed,blessed,999,69,26,Python,http://pypi.python.org/pypi/blessed,"Blessed is an easy, practical library for making python terminal apps",jquast,2024-01-11,2014-03-01,517,1.9307012700165653,,"Blessed is an easy, practical library for making python terminal apps","['cli', 'curses', 'terminal']","['cli', 'curses', 'terminal']",2023-12-17,"[('urwid/urwid', 0.7145931720733643, 'term', 0), ('pygamelib/pygamelib', 0.6294499635696411, 'gamedev', 0), ('willmcgugan/rich', 0.6152134537696838, 'term', 1), ('tmbo/questionary', 0.6140781044960022, 'term', 1), ('hoffstadt/dearpygui', 0.6031554937362671, 'gui', 0), ('pypy/pypy', 0.6016967296600342, 'util', 0), ('google/python-fire', 0.5881098508834839, 'term', 1), ('tiangolo/typer', 0.5800941586494446, 'term', 2), ('willmcgugan/textual', 0.5734678506851196, 'term', 2), ('xonsh/xonsh', 0.569438099861145, 'util', 2), ('tartley/colorama', 0.5634984970092773, 'util', 1), ('pexpect/pexpect', 0.5613400340080261, 'util', 0), ('pallets/click', 0.5604668855667114, 'term', 2), ('beeware/toga', 0.5573833584785461, 'gui', 0), ('r0x0r/pywebview', 0.5557140707969666, 'gui', 0), ('pytoolz/toolz', 0.5486643314361572, 'util', 0), ('textualize/trogon', 0.5472238063812256, 'term', 2), ('pyglet/pyglet', 0.5451313853263855, 'gamedev', 0), ('federicoceratto/dashing', 0.5442036986351013, 'term', 1), ('pypa/virtualenv', 0.5412343144416809, 'util', 0), ('rockhopper-technologies/enlighten', 0.5384596586227417, 'term', 0), ('samuelcolvin/python-devtools', 0.5346581935882568, 'debug', 0), ('python/cpython', 0.5344061255455017, 'util', 0), ('pyscript/pyscript-cli', 0.5302552580833435, 'web', 0), ('parthjadhav/tkinter-designer', 0.5266092419624329, 'gui', 0), ('erotemic/ubelt', 0.5253154635429382, 'util', 0), ('hugovk/pypistats', 0.5230196118354797, 'util', 1), ('carpedm20/emoji', 0.5172060132026672, 'util', 0), ('kellyjonbrazil/jc', 0.5097572207450867, 'util', 1), ('webpy/webpy', 0.5048279166221619, 'web', 0), ('pygame/pygame', 0.504092812538147, 'gamedev', 0), ('inducer/pudb', 0.5037537217140198, 'debug', 0), ('pypa/hatch', 0.5016807913780212, 'util', 1), ('pypa/installer', 0.5001348853111267, 'util', 0)]",26,3.0,,0.42,9,6,120,1,1,5,1,9.0,10.0,90.0,1.1,31
1667,util,https://github.com/requests/toolbelt,[],,[],[],,,,requests/toolbelt,toolbelt,966,184,21,Python,https://toolbelt.readthedocs.org,A toolbelt of useful classes and functions to be used with python-requests,requests,2024-01-12,2013-12-29,526,1.8355048859934853,https://avatars.githubusercontent.com/u/2805331?v=4,A toolbelt of useful classes and functions to be used with python-requests,"['http', 'python-requests', 'toolbox']","['http', 'python-requests', 'toolbox']",2023-10-12,"[('psf/requests', 0.7315183281898499, 'web', 2), ('getsentry/responses', 0.7001904249191284, 'testing', 0), ('encode/httpx', 0.6686779856681824, 'web', 1), ('falconry/falcon', 0.6299983263015747, 'web', 1), ('simple-salesforce/simple-salesforce', 0.610306441783905, 'data', 0), ('eleutherai/pyfra', 0.6041449308395386, 'ml', 0), ('roniemartinez/dude', 0.6033374071121216, 'util', 0), ('scrapy/scrapy', 0.5953943133354187, 'data', 0), ('cherrypy/cherrypy', 0.5835244059562683, 'web', 1), ('alirezamika/autoscraper', 0.5791863799095154, 'data', 0), ('encode/uvicorn', 0.5721771717071533, 'web', 1), ('masoniteframework/masonite', 0.5709607005119324, 'web', 0), ('hugapi/hug', 0.5709561705589294, 'util', 1), ('pytoolz/toolz', 0.5654045939445496, 'util', 0), ('pallets/werkzeug', 0.5638414025306702, 'web', 1), ('webpy/webpy', 0.563819169998169, 'web', 0), ('clips/pattern', 0.5543271899223328, 'nlp', 0), ('landscapeio/prospector', 0.5488244891166687, 'util', 0), ('taverntesting/tavern', 0.545853853225708, 'testing', 1), ('pylons/webob', 0.5443535447120667, 'web', 0), ('bottlepy/bottle', 0.5425326228141785, 'web', 0), ('secdev/scapy', 0.5415241122245789, 'util', 0), ('ethereum/web3.py', 0.5374890565872192, 'crypto', 0), ('googleapis/google-api-python-client', 0.5348877906799316, 'util', 0), ('locustio/locust', 0.5336475968360901, 'testing', 1), ('neoteroi/blacksheep', 0.5285502672195435, 'web', 1), ('pympler/pympler', 0.5279673337936401, 'perf', 0), ('pallets/flask', 0.5181925296783447, 'web', 0), ('nedbat/coveragepy', 0.5164726376533508, 'testing', 0), ('amaargiru/pyroad', 0.5161598324775696, 'study', 0), ('agronholm/apscheduler', 0.5136269330978394, 'util', 0), ('wolever/parameterized', 0.5131222009658813, 'testing', 0), ('nv7-github/googlesearch', 0.5127140879631042, 'util', 0), ('pyston/pyston', 0.5103037357330322, 'util', 0), ('aio-libs/aiohttp', 0.5097265839576721, 'web', 1), ('pallets/quart', 0.5077387094497681, 'web', 0), ('lukasschwab/arxiv.py', 0.5077224969863892, 'util', 0), ('pylons/waitress', 0.5072819590568542, 'web', 0), ('mitmproxy/pdoc', 0.5041638016700745, 'util', 0), ('hoffstadt/dearpygui', 0.5012592077255249, 'gui', 0), ('pylons/pyramid', 0.5011575222015381, 'web', 0)]",69,6.0,,0.15,6,2,122,3,0,2,2,6.0,3.0,90.0,0.5,31
1406,llm,https://github.com/ctlllll/llm-toolmaker,['language-model'],Large Language Models as Tool Makers,[],[],,,,ctlllll/llm-toolmaker,LLM-ToolMaker,961,94,16,Jupyter Notebook,,,ctlllll,2024-01-12,2023-05-25,35,26.908,,Large Language Models as Tool Makers,[],['language-model'],2023-05-29,"[('conceptofmind/toolformer', 0.7394540309906006, 'llm', 1), ('hannibal046/awesome-llm', 0.708982527256012, 'study', 1), ('keirp/automatic_prompt_engineer', 0.699556291103363, 'llm', 1), ('ai21labs/lm-evaluation', 0.6955636143684387, 'llm', 1), ('lianjiatech/belle', 0.6910099983215332, 'llm', 0), ('guidance-ai/guidance', 0.6901283860206604, 'llm', 1), ('freedomintelligence/llmzoo', 0.6779150366783142, 'llm', 1), ('juncongmoo/pyllama', 0.6501331925392151, 'llm', 0), ('cg123/mergekit', 0.6498943567276001, 'llm', 0), ('lm-sys/fastchat', 0.6395042538642883, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.6329768300056458, 'llm', 1), ('openbmb/toolbench', 0.6306527853012085, 'llm', 0), ('prefecthq/langchain-prefect', 0.6256077885627747, 'llm', 0), ('neulab/prompt2model', 0.623613178730011, 'llm', 1), ('young-geng/easylm', 0.6142538785934448, 'llm', 1), ('next-gpt/next-gpt', 0.5953162908554077, 'llm', 0), ('oobabooga/text-generation-webui', 0.5950998663902283, 'llm', 1), ('bigscience-workshop/biomedical', 0.5948196053504944, 'data', 0), ('jonasgeiping/cramming', 0.593092143535614, 'nlp', 1), ('microsoft/autogen', 0.5929686427116394, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5928089022636414, 'llm', 0), ('infinitylogesh/mutate', 0.5922251343727112, 'nlp', 1), ('guardrails-ai/guardrails', 0.5916648507118225, 'llm', 0), ('yizhongw/self-instruct', 0.5800374150276184, 'llm', 1), ('salesforce/xgen', 0.5751272439956665, 'llm', 1), ('togethercomputer/redpajama-data', 0.5702430605888367, 'llm', 0), ('thudm/codegeex', 0.5676085948944092, 'llm', 0), ('reasoning-machines/pal', 0.5670881867408752, 'llm', 1), ('databrickslabs/dolly', 0.5645289421081543, 'llm', 0), ('optimalscale/lmflow', 0.5597103238105774, 'llm', 1), ('lupantech/chameleon-llm', 0.5588576197624207, 'llm', 1), ('hazyresearch/h3', 0.5581068992614746, 'llm', 0), ('huggingface/text-generation-inference', 0.5557337403297424, 'llm', 0), ('hwchase17/langchain', 0.5530260801315308, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5522134304046631, 'llm', 1), ('night-chen/toolqa', 0.5460361242294312, 'llm', 0), ('1rgs/jsonformer', 0.5433797240257263, 'llm', 0), ('predibase/llm_distillation_playbook', 0.5375890731811523, 'llm', 0), ('srush/minichain', 0.5372619032859802, 'llm', 0), ('openlmlab/moss', 0.5362043976783752, 'llm', 1), ('thudm/chatglm-6b', 0.536123514175415, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5329034924507141, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5325697660446167, 'llm', 0), ('thudm/chatglm2-6b', 0.5311850309371948, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5290946364402771, 'nlp', 0), ('eleutherai/the-pile', 0.5270031690597534, 'data', 0), ('aiwaves-cn/agents', 0.5234825611114502, 'nlp', 1), ('openlmlab/leval', 0.5215489864349365, 'llm', 1), ('llmware-ai/llmware', 0.5208345055580139, 'llm', 0), ('explosion/spacy-llm', 0.520075261592865, 'llm', 0), ('mlc-ai/web-llm', 0.5185114145278931, 'llm', 1), ('facebookresearch/shepherd', 0.5185016393661499, 'llm', 1), ('jalammar/ecco', 0.5147423148155212, 'ml-interpretability', 0), ('minedojo/voyager', 0.5137326121330261, 'llm', 0), ('eugeneyan/obsidian-copilot', 0.5133623480796814, 'llm', 0), ('nat/openplayground', 0.5115349292755127, 'llm', 1), ('paperswithcode/galai', 0.5114402174949646, 'llm', 1), ('spcl/graph-of-thoughts', 0.5085147023200989, 'llm', 0), ('dylanhogg/llmgraph', 0.5081648826599121, 'ml', 0), ('hazyresearch/ama_prompting', 0.5080617666244507, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5077611804008484, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5077611804008484, 'llm', 0), ('hiyouga/llama-factory', 0.5054224133491516, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5054222941398621, 'llm', 1), ('langchain-ai/langgraph', 0.501153290271759, 'llm', 0), ('epfllm/meditron', 0.5010691285133362, 'llm', 1), ('lvwerra/trl', 0.5007656812667847, 'llm', 0)]",1,1.0,,0.06,2,0,8,8,0,0,0,2.0,0.0,90.0,0.0,31
488,viz,https://github.com/luispedro/mahotas,[],,[],[],,,,luispedro/mahotas,mahotas,816,155,50,Python,https://mahotas.rtfd.io,Computer Vision in Python,luispedro,2024-01-12,2010-01-31,730,1.1173708920187793,,Computer Vision in Python,"['c-plus-plus', 'computer-vision', 'numpy']","['c-plus-plus', 'computer-vision', 'numpy']",2024-01-08,"[('scikit-image/scikit-image', 0.6524010300636292, 'util', 1), ('xl0/lovely-numpy', 0.5749441981315613, 'util', 1), ('google/jax', 0.5661518573760986, 'ml', 1), ('mdbloice/augmentor', 0.5612508654594421, 'ml', 0), ('numpy/numpy', 0.5569568872451782, 'math', 1), ('roboflow/supervision', 0.5504482388496399, 'ml', 1), ('python-pillow/pillow', 0.5384292602539062, 'util', 0), ('open-mmlab/mmcv', 0.5330637693405151, 'ml', 1), ('lightly-ai/lightly', 0.5195291638374329, 'ml', 1), ('dfki-ric/pytransform3d', 0.5193596482276917, 'math', 0), ('cupy/cupy', 0.5168439745903015, 'math', 1)]",34,5.0,,0.15,1,0,170,0,0,4,4,1.0,1.0,90.0,1.0,31
18,nlp,https://github.com/explosion/spacy-streamlit,[],,[],[],,,,explosion/spacy-streamlit,spacy-streamlit,734,113,20,Python,https://share.streamlit.io/ines/spacy-streamlit-demo/master/app.py,👑 spaCy building blocks and visualizers for Streamlit apps,explosion,2024-01-10,2020-06-23,188,3.904255319148936,https://avatars.githubusercontent.com/u/20011530?v=4,👑 spaCy building blocks and visualizers for Streamlit apps,"['dependency-parsing', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'ner', 'nlp', 'part-of-speech-tagging', 'spacy', 'streamlit', 'text-classification', 'tokenization', 'visualizer', 'visualizers', 'word-vectors']","['dependency-parsing', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'ner', 'nlp', 'part-of-speech-tagging', 'spacy', 'streamlit', 'text-classification', 'tokenization', 'visualizer', 'visualizers', 'word-vectors']",2023-08-02,"[('streamlit/streamlit', 0.6851550340652466, 'viz', 2), ('explosion/spacy-models', 0.6529020071029663, 'nlp', 4), ('alphasecio/langchain-examples', 0.6193504929542542, 'llm', 1), ('explosion/spacy-transformers', 0.5857328772544861, 'llm', 4), ('sloria/textblob', 0.5445199012756348, 'nlp', 2), ('explosion/spacy-stanza', 0.5425686240196228, 'nlp', 4), ('huggingface/neuralcoref', 0.5396462082862854, 'nlp', 3), ('jbesomi/texthero', 0.5377371907234192, 'nlp', 2), ('nltk/nltk', 0.5315085053443909, 'nlp', 3), ('neuralmagic/sparseml', 0.5314697623252869, 'ml-dl', 1), ('gradio-app/gradio', 0.5299880504608154, 'viz', 1), ('flairnlp/flair', 0.5156773328781128, 'nlp', 4), ('lucidrains/toolformer-pytorch', 0.5099772214889526, 'llm', 0), ('openai/tiktoken', 0.5080421566963196, 'nlp', 0), ('run-llama/rags', 0.5017317533493042, 'llm', 1)]",14,5.0,,0.1,1,0,43,6,2,4,2,1.0,1.0,90.0,1.0,31
1408,llm,https://github.com/salesforce/xgen,[],,[],[],,,,salesforce/xgen,xgen,695,35,12,Python,,Salesforce open-source LLMs with 8k sequence length.,salesforce,2024-01-12,2023-06-23,31,22.013574660633484,https://avatars.githubusercontent.com/u/453694?v=4,Salesforce open-source LLMs with 8k sequence length.,"['language-model', 'large-language-models', 'llm', 'nlp']","['language-model', 'large-language-models', 'llm', 'nlp']",2023-10-16,"[('eugeneyan/open-llms', 0.6812090873718262, 'study', 2), ('young-geng/easylm', 0.6715541481971741, 'llm', 2), ('mooler0410/llmspracticalguide', 0.6607375741004944, 'study', 2), ('eleutherai/the-pile', 0.6535871624946594, 'data', 1), ('explosion/spacy-llm', 0.6350138187408447, 'llm', 3), ('cg123/mergekit', 0.6309936046600342, 'llm', 1), ('infinitylogesh/mutate', 0.621751070022583, 'nlp', 1), ('lianjiatech/belle', 0.6193069219589233, 'llm', 0), ('bigscience-workshop/petals', 0.6189985871315002, 'data', 2), ('thudm/chatglm2-6b', 0.6184314489364624, 'llm', 2), ('salesforce/codet5', 0.6172817945480347, 'nlp', 2), ('juncongmoo/pyllama', 0.6149995923042297, 'llm', 0), ('ray-project/ray-llm', 0.6136285662651062, 'llm', 2), ('artidoro/qlora', 0.6102448105812073, 'llm', 1), ('bobazooba/xllm', 0.607836902141571, 'llm', 2), ('hannibal046/awesome-llm', 0.6004391312599182, 'study', 1), ('alpha-vllm/llama2-accessory', 0.5997952222824097, 'llm', 0), ('tigerlab-ai/tiger', 0.5921275615692139, 'llm', 2), ('nomic-ai/gpt4all', 0.590821385383606, 'llm', 1), ('vllm-project/vllm', 0.58912593126297, 'llm', 1), ('hiyouga/llama-factory', 0.5888428688049316, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.5888428092002869, 'llm', 3), ('sjtu-ipads/powerinfer', 0.5852330327033997, 'llm', 2), ('microsoft/torchscale', 0.5822688937187195, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5792416334152222, 'perf', 0), ('jzhang38/tinyllama', 0.57785564661026, 'llm', 1), ('ai21labs/lm-evaluation', 0.5776482224464417, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5755364894866943, 'llm', 2), ('ctlllll/llm-toolmaker', 0.5751272439956665, 'llm', 1), ('li-plus/chatglm.cpp', 0.5746297240257263, 'llm', 2), ('dylanhogg/llmgraph', 0.5694348812103271, 'ml', 1), ('night-chen/toolqa', 0.5676834583282471, 'llm', 1), ('next-gpt/next-gpt', 0.5664035081863403, 'llm', 2), ('agenta-ai/agenta', 0.5658103227615356, 'llm', 2), ('freedomintelligence/llmzoo', 0.5646532773971558, 'llm', 1), ('bytedance/lightseq', 0.5642438530921936, 'nlp', 0), ('microsoft/autogen', 0.5639118552207947, 'llm', 0), ('hwchase17/langchain', 0.5602133274078369, 'llm', 1), ('nat/openplayground', 0.5570892095565796, 'llm', 1), ('epfllm/meditron', 0.5559400320053101, 'llm', 1), ('huggingface/text-generation-inference', 0.5519874691963196, 'llm', 1), ('alphasecio/langchain-examples', 0.5513124465942383, 'llm', 1), ('mlc-ai/web-llm', 0.5496227741241455, 'llm', 2), ('bentoml/openllm', 0.5479778051376343, 'ml-ops', 1), ('llmware-ai/llmware', 0.5470395684242249, 'llm', 2), ('lm-sys/fastchat', 0.546430230140686, 'llm', 1), ('argilla-io/argilla', 0.544439971446991, 'nlp', 2), ('hegelai/prompttools', 0.5444273352622986, 'llm', 1), ('run-llama/llama-hub', 0.542940616607666, 'data', 1), ('confident-ai/deepeval', 0.5417569875717163, 'testing', 2), ('nebuly-ai/nebullvm', 0.5392612814903259, 'perf', 2), ('togethercomputer/redpajama-data', 0.5379053950309753, 'llm', 0), ('prefecthq/langchain-prefect', 0.5343795418739319, 'llm', 1), ('aiwaves-cn/agents', 0.5333874225616455, 'nlp', 2), ('lightning-ai/lit-llama', 0.5283926129341125, 'llm', 1), ('opengvlab/omniquant', 0.5267922878265381, 'llm', 2), ('squeezeailab/squeezellm', 0.5263859629631042, 'llm', 2), ('deepset-ai/haystack', 0.525870144367218, 'llm', 3), ('tatsu-lab/stanford_alpaca', 0.52476966381073, 'llm', 1), ('ibm/dromedary', 0.5233420729637146, 'llm', 1), ('predibase/llm_distillation_playbook', 0.5224753022193909, 'llm', 0), ('citadel-ai/langcheck', 0.5217398405075073, 'llm', 1), ('zilliztech/gptcache', 0.5199717283248901, 'llm', 1), ('predibase/lorax', 0.5184742212295532, 'llm', 1), ('conceptofmind/toolformer', 0.5180943608283997, 'llm', 1), ('shishirpatil/gorilla', 0.5161482691764832, 'llm', 1), ('pathwaycom/llm-app', 0.5143515467643738, 'llm', 1), ('neuml/txtai', 0.5134238600730896, 'nlp', 4), ('baichuan-inc/baichuan-13b', 0.5127484798431396, 'llm', 1), ('microsoft/llama-2-onnx', 0.5118889212608337, 'llm', 1), ('cstankonrad/long_llama', 0.5117976665496826, 'llm', 1), ('microsoft/jarvis', 0.5117494463920593, 'llm', 0), ('princeton-nlp/alce', 0.5106900930404663, 'llm', 0), ('oobabooga/text-generation-webui', 0.5097959041595459, 'llm', 1), ('bigscience-workshop/biomedical', 0.5077841877937317, 'data', 0), ('ludwig-ai/ludwig', 0.5066179037094116, 'ml-ops', 1), ('iryna-kondr/scikit-llm', 0.5054061412811279, 'llm', 1), ('titanml/takeoff', 0.5039275884628296, 'llm', 2), ('thudm/codegeex', 0.5032574534416199, 'llm', 0), ('openlmlab/leval', 0.5019434690475464, 'llm', 1), ('langchain-ai/langgraph', 0.5001147389411926, 'llm', 0)]",2,0.0,,0.67,2,1,7,3,0,0,0,2.0,1.0,90.0,0.5,31
875,time-series,https://github.com/autoviml/auto_ts,[],,[],[],,,,autoviml/auto_ts,Auto_TS,655,106,17,Jupyter Notebook,,"Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.",autoviml,2024-01-13,2020-02-15,206,3.1730103806228374,,"Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.","['arima', 'auto-arima', 'auto-sklearn', 'auto-timeseries', 'autokeras', 'automl', 'autosklearn', 'prophet', 'sklearn', 'time-series', 'time-series-analysis', 'tpot']","['arima', 'auto-arima', 'auto-sklearn', 'auto-timeseries', 'autokeras', 'automl', 'autosklearn', 'prophet', 'sklearn', 'time-series', 'time-series-analysis', 'tpot']",2023-12-03,"[('dask/dask-ml', 0.5662448406219482, 'ml', 0), ('nixtla/statsforecast', 0.5572653412818909, 'time-series', 4), ('winedarksea/autots', 0.5450201034545898, 'time-series', 2), ('microsoft/flaml', 0.5320088863372803, 'ml', 1), ('alkaline-ml/pmdarima', 0.5241935849189758, 'time-series', 2), ('dask/distributed', 0.5237195491790771, 'perf', 0), ('pytroll/satpy', 0.5144683718681335, 'gis', 0), ('prefecthq/prefect-dask', 0.5042782425880432, 'util', 0), ('hi-primus/optimus', 0.5023893713951111, 'ml-ops', 0)]",11,3.0,,0.31,5,5,48,1,0,1,1,5.0,6.0,90.0,1.2,31
1151,util,https://github.com/erdewit/nest_asyncio,[],,[],[],,,,erdewit/nest_asyncio,nest_asyncio,599,54,17,Python,,Patch asyncio to allow nested event loops,erdewit,2024-01-13,2018-09-07,281,2.1273465246067986,,Patch asyncio to allow nested event loops,"['asyncio', 'event-loop', 'nested', 'recursive']","['asyncio', 'event-loop', 'nested', 'recursive']",2023-11-27,"[('magicstack/uvloop', 0.6544142365455627, 'util', 2), ('alex-sherman/unsync', 0.597553551197052, 'util', 0), ('pytest-dev/pytest-asyncio', 0.5122057199478149, 'testing', 1)]",12,4.0,,0.19,3,3,65,2,1,2,1,3.0,7.0,90.0,2.3,31
667,perf,https://github.com/brandtbucher/specialist,['cpython'],,[],[],,,,brandtbucher/specialist,specialist,596,10,9,Python,,"Visualize CPython 3.11's specializing, adaptive interpreter. :fire:",brandtbucher,2024-01-14,2022-06-01,86,6.8618421052631575,,"Visualize CPython 3.11's specializing, adaptive interpreter. 🔥",[],['cpython'],2023-08-06,"[('python/cpython', 0.7123744487762451, 'util', 1), ('faster-cpython/ideas', 0.6769110560417175, 'perf', 1), ('faster-cpython/tools', 0.6422561407089233, 'perf', 1), ('p403n1x87/austin', 0.5802419185638428, 'profiling', 0), ('markshannon/faster-cpython', 0.5744246244430542, 'perf', 0), ('ipython/ipyparallel', 0.5722348093986511, 'perf', 0), ('pyston/pyston', 0.5647898316383362, 'util', 0), ('alexmojaki/heartrate', 0.56365966796875, 'debug', 0), ('facebookincubator/cinder', 0.5538285374641418, 'perf', 1), ('altair-viz/altair', 0.5505017042160034, 'viz', 0), ('pypy/pypy', 0.5478411912918091, 'util', 1), ('vizzuhq/ipyvizzu', 0.5436729192733765, 'jupyter', 0), ('jalammar/ecco', 0.5420230031013489, 'ml-interpretability', 0), ('cohere-ai/notebooks', 0.5358579158782959, 'llm', 0), ('bokeh/bokeh', 0.5352582931518555, 'viz', 0), ('rustpython/rustpython', 0.5334833860397339, 'util', 0), ('has2k1/plotnine', 0.5308057069778442, 'viz', 0), ('maartenbreddels/ipyvolume', 0.5239526033401489, 'jupyter', 0), ('holoviz/holoviz', 0.5191949605941772, 'viz', 0), ('adafruit/circuitpython', 0.5155614018440247, 'util', 1), ('plotly/plotly.py', 0.5150900483131409, 'viz', 0), ('gotcha/ipdb', 0.5149388313293457, 'debug', 0), ('urwid/urwid', 0.5062293410301208, 'term', 0), ('pygments/pygments', 0.5001950263977051, 'util', 0)]",3,2.0,,0.29,1,1,20,5,3,8,3,1.0,1.0,90.0,1.0,31
477,gis,https://github.com/holoviz/geoviews,[],,[],[],,,,holoviz/geoviews,geoviews,546,73,25,Python,http://geoviews.org,"Simple, concise geographical visualization in Python",holoviz,2024-01-11,2016-04-19,406,1.3448275862068966,https://avatars.githubusercontent.com/u/51678735?v=4,"Simple, concise geographical visualization in Python","['cartopy', 'geographic-visualizations', 'geoviews', 'holoviews', 'holoviz', 'plotting']","['cartopy', 'geographic-visualizations', 'geoviews', 'holoviews', 'holoviz', 'plotting']",2023-12-22,"[('holoviz/holoviz', 0.7218708992004395, 'viz', 3), ('scitools/cartopy', 0.7182220220565796, 'gis', 1), ('residentmario/geoplot', 0.6962321400642395, 'gis', 0), ('raphaelquast/eomaps', 0.6664366126060486, 'gis', 2), ('artelys/geonetworkx', 0.663130521774292, 'gis', 0), ('holoviz/holoviews', 0.6530880331993103, 'viz', 3), ('altair-viz/altair', 0.6453191041946411, 'viz', 0), ('pyproj4/pyproj', 0.6277405023574829, 'gis', 0), ('matplotlib/matplotlib', 0.6272979974746704, 'viz', 1), ('gregorhd/mapcompare', 0.624692440032959, 'gis', 0), ('opengeos/leafmap', 0.6069517135620117, 'gis', 0), ('has2k1/plotnine', 0.6033970713615417, 'viz', 1), ('holoviz/hvplot', 0.6014821529388428, 'pandas', 3), ('bokeh/bokeh', 0.5875713229179382, 'viz', 1), ('mwaskom/seaborn', 0.5874853134155273, 'viz', 0), ('enthought/mayavi', 0.5856287479400635, 'viz', 0), ('dfki-ric/pytransform3d', 0.5822384357452393, 'math', 0), ('giswqs/geemap', 0.5793654918670654, 'gis', 0), ('geopandas/geopandas', 0.5721185803413391, 'gis', 0), ('vispy/vispy', 0.571336567401886, 'viz', 0), ('scitools/iris', 0.5698901414871216, 'gis', 0), ('plotly/plotly.py', 0.5691211223602295, 'viz', 0), ('marceloprates/prettymaps', 0.5674756765365601, 'viz', 0), ('contextlab/hypertools', 0.5654189586639404, 'ml', 0), ('kanaries/pygwalker', 0.5505314469337463, 'pandas', 0), ('cuemacro/chartpy', 0.5426660180091858, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5372338891029358, 'jupyter', 1), ('pysal/pysal', 0.535015881061554, 'gis', 0), ('geopandas/contextily', 0.5272794961929321, 'gis', 0), ('alexmojaki/heartrate', 0.5235070586204529, 'debug', 0), ('man-group/dtale', 0.5216120481491089, 'viz', 0), ('holoviz/panel', 0.521112859249115, 'viz', 2), ('earthlab/earthpy', 0.5206630229949951, 'gis', 0), ('vizzuhq/ipyvizzu', 0.5115540623664856, 'jupyter', 1), ('pyglet/pyglet', 0.5069261789321899, 'gamedev', 0), ('federicoceratto/dashing', 0.5021084547042847, 'term', 0)]",30,3.0,,1.31,35,29,94,1,4,11,4,35.0,13.0,90.0,0.4,31
906,ml,https://github.com/cvxgrp/pymde,[],,[],[],,,,cvxgrp/pymde,pymde,501,27,9,Python,https://pymde.org,Minimum-distortion embedding with PyTorch,cvxgrp,2024-01-09,2020-11-29,165,3.031114952463267,https://avatars.githubusercontent.com/u/2957335?v=4,Minimum-distortion embedding with PyTorch,"['cuda', 'dimensionality-reduction', 'embedding', 'feature-vectors', 'gpu', 'graph-embedding', 'machine-learning', 'pytorch', 'visualization']","['cuda', 'dimensionality-reduction', 'embedding', 'feature-vectors', 'gpu', 'graph-embedding', 'machine-learning', 'pytorch', 'visualization']",2023-07-01,"[('pyg-team/pytorch_geometric', 0.6165646910667419, 'ml-dl', 1), ('koaning/embetter', 0.6085571646690369, 'data', 0), ('blackhc/toma', 0.588315486907959, 'ml-dl', 3), ('graphistry/pygraphistry', 0.5851930379867554, 'data', 2), ('xl0/lovely-tensors', 0.5830146074295044, 'ml-dl', 2), ('pytorch/torchrec', 0.5692107677459717, 'ml-dl', 3), ('rentruewang/koila', 0.5673205852508545, 'ml', 2), ('pytorch/ignite', 0.5655942559242249, 'ml-dl', 2), ('cupy/cupy', 0.5531930327415466, 'math', 2), ('qdrant/fastembed', 0.5445080995559692, 'ml', 0), ('neuralmagic/sparseml', 0.5381171107292175, 'ml-dl', 1), ('skorch-dev/skorch', 0.5335275530815125, 'ml-dl', 2), ('facebookresearch/pytorch3d', 0.5327015519142151, 'ml-dl', 0), ('lightly-ai/lightly', 0.5311744809150696, 'ml', 2), ('danielegrattarola/spektral', 0.520326554775238, 'ml-dl', 0), ('huggingface/accelerate', 0.5172093510627747, 'ml', 0), ('lutzroeder/netron', 0.5167331695556641, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.5150303244590759, 'perf', 2), ('ddbourgin/numpy-ml', 0.51487797498703, 'ml', 1), ('timdettmers/bitsandbytes', 0.509398341178894, 'util', 1), ('oml-team/open-metric-learning', 0.5077699422836304, 'ml', 1), ('rasbt/machine-learning-book', 0.5070154070854187, 'study', 2), ('neuralmagic/deepsparse', 0.5062499642372131, 'nlp', 0), ('pytorch/pytorch', 0.5061772465705872, 'ml-dl', 2), ('isl-org/open3d', 0.5052924156188965, 'sim', 5), ('koaning/whatlies', 0.5038027167320251, 'nlp', 0), ('mrdbourke/pytorch-deep-learning', 0.5034160017967224, 'study', 2), ('pytorch/captum', 0.5022547245025635, 'ml-interpretability', 0)]",10,7.0,,0.04,1,0,38,7,0,6,6,1.0,1.0,90.0,1.0,31
937,web,https://github.com/aeternalis-ingenium/fastapi-backend-template,[],,[],[],,,,aeternalis-ingenium/fastapi-backend-template,FastAPI-Backend-Template,486,76,10,Python,,"A backend project template with FastAPI, PostgreSQL with asynchronous SQLAlchemy 2.0, Alembic for asynchronous database migration, and Docker.",aeternalis-ingenium,2024-01-12,2022-12-05,60,8.080760095011877,,"A backend project template with FastAPI, PostgreSQL with asynchronous SQLAlchemy 2.0, Alembic for asynchronous database migration, and Docker.","['alembic', 'asynchronous', 'asyncpg', 'codecov', 'coverage', 'docker', 'docker-compose', 'fastapi', 'githubactions', 'jwt', 'postgresql', 'pre-commit', 'pytest', 'sqlalchemy']","['alembic', 'asynchronous', 'asyncpg', 'codecov', 'coverage', 'docker', 'docker-compose', 'fastapi', 'githubactions', 'jwt', 'postgresql', 'pre-commit', 'pytest', 'sqlalchemy']",2023-12-13,"[('rawheel/fastapi-boilerplate', 0.7447752356529236, 'web', 6), ('tiangolo/full-stack-fastapi-postgresql', 0.6168069839477539, 'template', 4), ('aminalaee/sqladmin', 0.6071468591690063, 'data', 2), ('s3rius/fastapi-template', 0.5711674690246582, 'web', 3), ('sqlalchemy/alembic', 0.5487288236618042, 'data', 1), ('collerek/ormar', 0.5430356860160828, 'data', 3), ('tiangolo/sqlmodel', 0.5365174412727356, 'data', 2), ('backtick-se/cowait', 0.5334479212760925, 'util', 1), ('sqlalchemy/sqlalchemy', 0.5284538269042969, 'data', 1), ('starlite-api/starlite', 0.5256733298301697, 'web', 0), ('buuntu/fastapi-react', 0.5084453225135803, 'template', 4), ('darribas/gds_env', 0.5019727349281311, 'gis', 1)]",7,3.0,,0.27,11,5,14,1,0,0,0,11.0,1.0,90.0,0.1,31
1389,util,https://github.com/pycqa/docformatter,['pep257'],,[],[],,,,pycqa/docformatter,docformatter,478,57,9,Python,https://pypi.python.org/pypi/docformatter,Formats docstrings to follow PEP 257,pycqa,2024-01-07,2012-05-26,609,0.784341303328645,https://avatars.githubusercontent.com/u/8749848?v=4,Formats docstrings to follow PEP 257,"['autoformat', 'docstring', 'formatter']","['autoformat', 'docstring', 'formatter', 'pep257']",2023-10-15,"[('danielnoord/pydocstringformatter', 0.8163774013519287, 'util', 2), ('hhatto/autopep8', 0.591210126876831, 'util', 1), ('mkdocstrings/mkdocstrings', 0.5333818793296814, 'util', 0), ('mkdocstrings/python', 0.503516674041748, 'util', 0)]",30,4.0,,1.31,12,3,142,3,12,5,12,12.0,19.0,90.0,1.6,31
1772,jupyter,https://github.com/bloomberg/ipydatagrid,[],,[],[],,,,bloomberg/ipydatagrid,ipydatagrid,467,47,16,TypeScript,,Fast Datagrid widget for the Jupyter Notebook and JupyterLab,bloomberg,2024-01-12,2019-07-19,236,1.9740338164251208,https://avatars.githubusercontent.com/u/1416818?v=4,Fast Datagrid widget for the Jupyter Notebook and JupyterLab,"['datagrid', 'datatable', 'grid', 'jupyter-notebooks', 'jupyterlab-extension']","['datagrid', 'datatable', 'grid', 'jupyter-notebooks', 'jupyterlab-extension']",2023-12-09,"[('jupyter-widgets/ipywidgets', 0.6741766929626465, 'jupyter', 2), ('quantopian/qgrid', 0.6440353989601135, 'jupyter', 0), ('tkrabel/bamboolib', 0.6406834721565247, 'pandas', 0), ('aws/graph-notebook', 0.5870513916015625, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.5657516717910767, 'jupyter', 0), ('jupyter/notebook', 0.5550519824028015, 'jupyter', 0), ('jupyter/nbformat', 0.5524267554283142, 'jupyter', 0), ('koaning/drawdata', 0.551867663860321, 'jupyter', 0), ('holoviz/panel', 0.5404045581817627, 'viz', 0), ('voila-dashboards/voila', 0.5380343794822693, 'jupyter', 1), ('ipython/ipyparallel', 0.5364363193511963, 'perf', 0), ('jakevdp/pythondatasciencehandbook', 0.5277616381645203, 'study', 0), ('vizzuhq/ipyvizzu', 0.522794783115387, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.518395185470581, 'jupyter', 0), ('xiaohk/stickyland', 0.5169107913970947, 'jupyter', 1), ('pyqtgraph/pyqtgraph', 0.5133781433105469, 'viz', 0), ('jazzband/tablib', 0.5118611454963684, 'data', 0), ('jupyter-widgets/ipyleaflet', 0.5112629532814026, 'gis', 1), ('mwouts/jupytext', 0.5105863809585571, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.5007188320159912, 'jupyter', 0)]",19,4.0,,1.12,17,7,55,3,4,9,4,17.0,12.0,90.0,0.7,31
740,study,https://github.com/bayesianmodelingandcomputationinpython/bookcode_edition1,[],,[],[],,,,bayesianmodelingandcomputationinpython/bookcode_edition1,BookCode_Edition1,456,120,18,Jupyter Notebook,https://www.bayesiancomputationbook.com,,bayesianmodelingandcomputationinpython,2024-01-13,2021-08-17,128,3.5625,https://avatars.githubusercontent.com/u/84690770?v=4,bayesianmodelingandcomputationinpython/BookCode_Edition1,[],[],2023-12-28,"[('pymc-devs/pymc3', 0.6229053735733032, 'ml', 0), ('gerdm/prml', 0.5888375639915466, 'study', 0), ('pytorch/botorch', 0.5796522498130798, 'ml-dl', 0), ('probml/pyprobml', 0.5301855802536011, 'ml', 0), ('pyro-ppl/pyro', 0.5214691162109375, 'ml-dl', 0), ('scikit-optimize/scikit-optimize', 0.5192668437957764, 'ml', 0)]",10,4.0,,0.19,9,6,29,1,0,0,0,9.0,12.0,90.0,1.3,31
1629,data,https://github.com/databrickslabs/dbx,[],,[],[],,,,databrickslabs/dbx,dbx,411,114,21,Python,https://dbx.readthedocs.io,🧱 Databricks CLI eXtensions - aka dbx is a CLI tool for development and advanced Databricks workflows management.,databrickslabs,2024-01-04,2020-03-03,204,2.014705882352941,https://avatars.githubusercontent.com/u/49501376?v=4,🧱 Databricks CLI eXtensions - aka dbx is a CLI tool for development and advanced Databricks workflows management.,"['ci', 'cicd', 'databricks', 'databricks-api', 'databricks-cli', 'mlops']","['ci', 'cicd', 'databricks', 'databricks-api', 'databricks-cli', 'mlops']",2023-09-19,"[('databricks/dbt-databricks', 0.6466124057769775, 'data', 1), ('hyperqueryhq/whale', 0.5740757584571838, 'data', 0), ('dagster-io/dagster', 0.5274744033813477, 'ml-ops', 1), ('airbytehq/airbyte', 0.5199562907218933, 'data', 0), ('mage-ai/mage-ai', 0.5128315091133118, 'ml-ops', 0), ('airbnb/omniduct', 0.5089248418807983, 'data', 0)]",32,2.0,,0.62,21,4,47,4,11,17,11,21.0,21.0,90.0,1.0,31
1072,data,https://github.com/bigscience-workshop/biomedical,[],,[],[],,,,bigscience-workshop/biomedical,biomedical,399,106,28,Python,,Tools for curating biomedical training data for large-scale language modeling ,bigscience-workshop,2024-01-09,2021-09-16,123,3.225173210161663,https://avatars.githubusercontent.com/u/82455566?v=4,Tools for curating biomedical training data for large-scale language modeling ,[],[],2023-12-06,"[('qanastek/drbert', 0.6520806550979614, 'llm', 0), ('ai21labs/lm-evaluation', 0.6412531137466431, 'llm', 0), ('togethercomputer/redpajama-data', 0.6301681995391846, 'llm', 0), ('cg123/mergekit', 0.6185329556465149, 'llm', 0), ('freedomintelligence/llmzoo', 0.6107045412063599, 'llm', 0), ('hannibal046/awesome-llm', 0.6027185916900635, 'study', 0), ('ctlllll/llm-toolmaker', 0.5948196053504944, 'llm', 0), ('lm-sys/fastchat', 0.589521050453186, 'llm', 0), ('epfllm/meditron', 0.5808714032173157, 'llm', 0), ('openbmb/toolbench', 0.5767196416854858, 'llm', 0), ('infinitylogesh/mutate', 0.5588589310646057, 'nlp', 0), ('jonasgeiping/cramming', 0.5536470413208008, 'nlp', 0), ('eleutherai/lm-evaluation-harness', 0.5494688153266907, 'llm', 0), ('juncongmoo/pyllama', 0.5467652678489685, 'llm', 0), ('eleutherai/the-pile', 0.5412442684173584, 'data', 0), ('guidance-ai/guidance', 0.5397950410842896, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5387159585952759, 'nlp', 0), ('huggingface/text-generation-inference', 0.5347291231155396, 'llm', 0), ('yueyu1030/attrprompt', 0.5249584317207336, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5243796706199646, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5243796706199646, 'llm', 0), ('lianjiatech/belle', 0.5243057012557983, 'llm', 0), ('huggingface/evaluate', 0.5192505121231079, 'ml', 0), ('predibase/llm_distillation_playbook', 0.5187903642654419, 'llm', 0), ('anthropics/evals', 0.5112786889076233, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5104791522026062, 'study', 0), ('explosion/spacy-llm', 0.510345458984375, 'llm', 0), ('srush/minichain', 0.5102963447570801, 'llm', 0), ('koaning/embetter', 0.5097063183784485, 'data', 0), ('bytedance/lightseq', 0.5078558325767517, 'nlp', 0), ('salesforce/xgen', 0.5077841877937317, 'llm', 0), ('maartengr/bertopic', 0.5077448487281799, 'nlp', 0), ('yizhongw/self-instruct', 0.5062916278839111, 'llm', 0), ('microsoft/unilm', 0.5054998397827148, 'nlp', 0), ('openai/finetune-transformer-lm', 0.5047228336334229, 'llm', 0), ('databrickslabs/dolly', 0.5042515397071838, 'llm', 0)]",56,6.0,,0.71,10,8,28,1,0,0,0,10.0,1.0,90.0,0.1,31
1091,ml-interpretability,https://github.com/alignmentresearch/tuned-lens,[],,[],[],,,,alignmentresearch/tuned-lens,tuned-lens,318,32,6,Python,https://tuned-lens.readthedocs.io/en/latest/,Tools for understanding how transformer predictions are built layer-by-layer,alignmentresearch,2024-01-11,2022-10-03,69,4.599173553719008,https://avatars.githubusercontent.com/u/108596820?v=4,Tools for understanding how transformer predictions are built layer-by-layer,"['machine-learning', 'pytorch', 'transformers']","['machine-learning', 'pytorch', 'transformers']",2023-09-11,"[('eleutherai/knowledge-neurons', 0.6871256828308105, 'ml-interpretability', 1), ('huggingface/transformers', 0.6621186137199402, 'nlp', 2), ('opengeos/earthformer', 0.6345276832580566, 'gis', 0), ('cdpierse/transformers-interpret', 0.6314489245414734, 'ml-interpretability', 2), ('nvidia/megatron-lm', 0.6293410658836365, 'llm', 0), ('huggingface/optimum', 0.5732378363609314, 'ml', 2), ('marella/ctransformers', 0.5659533143043518, 'nlp', 1), ('nielsrogge/transformers-tutorials', 0.5491597652435303, 'study', 2), ('karpathy/mingpt', 0.5471770763397217, 'llm', 0), ('ist-daslab/gptq', 0.5439256429672241, 'llm', 0), ('apple/ml-ane-transformers', 0.5322527289390564, 'ml', 0), ('bigscience-workshop/megatron-deepspeed', 0.515650749206543, 'llm', 0), ('microsoft/megatron-deepspeed', 0.515650749206543, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.5096346735954285, 'nlp', 1), ('aistream-peelout/flow-forecast', 0.5054931640625, 'time-series', 1)]",5,3.0,,2.69,3,1,16,4,5,5,5,3.0,2.0,90.0,0.7,31
997,finance,https://github.com/lballabio/quantlib-swig,[],,[],[],,,,lballabio/quantlib-swig,QuantLib-SWIG,311,273,38,SWIG,,QuantLib wrappers to other languages,lballabio,2024-01-11,2015-12-17,423,0.7339851652056641,,QuantLib wrappers to other languages,['quantitative-finance'],['quantitative-finance'],2024-01-12,"[('enthought/pyql', 0.5862199664115906, 'finance', 0), ('goldmansachs/gs-quant', 0.5069200396537781, 'finance', 0), ('ta-lib/ta-lib-python', 0.505255401134491, 'finance', 1)]",82,2.0,,2.44,18,16,98,0,5,5,5,18.0,13.0,90.0,0.7,31
893,gis,https://github.com/amazon-science/earth-forecasting-transformer,[],,[],[],,,,amazon-science/earth-forecasting-transformer,earth-forecasting-transformer,310,53,12,Jupyter Notebook,,Official implementation of Earthformer,amazon-science,2024-01-10,2022-09-12,72,4.297029702970297,https://avatars.githubusercontent.com/u/70298811?v=4,Official implementation of Earthformer,[],[],2023-07-16,"[('opengeos/earthformer', 0.5462614893913269, 'gis', 0)]",7,4.0,,0.1,7,2,16,6,0,0,0,7.0,24.0,90.0,3.4,31
1400,finance,https://github.com/chancefocus/pixiu,"['language-model', 'llm']",,[],[],,,,chancefocus/pixiu,PIXIU,300,26,6,Python,,"This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).",chancefocus,2024-01-14,2023-06-02,34,8.677685950413224,https://avatars.githubusercontent.com/u/26429143?v=4,"This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).","['aifinance', 'chatgpt', 'fintech', 'gpt-4', 'large-language-models', 'llama', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'pixiu', 'question-answering', 'sentiment-analysis', 'stock-price-prediction', 'text-classification']","['aifinance', 'chatgpt', 'fintech', 'gpt-4', 'language-model', 'large-language-models', 'llama', 'llm', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'pixiu', 'question-answering', 'sentiment-analysis', 'stock-price-prediction', 'text-classification']",2023-12-03,"[('ai4finance-foundation/fingpt', 0.7477177381515503, 'finance', 8), ('ai4finance-foundation/finrl', 0.5721709132194519, 'finance', 1), ('mindsdb/mindsdb', 0.5329363346099854, 'data', 2), ('microsoft/qlib', 0.5296194553375244, 'finance', 2), ('argilla-io/argilla', 0.5267559289932251, 'nlp', 5), ('llmware-ai/llmware', 0.5248722434043884, 'llm', 4), ('numerai/example-scripts', 0.5142377018928528, 'finance', 1), ('nebuly-ai/nebullvm', 0.5107274651527405, 'perf', 2)]",7,1.0,,2.69,11,8,8,1,0,0,0,11.0,3.0,90.0,0.3,31
651,template,https://github.com/sqlalchemy/mako,[],,[],[],,,,sqlalchemy/mako,mako,293,55,12,Python,https://www.makotemplates.org,Mako Templates for Python,sqlalchemy,2024-01-04,2018-11-26,270,1.084611316763617,https://avatars.githubusercontent.com/u/6043126?v=4,Mako Templates for Python,[],[],2023-11-08,"[('pyscaffold/pyscaffold', 0.5707492828369141, 'template', 0), ('martinheinz/python-project-blueprint', 0.5644561648368835, 'template', 0), ('pallets/flask', 0.5588720440864563, 'web', 0), ('eugeneyan/python-collab-template', 0.5552263855934143, 'template', 0), ('klen/muffin', 0.5490294694900513, 'web', 0), ('pallets/jinja', 0.54783034324646, 'util', 0), ('pypy/pypy', 0.5476346015930176, 'util', 0), ('eleutherai/pyfra', 0.5347551107406616, 'ml', 0), ('pytoolz/toolz', 0.530997097492218, 'util', 0), ('pylons/pyramid', 0.5201253890991211, 'web', 0), ('hoffstadt/dearpygui', 0.5178695917129517, 'gui', 0), ('python/cpython', 0.5141631960868835, 'util', 0), ('brandon-rhodes/python-patterns', 0.5129835605621338, 'util', 0), ('bottlepy/bottle', 0.5123538970947266, 'web', 0), ('google/latexify_py', 0.5123310685157776, 'util', 0), ('ta-lib/ta-lib-python', 0.511563241481781, 'finance', 0), ('kubeflow/fairing', 0.5085417628288269, 'ml-ops', 0), ('connorferster/handcalcs', 0.5080623626708984, 'jupyter', 0), ('masoniteframework/masonite', 0.5073601603507996, 'web', 0), ('hhatto/autopep8', 0.5068827867507935, 'util', 0), ('xrudelis/pytrait', 0.505092203617096, 'util', 0), ('pdm-project/pdm', 0.5048580765724182, 'util', 0), ('pypa/hatch', 0.5044662356376648, 'util', 0)]",59,7.0,,0.08,3,2,62,2,1,13,1,3.0,9.0,90.0,3.0,31
1355,time-series,https://github.com/wilsonrljr/sysidentpy,['dynamical-systems'],,[],[],,,,wilsonrljr/sysidentpy,sysidentpy,282,55,12,Python,https://sysidentpy.org,A Python Package For System Identification Using NARMAX Models,wilsonrljr,2024-01-05,2019-03-13,254,1.1065022421524664,,A Python Package For System Identification Using NARMAX Models,"['data-science', 'dynamical-systems', 'machine-learning', 'narmax', 'narx', 'system-identification', 'time-series']","['data-science', 'dynamical-systems', 'machine-learning', 'narmax', 'narx', 'system-identification', 'time-series']",2023-11-22,"[('dynamicslab/pysindy/', 0.6205930113792419, 'math', 3), ('alkaline-ml/pmdarima', 0.5545415878295898, 'time-series', 2), ('artemyk/dynpy', 0.5530008673667908, 'sim', 0), ('unit8co/darts', 0.5214325785636902, 'time-series', 3), ('firmai/atspy', 0.5202825665473938, 'time-series', 1), ('ta-lib/ta-lib-python', 0.5129168033599854, 'finance', 0), ('pycaret/pycaret', 0.5120651125907898, 'ml', 3)]",12,7.0,,2.19,13,9,59,2,4,3,4,13.0,12.0,90.0,0.9,31
856,ml-ops,https://github.com/astronomer/airflow-chart,[],,[],[],,,,astronomer/airflow-chart,airflow-chart,261,93,47,Python,,A Helm chart to install Apache Airflow on Kubernetes,astronomer,2024-01-13,2020-01-22,209,1.2437031994554117,https://avatars.githubusercontent.com/u/12449437?v=4,A Helm chart to install Apache Airflow on Kubernetes,"['airflow', 'apache-airflow', 'helm-chart', 'kubernetes']","['airflow', 'apache-airflow', 'helm-chart', 'kubernetes']",2023-12-14,"[('astronomer/astronomer', 0.8572540283203125, 'ml-ops', 2)]",49,3.0,,1.42,16,15,48,1,22,28,22,16.0,9.0,90.0,0.6,31
1471,data,https://github.com/piccolo-orm/piccolo_admin,[],,[],[],,,,piccolo-orm/piccolo_admin,piccolo_admin,256,32,7,Python,https://piccolo-orm.com/ecosystem/,A powerful web admin for your database.,piccolo-orm,2024-01-13,2019-06-25,240,1.0666666666666667,https://avatars.githubusercontent.com/u/45742130?v=4,A powerful web admin for your database.,"['admin', 'asgi', 'asyncio', 'cms', 'content-management-system', 'dashboard', 'database', 'fastapi', 'piccolo', 'postgresql', 'sqlite', 'starlette', 'vuejs']","['admin', 'asgi', 'asyncio', 'cms', 'content-management-system', 'dashboard', 'database', 'fastapi', 'piccolo', 'postgresql', 'sqlite', 'starlette', 'vuejs']",2023-11-18,"[('tiangolo/full-stack-fastapi-postgresql', 0.6423637866973877, 'template', 2), ('aminalaee/sqladmin', 0.6254207491874695, 'data', 5), ('fastapi-admin/fastapi-admin', 0.6113201975822449, 'web', 3), ('zenodo/zenodo', 0.5565163493156433, 'util', 1), ('coleifer/peewee', 0.5553250908851624, 'data', 1), ('prefecthq/server', 0.5307790040969849, 'util', 0), ('tiangolo/sqlmodel', 0.5287275910377502, 'data', 1), ('django/django', 0.5213547348976135, 'web', 0), ('simonw/datasette', 0.5212988257408142, 'data', 2), ('airbytehq/airbyte', 0.5102675557136536, 'data', 1), ('wagtail/wagtail', 0.5042932033538818, 'web', 1)]",17,5.0,,1.38,30,26,55,2,24,28,24,30.0,17.0,90.0,0.6,31
1549,llm,https://github.com/rcgai/simplyretrieve,[],,[],[],,,,rcgai/simplyretrieve,SimplyRetrieve,175,15,6,Python,,"Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.",rcgai,2024-01-12,2023-07-24,27,6.447368421052632,,"Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.","['artificial-intelligence', 'chat-ai', 'generative-ai', 'huggingface', 'large-language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'prompt-engineering', 'retrieval-augmented-generation', 'retrieval-centric-generation']","['artificial-intelligence', 'chat-ai', 'generative-ai', 'huggingface', 'large-language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'prompt-engineering', 'retrieval-augmented-generation', 'retrieval-centric-generation']",2023-12-22,"[('embedchain/embedchain', 0.6877291202545166, 'llm', 0), ('deepset-ai/haystack', 0.67171311378479, 'llm', 4), ('larsbaunwall/bricky', 0.6707216501235962, 'llm', 0), ('deeppavlov/deeppavlov', 0.6620361804962158, 'nlp', 3), ('rasahq/rasa', 0.660693347454071, 'llm', 3), ('nomic-ai/gpt4all', 0.6575261950492859, 'llm', 0), ('weaviate/verba', 0.6520878076553345, 'llm', 0), ('cheshire-cat-ai/core', 0.6481109261512756, 'llm', 0), ('togethercomputer/openchatkit', 0.6417977213859558, 'nlp', 0), ('openlmlab/moss', 0.6414903998374939, 'llm', 2), ('lm-sys/fastchat', 0.6361554265022278, 'llm', 0), ('chatarena/chatarena', 0.6356537342071533, 'llm', 3), ('fasteval/fasteval', 0.6337692141532898, 'llm', 0), ('krohling/bondai', 0.6295303702354431, 'llm', 0), ('minimaxir/simpleaichat', 0.6199681162834167, 'llm', 0), ('run-llama/rags', 0.6102337837219238, 'llm', 0), ('nvidia/nemo', 0.6101846098899841, 'nlp', 1), ('gunthercox/chatterbot', 0.5998932123184204, 'nlp', 1), ('llmware-ai/llmware', 0.5904434323310852, 'llm', 5), ('pathwaycom/llm-app', 0.5839216113090515, 'llm', 2), ('prefecthq/marvin', 0.5794979929924011, 'nlp', 0), ('microsoft/generative-ai-for-beginners', 0.5786988139152527, 'study', 2), ('intel/intel-extension-for-transformers', 0.5712392330169678, 'perf', 0), ('intellabs/fastrag', 0.5627588629722595, 'nlp', 2), ('deep-diver/llm-as-chatbot', 0.5600287914276123, 'llm', 0), ('facebookresearch/parlai', 0.5574244856834412, 'nlp', 0), ('langchain-ai/chat-langchain', 0.5566864013671875, 'llm', 0), ('hwchase17/langchain', 0.5519610643386841, 'llm', 0), ('microsoft/autogen', 0.5516322255134583, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5365450978279114, 'llm', 1), ('argilla-io/argilla', 0.5302114486694336, 'nlp', 3), ('dylanhogg/llmgraph', 0.5281891226768494, 'ml', 0), ('bigscience-workshop/promptsource', 0.5266952514648438, 'nlp', 3), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5226764678955078, 'llm', 0), ('marqo-ai/marqo', 0.5212767124176025, 'ml', 3), ('microsoft/lmops', 0.5208574533462524, 'llm', 1), ('databrickslabs/dolly', 0.5198461413383484, 'llm', 0), ('openai/chatgpt-retrieval-plugin', 0.5177233815193176, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.5177233815193176, 'nlp', 0), ('eugeneyan/obsidian-copilot', 0.5125769972801208, 'llm', 3), ('blinkdl/chatrwkv', 0.5114522576332092, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5069754123687744, 'nlp', 0), ('nebuly-ai/nebullvm', 0.5062447786331177, 'perf', 2), ('killianlucas/open-interpreter', 0.5058812499046326, 'llm', 0), ('mindsdb/mindsdb', 0.5031827688217163, 'data', 3), ('openai/gpt-discord-bot', 0.5008484125137329, 'llm', 0), ('aiwaves-cn/agents', 0.5003217458724976, 'nlp', 0)]",2,0.0,,2.38,2,0,6,1,4,8,4,2.0,6.0,90.0,3.0,31
1341,data,https://github.com/prefecthq/prefect-aws,['aws'],,[],[],,,,prefecthq/prefect-aws,prefect-aws,83,39,11,Python,https://PrefectHQ.github.io/prefect-aws/,Prefect integrations with AWS.,prefecthq,2023-12-29,2022-01-04,108,0.7685185185185185,https://avatars.githubusercontent.com/u/39270919?v=4,Prefect integrations with AWS.,"['aws', 'prefect']","['aws', 'prefect']",2024-01-05,"[('aws/aws-sdk-pandas', 0.6139408946037292, 'pandas', 1), ('boto/boto3', 0.5230756402015686, 'util', 1), ('pynamodb/pynamodb', 0.5191414952278137, 'data', 1), ('rhinosecuritylabs/pacu', 0.5137910842895508, 'security', 1), ('prefecthq/prefect-dbt', 0.512976348400116, 'ml-ops', 1)]",34,4.0,,1.42,50,32,25,0,18,15,18,50.0,43.0,90.0,0.9,31
1659,data,https://github.com/unstructured-io/unstructured-inference,"['unstructured', 'inference', 'pipeline']",Hosted model inference code for layout parsing models.,[],[],,,,unstructured-io/unstructured-inference,unstructured-inference,61,18,15,Python,,,unstructured-io,2024-01-14,2022-12-20,58,1.0517241379310345,https://avatars.githubusercontent.com/u/108372208?v=4,Hosted model inference code for layout parsing models.,[],"['inference', 'pipeline', 'unstructured']",2024-01-10,"[('optimalscale/lmflow', 0.5030722618103027, 'llm', 0)]",24,3.0,,3.21,70,54,13,0,71,72,71,70.0,43.0,90.0,0.6,31
1038,term,https://github.com/manrajgrover/halo,[],,[],[],,,,manrajgrover/halo,halo,2816,146,24,Python,,"💫 Beautiful spinners for terminal, IPython and Jupyter",manrajgrover,2024-01-11,2017-09-03,334,8.423931623931624,,"💫 Beautiful spinners for terminal, IPython and Jupyter","['async', 'halo', 'ipython', 'jupyter', 'ora', 'spinner']","['async', 'halo', 'ipython', 'jupyter', 'ora', 'spinner']",2020-11-09,"[('ipython/ipyparallel', 0.5247726440429688, 'perf', 1)]",31,4.0,,0.0,4,0,77,39,0,0,0,4.0,1.0,90.0,0.2,30
1783,diffusion,https://github.com/openai/improved-diffusion,"['denoising', 'diffusion']",,[],[],,,,openai/improved-diffusion,improved-diffusion,2511,408,116,Python,,Release for Improved Denoising Diffusion Probabilistic Models,openai,2024-01-12,2021-02-08,155,16.185082872928177,https://avatars.githubusercontent.com/u/14957082?v=4,Release for Improved Denoising Diffusion Probabilistic Models,[],"['denoising', 'diffusion']",2022-01-12,"[('lllyasviel/controlnet', 0.5762985944747925, 'diffusion', 0), ('tanelp/tiny-diffusion', 0.5728610754013062, 'diffusion', 0), ('divamgupta/stable-diffusion-tensorflow', 0.5380927324295044, 'diffusion', 0)]",1,0.0,,0.0,28,2,36,24,0,0,0,28.0,28.0,90.0,1.0,30
800,web,https://github.com/flipkart-incubator/astra,[],,[],[],,,,flipkart-incubator/astra,Astra,2385,389,84,Python,,Automated Security Testing For REST API's,flipkart-incubator,2024-01-13,2018-01-10,315,7.550881953867028,https://avatars.githubusercontent.com/u/7090545?v=4,Automated Security Testing For REST API's,"['ci-cd', 'owasp', 'penetration-testing', 'penetration-testing-framework', 'postman-collection', 'restapiautomation', 'sdlc', 'security', 'security-automation']","['ci-cd', 'owasp', 'penetration-testing', 'penetration-testing-framework', 'postman-collection', 'restapiautomation', 'sdlc', 'security', 'security-automation']",2023-02-16,"[('rhinosecuritylabs/pacu', 0.5590912699699402, 'security', 2), ('taverntesting/tavern', 0.552314817905426, 'testing', 0), ('swisskyrepo/payloadsallthethings', 0.5459146499633789, 'security', 2), ('tox-dev/tox', 0.5185132026672363, 'testing', 0), ('tiangolo/fastapi', 0.5062249302864075, 'web', 0)]",12,3.0,,0.02,4,0,73,11,0,0,0,4.0,1.0,90.0,0.2,30
1328,ml-dl,https://github.com/google-research/electra,[],,[],[],,,,google-research/electra,electra,2269,350,61,Python,,ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators,google-research,2024-01-13,2020-03-10,203,11.177339901477833,https://avatars.githubusercontent.com/u/43830688?v=4,ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators,"['deep-learning', 'nlp', 'tensorflow']","['deep-learning', 'nlp', 'tensorflow']",2021-03-31,"[('huggingface/text-generation-inference', 0.648766815662384, 'llm', 2), ('minimaxir/textgenrnn', 0.6392484903335571, 'nlp', 2), ('amansrivastava17/embedding-as-service', 0.6076592803001404, 'nlp', 3), ('google/sentencepiece', 0.5957339406013489, 'nlp', 0), ('allenai/allennlp', 0.5719739198684692, 'nlp', 2), ('microsoft/unilm', 0.5669850707054138, 'nlp', 1), ('openai/clip', 0.5659348964691162, 'ml-dl', 1), ('minimaxir/gpt-2-simple', 0.5602125525474548, 'llm', 1), ('infinitylogesh/mutate', 0.553767204284668, 'nlp', 0), ('yueyu1030/attrprompt', 0.5535896420478821, 'llm', 0), ('deepset-ai/farm', 0.5499265789985657, 'nlp', 2), ('alibaba/easynlp', 0.5465848445892334, 'nlp', 2), ('bytedance/lightseq', 0.5465645790100098, 'nlp', 0), ('huggingface/transformers', 0.5317683219909668, 'nlp', 3), ('openai/finetune-transformer-lm', 0.5301187634468079, 'llm', 0), ('graykode/nlp-tutorial', 0.5271270871162415, 'study', 2), ('extreme-bert/extreme-bert', 0.5268334150314331, 'llm', 2), ('keras-team/keras-nlp', 0.5258187651634216, 'nlp', 3), ('qanastek/drbert', 0.5233448147773743, 'llm', 1), ('salesforce/blip', 0.5222206115722656, 'diffusion', 0), ('huggingface/text-embeddings-inference', 0.5215802788734436, 'llm', 0), ('nvidia/deeplearningexamples', 0.5167393684387207, 'ml-dl', 3), ('jonasgeiping/cramming', 0.5113855600357056, 'nlp', 0), ('explosion/spacy-transformers', 0.5100114941596985, 'llm', 1), ('huggingface/neuralcoref', 0.5090615153312683, 'nlp', 1), ('lucidrains/dalle2-pytorch', 0.5081148743629456, 'diffusion', 1), ('huggingface/setfit', 0.5080073475837708, 'nlp', 1), ('minimaxir/aitextgen', 0.5072848796844482, 'llm', 0), ('squeezeailab/squeezellm', 0.5039941072463989, 'llm', 0), ('llmware-ai/llmware', 0.5037044882774353, 'llm', 1), ('sharonzhou/long_stable_diffusion', 0.5007908344268799, 'diffusion', 0), ('plasticityai/magnitude', 0.500442385673523, 'nlp', 1)]",5,2.0,,0.0,1,1,47,34,0,0,0,1.0,1.0,90.0,1.0,30
848,profiling,https://github.com/jiffyclub/snakeviz,[],,[],[],,,,jiffyclub/snakeviz,snakeviz,2156,133,22,Python,https://jiffyclub.github.io/snakeviz/,An in-browser Python profile viewer,jiffyclub,2024-01-11,2012-06-26,605,3.5636363636363635,,An in-browser Python profile viewer,[],[],2023-05-14,"[('landscapeio/prospector', 0.582079291343689, 'util', 0), ('bokeh/bokeh', 0.5664529204368591, 'viz', 0), ('joerick/pyinstrument', 0.5636839866638184, 'profiling', 0), ('pyutils/line_profiler', 0.563589870929718, 'profiling', 0), ('gaogaotiantian/viztracer', 0.5514275431632996, 'profiling', 0), ('benfred/py-spy', 0.5489193797111511, 'profiling', 0), ('webpy/webpy', 0.5397558808326721, 'web', 0), ('urwid/urwid', 0.5396429300308228, 'term', 0), ('sumerc/yappi', 0.5380180478096008, 'profiling', 0), ('psf/requests', 0.5308777093887329, 'web', 0), ('pympler/pympler', 0.5296313762664795, 'perf', 0), ('roniemartinez/dude', 0.5278874635696411, 'util', 0), ('hoffstadt/dearpygui', 0.5256134867668152, 'gui', 0), ('seleniumbase/seleniumbase', 0.5255619287490845, 'testing', 0), ('eleutherai/pyfra', 0.5175898671150208, 'ml', 0), ('nedbat/coveragepy', 0.5139472484588623, 'testing', 0), ('pythonspeed/filprofiler', 0.5121504664421082, 'profiling', 0), ('scrapy/scrapy', 0.5070153474807739, 'data', 0), ('r0x0r/pywebview', 0.5045416951179504, 'gui', 0), ('pyglet/pyglet', 0.5042890310287476, 'gamedev', 0)]",26,7.0,,0.23,1,0,141,8,0,2,2,1.0,0.0,90.0,0.0,30
1049,util,https://github.com/kalliope-project/kalliope,[],,[],[],,,,kalliope-project/kalliope,kalliope,1683,241,82,Python,https://kalliope-project.github.io/,Kalliope is a framework that will help you to create your own personal assistant.,kalliope-project,2024-01-13,2016-10-11,381,4.417322834645669,https://avatars.githubusercontent.com/u/22769353?v=4,Kalliope is a framework that will help you to create your own personal assistant.,"['bot', 'bot-creation', 'home-automation', 'jarvis', 'linux', 'personal-assistant', 'raspberry', 'speech-recognition', 'speech-synthesis', 'speech-to-text']","['bot', 'bot-creation', 'home-automation', 'jarvis', 'linux', 'personal-assistant', 'raspberry', 'speech-recognition', 'speech-synthesis', 'speech-to-text']",2022-03-06,"[('rasahq/rasa', 0.5666339993476868, 'llm', 1), ('togethercomputer/openchatkit', 0.5493156909942627, 'nlp', 0), ('cheshire-cat-ai/core', 0.5292161107063293, 'llm', 0), ('speechbrain/speechbrain', 0.5283302664756775, 'nlp', 2), ('gunthercox/chatterbot', 0.518312394618988, 'nlp', 1), ('lucidrains/toolformer-pytorch', 0.5108852982521057, 'llm', 0), ('minimaxir/simpleaichat', 0.5094994902610779, 'llm', 0), ('willmcgugan/textual', 0.5060406923294067, 'term', 0)]",29,2.0,,0.0,4,2,88,23,0,3,3,4.0,4.0,90.0,1.0,30
1544,util,https://github.com/konradhalas/dacite,[],,[],[],,,,konradhalas/dacite,dacite,1577,95,14,Python,,Simple creation of data classes from dictionaries.,konradhalas,2024-01-12,2018-03-03,308,5.113015284854099,,Simple creation of data classes from dictionaries.,['dataclasses'],['dataclasses'],2023-05-12,"[('lidatong/dataclasses-json', 0.630731999874115, 'util', 1), ('fabiocaccamo/python-benedict', 0.5441532731056213, 'util', 0), ('marshmallow-code/marshmallow', 0.5163299441337585, 'util', 0)]",11,4.0,,0.06,5,0,71,8,2,7,2,5.0,1.0,90.0,0.2,30
458,nlp,https://github.com/google-research/language,[],,[],[],,,,google-research/language,language,1536,349,62,Python,https://ai.google/research/teams/language/,Shared repository for open-sourced projects from the Google AI Language team.,google-research,2024-01-12,2018-10-16,276,5.565217391304348,https://avatars.githubusercontent.com/u/43830688?v=4,Shared repository for open-sourced projects from the Google AI Language team.,"['machine-learning', 'natural-language-processing', 'research']","['machine-learning', 'natural-language-processing', 'research']",2023-10-19,"[('google-research/google-research', 0.6985517740249634, 'ml', 2), ('alirezadir/machine-learning-interview-enlightener', 0.6070800423622131, 'study', 1), ('googlecloudplatform/vertex-ai-samples', 0.6063291430473328, 'ml', 0), ('antonosika/gpt-engineer', 0.5954734683036804, 'llm', 0), ('rasahq/rasa', 0.5900284051895142, 'llm', 2), ('transformeroptimus/superagi', 0.5880876779556274, 'llm', 0), ('mlflow/mlflow', 0.5879070162773132, 'ml-ops', 1), ('tensorflow/tensorflow', 0.5603903532028198, 'ml-dl', 1), ('tensorflow/tensor2tensor', 0.5586530566215515, 'ml', 1), ('krohling/bondai', 0.5564771294593811, 'llm', 0), ('mindsdb/mindsdb', 0.5518535375595093, 'data', 1), ('argilla-io/argilla', 0.5392791628837585, 'nlp', 2), ('deeppavlov/deeppavlov', 0.5389516949653625, 'nlp', 1), ('unity-technologies/ml-agents', 0.5368104577064514, 'ml-rl', 1), ('aiwaves-cn/agents', 0.5324529409408569, 'nlp', 0), ('yueyu1030/attrprompt', 0.5311223864555359, 'llm', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5293827652931213, 'study', 1), ('rasbt/machine-learning-book', 0.5288284420967102, 'study', 1), ('doccano/doccano', 0.5283976197242737, 'nlp', 2), ('merantix-momentum/squirrel-core', 0.5260434150695801, 'ml', 2), ('bentoml/bentoml', 0.5239987969398499, 'ml-ops', 1), ('mlc-ai/mlc-llm', 0.5201672911643982, 'llm', 0), ('prefecthq/marvin', 0.5198028087615967, 'nlp', 0), ('nltk/nltk', 0.5192822217941284, 'nlp', 2), ('allenai/allennlp', 0.5185959935188293, 'nlp', 1), ('iterative/dvc', 0.5162245631217957, 'ml-ops', 1), ('microsoft/generative-ai-for-beginners', 0.5157017111778259, 'study', 0), ('patchy631/machine-learning', 0.5135775804519653, 'ml', 0), ('openlm-research/open_llama', 0.5121092796325684, 'llm', 0), ('oegedijk/explainerdashboard', 0.5075442790985107, 'ml-interpretability', 0), ('netflix/metaflow', 0.5073051452636719, 'ml-ops', 1), ('embedchain/embedchain', 0.5062578916549683, 'llm', 0), ('databrickslabs/dolly', 0.5051336288452148, 'llm', 0), ('aimhubio/aim', 0.5032878518104553, 'ml-ops', 1), ('lucidrains/toolformer-pytorch', 0.5021114945411682, 'llm', 0), ('microsoft/nni', 0.5012085437774658, 'ml', 1)]",10,3.0,,0.0,21,3,64,3,0,0,0,21.0,3.0,90.0,0.1,30
1309,study,https://github.com/chandlerbang/awesome-self-supervised-gnn,['awesome'],,[],[],,,,chandlerbang/awesome-self-supervised-gnn,awesome-self-supervised-gnn,1366,157,50,Python,,Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).,chandlerbang,2024-01-10,2020-05-27,191,7.1198808637379,,Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).,"['deep-learning', 'graph-mining', 'graph-neural-networks', 'graph-self-supervised-learning', 'machine-learning', 'pre-training', 'pretraining', 'self-supervised-learning']","['awesome', 'deep-learning', 'graph-mining', 'graph-neural-networks', 'graph-self-supervised-learning', 'machine-learning', 'pre-training', 'pretraining', 'self-supervised-learning']",2023-07-10,"[('stellargraph/stellargraph', 0.6943688988685608, 'graph', 3), ('danielegrattarola/spektral', 0.6770707964897156, 'ml-dl', 2), ('pyg-team/pytorch_geometric', 0.6452118158340454, 'ml-dl', 2), ('dmlc/dgl', 0.593945324420929, 'ml-dl', 2), ('google-deepmind/materials_discovery', 0.5740145444869995, 'sim', 0), ('rampasek/graphgps', 0.5653940439224243, 'graph', 0), ('googlecloudplatform/vertex-ai-samples', 0.5588842034339905, 'ml', 0), ('benedekrozemberczki/tigerlily', 0.5460976362228394, 'ml-dl', 2), ('microsoft/unilm', 0.5360260605812073, 'nlp', 0), ('accenture/ampligraph', 0.5335105061531067, 'data', 1), ('a-r-j/graphein', 0.5173624753952026, 'sim', 2), ('christoschristofidis/awesome-deep-learning', 0.5087785124778748, 'study', 3), ('graphistry/pygraphistry', 0.5049978494644165, 'data', 0)]",19,5.0,,0.33,1,0,44,6,0,0,0,1.0,0.0,90.0,0.0,30
1094,data,https://github.com/eleutherai/the-pile,"['training-data', 'llm']","The Pile is a large, diverse, open source language modelling data set that consists of many smaller datasets combined together.",[],[],,,,eleutherai/the-pile,the-pile,1334,112,31,Python,,,eleutherai,2024-01-12,2020-08-26,178,7.458466453674121,https://avatars.githubusercontent.com/u/68924597?v=4,"The Pile is a large, diverse, open source language modelling data set that consists of many smaller datasets combined together.",[],"['llm', 'training-data']",2021-06-16,"[('salesforce/xgen', 0.6535871624946594, 'llm', 1), ('togethercomputer/redpajama-data', 0.6279685497283936, 'llm', 0), ('infinitylogesh/mutate', 0.6196421980857849, 'nlp', 0), ('hannibal046/awesome-llm', 0.6086982488632202, 'study', 0), ('cg123/mergekit', 0.607460081577301, 'llm', 1), ('yueyu1030/attrprompt', 0.5999411940574646, 'llm', 0), ('juncongmoo/pyllama', 0.5945956707000732, 'llm', 0), ('databrickslabs/dolly', 0.5931678414344788, 'llm', 0), ('neuml/txtai', 0.5822166800498962, 'nlp', 1), ('mooler0410/llmspracticalguide', 0.5788910984992981, 'study', 0), ('paddlepaddle/paddlenlp', 0.5773162841796875, 'llm', 1), ('explosion/spacy-llm', 0.5771530270576477, 'llm', 1), ('lianjiatech/belle', 0.5712957978248596, 'llm', 0), ('lm-sys/fastchat', 0.568549394607544, 'llm', 0), ('young-geng/easylm', 0.5631842613220215, 'llm', 0), ('llmware-ai/llmware', 0.5548039078712463, 'llm', 0), ('night-chen/toolqa', 0.5525684952735901, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5507524013519287, 'llm', 0), ('ai21labs/lm-evaluation', 0.5482072830200195, 'llm', 0), ('freedomintelligence/llmzoo', 0.5475942492485046, 'llm', 0), ('bobazooba/xllm', 0.5469703674316406, 'llm', 1), ('argilla-io/argilla', 0.542231023311615, 'nlp', 1), ('bigscience-workshop/biomedical', 0.5412442684173584, 'data', 0), ('salesforce/codet5', 0.5400211811065674, 'nlp', 0), ('thudm/chatglm2-6b', 0.5338200330734253, 'llm', 1), ('deepset-ai/haystack', 0.5290297269821167, 'llm', 0), ('nebuly-ai/nebullvm', 0.528251588344574, 'perf', 1), ('dylanhogg/llmgraph', 0.5278661847114563, 'ml', 1), ('ctlllll/llm-toolmaker', 0.5270031690597534, 'llm', 0), ('epfllm/meditron', 0.5269107818603516, 'llm', 0), ('koaning/embetter', 0.5203182101249695, 'data', 1), ('openlm-research/open_llama', 0.5138996839523315, 'llm', 0), ('aiwaves-cn/agents', 0.5101150274276733, 'nlp', 1), ('nomic-ai/gpt4all', 0.5064859986305237, 'llm', 0), ('huggingface/text-generation-inference', 0.5064693093299866, 'llm', 0), ('optimalscale/lmflow', 0.5063433647155762, 'llm', 0), ('conceptofmind/toolformer', 0.5042285919189453, 'llm', 0), ('bigscience-workshop/petals', 0.5029622316360474, 'data', 0), ('tigerlab-ai/tiger', 0.5005179643630981, 'llm', 1)]",7,3.0,,0.0,5,0,41,31,0,0,0,5.0,8.0,90.0,1.6,30
1186,ml-rl,https://github.com/anthropics/hh-rlhf,"['rlhf', 'dataset']",,[],[],,,,anthropics/hh-rlhf,hh-rlhf,1304,99,19,,https://arxiv.org/abs/2204.05862,"Human preference data for ""Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback""",anthropics,2024-01-12,2022-04-10,94,13.83030303030303,https://avatars.githubusercontent.com/u/76263028?v=4,"Human preference data for ""Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback""",[],"['dataset', 'rlhf']",2023-09-19,[],4,2.0,,0.04,0,0,21,4,0,0,0,0.0,0.0,90.0,0.0,30
650,web,https://github.com/magicstack/httptools,[],,[],[],,,,magicstack/httptools,httptools,1148,76,41,Python,,Fast HTTP parser,magicstack,2024-01-04,2016-04-25,405,2.8335684062059237,https://avatars.githubusercontent.com/u/14324950?v=4,Fast HTTP parser,[],[],2023-10-16,"[('aio-libs/yarl', 0.5892772674560547, 'util', 0), ('psf/requests', 0.5530205965042114, 'web', 0)]",15,6.0,,0.12,5,4,94,3,2,2,2,2.0,0.0,90.0,0.0,30
219,template,https://github.com/tezromach/python-package-template,[],,[],[],1.0,,,tezromach/python-package-template,python-package-template,1056,147,9,Python,,🚀 Your next Python package needs a bleeding-edge project structure.,tezromach,2024-01-13,2020-04-15,197,5.337184115523466,,🚀 Your next Python package needs a bleeding-edge project structure.,"['best-practices', 'codestyle', 'cookiecutter', 'formatters', 'makefile', 'poetry', 'python-packages', 'semantic-versions', 'template']","['best-practices', 'codestyle', 'cookiecutter', 'formatters', 'makefile', 'poetry', 'python-packages', 'semantic-versions', 'template']",2022-05-18,"[('tedivm/robs_awesome_python_template', 0.647948682308197, 'template', 0), ('python-poetry/poetry', 0.6311818361282349, 'util', 1), ('pypa/hatch', 0.6175110936164856, 'util', 0), ('lyz-code/cookiecutter-python-project', 0.5960633754730225, 'template', 1), ('pypa/flit', 0.5902042984962463, 'util', 0), ('pypa/build', 0.5865764021873474, 'util', 0), ('mitsuhiko/rye', 0.5775073170661926, 'util', 0), ('jazzband/pip-tools', 0.5716038346290588, 'util', 0), ('regebro/pyroma', 0.5707094073295593, 'util', 0), ('pdm-project/pdm', 0.5643466114997864, 'util', 0), ('indygreg/pyoxidizer', 0.5635542273521423, 'util', 0), ('giswqs/pypackage', 0.5540108680725098, 'template', 2), ('pyscaffold/pyscaffold', 0.553695797920227, 'template', 0), ('asottile/reorder-python-imports', 0.5504276156425476, 'util', 0), ('pypa/pipenv', 0.5377501249313354, 'util', 0), ('pypi/warehouse', 0.5265879034996033, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5260007977485657, 'util', 0), ('cookiecutter/cookiecutter', 0.5202876925468445, 'template', 1), ('eugeneyan/python-collab-template', 0.518621563911438, 'template', 1), ('pyodide/micropip', 0.5130401253700256, 'util', 0)]",13,2.0,,0.0,3,0,46,20,0,6,6,3.0,3.0,90.0,1.0,30
204,debug,https://github.com/alexmojaki/snoop,[],,[],[],,,,alexmojaki/snoop,snoop,1042,33,20,Python,,"A powerful set of Python debugging tools, based on PySnooper",alexmojaki,2024-01-07,2019-05-13,246,4.233313987231573,,"A powerful set of Python debugging tools, based on PySnooper","['debugger', 'debugging', 'debugging-tools', 'logging']","['debugger', 'debugging', 'debugging-tools', 'logging']",2022-12-22,"[('samuelcolvin/python-devtools', 0.7110832929611206, 'debug', 0), ('alexmojaki/heartrate', 0.6388193964958191, 'debug', 1), ('gaogaotiantian/viztracer', 0.623710036277771, 'profiling', 2), ('inducer/pudb', 0.61795973777771, 'debug', 1), ('nedbat/coveragepy', 0.6032272577285767, 'testing', 0), ('metachris/logzero', 0.601128876209259, 'util', 1), ('alexmojaki/birdseye', 0.6003603935241699, 'debug', 2), ('pympler/pympler', 0.5967013835906982, 'perf', 0), ('hoffstadt/dearpygui', 0.5897102952003479, 'gui', 0), ('ionelmc/python-hunter', 0.5830564498901367, 'debug', 2), ('beeware/toga', 0.5817451477050781, 'gui', 0), ('reloadware/reloadium', 0.5736259818077087, 'profiling', 0), ('delgan/loguru', 0.5699717998504639, 'util', 1), ('pypy/pypy', 0.5637267827987671, 'util', 0), ('gotcha/ipdb', 0.5553923845291138, 'debug', 1), ('urwid/urwid', 0.5546684265136719, 'term', 0), ('pyston/pyston', 0.5509077310562134, 'util', 0), ('landscapeio/prospector', 0.5469942688941956, 'util', 0), ('trailofbits/pip-audit', 0.5462668538093567, 'security', 0), ('p403n1x87/austin', 0.5450026392936707, 'profiling', 1), ('secdev/scapy', 0.5415452122688293, 'util', 0), ('pyglet/pyglet', 0.5321446061134338, 'gamedev', 0), ('pyutils/line_profiler', 0.5304473638534546, 'profiling', 0), ('python/cpython', 0.5296629667282104, 'util', 0), ('willmcgugan/textual', 0.5284596681594849, 'term', 0), ('ionelmc/pytest-benchmark', 0.5151593685150146, 'testing', 0), ('faster-cpython/ideas', 0.5135080814361572, 'perf', 0), ('pytoolz/toolz', 0.5116428732872009, 'util', 0), ('teamhg-memex/eli5', 0.5058972835540771, 'ml', 0), ('micropython/micropython', 0.505521297454834, 'util', 0), ('pytest-dev/pytest-bdd', 0.5047500133514404, 'testing', 0), ('amaargiru/pyroad', 0.5022026896476746, 'study', 0), ('eleutherai/pyfra', 0.5008596777915955, 'ml', 0), ('klen/pylama', 0.500612735748291, 'util', 0)]",22,5.0,,0.0,1,0,57,13,0,1,1,1.0,1.0,90.0,1.0,30
627,util,https://github.com/pyca/pynacl,[],,[],[],,,,pyca/pynacl,pynacl,1009,228,28,C,https://pynacl.readthedocs.io/,Python binding to the Networking and Cryptography (NaCl) library,pyca,2024-01-13,2013-02-22,570,1.7684026039058587,https://avatars.githubusercontent.com/u/5615737?v=4,Python binding to the Networking and Cryptography (NaCl) library,"['cryptography', 'libsodium', 'nacl']","['cryptography', 'libsodium', 'nacl']",2023-12-17,"[('legrandin/pycryptodome', 0.7229923605918884, 'util', 1), ('pyca/cryptography', 0.659361720085144, 'util', 1), ('1200wd/bitcoinlib', 0.5711807608604431, 'crypto', 0), ('primal100/pybitcointools', 0.56072998046875, 'crypto', 0), ('secdev/scapy', 0.5417189002037048, 'util', 0), ('man-c/pycoingecko', 0.5348667502403259, 'crypto', 0), ('nvidia/cuda-python', 0.5136300921440125, 'ml', 0), ('paramiko/paramiko', 0.5029951930046082, 'util', 0)]",67,2.0,,0.27,9,7,133,1,0,1,1,9.0,8.0,90.0,0.9,30
346,nlp,https://github.com/norskregnesentral/skweak,[],,[],[],,,,norskregnesentral/skweak,skweak,902,74,28,Python,,skweak: A software toolkit for weak supervision applied to NLP tasks,norskregnesentral,2024-01-09,2021-03-16,150,6.013333333333334,https://avatars.githubusercontent.com/u/17080513?v=4,skweak: A software toolkit for weak supervision applied to NLP tasks,"['data-science', 'distant-supervision', 'natural-language-processing', 'nlp-library', 'nlp-machine-learning', 'spacy', 'training-data', 'weak-supervision']","['data-science', 'distant-supervision', 'natural-language-processing', 'nlp-library', 'nlp-machine-learning', 'spacy', 'training-data', 'weak-supervision']",2023-09-26,"[('alibaba/easynlp', 0.6338717341423035, 'nlp', 0), ('argilla-io/argilla', 0.6235673427581787, 'nlp', 2), ('explosion/spacy', 0.6230126619338989, 'nlp', 4), ('explosion/spacy-models', 0.608527660369873, 'nlp', 2), ('nltk/nltk', 0.608248770236969, 'nlp', 1), ('flairnlp/flair', 0.602255642414093, 'nlp', 1), ('allenai/allennlp', 0.5997143387794495, 'nlp', 2), ('explosion/spacy-llm', 0.5937286615371704, 'llm', 2), ('paddlepaddle/paddlenlp', 0.592394232749939, 'llm', 0), ('sloria/textblob', 0.5448392033576965, 'nlp', 1), ('infinitylogesh/mutate', 0.5382066965103149, 'nlp', 1), ('explosion/spacy-stanza', 0.5375661849975586, 'nlp', 3), ('openai/whisper', 0.5360553860664368, 'ml-dl', 0), ('graykode/nlp-tutorial', 0.5352105498313904, 'study', 1), ('huggingface/text-generation-inference', 0.5326890349388123, 'llm', 0), ('lexpredict/lexpredict-lexnlp', 0.5267290472984314, 'nlp', 0), ('rasahq/rasa', 0.5265606641769409, 'llm', 2), ('deepset-ai/farm', 0.5261529088020325, 'nlp', 1), ('keras-team/keras-nlp', 0.5260323882102966, 'nlp', 1), ('bytedance/lightseq', 0.5218310952186584, 'nlp', 0), ('llmware-ai/llmware', 0.5207846760749817, 'llm', 0), ('makcedward/nlpaug', 0.5184004902839661, 'nlp', 2), ('maartengr/bertopic', 0.5178706645965576, 'nlp', 0), ('databrickslabs/dolly', 0.5073420405387878, 'llm', 0), ('lm-sys/fastchat', 0.5055734515190125, 'llm', 0), ('jonasgeiping/cramming', 0.505377471446991, 'nlp', 0), ('yueyu1030/attrprompt', 0.5007184743881226, 'llm', 1), ('minimaxir/aitextgen', 0.5007104873657227, 'llm', 0)]",12,5.0,,0.29,1,0,34,4,0,1,1,1.0,0.0,90.0,0.0,30
1674,util,https://github.com/fastai/fastcore,[],,[],[],,,,fastai/fastcore,fastcore,880,256,19,Jupyter Notebook,http://fastcore.fast.ai,Python supercharged for the fastai library,fastai,2024-01-07,2019-12-02,217,4.052631578947368,https://avatars.githubusercontent.com/u/20547620?v=4,Python supercharged for the fastai library,"['data-structures', 'developer-tools', 'dispatch', 'documentation-generator', 'fastai', 'functional-programming', 'languages', 'parallel-processing']","['data-structures', 'developer-tools', 'dispatch', 'documentation-generator', 'fastai', 'functional-programming', 'languages', 'parallel-processing']",2023-06-25,"[('pypy/pypy', 0.6839970946311951, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.6752527952194214, 'template', 0), ('pyston/pyston', 0.6732315421104431, 'util', 0), ('pytoolz/toolz', 0.6410788297653198, 'util', 0), ('cython/cython', 0.6373258829116821, 'util', 0), ('tiangolo/fastapi', 0.63350909948349, 'web', 0), ('dylanhogg/awesome-python', 0.6330905556678772, 'study', 0), ('exaloop/codon', 0.6250977516174316, 'perf', 0), ('rawheel/fastapi-boilerplate', 0.6172206997871399, 'web', 0), ('gradio-app/gradio', 0.6137790679931641, 'viz', 0), ('timofurrer/awesome-asyncio', 0.6136905550956726, 'study', 0), ('s3rius/fastapi-template', 0.6101469993591309, 'web', 0), ('dagworks-inc/hamilton', 0.603600263595581, 'ml-ops', 0), ('klen/py-frameworks-bench', 0.6014686226844788, 'perf', 0), ('joblib/joblib', 0.6007087826728821, 'util', 0), ('faster-cpython/tools', 0.5940293669700623, 'perf', 0), ('pandas-dev/pandas', 0.5918874144554138, 'pandas', 0), ('intel/intel-extension-for-pytorch', 0.5917688012123108, 'perf', 0), ('ploomber/ploomber', 0.5885465741157532, 'ml-ops', 0), ('parallel-domain/pd-sdk', 0.5881737470626831, 'data', 0), ('vaexio/vaex', 0.5877453088760376, 'perf', 0), ('klen/muffin', 0.5848855376243591, 'web', 0), ('eleutherai/pyfra', 0.5840798616409302, 'ml', 0), ('pytorch/data', 0.5834670066833496, 'data', 0), ('python/cpython', 0.5814699530601501, 'util', 0), ('evhub/coconut', 0.5812950730323792, 'util', 1), ('willmcgugan/textual', 0.5812305808067322, 'term', 0), ('reloadware/reloadium', 0.5807570219039917, 'profiling', 0), ('tobymao/sqlglot', 0.5786949396133423, 'data', 0), ('micropython/micropython', 0.5780920386314392, 'util', 0), ('openai/openai-python', 0.5748746991157532, 'util', 0), ('eventual-inc/daft', 0.572355329990387, 'pandas', 0), ('merantix-momentum/squirrel-core', 0.572002649307251, 'ml', 0), ('backtick-se/cowait', 0.5709444284439087, 'util', 0), ('hoffstadt/dearpygui', 0.5673369765281677, 'gui', 0), ('ibis-project/ibis', 0.566156804561615, 'data', 0), ('vitalik/django-ninja', 0.5651664137840271, 'web', 0), ('sumerc/yappi', 0.5639339685440063, 'profiling', 0), ('collerek/ormar', 0.5605081915855408, 'data', 0), ('kubeflow/fairing', 0.5604775547981262, 'ml-ops', 0), ('neoteroi/blacksheep', 0.5601794719696045, 'web', 0), ('1200wd/bitcoinlib', 0.5587186813354492, 'crypto', 0), ('google/gin-config', 0.5576726198196411, 'util', 0), ('pytables/pytables', 0.5568447113037109, 'data', 0), ('google/pyglove', 0.5538975596427917, 'util', 0), ('tiangolo/sqlmodel', 0.5534236431121826, 'data', 0), ('faster-cpython/ideas', 0.5519487857818604, 'perf', 0), ('ipython/ipyparallel', 0.5499588251113892, 'perf', 0), ('python-trio/trio', 0.5491400361061096, 'perf', 0), ('lucidrains/toolformer-pytorch', 0.5489647388458252, 'llm', 0), ('samuelcolvin/fastui', 0.546245813369751, 'gui', 0), ('pyparsing/pyparsing', 0.5457038283348083, 'util', 0), ('alphasecio/langchain-examples', 0.5439481735229492, 'llm', 0), ('plotly/dash', 0.5438128113746643, 'viz', 0), ('falconry/falcon', 0.5430771708488464, 'web', 0), ('ashleve/lightning-hydra-template', 0.5420973300933838, 'util', 0), ('malloydata/malloy-py', 0.5410973429679871, 'data', 0), ('krzjoa/awesome-python-data-science', 0.5385904908180237, 'study', 0), ('plasma-umass/scalene', 0.5373175144195557, 'profiling', 0), ('holoviz/panel', 0.5372098684310913, 'viz', 0), ('explosion/thinc', 0.5361101627349854, 'ml-dl', 1), ('erotemic/ubelt', 0.5355119705200195, 'util', 0), ('libtcod/python-tcod', 0.5340726375579834, 'gamedev', 0), ('python-restx/flask-restx', 0.5335445404052734, 'web', 0), ('facebookincubator/cinder', 0.5330604910850525, 'perf', 0), ('google/tf-quant-finance', 0.5327866077423096, 'finance', 0), ('polyaxon/datatile', 0.5320842266082764, 'pandas', 0), ('lk-geimfari/mimesis', 0.5298691987991333, 'data', 0), ('ray-project/ray', 0.528685986995697, 'ml-ops', 0), ('airtai/faststream', 0.5284400582313538, 'perf', 0), ('renpy/renpy', 0.5279468894004822, 'viz', 0), ('fugue-project/fugue', 0.527552604675293, 'pandas', 0), ('dgilland/cacheout', 0.5273230671882629, 'perf', 0), ('huggingface/huggingface_hub', 0.5268593430519104, 'ml', 0), ('beeware/toga', 0.5267270803451538, 'gui', 0), ('scrapy/scrapy', 0.5240939855575562, 'data', 0), ('ml-tooling/opyrator', 0.5239315629005432, 'viz', 0), ('spotify/luigi', 0.5237264037132263, 'ml-ops', 0), ('agronholm/apscheduler', 0.5230339765548706, 'util', 0), ('pympler/pympler', 0.5204732418060303, 'perf', 0), ('pyinfra-dev/pyinfra', 0.5203496217727661, 'util', 0), ('python-cachier/cachier', 0.5200280547142029, 'perf', 0), ('python-odin/odin', 0.5195226669311523, 'util', 1), ('bottlepy/bottle', 0.5190497040748596, 'web', 0), ('lcompilers/lpython', 0.5177335739135742, 'util', 0), ('panda3d/panda3d', 0.5172508955001831, 'gamedev', 0), ('wxwidgets/phoenix', 0.5171084403991699, 'gui', 0), ('pypa/hatch', 0.5164702534675598, 'util', 0), ('starlite-api/starlite', 0.5158090591430664, 'web', 0), ('huggingface/datasets', 0.5157922506332397, 'nlp', 0), ('explosion/spacy', 0.5157615542411804, 'nlp', 0), ('cherrypy/cherrypy', 0.5150647759437561, 'web', 0), ('amaargiru/pyroad', 0.5148802995681763, 'study', 0), ('samuelcolvin/arq', 0.5146878957748413, 'data', 0), ('alirn76/panther', 0.5143014788627625, 'web', 0), ('python-rope/rope', 0.5134739875793457, 'util', 0), ('jmcarpenter2/swifter', 0.5132153034210205, 'pandas', 0), ('locustio/locust', 0.5126659870147705, 'testing', 0), ('marshmallow-code/marshmallow', 0.5120862722396851, 'util', 0), ('pallets/quart', 0.5118736624717712, 'web', 0), ('pallets/flask', 0.5115838050842285, 'web', 0), ('astronomer/astro-sdk', 0.5107561945915222, 'ml-ops', 0), ('pola-rs/polars', 0.5103496313095093, 'pandas', 0), ('fluentpython/example-code-2e', 0.5095949769020081, 'study', 0), ('fastapi-admin/fastapi-admin', 0.5095018148422241, 'web', 0), ('kestra-io/kestra', 0.5094014406204224, 'ml-ops', 0), ('magicstack/uvloop', 0.5093868970870972, 'util', 0), ('pytorch/glow', 0.509026288986206, 'ml', 0), ('huggingface/transformers', 0.5071895718574524, 'nlp', 0), ('rustpython/rustpython', 0.5066707134246826, 'util', 0), ('goldmansachs/gs-quant', 0.5064576268196106, 'finance', 0), ('ethereum/py-evm', 0.5062366724014282, 'crypto', 0), ('ta-lib/ta-lib-python', 0.5056002140045166, 'finance', 0), ('geeogi/async-python-lambda-template', 0.504927396774292, 'template', 0), ('nteract/papermill', 0.5034418106079102, 'jupyter', 0), ('adafruit/circuitpython', 0.5033023953437805, 'util', 0), ('kubeflow-kale/kale', 0.5032058358192444, 'ml-ops', 0), ('pyqtgraph/pyqtgraph', 0.5031821727752686, 'viz', 0), ('nvidia/warp', 0.5028382539749146, 'sim', 0), ('lianjiatech/belle', 0.502812385559082, 'llm', 0), ('uber/petastorm', 0.5022857189178467, 'data', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5009275078773499, 'study', 0), ('wesm/pydata-book', 0.5009101629257202, 'study', 0), ('astral-sh/ruff', 0.5008984804153442, 'util', 0), ('orchest/orchest', 0.5004292130470276, 'ml-ops', 0), ('mamba-org/mamba', 0.5001938343048096, 'util', 0)]",56,5.0,,0.27,3,0,50,7,2,18,2,3.0,0.0,90.0,0.0,30
633,ml,https://github.com/dask/dask-ml,[],,[],[],,,,dask/dask-ml,dask-ml,872,245,41,Python,http://ml.dask.org,Scalable Machine Learning with Dask,dask,2024-01-04,2017-06-15,345,2.522314049586777,https://avatars.githubusercontent.com/u/17131925?v=4,Scalable Machine Learning with Dask,[],[],2023-03-24,"[('scikit-learn-contrib/lightning', 0.5908797979354858, 'ml', 0), ('prefecthq/prefect-dask', 0.5907831192016602, 'util', 0), ('dmlc/xgboost', 0.5699902176856995, 'ml', 0), ('autoviml/auto_ts', 0.5662448406219482, 'time-series', 0), ('dask/distributed', 0.5617966055870056, 'perf', 0), ('scikit-learn-contrib/metric-learn', 0.5469420552253723, 'ml', 0), ('optuna/optuna', 0.5394331216812134, 'ml', 0), ('catboost/catboost', 0.5368636250495911, 'ml', 0), ('ray-project/ray', 0.5340972542762756, 'ml-ops', 0), ('rasbt/machine-learning-book', 0.5278743505477905, 'study', 0), ('dask/dask', 0.526610255241394, 'perf', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5264381170272827, 'ml', 0), ('paddlepaddle/paddle', 0.5259979963302612, 'ml-dl', 0), ('determined-ai/determined', 0.5248952507972717, 'ml-ops', 0), ('huggingface/evaluate', 0.5205085873603821, 'ml', 0), ('kubeflow-kale/kale', 0.5168511867523193, 'ml-ops', 0), ('tensorflow/data-validation', 0.5110467076301575, 'ml-ops', 0), ('koaning/scikit-lego', 0.5104072093963623, 'ml', 0), ('huggingface/datasets', 0.5084608197212219, 'nlp', 0), ('kubeflow/fairing', 0.5057757496833801, 'ml-ops', 0), ('automl/auto-sklearn', 0.5055193305015564, 'ml', 0), ('tensorflow/tensorflow', 0.5045599937438965, 'ml-dl', 0), ('nvidia/apex', 0.5042513608932495, 'ml-dl', 0)]",77,6.0,,0.06,5,1,80,10,1,6,1,5.0,4.0,90.0,0.8,30
731,perf,https://github.com/zerointensity/pointers.py,[],,[],[],,,,zerointensity/pointers.py,pointers.py,851,12,5,Python,https://pointers.zintensity.dev/,Bringing the hell of pointers to Python.,zerointensity,2024-01-08,2022-03-09,98,8.608381502890174,,Bringing the hell of pointers to Python.,"['pointers', 'python-pointers']","['pointers', 'python-pointers']",2023-11-29,"[('pyston/pyston', 0.523978590965271, 'util', 0), ('google/jax', 0.5109991431236267, 'ml', 0)]",8,2.0,,0.06,1,1,22,2,1,4,1,1.0,0.0,90.0,0.0,30
418,util,https://github.com/sethmmorton/natsort,[],,[],[],,,,sethmmorton/natsort,natsort,819,48,17,Python,https://pypi.org/project/natsort/,Simple yet flexible natural sorting in Python.,sethmmorton,2024-01-06,2012-05-03,612,1.336675215667988,,Simple yet flexible natural sorting in Python.,"['natsort', 'natural-sort', 'sorting', 'sorting-interface']","['natsort', 'natural-sort', 'sorting', 'sorting-interface']",2023-06-20,"[('pycqa/isort', 0.5276709794998169, 'util', 0)]",21,4.0,,0.92,2,2,142,7,0,5,5,2.0,4.0,90.0,2.0,30
981,llm,https://github.com/muennighoff/sgpt,[],,[],[],,,,muennighoff/sgpt,sgpt,761,49,8,Jupyter Notebook,https://arxiv.org/abs/2202.08904,SGPT: GPT Sentence Embeddings for Semantic Search,muennighoff,2024-01-12,2022-02-11,102,7.419220055710307,,SGPT: GPT Sentence Embeddings for Semantic Search,"['gpt', 'information-retrieval', 'language-model', 'large-language-models', 'neural-search', 'retrieval', 'semantic-search', 'sentence-embeddings', 'sgpt', 'text-embedding']","['gpt', 'information-retrieval', 'language-model', 'large-language-models', 'neural-search', 'retrieval', 'semantic-search', 'sentence-embeddings', 'sgpt', 'text-embedding']",2023-07-06,"[('neuml/txtai', 0.6413739323616028, 'nlp', 6), ('intellabs/fastrag', 0.6058968305587769, 'nlp', 2), ('ddangelov/top2vec', 0.5922024846076965, 'nlp', 1), ('ukplab/sentence-transformers', 0.5446346402168274, 'nlp', 3), ('jina-ai/clip-as-service', 0.5414046049118042, 'nlp', 1), ('llmware-ai/llmware', 0.5382522940635681, 'llm', 3), ('weaviate/demo-text2vec-openai', 0.5382280945777893, 'util', 0), ('paddlepaddle/rocketqa', 0.5352213978767395, 'nlp', 1), ('amansrivastava17/embedding-as-service', 0.5335803627967834, 'nlp', 0), ('ai21labs/in-context-ralm', 0.5289058685302734, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5275580286979675, 'llm', 1), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5272648334503174, 'data', 0), ('hannibal046/awesome-llm', 0.5209395885467529, 'study', 2), ('huggingface/text-generation-inference', 0.519349992275238, 'llm', 1), ('plasticityai/magnitude', 0.5156716704368591, 'nlp', 0), ('jina-ai/finetuner', 0.512883186340332, 'ml', 1), ('koaning/whatlies', 0.5074957013130188, 'nlp', 0), ('sebischair/lbl2vec', 0.5040941834449768, 'nlp', 0), ('bigscience-workshop/megatron-deepspeed', 0.5024375915527344, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5024375915527344, 'llm', 0)]",3,2.0,,0.12,3,0,23,6,0,0,0,3.0,5.0,90.0,1.7,30
473,viz,https://github.com/holoviz/holoviz,[],,[],[],1.0,,,holoviz/holoviz,holoviz,756,120,36,Shell,https://holoviz.org/,High-level tools to simplify visualization in Python.,holoviz,2024-01-13,2017-09-22,331,2.2800517018526496,https://avatars.githubusercontent.com/u/51678735?v=4,High-level tools to simplify visualization in Python.,"['colorcet', 'datashader', 'geoviews', 'holoviews', 'holoviz', 'hvplot', 'panel']","['colorcet', 'datashader', 'geoviews', 'holoviews', 'holoviz', 'hvplot', 'panel']",2023-12-04,"[('holoviz/panel', 0.7308956384658813, 'viz', 4), ('holoviz/geoviews', 0.7218708992004395, 'gis', 3), ('altair-viz/altair', 0.7110622525215149, 'viz', 0), ('man-group/dtale', 0.7002979516983032, 'viz', 0), ('residentmario/geoplot', 0.6968002319335938, 'gis', 0), ('pyqtgraph/pyqtgraph', 0.6720275282859802, 'viz', 0), ('holoviz/hvplot', 0.6662247776985168, 'pandas', 3), ('bokeh/bokeh', 0.6567468047142029, 'viz', 0), ('scitools/iris', 0.6550614833831787, 'gis', 0), ('giswqs/geemap', 0.6438645124435425, 'gis', 0), ('contextlab/hypertools', 0.6432879567146301, 'ml', 0), ('kanaries/pygwalker', 0.6368023753166199, 'pandas', 0), ('mwaskom/seaborn', 0.6366784572601318, 'viz', 0), ('enthought/mayavi', 0.6358242630958557, 'viz', 0), ('matplotlib/matplotlib', 0.6354397535324097, 'viz', 0), ('plotly/plotly.py', 0.6298113465309143, 'viz', 0), ('has2k1/plotnine', 0.6090349555015564, 'viz', 0), ('opengeos/leafmap', 0.6019681692123413, 'gis', 0), ('vispy/vispy', 0.6010532379150391, 'viz', 0), ('maartenbreddels/ipyvolume', 0.6008582711219788, 'jupyter', 0), ('holoviz/datashader', 0.5855922698974609, 'gis', 2), ('holoviz/holoviews', 0.585200309753418, 'viz', 2), ('pyglet/pyglet', 0.5789063572883606, 'gamedev', 0), ('pyvista/pyvista', 0.5743590593338013, 'viz', 0), ('alexmojaki/heartrate', 0.573762834072113, 'debug', 0), ('vizzuhq/ipyvizzu', 0.5736362338066101, 'jupyter', 0), ('graphistry/pygraphistry', 0.5680197477340698, 'data', 0), ('gaogaotiantian/viztracer', 0.5649738311767578, 'profiling', 0), ('gregorhd/mapcompare', 0.5640184879302979, 'gis', 0), ('hoffstadt/dearpygui', 0.5614981651306152, 'gui', 0), ('lux-org/lux', 0.5574216246604919, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5567380785942078, 'study', 0), ('cuemacro/chartpy', 0.5522693395614624, 'viz', 0), ('plotly/dash', 0.5496276617050171, 'viz', 0), ('mckinsey/vizro', 0.5490374565124512, 'viz', 0), ('beeware/toga', 0.5480042099952698, 'gui', 0), ('dfki-ric/pytransform3d', 0.5455291867256165, 'math', 0), ('marcomusy/vedo', 0.5432279706001282, 'viz', 0), ('artelys/geonetworkx', 0.5396808385848999, 'gis', 0), ('federicoceratto/dashing', 0.5380876064300537, 'term', 0), ('westhealth/pyvis', 0.5358419418334961, 'graph', 0), ('raphaelquast/eomaps', 0.5355204343795776, 'gis', 0), ('parthjadhav/tkinter-designer', 0.5346206426620483, 'gui', 0), ('scitools/cartopy', 0.5334916114807129, 'gis', 0), ('vaexio/vaex', 0.5222266316413879, 'perf', 0), ('pygraphviz/pygraphviz', 0.5206543207168579, 'viz', 0), ('wesm/pydata-book', 0.5204950571060181, 'study', 0), ('imageio/imageio', 0.5204654335975647, 'util', 0), ('eleutherai/pyfra', 0.5192615985870361, 'ml', 0), ('visgl/deck.gl', 0.5192416310310364, 'viz', 0), ('brandtbucher/specialist', 0.5191949605941772, 'perf', 0), ('bmabey/pyldavis', 0.518200159072876, 'ml', 0), ('ipython/ipyparallel', 0.5176970362663269, 'perf', 0), ('pandas-dev/pandas', 0.5176126956939697, 'pandas', 0), ('nomic-ai/deepscatter', 0.5163466930389404, 'viz', 0), ('geopandas/geopandas', 0.515374481678009, 'gis', 0), ('wxwidgets/phoenix', 0.5125094652175903, 'gui', 0), ('makepath/xarray-spatial', 0.510444700717926, 'gis', 1), ('earthlab/earthpy', 0.5084066987037659, 'gis', 0), ('amaargiru/pyroad', 0.5063855648040771, 'study', 0), ('quantopian/qgrid', 0.5044505596160889, 'jupyter', 0), ('jalammar/ecco', 0.5038740634918213, 'ml-interpretability', 0), ('gradio-app/gradio', 0.5025941729545593, 'viz', 0), ('pyston/pyston', 0.5020092725753784, 'util', 0)]",23,2.0,,0.4,11,3,77,1,1,13,1,11.0,10.0,90.0,0.9,30
1680,util,https://github.com/pycqa/mccabe,[],,[],[],,,,pycqa/mccabe,mccabe,615,58,17,Python,pypi.python.org/pypi/mccabe,McCabe complexity checker for Python,pycqa,2024-01-12,2013-02-20,570,1.0773273273273274,https://avatars.githubusercontent.com/u/8749848?v=4,McCabe complexity checker for Python,"['complexity', 'complexity-analysis', 'flake8', 'flake8-extensions', 'flake8-plugin', 'linter-flake8', 'linter-plugin', 'mccabe']","['complexity', 'complexity-analysis', 'flake8', 'flake8-extensions', 'flake8-plugin', 'linter-flake8', 'linter-plugin', 'mccabe']",2023-12-03,"[('pycqa/flake8', 0.6619266867637634, 'util', 3), ('facebook/pyre-check', 0.5869566202163696, 'typing', 0), ('google/pytype', 0.5844917893409729, 'typing', 0), ('agronholm/typeguard', 0.5824611186981201, 'typing', 0), ('pycqa/pycodestyle', 0.5681533813476562, 'util', 3), ('rubik/radon', 0.5480080842971802, 'util', 0), ('microsoft/pyright', 0.5418702960014343, 'typing', 0), ('pytoolz/toolz', 0.521766722202301, 'util', 0), ('astral-sh/ruff', 0.509772777557373, 'util', 0)]",24,7.0,,0.04,8,8,133,1,0,1,1,8.0,6.0,90.0,0.8,30
1483,util,https://github.com/ivankorobkov/python-inject,['dependency-injection'],,[],[],,,,ivankorobkov/python-inject,python-inject,607,98,17,Python,,Python dependency injection,ivankorobkov,2024-01-12,2010-02-08,729,0.8324843260188087,,Python dependency injection,[],['dependency-injection'],2023-11-23,"[('python-injector/injector', 0.7356547713279724, 'util', 1), ('allrod5/injectable', 0.640688955783844, 'util', 1), ('ets-labs/python-dependency-injector', 0.6299859881401062, 'util', 1), ('mitsuhiko/rye', 0.5525853037834167, 'util', 0), ('proofit404/dependencies', 0.547492265701294, 'util', 1), ('python-poetry/poetry', 0.534679651260376, 'util', 0)]",29,5.0,,0.31,10,7,170,2,0,2,2,10.0,21.0,90.0,2.1,30
522,gis,https://github.com/toblerity/rtree,[],,[],[],,,,toblerity/rtree,rtree,582,126,31,Python,https://rtree.readthedocs.io/en/latest/,Rtree: spatial index for Python GIS,toblerity,2024-01-04,2011-06-19,658,0.8841145833333334,https://avatars.githubusercontent.com/u/859968?v=4,Rtree: spatial index for Python GIS,[],[],2023-12-19,"[('pysal/pysal', 0.6110436320304871, 'gis', 0), ('uber/h3-py', 0.6043885350227356, 'gis', 0), ('artelys/geonetworkx', 0.597081184387207, 'gis', 0), ('makepath/xarray-spatial', 0.5867227911949158, 'gis', 0), ('pinecone-io/pinecone-python-client', 0.5536699295043945, 'data', 0), ('geopandas/geopandas', 0.5479511618614197, 'gis', 0), ('opengeos/leafmap', 0.5405087471008301, 'gis', 0), ('earthlab/earthpy', 0.5261022448539734, 'gis', 0), ('gregorhd/mapcompare', 0.5135495662689209, 'gis', 0)]",41,3.0,,0.63,13,10,153,1,1,1,1,13.0,23.0,90.0,1.8,30
1320,util,https://github.com/pycqa/pylint-django,"['django', 'pylint', 'linter']",,[],[],,,,pycqa/pylint-django,pylint-django,575,121,16,Python,,Pylint plugin for improving code analysis for when using Django,pycqa,2024-01-12,2013-10-01,539,1.0667903525046383,https://avatars.githubusercontent.com/u/121692054?v=4,Pylint plugin for improving code analysis for when using Django,[],"['django', 'linter', 'pylint']",2023-11-04,"[('psf/black', 0.5581016540527344, 'util', 0), ('pygments/pygments', 0.5438166856765747, 'util', 0), ('grantjenks/blue', 0.5386630892753601, 'util', 0), ('google/pytype', 0.5338144898414612, 'typing', 1), ('pycqa/flake8', 0.5301540493965149, 'util', 1), ('pylons/pyramid', 0.5214040279388428, 'web', 0), ('hhatto/autopep8', 0.5179296731948853, 'util', 0), ('klen/pylama', 0.5170671939849854, 'util', 1), ('landscapeio/prospector', 0.5079518556594849, 'util', 0), ('bottlepy/bottle', 0.5043782591819763, 'web', 0), ('feincms/feincms', 0.5040256381034851, 'web', 0)]",70,3.0,,0.6,23,14,125,2,1,5,1,23.0,28.0,90.0,1.2,30
485,gis,https://github.com/fatiando/verde,[],,[],[],,,,fatiando/verde,verde,550,69,21,Python,https://www.fatiando.org/verde,"Processing and gridding spatial data, machine-learning style",fatiando,2024-01-12,2018-04-25,300,1.8281101614434947,https://avatars.githubusercontent.com/u/8174113?v=4,"Processing and gridding spatial data, machine-learning style","['earth-science', 'fatiando-a-terra', 'geophysics', 'geoscience', 'geospatial', 'interpolation', 'machine-learning', 'scipy', 'scipy-stack']","['earth-science', 'fatiando-a-terra', 'geophysics', 'geoscience', 'geospatial', 'interpolation', 'machine-learning', 'scipy', 'scipy-stack']",2023-10-25,"[('osgeo/grass', 0.6331599950790405, 'gis', 2), ('krzjoa/awesome-python-data-science', 0.559866726398468, 'study', 1), ('microsoft/torchgeo', 0.5598282217979431, 'gis', 1), ('ddbourgin/numpy-ml', 0.5586436986923218, 'ml', 1), ('automl/auto-sklearn', 0.5549225807189941, 'ml', 0), ('scikit-learn/scikit-learn', 0.5504959225654602, 'ml', 1), ('developmentseed/label-maker', 0.5453664660453796, 'gis', 0), ('sentinel-hub/eo-learn', 0.5445219874382019, 'gis', 1), ('feast-dev/feast', 0.5436317324638367, 'ml-ops', 1), ('remotesensinglab/raster4ml', 0.5404156446456909, 'gis', 1), ('plant99/felicette', 0.533496618270874, 'gis', 2), ('scikit-mobility/scikit-mobility', 0.531174898147583, 'gis', 0), ('online-ml/river', 0.531051754951477, 'ml', 1), ('awslabs/autogluon', 0.5272731781005859, 'ml', 1), ('polyaxon/datatile', 0.5251547694206238, 'pandas', 0), ('opengeos/segment-geospatial', 0.5204988121986389, 'gis', 2), ('scitools/iris', 0.5199081301689148, 'gis', 1), ('firmai/industry-machine-learning', 0.5189895629882812, 'study', 1), ('milvus-io/bootcamp', 0.5155651569366455, 'data', 0), ('skops-dev/skops', 0.513488233089447, 'ml-ops', 1), ('earthlab/earthpy', 0.5133398771286011, 'gis', 0), ('r-barnes/richdem', 0.5110668540000916, 'gis', 1), ('geopandas/geopandas', 0.5108627080917358, 'gis', 1), ('sloria/textblob', 0.5090445876121521, 'nlp', 0), ('gradio-app/gradio', 0.5083851218223572, 'viz', 1), ('raphaelquast/eomaps', 0.5024335384368896, 'gis', 1)]",13,8.0,,0.13,4,2,70,3,1,2,1,4.0,4.0,90.0,1.0,30
1797,jupyter,https://github.com/rapidsai/jupyterlab-nvdashboard,['gpu'],,[],[],,,,rapidsai/jupyterlab-nvdashboard,jupyterlab-nvdashboard,531,74,16,TypeScript,,A JupyterLab extension for displaying dashboards of GPU usage.,rapidsai,2024-01-04,2019-08-12,233,2.2775735294117645,https://avatars.githubusercontent.com/u/43887749?v=4,A JupyterLab extension for displaying dashboards of GPU usage.,[],['gpu'],2024-01-12,"[('federicoceratto/dashing', 0.6266454458236694, 'term', 0), ('nvidia/warp', 0.5572924017906189, 'sim', 1), ('vizzuhq/ipyvizzu', 0.5411252975463867, 'jupyter', 0), ('datapane/datapane', 0.5317434668540955, 'viz', 0), ('holoviz/panel', 0.5303380489349365, 'viz', 0), ('voila-dashboards/voila', 0.5271745920181274, 'jupyter', 0), ('plotly/plotly.py', 0.5129841566085815, 'viz', 0), ('maartenbreddels/ipyvolume', 0.5118504762649536, 'jupyter', 0), ('xiaohk/stickyland', 0.5069721937179565, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.5053638815879822, 'jupyter', 0), ('graphistry/pygraphistry', 0.503430187702179, 'data', 1)]",19,2.0,,0.27,7,5,54,0,2,6,2,7.0,4.0,90.0,0.6,30
1669,testing,https://github.com/lundberg/respx,"['mocking', 'httpx']",,[],[],,,,lundberg/respx,respx,523,38,4,Python,https://lundberg.github.io/respx,Mock HTTPX with awesome request patterns and response side effects 🦋,lundberg,2024-01-12,2019-11-13,219,2.378817413905133,,Mock HTTPX with awesome request patterns and response side effects 🦋,"['httpx', 'mock', 'pytest', 'testing']","['httpx', 'mock', 'mocking', 'pytest', 'testing']",2023-07-20,"[('kevin1024/vcrpy', 0.6465907692909241, 'testing', 2), ('jamielennox/requests-mock', 0.6144221425056458, 'testing', 1), ('pytest-dev/pytest-mock', 0.5953378081321716, 'testing', 2), ('getsentry/responses', 0.5848559737205505, 'testing', 1), ('taverntesting/tavern', 0.5600868463516235, 'testing', 2)]",24,7.0,,0.15,5,0,51,6,1,11,1,5.0,6.0,90.0,1.2,30
921,util,https://github.com/heuer/segno,[],,[],[],,,,heuer/segno,segno,507,47,13,Python,https://pypi.org/project/segno/,Python QR Code and Micro QR Code encoder,heuer,2024-01-08,2016-08-04,390,1.2976234003656306,,Python QR Code and Micro QR Code encoder,"['barcode', 'iso-18004', 'matrix-barcode', 'micro-qr-code', 'micro-qrcode', 'python-qrcode', 'qr-code', 'qr-generator', 'qrcode', 'segno', 'structured-append']","['barcode', 'iso-18004', 'matrix-barcode', 'micro-qr-code', 'micro-qrcode', 'python-qrcode', 'qr-code', 'qr-generator', 'qrcode', 'segno', 'structured-append']",2023-11-30,"[('mnooner256/pyqrcode', 0.7471798658370972, 'util', 0)]",11,3.0,,1.46,12,10,91,1,2,6,2,12.0,19.0,90.0,1.6,30
830,gis,https://github.com/perrygeo/python-rasterstats,[],,[],[],,,,perrygeo/python-rasterstats,python-rasterstats,504,165,34,Python,,Summary statistics of geospatial raster datasets based on vector geometries.,perrygeo,2024-01-12,2013-09-18,540,0.9318541996830428,,Summary statistics of geospatial raster datasets based on vector geometries.,[],[],2023-10-05,"[('osgeo/gdal', 0.5707691311836243, 'gis', 0), ('remotesensinglab/raster4ml', 0.5588922500610352, 'gis', 0), ('osgeo/grass', 0.5056399703025818, 'gis', 0), ('makepath/xarray-spatial', 0.5050948262214661, 'gis', 0)]",31,7.0,,0.38,5,2,126,3,1,2,1,5.0,9.0,90.0,1.8,30
291,util,https://github.com/fastai/ghapi,[],,[],[],,,,fastai/ghapi,ghapi,496,55,9,Jupyter Notebook,https://ghapi.fast.ai/,A delightful and complete interface to GitHub's amazing API,fastai,2024-01-12,2020-11-21,166,2.9802575107296136,https://avatars.githubusercontent.com/u/20547620?v=4,A delightful and complete interface to GitHub's amazing API,"['api-client', 'github', 'github-api', 'nbdev', 'openapi']","['api-client', 'github', 'github-api', 'nbdev', 'openapi']",2023-06-14,"[('fauxpilot/fauxpilot', 0.5876653790473938, 'llm', 0), ('vitalik/django-ninja', 0.58127361536026, 'web', 1), ('openai/openai-python', 0.5810590982437134, 'util', 0), ('langchain-ai/opengpts', 0.5734665393829346, 'llm', 0), ('hugapi/hug', 0.5626925230026245, 'util', 0), ('pygithub/pygithub', 0.5488420724868774, 'util', 2), ('github/innovationgraph', 0.5455352067947388, 'data', 1), ('shishirpatil/gorilla', 0.5438264012336731, 'llm', 0), ('starlite-api/starlite', 0.5345987677574158, 'web', 1), ('googleapis/google-api-python-client', 0.5308494567871094, 'util', 0), ('tiangolo/fastapi', 0.5305969715118408, 'web', 1), ('prefecthq/server', 0.5246346592903137, 'util', 0), ('python-restx/flask-restx', 0.5162189602851868, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5124199986457825, 'data', 1), ('kivy/kivy', 0.5002898573875427, 'util', 0)]",16,7.0,,0.02,4,1,38,7,0,6,6,4.0,2.0,90.0,0.5,30
1273,ml,https://github.com/intellabs/bayesian-torch,[],,[],[],,,,intellabs/bayesian-torch,bayesian-torch,402,57,17,Python,,A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch,intellabs,2024-01-14,2020-12-17,162,2.4705882352941178,https://avatars.githubusercontent.com/u/1492758?v=4,A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch,"['bayesian-deep-learning', 'bayesian-inference', 'bayesian-layers', 'bayesian-neural-networks', 'deep-learning', 'deep-neural-networks', 'pytorch', 'stochastic-variational-inference', 'uncertainty-estimation', 'uncertainty-neural-networks', 'uncertainty-quantification']","['bayesian-deep-learning', 'bayesian-inference', 'bayesian-layers', 'bayesian-neural-networks', 'deep-learning', 'deep-neural-networks', 'pytorch', 'stochastic-variational-inference', 'uncertainty-estimation', 'uncertainty-neural-networks', 'uncertainty-quantification']",2024-01-02,"[('pyro-ppl/pyro', 0.6956607699394226, 'ml-dl', 3), ('pytorch/ignite', 0.6580493450164795, 'ml-dl', 2), ('pytorch/botorch', 0.6108170747756958, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5984524488449097, 'study', 2), ('rasbt/machine-learning-book', 0.5859395861625671, 'study', 2), ('intel/intel-extension-for-pytorch', 0.582083523273468, 'perf', 2), ('pyg-team/pytorch_geometric', 0.5782586932182312, 'ml-dl', 2), ('skorch-dev/skorch', 0.5576133131980896, 'ml-dl', 1), ('denys88/rl_games', 0.5465734004974365, 'ml-rl', 2), ('tensorlayer/tensorlayer', 0.5386697053909302, 'ml-rl', 1), ('thu-ml/tianshou', 0.5311893820762634, 'ml-rl', 1), ('nvidia/apex', 0.5274893045425415, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5250702500343323, 'ml', 1), ('karpathy/micrograd', 0.5212419629096985, 'study', 0), ('keras-team/keras', 0.5210490822792053, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5206497311592102, 'ml-dl', 2), ('aistream-peelout/flow-forecast', 0.518251359462738, 'time-series', 3), ('calculatedcontent/weightwatcher', 0.51715487241745, 'ml-dl', 0), ('rasbt/deeplearning-models', 0.516359806060791, 'ml-dl', 0), ('huggingface/transformers', 0.5125004053115845, 'nlp', 2), ('pytorch/rl', 0.5093467831611633, 'ml-rl', 1), ('udlbook/udlbook', 0.5086169838905334, 'study', 1), ('microsoft/deepspeed', 0.5001977682113647, 'ml-dl', 2)]",6,2.0,,0.63,8,7,37,0,2,2,2,8.0,10.0,90.0,1.2,30
1230,perf,https://github.com/dgilland/cacheout,[],,[],[],,,,dgilland/cacheout,cacheout,392,42,13,Python,https://cacheout.readthedocs.io,A caching library for Python,dgilland,2024-01-03,2018-01-12,315,1.2421910366681757,,A caching library for Python,"['caching', 'fifo', 'lfu', 'lifo', 'lru', 'memoization', 'mru', 'rr']","['caching', 'fifo', 'lfu', 'lifo', 'lru', 'memoization', 'mru', 'rr']",2023-12-22,"[('python-cachier/cachier', 0.7924980521202087, 'perf', 2), ('erotemic/ubelt', 0.6818086504936218, 'util', 0), ('joblib/joblib', 0.6794201135635376, 'util', 2), ('grantjenks/python-diskcache', 0.6435301899909973, 'util', 0), ('pythonspeed/filprofiler', 0.6149056553840637, 'profiling', 0), ('pytoolz/toolz', 0.6084659695625305, 'util', 0), ('pympler/pympler', 0.6028500199317932, 'perf', 0), ('spotify/annoy', 0.5922878980636597, 'ml', 0), ('pypy/pypy', 0.5562092661857605, 'util', 0), ('pythonprofilers/memory_profiler', 0.555606484413147, 'profiling', 0), ('pyston/pyston', 0.5537205338478088, 'util', 0), ('aio-libs/aiocache', 0.5477664470672607, 'data', 0), ('zilliztech/gptcache', 0.540678858757019, 'llm', 0), ('pytables/pytables', 0.5385268926620483, 'data', 0), ('klen/py-frameworks-bench', 0.5362305641174316, 'perf', 0), ('fastai/fastcore', 0.5273230671882629, 'util', 0), ('long2ice/fastapi-cache', 0.5193830728530884, 'web', 0), ('python-trio/trio', 0.5188122987747192, 'perf', 0), ('libtcod/python-tcod', 0.5180550813674927, 'gamedev', 0), ('qdrant/fastembed', 0.5054611563682556, 'ml', 0), ('dosisod/refurb', 0.5029307007789612, 'util', 0), ('sumerc/yappi', 0.5023199915885925, 'profiling', 0)]",6,1.0,,0.79,10,10,73,1,0,4,4,10.0,34.0,90.0,3.4,30
1548,llm,https://github.com/eugeneyan/obsidian-copilot,[],,[],[],,,,eugeneyan/obsidian-copilot,obsidian-copilot,342,23,6,Python,https://eugeneyan.com/writing/obsidian-copilot/,🤖 A prototype assistant for writing and thinking,eugeneyan,2024-01-12,2023-06-11,33,10.274678111587983,,🤖 A prototype assistant for writing and thinking,"['assistant', 'generative-ai', 'large-language-models', 'llm', 'obsidian-plugin', 'retrieval-augmented-generation']","['assistant', 'generative-ai', 'large-language-models', 'llm', 'obsidian-plugin', 'retrieval-augmented-generation']",2024-01-11,"[('kyegomez/tree-of-thoughts', 0.6086257100105286, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5925748348236084, 'study', 1), ('llmware-ai/llmware', 0.5871189832687378, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5580410361289978, 'llm', 1), ('lucidrains/toolformer-pytorch', 0.5504752397537231, 'llm', 0), ('lupantech/chameleon-llm', 0.5486295819282532, 'llm', 1), ('openlmlab/moss', 0.5385111570358276, 'llm', 1), ('intellabs/fastrag', 0.5370295643806458, 'nlp', 2), ('huggingface/text-generation-inference', 0.5260320901870728, 'llm', 0), ('ofa-sys/ofa', 0.5171679854393005, 'llm', 0), ('lm-sys/fastchat', 0.5163758993148804, 'llm', 0), ('srush/minichain', 0.5138395428657532, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5133623480796814, 'llm', 0), ('microsoft/lmops', 0.5132716298103333, 'llm', 1), ('rcgai/simplyretrieve', 0.5125769972801208, 'llm', 3), ('thilinarajapakse/simpletransformers', 0.5094960927963257, 'nlp', 0), ('reasoning-machines/pal', 0.5085762739181519, 'llm', 1), ('deepset-ai/haystack', 0.5081061124801636, 'llm', 2), ('prefecthq/marvin', 0.5069007277488708, 'nlp', 1), ('cheshire-cat-ai/core', 0.5033305287361145, 'llm', 2), ('lianjiatech/belle', 0.5000393390655518, 'llm', 0)]",5,2.0,,0.35,1,1,7,0,0,0,0,1.0,0.0,90.0,0.0,30
306,crypto,https://github.com/ethereum/eth-utils,[],,[],[],,,,ethereum/eth-utils,eth-utils,297,151,19,Python,https://eth-utils.readthedocs.io/en/latest/,Utility functions for working with ethereum related codebases.,ethereum,2024-01-03,2017-02-07,364,0.8159340659340659,https://avatars.githubusercontent.com/u/6250754?v=4,Utility functions for working with ethereum related codebases.,"['ethereum', 'utility-library']","['ethereum', 'utility-library']",2024-01-10,"[('pytoolz/toolz', 0.525811493396759, 'util', 0), ('suor/funcy', 0.5216156244277954, 'util', 0), ('tiiuae/sbomnix', 0.5169852375984192, 'util', 0)]",37,2.0,,1.9,17,11,84,0,0,10,10,17.0,8.0,90.0,0.5,30
1725,study,https://github.com/ray-project/ray-educational-materials,[],,[],[],,,,ray-project/ray-educational-materials,ray-educational-materials,232,42,11,Jupyter Notebook,,"This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.",ray-project,2024-01-10,2022-09-16,71,3.241516966067864,https://avatars.githubusercontent.com/u/22125274?v=4,"This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.","['deep-learning', 'distributed-machine-learning', 'generative-ai', 'llm', 'llm-inference', 'llm-serving', 'ray', 'ray-data', 'ray-distributed', 'ray-serve', 'ray-train', 'ray-tune']","['deep-learning', 'distributed-machine-learning', 'generative-ai', 'llm', 'llm-inference', 'llm-serving', 'ray', 'ray-data', 'ray-distributed', 'ray-serve', 'ray-train', 'ray-tune']",2024-01-09,"[('ray-project/ray', 0.7717517614364624, 'ml-ops', 3), ('ray-project/ray-llm', 0.6102384924888611, 'llm', 4), ('alpa-projects/alpa', 0.5506226420402527, 'ml-dl', 2), ('horovod/horovod', 0.5418562293052673, 'ml-ops', 2), ('aistream-peelout/flow-forecast', 0.5015262365341187, 'time-series', 1)]",8,2.0,,0.98,22,19,16,0,2,3,2,22.0,7.0,90.0,0.3,30
1478,web,https://github.com/alirn76/panther,[],,[],[],,,,alirn76/panther,panther,226,12,7,Python,https://pantherpy.github.io,Fast & Friendly Web Framework For Building Async APIs With Python 3.10+,alirn76,2024-01-13,2022-02-23,100,2.2407932011331444,,Fast & Friendly Web Framework For Building Async APIs With Python 3.10+,"['framework', 'panther']","['framework', 'panther']",2024-01-04,"[('pallets/quart', 0.7484593391418457, 'web', 0), ('neoteroi/blacksheep', 0.7121951580047607, 'web', 1), ('aio-libs/aiohttp', 0.7044265270233154, 'web', 0), ('encode/httpx', 0.6786492466926575, 'web', 0), ('klen/muffin', 0.6565958857536316, 'web', 0), ('python-trio/trio', 0.6534282565116882, 'perf', 0), ('geeogi/async-python-lambda-template', 0.6337937712669373, 'template', 0), ('python-restx/flask-restx', 0.6230086088180542, 'web', 0), ('encode/uvicorn', 0.6067794561386108, 'web', 0), ('huge-success/sanic', 0.6031531095504761, 'web', 1), ('magicstack/uvloop', 0.5940344929695129, 'util', 0), ('encode/starlette', 0.5935547351837158, 'web', 0), ('agronholm/anyio', 0.5871341824531555, 'perf', 0), ('timofurrer/awesome-asyncio', 0.5865374207496643, 'study', 0), ('tiangolo/asyncer', 0.5792798399925232, 'perf', 0), ('falconry/falcon', 0.5779109001159668, 'web', 1), ('samuelcolvin/arq', 0.5734840035438538, 'data', 0), ('klen/py-frameworks-bench', 0.564853310585022, 'perf', 0), ('starlite-api/starlite', 0.5622544288635254, 'web', 0), ('vitalik/django-ninja', 0.5587133169174194, 'web', 0), ('masoniteframework/masonite', 0.5462374091148376, 'web', 1), ('pallets/flask', 0.5419907569885254, 'web', 0), ('airtai/faststream', 0.5416358709335327, 'perf', 0), ('samuelcolvin/aioaws', 0.5380411148071289, 'data', 0), ('sumerc/yappi', 0.5367398858070374, 'profiling', 0), ('tornadoweb/tornado', 0.5357459187507629, 'web', 0), ('tiangolo/fastapi', 0.5322808027267456, 'web', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5177244544029236, 'template', 0), ('hugapi/hug', 0.5171224474906921, 'util', 0), ('ets-labs/python-dependency-injector', 0.516931414604187, 'util', 0), ('jordaneremieff/mangum', 0.5160053372383118, 'web', 0), ('fastai/fastcore', 0.5143014788627625, 'util', 0), ('bottlepy/bottle', 0.5107240676879883, 'web', 0), ('nficano/python-lambda', 0.5092719793319702, 'util', 0), ('pylons/pyramid', 0.5073546767234802, 'web', 0), ('hyperopt/hyperopt', 0.5043962597846985, 'ml', 0)]",6,1.0,,5.27,22,18,23,0,0,40,40,22.0,4.0,90.0,0.2,30
1173,data,https://github.com/pinecone-io/pinecone-python-client,['vector-search'],,[],[],,,,pinecone-io/pinecone-python-client,pinecone-python-client,205,50,21,Python,https://www.pinecone.io/docs,The Pinecone Python client ,pinecone-io,2024-01-12,2021-09-16,123,1.6570438799076213,https://avatars.githubusercontent.com/u/54333248?v=4,The Pinecone Python client ,[],['vector-search'],2024-01-14,"[('qdrant/qdrant-client', 0.6546476483345032, 'util', 1), ('weaviate/weaviate-python-client', 0.5670716762542725, 'util', 1), ('toblerity/rtree', 0.5536699295043945, 'gis', 0), ('qdrant/qdrant-haystack', 0.5180879831314087, 'data', 0), ('qdrant/vector-db-benchmark', 0.512586772441864, 'perf', 1), ('facebookresearch/faiss', 0.5026112794876099, 'ml', 1)]",28,2.0,,2.27,71,56,28,0,3,18,3,70.0,20.0,90.0,0.3,30
1865,llm,https://github.com/lamini-ai/llm-classifier,['classifier'],,[],[],,,,lamini-ai/llm-classifier,llm-classifier,124,13,4,Python,,Classify data instantly using an LLM,lamini-ai,2024-01-12,2023-09-20,18,6.575757575757576,https://avatars.githubusercontent.com/u/130713213?v=4,Classify data instantly using an LLM,[],['classifier'],2023-12-14,"[('microsoft/jarvis', 0.5053083300590515, 'llm', 0)]",6,1.0,,0.96,2,0,4,1,0,0,0,2.0,6.0,90.0,3.0,30
1557,util,https://github.com/tiiuae/sbomnix,[],,[],[],,,,tiiuae/sbomnix,sbomnix,72,18,8,Python,,A suite of utilities to help with software supply chain challenges on nix targets,tiiuae,2024-01-04,2022-12-08,59,1.2057416267942584,https://avatars.githubusercontent.com/u/59836348?v=4,A suite of utilities to help with software supply chain challenges on nix targets,"['bill-of-materials', 'cpe', 'cyclonedx', 'dependencies', 'nix', 'purl', 'sbom', 'sbom-generator', 'sbom-tool', 'security', 'software-bill-of-materials', 'software-supply-chain', 'software-supply-chain-security', 'spdx-sbom', 'static-analysis', 'vulnerability-scanners']","['bill-of-materials', 'cpe', 'cyclonedx', 'dependencies', 'nix', 'purl', 'sbom', 'sbom-generator', 'sbom-tool', 'security', 'software-bill-of-materials', 'software-supply-chain', 'software-supply-chain-security', 'spdx-sbom', 'static-analysis', 'vulnerability-scanners']",2024-01-03,"[('spack/spack', 0.5559228658676147, 'util', 0), ('trailofbits/pip-audit', 0.5466781258583069, 'security', 1), ('conda/conda', 0.5382207632064819, 'util', 0), ('aquasecurity/trivy', 0.5336388945579529, 'security', 2), ('chaostoolkit/chaostoolkit', 0.5200450420379639, 'util', 0), ('mamba-org/mamba', 0.5184597373008728, 'util', 0), ('ethereum/eth-utils', 0.5169852375984192, 'crypto', 0), ('aswinnnn/pyscan', 0.5072173476219177, 'security', 2)]",9,5.0,,3.15,17,16,13,1,12,11,12,17.0,13.0,90.0,0.8,30
760,study,https://github.com/fluentpython/example-code-2e,[],,[],[],,,,fluentpython/example-code-2e,example-code-2e,2683,763,68,Python,https://amzn.to/3J48u2J,"Example code for Fluent Python, 2nd edition (O'Reilly 2022) ",fluentpython,2024-01-13,2019-03-21,253,10.574887387387387,https://avatars.githubusercontent.com/u/9216311?v=4,"Example code for Fluent Python, 2nd edition (O'Reilly 2022) ","['concurrency', 'iterators', 'metaprogramming', 'special-methods']","['concurrency', 'iterators', 'metaprogramming', 'special-methods']",2022-04-24,"[('more-itertools/more-itertools', 0.5874441862106323, 'util', 0), ('python-trio/trio', 0.5376577377319336, 'perf', 0), ('python-greenlet/greenlet', 0.514401912689209, 'perf', 0), ('evhub/coconut', 0.5133896470069885, 'util', 0), ('fastai/fastcore', 0.5095949769020081, 'util', 0), ('pytoolz/toolz', 0.5072869062423706, 'util', 0), ('nteract/papermill', 0.5064553022384644, 'jupyter', 0), ('sumerc/yappi', 0.5051793456077576, 'profiling', 0), ('joblib/joblib', 0.5024613738059998, 'util', 0), ('koaning/clumper', 0.5001460909843445, 'util', 0)]",7,1.0,,0.0,3,1,59,21,0,0,0,3.0,1.0,90.0,0.3,29
1343,util,https://github.com/cdgriffith/box,[],,[],[],,,,cdgriffith/box,Box,2308,104,35,Python,https://github.com/cdgriffith/Box/wiki,Python dictionaries with advanced dot notation access,cdgriffith,2024-01-12,2017-03-11,359,6.421303656597774,,Python dictionaries with advanced dot notation access,"['addict', 'box', 'bunch', 'dictionaries', 'helper', 'object', 'python-box', 'python-types']","['addict', 'box', 'bunch', 'dictionaries', 'helper', 'object', 'python-box', 'python-types']",2023-08-26,[],1,0.0,,0.08,3,0,83,5,9,9,9,3.0,3.0,90.0,1.0,29
1474,util,https://github.com/ianmiell/shutit,[],,[],[],,,,ianmiell/shutit,shutit,2143,130,67,Python,http://ianmiell.github.io/shutit/,Automation framework for programmers,ianmiell,2024-01-13,2014-03-25,514,4.169260700389105,,Automation framework for programmers,"['docker', 'pexpect', 'vagrant']","['docker', 'pexpect', 'vagrant']",2022-06-29,"[('tox-dev/tox', 0.5560944080352783, 'testing', 0), ('pypa/pipenv', 0.549948513507843, 'util', 0), ('backtick-se/cowait', 0.5379471182823181, 'util', 1), ('martinheinz/python-project-blueprint', 0.5357551574707031, 'template', 1), ('pexpect/pexpect', 0.5116491317749023, 'util', 0), ('willmcgugan/textual', 0.5011722445487976, 'term', 0)]",24,6.0,,0.0,0,0,119,19,0,3,3,0.0,0.0,90.0,0.0,29
707,gis,https://github.com/mcordts/cityscapesscripts,[],,[],[],,,,mcordts/cityscapesscripts,cityscapesScripts,2053,608,45,Python,,README and scripts for the Cityscapes Dataset,mcordts,2024-01-12,2016-02-20,414,4.953809031368493,,README and scripts for the Cityscapes Dataset,[],[],2023-05-07,"[('udst/urbansim', 0.6591488718986511, 'sim', 0), ('pysal/momepy', 0.564961314201355, 'gis', 0), ('gregorhd/mapcompare', 0.5555253624916077, 'gis', 0), ('mattbierbaum/arxiv-public-datasets', 0.5378220677375793, 'data', 0), ('spatialucr/geosnap', 0.5296457409858704, 'gis', 0)]",18,3.0,,0.04,6,1,96,8,0,0,0,6.0,1.0,90.0,0.2,29
1002,study,https://github.com/cerlymarco/medium_notebook,[],,[],[],,,,cerlymarco/medium_notebook,MEDIUM_NoteBook,1972,966,100,Jupyter Notebook,,Repository containing notebooks of my posts on Medium,cerlymarco,2024-01-11,2019-04-22,249,7.915137614678899,,Repository containing notebooks of my posts on Medium,"['artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'notebooks']","['artificial-intelligence', 'data-science', 'deep-learning', 'machine-learning', 'notebooks']",2023-12-17,"[('firmai/industry-machine-learning', 0.6431946158409119, 'study', 2), ('zenodo/zenodo', 0.5398790240287781, 'util', 0), ('tensorflow/tensor2tensor', 0.5338510870933533, 'ml', 2), ('ageron/handson-ml2', 0.525178074836731, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5219733119010925, 'study', 2)]",1,0.0,,0.67,1,1,58,1,0,0,0,1.0,1.0,90.0,1.0,29
9,ml,https://github.com/contextlab/hypertools,[],,[],[],,,,contextlab/hypertools,hypertools,1796,163,61,Python,http://hypertools.readthedocs.io/en/latest/,A Python toolbox for gaining geometric insights into high-dimensional data,contextlab,2024-01-13,2016-09-27,383,4.689295039164491,https://avatars.githubusercontent.com/u/22374976?v=4,A Python toolbox for gaining geometric insights into high-dimensional data,"['data-visualization', 'data-wrangling', 'high-dimensional-data', 'text-vectorization', 'time-series', 'topic-modeling', 'visualization']","['data-visualization', 'data-wrangling', 'high-dimensional-data', 'text-vectorization', 'time-series', 'topic-modeling', 'visualization']",2022-02-12,"[('enthought/mayavi', 0.6949652433395386, 'viz', 1), ('residentmario/geoplot', 0.686218798160553, 'gis', 0), ('holoviz/holoviz', 0.6432879567146301, 'viz', 0), ('marcomusy/vedo', 0.6358500719070435, 'viz', 1), ('scitools/iris', 0.6321009993553162, 'gis', 0), ('mwaskom/seaborn', 0.6260726451873779, 'viz', 1), ('pyqtgraph/pyqtgraph', 0.6197599172592163, 'viz', 1), ('altair-viz/altair', 0.6141685843467712, 'viz', 1), ('holoviz/panel', 0.6038259267807007, 'viz', 0), ('holoviz/hvplot', 0.5875470042228699, 'pandas', 0), ('pyvista/pyvista', 0.5857540369033813, 'viz', 1), ('facebookresearch/hiplot', 0.5843124985694885, 'viz', 0), ('man-group/dtale', 0.5685189366340637, 'viz', 2), ('gregorhd/mapcompare', 0.565951406955719, 'gis', 0), ('holoviz/geoviews', 0.5654189586639404, 'gis', 0), ('bmabey/pyldavis', 0.5628495812416077, 'ml', 0), ('matplotlib/matplotlib', 0.5552871227264404, 'viz', 1), ('makepath/xarray-spatial', 0.5519405603408813, 'gis', 0), ('vaexio/vaex', 0.548748791217804, 'perf', 1), ('kanaries/pygwalker', 0.5455514192581177, 'pandas', 1), ('dfki-ric/pytransform3d', 0.5422584414482117, 'math', 1), ('pandas-dev/pandas', 0.5390833616256714, 'pandas', 0), ('bokeh/bokeh', 0.5384601950645447, 'viz', 1), ('earthlab/earthpy', 0.5363619923591614, 'gis', 0), ('pysal/pysal', 0.535285472869873, 'gis', 0), ('wesm/pydata-book', 0.5346917510032654, 'study', 0), ('eleutherai/pyfra', 0.5310801863670349, 'ml', 0), ('jakevdp/pythondatasciencehandbook', 0.5308850407600403, 'study', 0), ('graphistry/pygraphistry', 0.5276858806610107, 'data', 1), ('lux-org/lux', 0.5267577767372131, 'viz', 1), ('artelys/geonetworkx', 0.5169349908828735, 'gis', 0), ('has2k1/plotnine', 0.5157314538955688, 'viz', 0), ('geopandas/geopandas', 0.5144107341766357, 'gis', 0), ('tdameritrade/stumpy', 0.5135495662689209, 'time-series', 0), ('holoviz/datashader', 0.5120874643325806, 'gis', 0), ('pytables/pytables', 0.5119327306747437, 'data', 0), ('blaze/blaze', 0.5032052397727966, 'pandas', 0)]",21,7.0,,0.0,0,0,89,23,0,3,3,0.0,0.0,90.0,0.0,29
1682,util,https://github.com/rubik/radon,[],,[],[],,,,rubik/radon,radon,1566,114,34,Python,http://radon.readthedocs.org/,Various code metrics for Python code,rubik,2024-01-14,2012-09-20,592,2.6420824295010847,,Various code metrics for Python code,"['cli', 'code-analysis', 'quality-assurance', 'static-analysis']","['cli', 'code-analysis', 'quality-assurance', 'static-analysis']",2023-10-06,"[('google/pytype', 0.6680740714073181, 'typing', 1), ('sourcery-ai/sourcery', 0.6180241703987122, 'util', 0), ('psf/black', 0.609166145324707, 'util', 0), ('nedbat/coveragepy', 0.6033869981765747, 'testing', 0), ('grantjenks/blue', 0.6026664972305298, 'util', 0), ('facebook/pyre-check', 0.6015112400054932, 'typing', 1), ('dosisod/refurb', 0.5743918418884277, 'util', 1), ('pyutils/line_profiler', 0.5716840028762817, 'profiling', 0), ('landscapeio/prospector', 0.5695351362228394, 'util', 0), ('pympler/pympler', 0.568540632724762, 'perf', 0), ('pycqa/flake8', 0.5651803612709045, 'util', 1), ('hhatto/autopep8', 0.5594862699508667, 'util', 0), ('pythonprofilers/memory_profiler', 0.5579990148544312, 'profiling', 0), ('regebro/pyroma', 0.5549668669700623, 'util', 0), ('ionelmc/pytest-benchmark', 0.554404079914093, 'testing', 0), ('jendrikseipp/vulture', 0.5527566075325012, 'util', 0), ('klen/py-frameworks-bench', 0.5515268445014954, 'perf', 0), ('python/cpython', 0.5486265420913696, 'util', 0), ('pycqa/mccabe', 0.5480080842971802, 'util', 0), ('klen/pylama', 0.5425077676773071, 'util', 0), ('google/yapf', 0.5423753261566162, 'util', 0), ('agronholm/typeguard', 0.5345003604888916, 'typing', 0), ('ydataai/ydata-quality', 0.5341893434524536, 'data', 0), ('microsoft/pyright', 0.533168375492096, 'typing', 0), ('pypa/hatch', 0.5330091714859009, 'util', 1), ('eugeneyan/python-collab-template', 0.5297834873199463, 'template', 0), ('astral-sh/ruff', 0.528230607509613, 'util', 1), ('samuelcolvin/python-devtools', 0.5275425314903259, 'debug', 0), ('amaargiru/pyroad', 0.5273672342300415, 'study', 0), ('gaogaotiantian/viztracer', 0.5236186385154724, 'profiling', 0), ('cython/cython', 0.5208148956298828, 'util', 0), ('ydataai/ydata-profiling', 0.5186936259269714, 'pandas', 0), ('aswinnnn/pyscan', 0.5182473659515381, 'security', 0), ('mynameisfiber/high_performance_python_2e', 0.5166555643081665, 'study', 0), ('eleutherai/pyfra', 0.514139711856842, 'ml', 0), ('pypy/pypy', 0.5132455825805664, 'util', 0), ('instagram/monkeytype', 0.5105746984481812, 'typing', 0), ('citadel-ai/langcheck', 0.505801796913147, 'llm', 0), ('pypi/warehouse', 0.5054935216903687, 'util', 0), ('facebookincubator/bowler', 0.5027236938476562, 'util', 0), ('microsoft/pycodegpt', 0.5025046467781067, 'llm', 0)]",60,2.0,,0.33,7,1,138,3,0,4,4,7.0,1.0,90.0,0.1,29
390,data,https://github.com/mchong6/jojogan,[],,[],[],,,,mchong6/jojogan,JoJoGAN,1395,207,26,Jupyter Notebook,,Official PyTorch repo for JoJoGAN: One Shot Face Stylization,mchong6,2024-01-08,2021-12-17,110,12.616279069767442,,Official PyTorch repo for JoJoGAN: One Shot Face Stylization,"['anime', 'gans', 'image-translation']","['anime', 'gans', 'image-translation']",2022-02-05,"[('tencentarc/gfpgan', 0.5290706753730774, 'ml', 0), ('williamyang1991/vtoonify', 0.515015184879303, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5051544308662415, 'ml-dl', 0)]",3,1.0,,0.0,1,0,25,24,0,0,0,1.0,1.0,90.0,1.0,29
562,gis,https://github.com/gboeing/osmnx-examples,[],,[],[],,,,gboeing/osmnx-examples,osmnx-examples,1386,493,59,Jupyter Notebook,https://osmnx.readthedocs.io,"Gallery of OSMnx tutorials, usage examples, and feature demonstations.",gboeing,2024-01-10,2017-07-22,340,4.071338648762064,,"Gallery of OSMnx tutorials, usage examples, and feature demonstations.","['accessibility', 'binder', 'cities', 'city', 'jupyter-notebook', 'network-analysis', 'notebooks', 'openstreetmap', 'public-transport', 'street-networks', 'transit', 'transport', 'transportation', 'urban-analytics', 'urban-data-science', 'urban-design', 'urban-planning']","['accessibility', 'binder', 'cities', 'city', 'jupyter-notebook', 'network-analysis', 'notebooks', 'openstreetmap', 'public-transport', 'street-networks', 'transit', 'transport', 'transportation', 'urban-analytics', 'urban-data-science', 'urban-design', 'urban-planning']",2023-12-31,"[('gboeing/osmnx', 0.7930247187614441, 'gis', 5), ('marceloprates/prettymaps', 0.562412440776825, 'viz', 2)]",1,1.0,,1.1,4,4,79,0,0,3,3,4.0,0.0,90.0,0.0,29
1225,perf,https://github.com/nschloe/perfplot,[],,[],[],,,,nschloe/perfplot,perfplot,1261,63,18,Python,,:chart_with_upwards_trend: Performance analysis for Python snippets,nschloe,2024-01-12,2017-02-21,362,3.483425414364641,,:chart_with_upwards_trend: Performance analysis for Python snippets,['performance-analysis'],['performance-analysis'],2022-06-06,"[('altair-viz/altair', 0.5681192278862, 'viz', 0), ('pyutils/line_profiler', 0.535541832447052, 'profiling', 0), ('gaogaotiantian/viztracer', 0.528630793094635, 'profiling', 0), ('has2k1/plotnine', 0.5270527005195618, 'viz', 0), ('alexmojaki/heartrate', 0.5038774013519287, 'debug', 0), ('vizzuhq/ipyvizzu', 0.5008931756019592, 'jupyter', 0)]",13,4.0,,0.0,5,1,84,20,0,10,10,5.0,1.0,90.0,0.2,29
192,ml,https://github.com/awslabs/dgl-ke,[],,[],[],,,,awslabs/dgl-ke,dgl-ke,1202,197,27,Python,https://dglke.dgl.ai/doc/,"High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.",awslabs,2024-01-11,2020-03-03,204,5.892156862745098,https://avatars.githubusercontent.com/u/3299148?v=4,"High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.","['dgl', 'graph-learning', 'knowledge-graph', 'knowledge-graphs-embeddings', 'machine-learning']","['dgl', 'graph-learning', 'knowledge-graph', 'knowledge-graphs-embeddings', 'machine-learning']",2023-03-20,"[('accenture/ampligraph', 0.7346105575561523, 'data', 2), ('dylanhogg/llmgraph', 0.6202223300933838, 'ml', 1), ('facebookresearch/pytorch-biggraph', 0.6120842099189758, 'ml-dl', 0), ('zjunlp/deepke', 0.5722960233688354, 'ml', 1), ('neuml/txtai', 0.5560591220855713, 'nlp', 1), ('deepgraphlearning/ultra', 0.5505498647689819, 'ml', 1), ('dmlc/dgl', 0.5391981601715088, 'ml-dl', 0), ('koaning/embetter', 0.5223193764686584, 'data', 0), ('stellargraph/stellargraph', 0.5202558040618896, 'graph', 1), ('plasticityai/magnitude', 0.5072051286697388, 'nlp', 1), ('qdrant/fastembed', 0.5057823061943054, 'ml', 0), ('benedekrozemberczki/tigerlily', 0.50465327501297, 'ml-dl', 2)]",26,4.0,,0.02,1,0,47,10,0,1,1,1.0,0.0,90.0,0.0,29
616,util,https://github.com/pytoolz/cytoolz,[],,[],[],,,,pytoolz/cytoolz,cytoolz,954,67,25,Python,,Cython implementation of Toolz: High performance functional utilities,pytoolz,2024-01-13,2014-04-04,512,1.8612040133779264,https://avatars.githubusercontent.com/u/5448828?v=4,Cython implementation of Toolz: High performance functional utilities,[],[],2023-07-21,"[('scikit-build/scikit-build', 0.5701683759689331, 'ml', 0), ('suor/funcy', 0.5295758247375488, 'util', 0), ('cython/cython', 0.5252465009689331, 'util', 0)]",21,5.0,,0.08,3,1,119,6,1,2,1,3.0,4.0,90.0,1.3,29
789,graph,https://github.com/westhealth/pyvis,[],,[],[],,,,westhealth/pyvis,pyvis,850,145,19,HTML,http://pyvis.readthedocs.io/en/latest/,Python package for creating and visualizing interactive network graphs.,westhealth,2024-01-11,2018-05-10,298,2.845528455284553,https://avatars.githubusercontent.com/u/22085795?v=4,Python package for creating and visualizing interactive network graphs.,"['network-visualization', 'networkx']","['network-visualization', 'networkx']",2023-02-10,"[('pygraphviz/pygraphviz', 0.7577512264251709, 'viz', 0), ('graphistry/pygraphistry', 0.6478259563446045, 'data', 2), ('networkx/networkx', 0.6360735297203064, 'graph', 0), ('plotly/plotly.py', 0.6326491832733154, 'viz', 0), ('h4kor/graph-force', 0.6132168173789978, 'graph', 0), ('holoviz/hvplot', 0.5998131036758423, 'pandas', 0), ('artelys/geonetworkx', 0.5923200249671936, 'gis', 0), ('altair-viz/altair', 0.5836617946624756, 'viz', 0), ('matplotlib/matplotlib', 0.5532649159431458, 'viz', 0), ('bokeh/bokeh', 0.55198734998703, 'viz', 0), ('vizzuhq/ipyvizzu', 0.5483621954917908, 'jupyter', 0), ('has2k1/plotnine', 0.5456839203834534, 'viz', 0), ('pydot/pydot', 0.542072594165802, 'viz', 0), ('mwaskom/seaborn', 0.5389178395271301, 'viz', 0), ('holoviz/holoviz', 0.5358419418334961, 'viz', 0), ('secdev/scapy', 0.5347074866294861, 'util', 1), ('enthought/mayavi', 0.5338135361671448, 'viz', 0), ('gboeing/osmnx', 0.5170513987541199, 'gis', 1), ('cuemacro/chartpy', 0.5167423486709595, 'viz', 0), ('dmlc/dgl', 0.5159277319908142, 'ml-dl', 0), ('kuanb/peartree', 0.5152437090873718, 'gis', 0), ('graphql-python/graphene', 0.5054649114608765, 'web', 0), ('pyqtgraph/pyqtgraph', 0.5053408741950989, 'viz', 0), ('holoviz/panel', 0.5053226947784424, 'viz', 0), ('scitools/iris', 0.503166139125824, 'gis', 0), ('comfyanonymous/comfyui', 0.5001736283302307, 'diffusion', 0)]",32,3.0,,0.06,23,3,69,11,0,1,1,23.0,21.0,90.0,0.9,29
1582,nlp,https://github.com/paddlepaddle/rocketqa,['question-answering'],,[],[],,,,paddlepaddle/rocketqa,RocketQA,713,124,19,Python,,"🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models. ",paddlepaddle,2024-01-12,2021-09-07,125,5.704,https://avatars.githubusercontent.com/u/23534030?v=4,"🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models. ","['dense-retrieval', 'information-retrieval', 'nlp', 'question-answering']","['dense-retrieval', 'information-retrieval', 'nlp', 'question-answering']",2022-12-03,"[('intellabs/fastrag', 0.6380553841590881, 'nlp', 3), ('facebookresearch/dpr-scale', 0.6294921636581421, 'nlp', 0), ('ai21labs/in-context-ralm', 0.5692198872566223, 'llm', 0), ('srush/minichain', 0.5608699321746826, 'llm', 1), ('paddlepaddle/paddlenlp', 0.560157835483551, 'llm', 2), ('muennighoff/sgpt', 0.5352213978767395, 'llm', 1), ('lianjiatech/belle', 0.5283323526382446, 'llm', 0), ('freedomintelligence/llmzoo', 0.5235476493835449, 'llm', 0), ('castorini/pyserini', 0.5224674940109253, 'ml', 1), ('deepset-ai/farm', 0.5212914347648621, 'nlp', 2), ('neuml/txtai', 0.5149143934249878, 'nlp', 2), ('llmware-ai/llmware', 0.510935366153717, 'llm', 3), ('baichuan-inc/baichuan-13b', 0.5105500817298889, 'llm', 0), ('night-chen/toolqa', 0.5089247226715088, 'llm', 1), ('jina-ai/clip-as-service', 0.506847620010376, 'nlp', 0), ('explosion/spacy-models', 0.5023024678230286, 'nlp', 1)]",12,3.0,,0.0,4,0,29,14,0,0,0,4.0,5.0,90.0,1.2,29
1105,study,https://github.com/davidadsp/generative_deep_learning_2nd_edition,[],,[],[],,,,davidadsp/generative_deep_learning_2nd_edition,Generative_Deep_Learning_2nd_Edition,663,223,18,Jupyter Notebook,https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/,"The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.",davidadsp,2024-01-14,2022-03-25,96,6.865384615384615,,"The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.","['chatgpt', 'dalle2', 'data-science', 'deep-learning', 'diffusion-models', 'generative-adversarial-network', 'gpt-3', 'machine-learning', 'stable-diffusion', 'tensorflow']","['chatgpt', 'dalle2', 'data-science', 'deep-learning', 'diffusion-models', 'generative-adversarial-network', 'gpt-3', 'machine-learning', 'stable-diffusion', 'tensorflow']",2023-07-18,"[('openai/image-gpt', 0.6263450980186462, 'llm', 0), ('mrdbourke/pytorch-deep-learning', 0.5829065442085266, 'study', 2), ('rasbt/machine-learning-book', 0.5600119233131409, 'study', 2), ('d2l-ai/d2l-en', 0.5406649708747864, 'study', 4), ('tensorlayer/tensorlayer', 0.5377876162528992, 'ml-rl', 2), ('open-mmlab/mmediting', 0.5358924269676208, 'ml', 3), ('microsoft/generative-ai-for-beginners', 0.5330820679664612, 'study', 1), ('lupantech/chameleon-llm', 0.5299697518348694, 'llm', 1), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5259917974472046, 'study', 2), ('lucidrains/imagen-pytorch', 0.5147601962089539, 'ml-dl', 1), ('nvidia/deeplearningexamples', 0.5120260119438171, 'ml-dl', 2), ('sharonzhou/long_stable_diffusion', 0.5068796277046204, 'diffusion', 0), ('automatic1111/stable-diffusion-webui', 0.5017077922821045, 'diffusion', 2)]",4,1.0,,1.35,9,4,22,6,0,0,0,9.0,8.0,90.0,0.9,29
435,pandas,https://github.com/polyaxon/datatile,[],,[],[],,,,polyaxon/datatile,traceml,488,43,14,Python,,"Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.",polyaxon,2024-01-12,2016-03-25,409,1.1914893617021276,https://avatars.githubusercontent.com/u/24544827?v=4,"Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.","['dask', 'data-exploration', 'data-profiling', 'data-quality', 'data-quality-checks', 'data-science', 'data-visualization', 'dataframes', 'dataops', 'explainable-ai', 'matplotlib', 'mlops', 'pandas', 'pandas-summary', 'plotly', 'pytorch', 'spark', 'statistics', 'tensorflow', 'tracking']","['dask', 'data-exploration', 'data-profiling', 'data-quality', 'data-quality-checks', 'data-science', 'data-visualization', 'dataframes', 'dataops', 'explainable-ai', 'matplotlib', 'mlops', 'pandas', 'pandas-summary', 'plotly', 'pytorch', 'spark', 'statistics', 'tensorflow', 'tracking']",2024-01-04,"[('plotly/dash', 0.6874310970306396, 'viz', 3), ('wandb/client', 0.6632310748100281, 'ml', 4), ('krzjoa/awesome-python-data-science', 0.6403499245643616, 'study', 3), ('aimhubio/aim', 0.6386370062828064, 'ml-ops', 5), ('huggingface/datasets', 0.6301923990249634, 'nlp', 3), ('dagworks-inc/hamilton', 0.6286333203315735, 'ml-ops', 3), ('holoviz/panel', 0.6254085302352905, 'viz', 2), ('pandas-dev/pandas', 0.6224325299263, 'pandas', 2), ('gradio-app/gradio', 0.6204770803451538, 'viz', 2), ('mlflow/mlflow', 0.6103001236915588, 'ml-ops', 0), ('ydataai/ydata-profiling', 0.6102992296218872, 'pandas', 6), ('ranaroussi/quantstats', 0.6079445481300354, 'finance', 0), ('dylanhogg/awesome-python', 0.6012967228889465, 'study', 2), ('whylabs/whylogs', 0.5969440937042236, 'util', 4), ('man-group/dtale', 0.5954803824424744, 'viz', 3), ('oegedijk/explainerdashboard', 0.5954424142837524, 'ml-interpretability', 1), ('merantix-momentum/squirrel-core', 0.5911334156990051, 'ml', 4), ('quantconnect/lean', 0.590923011302948, 'finance', 0), ('netflix/metaflow', 0.5876936316490173, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5872251987457275, 'ml-ops', 4), ('activeloopai/deeplake', 0.585174024105072, 'ml-ops', 4), ('avaiga/taipy', 0.5837622880935669, 'data', 2), ('csinva/imodels', 0.5763207674026489, 'ml', 3), ('xplainable/xplainable', 0.5760681629180908, 'ml-interpretability', 3), ('districtdatalabs/yellowbrick', 0.5754048228263855, 'ml', 1), ('gventuri/pandas-ai', 0.5753474235534668, 'pandas', 2), ('hi-primus/optimus', 0.5730414986610413, 'ml-ops', 5), ('firmai/industry-machine-learning', 0.5717259049415588, 'study', 1), ('feast-dev/feast', 0.570514976978302, 'ml-ops', 3), ('pycaret/pycaret', 0.5685513615608215, 'ml', 1), ('mito-ds/monorepo', 0.5668829083442688, 'jupyter', 3), ('meltano/meltano', 0.5661336183547974, 'ml-ops', 1), ('rasbt/mlxtend', 0.5656301975250244, 'ml', 1), ('online-ml/river', 0.5639887452125549, 'ml', 1), ('vaexio/vaex', 0.5635982155799866, 'perf', 1), ('salesforce/logai', 0.5616445541381836, 'util', 0), ('unionai-oss/pandera', 0.55992591381073, 'pandas', 2), ('mage-ai/mage-ai', 0.5572016835212708, 'ml-ops', 2), ('polakowo/vectorbt', 0.5571960806846619, 'finance', 2), ('googlecloudplatform/vertex-ai-samples', 0.5558412075042725, 'ml', 2), ('plotly/plotly.py', 0.554128885269165, 'viz', 1), ('hazyresearch/meerkat', 0.5525878071784973, 'viz', 2), ('fugue-project/fugue', 0.5508334040641785, 'pandas', 3), ('teamhg-memex/eli5', 0.5490293502807617, 'ml', 1), ('reloadware/reloadium', 0.5487061738967896, 'profiling', 1), ('bokeh/bokeh', 0.5476343631744385, 'viz', 0), ('mindsdb/mindsdb', 0.5471445918083191, 'data', 0), ('goldmansachs/gs-quant', 0.5469740033149719, 'finance', 0), ('eventual-inc/daft', 0.5458943843841553, 'pandas', 1), ('google/tf-quant-finance', 0.5451685190200806, 'finance', 1), ('airbytehq/airbyte', 0.5435851812362671, 'data', 0), ('ploomber/ploomber', 0.5431307554244995, 'ml-ops', 2), ('scikit-learn/scikit-learn', 0.5421066284179688, 'ml', 2), ('streamlit/streamlit', 0.5420833230018616, 'viz', 2), ('selfexplainml/piml-toolbox', 0.5384085178375244, 'ml-interpretability', 0), ('great-expectations/great_expectations', 0.5381271839141846, 'ml-ops', 4), ('bentoml/bentoml', 0.5369633436203003, 'ml-ops', 1), ('orchest/orchest', 0.5361875891685486, 'ml-ops', 1), ('dagster-io/dagster', 0.5342531800270081, 'ml-ops', 2), ('rapidsai/cudf', 0.5338151454925537, 'pandas', 3), ('backtick-se/cowait', 0.533811628818512, 'util', 3), ('fastai/fastcore', 0.5320842266082764, 'util', 0), ('tensorlayer/tensorlayer', 0.5311883687973022, 'ml-rl', 1), ('apache/spark', 0.529706597328186, 'data', 1), ('awslabs/autogluon', 0.5291432738304138, 'ml', 2), ('pola-rs/polars', 0.5283935070037842, 'pandas', 1), ('deepchecks/deepchecks', 0.5274471640586853, 'data', 3), ('willmcgugan/textual', 0.5274295806884766, 'term', 0), ('doccano/doccano', 0.5258282423019409, 'nlp', 0), ('simonw/datasette', 0.525797963142395, 'data', 0), ('fatiando/verde', 0.5251547694206238, 'gis', 0), ('pathwaycom/pathway', 0.5242671370506287, 'data', 0), ('tensorflow/tensorflow', 0.524250328540802, 'ml-dl', 1), ('gaogaotiantian/viztracer', 0.5233743786811829, 'profiling', 0), ('featurelabs/featuretools', 0.5225564241409302, 'ml', 1), ('alirezadir/machine-learning-interview-enlightener', 0.521611213684082, 'study', 0), ('nccr-itmo/fedot', 0.521264910697937, 'ml-ops', 0), ('interpretml/interpret', 0.5212517976760864, 'ml-interpretability', 1), ('mckinsey/vizro', 0.5210312008857727, 'viz', 2), ('lutzroeder/netron', 0.5207769274711609, 'ml', 2), ('kubeflow-kale/kale', 0.5204256772994995, 'ml-ops', 0), ('carla-recourse/carla', 0.5203560590744019, 'ml', 3), ('tensorflow/data-validation', 0.5198504328727722, 'ml-ops', 0), ('isl-org/open3d', 0.5179375410079956, 'sim', 2), ('clips/pattern', 0.5179307460784912, 'nlp', 0), ('giswqs/geemap', 0.5178652405738831, 'gis', 1), ('microsoft/nni', 0.5166768431663513, 'ml', 4), ('determined-ai/determined', 0.516498863697052, 'ml-ops', 4), ('jovianml/opendatasets', 0.5163763165473938, 'data', 1), ('pyvista/pyvista', 0.5150445699691772, 'viz', 0), ('cheshire-cat-ai/core', 0.5147360563278198, 'llm', 0), ('kubeflow/fairing', 0.514506459236145, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5134544372558594, 'viz', 0), ('wesm/pydata-book', 0.513410747051239, 'study', 0), ('alkaline-ml/pmdarima', 0.5126562118530273, 'time-series', 0), ('statsmodels/statsmodels', 0.5121821165084839, 'ml', 2), ('superduperdb/superduperdb', 0.5108543634414673, 'data', 2), ('saulpw/visidata', 0.5101152658462524, 'term', 1), ('explosion/thinc', 0.5097380876541138, 'ml-dl', 2), ('cleanlab/cleanlab', 0.5088824033737183, 'ml', 4), ('pyqtgraph/pyqtgraph', 0.5080159902572632, 'viz', 0), ('panda3d/panda3d', 0.5070898532867432, 'gamedev', 0), ('onnx/onnx', 0.5065220594406128, 'ml', 2), ('roboflow/supervision', 0.5057425498962402, 'ml', 3), ('ddbourgin/numpy-ml', 0.5043874382972717, 'ml', 0), ('google/mediapipe', 0.5041832327842712, 'ml', 0), ('firmai/atspy', 0.5035507678985596, 'time-series', 0), ('zenodo/zenodo', 0.5025171041488647, 'util', 0), ('sktime/sktime', 0.5024852752685547, 'time-series', 1), ('flyteorg/flyte', 0.5018032789230347, 'ml-ops', 3), ('ray-project/ray', 0.5014405846595764, 'ml-ops', 3), ('ta-lib/ta-lib-python', 0.500678539276123, 'finance', 0), ('ashleve/lightning-hydra-template', 0.5006352066993713, 'util', 2), ('mwaskom/seaborn', 0.500043511390686, 'viz', 4)]",99,3.0,,2.27,0,0,95,0,0,6,6,0.0,0.0,90.0,0.0,29
1416,jupyter,https://github.com/xiaohk/stickyland,[],,[],[],,,,xiaohk/stickyland,stickyland,470,30,9,TypeScript,https://xiaohk.github.io/stickyland/,Break the linear presentation of Jupyter Notebooks with sticky cells!,xiaohk,2024-01-12,2021-11-02,117,4.017094017094017,,Break the linear presentation of Jupyter Notebooks with sticky cells!,"['dashboard', 'jupyter', 'jupyterlab', 'jupyterlab-extension', 'notebook']","['dashboard', 'jupyter', 'jupyterlab', 'jupyterlab-extension', 'notebook']",2023-12-24,"[('jupyter-widgets/ipywidgets', 0.6346691250801086, 'jupyter', 1), ('jupyter/notebook', 0.6330485939979553, 'jupyter', 2), ('voila-dashboards/voila', 0.5852877497673035, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.5726215243339539, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.5476192235946655, 'jupyter', 2), ('jupyter/nbformat', 0.5371445417404175, 'jupyter', 0), ('jupyter/nbconvert', 0.536399781703949, 'jupyter', 0), ('bloomberg/ipydatagrid', 0.5169107913970947, 'jupyter', 1), ('mwouts/jupytext', 0.5160862803459167, 'jupyter', 2), ('quantopian/qgrid', 0.5084087252616882, 'jupyter', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5069721937179565, 'jupyter', 0)]",2,1.0,,0.19,2,2,27,1,1,4,1,2.0,4.0,90.0,2.0,29
1378,diffusion,https://github.com/nvlabs/gcvit,[],,[],[],,,,nvlabs/gcvit,GCVit,412,49,10,Python,https://arxiv.org/abs/2206.09959,[ICML 2023] Official PyTorch implementation of Global Context Vision Transformers,nvlabs,2024-01-12,2022-06-18,84,4.879864636209814,https://avatars.githubusercontent.com/u/2695301?v=4,[ICML 2023] Official PyTorch implementation of Global Context Vision Transformers,"['ade20k', 'backbone', 'coco', 'deep-learning', 'imagenet', 'imagenet-classification', 'object-detection', 'pre-train', 'pre-trained-model', 'self-attention', 'semantic-segmentation', 'vision-transformer', 'visual-recognition']","['ade20k', 'backbone', 'coco', 'deep-learning', 'imagenet', 'imagenet-classification', 'object-detection', 'pre-train', 'pre-trained-model', 'self-attention', 'semantic-segmentation', 'vision-transformer', 'visual-recognition']",2023-12-22,"[('microsoft/swin-transformer', 0.6548908352851868, 'ml', 4), ('lucidrains/vit-pytorch', 0.6527162790298462, 'ml-dl', 0), ('roboflow/supervision', 0.6431651711463928, 'ml', 3), ('huggingface/transformers', 0.6319802403450012, 'nlp', 1), ('rwightman/pytorch-image-models', 0.6296912431716919, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.6284846067428589, 'ml-dl', 1), ('deci-ai/super-gradients', 0.6253355145454407, 'ml-dl', 4), ('google-research/maxvit', 0.6166060566902161, 'ml', 2), ('lucidrains/imagen-pytorch', 0.6131131052970886, 'ml-dl', 1), ('salesforce/blip', 0.5959486365318298, 'diffusion', 0), ('intel/intel-extension-for-pytorch', 0.5956222414970398, 'perf', 1), ('open-mmlab/mmsegmentation', 0.5941908359527588, 'ml', 1), ('nielsrogge/transformers-tutorials', 0.5923160910606384, 'study', 1), ('mcahny/deep-video-inpainting', 0.5919110774993896, 'ml-dl', 0), ('pytorch/ignite', 0.5840114951133728, 'ml-dl', 1), ('karpathy/mingpt', 0.5820482969284058, 'llm', 0), ('roboflow/notebooks', 0.5799825191497803, 'study', 2), ('microsoft/focal-transformer', 0.5749183893203735, 'ml', 0), ('idea-research/groundingdino', 0.5644093751907349, 'diffusion', 1), ('microsoft/torchgeo', 0.5610058307647705, 'gis', 1), ('open-mmlab/mmdetection', 0.559407114982605, 'ml', 2), ('nyandwi/modernconvnets', 0.5547017455101013, 'ml-dl', 0), ('blakeblackshear/frigate', 0.5532398223876953, 'util', 1), ('open-mmlab/mmediting', 0.5518020987510681, 'ml', 1), ('google/automl', 0.5431532859802246, 'ml', 1), ('lutzroeder/netron', 0.5401598811149597, 'ml', 1), ('lightly-ai/lightly', 0.5394826531410217, 'ml', 1), ('kornia/kornia', 0.5384776592254639, 'ml-dl', 1), ('huggingface/exporters', 0.5337070822715759, 'ml', 1), ('huggingface/optimum', 0.5319724678993225, 'ml', 0), ('skorch-dev/skorch', 0.531648576259613, 'ml-dl', 0), ('matterport/mask_rcnn', 0.5284178256988525, 'ml-dl', 1), ('nvlabs/prismer', 0.5274479389190674, 'diffusion', 0), ('google-research/deeplab2', 0.5264610648155212, 'ml', 0), ('rasbt/machine-learning-book', 0.5256971716880798, 'study', 1), ('pyg-team/pytorch_geometric', 0.5232054591178894, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.520950198173523, 'study', 1), ('albumentations-team/albumentations', 0.5185796618461609, 'ml-dl', 2), ('nicolas-chaulet/torch-points3d', 0.5170150995254517, 'ml', 0), ('facebookresearch/detr', 0.5166937112808228, 'ml-dl', 0), ('mdbloice/augmentor', 0.5139862895011902, 'ml', 1), ('kshitij12345/torchnnprofiler', 0.5133840441703796, 'profiling', 0), ('facebookresearch/pytorch3d', 0.5093544125556946, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5083329081535339, 'ml', 1), ('azavea/raster-vision', 0.5082259178161621, 'gis', 3), ('huggingface/datasets', 0.5082133412361145, 'nlp', 1), ('graykode/nlp-tutorial', 0.5081471800804138, 'study', 0), ('huggingface/huggingface_hub', 0.5078686475753784, 'ml', 1), ('lucidrains/dalle2-pytorch', 0.5059126019477844, 'diffusion', 1), ('facebookresearch/detectron2', 0.504278838634491, 'ml-dl', 0)]",6,1.0,,0.63,2,1,19,1,0,1,1,2.0,2.0,90.0,1.0,29
1732,testing,https://github.com/kiwicom/pytest-recording,[],,[],[],,,,kiwicom/pytest-recording,pytest-recording,347,31,4,Python,,A pytest plugin that allows recording network interactions via VCR.py,kiwicom,2024-01-11,2019-07-16,237,1.4641350210970465,https://avatars.githubusercontent.com/u/25227300?v=4,A pytest plugin that allows recording network interactions via VCR.py,"['cassettes', 'pytest', 'testing', 'vcr']","['cassettes', 'pytest', 'testing', 'vcr']",2023-12-06,"[('pytest-dev/pytest-xdist', 0.5940394401550293, 'testing', 1), ('irmen/pyminiaudio', 0.5641629099845886, 'util', 0), ('samuelcolvin/pytest-pretty', 0.544182538986206, 'testing', 1), ('computationalmodelling/nbval', 0.535578191280365, 'jupyter', 2), ('ionelmc/pytest-benchmark', 0.5216888785362244, 'testing', 1), ('teemu/pytest-sugar', 0.5215980410575867, 'testing', 2), ('pytest-dev/pytest-cov', 0.52159583568573, 'testing', 1)]",13,3.0,,0.71,14,10,55,1,3,5,3,14.0,16.0,90.0,1.1,29
1404,llm,https://github.com/approximatelabs/datadm,['conversational'],,[],[],,,,approximatelabs/datadm,datadm,315,25,8,Python,,DataDM is your private data assistant. Slide into your data's DMs,approximatelabs,2024-01-04,2023-05-25,35,8.82,https://avatars.githubusercontent.com/u/106505054?v=4,DataDM is your private data assistant. Slide into your data's DMs,[],['conversational'],2023-09-11,[],3,1.0,,0.98,0,0,8,4,0,21,21,0.0,0.0,90.0,0.0,29
664,gis,https://github.com/cgal/cgal-swig-bindings,[],,[],[],,,,cgal/cgal-swig-bindings,cgal-swig-bindings,305,91,28,C++,,CGAL bindings using SWIG,cgal,2024-01-05,2015-03-14,463,0.658138101109741,https://avatars.githubusercontent.com/u/5746664?v=4,CGAL bindings using SWIG,[],[],2023-12-20,[],22,3.0,,0.75,15,6,108,1,7,1,7,15.0,19.0,90.0,1.3,29
478,pandas,https://github.com/holoviz/spatialpandas,[],,[],[],,,,holoviz/spatialpandas,spatialpandas,293,24,23,Python,,Pandas extension arrays for spatial/geometric operations,holoviz,2024-01-04,2019-10-28,222,1.3189710610932477,https://avatars.githubusercontent.com/u/51678735?v=4,Pandas extension arrays for spatial/geometric operations,"['geographic-data', 'geopandas', 'holoviz', 'pandas', 'spatialpandas']","['geographic-data', 'geopandas', 'holoviz', 'pandas', 'spatialpandas']",2024-01-11,"[('geopandas/geopandas', 0.6860671043395996, 'gis', 2), ('residentmario/geoplot', 0.6135755777359009, 'gis', 1), ('anitagraser/movingpandas', 0.5773379802703857, 'gis', 1), ('jmcarpenter2/swifter', 0.562759518623352, 'pandas', 1), ('nalepae/pandarallel', 0.5524942874908447, 'pandas', 1), ('makepath/xarray-spatial', 0.5339615941047668, 'gis', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5225083231925964, 'pandas', 0), ('man-group/dtale', 0.5217031240463257, 'viz', 1), ('blaze/blaze', 0.5175127387046814, 'pandas', 0), ('rapidsai/cudf', 0.5126527547836304, 'pandas', 1), ('earthlab/earthpy', 0.5123425722122192, 'gis', 0), ('mwaskom/seaborn', 0.5058495402336121, 'viz', 1), ('adamerose/pandasgui', 0.5028727054595947, 'pandas', 1), ('holoviz/hvplot', 0.5009598135948181, 'pandas', 1)]",12,5.0,,0.46,6,5,51,0,4,10,4,6.0,3.0,90.0,0.5,29
1713,diffusion,https://github.com/bentoml/onediffusion,[],,[],[],,,,bentoml/onediffusion,OneDiffusion,285,17,12,Python,https://bentoml.com,OneDiffusion: Run any Stable Diffusion models and fine-tuned weights with ease,bentoml,2024-01-05,2023-06-12,33,8.599137931034482,https://avatars.githubusercontent.com/u/49176046?v=4,OneDiffusion: Run any Stable Diffusion models and fine-tuned weights with ease,"['ai', 'diffusion-models', 'fine-tuning', 'kubernetes', 'lora', 'model-serving', 'stable-diffusion']","['ai', 'diffusion-models', 'fine-tuning', 'kubernetes', 'lora', 'model-serving', 'stable-diffusion']",2023-12-08,"[('carson-katri/dream-textures', 0.6899959444999695, 'diffusion', 2), ('stability-ai/stability-sdk', 0.6665179133415222, 'diffusion', 1), ('divamgupta/stable-diffusion-tensorflow', 0.6373262405395508, 'diffusion', 0), ('lllyasviel/controlnet', 0.6226494908332825, 'diffusion', 0), ('mlc-ai/web-stable-diffusion', 0.6144503355026245, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6026777625083923, 'diffusion', 2), ('comfyanonymous/comfyui', 0.5863965749740601, 'diffusion', 1), ('divamgupta/diffusionbee-stable-diffusion-ui', 0.5153639316558838, 'diffusion', 1), ('tanelp/tiny-diffusion', 0.5116053223609924, 'diffusion', 0), ('civitai/sd_civitai_extension', 0.5044161081314087, 'llm', 0), ('thereforegames/unprompted', 0.5029755234718323, 'diffusion', 1)]",5,1.0,,0.87,7,3,7,1,0,0,0,7.0,2.0,90.0,0.3,29
124,util,https://github.com/mgedmin/check-manifest,[],,[],[],,,,mgedmin/check-manifest,check-manifest,283,38,7,Python,https://pypi.org/p/check-manifest,Tool to check the completeness of MANIFEST.in for Python packages,mgedmin,2024-01-04,2013-03-05,569,0.4973637961335677,,Tool to check the completeness of MANIFEST.in for Python packages,[],[],2023-12-18,"[('pypi/warehouse', 0.5615488886833191, 'util', 0), ('mkdocstrings/griffe', 0.537682056427002, 'util', 0), ('nedbat/coveragepy', 0.5218181610107422, 'testing', 0), ('indygreg/pyoxidizer', 0.5157642364501953, 'util', 0), ('mitsuhiko/rye', 0.5007199645042419, 'util', 0)]",22,6.0,,0.12,1,1,132,1,0,5,5,1.0,2.0,90.0,2.0,29
273,data,https://github.com/amzn/ion-python,[],,[],[],,,,amzn/ion-python,ion-python,246,52,25,Python,https://amazon-ion.github.io/ion-docs/,A Python implementation of Amazon Ion.,amzn,2024-01-06,2016-04-07,407,0.6033637000700771,https://avatars.githubusercontent.com/u/105071691?v=4,A Python implementation of Amazon Ion.,[],[],2024-01-10,"[('pynamodb/pynamodb', 0.6932819485664368, 'data', 0), ('geeogi/async-python-lambda-template', 0.6108747720718384, 'template', 0), ('primal100/pybitcointools', 0.5686578154563904, 'crypto', 0), ('nficano/python-lambda', 0.5418636798858643, 'util', 0), ('falconry/falcon', 0.5367324352264404, 'web', 0), ('ethereum/py-evm', 0.5310432314872742, 'crypto', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.524419903755188, 'util', 0), ('pytables/pytables', 0.5214452147483826, 'data', 0), ('awslabs/python-deequ', 0.5171683430671692, 'ml', 0), ('ethereum/web3.py', 0.5136985778808594, 'crypto', 0), ('boto/boto3', 0.5133451223373413, 'util', 0), ('oracle/graalpython', 0.5121100544929504, 'util', 0), ('pyston/pyston', 0.5089343786239624, 'util', 0), ('encode/httpx', 0.5086729526519775, 'web', 0), ('aws/aws-sdk-pandas', 0.5067648887634277, 'pandas', 0), ('aws/chalice', 0.5045038461685181, 'web', 0)]",28,3.0,,1.04,48,35,95,0,4,2,4,48.0,30.0,90.0,0.6,29
1459,util,https://github.com/mamba-org/boa,[],,[],[],,,,mamba-org/boa,boa,245,54,9,Python,https://boa-build.readthedocs.io/en/latest/,"The fast conda package builder, based on mamba",mamba-org,2024-01-04,2020-05-27,191,1.2769918093819805,https://avatars.githubusercontent.com/u/66118895?v=4,"The fast conda package builder, based on mamba","['conda', 'conda-packages', 'mamba']","['conda', 'conda-packages', 'mamba']",2023-11-19,"[('conda/conda-build', 0.7872036695480347, 'util', 1), ('mamba-org/quetz', 0.7533841729164124, 'util', 1), ('mamba-org/mamba', 0.7310133576393127, 'util', 1), ('conda/constructor', 0.7149392366409302, 'util', 1), ('conda/conda-pack', 0.7062498927116394, 'util', 1), ('mamba-org/micromamba-docker', 0.6690220236778259, 'util', 2), ('conda/conda', 0.5487179756164551, 'util', 1), ('mamba-org/gator', 0.5324650406837463, 'jupyter', 1), ('conda-forge/miniforge', 0.5230752825737, 'util', 0), ('conda-forge/feedstocks', 0.5122072696685791, 'util', 1), ('pomponchik/instld', 0.5095345377922058, 'util', 0), ('spack/spack', 0.5025382041931152, 'util', 0)]",32,4.0,,0.46,20,8,44,2,3,11,3,20.0,19.0,90.0,0.9,29
1838,finance,https://github.com/hydrosquall/tiingo-python,[],,[],[],,,,hydrosquall/tiingo-python,tiingo-python,227,51,8,Python,https://pypi.org/project/tiingo/,Python client for interacting with the Tiingo Financial Data API (stock ticker and news data),hydrosquall,2024-01-12,2017-08-25,335,0.676458067262665,,Python client for interacting with the Tiingo Financial Data API (stock ticker and news data),"['finance', 'stock-market', 'stock-prices', 'stocks', 'ticker-data']","['finance', 'stock-market', 'stock-prices', 'stocks', 'ticker-data']",2023-12-13,"[('cuemacro/findatapy', 0.6793490052223206, 'finance', 0), ('plotly/dash', 0.5758013725280762, 'viz', 1), ('ranaroussi/yfinance', 0.5750361084938049, 'finance', 0), ('matplotlib/mplfinance', 0.5679528713226318, 'finance', 1), ('nasdaq/data-link-python', 0.5673314332962036, 'finance', 0), ('pmorissette/ffn', 0.5629584789276123, 'finance', 0), ('gbeced/pyalgotrade', 0.5620060563087463, 'finance', 0), ('ethereum/web3.py', 0.5618115067481995, 'crypto', 0), ('gbeced/basana', 0.5487340688705444, 'finance', 0), ('ta-lib/ta-lib-python', 0.5465307831764221, 'finance', 1), ('goldmansachs/gs-quant', 0.5450910329818726, 'finance', 0), ('simple-salesforce/simple-salesforce', 0.5396576523780823, 'data', 0), ('holoviz/panel', 0.5386637449264526, 'viz', 0), ('quantconnect/lean', 0.5375146865844727, 'finance', 1), ('cuemacro/finmarketpy', 0.5366682410240173, 'finance', 0), ('stefmolin/stock-analysis', 0.5315351486206055, 'finance', 2), ('pmaji/crypto-whale-watching-app', 0.5264788269996643, 'crypto', 0), ('ccxt/ccxt', 0.5252950191497803, 'crypto', 0), ('googleapis/google-api-python-client', 0.5202198624610901, 'util', 0), ('ranaroussi/quantstats', 0.5174421072006226, 'finance', 1), ('robcarver17/pysystemtrade', 0.5165610313415527, 'finance', 0), ('hugapi/hug', 0.5074604749679565, 'util', 0), ('quantopian/zipline', 0.5056824684143066, 'finance', 0), ('firmai/atspy', 0.5044682025909424, 'time-series', 1), ('snyk-labs/pysnyk', 0.5022001266479492, 'security', 0), ('encode/httpx', 0.5019758939743042, 'web', 0), ('qdrant/qdrant-client', 0.501908540725708, 'util', 0)]",13,5.0,,0.83,26,19,78,1,0,3,3,26.0,28.0,90.0,1.1,29
1495,math,https://github.com/deepmind/synjax,"['probability', 'distributions', 'jax']",SynJax is a neural network library for JAX structured probability distributions,[],[],,,,deepmind/synjax,synjax,220,14,12,Python,,,deepmind,2024-01-04,2023-08-04,25,8.603351955307263,https://avatars.githubusercontent.com/u/8596759?v=4,SynJax is a neural network library for JAX structured probability distributions,[],"['distributions', 'jax', 'probability']",2024-01-08,"[('deepmind/dm-haiku', 0.7001689076423645, 'ml-dl', 1), ('google/flax', 0.6082916259765625, 'ml-dl', 1), ('google/evojax', 0.5408310890197754, 'sim', 1), ('deepmind/chex', 0.5291113257408142, 'ml-dl', 1)]",5,3.0,,0.4,0,0,5,0,0,0,0,0.0,0.0,90.0,0.0,29
406,data,https://github.com/google/weather-tools,[],,[],[],,,,google/weather-tools,weather-tools,186,35,15,Python,https://weather-tools.readthedocs.io/,Apache Beam pipelines to make weather data accessible and useful.,google,2024-01-11,2021-11-22,114,1.6295369211514392,https://avatars.githubusercontent.com/u/1342004?v=4,Apache Beam pipelines to make weather data accessible and useful.,"['apache-beam', 'weather']","['apache-beam', 'weather']",2024-01-10,[],31,2.0,,1.25,32,27,26,0,0,5,5,32.0,6.0,90.0,0.2,29
1502,math,https://github.com/deepmind/kfac-jax,['jax'],,[],[],,,,deepmind/kfac-jax,kfac-jax,177,14,8,Python,,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,deepmind,2024-01-04,2022-03-18,97,1.8140556368960468,https://avatars.githubusercontent.com/u/8596759?v=4,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,"['bayesian-deep-learning', 'machine-learning', 'optimization']","['bayesian-deep-learning', 'jax', 'machine-learning', 'optimization']",2024-01-04,"[('deepmind/dm-haiku', 0.5966999530792236, 'ml-dl', 2), ('pytorch/botorch', 0.55311119556427, 'ml-dl', 0)]",11,4.0,,1.67,19,16,22,0,2,2,2,19.0,6.0,90.0,0.3,29
861,util,https://github.com/hugovk/pypistats,[],,[],[],,,,hugovk/pypistats,pypistats,174,30,5,Python,https://pypistats.org/api/,Command-line interface to PyPI Stats API to get download stats for Python packages,hugovk,2024-01-10,2018-09-22,279,0.6226993865030674,,Command-line interface to PyPI Stats API to get download stats for Python packages,"['api', 'cli', 'command-line', 'command-line-tool', 'downloads', 'statistics', 'stats']","['api', 'cli', 'command-line', 'command-line-tool', 'downloads', 'statistics', 'stats']",2024-01-01,"[('ofek/pypinfo', 0.7068412899971008, 'util', 1), ('pypi/warehouse', 0.6038178205490112, 'util', 0), ('cuemacro/findatapy', 0.562679648399353, 'finance', 0), ('urwid/urwid', 0.5486549735069275, 'term', 0), ('google/python-fire', 0.5451176166534424, 'term', 1), ('tox-dev/pipdeptree', 0.5279873609542847, 'util', 1), ('wolph/python-progressbar', 0.5268552303314209, 'util', 1), ('jquast/blessed', 0.5230196118354797, 'term', 1), ('pyodide/micropip', 0.5070849657058716, 'util', 0), ('samuelcolvin/pytest-pretty', 0.5067400932312012, 'testing', 0), ('pypa/gh-action-pypi-publish', 0.5029650926589966, 'util', 0)]",13,4.0,,0.92,11,9,65,0,3,5,3,11.0,18.0,90.0,1.6,29
767,sim,https://github.com/openfisca/openfisca-core,[],,[],[],,,,openfisca/openfisca-core,openfisca-core,157,74,26,Python,https://openfisca.org,OpenFisca core engine. See other repositories for countries-specific code & data.,openfisca,2023-12-26,2013-12-29,526,0.2983170466883822,https://avatars.githubusercontent.com/u/1794404?v=4,OpenFisca core engine. See other repositories for countries-specific code & data.,"['better-rules', 'legislation-as-code', 'microsimulation', 'rules-as-code']","['better-rules', 'legislation-as-code', 'microsimulation', 'rules-as-code']",2023-12-18,[],61,2.0,,1.88,6,3,122,1,0,39,39,6.0,10.0,90.0,1.7,29
1399,llm,https://github.com/openbioml/chemnlp,['chemistry'],,[],[],,,,openbioml/chemnlp,chemnlp,120,43,3,Python,,ChemNLP project,openbioml,2024-01-12,2023-02-13,50,2.393162393162393,https://avatars.githubusercontent.com/u/106522429?v=4,ChemNLP project,[],['chemistry'],2023-12-09,[],26,2.0,,5.56,113,92,11,1,0,0,0,113.0,71.0,90.0,0.6,29
326,security,https://github.com/sonatype-nexus-community/jake,[],,[],[],,,,sonatype-nexus-community/jake,jake,95,28,8,Python,https://jake.readthedocs.io/,Check your Python environments for vulnerable Open Source packages with OSS Index or Sonatype Nexus Lifecycle.,sonatype-nexus-community,2023-12-08,2019-10-10,224,0.4227590591226955,https://avatars.githubusercontent.com/u/33330803?v=4,Check your Python environments for vulnerable Open Source packages with OSS Index or Sonatype Nexus Lifecycle.,"['nexus-iq', 'ossindex', 'sonatype-iq', 'vulnerabilities', 'vulnerability-scanners']","['nexus-iq', 'ossindex', 'sonatype-iq', 'vulnerabilities', 'vulnerability-scanners']",2023-12-08,"[('pyupio/safety', 0.607435405254364, 'security', 1)]",17,4.0,,1.23,7,3,52,1,7,32,7,7.0,19.0,90.0,2.7,29
1645,util,https://github.com/danielnoord/pydocstringformatter,"['pep257', 'pep8', 'docstrings']",,[],[],,,,danielnoord/pydocstringformatter,pydocstringformatter,62,8,2,Python,,Automatically format your Python docstrings to conform with PEP 8 and PEP 257,danielnoord,2023-12-18,2022-01-01,108,0.5718050065876152,,Automatically format your Python docstrings to conform with PEP 8 and PEP 257,"['docstrings', 'formatter']","['docstrings', 'formatter', 'pep257', 'pep8']",2024-01-08,"[('pycqa/docformatter', 0.8163774013519287, 'util', 2), ('hhatto/autopep8', 0.7380919456481934, 'util', 2), ('google/yapf', 0.6070597171783447, 'util', 1), ('mkdocstrings/python', 0.563347578048706, 'util', 0), ('pdoc3/pdoc', 0.5444629192352295, 'util', 1), ('grantjenks/blue', 0.5090311169624329, 'util', 1), ('mitmproxy/pdoc', 0.5013178586959839, 'util', 1)]",7,2.0,,1.85,24,20,25,0,0,7,7,23.0,55.0,90.0,2.4,29
78,jupyter,https://github.com/quantopian/qgrid,[],,[],[],,,,quantopian/qgrid,qgrid,3007,433,89,Python,,"An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks",quantopian,2024-01-13,2014-09-30,487,6.174537987679671,https://avatars.githubusercontent.com/u/1393215?v=4,"An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks",[],[],2020-04-07,"[('tkrabel/bamboolib', 0.7011144161224365, 'pandas', 0), ('jakevdp/pythondatasciencehandbook', 0.6440531611442566, 'study', 0), ('bloomberg/ipydatagrid', 0.6440353989601135, 'jupyter', 0), ('jupyter/nbformat', 0.6320311427116394, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.6274958848953247, 'jupyter', 0), ('jupyter/notebook', 0.613193154335022, 'jupyter', 0), ('ipython/ipyparallel', 0.611356794834137, 'perf', 0), ('vizzuhq/ipyvizzu', 0.608697235584259, 'jupyter', 0), ('jupyter/nbdime', 0.5866443514823914, 'jupyter', 0), ('holoviz/panel', 0.5826691389083862, 'viz', 0), ('opengeos/leafmap', 0.5799870491027832, 'gis', 0), ('cmudig/autoprofiler', 0.5721290111541748, 'jupyter', 0), ('aws/graph-notebook', 0.571941614151001, 'jupyter', 0), ('mwouts/jupytext', 0.5685391426086426, 'jupyter', 0), ('adamerose/pandasgui', 0.5642687678337097, 'pandas', 0), ('jupyterlab/jupyterlab', 0.5634688138961792, 'jupyter', 0), ('man-group/dtale', 0.560211718082428, 'viz', 0), ('cohere-ai/notebooks', 0.5572461485862732, 'llm', 0), ('jupyterlab/jupyterlab-desktop', 0.5566320419311523, 'jupyter', 0), ('lux-org/lux', 0.5451768040657043, 'viz', 0), ('kanaries/pygwalker', 0.5393330454826355, 'pandas', 0), ('jupyter/nbconvert', 0.538368284702301, 'jupyter', 0), ('voila-dashboards/voila', 0.5343106985092163, 'jupyter', 0), ('koaning/drawdata', 0.5323299765586853, 'jupyter', 0), ('jupyter/nbgrader', 0.5307871103286743, 'jupyter', 0), ('wesm/pydata-book', 0.5240903496742249, 'study', 0), ('jazzband/tablib', 0.5186475515365601, 'data', 0), ('nteract/papermill', 0.516223669052124, 'jupyter', 0), ('vaexio/vaex', 0.5162068009376526, 'perf', 0), ('maartenbreddels/ipyvolume', 0.5143440961837769, 'jupyter', 0), ('ipython/ipykernel', 0.514000654220581, 'util', 0), ('jupyter-widgets/ipyleaflet', 0.5120397806167603, 'gis', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5115215182304382, 'study', 0), ('ageron/handson-ml2', 0.5087634921073914, 'ml', 0), ('xiaohk/stickyland', 0.5084087252616882, 'jupyter', 0), ('pyqtgraph/pyqtgraph', 0.5077592730522156, 'viz', 0), ('saulpw/visidata', 0.5062557458877563, 'term', 0), ('holoviz/holoviz', 0.5044505596160889, 'viz', 0)]",30,2.0,,0.0,1,1,113,46,0,2,2,1.0,0.0,90.0,0.0,28
873,time-series,https://github.com/rjt1990/pyflux,[],,[],[],,,,rjt1990/pyflux,pyflux,2074,243,71,Python,,Open source time series library for Python,rjt1990,2024-01-05,2016-02-16,415,4.997590361445783,,Open source time series library for Python,"['statistics', 'time-series']","['statistics', 'time-series']",2018-12-16,"[('alkaline-ml/pmdarima', 0.7352306842803955, 'time-series', 1), ('tdameritrade/stumpy', 0.6353744268417358, 'time-series', 0), ('awslabs/gluonts', 0.623365581035614, 'time-series', 1), ('firmai/atspy', 0.6143859624862671, 'time-series', 1), ('unit8co/darts', 0.5929312109947205, 'time-series', 1), ('dateutil/dateutil', 0.5882995128631592, 'util', 0), ('google/temporian', 0.5719876289367676, 'time-series', 1), ('pycaret/pycaret', 0.5557315349578857, 'ml', 1), ('pastas/pastas', 0.5493255257606506, 'time-series', 0), ('statsmodels/statsmodels', 0.5408421158790588, 'ml', 1), ('stan-dev/pystan', 0.5393368601799011, 'ml', 0), ('sdispater/pendulum', 0.5265621542930603, 'util', 0), ('pandas-dev/pandas', 0.5234712958335876, 'pandas', 0), ('ta-lib/ta-lib-python', 0.5227634906768799, 'finance', 0), ('mwaskom/seaborn', 0.522447407245636, 'viz', 0), ('altair-viz/altair', 0.5199966430664062, 'viz', 0), ('andgoldschmidt/derivative', 0.5164141654968262, 'math', 0), ('wesm/pydata-book', 0.5128363966941833, 'study', 0), ('stub42/pytz', 0.5127301812171936, 'util', 0), ('pmorissette/ffn', 0.5010930895805359, 'finance', 0)]",6,2.0,,0.0,1,0,96,62,0,5,5,1.0,1.0,90.0,1.0,28
1041,llm,https://github.com/openai/gpt-2-output-dataset,[],,[],[],,,,openai/gpt-2-output-dataset,gpt-2-output-dataset,1844,528,76,Python,,"Dataset of GPT-2 outputs for research in detection, biases, and more",openai,2024-01-12,2019-05-03,247,7.448355452971725,https://avatars.githubusercontent.com/u/14957082?v=4,"Dataset of GPT-2 outputs for research in detection, biases, and more",[],[],2023-12-13,"[('karpathy/nanogpt', 0.5051628351211548, 'llm', 0)]",5,1.0,,0.02,1,0,57,1,0,0,0,1.0,0.0,90.0,0.0,28
496,ml-dl,https://github.com/vt-vl-lab/fgvc,[],,[],[],,,,vt-vl-lab/fgvc,FGVC,1523,279,70,Python,,[ECCV 2020] Flow-edge Guided Video Completion ,vt-vl-lab,2024-01-12,2020-09-09,176,8.611470113085621,https://avatars.githubusercontent.com/u/31048446?v=4,[ECCV 2020] Flow-edge Guided Video Completion ,[],[],2021-12-14,"[('researchmm/sttn', 0.6461269855499268, 'ml-dl', 0), ('mcahny/deep-video-inpainting', 0.5231187343597412, 'ml-dl', 0)]",3,2.0,,0.0,1,1,41,25,0,0,0,1.0,1.0,90.0,1.0,28
283,data,https://github.com/sdispater/orator,[],,[],[],,,,sdispater/orator,orator,1420,174,45,Python,https://orator-orm.com,The Orator ORM provides a simple yet beautiful ActiveRecord implementation.,sdispater,2024-01-04,2015-05-24,453,3.1326820044122283,,The Orator ORM provides a simple yet beautiful ActiveRecord implementation.,"['database', 'orm']","['database', 'orm']",2022-03-13,"[('mcfunley/pugsql', 0.5235227346420288, 'data', 1)]",32,4.0,,0.0,4,0,105,22,0,3,3,4.0,2.0,90.0,0.5,28
732,pandas,https://github.com/machow/siuba,[],,[],[],,,,machow/siuba,siuba,1074,50,21,Python,https://siuba.org,Python library for using dplyr like syntax with pandas and SQL,machow,2024-01-13,2019-02-09,259,4.139867841409692,,Python library for using dplyr like syntax with pandas and SQL,"['data-analysis', 'dplyr', 'pandas', 'sql']","['data-analysis', 'dplyr', 'pandas', 'sql']",2023-09-19,"[('ibis-project/ibis', 0.6308576464653015, 'data', 2), ('tobymao/sqlglot', 0.6064596176147461, 'data', 1), ('tiangolo/sqlmodel', 0.5724479556083679, 'data', 1), ('andialbrecht/sqlparse', 0.5550650358200073, 'data', 0), ('sqlalchemy/sqlalchemy', 0.5513966679573059, 'data', 1), ('pandas-dev/pandas', 0.5513116717338562, 'pandas', 2), ('malloydata/malloy-py', 0.513921856880188, 'data', 1)]",10,2.0,,0.71,3,1,60,4,2,8,2,3.0,0.0,90.0,0.0,28
706,ml,https://github.com/google-research/deeplab2,[],,[],[],,,,google-research/deeplab2,deeplab2,965,160,23,Python,,"DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.",google-research,2024-01-13,2021-05-12,141,6.802618328298086,https://avatars.githubusercontent.com/u/43830688?v=4,"DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.",[],[],2023-04-17,"[('open-mmlab/mmsegmentation', 0.5815913081169128, 'ml', 0), ('dmlc/dgl', 0.5739299058914185, 'ml-dl', 0), ('mdbloice/augmentor', 0.5629528760910034, 'ml', 0), ('lightly-ai/lightly', 0.5617728233337402, 'ml', 0), ('microsoft/deepspeed', 0.5402511954307556, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.5299793481826782, 'ml-dl', 0), ('nvlabs/gcvit', 0.5264610648155212, 'diffusion', 0), ('deepmind/deepmind-research', 0.5203995704650879, 'ml', 0), ('pytorch/ignite', 0.5203951597213745, 'ml-dl', 0), ('lutzroeder/netron', 0.5186880826950073, 'ml', 0), ('azavea/raster-vision', 0.5186462998390198, 'gis', 0), ('open-mmlab/mmdetection', 0.516015350818634, 'ml', 0), ('nvidia/deeplearningexamples', 0.5127051472663879, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5118420720100403, 'ml-dl', 0), ('albumentations-team/albumentations', 0.5106143355369568, 'ml-dl', 0), ('tensorflow/addons', 0.5076053738594055, 'ml', 0), ('roboflow/supervision', 0.5049787163734436, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.5000344514846802, 'study', 0)]",12,4.0,,0.13,2,0,33,9,0,0,0,2.0,0.0,90.0,0.0,28
226,sim,https://github.com/facebookresearch/droidlet,[],,[],[],,,,facebookresearch/droidlet,fairo,828,83,39,Jupyter Notebook,,A modular embodied agent architecture and platform for building embodied agents,facebookresearch,2024-01-11,2020-11-02,169,4.89527027027027,https://avatars.githubusercontent.com/u/16943930?v=4,A modular embodied agent architecture and platform for building embodied agents,[],[],2023-02-01,"[('minedojo/voyager', 0.6723781228065491, 'llm', 0), ('facebookresearch/habitat-lab', 0.6688793897628784, 'sim', 0), ('operand/agency', 0.5389538407325745, 'llm', 0), ('humanoidagents/humanoidagents', 0.5291570425033569, 'sim', 0)]",43,2.0,,0.08,2,0,39,12,0,0,0,2.0,2.0,90.0,1.0,28
1221,debug,https://github.com/ionelmc/python-hunter,[],,[],[],,,,ionelmc/python-hunter,python-hunter,768,45,14,Python,https://python-hunter.readthedocs.io/,Hunter is a flexible code tracing toolkit. ,ionelmc,2024-01-13,2015-03-16,463,1.6582356570018506,,Hunter is a flexible code tracing toolkit. ,"['debugger', 'debugging', 'tracer']","['debugger', 'debugging', 'tracer']",2023-04-26,"[('gaogaotiantian/viztracer', 0.6034563779830933, 'profiling', 2), ('alexmojaki/snoop', 0.5830564498901367, 'debug', 2), ('alexmojaki/heartrate', 0.5184060335159302, 'debug', 1), ('teamhg-memex/eli5', 0.5018780827522278, 'ml', 0), ('abnamro/repository-scanner', 0.5003989338874817, 'security', 0)]",9,3.0,,0.37,1,0,108,9,0,6,6,1.0,2.0,90.0,2.0,28
1805,sim,https://github.com/google/evojax,"['gpu', 'tpu', 'neuroevolution', 'jax']","EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit built on the JAX library",[],[],,,,google/evojax,evojax,728,64,23,Jupyter Notebook,,,google,2024-01-12,2021-12-07,112,6.5,https://avatars.githubusercontent.com/u/1342004?v=4,"EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit built on the JAX library",[],"['gpu', 'jax', 'neuroevolution', 'tpu']",2023-08-29,"[('deepmind/dm-haiku', 0.5894790291786194, 'ml-dl', 1), ('deepmind/synjax', 0.5408310890197754, 'math', 1)]",14,3.0,,0.29,0,0,26,5,1,12,1,0.0,0.0,90.0,0.0,28
357,data,https://github.com/hyperqueryhq/whale,[],,[],[],,,,hyperqueryhq/whale,whale,724,39,42,Python,https://rsyi.gitbook.io/whale,🐳 The stupidly simple CLI workspace for your data warehouse.,hyperqueryhq,2024-01-04,2020-05-27,191,3.7736411020104246,,🐳 The stupidly simple CLI workspace for your data warehouse.,"['data-catalog', 'data-discovery', 'data-documentation']","['data-catalog', 'data-discovery', 'data-documentation']",2022-10-13,"[('intake/intake', 0.5861302614212036, 'data', 1), ('saulpw/visidata', 0.5835681557655334, 'term', 0), ('databrickslabs/dbx', 0.5740757584571838, 'data', 0), ('google/ml-metadata', 0.5290652513504028, 'ml-ops', 0), ('airbnb/knowledge-repo', 0.520332932472229, 'data', 0), ('airbnb/omniduct', 0.5135779976844788, 'data', 0), ('simonw/datasette', 0.5066918134689331, 'data', 0)]",17,7.0,,0.0,0,0,44,15,0,7,7,0.0,0.0,90.0,0.0,28
1870,ml,https://github.com/davidmrau/mixture-of-experts,[],,[],[],,,,davidmrau/mixture-of-experts,mixture-of-experts,716,80,4,Python,,"PyTorch Re-Implementation of ""The Sparsely-Gated Mixture-of-Experts Layer"" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538",davidmrau,2024-01-13,2019-07-19,236,3.026570048309179,,"PyTorch Re-Implementation of ""The Sparsely-Gated Mixture-of-Experts Layer"" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538","['mixture-of-experts', 'moe', 'pytorch', 're-implementation', 'sparsely-gated-mixture-of-experts']","['mixture-of-experts', 'moe', 'pytorch', 're-implementation', 'sparsely-gated-mixture-of-experts']",2023-12-10,"[('laekov/fastmoe', 0.5889419913291931, 'ml', 1), ('nvidia/apex', 0.5525276064872742, 'ml-dl', 0), ('pytorch/ignite', 0.5471916198730469, 'ml-dl', 1), ('pytorch/botorch', 0.5366146564483643, 'ml-dl', 0), ('skorch-dev/skorch', 0.5069704055786133, 'ml-dl', 1)]",4,2.0,,0.12,7,5,55,1,0,0,0,7.0,7.0,90.0,1.0,28
1036,finance,https://github.com/numerai/example-scripts,[],,[],[],,,,numerai/example-scripts,example-scripts,703,259,67,Jupyter Notebook,https://numer.ai/,A collection of scripts and notebooks to help you get started quickly.,numerai,2024-01-13,2017-01-06,368,1.9073643410852714,https://avatars.githubusercontent.com/u/15222762?v=4,A collection of scripts and notebooks to help you get started quickly.,"['cryptocurrency', 'machine-learning', 'numerai', 'quant-finance']","['cryptocurrency', 'machine-learning', 'numerai', 'quant-finance']",2024-01-13,"[('ccxt/ccxt', 0.6066434979438782, 'crypto', 1), ('gbeced/basana', 0.6021682024002075, 'finance', 1), ('zvtvz/zvt', 0.5974596738815308, 'finance', 2), ('polakowo/vectorbt', 0.5881688594818115, 'finance', 2), ('ofek/bit', 0.5733424425125122, 'crypto', 0), ('1200wd/bitcoinlib', 0.5599415898323059, 'crypto', 0), ('dylanhogg/crazy-awesome-crypto', 0.5541864633560181, 'crypto', 1), ('goldmansachs/gs-quant', 0.5451831221580505, 'finance', 0), ('openbb-finance/openbbterminal', 0.5369656682014465, 'finance', 2), ('primal100/pybitcointools', 0.5286599397659302, 'crypto', 0), ('ranaroussi/quantstats', 0.5179154872894287, 'finance', 0), ('chancefocus/pixiu', 0.5142377018928528, 'finance', 1), ('microsoft/qlib', 0.5105475187301636, 'finance', 1), ('gbeced/pyalgotrade', 0.5094029307365417, 'finance', 0), ('opentensor/bittensor', 0.5083948969841003, 'ml', 2), ('quantconnect/lean', 0.5045038461685181, 'finance', 0)]",46,2.0,,0.9,14,8,85,0,0,0,0,14.0,2.0,90.0,0.1,28
1671,util,https://github.com/erotemic/ubelt,[],,[],[],,,,erotemic/ubelt,ubelt,702,46,18,Python,,"A Python utility library with a stdlib like feel and extra batteries. Paths, Progress, Dicts, Downloads, Caching, Hashing: ubelt makes it easy!",erotemic,2024-01-04,2017-01-30,365,1.9225352112676057,,"A Python utility library with a stdlib like feel and extra batteries. Paths, Progress, Dicts, Downloads, Caching, Hashing: ubelt makes it easy!","['cross-platform', 'utilities', 'utility-library']","['cross-platform', 'utilities', 'utility-library']",2023-10-27,"[('dgilland/cacheout', 0.6818086504936218, 'perf', 0), ('pytoolz/toolz', 0.6275792717933655, 'util', 0), ('pytables/pytables', 0.615244448184967, 'data', 0), ('pypy/pypy', 0.6026424169540405, 'util', 0), ('pytorch/data', 0.5979729294776917, 'data', 0), ('python-cachier/cachier', 0.5962907671928406, 'perf', 0), ('tqdm/tqdm', 0.5793629884719849, 'term', 1), ('pympler/pympler', 0.574570894241333, 'perf', 0), ('pypa/installer', 0.5646870136260986, 'util', 0), ('grantjenks/python-diskcache', 0.5623111724853516, 'util', 0), ('spotify/annoy', 0.5622727870941162, 'ml', 0), ('agronholm/apscheduler', 0.5611792802810669, 'util', 0), ('1200wd/bitcoinlib', 0.5576600432395935, 'crypto', 0), ('imageio/imageio', 0.5557732582092285, 'util', 0), ('pyston/pyston', 0.5508465766906738, 'util', 0), ('libtcod/python-tcod', 0.5495151877403259, 'gamedev', 0), ('scrapy/scrapy', 0.5488511323928833, 'data', 0), ('hoffstadt/dearpygui', 0.5396130681037903, 'gui', 1), ('rasbt/watermark', 0.5379260182380676, 'util', 0), ('qdrant/fastembed', 0.5371770262718201, 'ml', 0), ('fastai/fastcore', 0.5355119705200195, 'util', 0), ('jovianml/opendatasets', 0.5337167978286743, 'data', 0), ('mkdocstrings/griffe', 0.5309567451477051, 'util', 0), ('spotify/voyager', 0.5305669903755188, 'ml', 0), ('pypa/hatch', 0.5291941165924072, 'util', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.5286123752593994, 'data', 0), ('wxwidgets/phoenix', 0.5278312563896179, 'gui', 1), ('linkedin/shiv', 0.5263099670410156, 'util', 0), ('jquast/blessed', 0.5253154635429382, 'term', 0), ('beeware/toga', 0.5228415727615356, 'gui', 0), ('dlt-hub/dlt', 0.5195381045341492, 'data', 0), ('landscapeio/prospector', 0.518781840801239, 'util', 0), ('platformdirs/platformdirs', 0.5185686945915222, 'util', 1), ('quantopian/zipline', 0.5161089897155762, 'finance', 0), ('faster-cpython/tools', 0.5155495405197144, 'perf', 0), ('ta-lib/ta-lib-python', 0.513969361782074, 'finance', 0), ('dosisod/refurb', 0.513725996017456, 'util', 0), ('joblib/joblib', 0.5093076825141907, 'util', 0), ('samuelcolvin/watchfiles', 0.5087971091270447, 'util', 0), ('micropython/micropython', 0.5087762475013733, 'util', 0), ('eleutherai/pyfra', 0.5078774094581604, 'ml', 0), ('pythonprofilers/memory_profiler', 0.5078474879264832, 'profiling', 0), ('python/cpython', 0.5061374306678772, 'util', 0), ('faster-cpython/ideas', 0.5061014890670776, 'perf', 0), ('rhettbull/osxphotos', 0.5059564709663391, 'util', 0), ('timofurrer/awesome-asyncio', 0.5056865811347961, 'study', 0), ('prompt-toolkit/ptpython', 0.5056498646736145, 'util', 0), ('python-odin/odin', 0.5053083896636963, 'util', 0), ('pyglet/pyglet', 0.5050175786018372, 'gamedev', 0), ('pyodide/micropip', 0.5013461709022522, 'util', 0), ('legrandin/pycryptodome', 0.5003699660301208, 'util', 0)]",4,2.0,,2.15,3,2,85,3,5,9,5,3.0,2.0,90.0,0.7,28
396,web,https://github.com/klen/muffin,[],,[],[],,,,klen/muffin,muffin,659,25,31,Python,,"Muffin is a fast, simple and asyncronous web-framework for Python 3",klen,2024-01-13,2015-02-03,469,1.4051172707889126,,"Muffin is a fast, simple and asyncronous web-framework for Python 3","['asgi', 'asyncio', 'curio', 'muffin', 'trio', 'webframework']","['asgi', 'asyncio', 'curio', 'muffin', 'trio', 'webframework']",2023-10-11,"[('neoteroi/blacksheep', 0.7668511271476746, 'web', 2), ('masoniteframework/masonite', 0.7306077480316162, 'web', 1), ('pallets/quart', 0.7141019701957703, 'web', 2), ('pallets/flask', 0.6954807639122009, 'web', 0), ('alirn76/panther', 0.6565958857536316, 'web', 0), ('falconry/falcon', 0.6537138819694519, 'web', 1), ('encode/uvicorn', 0.6518058180809021, 'web', 2), ('webpy/webpy', 0.6442074179649353, 'web', 0), ('timofurrer/awesome-asyncio', 0.6332191228866577, 'study', 1), ('pylons/pyramid', 0.6305515766143799, 'web', 0), ('bottlepy/bottle', 0.6195411086082458, 'web', 0), ('encode/httpx', 0.6186836957931519, 'web', 2), ('willmcgugan/textual', 0.6039458513259888, 'term', 0), ('sumerc/yappi', 0.5936639904975891, 'profiling', 2), ('pypy/pypy', 0.5929686427116394, 'util', 0), ('scrapy/scrapy', 0.5914323329925537, 'data', 0), ('eleutherai/pyfra', 0.5880747437477112, 'ml', 0), ('cherrypy/cherrypy', 0.5874270796775818, 'web', 0), ('fastai/fastcore', 0.5848855376243591, 'util', 0), ('reflex-dev/reflex', 0.5825878977775574, 'web', 0), ('holoviz/panel', 0.5814103484153748, 'viz', 0), ('r0x0r/pywebview', 0.5798044800758362, 'gui', 0), ('aio-libs/aiohttp', 0.5731057524681091, 'web', 1), ('pallets/werkzeug', 0.5703849196434021, 'web', 0), ('huge-success/sanic', 0.5701971054077148, 'web', 2), ('flet-dev/flet', 0.5684182643890381, 'web', 0), ('emmett-framework/emmett', 0.56635981798172, 'web', 2), ('klen/py-frameworks-bench', 0.5657544732093811, 'perf', 0), ('ets-labs/python-dependency-injector', 0.5598342418670654, 'util', 1), ('python-trio/trio', 0.5527318716049194, 'perf', 1), ('dylanhogg/awesome-python', 0.5525697469711304, 'study', 0), ('sqlalchemy/mako', 0.5490294694900513, 'template', 0), ('starlite-api/starlite', 0.548690915107727, 'web', 2), ('pyston/pyston', 0.5434106588363647, 'util', 0), ('python-restx/flask-restx', 0.5422064065933228, 'web', 0), ('agronholm/anyio', 0.5409852266311646, 'perf', 3), ('hoffstadt/dearpygui', 0.5390740036964417, 'gui', 0), ('python/cpython', 0.5362712144851685, 'util', 0), ('bokeh/bokeh', 0.5334693193435669, 'viz', 0), ('plotly/dash', 0.5317702889442444, 'viz', 0), ('tiangolo/fastapi', 0.5296502709388733, 'web', 1), ('ipython/ipyparallel', 0.5273743271827698, 'perf', 0), ('eventual-inc/daft', 0.5256049036979675, 'pandas', 0), ('backtick-se/cowait', 0.5236403942108154, 'util', 0), ('encode/starlette', 0.5224829912185669, 'web', 0), ('voila-dashboards/voila', 0.5211663246154785, 'jupyter', 0), ('joblib/joblib', 0.5203728675842285, 'util', 0), ('tornadoweb/tornado', 0.5200356245040894, 'web', 0), ('locustio/locust', 0.5192926526069641, 'testing', 0), ('pytoolz/toolz', 0.5191732048988342, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5174252390861511, 'study', 0), ('roniemartinez/dude', 0.5161595940589905, 'util', 0), ('pyodide/pyodide', 0.5121115446090698, 'util', 0), ('psf/requests', 0.5109789967536926, 'web', 0), ('clips/pattern', 0.5102404356002808, 'nlp', 0), ('s3rius/fastapi-template', 0.5076808333396912, 'web', 1), ('wxwidgets/phoenix', 0.5061802864074707, 'gui', 0), ('pylons/waitress', 0.5056185126304626, 'web', 0), ('maartenbreddels/ipyvolume', 0.50477135181427, 'jupyter', 0), ('indico/indico', 0.5037544369697571, 'web', 0), ('django/django', 0.5025768876075745, 'web', 0), ('pyinfra-dev/pyinfra', 0.5019458532333374, 'util', 0), ('ibis-project/ibis', 0.5011864900588989, 'data', 0), ('google/gin-config', 0.5011733770370483, 'util', 0), ('benoitc/gunicorn', 0.5010045766830444, 'web', 0), ('plotly/plotly.py', 0.5007705092430115, 'viz', 0)]",13,5.0,,2.56,1,0,109,3,0,43,43,1.0,0.0,90.0,0.0,28
1161,jupyter,https://github.com/linealabs/lineapy,[],,[],[],,,,linealabs/lineapy,lineapy,641,49,21,Jupyter Notebook,https://lineapy.org,"Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.",linealabs,2024-01-11,2021-07-28,130,4.898471615720524,https://avatars.githubusercontent.com/u/76981099?v=4,"Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.",[],[],2023-08-10,"[('ploomber/ploomber', 0.739909827709198, 'ml-ops', 0), ('mage-ai/mage-ai', 0.6465555429458618, 'ml-ops', 0), ('orchest/orchest', 0.6248028874397278, 'ml-ops', 0), ('unstructured-io/pipeline-sec-filings', 0.6072432994842529, 'data', 0), ('meltano/meltano', 0.5844327211380005, 'ml-ops', 0), ('paperswithcode/sota-extractor', 0.5607438087463379, 'data', 0), ('hi-primus/optimus', 0.5603882074356079, 'ml-ops', 0), ('kubeflow-kale/kale', 0.5481640100479126, 'ml-ops', 0), ('nteract/papermill', 0.5371261835098267, 'jupyter', 0), ('airbytehq/airbyte', 0.5353677272796631, 'data', 0), ('saulpw/visidata', 0.5347519516944885, 'term', 0), ('astronomer/astro-sdk', 0.5296847224235535, 'ml-ops', 0), ('koaning/clumper', 0.5260019302368164, 'util', 0), ('intake/intake', 0.5228672623634338, 'data', 0), ('koaning/scikit-lego', 0.5148707032203674, 'ml', 0), ('kestra-io/kestra', 0.5073179602622986, 'ml-ops', 0), ('lean-dojo/leandojo', 0.5060582756996155, 'math', 0), ('dagworks-inc/hamilton', 0.5050464868545532, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5049693584442139, 'ml-ops', 0), ('google/ml-metadata', 0.5038784146308899, 'ml-ops', 0), ('koaning/scikit-partial', 0.5030436515808105, 'data', 0)]",24,2.0,,0.19,0,0,30,5,0,4,4,0.0,0.0,90.0,0.0,28
1223,ml,https://github.com/hpcaitech/energonai,[],,[],[],,,,hpcaitech/energonai,EnergonAI,629,92,23,Python,,Large-scale model inference.,hpcaitech,2024-01-12,2022-01-24,105,5.982336956521739,https://avatars.githubusercontent.com/u/88699314?v=4,Large-scale model inference.,[],[],2023-03-08,"[('optimalscale/lmflow', 0.6059041619300842, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5440476536750793, 'llm', 0), ('squeezeailab/squeezellm', 0.5279243588447571, 'llm', 0), ('huggingface/text-embeddings-inference', 0.5272306799888611, 'llm', 0), ('ai21labs/lm-evaluation', 0.5110083818435669, 'llm', 0)]",13,6.0,,0.13,0,0,24,10,0,1,1,0.0,0.0,90.0,0.0,28
1363,gamedev,https://github.com/lordmauve/pgzero,[],,[],[],,,,lordmauve/pgzero,pgzero,492,188,29,Python,https://pygame-zero.readthedocs.io/,"A zero-boilerplate games programming framework for Python 3, based on Pygame.",lordmauve,2024-01-11,2018-02-25,309,1.5907621247113164,,"A zero-boilerplate games programming framework for Python 3, based on Pygame.","['education', 'game-framework', 'pygame', 'python-game-development']","['education', 'game-framework', 'pygame', 'python-game-development']",2022-06-30,"[('pygame/pygame', 0.6985493302345276, 'gamedev', 1), ('pokepetter/ursina', 0.6621728539466858, 'gamedev', 0), ('kitao/pyxel', 0.6230867505073547, 'gamedev', 0), ('pygamelib/pygamelib', 0.6199098229408264, 'gamedev', 0), ('pythonarcade/arcade', 0.6107795238494873, 'gamedev', 0), ('panda3d/panda3d', 0.5944162011146545, 'gamedev', 0), ('pyglet/pyglet', 0.544232964515686, 'gamedev', 0), ('ljvmiranda921/seagull', 0.5329146981239319, 'sim', 0), ('amaargiru/pyroad', 0.5130576491355896, 'study', 0), ('alephalpha/golly', 0.5084664821624756, 'sim', 0), ('renpy/pygame_sdl2', 0.5067479610443115, 'gamedev', 1), ('projectmesa/mesa', 0.5056121349334717, 'sim', 0)]",45,5.0,,0.0,3,1,72,19,0,2,2,3.0,6.0,90.0,2.0,28
836,perf,https://github.com/joblib/loky,[],,[],[],,,,joblib/loky,loky,490,45,12,Python,http://loky.readthedocs.io/en/stable/,Robust and reusable Executor for joblib,joblib,2024-01-07,2015-12-25,422,1.1595672751859365,https://avatars.githubusercontent.com/u/332661?v=4,Robust and reusable Executor for joblib,['multiprocessing-library'],['multiprocessing-library'],2023-06-29,"[('agronholm/apscheduler', 0.5760906934738159, 'util', 0), ('samuelcolvin/arq', 0.5648357272148132, 'data', 0), ('bogdanp/dramatiq', 0.5552358031272888, 'util', 0), ('noxdafox/pebble', 0.5490549802780151, 'perf', 0), ('dask/dask', 0.5317108035087585, 'perf', 0), ('python-trio/trio', 0.5253786444664001, 'perf', 0), ('joblib/joblib', 0.5221757292747498, 'util', 0), ('sumerc/yappi', 0.5158117413520813, 'profiling', 0), ('ipython/ipyparallel', 0.5099025964736938, 'perf', 0)]",18,6.0,,0.35,2,1,98,7,0,5,5,2.0,2.0,90.0,1.0,28
494,ml,https://github.com/linkedin/fasttreeshap,[],,[],[],,,,linkedin/fasttreeshap,FastTreeSHAP,477,29,7,Python,,Fast SHAP value computation for interpreting tree-based models,linkedin,2024-01-10,2022-01-24,105,4.536684782608695,https://avatars.githubusercontent.com/u/357098?v=4,Fast SHAP value computation for interpreting tree-based models,"['explainable-ai', 'interpretability', 'lightgbm', 'machine-learning', 'random-forest', 'shap', 'xgboost']","['explainable-ai', 'interpretability', 'lightgbm', 'machine-learning', 'random-forest', 'shap', 'xgboost']",2023-06-26,"[('maif/shapash', 0.6388174295425415, 'ml', 3), ('slundberg/shap', 0.5951489806175232, 'ml-interpretability', 3), ('selfexplainml/piml-toolbox', 0.5518995523452759, 'ml-interpretability', 0), ('teamhg-memex/eli5', 0.542718231678009, 'ml', 3), ('csinva/imodels', 0.5407923460006714, 'ml', 3), ('interpretml/interpret', 0.5312417149543762, 'ml-interpretability', 3), ('marcotcr/lime', 0.5298793315887451, 'ml-interpretability', 0), ('catboost/catboost', 0.5170351266860962, 'ml', 1), ('seldonio/alibi', 0.5031470060348511, 'ml-interpretability', 2)]",6,2.0,,0.17,1,0,24,7,3,3,3,1.0,1.0,90.0,1.0,28
186,math,https://github.com/willianfuks/tfcausalimpact,[],,[],[],,,,willianfuks/tfcausalimpact,tfcausalimpact,475,62,12,Python,,Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.,willianfuks,2024-01-04,2020-08-17,180,2.6367961934972244,,Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.,"['causal-inference', 'causalimpact', 'tensorflow-probability']","['causal-inference', 'causalimpact', 'tensorflow-probability']",2023-11-21,"[('mckinsey/causalnex', 0.6074860692024231, 'math', 1), ('py-why/dowhy', 0.6020914912223816, 'ml', 1)]",4,1.0,,0.02,10,5,42,2,1,5,1,10.0,27.0,90.0,2.7,28
236,ml-rl,https://github.com/salesforce/warp-drive,[],,[],[],,,,salesforce/warp-drive,warp-drive,425,77,14,Python,,Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022),salesforce,2024-01-14,2021-08-25,126,3.350225225225225,https://avatars.githubusercontent.com/u/453694?v=4,Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022),"['cuda', 'deep-learning', 'gpu', 'high-throughput', 'multiagent-reinforcement-learning', 'numba', 'pytorch', 'reinforcement-learning']","['cuda', 'deep-learning', 'gpu', 'high-throughput', 'multiagent-reinforcement-learning', 'numba', 'pytorch', 'reinforcement-learning']",2023-12-20,"[('thu-ml/tianshou', 0.6808977723121643, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.6577045321464539, 'ml-rl', 2), ('denys88/rl_games', 0.6500195264816284, 'ml-rl', 3), ('google/trax', 0.640018880367279, 'ml-dl', 2), ('inspirai/timechamber', 0.6329183578491211, 'sim', 1), ('keras-rl/keras-rl', 0.6099841594696045, 'ml-rl', 1), ('pytorch/rl', 0.6080675721168518, 'ml-rl', 2), ('openai/baselines', 0.5866561532020569, 'ml-rl', 0), ('tensorflow/tensor2tensor', 0.5827850699424744, 'ml', 2), ('tensorlayer/tensorlayer', 0.5802738666534424, 'ml-rl', 2), ('pytorchlightning/pytorch-lightning', 0.5696126222610474, 'ml-dl', 2), ('deepmind/dm_control', 0.5524762272834778, 'ml-rl', 2), ('microsoft/deepspeed', 0.5514455437660217, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5466130971908569, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.5405166745185852, 'study', 3), ('determined-ai/determined', 0.5399549603462219, 'ml-ops', 2), ('facebookresearch/habitat-lab', 0.5386927127838135, 'sim', 2), ('apache/incubator-mxnet', 0.5272499322891235, 'ml-dl', 0), ('huggingface/accelerate', 0.5263185501098633, 'ml', 0), ('pettingzoo-team/pettingzoo', 0.5245906710624695, 'ml-rl', 2), ('ai4finance-foundation/finrl', 0.5230139493942261, 'finance', 1), ('microsoft/onnxruntime', 0.5185619592666626, 'ml', 2), ('openai/spinningup', 0.5154350399971008, 'study', 0), ('nvidia-omniverse/orbit', 0.513428270816803, 'sim', 0), ('ray-project/ray', 0.5116983652114868, 'ml-ops', 3), ('pytorch/pytorch', 0.5105277895927429, 'ml-dl', 2), ('aiqc/aiqc', 0.509090006351471, 'ml-ops', 0), ('horovod/horovod', 0.5066569447517395, 'ml-ops', 2), ('farama-foundation/gymnasium', 0.5065247416496277, 'ml-rl', 1), ('google/tf-quant-finance', 0.5037093162536621, 'finance', 1), ('google/dopamine', 0.5014780759811401, 'ml-rl', 0), ('deepmind/acme', 0.5006597638130188, 'ml-rl', 1), ('sail-sg/envpool', 0.5001153945922852, 'sim', 1)]",7,2.0,,0.67,7,6,29,1,4,3,4,7.0,0.0,90.0,0.0,28
1427,llm,https://github.com/declare-lab/instruct-eval,[],,[],[],,,,declare-lab/instruct-eval,instruct-eval,404,29,12,Python,https://declare-lab.net/instruct-eval/,This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks. ,declare-lab,2024-01-12,2023-03-28,44,9.181818181818182,https://avatars.githubusercontent.com/u/59164695?v=4,This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks. ,"['instruct-tuning', 'llm']","['instruct-tuning', 'llm']",2023-09-26,"[('instruction-tuning-with-gpt-4/gpt-4-llm', 0.653624415397644, 'llm', 0), ('tiger-ai-lab/mammoth', 0.6364500522613525, 'llm', 0), ('yizhongw/self-instruct', 0.550851047039032, 'llm', 0), ('hiyouga/llama-factory', 0.5344966650009155, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5344966053962708, 'llm', 1), ('zrrskywalker/llama-adapter', 0.5260562896728516, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5018754005432129, 'llm', 0)]",3,1.0,,2.75,7,0,10,4,0,0,0,7.0,2.0,90.0,0.3,28
1619,testing,https://github.com/samuelcolvin/pytest-pretty,['pytest'],,[],[],,,,samuelcolvin/pytest-pretty,pytest-pretty,388,6,7,Python,,pytest plugin for pretty printing the test summary.,samuelcolvin,2024-01-04,2022-10-25,66,5.878787878787879,,pytest plugin for pretty printing the test summary.,[],['pytest'],2023-05-04,"[('pytest-dev/pytest-cov', 0.6255528926849365, 'testing', 1), ('pytest-dev/pytest', 0.5827073454856873, 'testing', 0), ('teemu/pytest-sugar', 0.5765059590339661, 'testing', 1), ('pytest-dev/pytest-mock', 0.5683526992797852, 'testing', 1), ('pytest-dev/pytest-xdist', 0.5537928342819214, 'testing', 1), ('inducer/pudb', 0.5497497320175171, 'debug', 1), ('ionelmc/pytest-benchmark', 0.5456304550170898, 'testing', 1), ('kiwicom/pytest-recording', 0.544182538986206, 'testing', 1), ('computationalmodelling/nbval', 0.5243958234786987, 'jupyter', 1), ('samuelcolvin/dirty-equals', 0.5239970088005066, 'util', 1), ('taverntesting/tavern', 0.5094279050827026, 'testing', 1), ('hugovk/pypistats', 0.5067400932312012, 'util', 0), ('nedbat/coveragepy', 0.506611704826355, 'testing', 0)]",5,4.0,,0.29,0,0,15,8,5,4,5,0.0,0.0,90.0,0.0,28
708,ml-ops,https://github.com/unionai-oss/unionml,[],,[],[],,,,unionai-oss/unionml,unionml,323,43,4,Python,https://www.union.ai/unionml,UnionML: the easiest way to build and deploy machine learning microservices,unionai-oss,2024-01-11,2021-11-17,114,2.8121890547263684,https://avatars.githubusercontent.com/u/94206482?v=4,UnionML: the easiest way to build and deploy machine learning microservices,"['machine-learning', 'mlops']","['machine-learning', 'mlops']",2023-09-27,"[('ml-tooling/opyrator', 0.6805552840232849, 'viz', 1), ('polyaxon/polyaxon', 0.6547122597694397, 'ml-ops', 2), ('kubeflow/pipelines', 0.6105091571807861, 'ml-ops', 2), ('fmind/mlops-python-package', 0.5882454514503479, 'template', 1), ('bodywork-ml/bodywork-core', 0.5865074396133423, 'ml-ops', 2), ('titanml/takeoff', 0.56184321641922, 'llm', 0), ('microsoft/nni', 0.5530210137367249, 'ml', 2), ('zenml-io/zenml', 0.5419542193412781, 'ml-ops', 2), ('allegroai/clearml', 0.536503255367279, 'ml-ops', 2), ('mlflow/mlflow', 0.5361176133155823, 'ml-ops', 1), ('ajndkr/lanarky', 0.5264783501625061, 'llm', 0), ('zenml-io/mlstacks', 0.5240914225578308, 'ml-ops', 1), ('janetech-inc/fast-api-admin-template', 0.5197975635528564, 'template', 0), ('kubeflow/fairing', 0.5197477340698242, 'ml-ops', 0), ('flyteorg/flyte', 0.5188993215560913, 'ml-ops', 2), ('onnx/onnx', 0.5090394020080566, 'ml', 1), ('netflix/metaflow', 0.5082710981369019, 'ml-ops', 2), ('bentoml/bentoml', 0.5069453716278076, 'ml-ops', 2), ('alpa-projects/alpa', 0.50523841381073, 'ml-dl', 1), ('automl/auto-sklearn', 0.5017037987709045, 'ml', 0)]",16,6.0,,0.08,1,0,26,4,0,8,8,1.0,0.0,90.0,0.0,28
84,ml,https://github.com/stan-dev/pystan,[],,[],[],,,,stan-dev/pystan,pystan,296,56,13,Python,,"PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io",stan-dev,2024-01-13,2017-09-17,332,0.8907996560619088,https://avatars.githubusercontent.com/u/3374820?v=4,"PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io",[],[],2024-01-05,"[('firmai/atspy', 0.5848978757858276, 'time-series', 0), ('pandas-dev/pandas', 0.5815196633338928, 'pandas', 0), ('alkaline-ml/pmdarima', 0.5813616514205933, 'time-series', 0), ('eleutherai/pyfra', 0.5657259225845337, 'ml', 0), ('selfexplainml/piml-toolbox', 0.5616912245750427, 'ml-interpretability', 0), ('pymc-devs/pymc3', 0.5608965158462524, 'ml', 0), ('statsmodels/statsmodels', 0.5519436001777649, 'ml', 0), ('crflynn/stochastic', 0.5423630475997925, 'sim', 0), ('rjt1990/pyflux', 0.5393368601799011, 'time-series', 0), ('udst/urbansim', 0.5362882018089294, 'sim', 0), ('pysal/pysal', 0.5347137451171875, 'gis', 0), ('pytoolz/toolz', 0.5338031053543091, 'util', 0), ('pmorissette/ffn', 0.5250624418258667, 'finance', 0), ('uber/orbit', 0.5150203704833984, 'time-series', 0), ('mwaskom/seaborn', 0.5127715468406677, 'viz', 0), ('google/temporian', 0.5121117234230042, 'time-series', 0), ('brokenloop/jsontopydantic', 0.5074247717857361, 'util', 0), ('altair-viz/altair', 0.506462574005127, 'viz', 0), ('rasbt/mlxtend', 0.5054618716239929, 'ml', 0), ('scikit-learn/scikit-learn', 0.5038464665412903, 'ml', 0), ('probml/pyprobml', 0.5016106963157654, 'ml', 0)]",14,4.0,,0.17,9,7,77,1,0,3,3,9.0,12.0,90.0,1.3,28
945,diffusion,https://github.com/lunarring/latentblending,[],,[],[],,,,lunarring/latentblending,latentblending,290,23,14,Python,,"Create butter-smooth transitions between prompts, powered by stable diffusion",lunarring,2024-01-08,2022-11-19,62,4.645308924485126,https://avatars.githubusercontent.com/u/78172771?v=4,"Create butter-smooth transitions between prompts, powered by stable diffusion","['animation', 'diffusion', 'stable-diffusion']","['animation', 'diffusion', 'stable-diffusion']",2024-01-10,"[('carson-katri/dream-textures', 0.5618883967399597, 'diffusion', 1), ('nateraw/stable-diffusion-videos', 0.5257456302642822, 'diffusion', 1)]",12,1.0,,2.0,3,2,14,0,0,0,0,3.0,1.0,90.0,0.3,28
1705,util,https://github.com/mtkennerly/dunamai,[],,[],[],,,,mtkennerly/dunamai,dunamai,276,23,3,Python,https://dunamai.readthedocs.io/en/latest,Dynamic versioning library and CLI,mtkennerly,2024-01-13,2019-03-26,253,1.0909090909090908,,Dynamic versioning library and CLI,"['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning']","['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning']",2023-12-09,"[('mtkennerly/poetry-dynamic-versioning', 0.7415024042129517, 'util', 11), ('pypa/setuptools_scm', 0.6616849303245544, 'util', 2), ('callowayproject/bump-my-version', 0.6012407541275024, 'util', 1), ('python-versioneer/python-versioneer', 0.5904924869537354, 'util', 0), ('pypa/hatch', 0.56247878074646, 'util', 2), ('spack/spack', 0.5440518856048584, 'util', 0)]",14,4.0,,0.58,1,1,58,1,6,9,6,1.0,1.0,90.0,1.0,28
454,gis,https://github.com/graal-research/deepparse,[],,[],[],,,,graal-research/deepparse,deepparse,265,28,4,Python,https://deepparse.org/,Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning,graal-research,2024-01-04,2020-07-01,186,1.4181957186544343,https://avatars.githubusercontent.com/u/7155143?v=4,Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning,"['addresses-parsing', 'machine-learning']","['addresses-parsing', 'machine-learning']",2023-12-17,"[('jasonrig/address-net', 0.7487471699714661, 'gis', 1)]",8,2.0,,1.38,2,1,43,1,6,14,6,2.0,3.0,90.0,1.5,28
207,util,https://github.com/aws/aws-lambda-python-runtime-interface-client,[],,[],[],,,,aws/aws-lambda-python-runtime-interface-client,aws-lambda-python-runtime-interface-client,237,65,17,Python,,,aws,2024-01-05,2020-09-02,177,1.3325301204819278,https://avatars.githubusercontent.com/u/2232217?v=4,aws/aws-lambda-python-runtime-interface-client,[],[],2023-10-30,"[('nficano/python-lambda', 0.706771194934845, 'util', 0), ('aws/chalice', 0.672527015209198, 'web', 0), ('geeogi/async-python-lambda-template', 0.6352989673614502, 'template', 0), ('jordaneremieff/mangum', 0.6282299160957336, 'web', 0), ('boto/boto3', 0.6017659902572632, 'util', 0), ('developmentseed/geolambda', 0.5933845043182373, 'gis', 0), ('pynamodb/pynamodb', 0.5816658735275269, 'data', 0), ('rpgreen/apilogs', 0.5472556948661804, 'util', 0), ('samuelcolvin/aioaws', 0.5471480488777161, 'data', 0), ('amzn/ion-python', 0.524419903755188, 'data', 0), ('awslabs/python-deequ', 0.5195323824882507, 'ml', 0)]",27,4.0,,0.37,19,11,41,3,4,3,4,19.0,12.0,90.0,0.6,28
1251,study,https://github.com/stanford-crfm/ecosystem-graphs,[],,[],[],,,,stanford-crfm/ecosystem-graphs,ecosystem-graphs,214,25,14,JavaScript,,,stanford-crfm,2024-01-08,2022-03-10,98,2.167872648335745,https://avatars.githubusercontent.com/u/75054807?v=4,stanford-crfm/ecosystem-graphs,[],[],2024-01-09,[],15,4.0,,3.42,13,13,22,0,0,0,0,13.0,1.0,90.0,0.1,28
1701,llm,https://github.com/llm-tuning-safety/llms-finetuning-safety,[],,[],[],,,,llm-tuning-safety/llms-finetuning-safety,LLMs-Finetuning-Safety,110,8,3,Python,https://llm-tuning-safety.github.io/,"We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.",llm-tuning-safety,2024-01-13,2023-10-06,16,6.637931034482759,,"We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.","['alignment', 'llm', 'llm-finetuning']","['alignment', 'llm', 'llm-finetuning']",2023-11-21,"[('guardrails-ai/guardrails', 0.6136285066604614, 'llm', 1), ('nvidia/nemo-guardrails', 0.5286512970924377, 'llm', 0)]",4,2.0,,0.27,4,4,3,2,0,0,0,4.0,4.0,90.0,1.0,28
1775,llm,https://github.com/aws-samples/serverless-pdf-chat,[],,[],[],,,,aws-samples/serverless-pdf-chat,serverless-pdf-chat,96,94,7,TypeScript,https://aws.amazon.com/blogs/compute/building-a-serverless-document-chat-with-aws-lambda-and-amazon-bedrock/,LLM-powered document chat using Amazon Bedrock and AWS Serverless,aws-samples,2024-01-09,2023-09-30,17,5.508196721311475,https://avatars.githubusercontent.com/u/8931462?v=4,LLM-powered document chat using Amazon Bedrock and AWS Serverless,"['ai', 'amazon-bedrock', 'serverless']","['ai', 'amazon-bedrock', 'serverless']",2024-01-11,"[('deep-diver/llm-as-chatbot', 0.5605927109718323, 'llm', 0), ('nomic-ai/gpt4all', 0.5228908061981201, 'llm', 0), ('aws/chalice', 0.5006961226463318, 'web', 1), ('intel/intel-extension-for-transformers', 0.5002910494804382, 'perf', 0)]",3,1.0,,0.56,20,20,4,0,0,0,0,20.0,15.0,90.0,0.8,28
762,ml-dl,https://github.com/praw-dev/asyncpraw,[],,[],[],,,,praw-dev/asyncpraw,asyncpraw,92,17,4,Python,https://asyncpraw.readthedocs.io,"Async PRAW, an abbreviation for ""Asynchronous Python Reddit API Wrapper"", is a python package that allows for simple access to Reddit's API.",praw-dev,2023-11-29,2019-02-05,260,0.35384615384615387,https://avatars.githubusercontent.com/u/1696888?v=4,"Async PRAW, an abbreviation for ""Asynchronous Python Reddit API Wrapper"", is a python package that allows for simple access to Reddit's API.","['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api']","['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api']",2024-01-10,"[('praw-dev/praw', 0.8785129189491272, 'data', 5), ('tornadoweb/tornado', 0.5063261389732361, 'web', 0)]",233,4.0,,1.04,19,19,60,0,2,2,2,19.0,6.0,90.0,0.3,28
325,security,https://github.com/snyk-labs/pysnyk,[],,[],[],,,,snyk-labs/pysnyk,pysnyk,73,116,11,Python,https://snyk.docs.apiary.io/,A Python client for the Snyk API.,snyk-labs,2023-12-26,2019-02-03,260,0.2804610318331504,https://avatars.githubusercontent.com/u/47793611?v=4,A Python client for the Snyk API.,"['api', 'snyk']","['api', 'snyk']",2024-01-13,"[('simple-salesforce/simple-salesforce', 0.6529852151870728, 'data', 1), ('cohere-ai/cohere-python', 0.6142659187316895, 'util', 0), ('googleapis/google-api-python-client', 0.5522992610931396, 'util', 0), ('encode/httpx', 0.5362645983695984, 'web', 0), ('shishirpatil/gorilla', 0.5202717185020447, 'llm', 1), ('psf/requests', 0.518162727355957, 'web', 0), ('hugapi/hug', 0.515166163444519, 'util', 0), ('falconry/falcon', 0.5076401829719543, 'web', 1), ('meilisearch/meilisearch-python', 0.5068243741989136, 'data', 1), ('python-restx/flask-restx', 0.5058279633522034, 'web', 1), ('ethereum/web3.py', 0.5023728013038635, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5022001266479492, 'finance', 0)]",41,4.0,,0.81,19,14,60,0,14,7,14,19.0,10.0,90.0,0.5,28
1024,finance,https://github.com/borisbanushev/stockpredictionai,[],,[],[],,,,borisbanushev/stockpredictionai,stockpredictionai,3844,1627,266,,,"       In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.",borisbanushev,2024-01-13,2019-01-09,263,14.568489442338928,,"       In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.",[],[],2019-02-11,"[('ydataai/ydata-synthetic', 0.5105303525924683, 'data', 0)]",1,0.0,,0.0,6,0,61,60,0,0,0,6.0,4.0,90.0,0.7,27
596,gis,https://github.com/plant99/felicette,[],,[],[],,,,plant99/felicette,felicette,1810,88,40,Python,,Satellite imagery for dummies.,plant99,2024-01-12,2020-07-12,185,9.76869699306091,,Satellite imagery for dummies.,"['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images']","['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images']",2021-09-08,"[('sentinelsat/sentinelsat', 0.6691089272499084, 'gis', 1), ('developmentseed/label-maker', 0.6269397139549255, 'gis', 1), ('microsoft/torchgeo', 0.554535448551178, 'gis', 3), ('fatiando/verde', 0.533496618270874, 'gis', 2), ('giswqs/aws-open-data-geo', 0.5306439995765686, 'gis', 2), ('developmentseed/landsat-util', 0.5221068263053894, 'gis', 0), ('azavea/raster-vision', 0.5094917416572571, 'gis', 1)]",6,2.0,,0.0,0,0,43,29,0,1,1,0.0,0.0,90.0,0.0,27
1000,finance,https://github.com/cuemacro/findatapy,[],,[],[],1.0,,,cuemacro/findatapy,findatapy,1501,196,91,Python,,"Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.",cuemacro,2024-01-13,2016-08-03,390,3.8402777777777777,https://avatars.githubusercontent.com/u/20479975?v=4,"Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.","['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl']","['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl']",2023-12-01,"[('hydrosquall/tiingo-python', 0.6793490052223206, 'finance', 0), ('ranaroussi/yfinance', 0.6290664672851562, 'finance', 1), ('jovianml/opendatasets', 0.6065632104873657, 'data', 0), ('gbeced/pyalgotrade', 0.5724738836288452, 'finance', 0), ('quandl/quandl-python', 0.5631470084190369, 'finance', 1), ('hugovk/pypistats', 0.562679648399353, 'util', 0), ('quantopian/zipline', 0.5615792870521545, 'finance', 0), ('nv7-github/googlesearch', 0.5598644614219666, 'util', 0), ('nasdaq/data-link-python', 0.538777232170105, 'finance', 0), ('pydata/pandas-datareader', 0.5381442308425903, 'pandas', 1), ('man-c/pycoingecko', 0.5231452584266663, 'crypto', 0), ('sentinel-hub/sentinelhub-py', 0.5211452841758728, 'gis', 0), ('goldmansachs/gs-quant', 0.5208711624145508, 'finance', 0), ('cuemacro/finmarketpy', 0.5173200368881226, 'finance', 0), ('domokane/financepy', 0.5130403637886047, 'finance', 0), ('pmorissette/ffn', 0.5108827948570251, 'finance', 0), ('matplotlib/mplfinance', 0.5023597478866577, 'finance', 1), ('openai/openai-python', 0.5010272860527039, 'util', 0)]",7,1.0,,0.08,1,1,91,1,3,4,3,1.0,0.0,90.0,0.0,27
896,crypto,https://github.com/ofek/bit,[],,[],[],,,,ofek/bit,bit,1181,205,49,Python,https://ofek.dev/bit/,Bitcoin made easy.,ofek,2024-01-13,2016-11-12,376,3.137381404174573,,Bitcoin made easy.,"['bitcoin', 'cryptocurrencies', 'libraries', 'payments']","['bitcoin', 'cryptocurrencies', 'libraries', 'payments']",2023-11-13,"[('numerai/example-scripts', 0.5733424425125122, 'finance', 0), ('1200wd/bitcoinlib', 0.5140418410301208, 'crypto', 1)]",16,1.0,,0.04,6,2,87,2,0,0,0,6.0,4.0,90.0,0.7,27
1209,llm,https://github.com/keirp/automatic_prompt_engineer,"['prompt-engineering', 'language-model']",Large Language Models Are Human-Level Prompt Engineers,[],[],,,,keirp/automatic_prompt_engineer,automatic_prompt_engineer,860,109,16,Python,,,keirp,2024-01-13,2022-10-24,66,13.002159827213823,,Large Language Models Are Human-Level Prompt Engineers,[],"['language-model', 'prompt-engineering']",2023-05-25,"[('hazyresearch/ama_prompting', 0.7995690107345581, 'llm', 1), ('guidance-ai/guidance', 0.7347609996795654, 'llm', 2), ('ctlllll/llm-toolmaker', 0.699556291103363, 'llm', 1), ('neulab/prompt2model', 0.6848369836807251, 'llm', 1), ('microsoft/promptbase', 0.6507399082183838, 'llm', 1), ('kyegomez/tree-of-thoughts', 0.6476341485977173, 'llm', 1), ('srush/minichain', 0.6470367908477783, 'llm', 1), ('thudm/p-tuning-v2', 0.6158888339996338, 'nlp', 0), ('1rgs/jsonformer', 0.5909636616706848, 'llm', 1), ('stanfordnlp/dspy', 0.5835668444633484, 'llm', 0), ('promptslab/promptify', 0.5769301056861877, 'nlp', 1), ('agenta-ai/agenta', 0.5762568116188049, 'llm', 1), ('thudm/chatglm-6b', 0.5738417506217957, 'llm', 1), ('hannibal046/awesome-llm', 0.5738133192062378, 'study', 1), ('spcl/graph-of-thoughts', 0.5692050457000732, 'llm', 1), ('yizhongw/self-instruct', 0.5658340454101562, 'llm', 1), ('lianjiatech/belle', 0.5627188682556152, 'llm', 0), ('ai21labs/lm-evaluation', 0.5610719919204712, 'llm', 1), ('lm-sys/fastchat', 0.5581333041191101, 'llm', 1), ('bigscience-workshop/promptsource', 0.5572788715362549, 'nlp', 0), ('hazyresearch/manifest', 0.5553961396217346, 'llm', 1), ('facebookresearch/shepherd', 0.5500012636184692, 'llm', 1), ('freedomintelligence/llmzoo', 0.5354942083358765, 'llm', 1), ('promptslab/awesome-prompt-engineering', 0.5347884297370911, 'study', 1), ('conceptofmind/toolformer', 0.5263444185256958, 'llm', 1), ('next-gpt/next-gpt', 0.525351881980896, 'llm', 0), ('airi-institute/probing_framework', 0.5204843282699585, 'nlp', 0), ('juncongmoo/pyllama', 0.518515944480896, 'llm', 0), ('suno-ai/bark', 0.5167652368545532, 'ml', 0), ('likenneth/honest_llama', 0.5166599750518799, 'llm', 1), ('jina-ai/thinkgpt', 0.5156129002571106, 'llm', 1), ('openai/finetune-transformer-lm', 0.5153794884681702, 'llm', 0), ('hazyresearch/h3', 0.5113564729690552, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.511340320110321, 'llm', 1), ('jonasgeiping/cramming', 0.5112678408622742, 'nlp', 1), ('microsoft/autogen', 0.5107211470603943, 'llm', 0), ('young-geng/easylm', 0.5091218948364258, 'llm', 1), ('openbmb/toolbench', 0.5084372758865356, 'llm', 0), ('reasoning-machines/pal', 0.5067288279533386, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5003429055213928, 'llm', 0)]",4,0.0,,0.04,6,1,15,8,0,0,0,6.0,2.0,90.0,0.3,27
652,util,https://github.com/rasbt/watermark,[],,[],[],,,,rasbt/watermark,watermark,849,89,13,Python,,"An IPython magic extension for printing date and time stamps, version numbers, and hardware information",rasbt,2024-01-09,2014-07-30,495,1.7121866897147795,,"An IPython magic extension for printing date and time stamps, version numbers, and hardware information","['ipython', 'jupyter', 'magic-extension']","['ipython', 'jupyter', 'magic-extension']",2023-07-02,"[('python/cpython', 0.5787110328674316, 'util', 0), ('jupyter/nbformat', 0.5627220869064331, 'jupyter', 0), ('wesm/pydata-book', 0.5561281442642212, 'study', 0), ('ipython/ipython', 0.5411689281463623, 'util', 2), ('ipython/ipykernel', 0.541002631187439, 'util', 2), ('erotemic/ubelt', 0.5379260182380676, 'util', 0), ('ipython/ipyparallel', 0.5231406688690186, 'perf', 1), ('faster-cpython/ideas', 0.5214296579360962, 'perf', 0), ('dateutil/dateutil', 0.5191126465797424, 'util', 0), ('pyston/pyston', 0.5138229131698608, 'util', 0), ('pypy/pypy', 0.5103526711463928, 'util', 0), ('faster-cpython/tools', 0.507169783115387, 'perf', 0), ('gotcha/ipdb', 0.5067563652992249, 'debug', 1), ('cohere-ai/notebooks', 0.5048350095748901, 'llm', 0)]",19,5.0,,0.38,1,0,115,7,1,2,1,1.0,1.0,90.0,1.0,27
695,profiling,https://github.com/pythonspeed/filprofiler,[],,[],[],,,,pythonspeed/filprofiler,filprofiler,802,24,9,Rust,https://pythonspeed.com/products/filmemoryprofiler/,A Python memory profiler for data processing and scientific computing applications,pythonspeed,2024-01-14,2020-06-18,188,4.249810749432248,,A Python memory profiler for data processing and scientific computing applications,"['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling']","['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling']",2023-03-18,"[('pympler/pympler', 0.7303571105003357, 'perf', 0), ('bloomberg/memray', 0.7156088352203369, 'profiling', 4), ('benfred/py-spy', 0.7144114971160889, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.664732813835144, 'profiling', 0), ('sumerc/yappi', 0.6349960565567017, 'profiling', 0), ('pyutils/line_profiler', 0.6149646043777466, 'profiling', 0), ('dgilland/cacheout', 0.6149056553840637, 'perf', 0), ('python-cachier/cachier', 0.605586588382721, 'perf', 0), ('plasma-umass/scalene', 0.6020299196243286, 'profiling', 0), ('joblib/joblib', 0.5940987467765808, 'util', 0), ('pyston/pyston', 0.5313695073127747, 'util', 0), ('pytables/pytables', 0.5271565914154053, 'data', 0), ('joerick/pyinstrument', 0.5218971967697144, 'profiling', 0), ('xrudelis/pytrait', 0.5162791013717651, 'util', 0), ('rasbt/mlxtend', 0.5145261883735657, 'ml', 0), ('micropython/micropython', 0.5140711069107056, 'util', 0), ('numpy/numpy', 0.5123228430747986, 'math', 0), ('jiffyclub/snakeviz', 0.5121504664421082, 'profiling', 0), ('cython/cython', 0.5108945369720459, 'util', 0), ('google/pytype', 0.5083953738212585, 'typing', 0), ('spotify/annoy', 0.5058916807174683, 'ml', 0), ('exaloop/codon', 0.5046871900558472, 'perf', 0), ('eleutherai/pyfra', 0.5045285820960999, 'ml', 0), ('p403n1x87/austin', 0.5042270421981812, 'profiling', 0), ('pypy/pypy', 0.5019063353538513, 'util', 0)]",6,4.0,,0.33,1,0,44,10,3,18,3,1.0,0.0,90.0,0.0,27
832,finance,https://github.com/idanya/algo-trader,[],,[],[],,,,idanya/algo-trader,algo-trader,726,90,29,Python,,"Trading bot with support for realtime trading, backtesting, custom strategies and much more.",idanya,2024-01-13,2021-09-14,124,5.854838709677419,,"Trading bot with support for realtime trading, backtesting, custom strategies and much more.","['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies']","['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies']",2023-11-20,"[('freqtrade/freqtrade', 0.8236120939254761, 'crypto', 2), ('polakowo/vectorbt', 0.6762666702270508, 'finance', 3), ('gbeced/basana', 0.6138817071914673, 'finance', 3), ('quantconnect/lean', 0.5937037467956543, 'finance', 2), ('ccxt/ccxt', 0.5679965019226074, 'crypto', 0), ('blankly-finance/blankly', 0.5535969138145447, 'finance', 2), ('zvtvz/zvt', 0.5369071364402771, 'finance', 5), ('kernc/backtesting.py', 0.522881269454956, 'finance', 3), ('ai4finance-foundation/finrl', 0.5114476084709167, 'finance', 1), ('openbb-finance/openbbterminal', 0.5061081051826477, 'finance', 0)]",4,2.0,,0.08,1,1,28,2,1,3,1,1.0,0.0,90.0,0.0,27
305,crypto,https://github.com/palkeo/panoramix,[],,[],[],,,,palkeo/panoramix,panoramix,714,194,35,Python,,Ethereum decompiler,palkeo,2024-01-12,2020-02-17,206,3.4636174636174637,,Ethereum decompiler,[],[],2023-06-14,"[('ethtx/ethtx_ce', 0.6499204635620117, 'crypto', 0)]",4,2.0,,0.4,3,1,48,7,0,1,1,3.0,3.0,90.0,1.0,27
568,gis,https://github.com/developmentseed/landsat-util,[],,[],[],,,,developmentseed/landsat-util,landsat-util,687,153,127,Python,,"A utility to search, download and process Landsat 8 satellite imagery",developmentseed,2024-01-10,2014-08-01,495,1.3862784664168348,https://avatars.githubusercontent.com/u/92384?v=4,"A utility to search, download and process Landsat 8 satellite imagery",[],[],2018-07-30,"[('plant99/felicette', 0.5221068263053894, 'gis', 0), ('sentinelsat/sentinelsat', 0.5123438239097595, 'gis', 0)]",25,7.0,,0.0,1,0,115,66,0,2,2,1.0,3.0,90.0,3.0,27
513,ml-dl,https://github.com/kakaobrain/rq-vae-transformer,[],,[],[],,,,kakaobrain/rq-vae-transformer,rq-vae-transformer,647,74,16,Jupyter Notebook,,The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22),kakaobrain,2024-01-12,2022-03-03,99,6.488538681948424,https://avatars.githubusercontent.com/u/25736994?v=4,The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22),[],[],2024-01-03,"[('stability-ai/stablediffusion', 0.5072489380836487, 'diffusion', 0), ('compvis/latent-diffusion', 0.5072487592697144, 'diffusion', 0)]",2,2.0,,0.02,1,0,23,0,0,0,0,1.0,0.0,90.0,0.0,27
1078,ml-ops,https://github.com/kubeflow-kale/kale,[],,[],[],,,,kubeflow-kale/kale,kale,613,129,17,Python,http://kubeflow-kale.github.io,Kubeflow’s superfood for Data Scientists,kubeflow-kale,2024-01-05,2019-01-24,261,2.342248908296943,https://avatars.githubusercontent.com/u/52384265?v=4,Kubeflow’s superfood for Data Scientists,"['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning']","['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning']",2021-10-20,"[('kubeflow/pipelines', 0.692866325378418, 'ml-ops', 3), ('orchest/orchest', 0.6008453965187073, 'ml-ops', 1), ('getindata/kedro-kubeflow', 0.5977193117141724, 'ml-ops', 2), ('firmai/industry-machine-learning', 0.5946695804595947, 'study', 2), ('determined-ai/determined', 0.5921743512153625, 'ml-ops', 1), ('ploomber/ploomber', 0.5709792971611023, 'ml-ops', 1), ('flyteorg/flyte', 0.5685352683067322, 'ml-ops', 1), ('gradio-app/gradio', 0.5675045847892761, 'viz', 1), ('superduperdb/superduperdb', 0.5663890838623047, 'data', 0), ('polyaxon/polyaxon', 0.5640344619750977, 'ml-ops', 1), ('linealabs/lineapy', 0.5481640100479126, 'jupyter', 0), ('dagworks-inc/hamilton', 0.5480688214302063, 'ml-ops', 1), ('mito-ds/monorepo', 0.5466421842575073, 'jupyter', 0), ('ageron/handson-ml2', 0.5455378293991089, 'ml', 0), ('kedro-org/kedro-viz', 0.5434872508049011, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5426039695739746, 'ml-ops', 1), ('netflix/metaflow', 0.5416892170906067, 'ml-ops', 1), ('backtick-se/cowait', 0.5415117740631104, 'util', 0), ('astronomer/astro-sdk', 0.5413955450057983, 'ml-ops', 0), ('hi-primus/optimus', 0.5404044389724731, 'ml-ops', 1), ('mage-ai/mage-ai', 0.5401076674461365, 'ml-ops', 1), ('jovianml/opendatasets', 0.5395414233207703, 'data', 1), ('dylanhogg/awesome-python', 0.5359322428703308, 'study', 1), ('skops-dev/skops', 0.5353480577468872, 'ml-ops', 1), ('huggingface/datasets', 0.5326496362686157, 'nlp', 1), ('mlflow/mlflow', 0.5298233032226562, 'ml-ops', 1), ('kedro-org/kedro', 0.5225927233695984, 'ml-ops', 1), ('polyaxon/datatile', 0.5204256772994995, 'pandas', 0), ('googlecloudplatform/vertex-ai-samples', 0.5194539427757263, 'ml', 0), ('dask/dask-ml', 0.5168511867523193, 'ml', 0), ('wandb/client', 0.5164470076560974, 'ml', 1), ('kubeflow/fairing', 0.5162668824195862, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.5156881809234619, 'ml', 1), ('eventual-inc/daft', 0.5150558352470398, 'pandas', 1), ('airbytehq/airbyte', 0.5148319005966187, 'data', 0), ('koaning/scikit-lego', 0.5133776664733887, 'ml', 1), ('vaexio/vaex', 0.5090245008468628, 'perf', 1), ('ashleve/lightning-hydra-template', 0.5069912075996399, 'util', 0), ('gefyrahq/gefyra', 0.5049176216125488, 'util', 0), ('intake/intake', 0.5041489005088806, 'data', 0), ('fastai/fastcore', 0.5032058358192444, 'util', 0)]",10,4.0,,0.0,2,0,60,27,0,5,5,2.0,3.0,90.0,1.5,27
1349,ml,https://github.com/ray-project/tune-sklearn,[],,[],[],,,,ray-project/tune-sklearn,tune-sklearn,462,51,18,Python,https://docs.ray.io/en/master/tune/api_docs/sklearn.html,A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.,ray-project,2024-01-06,2019-11-28,217,2.122047244094488,https://avatars.githubusercontent.com/u/22125274?v=4,A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.,"['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn']","['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn']",2023-11-04,"[('automl/auto-sklearn', 0.6818026304244995, 'ml', 4), ('microsoft/flaml', 0.6497718095779419, 'ml', 2), ('kubeflow/katib', 0.626979410648346, 'ml', 0), ('microsoft/nni', 0.580021321773529, 'ml', 3), ('mljar/mljar-supervised', 0.5580164194107056, 'ml', 2), ('scikit-optimize/scikit-optimize', 0.550593912601471, 'ml', 3), ('google/vizier', 0.5394313931465149, 'ml', 2), ('determined-ai/determined', 0.5187373161315918, 'ml-ops', 1), ('optuna/optuna', 0.5114966034889221, 'ml', 0), ('rasbt/machine-learning-book', 0.5028691291809082, 'study', 1)]",14,3.0,,0.15,12,8,50,2,2,3,2,12.0,3.0,90.0,0.2,27
1115,web,https://github.com/pylons/webob,['wsgi'],,[],[],,,,pylons/webob,webob,428,189,20,Python,https://webob.org/,WSGI request and response objects,pylons,2024-01-12,2011-09-17,645,0.6631252766710934,https://avatars.githubusercontent.com/u/452227?v=4,WSGI request and response objects,[],['wsgi'],2023-09-05,"[('pallets/werkzeug', 0.6051927804946899, 'web', 1), ('pylons/waitress', 0.5591251254081726, 'web', 0), ('requests/toolbelt', 0.5443535447120667, 'util', 0), ('benoitc/gunicorn', 0.5041623711585999, 'web', 1)]",110,3.0,,0.13,3,0,150,4,0,6,6,3.0,3.0,90.0,1.0,27
516,gis,https://github.com/scikit-geometry/scikit-geometry,[],,[],[],,,,scikit-geometry/scikit-geometry,scikit-geometry,398,53,14,Jupyter Notebook,https://scikit-geometry.github.io/scikit-geometry,Scientific Python Geometric Algorithms Library,scikit-geometry,2024-01-09,2016-03-28,409,0.9727653631284916,https://avatars.githubusercontent.com/u/59055868?v=4,Scientific Python Geometric Algorithms Library,"['cgal', 'geometric-algorithms', 'geometry', 'wrapper']","['cgal', 'geometric-algorithms', 'geometry', 'wrapper']",2023-12-04,"[('scipy/scipy', 0.6300176382064819, 'math', 0), ('shapely/shapely', 0.5902884602546692, 'gis', 2), ('pysal/pysal', 0.5866171717643738, 'gis', 0), ('fredrik-johansson/mpmath', 0.5644670724868774, 'math', 0), ('albahnsen/pycircular', 0.5635026693344116, 'math', 0), ('marcomusy/vedo', 0.5506011843681335, 'viz', 0), ('numpy/numpy', 0.5440155267715454, 'math', 0), ('sympy/sympy', 0.5399286150932312, 'math', 0), ('kornia/kornia', 0.5317684412002563, 'ml-dl', 0), ('artelys/geonetworkx', 0.5264812707901001, 'gis', 0), ('benbovy/spherely', 0.5209958553314209, 'gis', 2), ('dfki-ric/pytransform3d', 0.5100734233856201, 'math', 0)]",18,6.0,,0.02,9,4,95,1,0,0,0,9.0,11.0,90.0,1.2,27
840,gis,https://github.com/mapbox/mercantile,[],,[],[],,,,mapbox/mercantile,mercantile,384,62,124,Python,,Spherical mercator tile and coordinate utilities,mapbox,2024-01-14,2014-02-12,519,0.7386644682605111,https://avatars.githubusercontent.com/u/600935?v=4,Spherical mercator tile and coordinate utilities,"['imagery', 'pxm', 'satellite']","['imagery', 'pxm', 'satellite']",2023-11-02,[],23,5.0,,0.0,1,1,121,2,0,4,4,1.0,1.0,90.0,1.0,27
1157,gamedev,https://github.com/libtcod/python-tcod,[],,[],[],,,,libtcod/python-tcod,python-tcod,382,37,19,Python,,A high-performance Python port of libtcod.  Includes the libtcodpy module for backwards compatibility with older projects.,libtcod,2024-01-13,2015-03-14,463,0.8242909987669543,https://avatars.githubusercontent.com/u/40313210?v=4,A high-performance Python port of libtcod.  Includes the libtcodpy module for backwards compatibility with older projects.,"['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod']","['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod']",2024-01-08,"[('pypy/pypy', 0.6247069835662842, 'util', 0), ('pyodide/micropip', 0.5887089967727661, 'util', 0), ('pyodide/pyodide', 0.5775824785232544, 'util', 0), ('cython/cython', 0.5713533759117126, 'util', 0), ('1200wd/bitcoinlib', 0.5591480731964111, 'crypto', 0), ('pyston/pyston', 0.5553798079490662, 'util', 0), ('pyo3/maturin', 0.5518137812614441, 'util', 1), ('erotemic/ubelt', 0.5495151877403259, 'util', 0), ('hoffstadt/dearpygui', 0.5448720455169678, 'gui', 0), ('pdm-project/pdm', 0.5411107540130615, 'util', 0), ('pytoolz/toolz', 0.5379080176353455, 'util', 0), ('fastai/fastcore', 0.5340726375579834, 'util', 0), ('pypa/hatch', 0.5316311120986938, 'util', 0), ('pytest-dev/pytest-bdd', 0.5265241265296936, 'testing', 0), ('pympler/pympler', 0.52317214012146, 'perf', 0), ('klen/py-frameworks-bench', 0.5221824049949646, 'perf', 0), ('dgilland/cacheout', 0.5180550813674927, 'perf', 0), ('primal100/pybitcointools', 0.5110588669776917, 'crypto', 0), ('dosisod/refurb', 0.5105434060096741, 'util', 0), ('beeware/toga', 0.5042990446090698, 'gui', 0), ('paramiko/paramiko', 0.502030611038208, 'util', 0)]",23,1.0,,2.67,10,10,108,0,10,20,10,10.0,1.0,90.0,0.1,27
1874,ml,https://github.com/oneil512/insight,[],,[],[],,,,oneil512/insight,INSIGHT,373,54,13,Python,,INSIGHT is an autonomous AI that can do medical research!,oneil512,2024-01-12,2023-04-08,42,8.79124579124579,,INSIGHT is an autonomous AI that can do medical research!,"['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml']","['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml']",2023-10-21,"[('lucidrains/medical-chatgpt', 0.6099081635475159, 'llm', 0), ('torantulino/auto-gpt', 0.5754587054252625, 'llm', 1), ('mindsdb/mindsdb', 0.5716148614883423, 'data', 4), ('microsoft/lmops', 0.5636782050132751, 'llm', 2), ('assafelovic/gpt-researcher', 0.5495372414588928, 'llm', 0), ('gventuri/pandas-ai', 0.5358114242553711, 'pandas', 2), ('google-research/google-research', 0.5356377363204956, 'ml', 1), ('prefecthq/marvin', 0.5314114093780518, 'nlp', 3), ('project-monai/monai', 0.5232803225517273, 'ml', 0), ('oegedijk/explainerdashboard', 0.5162001848220825, 'ml-interpretability', 0), ('antonosika/gpt-engineer', 0.5132570862770081, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5009233355522156, 'study', 1)]",3,0.0,,1.42,3,3,9,3,0,0,0,3.0,2.0,90.0,0.7,27
1351,util,https://github.com/nv7-github/googlesearch,['google-crawler'],,[],[],,,,nv7-github/googlesearch,googlesearch,345,91,6,Python,https://pypi.org/project/googlesearch-python/,A Python library for scraping the Google search engine.,nv7-github,2024-01-12,2020-07-05,186,1.8519938650306749,,A Python library for scraping the Google search engine.,[],['google-crawler'],2023-05-30,"[('scrapy/scrapy', 0.7149690985679626, 'data', 0), ('alirezamika/autoscraper', 0.7142531275749207, 'data', 0), ('googleapis/google-api-python-client', 0.6702570915222168, 'util', 0), ('serpapi/google-search-results-python', 0.6652640700340271, 'util', 1), ('roniemartinez/dude', 0.6479972004890442, 'util', 0), ('binux/pyspider', 0.6098272204399109, 'data', 0), ('scholarly-python-package/scholarly', 0.6054853796958923, 'data', 0), ('jovianml/opendatasets', 0.5820246338844299, 'data', 0), ('clips/pattern', 0.5700188279151917, 'nlp', 0), ('cuemacro/findatapy', 0.5598644614219666, 'finance', 0), ('goldsmith/wikipedia', 0.5323612093925476, 'data', 0), ('meilisearch/meilisearch-python', 0.5313740968704224, 'data', 0), ('requests/toolbelt', 0.5127140879631042, 'util', 0), ('psf/requests', 0.5044116973876953, 'web', 0), ('qdrant/qdrant-client', 0.5012821555137634, 'util', 0)]",8,4.0,,0.15,9,1,43,8,0,1,1,9.0,18.0,90.0,2.0,27
341,term,https://github.com/rockhopper-technologies/enlighten,[],,[],[],,,,rockhopper-technologies/enlighten,enlighten,335,23,5,Python,https://python-enlighten.readthedocs.io,Enlighten Progress Bar for Python Console Apps,rockhopper-technologies,2024-01-11,2017-09-22,331,1.010340370529944,https://avatars.githubusercontent.com/u/20388549?v=4,Enlighten Progress Bar for Python Console Apps,[],[],2023-12-25,"[('wolph/python-progressbar', 0.7673426270484924, 'util', 0), ('tqdm/tqdm', 0.7503482699394226, 'term', 0), ('urwid/urwid', 0.5648614764213562, 'term', 0), ('rsalmei/alive-progress', 0.5524495244026184, 'util', 0), ('inducer/pudb', 0.5411313772201538, 'debug', 0), ('jquast/blessed', 0.5384596586227417, 'term', 0), ('willmcgugan/rich', 0.5262578725814819, 'term', 0), ('alexmojaki/heartrate', 0.5111202597618103, 'debug', 0), ('teemu/pytest-sugar', 0.5103371739387512, 'testing', 0)]",6,2.0,,0.92,2,2,77,1,6,5,6,2.0,3.0,90.0,1.5,27
1649,llm,https://github.com/lchen001/llmdrift,"['drift', 'language-model']",LLM Drifts: How Is ChatGPT’s Behavior Changing over Time?,[],[],,,,lchen001/llmdrift,LLMDrift,320,28,15,Jupyter Notebook,,,lchen001,2024-01-12,2023-07-18,28,11.428571428571429,,LLM Drifts: How Is ChatGPT’s Behavior Changing over Time?,[],"['drift', 'language-model']",2024-01-03,"[('thudm/chatglm2-6b', 0.5880559086799622, 'llm', 0), ('hwchase17/langchain', 0.5510751008987427, 'llm', 1), ('microsoft/autogen', 0.5390075445175171, 'llm', 0), ('nomic-ai/gpt4all', 0.5283440351486206, 'llm', 1), ('fasteval/fasteval', 0.5002023577690125, 'llm', 0)]",5,0.0,,0.48,1,1,6,0,0,0,0,1.0,0.0,90.0,0.0,27
986,time-series,https://github.com/microprediction/microprediction,[],,[],[],,,,microprediction/microprediction,microprediction,311,55,15,Jupyter Notebook,http://www.microprediction.org,"If you can measure it, consider it predicted",microprediction,2024-01-09,2020-02-20,205,1.5118055555555556,,"If you can measure it, consider it predicted","['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn']","['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn']",2024-01-05,"[('ourownstory/neural_prophet', 0.5824498534202576, 'ml', 3), ('alkaline-ml/pmdarima', 0.5424190759658813, 'time-series', 2), ('awslabs/gluonts', 0.5331199765205383, 'time-series', 2), ('unit8co/darts', 0.5138264298439026, 'time-series', 1), ('salesforce/merlion', 0.5122169852256775, 'time-series', 1), ('firmai/atspy', 0.5109694004058838, 'time-series', 1), ('sktime/sktime', 0.5038242340087891, 'time-series', 1)]",14,3.0,,4.37,0,0,47,0,5,36,5,0.0,0.0,90.0,0.0,27
1448,util,https://github.com/salesforce/logai,[],,[],[],,,,salesforce/logai,logai,298,39,15,Python,,LogAI - An open-source library for log analytics and intelligence,salesforce,2024-01-10,2022-10-27,65,4.534782608695652,https://avatars.githubusercontent.com/u/453694?v=4,LogAI - An open-source library for log analytics and intelligence,"['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning']","['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning']",2023-03-02,"[('whylabs/whylogs', 0.7454851269721985, 'util', 1), ('aimhubio/aim', 0.5716543197631836, 'ml-ops', 2), ('polyaxon/datatile', 0.5616445541381836, 'pandas', 0), ('pycaret/pycaret', 0.5456848740577698, 'ml', 2), ('ray-project/ray', 0.5410916805267334, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5326856970787048, 'data', 2), ('metachris/logzero', 0.5321155786514282, 'util', 0), ('yzhao062/pyod', 0.5268727540969849, 'data', 2), ('mlflow/mlflow', 0.5248235464096069, 'ml-ops', 2), ('larsbaunwall/bricky', 0.5161862969398499, 'llm', 1), ('oegedijk/explainerdashboard', 0.5160885453224182, 'ml-interpretability', 0), ('unit8co/darts', 0.5133403539657593, 'time-series', 2), ('nebuly-ai/nebullvm', 0.5123262405395508, 'perf', 1), ('netflix/metaflow', 0.5108799934387207, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5083866119384766, 'study', 2), ('wandb/client', 0.5036634206771851, 'ml', 1), ('fmind/mlops-python-package', 0.5032975077629089, 'template', 1)]",5,2.0,,0.92,2,0,15,11,4,4,4,2.0,2.0,90.0,1.0,27
1620,data,https://github.com/samuelcolvin/rtoml,"['toml', 'rust']",,[],[],,,,samuelcolvin/rtoml,rtoml,285,29,8,Python,https://pypi.org/project/rtoml/,A fast TOML library for python implemented in rust.,samuelcolvin,2024-01-12,2020-01-07,212,1.3443396226415094,,A fast TOML library for python implemented in rust.,"['deserialization', 'parser', 'rust', 'toml']","['deserialization', 'parser', 'rust', 'toml']",2023-12-21,"[('astral-sh/ruff', 0.5641447305679321, 'util', 1), ('rustpython/rustpython', 0.5515300035476685, 'util', 1), ('yukinarit/pyserde', 0.5448687076568604, 'util', 1), ('pyo3/pyo3', 0.5289204716682434, 'util', 1), ('marshmallow-code/marshmallow', 0.5252963900566101, 'util', 1), ('deepmind/chex', 0.5042668581008911, 'ml-dl', 0), ('sfu-db/connector-x', 0.5036177039146423, 'data', 1)]",14,3.0,,0.13,8,6,49,1,1,3,1,8.0,8.0,90.0,1.0,27
521,util,https://github.com/venth/aws-adfs,[],,[],[],,,,venth/aws-adfs,aws-adfs,283,96,11,Python,,Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory),venth,2024-01-11,2016-06-25,396,0.7138738738738739,,Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory),"['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools']","['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools']",2023-12-16,[],50,2.0,,0.87,15,12,92,1,6,16,6,15.0,7.0,90.0,0.5,27
726,ml,https://github.com/autonlab/auton-survival,[],,[],[],,,,autonlab/auton-survival,auton-survival,275,70,8,Python,http://autonlab.github.io/auton-survival,"Auton Survival -  an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events ",autonlab,2024-01-12,2020-04-01,199,1.3759828448892066,https://avatars.githubusercontent.com/u/11739208?v=4,"Auton Survival -  an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events ","['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event']","['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event']",2023-10-16,[],12,4.0,,0.31,26,2,46,3,2,1,2,24.0,26.0,90.0,1.1,27
1786,util,https://github.com/cohere-ai/cohere-python,[],,[],[],,,,cohere-ai/cohere-python,cohere-python,157,33,23,Python,https://docs.cohere.ai,Python Library for Accessing the Cohere API,cohere-ai,2024-01-09,2021-01-20,157,0.9945701357466064,https://avatars.githubusercontent.com/u/54850923?v=4,Python Library for Accessing the Cohere API,['sdk'],['sdk'],2024-01-10,"[('snyk-labs/pysnyk', 0.6142659187316895, 'security', 0), ('cohere-ai/notebooks', 0.5595293641090393, 'llm', 0), ('simple-salesforce/simple-salesforce', 0.5442338585853577, 'data', 0), ('openai/openai-python', 0.5279492735862732, 'util', 0), ('open-telemetry/opentelemetry-python', 0.5118368864059448, 'util', 1), ('anthropics/anthropic-sdk-python', 0.5114951133728027, 'util', 1), ('googleapis/google-api-python-client', 0.5053079128265381, 'util', 0), ('kubeflow/fairing', 0.5045545697212219, 'ml-ops', 0)]",37,2.0,,2.9,48,38,36,0,0,0,0,48.0,30.0,90.0,0.6,27
1546,llm,https://github.com/luohongyin/sail,"['search-augmentation', 'search', 'language-model']",,[],[],,,,luohongyin/sail,SAIL,147,14,3,Python,,SAIL: Search Augmented Instruction Learning,luohongyin,2024-01-04,2023-05-24,35,4.099601593625498,,SAIL: Search Augmented Instruction Learning,[],"['language-model', 'search', 'search-augmentation']",2023-06-06,"[('ai21labs/in-context-ralm', 0.5805896520614624, 'llm', 1), ('openbmb/toolbench', 0.5190768241882324, 'llm', 0), ('intellabs/fastrag', 0.5123240947723389, 'nlp', 0), ('yizhongw/self-instruct', 0.5114395022392273, 'llm', 1), ('srush/minichain', 0.5050071477890015, 'llm', 0)]",2,1.0,,0.19,3,2,8,7,0,0,0,3.0,19.0,90.0,6.3,27
904,security,https://github.com/abnamro/repository-scanner,[],,[],[],,,,abnamro/repository-scanner,repository-scanner,141,13,7,Python,,Tool to detect secrets in source code management systems.,abnamro,2024-01-09,2022-09-08,72,1.9390962671905698,https://avatars.githubusercontent.com/u/42280701?v=4,Tool to detect secrets in source code management systems.,[],[],2023-12-20,"[('ionelmc/python-hunter', 0.5003989338874817, 'debug', 0)]",10,1.0,,4.21,23,22,16,1,9,8,9,23.0,5.0,90.0,0.2,27
1600,llm,https://github.com/krohling/bondai,"['autonomous-agents', 'agents']",Open-source framework tailored for integrating and customizing Conversational AI Agents,[],[],,,,krohling/bondai,bondai,128,20,11,Python,,,krohling,2024-01-12,2023-07-16,28,4.525252525252525,,Open-source framework tailored for integrating and customizing Conversational AI Agents,[],"['agents', 'autonomous-agents']",2024-01-14,"[('rasahq/rasa', 0.7222825288772583, 'llm', 0), ('nvidia/nemo', 0.7121801972389221, 'nlp', 0), ('facebookresearch/parlai', 0.680033266544342, 'nlp', 0), ('aiwaves-cn/agents', 0.6704409718513489, 'nlp', 1), ('deeppavlov/deeppavlov', 0.6658996939659119, 'nlp', 0), ('prefecthq/marvin', 0.6641014814376831, 'nlp', 1), ('rcgai/simplyretrieve', 0.6295303702354431, 'llm', 0), ('embedchain/embedchain', 0.6167078018188477, 'llm', 0), ('openlmlab/moss', 0.6103507876396179, 'llm', 0), ('togethercomputer/openchatkit', 0.596705973148346, 'nlp', 0), ('minimaxir/simpleaichat', 0.5760908722877502, 'llm', 0), ('larsbaunwall/bricky', 0.5706357359886169, 'llm', 0), ('cheshire-cat-ai/core', 0.5621045827865601, 'llm', 0), ('chatarena/chatarena', 0.5611550807952881, 'llm', 0), ('google-research/language', 0.5564771294593811, 'nlp', 0), ('antonosika/gpt-engineer', 0.5529053211212158, 'llm', 0), ('transformeroptimus/superagi', 0.5482898354530334, 'llm', 2), ('lm-sys/fastchat', 0.5441566109657288, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.534867525100708, 'nlp', 0), ('operand/agency', 0.5317063331604004, 'llm', 2), ('nomic-ai/gpt4all', 0.5283734798431396, 'llm', 0), ('smol-ai/developer', 0.5269186496734619, 'llm', 0), ('run-llama/rags', 0.5235958099365234, 'llm', 0), ('humanoidagents/humanoidagents', 0.5202670693397522, 'sim', 1), ('laion-ai/open-assistant', 0.5133852362632751, 'llm', 0), ('unity-technologies/ml-agents', 0.5124642252922058, 'ml-rl', 0), ('gunthercox/chatterbot', 0.5118715763092041, 'nlp', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5104005932807922, 'llm', 0), ('deepset-ai/haystack', 0.5101301074028015, 'llm', 0), ('minimaxir/aitextgen', 0.5016161799430847, 'llm', 0)]",2,0.0,,2.62,7,6,6,0,0,71,71,7.0,1.0,90.0,0.1,27
558,gis,https://github.com/geopandas/xyzservices,[],,[],[],,,,geopandas/xyzservices,xyzservices,125,23,14,Python,https://xyzservices.readthedocs.io/,Source of XYZ tiles providers,geopandas,2024-01-09,2021-05-21,140,0.8892276422764228,https://avatars.githubusercontent.com/u/8130715?v=4,Source of XYZ tiles providers,[],[],2023-12-15,[],18,5.0,,0.77,5,3,32,1,5,8,5,5.0,3.0,90.0,0.6,27
1445,util,https://github.com/pypa/installer,['wheel'],,[],[],,,,pypa/installer,installer,105,51,15,Python,https://installer.readthedocs.io/,A low-level library for installing from a Python wheel distribution.,pypa,2024-01-08,2020-04-11,198,0.5291576673866091,https://avatars.githubusercontent.com/u/647025?v=4,A low-level library for installing from a Python wheel distribution.,['wheel'],['wheel'],2024-01-05,"[('pyo3/maturin', 0.6027328372001648, 'util', 0), ('pyodide/micropip', 0.5991305708885193, 'util', 0), ('pytoolz/toolz', 0.5778533220291138, 'util', 0), ('getsentry/milksnake', 0.5740995407104492, 'util', 0), ('indygreg/pyoxidizer', 0.5681904554367065, 'util', 0), ('erotemic/ubelt', 0.5646870136260986, 'util', 0), ('pdm-project/pdm', 0.5561202168464661, 'util', 0), ('pypy/pypy', 0.5516636371612549, 'util', 0), ('ofek/pyapp', 0.5315470695495605, 'util', 0), ('pyston/pyston', 0.5297871828079224, 'util', 0), ('mitsuhiko/rye', 0.5275384187698364, 'util', 0), ('pypi/warehouse', 0.5235101580619812, 'util', 0), ('legrandin/pycryptodome', 0.5224993228912354, 'util', 0), ('python-poetry/poetry', 0.522213339805603, 'util', 0), ('scitools/cartopy', 0.5148464441299438, 'gis', 0), ('python/cpython', 0.505916953086853, 'util', 0), ('urwid/urwid', 0.5019884705543518, 'term', 0), ('jquast/blessed', 0.5001348853111267, 'term', 0)]",24,3.0,,0.63,16,11,46,0,0,3,3,16.0,16.0,90.0,1.0,27
994,finance,https://github.com/quantopian/research_public,[],,[],[],,,,quantopian/research_public,research_public,2262,1544,201,Jupyter Notebook,https://www.quantopian.com/lectures,Quantitative research and educational materials,quantopian,2024-01-13,2015-02-26,465,4.8570552147239265,https://avatars.githubusercontent.com/u/1393215?v=4,Quantitative research and educational materials,[],[],2020-10-30,[],52,4.0,,0.0,0,0,108,39,0,0,0,0.0,0.0,90.0,0.0,26
266,nlp,https://github.com/arxiv-vanity/arxiv-vanity,[],,[],[],,,,arxiv-vanity/arxiv-vanity,arxiv-vanity,1575,102,23,Python,https://www.arxiv-vanity.com,Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.,arxiv-vanity,2024-01-12,2017-08-12,337,4.667654530059272,https://avatars.githubusercontent.com/u/31142715?v=4,Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.,"['academic-publishing', 'arxiv', 'latex', 'science']","['academic-publishing', 'arxiv', 'latex', 'science']",2022-01-18,"[('lukasschwab/arxiv.py', 0.5270992517471313, 'util', 1)]",9,3.0,,0.0,2,0,78,24,0,0,0,2.0,1.0,90.0,0.5,26
1124,nlp,https://github.com/gunthercox/chatterbot-corpus,[],,[],[],,,,gunthercox/chatterbot-corpus,chatterbot-corpus,1333,1151,69,Python,http://chatterbot-corpus.readthedocs.io,A multilingual dialog corpus,gunthercox,2024-01-12,2017-01-11,367,3.6236893203883493,,A multilingual dialog corpus,"['chatterbot', 'corpus', 'dialog', 'language', 'yaml']","['chatterbot', 'corpus', 'dialog', 'language', 'yaml']",2020-08-24,"[('lm-sys/fastchat', 0.6321564316749573, 'llm', 0), ('deeppavlov/deeppavlov', 0.6254500150680542, 'nlp', 0), ('thudm/chatglm-6b', 0.6088327765464783, 'llm', 0), ('rasahq/rasa', 0.6020722985267639, 'llm', 0), ('gunthercox/chatterbot', 0.5911657214164734, 'nlp', 2), ('langchain-ai/chat-langchain', 0.5771211981773376, 'llm', 0), ('fasteval/fasteval', 0.5639832019805908, 'llm', 0), ('nvidia/nemo', 0.5635098218917847, 'nlp', 0), ('bigscience-workshop/promptsource', 0.5633988976478577, 'nlp', 0), ('nomic-ai/gpt4all', 0.5547274947166443, 'llm', 0), ('thudm/chatglm2-6b', 0.5543924570083618, 'llm', 0), ('openlmlab/moss', 0.5456989407539368, 'llm', 0), ('killianlucas/open-interpreter', 0.5440534949302673, 'llm', 0), ('databrickslabs/dolly', 0.541381299495697, 'llm', 0), ('pemistahl/lingua-py', 0.5412878394126892, 'nlp', 0), ('run-llama/rags', 0.5412566065788269, 'llm', 0), ('srush/minichain', 0.5370650291442871, 'llm', 0), ('facebookresearch/parlai', 0.5316453576087952, 'nlp', 0), ('nltk/nltk', 0.5303866267204285, 'nlp', 0), ('togethercomputer/openchatkit', 0.5263169407844543, 'nlp', 0), ('pndurette/gtts', 0.5201086401939392, 'util', 0), ('blinkdl/chatrwkv', 0.5196929574012756, 'llm', 0), ('suno-ai/bark', 0.5169947743415833, 'ml', 0), ('facebookresearch/seamless_communication', 0.5149349570274353, 'nlp', 0), ('guidance-ai/guidance', 0.5125293135643005, 'llm', 0), ('embedchain/embedchain', 0.5088998079299927, 'llm', 0), ('aiwaves-cn/agents', 0.5078116059303284, 'nlp', 0), ('minimaxir/simpleaichat', 0.5073516964912415, 'llm', 0), ('rcgai/simplyretrieve', 0.5069754123687744, 'llm', 0), ('lingjzhu/charsiug2p', 0.5040284395217896, 'nlp', 0), ('explosion/spacy-models', 0.5032789707183838, 'nlp', 0)]",72,5.0,,0.0,0,0,85,41,0,1,1,0.0,0.0,90.0,0.0,26
1426,util,https://github.com/py4j/py4j,[],,[],[],,,,py4j/py4j,py4j,1123,206,41,Java,https://www.py4j.org,Py4J enables Python programs to dynamically access arbitrary Java objects,py4j,2024-01-13,2010-11-02,691,1.6251808972503619,https://avatars.githubusercontent.com/u/99001623?v=4,Py4J enables Python programs to dynamically access arbitrary Java objects,"['distributed-systems', 'java', 'programming-languages']","['distributed-systems', 'java', 'programming-languages']",2023-02-12,"[('pympler/pympler', 0.5723164677619934, 'perf', 0), ('pyston/pyston', 0.5638821721076965, 'util', 0), ('oracle/graalpython', 0.5628305077552795, 'util', 1), ('pypy/pypy', 0.5454596877098083, 'util', 0), ('micropython/micropython', 0.5158247947692871, 'util', 0), ('backtick-se/cowait', 0.5144885182380676, 'util', 0), ('numba/llvmlite', 0.5133116245269775, 'util', 0), ('pyglet/pyglet', 0.5067098140716553, 'gamedev', 0), ('secdev/scapy', 0.5051878690719604, 'util', 0), ('hoffstadt/dearpygui', 0.5022397041320801, 'gui', 0)]",38,5.0,,0.02,6,1,161,11,0,2,2,6.0,2.0,90.0,0.3,26
1681,util,https://github.com/klen/pylama,['linter'],,[],[],,,,klen/pylama,pylama,1034,102,20,Python,,Code audit tool for python.,klen,2024-01-09,2012-08-17,597,1.7303370786516854,,Code audit tool for python.,[],['linter'],2022-08-08,"[('pycqa/pyflakes', 0.6928360462188721, 'util', 1), ('landscapeio/prospector', 0.5915239453315735, 'util', 0), ('nedbat/coveragepy', 0.5741574764251709, 'testing', 0), ('google/pytype', 0.5735052824020386, 'typing', 1), ('instagram/fixit', 0.5459169745445251, 'util', 1), ('rubik/radon', 0.5425077676773071, 'util', 0), ('trailofbits/pip-audit', 0.5406633019447327, 'security', 0), ('gaogaotiantian/viztracer', 0.5285407304763794, 'profiling', 0), ('pycqa/pylint-django', 0.5170671939849854, 'util', 1), ('astral-sh/ruff', 0.5164267420768738, 'util', 1), ('pycqa/pylint', 0.5042141675949097, 'util', 1), ('alexmojaki/snoop', 0.500612735748291, 'debug', 0)]",46,5.0,,0.0,3,0,139,17,0,12,12,3.0,0.0,90.0,0.0,26
1020,finance,https://github.com/enthought/pyql,[],,[],[],,,,enthought/pyql,pyql,889,192,108,Cython,,Cython QuantLib wrappers,enthought,2024-01-12,2012-03-08,620,1.4322209436133486,https://avatars.githubusercontent.com/u/539651?v=4,Cython QuantLib wrappers,"['cython', 'quantlib']","['cython', 'quantlib']",2023-11-22,"[('lballabio/quantlib-swig', 0.5862199664115906, 'finance', 0)]",24,4.0,,1.37,12,12,144,2,0,0,0,12.0,0.0,90.0,0.0,26
503,gis,https://github.com/scikit-mobility/scikit-mobility,[],,[],[],,,,scikit-mobility/scikit-mobility,scikit-mobility,672,151,29,Python,https://scikit-mobility.github.io/scikit-mobility/,scikit-mobility: mobility analysis in Python,scikit-mobility,2024-01-09,2019-04-30,248,2.7096774193548385,https://avatars.githubusercontent.com/u/45601440?v=4,scikit-mobility: mobility analysis in Python,"['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows']","['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows']",2023-01-20,"[('ranaroussi/quantstats', 0.6154873967170715, 'finance', 0), ('networkx/networkx', 0.614628255367279, 'graph', 0), ('statsmodels/statsmodels', 0.5975031852722168, 'ml', 3), ('scikit-learn/scikit-learn', 0.5857199430465698, 'ml', 3), ('pandas-dev/pandas', 0.5808542966842651, 'pandas', 2), ('goldmansachs/gs-quant', 0.5793623328208923, 'finance', 0), ('wesm/pydata-book', 0.5627614259719849, 'study', 0), ('plotly/dash', 0.5490847826004028, 'viz', 1), ('firmai/atspy', 0.5477433800697327, 'time-series', 0), ('eleutherai/pyfra', 0.5473421216011047, 'ml', 0), ('thealgorithms/python', 0.5455461740493774, 'study', 0), ('atsushisakai/pythonrobotics', 0.5399863719940186, 'sim', 0), ('anitagraser/movingpandas', 0.5389496684074402, 'gis', 0), ('geopandas/geopandas', 0.5326095819473267, 'gis', 0), ('krzjoa/awesome-python-data-science', 0.5322821736335754, 'study', 3), ('fatiando/verde', 0.531174898147583, 'gis', 0), ('pysal/pysal', 0.5261431336402893, 'gis', 0), ('rasbt/mlxtend', 0.5235726833343506, 'ml', 1), ('python-odin/odin', 0.5235087275505066, 'util', 0), ('pycaret/pycaret', 0.5205168128013611, 'ml', 1), ('online-ml/river', 0.5196621417999268, 'ml', 1), ('dagworks-inc/hamilton', 0.5164218544960022, 'ml-ops', 2), ('ta-lib/ta-lib-python', 0.5160692930221558, 'finance', 0), ('makepath/xarray-spatial', 0.5155532956123352, 'gis', 0), ('sympy/sympy', 0.5121610164642334, 'math', 0), ('facebook/pyre-check', 0.5081936120986938, 'typing', 0), ('projectmesa/mesa', 0.5076199769973755, 'sim', 1), ('opengeos/leafmap', 0.5058870911598206, 'gis', 1), ('cuemacro/finmarketpy', 0.5044008493423462, 'finance', 0), ('keon/algorithms', 0.5026717782020569, 'util', 0), ('alkaline-ml/pmdarima', 0.5019405484199524, 'time-series', 0), ('amaargiru/pyroad', 0.5013498067855835, 'study', 0), ('quantecon/quantecon.py', 0.5013092756271362, 'sim', 0), ('artelys/geonetworkx', 0.5012951493263245, 'gis', 0), ('pytoolz/toolz', 0.5007225871086121, 'util', 0)]",23,3.0,,0.0,9,1,57,12,0,2,2,9.0,2.0,90.0,0.2,26
1204,util,https://github.com/serpapi/google-search-results-python,[],,[],[],,,,serpapi/google-search-results-python,google-search-results-python,474,89,14,Python,,Google Search Results via SERP API pip Python Package,serpapi,2024-01-12,2018-01-10,315,1.5006784260515604,https://avatars.githubusercontent.com/u/34724717?v=4,Google Search Results via SERP API pip Python Package,"['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping']","['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping']",2023-09-01,"[('nv7-github/googlesearch', 0.6652640700340271, 'util', 1), ('alirezamika/autoscraper', 0.5220003724098206, 'data', 2), ('googleapis/google-api-python-client', 0.5091744065284729, 'util', 0)]",17,2.0,,0.17,12,7,73,4,0,1,1,12.0,12.0,90.0,1.0,26
1114,util,https://github.com/pylons/colander,[],,[],[],,,,pylons/colander,colander,438,146,28,Python,https://docs.pylonsproject.org/projects/colander/en/latest/,"A serialization/deserialization/validation library for strings, mappings and lists.",pylons,2024-01-03,2011-02-16,675,0.6480659480025365,https://avatars.githubusercontent.com/u/452227?v=4,"A serialization/deserialization/validation library for strings, mappings and lists.","['deserialization', 'forms', 'serialization', 'validation']","['deserialization', 'forms', 'serialization', 'validation']",2023-09-09,"[('yukinarit/pyserde', 0.6523554921150208, 'util', 1), ('marshmallow-code/marshmallow', 0.6454058885574341, 'util', 3), ('python-odin/odin', 0.63074791431427, 'util', 1), ('pyeve/cerberus', 0.5786774754524231, 'data', 0), ('pydantic/pydantic', 0.531548798084259, 'util', 2), ('lidatong/dataclasses-json', 0.5289405584335327, 'util', 0), ('google/flatbuffers', 0.5039762258529663, 'perf', 1)]",111,4.0,,0.02,3,2,157,4,0,4,4,3.0,2.0,90.0,0.7,26
820,gis,https://github.com/datasystemslab/geotorch,[],,[],[],,,,datasystemslab/geotorch,GeoTorchAI,433,31,13,Jupyter Notebook,https://kanchanchy.github.io/geotorchai/,GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale,datasystemslab,2024-01-10,2022-05-23,88,4.9124797406807135,https://avatars.githubusercontent.com/u/92130061?v=4,GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale,"['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet']","['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet']",2023-10-22,"[('azavea/raster-vision', 0.6860873103141785, 'gis', 1), ('microsoft/torchgeo', 0.6509654521942139, 'gis', 1), ('developmentseed/label-maker', 0.5983750224113464, 'gis', 1), ('tensorflow/tensorflow', 0.5481935739517212, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.5321657061576843, 'ml-dl', 0), ('rwightman/pytorch-image-models', 0.5261633992195129, 'ml-dl', 0), ('rasbt/deeplearning-models', 0.5235190391540527, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5219647884368896, 'ml', 1), ('roboflow/notebooks', 0.5124086141586304, 'study', 2), ('aiqc/aiqc', 0.5082912445068359, 'ml-ops', 0), ('aistream-peelout/flow-forecast', 0.5054202675819397, 'time-series', 2)]",5,2.0,,1.21,1,0,20,3,0,1,1,1.0,0.0,90.0,0.0,26
749,ml-dl,https://github.com/samuela/git-re-basin,[],,[],[],,,,samuela/git-re-basin,git-re-basin,429,36,8,Python,https://arxiv.org/abs/2209.04836,"Code release for ""Git Re-Basin: Merging Models modulo Permutation Symmetries""",samuela,2024-01-12,2022-09-13,72,5.958333333333333,,"Code release for ""Git Re-Basin: Merging Models modulo Permutation Symmetries""","['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks']","['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks']",2023-03-07,"[('rasbt/machine-learning-book', 0.564606785774231, 'study', 3), ('huggingface/transformers', 0.5258132219314575, 'nlp', 3), ('alpa-projects/alpa', 0.507290780544281, 'ml-dl', 3)]",2,0.0,,0.12,1,1,16,10,0,0,0,1.0,4.0,90.0,4.0,26
559,gis,https://github.com/geopandas/dask-geopandas,[],,[],[],,,,geopandas/dask-geopandas,dask-geopandas,424,45,23,Python,https://dask-geopandas.readthedocs.io/,Parallel GeoPandas with Dask,geopandas,2024-01-05,2020-02-13,206,2.0511402902557014,https://avatars.githubusercontent.com/u/8130715?v=4,Parallel GeoPandas with Dask,[],[],2023-05-19,"[('dask/dask', 0.5673131942749023, 'perf', 0), ('nalepae/pandarallel', 0.5014110207557678, 'pandas', 0)]",20,7.0,,0.19,3,0,48,8,2,4,2,3.0,1.0,90.0,0.3,26
1668,testing,https://github.com/jamielennox/requests-mock,['mocking'],,[],[],,,,jamielennox/requests-mock,requests-mock,400,65,5,Python,https://requests-mock.readthedocs.io,Mocked responses for the requests library,jamielennox,2024-01-11,2014-12-16,476,0.8403361344537815,,Mocked responses for the requests library,[],['mocking'],2023-11-04,"[('getsentry/responses', 0.7649958729743958, 'testing', 1), ('kevin1024/vcrpy', 0.6570014953613281, 'testing', 1), ('lundberg/respx', 0.6144221425056458, 'testing', 1)]",51,5.0,,0.17,13,4,110,3,1,3,1,13.0,4.0,90.0,0.3,26
12,nlp,https://github.com/dialogflow/dialogflow-python-client-v2,[],,[],[],,,,dialogflow/dialogflow-python-client-v2,python-dialogflow,398,187,56,,https://dialogflow.com/,This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow,dialogflow,2024-01-10,2017-10-24,327,1.217125382262997,https://avatars.githubusercontent.com/u/16785467?v=4,This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow,"['dialogflow', 'machine-learning']","['dialogflow', 'machine-learning']",2023-09-21,"[('googleapis/python-speech', 0.743192732334137, 'ml', 0), ('googleapis/google-api-python-client', 0.5611771941184998, 'util', 0), ('deeppavlov/deeppavlov', 0.5524868369102478, 'nlp', 1), ('pndurette/gtts', 0.5459315776824951, 'util', 0), ('googlecloudplatform/vertex-ai-samples', 0.5129502415657043, 'ml', 0), ('google/vizier', 0.5073549151420593, 'ml', 1)]",37,5.0,,0.96,0,0,76,4,10,9,10,0.0,0.0,90.0,0.0,26
1190,llm,https://github.com/microsoft/chatgpt-robot-manipulation-prompts,[],,[],[],,,,microsoft/chatgpt-robot-manipulation-prompts,ChatGPT-Robot-Manipulation-Prompts,304,30,8,,,,microsoft,2024-01-10,2023-04-06,42,7.117056856187291,https://avatars.githubusercontent.com/u/6154722?v=4,microsoft/ChatGPT-Robot-Manipulation-Prompts,[],[],2023-11-28,"[('embedchain/embedchain', 0.5969848036766052, 'llm', 0), ('microsoft/promptcraft-robotics', 0.580747663974762, 'sim', 0), ('togethercomputer/openchatkit', 0.5749940872192383, 'nlp', 0), ('minimaxir/simpleaichat', 0.5525240302085876, 'llm', 0), ('weaviate/verba', 0.5466259121894836, 'llm', 0), ('run-llama/rags', 0.5412412881851196, 'llm', 0), ('cheshire-cat-ai/core', 0.5411517024040222, 'llm', 0), ('promptslab/promptify', 0.541034460067749, 'nlp', 0), ('prefecthq/marvin', 0.5360046029090881, 'nlp', 0), ('nomic-ai/gpt4all', 0.5312781929969788, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5258796811103821, 'study', 0), ('rcgai/simplyretrieve', 0.5226764678955078, 'llm', 0), ('killianlucas/open-interpreter', 0.5223796367645264, 'llm', 0), ('humanoidagents/humanoidagents', 0.5163865685462952, 'sim', 0), ('krohling/bondai', 0.5104005932807922, 'llm', 0), ('gunthercox/chatterbot', 0.5103239417076111, 'nlp', 0), ('chatarena/chatarena', 0.5086156725883484, 'llm', 0), ('guidance-ai/guidance', 0.5060887932777405, 'llm', 0), ('microsoft/autogen', 0.5033249258995056, 'llm', 0), ('xtekky/gpt4free', 0.50308758020401, 'llm', 0)]",3,1.0,,0.42,2,2,9,2,0,0,0,2.0,1.0,90.0,0.5,26
834,jupyter,https://github.com/cmudig/autoprofiler,[],,[],[],,,,cmudig/autoprofiler,AutoProfiler,294,8,6,Svelte,,Automatically profile dataframes in the Jupyter sidebar,cmudig,2024-01-12,2022-03-24,96,3.039881831610044,https://avatars.githubusercontent.com/u/56060038?v=4,Automatically profile dataframes in the Jupyter sidebar,"['jupyter', 'pandas']","['jupyter', 'pandas']",2023-09-26,"[('tkrabel/bamboolib', 0.6205199956893921, 'pandas', 1), ('quantopian/qgrid', 0.5721290111541748, 'jupyter', 0), ('lux-org/lux', 0.5497778058052063, 'viz', 2), ('koaning/drawdata', 0.5204150080680847, 'jupyter', 1), ('adamerose/pandasgui', 0.5127207040786743, 'pandas', 1), ('jakevdp/pythondatasciencehandbook', 0.5069370865821838, 'study', 1)]",4,2.0,,1.17,1,0,22,4,0,0,0,1.0,2.0,90.0,2.0,26
915,gis,https://github.com/giswqs/aws-open-data-geo,[],,[],[],,,,giswqs/aws-open-data-geo,aws-open-data-geo,263,7,11,Python,,A list of open geospatial datasets on AWS,giswqs,2024-01-05,2022-12-18,58,4.512254901960785,https://avatars.githubusercontent.com/u/129896036?v=4,A list of open geospatial datasets on AWS,"['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability']","['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability']",2024-01-13,"[('sentinelsat/sentinelsat', 0.6044768691062927, 'gis', 2), ('plant99/felicette', 0.5306439995765686, 'gis', 2), ('developmentseed/geolambda', 0.507233738899231, 'gis', 0)]",2,2.0,,2.15,1,1,13,0,0,0,0,1.0,0.0,90.0,0.0,26
1590,util,https://github.com/soft-matter/pims,"['formats', 'video']",,[],[],,,,soft-matter/pims,pims,256,66,14,Python,http://soft-matter.github.io/pims/,"Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface.",soft-matter,2024-01-04,2013-11-12,533,0.4803001876172608,https://avatars.githubusercontent.com/u/5857177?v=4,"Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface.",[],"['formats', 'video']",2023-11-26,"[('zulko/moviepy', 0.6140278577804565, 'util', 1), ('imageio/imageio', 0.5806184411048889, 'util', 1)]",38,5.0,,0.1,3,1,124,2,0,2,2,3.0,3.0,90.0,1.0,26
1648,nlp,https://github.com/microsoft/vert-papers,[],,[],[],,,,microsoft/vert-papers,vert-papers,256,90,12,Python,,"This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).",microsoft,2024-01-08,2019-07-25,235,1.0860606060606062,https://avatars.githubusercontent.com/u/6154722?v=4,"This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).","['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner']","['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner']",2023-10-07,"[('zjunlp/deepke', 0.5728095769882202, 'ml', 3), ('babelscape/rebel', 0.5618175864219666, 'nlp', 2), ('franck-dernoncourt/neuroner', 0.5278816223144531, 'nlp', 2), ('neuml/txtai', 0.5036661028862, 'nlp', 1), ('dylanhogg/llmgraph', 0.5032587647438049, 'ml', 0)]",13,5.0,,0.13,2,1,54,3,0,0,0,2.0,5.0,90.0,2.5,26
580,gis,https://github.com/spatialucr/geosnap,[],,[],[],,,,spatialucr/geosnap,geosnap,218,31,17,Python,https://oturns.github.io/geosnap/,The Geospatial Neighborhood Analysis Package,spatialucr,2024-01-04,2018-09-19,279,0.7789688616641144,https://avatars.githubusercontent.com/u/122131626?v=4,The Geospatial Neighborhood Analysis Package,"['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling']","['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling']",2023-12-11,"[('udst/urbansim', 0.5915238857269287, 'sim', 0), ('pysal/momepy', 0.5846999287605286, 'gis', 0), ('gregorhd/mapcompare', 0.54576176404953, 'gis', 0), ('mcordts/cityscapesscripts', 0.5296457409858704, 'gis', 0)]",9,6.0,,1.15,7,6,65,1,5,6,5,7.0,1.0,90.0,0.1,26
1785,llm,https://github.com/cohere-ai/notebooks,"['notebooks', 'cohere']",,[],[],,,,cohere-ai/notebooks,notebooks,204,54,12,Jupyter Notebook,,Code examples and jupyter notebooks for the Cohere Platform,cohere-ai,2024-01-12,2021-10-06,120,1.6879432624113475,https://avatars.githubusercontent.com/u/54850923?v=4,Code examples and jupyter notebooks for the Cohere Platform,[],"['cohere', 'notebooks']",2024-01-14,"[('fchollet/deep-learning-with-python-notebooks', 0.7323339581489563, 'study', 0), ('jupyter/nbformat', 0.7138713598251343, 'jupyter', 0), ('aws/graph-notebook', 0.6492716073989868, 'jupyter', 0), ('jupyter/notebook', 0.6436967253684998, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.6400914192199707, 'jupyter', 0), ('mwouts/jupytext', 0.6391822695732117, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.6190292835235596, 'study', 0), ('jupyter/nbconvert', 0.6110220551490784, 'jupyter', 0), ('python/cpython', 0.603980302810669, 'util', 0), ('voila-dashboards/voila', 0.60094153881073, 'jupyter', 0), ('wesm/pydata-book', 0.5929689407348633, 'study', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5917092561721802, 'jupyter', 0), ('jupyter/nbgrader', 0.5908788442611694, 'jupyter', 0), ('ageron/handson-ml2', 0.5897052884101868, 'ml', 0), ('jupyterlab/jupyterlab-desktop', 0.5881903767585754, 'jupyter', 0), ('faster-cpython/ideas', 0.5839570760726929, 'perf', 0), ('koaning/calm-notebooks', 0.5802757143974304, 'study', 0), ('nteract/papermill', 0.5747708082199097, 'jupyter', 1), ('mynameisfiber/high_performance_python_2e', 0.5679949522018433, 'study', 0), ('alphasecio/langchain-examples', 0.5620157718658447, 'llm', 0), ('vizzuhq/ipyvizzu', 0.5615155100822449, 'jupyter', 0), ('jupyter/nbdime', 0.559609591960907, 'jupyter', 0), ('cohere-ai/cohere-python', 0.5595293641090393, 'util', 0), ('ipython/ipyparallel', 0.5586426258087158, 'perf', 0), ('quantopian/qgrid', 0.5572461485862732, 'jupyter', 0), ('ipython/ipykernel', 0.5502215623855591, 'util', 0), ('nbqa-dev/nbqa', 0.550182044506073, 'jupyter', 0), ('opengeos/leafmap', 0.546214759349823, 'gis', 0), ('jupyterlab/jupyterlab', 0.5374837517738342, 'jupyter', 0), ('brandtbucher/specialist', 0.5358579158782959, 'perf', 0), ('adafruit/circuitpython', 0.5329226851463318, 'util', 0), ('huggingface/notebooks', 0.529309868812561, 'ml', 0), ('masoniteframework/masonite', 0.5226303935050964, 'web', 0), ('maartenbreddels/ipyvolume', 0.5213759541511536, 'jupyter', 0), ('eleutherai/pyfra', 0.5172991752624512, 'ml', 0), ('giswqs/mapwidget', 0.51350337266922, 'gis', 0), ('holoviz/panel', 0.5109737515449524, 'viz', 0), ('pypy/pypy', 0.5104973316192627, 'util', 0), ('faster-cpython/tools', 0.5094420313835144, 'perf', 0), ('mito-ds/monorepo', 0.5074410438537598, 'jupyter', 0), ('rasbt/watermark', 0.5048350095748901, 'util', 0), ('willmcgugan/textual', 0.5048252940177917, 'term', 0), ('wxwidgets/phoenix', 0.5029077529907227, 'gui', 0), ('timofurrer/awesome-asyncio', 0.5014423131942749, 'study', 0), ('pytoolz/toolz', 0.5008567571640015, 'util', 0)]",9,3.0,,1.56,33,25,28,0,0,0,0,33.0,7.0,90.0,0.2,26
1099,gis,https://github.com/giswqs/mapwidget,[],,[],[],,,,giswqs/mapwidget,mapwidget,201,12,9,Python,http://mapwidget.gishub.org,"Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers",giswqs,2024-01-04,2023-01-21,53,3.7620320855614975,https://avatars.githubusercontent.com/u/129896036?v=4,"Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers","['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers']","['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers']",2023-03-24,"[('jupyter-widgets/ipyleaflet', 0.6565911769866943, 'gis', 2), ('maartenbreddels/ipyvolume', 0.6517302989959717, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.6259199976921082, 'jupyter', 0), ('opengeos/leafmap', 0.5904461741447449, 'gis', 4), ('python-visualization/folium', 0.5697619915008545, 'gis', 0), ('vizzuhq/ipyvizzu', 0.5461418032646179, 'jupyter', 1), ('bokeh/bokeh', 0.5409641861915588, 'viz', 1), ('voila-dashboards/voila', 0.5279869437217712, 'jupyter', 1), ('wxwidgets/phoenix', 0.5239470601081848, 'gui', 0), ('aws/graph-notebook', 0.5196599364280701, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.5143319964408875, 'jupyter', 1), ('cohere-ai/notebooks', 0.51350337266922, 'llm', 0), ('plotly/plotly.py', 0.504154622554779, 'viz', 0)]",1,1.0,,0.9,1,0,12,10,8,8,8,1.0,2.0,90.0,2.0,26
739,data,https://github.com/ktrueda/parquet-tools,[],,[],[],,,,ktrueda/parquet-tools,parquet-tools,136,18,4,Python,,easy install parquet-tools,ktrueda,2024-01-04,2020-05-02,195,0.695906432748538,,easy install parquet-tools,"['cli', 'parquet', 'parquet-tools']","['cli', 'parquet', 'parquet-tools']",2024-01-02,"[('dask/fastparquet', 0.5958738327026367, 'data', 0)]",14,2.0,,0.13,7,6,45,0,4,5,4,7.0,10.0,90.0,1.4,26
1903,data,https://github.com/typesense/typesense-python,"['search-engine', 'sdk', 'api']","Open Source alternative to to ElasticSearch. Fast, typo tolerant, in-memory fuzzy Search Engine.",[],[],,,,typesense/typesense-python,typesense-python,125,28,5,Python,,Python client for Typesense: https://github.com/typesense/typesense,typesense,2024-01-16,2018-01-30,313,0.3993610223642173,https://avatars.githubusercontent.com/u/19822348?v=4,Python client for Typesense: https://github.com/typesense/typesense,[],"['api', 'sdk', 'search-engine']",2024-01-03,"[('meilisearch/meilisearch-python', 0.5989935994148254, 'data', 3), ('googleapis/google-api-python-client', 0.5588173866271973, 'util', 0), ('qdrant/qdrant-client', 0.5577874779701233, 'util', 0), ('tiangolo/typer', 0.5246941447257996, 'term', 0), ('strawberry-graphql/strawberry', 0.5120099782943726, 'web', 0), ('simple-salesforce/simple-salesforce', 0.503971517086029, 'data', 1)]",14,5.0,,0.4,8,5,73,0,0,0,0,8.0,17.0,90.0,2.1,26
1430,sim,https://github.com/srivatsankrishnan/oss-arch-gym,"['architecture', 'simulator']",,[],[],,,,srivatsankrishnan/oss-arch-gym,oss-arch-gym,91,15,6,Jupyter Notebook,,Open source version of ArchGym project.,srivatsankrishnan,2024-01-04,2023-04-11,42,2.1666666666666665,,Open source version of ArchGym project.,[],"['architecture', 'simulator']",2023-12-28,[],14,2.0,,5.33,28,22,9,1,0,0,0,28.0,14.0,90.0,0.5,26
884,util,https://github.com/pyodide/micropip,[],,[],[],,,,pyodide/micropip,micropip,42,12,6,Python,https://micropip.pyodide.org,A lightweight Python package installer for Pyodide,pyodide,2024-01-03,2022-09-15,71,0.5856573705179283,https://avatars.githubusercontent.com/u/77002075?v=4,A lightweight Python package installer for Pyodide,"['package-installer', 'pyodide', 'webassembly']","['package-installer', 'pyodide', 'webassembly']",2024-01-03,"[('pyodide/pyodide', 0.7593028545379639, 'util', 1), ('indygreg/pyoxidizer', 0.6752342581748962, 'util', 0), ('ofek/pyapp', 0.6668835282325745, 'util', 0), ('mitsuhiko/rye', 0.6547753810882568, 'util', 0), ('pypi/warehouse', 0.6480095386505127, 'util', 0), ('pypa/flit', 0.6164460778236389, 'util', 0), ('python-poetry/poetry', 0.609188437461853, 'util', 0), ('pdm-project/pdm', 0.6069954037666321, 'util', 0), ('pypa/installer', 0.5991305708885193, 'util', 0), ('pypa/hatch', 0.5937002897262573, 'util', 0), ('pypy/pypy', 0.5889347195625305, 'util', 0), ('libtcod/python-tcod', 0.5887089967727661, 'gamedev', 0), ('bottlepy/bottle', 0.5800544023513794, 'web', 0), ('webpy/webpy', 0.5706849694252014, 'web', 0), ('regebro/pyroma', 0.5652766227722168, 'util', 0), ('beeware/briefcase', 0.561262845993042, 'util', 0), ('mamba-org/mamba', 0.5484160780906677, 'util', 0), ('pallets/flask', 0.5379033088684082, 'web', 0), ('pyo3/maturin', 0.5349652171134949, 'util', 0), ('conda/constructor', 0.5295533537864685, 'util', 0), ('hoffstadt/dearpygui', 0.5266197919845581, 'gui', 0), ('pyinstaller/pyinstaller', 0.5264869332313538, 'util', 0), ('pytables/pytables', 0.5223343968391418, 'data', 0), ('malloydata/malloy-py', 0.5204178690910339, 'data', 0), ('pypa/build', 0.5155296325683594, 'util', 0), ('tezromach/python-package-template', 0.5130401253700256, 'template', 0), ('tox-dev/pipdeptree', 0.5127100944519043, 'util', 0), ('linkedin/shiv', 0.5121049284934998, 'util', 0), ('pyinfra-dev/pyinfra', 0.5114571452140808, 'util', 0), ('pomponchik/instld', 0.5101442337036133, 'util', 0), ('hugovk/pypistats', 0.5070849657058716, 'util', 0), ('erotemic/ubelt', 0.5013461709022522, 'util', 0), ('pypa/virtualenv', 0.5000215172767639, 'util', 0)]",8,2.0,,0.48,5,2,16,0,0,5,5,5.0,14.0,90.0,2.8,26
903,data,https://github.com/malloydata/malloy-py,[],,[],[],,,,malloydata/malloy-py,malloy-py,15,6,8,JavaScript,,Python package for executing Malloy,malloydata,2024-01-12,2022-11-02,64,0.23127753303964757,https://avatars.githubusercontent.com/u/115666028?v=4,Python package for executing Malloy,"['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql']","['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql']",2024-01-12,"[('tiangolo/sqlmodel', 0.6022137403488159, 'data', 1), ('ibis-project/ibis', 0.5992289185523987, 'data', 1), ('plotly/dash', 0.5759779810905457, 'viz', 0), ('sqlalchemy/sqlalchemy', 0.5755621790885925, 'data', 1), ('tobymao/sqlglot', 0.5748046040534973, 'data', 1), ('krzjoa/awesome-python-data-science', 0.5664848685264587, 'study', 0), ('eleutherai/pyfra', 0.5658804178237915, 'ml', 0), ('willmcgugan/textual', 0.5623626708984375, 'term', 0), ('pympler/pympler', 0.5531739592552185, 'perf', 0), ('pypa/hatch', 0.547518789768219, 'util', 0), ('kubeflow/fairing', 0.5466133952140808, 'ml-ops', 0), ('goldmansachs/gs-quant', 0.5456441044807434, 'finance', 0), ('fastai/fastcore', 0.5410973429679871, 'util', 0), ('pdm-project/pdm', 0.5352970957756042, 'util', 0), ('gradio-app/gradio', 0.5345299243927002, 'viz', 0), ('dagworks-inc/hamilton', 0.532253086566925, 'ml-ops', 0), ('python-odin/odin', 0.5316958427429199, 'util', 0), ('pandas-dev/pandas', 0.5308995246887207, 'pandas', 0), ('pytables/pytables', 0.5297611951828003, 'data', 0), ('indygreg/pyoxidizer', 0.5247126221656799, 'util', 0), ('omry/omegaconf', 0.5221198201179504, 'util', 0), ('ploomber/ploomber', 0.5213688015937805, 'ml-ops', 0), ('pyodide/micropip', 0.5204178690910339, 'util', 0), ('holoviz/panel', 0.5195130109786987, 'viz', 0), ('wesm/pydata-book', 0.5186687707901001, 'study', 0), ('ranaroussi/quantstats', 0.5185449123382568, 'finance', 0), ('amaargiru/pyroad', 0.5178565979003906, 'study', 0), ('ta-lib/ta-lib-python', 0.5163371562957764, 'finance', 0), ('machow/siuba', 0.513921856880188, 'pandas', 1), ('nteract/papermill', 0.5121511220932007, 'jupyter', 0), ('dylanhogg/awesome-python', 0.5110769867897034, 'study', 1), ('spotify/luigi', 0.5109559893608093, 'ml-ops', 0), ('macbre/sql-metadata', 0.5103316307067871, 'data', 1), ('pypy/pypy', 0.5085586309432983, 'util', 0), ('python/cpython', 0.5069926381111145, 'util', 0), ('pytoolz/toolz', 0.5053905844688416, 'util', 0), ('saulpw/visidata', 0.5046626925468445, 'term', 0)]",9,4.0,,4.63,16,15,15,0,46,92,46,16.0,10.0,90.0,0.6,26
160,data,https://github.com/goldsmith/wikipedia,[],,[],[],,,,goldsmith/wikipedia,Wikipedia,2774,569,83,Python,https://wikipedia.readthedocs.org/,A Pythonic wrapper for the Wikipedia API,goldsmith,2024-01-12,2013-08-20,545,5.089908256880734,,A Pythonic wrapper for the Wikipedia API,[],[],2020-10-09,"[('harangju/wikinet', 0.7525506615638733, 'data', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.709117591381073, 'data', 0), ('mediawiki-client-tools/wikitools3', 0.708003044128418, 'data', 0), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5848309397697449, 'data', 0), ('urschrei/pyzotero', 0.5380213856697083, 'util', 0), ('facebookresearch/drqa', 0.5370073318481445, 'nlp', 0), ('nv7-github/googlesearch', 0.5323612093925476, 'util', 0), ('meilisearch/meilisearch-python', 0.5276321768760681, 'data', 0), ('dit/dit', 0.525985062122345, 'math', 0), ('googleapis/google-api-python-client', 0.5227052569389343, 'util', 0), ('pytoolz/toolz', 0.5172760486602783, 'util', 0), ('scholarly-python-package/scholarly', 0.5016988515853882, 'data', 0)]",23,3.0,,0.0,0,0,127,40,0,0,0,0.0,0.0,90.0,0.0,25
1256,llm,https://github.com/openai/finetune-transformer-lm,[],,[],[],,,,openai/finetune-transformer-lm,finetune-transformer-lm,1996,485,73,Python,https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf,"Code and model for the paper ""Improving Language Understanding by Generative Pre-Training""",openai,2024-01-12,2018-06-11,294,6.785818358426421,https://avatars.githubusercontent.com/u/14957082?v=4,"Code and model for the paper ""Improving Language Understanding by Generative Pre-Training""",['paper'],['paper'],2018-11-22,"[('openai/gpt-2', 0.6553829312324524, 'llm', 1), ('srush/minichain', 0.6227275133132935, 'llm', 0), ('openai/image-gpt', 0.6050621867179871, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5976941585540771, 'nlp', 0), ('thudm/glm-130b', 0.590366780757904, 'llm', 0), ('yizhongw/self-instruct', 0.5861937999725342, 'llm', 0), ('jonasgeiping/cramming', 0.5850217342376709, 'nlp', 0), ('salesforce/blip', 0.5705159902572632, 'diffusion', 0), ('microsoft/unilm', 0.5644842982292175, 'nlp', 0), ('facebookresearch/shepherd', 0.5617777705192566, 'llm', 0), ('yueyu1030/attrprompt', 0.5512840747833252, 'llm', 0), ('togethercomputer/redpajama-data', 0.5497167706489563, 'llm', 0), ('qanastek/drbert', 0.5415375828742981, 'llm', 0), ('suno-ai/bark', 0.5396863222122192, 'ml', 0), ('google-research/electra', 0.5301187634468079, 'ml-dl', 0), ('tatsu-lab/stanford_alpaca', 0.5281931757926941, 'llm', 0), ('hannibal046/awesome-llm', 0.527682363986969, 'study', 0), ('cg123/mergekit', 0.5270984768867493, 'llm', 0), ('openai/clip', 0.5225598812103271, 'ml-dl', 0), ('microsoft/lora', 0.520248532295227, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5154716968536377, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5153794884681702, 'llm', 0), ('huggingface/text-generation-inference', 0.5149126648902893, 'llm', 0), ('lupantech/chameleon-llm', 0.5111390352249146, 'llm', 0), ('graykode/nlp-tutorial', 0.5100629329681396, 'study', 1), ('bigscience-workshop/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('guidance-ai/guidance', 0.5050269961357117, 'llm', 0), ('bigscience-workshop/biomedical', 0.5047228336334229, 'data', 0), ('extreme-bert/extreme-bert', 0.504263699054718, 'llm', 0), ('thudm/codegeex', 0.5009891390800476, 'llm', 0)]",5,1.0,,0.0,1,0,68,63,0,0,0,1.0,1.0,90.0,1.0,25
205,debug,https://github.com/alexmojaki/heartrate,[],,[],[],,,,alexmojaki/heartrate,heartrate,1685,124,33,Python,,Simple real time visualisation of the execution of a Python program.,alexmojaki,2024-01-13,2019-04-24,248,6.770952927669345,,Simple real time visualisation of the execution of a Python program.,"['debugger', 'visualization']","['debugger', 'visualization']",2021-11-13,"[('gaogaotiantian/viztracer', 0.6707364320755005, 'profiling', 1), ('altair-viz/altair', 0.6655700206756592, 'viz', 1), ('alexmojaki/snoop', 0.6388193964958191, 'debug', 1), ('pympler/pympler', 0.57415771484375, 'perf', 0), ('holoviz/holoviz', 0.573762834072113, 'viz', 0), ('bokeh/bokeh', 0.571494460105896, 'viz', 1), ('inducer/pudb', 0.5674254894256592, 'debug', 1), ('brandtbucher/specialist', 0.56365966796875, 'perf', 0), ('mwaskom/seaborn', 0.5591291785240173, 'viz', 0), ('alexmojaki/birdseye', 0.5575025081634521, 'debug', 1), ('p403n1x87/austin', 0.5422681570053101, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.5310880541801453, 'profiling', 0), ('kanaries/pygwalker', 0.5303294658660889, 'pandas', 1), ('holoviz/geoviews', 0.5235070586204529, 'gis', 0), ('pyutils/line_profiler', 0.522416353225708, 'profiling', 0), ('samuelcolvin/python-devtools', 0.5222712755203247, 'debug', 0), ('pyqtgraph/pyqtgraph', 0.5196880102157593, 'viz', 1), ('has2k1/plotnine', 0.5188327431678772, 'viz', 0), ('ionelmc/python-hunter', 0.5184060335159302, 'debug', 1), ('pyglet/pyglet', 0.5163092613220215, 'gamedev', 0), ('rockhopper-technologies/enlighten', 0.5111202597618103, 'term', 0), ('plotly/plotly.py', 0.5061368942260742, 'viz', 1), ('nschloe/perfplot', 0.5038774013519287, 'perf', 0), ('residentmario/geoplot', 0.5028942823410034, 'gis', 0), ('holoviz/panel', 0.5002499222755432, 'viz', 0)]",3,0.0,,0.0,1,1,58,26,0,0,0,1.0,2.0,90.0,2.0,25
1503,util,https://github.com/asweigart/pyperclip,['clipboard'],,[],[],,,,asweigart/pyperclip,pyperclip,1497,184,35,Python,https://pypi.python.org/pypi/pyperclip,Python module for cross-platform clipboard functions.,asweigart,2024-01-14,2011-06-15,658,2.2721162185602775,,Python module for cross-platform clipboard functions.,[],['clipboard'],2021-10-12,"[('taylorsmarks/playsound', 0.5268693566322327, 'util', 0), ('pytoolz/toolz', 0.5117189288139343, 'util', 0), ('hoffstadt/dearpygui', 0.5023604035377502, 'gui', 0), ('p403n1x87/austin', 0.5013977885246277, 'profiling', 0)]",34,3.0,,0.0,4,1,153,27,0,0,0,4.0,4.0,90.0,1.0,25
995,finance,https://github.com/quantopian/empyrical,[],,[],[],,,,quantopian/empyrical,empyrical,1189,365,71,Python,https://quantopian.github.io/empyrical,Common financial risk and performance metrics. Used by zipline and pyfolio.,quantopian,2024-01-13,2016-03-18,410,2.895963813500348,https://avatars.githubusercontent.com/u/1393215?v=4,Common financial risk and performance metrics. Used by zipline and pyfolio.,[],[],2020-10-14,"[('quantopian/pyfolio', 0.6045350432395935, 'finance', 0)]",22,4.0,,0.0,3,0,95,40,0,4,4,3.0,0.0,90.0,0.0,25
615,testing,https://github.com/wolever/parameterized,[],,[],[],,,,wolever/parameterized,parameterized,797,104,18,Python,,Parameterized testing with any Python test framework,wolever,2024-01-13,2012-03-10,620,1.28459590145061,,Parameterized testing with any Python test framework,[],[],2023-03-27,"[('nedbat/coveragepy', 0.6841293573379517, 'testing', 0), ('pmorissette/bt', 0.620602011680603, 'finance', 0), ('getsentry/responses', 0.6193545460700989, 'testing', 0), ('ionelmc/pytest-benchmark', 0.6030191779136658, 'testing', 0), ('klen/py-frameworks-bench', 0.5862182974815369, 'perf', 0), ('spulec/freezegun', 0.5846211314201355, 'testing', 0), ('locustio/locust', 0.5832534432411194, 'testing', 0), ('buildbot/buildbot', 0.5751279592514038, 'util', 0), ('pytest-dev/pytest', 0.5712449550628662, 'testing', 0), ('pytest-dev/pytest-bdd', 0.5707379579544067, 'testing', 0), ('eleutherai/pyfra', 0.5701524615287781, 'ml', 0), ('taverntesting/tavern', 0.5629963874816895, 'testing', 0), ('pytest-dev/pytest-xdist', 0.5527563095092773, 'testing', 0), ('eugeneyan/python-collab-template', 0.5470243692398071, 'template', 0), ('cobrateam/splinter', 0.5428141355514526, 'testing', 0), ('computationalmodelling/nbval', 0.5427061319351196, 'jupyter', 0), ('seleniumbase/seleniumbase', 0.5371958613395691, 'testing', 0), ('pyeve/cerberus', 0.5334105491638184, 'data', 0), ('pytoolz/toolz', 0.5330343842506409, 'util', 0), ('mementum/backtrader', 0.5309708714485168, 'finance', 0), ('samuelcolvin/dirty-equals', 0.524022102355957, 'util', 0), ('requests/toolbelt', 0.5131222009658813, 'util', 0), ('pytest-dev/pytest-mock', 0.5089722275733948, 'testing', 0), ('unionai-oss/pandera', 0.5074113607406616, 'pandas', 0), ('cuemacro/finmarketpy', 0.5073219537734985, 'finance', 0), ('pympler/pympler', 0.5015774965286255, 'perf', 0)]",31,5.0,,0.27,6,0,144,10,0,1,1,6.0,4.0,90.0,0.7,25
1388,nlp,https://github.com/keredson/wordninja,['tokeniser'],,[],[],,,,keredson/wordninja,wordninja,743,107,10,Python,,Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies.,keredson,2024-01-11,2017-04-20,353,2.1005654281098547,,Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies.,[],['tokeniser'],2023-02-14,[],6,3.0,,0.0,1,0,82,11,0,0,0,1.0,2.0,90.0,2.0,25
169,gis,https://github.com/openeventdata/mordecai,[],,[],[],,,,openeventdata/mordecai,mordecai,722,98,34,Python,,Full text geoparsing as a Python library,openeventdata,2024-01-04,2016-06-23,396,1.8199495858840475,https://avatars.githubusercontent.com/u/1460393?v=4,Full text geoparsing as a Python library,"['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution']","['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution']",2021-02-01,"[('geopandas/geopandas', 0.6333655714988708, 'gis', 0), ('kagisearch/vectordb', 0.5354642868041992, 'data', 0), ('opengeos/leafmap', 0.5265222191810608, 'gis', 0), ('artelys/geonetworkx', 0.5222339630126953, 'gis', 0), ('pemistahl/lingua-py', 0.5192466974258423, 'nlp', 1)]",6,3.0,,0.0,2,0,92,36,0,1,1,2.0,7.0,90.0,3.5,25
165,nlp,https://github.com/explosion/spacy-stanza,[],,[],[],,,,explosion/spacy-stanza,spacy-stanza,705,57,26,Python,,💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy,explosion,2024-01-04,2019-01-31,260,2.7041095890410958,https://avatars.githubusercontent.com/u/20011530?v=4,💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy,"['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza']","['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza']",2023-10-09,"[('explosion/spacy-models', 0.7454922199249268, 'nlp', 4), ('huggingface/neuralcoref', 0.6504446864128113, 'nlp', 4), ('explosion/spacy-transformers', 0.6275186538696289, 'llm', 5), ('iclrandd/blackstone', 0.5856739282608032, 'nlp', 1), ('explosion/spacy-llm', 0.5577221512794495, 'llm', 4), ('explosion/spacy-streamlit', 0.5425686240196228, 'nlp', 4), ('norskregnesentral/skweak', 0.5375661849975586, 'nlp', 3), ('explosion/spacy', 0.5188982486724854, 'nlp', 5)]",8,4.0,,0.17,0,0,60,3,2,3,2,0.0,0.0,90.0,0.0,25
1413,llm,https://github.com/hazyresearch/ama_prompting,['prompt-engineering'],,[],[],,,,hazyresearch/ama_prompting,ama_prompting,522,45,24,Python,,Ask Me Anything language model prompting,hazyresearch,2024-01-09,2022-10-01,69,7.518518518518518,https://avatars.githubusercontent.com/u/2165246?v=4,Ask Me Anything language model prompting,[],['prompt-engineering'],2023-07-05,"[('keirp/automatic_prompt_engineer', 0.7995690107345581, 'llm', 1), ('microsoft/promptbase', 0.7119161486625671, 'llm', 1), ('neulab/prompt2model', 0.687862753868103, 'llm', 0), ('guidance-ai/guidance', 0.6410424709320068, 'llm', 1), ('hazyresearch/manifest', 0.5880966782569885, 'llm', 1), ('srush/minichain', 0.5800570845603943, 'llm', 1), ('1rgs/jsonformer', 0.5634579062461853, 'llm', 1), ('promptslab/promptify', 0.5550077557563782, 'nlp', 1), ('stanfordnlp/dspy', 0.540431022644043, 'llm', 0), ('suno-ai/bark', 0.5254032015800476, 'ml', 0), ('bigscience-workshop/promptsource', 0.512933611869812, 'nlp', 0), ('ctlllll/llm-toolmaker', 0.5080617666244507, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5045536160469055, 'llm', 1), ('agenta-ai/agenta', 0.5027536153793335, 'llm', 1)]",6,2.0,,0.02,0,0,16,6,0,0,0,0.0,0.0,90.0,0.0,25
1088,graph,https://github.com/rampasek/graphgps,[],,[],[],,,,rampasek/graphgps,GraphGPS,520,95,9,Python,,"Recipe for a General, Powerful, Scalable Graph Transformer",rampasek,2024-01-12,2022-05-24,88,5.909090909090909,,"Recipe for a General, Powerful, Scalable Graph Transformer","['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence']","['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence']",2023-02-17,"[('hamed1375/exphormer', 0.6791350841522217, 'graph', 0), ('pyg-team/pytorch_geometric', 0.6243663430213928, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5940183997154236, 'ml-dl', 0), ('stellargraph/stellargraph', 0.5865841507911682, 'graph', 0), ('dmlc/dgl', 0.5740870833396912, 'ml-dl', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5653940439224243, 'study', 0), ('graphistry/pygraphistry', 0.5173921585083008, 'data', 0)]",2,0.0,,0.13,12,6,20,11,1,1,1,12.0,13.0,90.0,1.1,25
1830,data,https://github.com/koaning/doubtlab,['data-quality'],,[],[],,,,koaning/doubtlab,doubtlab,485,19,7,Python,https://koaning.github.io/doubtlab/,"Doubt your data, find bad labels. ",koaning,2024-01-09,2021-11-05,116,4.160539215686274,,"Doubt your data, find bad labels. ",[],['data-quality'],2022-11-25,"[('koaning/bulk', 0.5618610382080078, 'data', 1), ('ydataai/ydata-quality', 0.5491899251937866, 'data', 0)]",6,3.0,,0.0,2,2,27,14,0,4,4,2.0,0.0,90.0,0.0,25
735,nlp,https://github.com/koaning/whatlies,[],,[],[],,,,koaning/whatlies,whatlies,463,53,15,Python,https://koaning.github.io/whatlies/,"Toolkit to help understand ""what lies"" in word embeddings. Also benchmarking! ",koaning,2024-01-04,2020-02-22,205,2.253824756606398,,"Toolkit to help understand ""what lies"" in word embeddings. Also benchmarking! ","['embeddings', 'nlp', 'visualisations']","['embeddings', 'nlp', 'visualisations']",2023-02-06,"[('plasticityai/magnitude', 0.6194629669189453, 'nlp', 2), ('koaning/embetter', 0.6023291945457458, 'data', 0), ('qdrant/fastembed', 0.5915380120277405, 'ml', 1), ('ddangelov/top2vec', 0.5833550095558167, 'nlp', 0), ('sebischair/lbl2vec', 0.5767074823379517, 'nlp', 1), ('allenai/allennlp', 0.570354700088501, 'nlp', 1), ('alibaba/easynlp', 0.5676613450050354, 'nlp', 1), ('jina-ai/clip-as-service', 0.553788423538208, 'nlp', 0), ('chroma-core/chroma', 0.5464308261871338, 'data', 1), ('jalammar/ecco', 0.5461040735244751, 'ml-interpretability', 1), ('huggingface/text-embeddings-inference', 0.5444438457489014, 'llm', 1), ('flairnlp/flair', 0.5374947190284729, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5240222215652466, 'llm', 1), ('maartengr/bertopic', 0.5192699432373047, 'nlp', 1), ('neuml/txtai', 0.5187655687332153, 'nlp', 2), ('milvus-io/bootcamp', 0.5146546363830566, 'data', 2), ('explosion/spacy-models', 0.5144206881523132, 'nlp', 1), ('jbesomi/texthero', 0.5086743831634521, 'nlp', 1), ('muennighoff/sgpt', 0.5074957013130188, 'llm', 0), ('amansrivastava17/embedding-as-service', 0.5058193802833557, 'nlp', 2), ('ukplab/sentence-transformers', 0.5054284334182739, 'nlp', 0), ('mitvis/vistext', 0.5051085948944092, 'data', 0), ('jina-ai/vectordb', 0.5039339661598206, 'data', 0), ('cvxgrp/pymde', 0.5038027167320251, 'ml', 0), ('llmware-ai/llmware', 0.501112163066864, 'llm', 2)]",13,6.0,,0.06,0,0,47,11,0,7,7,0.0,0.0,90.0,0.0,25
699,ml-ops,https://github.com/bodywork-ml/bodywork-core,[],,[],[],,,,bodywork-ml/bodywork-core,bodywork-core,431,22,11,Python,https://bodywork.readthedocs.io/en/latest/,ML pipeline orchestration and model deployments on Kubernetes.,bodywork-ml,2024-01-04,2020-11-17,167,2.5808383233532934,https://avatars.githubusercontent.com/u/74599515?v=4,ML pipeline orchestration and model deployments on Kubernetes.,"['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving']","['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving']",2022-07-04,"[('kubeflow/pipelines', 0.8104010820388794, 'ml-ops', 5), ('polyaxon/polyaxon', 0.6861110925674438, 'ml-ops', 4), ('flyteorg/flyte', 0.6820202469825745, 'ml-ops', 4), ('getindata/kedro-kubeflow', 0.6706922650337219, 'ml-ops', 1), ('orchest/orchest', 0.6645437479019165, 'ml-ops', 3), ('allegroai/clearml', 0.6049355268478394, 'ml-ops', 3), ('bentoml/bentoml', 0.5959450602531433, 'ml-ops', 3), ('zenml-io/zenml', 0.5911761522293091, 'ml-ops', 3), ('unionai-oss/unionml', 0.5865074396133423, 'ml-ops', 2), ('netflix/metaflow', 0.5834348201751709, 'ml-ops', 4), ('dagster-io/dagster', 0.5800526142120361, 'ml-ops', 3), ('mage-ai/mage-ai', 0.5791720151901245, 'ml-ops', 4), ('ploomber/ploomber', 0.5688678026199341, 'ml-ops', 3), ('gefyrahq/gefyra', 0.5484521389007568, 'util', 1), ('backtick-se/cowait', 0.547731876373291, 'util', 2), ('kubeflow-kale/kale', 0.5426039695739746, 'ml-ops', 1), ('skypilot-org/skypilot', 0.5361641049385071, 'llm', 2), ('kestra-io/kestra', 0.5349816083908081, 'ml-ops', 2), ('feast-dev/feast', 0.5267590284347534, 'ml-ops', 3), ('zenml-io/mlstacks', 0.5243047475814819, 'ml-ops', 1), ('jina-ai/jina', 0.523324728012085, 'ml', 6), ('tox-dev/tox', 0.5215947031974792, 'testing', 0), ('avaiga/taipy', 0.5173805952072144, 'data', 3), ('apache/airflow', 0.5161139965057373, 'ml-ops', 4), ('onnx/onnx', 0.5088913440704346, 'ml', 1)]",4,2.0,,0.0,1,1,38,19,0,19,19,1.0,1.0,90.0,1.0,25
1227,time-series,https://github.com/microsoft/robustlearn,[],,[],[],,,,microsoft/robustlearn,robustlearn,384,45,7,Python,http://aka.ms/roblearn,Robust machine learning for responsible AI,microsoft,2024-01-13,2022-10-20,66,5.755888650963597,https://avatars.githubusercontent.com/u/6154722?v=4,Robust machine learning for responsible AI,[],[],2023-10-08,"[('seldonio/alibi', 0.5054094791412354, 'ml-interpretability', 0), ('maif/shapash', 0.5023934841156006, 'ml', 0)]",8,1.0,,1.54,1,1,15,3,0,0,0,1.0,0.0,90.0,0.0,25
1364,gamedev,https://github.com/renpy/pygame_sdl2,"['pygame', 'sdl2']",,[],[],,,,renpy/pygame_sdl2,pygame_sdl2,311,63,29,Python,,Reimplementation of portions of the pygame API using SDL2.,renpy,2023-12-27,2014-10-23,483,0.6429415239220319,https://avatars.githubusercontent.com/u/1900740?v=4,Reimplementation of portions of the pygame API using SDL2.,[],"['pygame', 'sdl2']",2023-12-20,"[('pygame/pygame', 0.7130681872367859, 'gamedev', 2), ('lordmauve/pgzero', 0.5067479610443115, 'gamedev', 1)]",26,1.0,,0.44,3,3,112,1,0,33,33,3.0,2.0,90.0,0.7,25
990,util,https://github.com/stub42/pytz,[],,[],[],,,,stub42/pytz,pytz,294,80,15,C,,pytz Python historical timezone library and database,stub42,2024-01-13,2016-07-12,394,0.7461928934010152,,pytz Python historical timezone library and database,[],[],2023-09-05,"[('sdispater/pendulum', 0.646413266658783, 'util', 0), ('dateutil/dateutil', 0.621961236000061, 'util', 0), ('arrow-py/arrow', 0.5504962205886841, 'util', 0), ('rjt1990/pyflux', 0.5127301812171936, 'time-series', 0)]",21,3.0,,0.23,3,1,91,4,0,10,10,3.0,2.0,90.0,0.7,25
1096,ml,https://github.com/eleutherai/oslo,[],,[],[],,,,eleutherai/oslo,oslo,169,29,5,Python,https://oslo.eleuther.ai,OSLO: Open Source for Large-scale Optimization,eleutherai,2024-01-10,2022-08-25,74,2.2619502868068833,https://avatars.githubusercontent.com/u/68924597?v=4,OSLO: Open Source for Large-scale Optimization,[],[],2023-09-09,"[('determined-ai/determined', 0.56780606508255, 'ml-ops', 0), ('optuna/optuna', 0.5354965925216675, 'ml', 0), ('tensorflow/tensorflow', 0.5216479301452637, 'ml-dl', 0), ('microsoft/olive', 0.5006281733512878, 'ml', 0)]",50,2.0,,1.33,1,0,17,4,0,7,7,1.0,0.0,90.0,0.0,25
1011,finance,https://github.com/daxm/fmpsdk,[],,[],[],,,,daxm/fmpsdk,fmpsdk,125,48,8,Python,,SDK for Financial Modeling Prep's (FMP) API,daxm,2024-01-12,2020-12-06,164,0.7608695652173914,,SDK for Financial Modeling Prep's (FMP) API,[],[],2024-01-13,"[('pmorissette/ffn', 0.5664402842521667, 'finance', 0)]",15,3.0,,0.4,6,6,38,0,0,0,0,6.0,8.0,90.0,1.3,25
1658,data,https://github.com/unstructured-io/pipeline-sec-filings,"['unstructured', 'sec', 'pipeline']",,[],[],,,,unstructured-io/pipeline-sec-filings,pipeline-sec-filings,119,21,12,Jupyter Notebook,,Preprocessing pipeline notebooks and API supporting text extraction from SEC documents,unstructured-io,2024-01-04,2022-09-27,70,1.7,https://avatars.githubusercontent.com/u/108372208?v=4,Preprocessing pipeline notebooks and API supporting text extraction from SEC documents,[],"['pipeline', 'sec', 'unstructured']",2023-10-02,"[('linealabs/lineapy', 0.6072432994842529, 'jupyter', 0), ('unstructured-io/unstructured-api', 0.5717188715934753, 'data', 1), ('paperswithcode/sota-extractor', 0.537769615650177, 'data', 0)]",15,5.0,,0.4,14,9,16,3,0,0,0,14.0,9.0,90.0,0.6,25
1756,ml,https://github.com/rom1504/embedding-reader,"['filesystem', 'embeddings']",,[],[],,,,rom1504/embedding-reader,embedding-reader,77,16,4,Python,,Efficiently read embedding in streaming from any filesystem,rom1504,2024-01-09,2022-02-27,100,0.7678062678062678,,Efficiently read embedding in streaming from any filesystem,[],"['embeddings', 'filesystem']",2024-01-11,"[('vhranger/nodevectors', 0.53115314245224, 'viz', 0)]",8,3.0,,0.19,8,8,23,0,3,12,3,8.0,10.0,90.0,1.2,25
1723,study,https://github.com/giswqs/geog-414,[],,[],[],,,,giswqs/geog-414,geog-414,66,16,7,HTML,https://geog-414.gishub.org,A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee,giswqs,2023-12-31,2023-08-16,23,2.7664670658682633,,A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee,"['database', 'earthengine', 'geospatial', 'postgis']","['database', 'earthengine', 'geospatial', 'postgis']",2023-12-04,"[('apache/incubator-sedona', 0.560769259929657, 'gis', 1)]",1,1.0,,1.08,1,1,5,1,0,0,0,1.0,4.0,90.0,4.0,25
1226,util,https://github.com/joowani/binarytree,[],,[],[],,,,joowani/binarytree,binarytree,1796,173,46,Python,http://binarytree.readthedocs.io,Python Library for Studying Binary Trees,joowani,2024-01-12,2016-09-20,384,4.677083333333333,,Python Library for Studying Binary Trees,"['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise']","['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise']",2022-06-28,"[('keon/algorithms', 0.6219033002853394, 'util', 2), ('krzjoa/awesome-python-data-science', 0.5454331636428833, 'study', 0), ('pandas-dev/pandas', 0.5415753722190857, 'pandas', 0), ('thealgorithms/python', 0.5343596935272217, 'study', 2), ('pyparsing/pyparsing', 0.5169668197631836, 'util', 0)]",9,1.0,,0.0,1,0,89,19,0,2,2,1.0,0.0,90.0,0.0,24
1131,ml,https://github.com/scikit-learn-contrib/lightning,[],,[],[],,,,scikit-learn-contrib/lightning,lightning,1695,215,38,Python,https://contrib.scikit-learn.org/lightning/,"Large-scale linear classification, regression and ranking in Python",scikit-learn-contrib,2024-01-11,2012-01-11,628,2.6953657428441615,https://avatars.githubusercontent.com/u/17349883?v=4,"Large-scale linear classification, regression and ranking in Python",['machine-learning'],['machine-learning'],2022-01-30,"[('scikit-learn/scikit-learn', 0.6304967403411865, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.6158003807067871, 'ml', 1), ('dask/dask-ml', 0.5908797979354858, 'ml', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5669341087341309, 'ml', 1), ('rasbt/mlxtend', 0.5572295188903809, 'ml', 1), ('pycaret/pycaret', 0.5490888953208923, 'ml', 1), ('amzn/pecos', 0.5417201519012451, 'ml', 0), ('lmcinnes/pynndescent', 0.5341041684150696, 'ml', 0), ('huggingface/evaluate', 0.5336165428161621, 'ml', 1), ('ggerganov/ggml', 0.5173808336257935, 'ml', 1), ('ageron/handson-ml2', 0.5109522938728333, 'ml', 0), ('gradio-app/gradio', 0.5077913403511047, 'viz', 1), ('catboost/catboost', 0.5035675764083862, 'ml', 1)]",17,6.0,,0.0,0,0,146,24,0,1,1,0.0,0.0,90.0,0.0,24
144,nlp,https://github.com/plasticityai/magnitude,[],,[],[],,,,plasticityai/magnitude,magnitude,1608,117,38,Python,,"A fast, efficient universal vector embedding utility package.",plasticityai,2024-01-12,2018-02-24,309,5.196675900277008,https://avatars.githubusercontent.com/u/36324344?v=4,"A fast, efficient universal vector embedding utility package.","['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec']","['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec']",2020-07-17,"[('qdrant/fastembed', 0.6370154619216919, 'ml', 1), ('amansrivastava17/embedding-as-service', 0.6195082068443298, 'nlp', 5), ('koaning/whatlies', 0.6194629669189453, 'nlp', 2), ('sebischair/lbl2vec', 0.5967115163803101, 'nlp', 4), ('ddangelov/top2vec', 0.584179162979126, 'nlp', 1), ('jina-ai/vectordb', 0.5780693888664246, 'data', 0), ('chroma-core/chroma', 0.5727373957633972, 'data', 1), ('jina-ai/clip-as-service', 0.5636166334152222, 'nlp', 0), ('huggingface/text-embeddings-inference', 0.5618109703063965, 'llm', 1), ('llmware-ai/llmware', 0.5475419759750366, 'llm', 3), ('jina-ai/finetuner', 0.5438824892044067, 'ml', 0), ('facebookresearch/faiss', 0.5381598472595215, 'ml', 1), ('kagisearch/vectordb', 0.5367014408111572, 'data', 1), ('koaning/embetter', 0.5330700278282166, 'data', 0), ('flairnlp/flair', 0.5328223705291748, 'nlp', 4), ('allenai/allennlp', 0.5297778248786926, 'nlp', 2), ('neuml/txtai', 0.526243269443512, 'nlp', 3), ('muennighoff/sgpt', 0.5156716704368591, 'llm', 0), ('awslabs/dgl-ke', 0.5072051286697388, 'ml', 1), ('extreme-bert/extreme-bert', 0.5061821341514587, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5057362914085388, 'llm', 1), ('google-research/electra', 0.500442385673523, 'ml-dl', 1)]",4,1.0,,0.0,1,0,72,42,0,23,23,1.0,0.0,90.0,0.0,24
541,ml,https://github.com/borealisai/advertorch,[],,[],[],,,,borealisai/advertorch,advertorch,1243,192,27,Jupyter Notebook,,A Toolbox for Adversarial Robustness Research,borealisai,2024-01-09,2018-11-29,269,4.608580508474576,https://avatars.githubusercontent.com/u/38730800?v=4,A Toolbox for Adversarial Robustness Research,"['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox']","['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox']",2022-05-29,"[('cleverhans-lab/cleverhans', 0.7116900086402893, 'ml', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5085124969482422, 'study', 2)]",21,3.0,,0.0,0,0,62,20,0,0,0,0.0,0.0,90.0,0.0,24
447,gis,https://github.com/residentmario/geoplot,[],,[],[],1.0,,,residentmario/geoplot,geoplot,1101,97,35,Python,https://residentmario.github.io/geoplot/index.html,High-level geospatial data visualization library for Python.,residentmario,2024-01-13,2016-06-29,395,2.781306387585709,,High-level geospatial data visualization library for Python.,"['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis']","['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis']",2023-07-05,"[('geopandas/geopandas', 0.7451832890510559, 'gis', 1), ('mwaskom/seaborn', 0.7261803150177002, 'viz', 1), ('gregorhd/mapcompare', 0.7236021161079407, 'gis', 0), ('raphaelquast/eomaps', 0.707546055316925, 'gis', 1), ('holoviz/holoviz', 0.6968002319335938, 'viz', 0), ('holoviz/geoviews', 0.6962321400642395, 'gis', 0), ('opengeos/leafmap', 0.6929628252983093, 'gis', 0), ('contextlab/hypertools', 0.686218798160553, 'ml', 0), ('altair-viz/altair', 0.6802260875701904, 'viz', 0), ('giswqs/geemap', 0.6778126358985901, 'gis', 0), ('man-group/dtale', 0.6714649796485901, 'viz', 0), ('scitools/iris', 0.6692157983779907, 'gis', 0), ('scitools/cartopy', 0.6602010726928711, 'gis', 1), ('holoviz/panel', 0.6484428644180298, 'viz', 1), ('earthlab/earthpy', 0.63930344581604, 'gis', 0), ('artelys/geonetworkx', 0.6383180022239685, 'gis', 0), ('bokeh/bokeh', 0.6318153738975525, 'viz', 0), ('enthought/mayavi', 0.6169453859329224, 'viz', 0), ('holoviz/hvplot', 0.6143922805786133, 'pandas', 0), ('holoviz/spatialpandas', 0.6135755777359009, 'pandas', 1), ('kanaries/pygwalker', 0.6120375394821167, 'pandas', 1), ('matplotlib/matplotlib', 0.6070235967636108, 'viz', 1), ('makepath/xarray-spatial', 0.59319007396698, 'gis', 1), ('has2k1/plotnine', 0.5918059349060059, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5889087915420532, 'viz', 0), ('plotly/plotly.py', 0.5845940709114075, 'viz', 0), ('pysal/pysal', 0.579075276851654, 'gis', 0), ('pyproj4/pyproj', 0.5774697065353394, 'gis', 0), ('visgl/deck.gl', 0.5753275156021118, 'viz', 0), ('dfki-ric/pytransform3d', 0.5743772983551025, 'math', 1), ('anitagraser/movingpandas', 0.5739251375198364, 'gis', 1), ('pandas-dev/pandas', 0.5717509984970093, 'pandas', 0), ('graphistry/pygraphistry', 0.56211256980896, 'data', 0), ('jakevdp/pythondatasciencehandbook', 0.5592259168624878, 'study', 1), ('cuemacro/chartpy', 0.5584993958473206, 'viz', 1), ('wesm/pydata-book', 0.5581743121147156, 'study', 0), ('lux-org/lux', 0.5569419860839844, 'viz', 0), ('plotly/dash', 0.5490462779998779, 'viz', 0), ('vispy/vispy', 0.5477085709571838, 'viz', 0), ('marcomusy/vedo', 0.5391072034835815, 'viz', 0), ('vaexio/vaex', 0.5356773734092712, 'perf', 0), ('marceloprates/prettymaps', 0.5294914245605469, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5270797610282898, 'jupyter', 0), ('imageio/imageio', 0.525743305683136, 'util', 0), ('pyvista/pyvista', 0.5253320336341858, 'viz', 0), ('nomic-ai/deepscatter', 0.5221992135047913, 'viz', 0), ('opengeos/segment-geospatial', 0.5210736989974976, 'gis', 0), ('osgeo/gdal', 0.5153622627258301, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.5134770274162292, 'study', 0), ('eleutherai/pyfra', 0.506131649017334, 'ml', 0), ('vizzuhq/ipyvizzu', 0.5058284997940063, 'jupyter', 0), ('matplotlib/mplfinance', 0.5038678050041199, 'finance', 1), ('alexmojaki/heartrate', 0.5028942823410034, 'debug', 0), ('mckinsey/vizro', 0.5022686719894409, 'viz', 0), ('federicoceratto/dashing', 0.5019742846488953, 'term', 0), ('uber/h3-py', 0.500649094581604, 'gis', 0)]",6,2.0,,0.04,1,0,92,6,0,3,3,1.0,0.0,90.0,0.0,24
1580,data,https://github.com/brettkromkamp/contextualise,['knowledge-graph'],,[],[],,,,brettkromkamp/contextualise,contextualise,1023,43,26,Python,https://contextualise.dev/,Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources,brettkromkamp,2024-01-04,2019-04-22,249,4.106077981651376,,Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources,"['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs']","['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs']",2023-09-30,"[('wagtail/wagtail', 0.5667254328727722, 'web', 0), ('indico/indico', 0.5198062658309937, 'web', 0), ('zenodo/zenodo', 0.5124539136886597, 'util', 0), ('airbnb/knowledge-repo', 0.5030013918876648, 'data', 0)]",5,2.0,,0.37,0,0,58,4,0,0,0,0.0,0.0,90.0,0.0,24
1601,ml-dl,https://github.com/whitead/dmol-book,['molecules'],,[],[],,,,whitead/dmol-book,dmol-book,553,108,17,Jupyter Notebook,https://dmol.pub,Deep learning for molecules and materials book,whitead,2024-01-04,2020-08-19,179,3.074662430500397,,Deep learning for molecules and materials book,"['chemistry', 'deep-learning', 'materials-informatics']","['chemistry', 'deep-learning', 'materials-informatics', 'molecules']",2023-07-02,"[('deepmodeling/deepmd-kit', 0.702418863773346, 'sim', 1), ('google-deepmind/materials_discovery', 0.5389830470085144, 'sim', 0), ('mrdbourke/pytorch-deep-learning', 0.5192140340805054, 'study', 1), ('udacity/deep-learning-v2-pytorch', 0.5150943994522095, 'study', 1), ('d2l-ai/d2l-en', 0.5019837021827698, 'study', 1)]",19,5.0,,0.08,0,0,41,6,0,0,0,0.0,0.0,90.0,0.0,24
894,util,https://github.com/ofek/pypinfo,[],,[],[],,,,ofek/pypinfo,pypinfo,386,38,14,Python,,Easily view PyPI download statistics via Google's BigQuery.,ofek,2024-01-06,2017-05-13,350,1.1015083571137383,,Easily view PyPI download statistics via Google's BigQuery.,"['bigquery', 'statistics']","['bigquery', 'statistics']",2023-10-05,"[('hugovk/pypistats', 0.7068412899971008, 'util', 1), ('googleapis/python-bigquery', 0.5070151686668396, 'data', 0)]",11,7.0,,0.19,0,0,81,3,0,4,4,0.0,0.0,90.0,0.0,24
1730,util,https://github.com/grantjenks/blue,['code-quality'],,[],[],,,,grantjenks/blue,blue,370,21,9,Python,https://blue.readthedocs.io/,The slightly less uncompromising Python code formatter.,grantjenks,2024-01-12,2020-09-02,177,2.0803212851405624,,The slightly less uncompromising Python code formatter.,"['autopep8', 'black', 'code', 'codeformatter', 'formatter', 'gofmt', 'pyfmt', 'yapf']","['autopep8', 'black', 'code', 'code-quality', 'codeformatter', 'formatter', 'gofmt', 'pyfmt', 'yapf']",2022-10-27,"[('psf/black', 0.9170903563499451, 'util', 7), ('hhatto/autopep8', 0.7624608278274536, 'util', 2), ('google/yapf', 0.749129056930542, 'util', 2), ('astral-sh/ruff', 0.6735846996307373, 'util', 1), ('pycqa/flake8', 0.6205138564109802, 'util', 1), ('google/pytype', 0.6101509928703308, 'typing', 1), ('rubik/radon', 0.6026664972305298, 'util', 0), ('pygments/pygments', 0.5901092886924744, 'util', 0), ('instagram/monkeytype', 0.5733786225318909, 'typing', 1), ('landscapeio/prospector', 0.5565185546875, 'util', 0), ('nedbat/coveragepy', 0.5528746843338013, 'testing', 0), ('pypy/pypy', 0.5494239926338196, 'util', 0), ('python/mypy', 0.5406695604324341, 'typing', 1), ('pycqa/pylint-django', 0.5386630892753601, 'util', 0), ('agronholm/typeguard', 0.5374644994735718, 'typing', 1), ('willmcgugan/rich', 0.5356051325798035, 'term', 0), ('google/latexify_py', 0.5342947244644165, 'util', 0), ('hoffstadt/dearpygui', 0.5329349040985107, 'gui', 0), ('eugeneyan/python-collab-template', 0.5308860540390015, 'template', 0), ('microsoft/pyright', 0.529589056968689, 'typing', 1), ('nbqa-dev/nbqa', 0.5135296583175659, 'jupyter', 2), ('facebook/pyre-check', 0.5125738382339478, 'typing', 1), ('danielnoord/pydocstringformatter', 0.5090311169624329, 'util', 1), ('pytoolz/toolz', 0.5073704719543457, 'util', 0), ('cython/cython', 0.5051524043083191, 'util', 0), ('hadialqattan/pycln', 0.5049071311950684, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5027827024459839, 'study', 0), ('microsoft/pycodegpt', 0.5024159550666809, 'llm', 0), ('pycqa/isort', 0.5017447471618652, 'util', 2)]",11,3.0,,0.0,2,0,41,15,0,3,3,2.0,2.0,90.0,1.0,24
1300,diffusion,https://github.com/albarji/mixture-of-diffusers,[],,['2302.02412'],[],,,,albarji/mixture-of-diffusers,mixture-of-diffusers,356,34,7,Python,,Mixture of Diffusers for scene composition and high resolution image generation,albarji,2024-01-13,2022-08-23,75,4.746666666666667,,Mixture of Diffusers for scene composition and high resolution image generation,"['ai', 'computer-vision', 'diffusion-models', 'stable-diffusion']","['ai', 'computer-vision', 'diffusion-models', 'stable-diffusion']",2023-05-21,"[('compvis/latent-diffusion', 0.6906744837760925, 'diffusion', 0), ('stability-ai/stablediffusion', 0.690674364566803, 'diffusion', 0), ('huggingface/diffusers', 0.6519318222999573, 'diffusion', 1), ('carson-katri/dream-textures', 0.6328878998756409, 'diffusion', 2), ('nateraw/stable-diffusion-videos', 0.5622400045394897, 'diffusion', 1), ('compvis/stable-diffusion', 0.5530049204826355, 'diffusion', 0), ('openai/glide-text2im', 0.5093878507614136, 'diffusion', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5000970959663391, 'web', 0)]",3,1.0,,0.81,2,1,17,8,6,6,6,2.0,0.0,90.0,0.0,24
210,util,https://github.com/tiangolo/poetry-version-plugin,[],,[],[],,,,tiangolo/poetry-version-plugin,poetry-version-plugin,332,30,4,Python,,Poetry plugin for dynamically extracting the package version from a __version__ variable or a Git tag.,tiangolo,2024-01-13,2021-05-27,139,2.376278118609407,,Poetry plugin for dynamically extracting the package version from a __version__ variable or a Git tag.,"['packaging', 'packaging-for-pypi', 'python-poetry']","['packaging', 'packaging-for-pypi', 'python-poetry']",2023-11-04,"[('mtkennerly/poetry-dynamic-versioning', 0.6403163075447083, 'util', 0), ('python-poetry/poetry', 0.6390268206596375, 'util', 1), ('mitsuhiko/rye', 0.5633344054222107, 'util', 1), ('python-poetry/install.python-poetry.org', 0.5517739653587341, 'util', 0), ('indygreg/pyoxidizer', 0.5493191480636597, 'util', 1), ('thoth-station/micropipenv', 0.5446652173995972, 'util', 0), ('pdm-project/pdm', 0.54221111536026, 'util', 1), ('pypi/warehouse', 0.537349283695221, 'util', 0), ('tezromach/python-package-template', 0.5260007977485657, 'template', 0), ('pypa/flit', 0.519473671913147, 'util', 1), ('pypa/hatch', 0.5143115520477295, 'util', 1)]",3,1.0,,0.15,19,5,32,2,1,1,1,19.0,15.0,90.0,0.8,24
452,gis,https://github.com/openaddresses/pyesridump,[],,[],[],,,,openaddresses/pyesridump,pyesridump,294,68,14,Python,,Scrapes an ESRI MapServer REST endpoint to spit out more generally-usable geodata.,openaddresses,2024-01-11,2013-12-06,529,0.5551659023469112,https://avatars.githubusercontent.com/u/6895392?v=4,Scrapes an ESRI MapServer REST endpoint to spit out more generally-usable geodata.,[],[],2023-09-26,"[('nasdaq/data-link-python', 0.5270365476608276, 'finance', 0)]",14,5.0,,0.13,4,1,123,4,1,2,1,4.0,5.0,90.0,1.2,24
465,math,https://github.com/lukaszahradnik/pyneuralogic,[],,[],[],,,,lukaszahradnik/pyneuralogic,PyNeuraLogic,261,19,6,Python,https://pyneuralogic.readthedocs.io/,PyNeuraLogic lets you use Python to create Differentiable Logic Programs,lukaszahradnik,2024-01-06,2020-12-06,164,1.588695652173913,,PyNeuraLogic lets you use Python to create Differentiable Logic Programs,"['deep-learning', 'differentiable-programming', 'geometric-deep-learning', 'graph-neural-networks', 'logic-programming', 'machine-learning', 'pytorch', 'relational-learning']","['deep-learning', 'differentiable-programming', 'geometric-deep-learning', 'graph-neural-networks', 'logic-programming', 'machine-learning', 'pytorch', 'relational-learning']",2024-01-10,"[('pypy/pypy', 0.5711145997047424, 'util', 0), ('google/pyglove', 0.5553054809570312, 'util', 1), ('explosion/thinc', 0.5442025065422058, 'ml-dl', 3), ('gradio-app/gradio', 0.5357550978660583, 'viz', 2), ('explosion/spacy', 0.5333031415939331, 'nlp', 2), ('evhub/coconut', 0.5328680872917175, 'util', 0), ('pyro-ppl/pyro', 0.5193347930908203, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.5184630155563354, 'ml', 1), ('pytorch/rl', 0.5153552889823914, 'ml-rl', 2), ('dylanhogg/awesome-python', 0.5141112804412842, 'study', 2), ('cython/cython', 0.5075069665908813, 'util', 0), ('ml-tooling/opyrator', 0.5073249936103821, 'viz', 1), ('pytoolz/toolz', 0.5010553002357483, 'util', 0)]",3,2.0,,0.96,0,0,38,0,9,14,9,0.0,0.0,90.0,0.0,24
1184,llm,https://github.com/ai21labs/in-context-ralm,"['retrieval-augmentation', 'language-model']",In-Context Retrieval-Augmented Language Models,[],[],,,,ai21labs/in-context-ralm,in-context-ralm,199,19,5,Python,,,ai21labs,2024-01-12,2023-01-26,52,3.7750677506775068,https://avatars.githubusercontent.com/u/33798954?v=4,In-Context Retrieval-Augmented Language Models,[],"['language-model', 'retrieval-augmentation']",2023-12-20,"[('intellabs/fastrag', 0.6507914066314697, 'nlp', 1), ('srush/minichain', 0.5984602570533752, 'llm', 1), ('luohongyin/sail', 0.5805896520614624, 'llm', 1), ('facebookresearch/dpr-scale', 0.5703505873680115, 'nlp', 0), ('paddlepaddle/rocketqa', 0.5692198872566223, 'nlp', 0), ('freedomintelligence/llmzoo', 0.5410796403884888, 'llm', 1), ('ai21labs/lm-evaluation', 0.5293609499931335, 'llm', 1), ('muennighoff/sgpt', 0.5289058685302734, 'llm', 1), ('openlmlab/moss', 0.5220905542373657, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5203155875205994, 'llm', 1), ('ddangelov/top2vec', 0.5187664031982422, 'nlp', 0), ('ukplab/sentence-transformers', 0.5168312788009644, 'nlp', 0), ('sebischair/lbl2vec', 0.5090823173522949, 'nlp', 0), ('openlmlab/leval', 0.5012268424034119, 'llm', 1)]",1,0.0,,0.35,5,5,12,1,0,0,0,5.0,10.0,90.0,2.0,24
399,study,https://github.com/dylanhogg/awesome-python,['awesome'],,[],['<hide>'],,,,dylanhogg/awesome-python,awesome-python,198,15,7,HTML,https://www.awesomepython.org,"🐍 Hand-picked awesome Python libraries and frameworks, with an emphasis on data and machine learning, organised by category",dylanhogg,2024-01-12,2020-06-20,188,1.0507960576194086,,"🐍 Hand-picked awesome Python libraries and frameworks, with an emphasis on data and machine learning, organised by category","['awesome', 'awesome-list', 'awesome-python', 'chatgpt', 'data', 'data-science', 'deep-learning', 'jupyter', 'machine-learning', 'natural-language-processing', 'nlp', 'open-source', 'pandas']","['awesome', 'awesome-list', 'awesome-python', 'chatgpt', 'data', 'data-science', 'deep-learning', 'jupyter', 'machine-learning', 'natural-language-processing', 'nlp', 'open-source', 'pandas']",2024-01-14,"[('krzjoa/awesome-python-data-science', 0.7601116299629211, 'study', 6), ('timofurrer/awesome-asyncio', 0.7224661111831665, 'study', 2), ('gradio-app/gradio', 0.6672014594078064, 'viz', 3), ('christoschristofidis/awesome-deep-learning', 0.6466501355171204, 'study', 4), ('fastai/fastcore', 0.6330905556678772, 'util', 0), ('pandas-dev/pandas', 0.6308945417404175, 'pandas', 2), ('plotly/dash', 0.619914174079895, 'viz', 2), ('merantix-momentum/squirrel-core', 0.6165292263031006, 'ml', 5), ('paddlepaddle/paddlenlp', 0.6097233295440674, 'llm', 1), ('rasbt/mlxtend', 0.6053752899169922, 'ml', 2), ('featurelabs/featuretools', 0.6018655300140381, 'ml', 2), ('polyaxon/datatile', 0.6012967228889465, 'pandas', 2), ('pytoolz/toolz', 0.5974959135055542, 'util', 0), ('trananhkma/fucking-awesome-python', 0.5963839888572693, 'study', 1), ('ta-lib/ta-lib-python', 0.59423828125, 'finance', 0), ('holoviz/panel', 0.5920050144195557, 'viz', 1), ('pycaret/pycaret', 0.5809772610664368, 'ml', 2), ('goldmansachs/gs-quant', 0.5735217928886414, 'finance', 0), ('pypy/pypy', 0.5712080597877502, 'util', 0), ('firmai/industry-machine-learning', 0.5685208439826965, 'study', 2), ('masoniteframework/masonite', 0.5680248141288757, 'web', 0), ('eventual-inc/daft', 0.5679042935371399, 'pandas', 3), ('eleutherai/pyfra', 0.5674682259559631, 'ml', 0), ('dylanhogg/crazy-awesome-crypto', 0.5653820037841797, 'crypto', 3), ('fchollet/deep-learning-with-python-notebooks', 0.5646929144859314, 'study', 0), ('willmcgugan/textual', 0.5644351243972778, 'term', 0), ('huggingface/huggingface_hub', 0.5635517835617065, 'ml', 3), ('1200wd/bitcoinlib', 0.5614901185035706, 'crypto', 0), ('cython/cython', 0.5576968193054199, 'util', 0), ('dagworks-inc/hamilton', 0.5567782521247864, 'ml-ops', 3), ('pallets/flask', 0.5550673604011536, 'web', 0), ('tensorly/tensorly', 0.5549004673957825, 'ml-dl', 1), ('explosion/thinc', 0.5546610951423645, 'ml-dl', 4), ('klen/muffin', 0.5525697469711304, 'web', 0), ('huggingface/datasets', 0.5519663095474243, 'nlp', 5), ('jovianml/opendatasets', 0.5494063496589661, 'data', 2), ('plotly/plotly.py', 0.549170970916748, 'viz', 0), ('clips/pattern', 0.5490561723709106, 'nlp', 2), ('wesm/pydata-book', 0.548250138759613, 'study', 0), ('explosion/spacy', 0.5455989241600037, 'nlp', 5), ('man-group/dtale', 0.5450599193572998, 'viz', 2), ('probml/pyprobml', 0.5428842306137085, 'ml', 1), ('vaexio/vaex', 0.5425941944122314, 'perf', 2), ('huggingface/transformers', 0.5408051609992981, 'nlp', 4), ('evhub/coconut', 0.5391582250595093, 'util', 0), ('rasahq/rasa', 0.536875307559967, 'llm', 3), ('kubeflow-kale/kale', 0.5359322428703308, 'ml-ops', 1), ('hoffstadt/dearpygui', 0.5330666899681091, 'gui', 0), ('ibis-project/ibis', 0.5329792499542236, 'data', 1), ('ml-tooling/opyrator', 0.532485842704773, 'viz', 1), ('reloadware/reloadium', 0.5307287573814392, 'profiling', 2), ('ageron/handson-ml2', 0.5303460955619812, 'ml', 0), ('python/cpython', 0.5265739560127258, 'util', 0), ('ranaroussi/quantstats', 0.5245246887207031, 'finance', 0), ('r0x0r/pywebview', 0.5234578251838684, 'gui', 0), ('falconry/falcon', 0.5216991901397705, 'web', 0), ('kubeflow/fairing', 0.5207975506782532, 'ml-ops', 0), ('unionai-oss/pandera', 0.5203728079795837, 'pandas', 1), ('imageio/imageio', 0.5201471447944641, 'util', 0), ('thealgorithms/python', 0.5201041102409363, 'study', 0), ('pyparsing/pyparsing', 0.5186076164245605, 'util', 0), ('aws/sagemaker-python-sdk', 0.5184698104858398, 'ml', 1), ('bottlepy/bottle', 0.5162600874900818, 'web', 0), ('skops-dev/skops', 0.5161980986595154, 'ml-ops', 1), ('online-ml/river', 0.5161080360412598, 'ml', 2), ('alphasecio/langchain-examples', 0.5161072611808777, 'llm', 0), ('pygamelib/pygamelib', 0.5159634947776794, 'gamedev', 0), ('webpy/webpy', 0.5156476497650146, 'web', 0), ('google/pyglove', 0.5149620175361633, 'util', 1), ('scrapy/scrapy', 0.5145118832588196, 'data', 0), ('google/tf-quant-finance', 0.5142082571983337, 'finance', 0), ('mljar/mljar-supervised', 0.5141728520393372, 'ml', 2), ('lukaszahradnik/pyneuralogic', 0.5141112804412842, 'math', 2), ('scikit-learn/scikit-learn', 0.5138348937034607, 'ml', 2), ('reflex-dev/reflex', 0.5130147337913513, 'web', 1), ('backtick-se/cowait', 0.5124981999397278, 'util', 1), ('sloria/textblob', 0.5117655992507935, 'nlp', 2), ('malloydata/malloy-py', 0.5110769867897034, 'data', 1), ('indico/indico', 0.5107914209365845, 'web', 0), ('adap/flower', 0.510422945022583, 'ml-ops', 2), ('scikit-learn-contrib/imbalanced-learn', 0.5090502500534058, 'ml', 2), ('tensorflow/tensorflow', 0.5077972412109375, 'ml-dl', 2), ('uber/petastorm', 0.5067077875137329, 'data', 2), ('rasbt/machine-learning-book', 0.5065024495124817, 'study', 2), ('mlflow/mlflow', 0.5060958862304688, 'ml-ops', 1), ('openai/openai-python', 0.5051689147949219, 'util', 0), ('nevronai/metisfl', 0.5050832033157349, 'ml', 2), ('tkrabel/bamboolib', 0.5048226118087769, 'pandas', 1), ('pylons/pyramid', 0.5040434002876282, 'web', 0), ('pemistahl/lingua-py', 0.5016226172447205, 'nlp', 2), ('tensorlayer/tensorlayer', 0.5007988810539246, 'ml-rl', 1), ('rapidsai/cudf', 0.5004510283470154, 'pandas', 2), ('stanfordnlp/dspy', 0.5004346966743469, 'llm', 0)]",2,1.0,,2.81,1,1,43,0,0,3,3,1.0,1.0,90.0,1.0,24
1574,llm,https://github.com/facebookresearch/shepherd,"['language-model', 'critic']",,[],[],,,,facebookresearch/shepherd,Shepherd,189,9,5,Jupyter Notebook,,This is the repo for the paper Shepherd -- A Critic for Language Model Generation,facebookresearch,2024-01-10,2023-07-29,26,7.151351351351352,https://avatars.githubusercontent.com/u/16943930?v=4,This is the repo for the paper Shepherd -- A Critic for Language Model Generation,[],"['critic', 'language-model']",2023-08-10,"[('yueyu1030/attrprompt', 0.572687566280365, 'llm', 0), ('openai/finetune-transformer-lm', 0.5617777705192566, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5500012636184692, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5445735454559326, 'llm', 1), ('huggingface/text-generation-inference', 0.5433861017227173, 'llm', 0), ('ai21labs/lm-evaluation', 0.5377524495124817, 'llm', 1), ('lvwerra/trl', 0.5294312834739685, 'llm', 0), ('hannibal046/awesome-llm', 0.5266714692115784, 'study', 1), ('neulab/prompt2model', 0.52313631772995, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5185016393661499, 'llm', 1), ('guidance-ai/guidance', 0.5177493095397949, 'llm', 1), ('lianjiatech/belle', 0.5161774158477783, 'llm', 0), ('openai/gpt-2', 0.5036864280700684, 'llm', 0), ('juncongmoo/pyllama', 0.5019182562828064, 'llm', 0), ('reasoning-machines/pal', 0.5001876950263977, 'llm', 1)]",4,3.0,,0.1,0,0,6,5,0,0,0,0.0,0.0,90.0,0.0,24
1715,security,https://github.com/aswinnnn/pyscan,['code-quality'],,[],[],,,,aswinnnn/pyscan,pyscan,171,4,4,Rust,,"python dependency vulnerability scanner, written in Rust.",aswinnnn,2024-01-09,2023-05-16,37,4.621621621621622,,"python dependency vulnerability scanner, written in Rust.","['cve', 'hacking', 'ossf', 'osv', 'rust', 'security', 'security-audit', 'security-automation', 'security-tools', 'vulnerabilities', 'vulnerability', 'vulnerability-scanners']","['code-quality', 'cve', 'hacking', 'ossf', 'osv', 'rust', 'security', 'security-audit', 'security-automation', 'security-tools', 'vulnerabilities', 'vulnerability', 'vulnerability-scanners']",2023-10-29,"[('astral-sh/ruff', 0.6466583609580994, 'util', 2), ('rustpython/rustpython', 0.6372954249382019, 'util', 1), ('pyupio/safety', 0.6276425123214722, 'security', 2), ('trailofbits/pip-audit', 0.6042621731758118, 'security', 2), ('pyo3/maturin', 0.552875280380249, 'util', 1), ('facebook/pyre-check', 0.5478062033653259, 'typing', 2), ('pola-rs/polars', 0.5435234308242798, 'pandas', 1), ('aquasecurity/trivy', 0.5372655987739563, 'security', 4), ('pycqa/bandit', 0.5267046093940735, 'security', 3), ('nedbat/coveragepy', 0.525583028793335, 'testing', 0), ('pyca/cryptography', 0.5230307579040527, 'util', 0), ('rubik/radon', 0.5182473659515381, 'util', 0), ('python-odin/odin', 0.5133723616600037, 'util', 0), ('mdmzfzl/neetcode-solutions', 0.5120133757591248, 'study', 1), ('pyo3/rust-numpy', 0.5087146759033203, 'util', 1), ('tiiuae/sbomnix', 0.5072173476219177, 'util', 2), ('ta-lib/ta-lib-python', 0.501020610332489, 'finance', 0), ('google/pytype', 0.5005221366882324, 'typing', 1)]",2,1.0,,2.44,1,0,8,3,4,9,4,1.0,0.0,90.0,0.0,24
601,web,https://github.com/pyscript/pyscript-cli,[],,[],[],,,,pyscript/pyscript-cli,pyscript-cli,156,19,13,Python,,A CLI for PyScript,pyscript,2024-01-04,2022-05-01,91,1.7089201877934272,https://avatars.githubusercontent.com/u/100553281?v=4,A CLI for PyScript,[],[],2023-08-17,"[('tiangolo/typer', 0.6438218355178833, 'term', 0), ('google/python-fire', 0.6401512026786804, 'term', 0), ('pyscript/pyscript', 0.606472909450531, 'web', 0), ('python/cpython', 0.5967158079147339, 'util', 0), ('kellyjonbrazil/jc', 0.5959556102752686, 'util', 0), ('pytoolz/toolz', 0.5934836864471436, 'util', 0), ('python-poetry/cleo', 0.5874193906784058, 'term', 0), ('pypy/pypy', 0.5749022364616394, 'util', 0), ('urwid/urwid', 0.5667005181312561, 'term', 0), ('hoffstadt/dearpygui', 0.5643460750579834, 'gui', 0), ('pexpect/pexpect', 0.5519907474517822, 'util', 0), ('renpy/renpy', 0.5380860567092896, 'viz', 0), ('jquast/blessed', 0.5302552580833435, 'term', 0), ('microsoft/playwright-python', 0.5237164497375488, 'testing', 0), ('eleutherai/pyfra', 0.5200616717338562, 'ml', 0), ('willmcgugan/textual', 0.5197693109512329, 'term', 0), ('textualize/trogon', 0.516979992389679, 'term', 0), ('pyston/pyston', 0.5076680183410645, 'util', 0), ('pygamelib/pygamelib', 0.5070560574531555, 'gamedev', 0), ('prompt-toolkit/ptpython', 0.5022722482681274, 'util', 0)]",10,3.0,,0.83,2,0,21,5,0,3,3,2.0,2.0,90.0,1.0,24
1410,math,https://github.com/lean-dojo/reprover,['retrieval-augmentation'],,[],[],,,,lean-dojo/reprover,ReProver,134,19,9,Python,https://leandojo.org,Retrieval-Augmented Theorem Provers for Lean,lean-dojo,2024-01-09,2023-03-16,45,2.93125,https://avatars.githubusercontent.com/u/136513911?v=4,Retrieval-Augmented Theorem Provers for Lean,"['lean', 'machine-learning', 'theorem-proving']","['lean', 'machine-learning', 'retrieval-augmentation', 'theorem-proving']",2023-12-26,"[('lean-dojo/leandojo', 0.6379106640815735, 'math', 3)]",6,1.0,,1.02,11,9,10,1,0,0,0,11.0,8.0,90.0,0.7,24
579,gis,https://github.com/developmentseed/cogeo-mosaic,[],,[],[],,,,developmentseed/cogeo-mosaic,cogeo-mosaic,92,25,8,Python,https://developmentseed.org/cogeo-mosaic/,Create and use COG mosaic based on mosaicJSON,developmentseed,2023-12-12,2019-05-14,246,0.37398373983739835,https://avatars.githubusercontent.com/u/92384?v=4,Create and use COG mosaic based on mosaicJSON,[],[],2023-12-06,[],11,7.0,,0.48,2,2,57,1,0,10,10,2.0,0.0,90.0,0.0,24
1676,util,https://github.com/lyz-code/autoimport,[],,[],[],,,,lyz-code/autoimport,autoimport,80,25,2,Python,https://lyz-code.github.io/autoimport,Autoimport automatically fixes wrong import statements.,lyz-code,2024-01-12,2020-10-16,171,0.466278101582015,,Autoimport automatically fixes wrong import statements.,[],[],2023-11-23,[],15,3.0,,0.25,5,2,39,2,0,13,13,5.0,4.0,90.0,0.8,24
1714,ml,https://github.com/dylanhogg/llmgraph,[],,[],[],,,,dylanhogg/llmgraph,llmgraph,22,2,4,Python,,Create knowledge graphs with LLMs,dylanhogg,2024-01-14,2023-10-05,16,1.3162393162393162,,Create knowledge graphs with LLMs,"['chatgpt', 'gephi', 'knowledge-graph', 'large-language-model', 'llama2', 'llm']","['chatgpt', 'gephi', 'knowledge-graph', 'large-language-model', 'llama2', 'llm']",2024-01-04,"[('mooler0410/llmspracticalguide', 0.6462394595146179, 'study', 0), ('nomic-ai/gpt4all', 0.6398856043815613, 'llm', 0), ('microsoft/autogen', 0.6262103915214539, 'llm', 1), ('awslabs/dgl-ke', 0.6202223300933838, 'ml', 1), ('hwchase17/langchain', 0.6144862174987793, 'llm', 0), ('spcl/graph-of-thoughts', 0.6043452024459839, 'llm', 1), ('young-geng/easylm', 0.6026452779769897, 'llm', 0), ('run-llama/rags', 0.5997524857521057, 'llm', 2), ('zilliztech/gptcache', 0.5875337719917297, 'llm', 2), ('shishirpatil/gorilla', 0.5875014662742615, 'llm', 2), ('deepset-ai/haystack', 0.5867999792098999, 'llm', 1), ('langchain-ai/langgraph', 0.5856835842132568, 'llm', 0), ('bobazooba/xllm', 0.5852743983268738, 'llm', 3), ('explosion/spacy-llm', 0.5829962491989136, 'llm', 1), ('zjunlp/deepke', 0.5825223922729492, 'ml', 1), ('confident-ai/deepeval', 0.581521213054657, 'testing', 2), ('accenture/ampligraph', 0.5810795426368713, 'data', 1), ('thudm/chatglm2-6b', 0.580618679523468, 'llm', 1), ('lupantech/chameleon-llm', 0.574148416519165, 'llm', 2), ('salesforce/xgen', 0.5694348812103271, 'llm', 1), ('lianjiatech/belle', 0.5655115842819214, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5651019811630249, 'study', 1), ('eth-sri/lmql', 0.5644213557243347, 'llm', 1), ('salesforce/codet5', 0.563266396522522, 'nlp', 0), ('intel/intel-extension-for-transformers', 0.5522487163543701, 'perf', 1), ('guidance-ai/guidance', 0.5485440492630005, 'llm', 1), ('deepgraphlearning/ultra', 0.5485401749610901, 'ml', 1), ('embedchain/embedchain', 0.5462237596511841, 'llm', 2), ('hiyouga/llama-factory', 0.5430908203125, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5430907607078552, 'llm', 1), ('next-gpt/next-gpt', 0.5404556393623352, 'llm', 2), ('juncongmoo/pyllama', 0.5386449694633484, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5368094444274902, 'llm', 1), ('mlc-ai/web-llm', 0.5363897085189819, 'llm', 2), ('hannibal046/awesome-llm', 0.5356044769287109, 'study', 0), ('hegelai/prompttools', 0.5341759324073792, 'llm', 0), ('eugeneyan/open-llms', 0.5341588258743286, 'study', 1), ('llmware-ai/llmware', 0.5296906232833862, 'llm', 0), ('rcgai/simplyretrieve', 0.5281891226768494, 'llm', 0), ('cg123/mergekit', 0.5279366970062256, 'llm', 1), ('eleutherai/the-pile', 0.5278661847114563, 'data', 1), ('nebuly-ai/nebullvm', 0.527693510055542, 'perf', 1), ('microsoft/jarvis', 0.5276885032653809, 'llm', 0), ('li-plus/chatglm.cpp', 0.524939239025116, 'llm', 0), ('fasteval/fasteval', 0.5239781141281128, 'llm', 1), ('nicolas-hbt/pygraft', 0.5217655897140503, 'ml', 1), ('microsoft/promptcraft-robotics', 0.5207331776618958, 'sim', 2), ('epfllm/meditron', 0.5147185921669006, 'llm', 0), ('argilla-io/argilla', 0.5142863988876343, 'nlp', 1), ('microsoft/torchscale', 0.5129197835922241, 'llm', 0), ('oobabooga/text-generation-webui', 0.5120260715484619, 'llm', 0), ('night-chen/toolqa', 0.5115826725959778, 'llm', 0), ('mindsdb/mindsdb', 0.5112169981002808, 'data', 1), ('lm-sys/fastchat', 0.5098823308944702, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5098133087158203, 'llm', 3), ('xtekky/gpt4free', 0.509717583656311, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5081648826599121, 'llm', 0), ('bigscience-workshop/petals', 0.5068194270133972, 'data', 1), ('neuml/txtai', 0.5045521259307861, 'nlp', 1), ('microsoft/promptflow', 0.5038095712661743, 'llm', 2), ('microsoft/vert-papers', 0.5032587647438049, 'nlp', 0), ('ray-project/ray-llm', 0.5024334192276001, 'llm', 1), ('opengenerativeai/genossgpt', 0.5003108382225037, 'llm', 1)]",2,1.0,,1.12,4,3,3,0,0,16,16,4.0,8.0,90.0,2.0,24
1314,study,https://github.com/trananhkma/fucking-awesome-python,"['awesome', 'python']",,[],['<hide>'],,,,trananhkma/fucking-awesome-python,fucking-awesome-python,1941,297,72,,,awesome-python with :octocat: :star: and :fork_and_knife:,trananhkma,2024-01-13,2015-09-19,436,4.44746317512275,,awesome-python with :octocat: ⭐ and 🍴,[],['awesome'],2023-07-16,"[('dylanhogg/awesome-python', 0.5963839888572693, 'study', 1), ('timofurrer/awesome-asyncio', 0.5646189451217651, 'study', 1), ('carpedm20/emoji', 0.5481189489364624, 'util', 0)]",7,1.0,,0.1,0,0,101,6,0,0,0,0.0,0.0,90.0,0.0,23
701,util,https://github.com/brandon-rhodes/python-patterns,[],,[],[],,,,brandon-rhodes/python-patterns,python-patterns,1203,140,312,Python,,Source code behind the python-patterns.guide site by Brandon Rhodes,brandon-rhodes,2024-01-13,2018-01-31,312,3.845205479452055,,Source code behind the python-patterns.guide site by Brandon Rhodes,[],[],2023-12-27,"[('faif/python-patterns', 0.6763890385627747, 'util', 0), ('gerdm/prml', 0.5963156819343567, 'study', 0), ('python/cpython', 0.5899462103843689, 'util', 0), ('amaargiru/pyroad', 0.5854448676109314, 'study', 0), ('landscapeio/prospector', 0.5751134157180786, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.55435711145401, 'study', 0), ('eleutherai/pyfra', 0.5504909157752991, 'ml', 0), ('pytoolz/toolz', 0.5499976277351379, 'util', 0), ('realpython/python-guide', 0.5476312637329102, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5350989699363708, 'study', 0), ('wesm/pydata-book', 0.5279967188835144, 'study', 0), ('ta-lib/ta-lib-python', 0.5266119837760925, 'finance', 0), ('google/latexify_py', 0.5185614824295044, 'util', 0), ('hhatto/autopep8', 0.5134424567222595, 'util', 0), ('sympy/sympy', 0.5133131742477417, 'math', 0), ('sqlalchemy/mako', 0.5129835605621338, 'template', 0), ('probml/pyprobml', 0.5112978219985962, 'ml', 0), ('nedbat/coveragepy', 0.5084773302078247, 'testing', 0), ('xrudelis/pytrait', 0.5037622451782227, 'util', 0)]",4,1.0,,0.02,0,0,72,1,0,0,0,0.0,0.0,90.0,0.0,23
638,util,https://github.com/metachris/logzero,[],,[],[],,,,metachris/logzero,logzero,1031,76,26,Python,https://logzero.readthedocs.io,Robust and effective logging for Python 2 and 3.,metachris,2024-01-04,2017-06-12,346,2.9785390012381345,,Robust and effective logging for Python 2 and 3.,"['logfiles', 'logging', 'logzero']","['logfiles', 'logging', 'logzero']",2021-03-17,"[('delgan/loguru', 0.7877286672592163, 'util', 1), ('alexmojaki/snoop', 0.601128876209259, 'debug', 1), ('salesforce/logai', 0.5321155786514282, 'util', 0)]",12,5.0,,0.0,0,0,80,34,0,2,2,0.0,0.0,90.0,0.0,23
270,crypto,https://github.com/man-c/pycoingecko,[],,[],[],,,,man-c/pycoingecko,pycoingecko,1008,262,31,Python,,Python wrapper for the CoinGecko API,man-c,2024-01-12,2018-08-24,283,3.5546599496221662,,Python wrapper for the CoinGecko API,"['api', 'api-wrapper', 'coingecko', 'crypto', 'cryptocurrency', 'nft', 'nfts', 'wrapper']","['api', 'api-wrapper', 'coingecko', 'crypto', 'cryptocurrency', 'nft', 'nfts', 'wrapper']",2022-10-26,"[('1200wd/bitcoinlib', 0.6038165092468262, 'crypto', 0), ('legrandin/pycryptodome', 0.5711352229118347, 'util', 0), ('pyca/cryptography', 0.5643881559371948, 'util', 0), ('ethereum/web3.py', 0.5624127388000488, 'crypto', 0), ('lukasschwab/arxiv.py', 0.5484018921852112, 'util', 0), ('ta-lib/ta-lib-python', 0.5362427234649658, 'finance', 0), ('pyca/pynacl', 0.5348667502403259, 'util', 0), ('openai/openai-python', 0.5335339307785034, 'util', 0), ('meilisearch/meilisearch-python', 0.5315601229667664, 'data', 1), ('bottlepy/bottle', 0.5294601917266846, 'web', 0), ('primal100/pybitcointools', 0.5288224220275879, 'crypto', 0), ('ccxt/ccxt', 0.5272769927978516, 'crypto', 3), ('cuemacro/findatapy', 0.5231452584266663, 'finance', 0), ('pytoolz/toolz', 0.5226123929023743, 'util', 0), ('masoniteframework/masonite', 0.5191899538040161, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5065068602561951, 'data', 1)]",14,1.0,,0.0,1,0,66,15,0,4,4,1.0,0.0,90.0,0.0,23
1876,sim,https://github.com/hardmaru/estool,[],,[],[],,,,hardmaru/estool,estool,913,162,33,Jupyter Notebook,,Evolution Strategies Tool,hardmaru,2024-01-04,2017-10-29,326,2.7981611208406303,,Evolution Strategies Tool,[],[],2022-01-20,[],9,2.0,,0.0,1,1,76,24,0,0,0,1.0,1.0,90.0,1.0,23
161,nlp,https://github.com/nipunsadvilkar/pysbd,[],,[],[],,,,nipunsadvilkar/pysbd,pySBD,690,76,13,Python,,🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.,nipunsadvilkar,2024-01-13,2017-06-11,346,1.9925742574257426,,🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.,"['rule-based', 'segmentation', 'sentence', 'sentence-boundary-detection', 'sentence-tokenizer']","['rule-based', 'segmentation', 'sentence', 'sentence-boundary-detection', 'sentence-tokenizer']",2021-02-11,"[('pemistahl/lingua-py', 0.5244992971420288, 'nlp', 0)]",7,2.0,,0.0,2,1,80,36,0,2,2,2.0,3.0,90.0,1.5,23
745,diffusion,https://github.com/sharonzhou/long_stable_diffusion,[],,[],[],,,,sharonzhou/long_stable_diffusion,long_stable_diffusion,671,55,16,Python,,"Long-form text-to-images generation, using a pipeline of deep generative models (GPT-3 and Stable Diffusion)",sharonzhou,2024-01-12,2022-09-04,73,9.155945419103315,,"Long-form text-to-images generation, using a pipeline of deep generative models (GPT-3 and Stable Diffusion)",[],[],2022-10-30,"[('compvis/stable-diffusion', 0.6926607489585876, 'diffusion', 0), ('huggingface/diffusers', 0.6350945830345154, 'diffusion', 0), ('openai/glide-text2im', 0.6345992684364319, 'diffusion', 0), ('saharmor/dalle-playground', 0.6195039749145508, 'diffusion', 0), ('thudm/cogvideo', 0.6085971593856812, 'ml', 0), ('huggingface/text-generation-inference', 0.5840281844139099, 'llm', 0), ('compvis/latent-diffusion', 0.5753589272499084, 'diffusion', 0), ('stability-ai/stablediffusion', 0.5753588676452637, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5584306120872498, 'diffusion', 0), ('minimaxir/gpt-2-simple', 0.5567746758460999, 'llm', 0), ('google/sentencepiece', 0.5528421401977539, 'nlp', 0), ('lucidrains/deep-daze', 0.5463470220565796, 'ml', 0), ('yoadtew/zero-shot-image-to-text', 0.5378433465957642, 'nlp', 0), ('ashawkey/stable-dreamfusion', 0.5333707332611084, 'diffusion', 0), ('minimaxir/aitextgen', 0.5227047801017761, 'llm', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.513484537601471, 'web', 0), ('lucidrains/imagen-pytorch', 0.5128797292709351, 'ml-dl', 0), ('nateraw/stable-diffusion-videos', 0.512672483921051, 'diffusion', 0), ('davidadsp/generative_deep_learning_2nd_edition', 0.5068796277046204, 'study', 0), ('nv-tlabs/get3d', 0.5064239501953125, 'ml', 0), ('google-research/electra', 0.5007908344268799, 'ml-dl', 0)]",2,1.0,,0.0,0,0,17,15,0,0,0,0.0,0.0,90.0,0.0,23
969,diffusion,https://github.com/tanelp/tiny-diffusion,[],,[],[],,,,tanelp/tiny-diffusion,tiny-diffusion,483,43,8,Jupyter Notebook,,A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets.,tanelp,2024-01-12,2023-01-13,54,8.850785340314136,,A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets.,[],[],2023-02-19,"[('divamgupta/stable-diffusion-tensorflow', 0.6014887690544128, 'diffusion', 0), ('huggingface/diffusers', 0.5994593501091003, 'diffusion', 0), ('openai/improved-diffusion', 0.5728610754013062, 'diffusion', 0), ('pytorch/botorch', 0.5327487587928772, 'ml-dl', 0), ('comfyanonymous/comfyui', 0.5260990858078003, 'diffusion', 0), ('openai/point-e', 0.5226495265960693, 'util', 0), ('carson-katri/dream-textures', 0.5146340727806091, 'diffusion', 0), ('bentoml/onediffusion', 0.5116053223609924, 'diffusion', 0)]",1,0.0,,0.06,3,0,12,11,0,0,0,3.0,2.0,90.0,0.7,23
1374,llm,https://github.com/amazon-science/alexa-teacher-models,"['aws', 'language-model', 'sagemaker']",AlexaTM 20B is a 20B-Parameter sequence-to-sequence transformer model,[],[],,,,amazon-science/alexa-teacher-models,alexa-teacher-models,362,26,36,Python,,,amazon-science,2024-01-04,2022-08-04,77,4.658088235294118,https://avatars.githubusercontent.com/u/70298811?v=4,AlexaTM 20B is a 20B-Parameter sequence-to-sequence transformer model,[],"['aws', 'language-model', 'sagemaker']",2023-04-09,[],5,2.0,,0.15,0,0,18,9,2,1,2,0.0,0.0,90.0,0.0,23
862,ml,https://github.com/infer-actively/pymdp,[],,[],[],,,,infer-actively/pymdp,pymdp,347,56,30,Python,,A Python implementation of active inference for Markov Decision Processes,infer-actively,2024-01-11,2019-11-27,217,1.5927868852459017,https://avatars.githubusercontent.com/u/75545318?v=4,A Python implementation of active inference for Markov Decision Processes,[],[],2023-08-19,"[('pymc-devs/pymc3', 0.5866931080818176, 'ml', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5399729013442993, 'study', 0), ('guyallard/markov_clustering', 0.5361882448196411, 'graph', 0), ('artemyk/dynpy', 0.5208140015602112, 'sim', 0)]",14,2.0,,0.33,4,2,50,5,1,1,1,4.0,2.0,90.0,0.5,23
1301,data,https://github.com/mattbierbaum/arxiv-public-datasets,[],,[],[],,,,mattbierbaum/arxiv-public-datasets,arxiv-public-datasets,338,55,15,Python,https://arxiv.org/abs/1905.00075,A set of scripts to grab public datasets from resources related to arXiv,mattbierbaum,2024-01-11,2019-01-29,261,1.2950191570881227,,A set of scripts to grab public datasets from resources related to arXiv,[],[],2022-07-15,"[('mcordts/cityscapesscripts', 0.5378220677375793, 'gis', 0), ('jovianml/opendatasets', 0.5266126990318298, 'data', 0), ('lukasschwab/arxiv.py', 0.5195563435554504, 'util', 0), ('simonw/datasette', 0.5164351463317871, 'data', 0)]",9,3.0,,0.0,2,2,60,18,0,1,1,2.0,4.0,90.0,2.0,23
991,finance,https://github.com/nasdaq/data-link-python,[],,[],[],,,,nasdaq/data-link-python,data-link-python,333,59,10,Python,,A Python library for Nasdaq Data Link's RESTful API,nasdaq,2024-01-05,2021-11-02,117,2.8461538461538463,https://avatars.githubusercontent.com/u/13860626?v=4,A Python library for Nasdaq Data Link's RESTful API,[],[],2022-08-29,"[('pynamodb/pynamodb', 0.5948277711868286, 'data', 0), ('hydrosquall/tiingo-python', 0.5673314332962036, 'finance', 0), ('tiangolo/sqlmodel', 0.5559763312339783, 'data', 0), ('falconry/falcon', 0.5472946763038635, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5415635704994202, 'data', 0), ('cuemacro/findatapy', 0.538777232170105, 'finance', 0), ('airbnb/omniduct', 0.5350242853164673, 'data', 0), ('mcfunley/pugsql', 0.5342159867286682, 'data', 0), ('datastax/python-driver', 0.5312590599060059, 'data', 0), ('awslabs/python-deequ', 0.5292037725448608, 'ml', 0), ('openaddresses/pyesridump', 0.5270365476608276, 'gis', 0), ('openai/openai-python', 0.5095574855804443, 'util', 0), ('sqlalchemy/sqlalchemy', 0.5085864067077637, 'data', 0), ('encode/httpx', 0.5050049424171448, 'web', 0), ('ethereum/web3.py', 0.5034674406051636, 'crypto', 0), ('taverntesting/tavern', 0.5025106072425842, 'testing', 0)]",4,1.0,,0.0,3,0,27,17,0,2,2,3.0,6.0,90.0,2.0,23
1461,jupyter,https://github.com/mamba-org/gator,"['conda', 'packages']",,[],[],,,,mamba-org/gator,gator,252,29,6,TypeScript,,Conda environment and package management extension from within Jupyter,mamba-org,2024-01-04,2018-08-02,286,0.8789237668161435,https://avatars.githubusercontent.com/u/66118895?v=4,Conda environment and package management extension from within Jupyter,"['conda', 'jupyter-notebook', 'jupyterlab-extension']","['conda', 'jupyter-notebook', 'jupyterlab-extension', 'packages']",2023-10-26,"[('conda/conda-pack', 0.6601476073265076, 'util', 1), ('conda/conda-build', 0.6426661014556885, 'util', 1), ('jupyter-widgets/ipywidgets', 0.6345846056938171, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.6041745543479919, 'jupyter', 0), ('chaoleili/jupyterlab_tensorboard', 0.5936612486839294, 'jupyter', 1), ('jupyter/notebook', 0.5904126763343811, 'jupyter', 1), ('mamba-org/quetz', 0.5806750655174255, 'util', 2), ('conda/constructor', 0.5621293783187866, 'util', 1), ('jupyterlab/jupyterlab-desktop', 0.5549662709236145, 'jupyter', 1), ('pypa/flit', 0.5534806847572327, 'util', 0), ('jupyterlite/jupyterlite', 0.5473883152008057, 'jupyter', 1), ('voila-dashboards/voila', 0.5456347465515137, 'jupyter', 2), ('aws/graph-notebook', 0.5385718941688538, 'jupyter', 1), ('pypa/hatch', 0.5378934741020203, 'util', 0), ('mwouts/jupytext', 0.5371402502059937, 'jupyter', 2), ('indygreg/pyoxidizer', 0.5344242453575134, 'util', 0), ('mitsuhiko/rye', 0.5343549847602844, 'util', 0), ('mamba-org/boa', 0.5324650406837463, 'util', 1), ('jupyter/nbformat', 0.5302620530128479, 'jupyter', 0), ('python-poetry/poetry', 0.5295340418815613, 'util', 0), ('ipython/ipykernel', 0.5081201791763306, 'util', 1), ('mamba-org/mamba', 0.5042513012886047, 'util', 1)]",26,4.0,,0.13,12,7,66,3,1,11,1,12.0,3.0,90.0,0.2,23
829,gis,https://github.com/r-barnes/richdem,[],,[],[],,,,r-barnes/richdem,richdem,234,62,14,C++,,High-performance Terrain and Hydrology Analysis ,r-barnes,2024-01-03,2013-01-06,577,0.4053452115812918,,High-performance Terrain and Hydrology Analysis ,"['big-data', 'digital-elevation-model', 'geosciences', 'geospatial', 'hydrologic-modeling', 'hydrology']","['big-data', 'digital-elevation-model', 'geosciences', 'geospatial', 'hydrologic-modeling', 'hydrology']",2023-07-06,"[('fatiando/verde', 0.5110668540000916, 'gis', 1), ('osgeo/grass', 0.5100005269050598, 'gis', 1)]",4,3.0,,0.21,6,0,134,6,0,2,2,6.0,15.0,90.0,2.5,23
1338,util,https://github.com/prefecthq/server,[],,[],[],,,,prefecthq/server,server,223,98,16,Python,,The Prefect API and backend,prefecthq,2024-01-14,2020-07-29,182,1.21953125,https://avatars.githubusercontent.com/u/39270919?v=4,The Prefect API and backend,"['automation', 'orchestration', 'prefect', 'workflow', 'workflow-engine']","['automation', 'orchestration', 'prefect', 'workflow', 'workflow-engine']",2023-06-23,"[('prefecthq/prefect', 0.7347409129142761, 'ml-ops', 5), ('kestra-io/kestra', 0.6529881954193115, 'ml-ops', 3), ('flyteorg/flyte', 0.6038605570793152, 'ml-ops', 1), ('avaiga/taipy', 0.5884959697723389, 'data', 3), ('apache/airflow', 0.5666928291320801, 'ml-ops', 4), ('tiangolo/full-stack-fastapi-postgresql', 0.5580776333808899, 'template', 0), ('zenml-io/zenml', 0.5477404594421387, 'ml-ops', 1), ('astronomer/astro-sdk', 0.5464804768562317, 'ml-ops', 0), ('dagster-io/dagster', 0.5430384278297424, 'ml-ops', 2), ('vitalik/django-ninja', 0.5366072654724121, 'web', 0), ('piccolo-orm/piccolo_admin', 0.5307790040969849, 'data', 0), ('tiangolo/fastapi', 0.5276178121566772, 'web', 0), ('fastai/ghapi', 0.5246346592903137, 'util', 0), ('mage-ai/mage-ai', 0.5205321311950684, 'ml-ops', 1), ('willmcgugan/textual', 0.5155190229415894, 'term', 0), ('cheshire-cat-ai/core', 0.5153402090072632, 'llm', 0), ('home-assistant/supervisor', 0.5141927599906921, 'util', 0), ('hugapi/hug', 0.5119301676750183, 'util', 0), ('starlite-api/starlite', 0.5044628381729126, 'web', 0), ('numerai/numerai-cli', 0.5039493441581726, 'finance', 0), ('orchest/orchest', 0.5037789344787598, 'ml-ops', 0), ('pythagora-io/gpt-pilot', 0.500454843044281, 'llm', 0), ('ploomber/ploomber', 0.5003852248191833, 'ml-ops', 1)]",48,4.0,,0.06,0,0,42,7,0,14,14,0.0,0.0,90.0,0.0,23
1609,llm,https://github.com/hazyresearch/legalbench,"['benchmark', 'legal']",,[],[],,,,hazyresearch/legalbench,legalbench,220,23,40,Python,,An open science effort to benchmark legal reasoning in foundation models,hazyresearch,2024-01-14,2022-08-19,75,2.9111531190926274,https://avatars.githubusercontent.com/u/2165246?v=4,An open science effort to benchmark legal reasoning in foundation models,[],"['benchmark', 'legal']",2023-12-02,"[('coastalcph/lex-glue', 0.5063652992248535, 'nlp', 2)]",4,1.0,,1.46,3,1,17,1,0,0,0,3.0,2.0,90.0,0.7,23
1694,util,https://github.com/regebro/pyroma,['quality'],,[],[],,,,regebro/pyroma,pyroma,201,24,7,Python,,Rate your Python packages package friendliness,regebro,2023-12-11,2017-04-15,354,0.5671100362756953,,Rate your Python packages package friendliness,['packaging'],"['packaging', 'quality']",2023-10-10,"[('pypa/flit', 0.7084984183311462, 'util', 1), ('indygreg/pyoxidizer', 0.7064893245697021, 'util', 1), ('python-poetry/poetry', 0.6802058219909668, 'util', 1), ('mitsuhiko/rye', 0.661931574344635, 'util', 1), ('pypi/warehouse', 0.6017106771469116, 'util', 0), ('tezromach/python-package-template', 0.5707094073295593, 'template', 0), ('pdm-project/pdm', 0.568504810333252, 'util', 1), ('pyodide/micropip', 0.5652766227722168, 'util', 0), ('pypa/hatch', 0.5634012818336487, 'util', 1), ('rubik/radon', 0.5549668669700623, 'util', 0)]",21,6.0,,0.15,1,0,82,3,0,7,7,1.0,0.0,90.0,0.0,23
1771,sim,https://github.com/humanoidagents/humanoidagents,"['simulation', 'chatgpt', 'agents', 'language-model']",,[],[],,,,humanoidagents/humanoidagents,HumanoidAgents,197,20,5,Python,http://www.humanoidagents.com/,Humanoid Agents: Platform for Simulating Human-like Generative Agents,humanoidagents,2024-01-14,2023-10-09,16,12.20353982300885,https://avatars.githubusercontent.com/u/147342724?v=4,Humanoid Agents: Platform for Simulating Human-like Generative Agents,[],"['agents', 'chatgpt', 'language-model', 'simulation']",2023-10-19,"[('google-deepmind/concordia', 0.640636682510376, 'sim', 0), ('minedojo/voyager', 0.56615149974823, 'llm', 0), ('prefecthq/marvin', 0.5612106919288635, 'nlp', 1), ('microsoft/autogen', 0.5427013039588928, 'llm', 1), ('aiwaves-cn/agents', 0.5400265455245972, 'nlp', 1), ('projectmesa/mesa', 0.5313592553138733, 'sim', 1), ('facebookresearch/droidlet', 0.5291570425033569, 'sim', 0), ('facebookresearch/habitat-lab', 0.5243606567382812, 'sim', 0), ('krohling/bondai', 0.5202670693397522, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5163865685462952, 'llm', 0), ('microsoft/promptcraft-robotics', 0.51203453540802, 'sim', 2), ('mnotgod96/appagent', 0.5099534392356873, 'llm', 1), ('activitysim/populationsim', 0.5014925003051758, 'sim', 0), ('noahshinn/reflexion', 0.5013625621795654, 'llm', 0)]",1,0.0,,0.12,2,1,3,3,0,0,0,2.0,0.0,90.0,0.0,23
768,sim,https://github.com/activitysim/activitysim,[],,[],[],,,,activitysim/activitysim,activitysim,176,92,44,Jupyter Notebook,https://activitysim.github.io,An Open Platform for Activity-Based Travel Modeling,activitysim,2024-01-03,2014-06-18,501,0.3506974096214062,https://avatars.githubusercontent.com/u/25851945?v=4,An Open Platform for Activity-Based Travel Modeling,"['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'travel-modeling']","['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'travel-modeling']",2023-05-09,[],32,4.0,,0.23,41,9,117,8,3,2,3,41.0,22.0,90.0,0.5,23
828,typing,https://github.com/jellezijlstra/autotyping,[],,[],[],,,,jellezijlstra/autotyping,autotyping,175,12,5,Python,,,jellezijlstra,2024-01-04,2021-06-25,135,1.2908324552160169,,jellezijlstra/autotyping,[],[],2023-03-28,[],8,6.0,,0.08,1,0,31,10,2,2,2,1.0,1.0,90.0,1.0,23
1664,util,https://github.com/python-poetry/install.python-poetry.org,[],,[],[],,,,python-poetry/install.python-poetry.org,install.python-poetry.org,157,50,8,Python,https://install.python-poetry.org,The official Poetry installation script,python-poetry,2024-01-09,2021-11-12,115,1.3584672435105067,https://avatars.githubusercontent.com/u/48722593?v=4,The official Poetry installation script,['poetry'],['poetry'],2023-09-19,"[('snok/install-poetry', 0.6302388310432434, 'util', 0), ('tiangolo/poetry-version-plugin', 0.5517739653587341, 'util', 0), ('mtkennerly/poetry-dynamic-versioning', 0.5113168358802795, 'util', 1)]",17,2.0,,0.27,13,6,26,4,0,0,0,13.0,21.0,90.0,1.6,23
1415,llm,https://github.com/yueyu1030/attrprompt,[],,[],[],,,,yueyu1030/attrprompt,AttrPrompt,107,7,3,Python,https://arxiv.org/abs/2306.15895,[NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.,yueyu1030,2024-01-12,2023-05-31,34,3.069672131147541,,[NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.,"['attributed-text', 'data-centric-ai', 'large-language-models', 'natural-language-processing', 'pretrained-language-model', 'text-classification', 'training-data-generation', 'zero-shot-learning']","['attributed-text', 'data-centric-ai', 'large-language-models', 'natural-language-processing', 'pretrained-language-model', 'text-classification', 'training-data-generation', 'zero-shot-learning']",2023-11-02,"[('togethercomputer/redpajama-data', 0.6486657857894897, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.6001567840576172, 'llm', 2), ('eleutherai/the-pile', 0.5999411940574646, 'data', 0), ('huggingface/text-generation-inference', 0.5905746221542358, 'llm', 0), ('facebookresearch/shepherd', 0.572687566280365, 'llm', 0), ('microsoft/unilm', 0.5726070404052734, 'nlp', 0), ('openai/gpt-2', 0.5717838406562805, 'llm', 0), ('princeton-nlp/alce', 0.5694684982299805, 'llm', 0), ('lm-sys/fastchat', 0.5685467720031738, 'llm', 0), ('microsoft/lora', 0.5668091177940369, 'llm', 0), ('hannibal046/awesome-llm', 0.5640091896057129, 'study', 0), ('databrickslabs/dolly', 0.5601263642311096, 'llm', 0), ('lupantech/chameleon-llm', 0.5579221248626709, 'llm', 0), ('infinitylogesh/mutate', 0.5573945045471191, 'nlp', 0), ('lianjiatech/belle', 0.5536043047904968, 'llm', 0), ('google-research/electra', 0.5535896420478821, 'ml-dl', 0), ('openai/finetune-transformer-lm', 0.5512840747833252, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5511519312858582, 'llm', 0), ('explosion/spacy-models', 0.5483715534210205, 'nlp', 1), ('minimaxir/textgenrnn', 0.545197069644928, 'nlp', 0), ('openai/clip', 0.5419546961784363, 'ml-dl', 0), ('jonasgeiping/cramming', 0.5370364189147949, 'nlp', 0), ('eleutherai/lm-evaluation-harness', 0.5361191034317017, 'llm', 0), ('alibaba/easynlp', 0.5339798331260681, 'nlp', 1), ('google-research/language', 0.5311223864555359, 'nlp', 1), ('reasoning-machines/pal', 0.5289052128791809, 'llm', 1), ('next-gpt/next-gpt', 0.5283357501029968, 'llm', 1), ('openlm-research/open_llama', 0.5280580520629883, 'llm', 0), ('freedomintelligence/llmzoo', 0.5263165831565857, 'llm', 0), ('bigscience-workshop/biomedical', 0.5249584317207336, 'data', 0), ('nltk/nltk', 0.5240321755409241, 'nlp', 1), ('eureka-research/eureka', 0.522046685218811, 'ml-rl', 0), ('ai21labs/lm-evaluation', 0.5179035067558289, 'llm', 0), ('llmware-ai/llmware', 0.5132665038108826, 'llm', 1), ('ofa-sys/ofa', 0.5103440284729004, 'llm', 0), ('openlmlab/moss', 0.5103434920310974, 'llm', 2), ('thudm/codegeex', 0.510209858417511, 'llm', 0), ('deepset-ai/farm', 0.5037703514099121, 'nlp', 0), ('microsoft/autogen', 0.5033207535743713, 'llm', 0), ('prithivirajdamodaran/styleformer', 0.5027515292167664, 'nlp', 0), ('norskregnesentral/skweak', 0.5007184743881226, 'nlp', 1)]",3,2.0,,0.44,4,4,8,2,0,0,0,4.0,3.0,90.0,0.8,23
1339,ml-ops,https://github.com/prefecthq/prefect-dbt,[],,[],[],,,,prefecthq/prefect-dbt,prefect-dbt,77,8,8,Python,https://prefecthq.github.io/prefect-dbt/,Collection of Prefect integrations for working with dbt with your Prefect flows.,prefecthq,2024-01-04,2022-05-26,87,0.8778501628664495,https://avatars.githubusercontent.com/u/39270919?v=4,Collection of Prefect integrations for working with dbt with your Prefect flows.,"['dbt', 'prefect']","['dbt', 'prefect']",2023-11-02,"[('prefecthq/prefect', 0.5904790759086609, 'ml-ops', 1), ('prefecthq/prefect-aws', 0.512976348400116, 'data', 1)]",14,4.0,,0.5,11,4,20,2,4,8,4,11.0,2.0,90.0,0.2,23
1661,data,https://github.com/mediawiki-client-tools/mediawiki-dump-generator,"['wikimedia', 'wikipedia']",,[],[],,,,mediawiki-client-tools/mediawiki-dump-generator,mediawiki-dump-generator,68,14,5,HTML,https://github.com/mediawiki-client-tools/mediawiki-scraper,Python 3 tools for downloading and preserving wikis,mediawiki-client-tools,2024-01-12,2021-05-27,139,0.4867075664621677,https://avatars.githubusercontent.com/u/122663498?v=4,Python 3 tools for downloading and preserving wikis,[],"['wikimedia', 'wikipedia']",2023-12-18,"[('mediawiki-client-tools/wikitools3', 0.8232905268669128, 'data', 2), ('goldsmith/wikipedia', 0.709117591381073, 'data', 0), ('harangju/wikinet', 0.6572245359420776, 'data', 0), ('executablebooks/jupyter-book', 0.535974383354187, 'jupyter', 0), ('erotemic/ubelt', 0.5286123752593994, 'util', 0)]",44,3.0,,1.94,33,22,32,1,0,0,0,33.0,19.0,90.0,0.6,23
913,util,https://github.com/python-odin/odin,[],,[],[],,,,python-odin/odin,odin,35,9,3,Python,https://odin.readthedocs.org/en/latest/,"Data-structure definition/validation/traversal, mapping and serialisation toolkit for Python",python-odin,2023-11-27,2013-08-20,545,0.06422018348623854,https://avatars.githubusercontent.com/u/14675500?v=4,"Data-structure definition/validation/traversal, mapping and serialisation toolkit for Python","['csv', 'data-mapping', 'data-structures', 'de-serialize', 'json', 'msgpack', 'serialize', 'validation', 'xml', 'yaml']","['csv', 'data-mapping', 'data-structures', 'de-serialize', 'json', 'msgpack', 'serialize', 'validation', 'xml', 'yaml']",2024-01-13,"[('marshmallow-code/marshmallow', 0.6529213190078735, 'util', 1), ('pydantic/pydantic', 0.6526608467102051, 'util', 1), ('pandas-dev/pandas', 0.6475261449813843, 'pandas', 0), ('dagworks-inc/hamilton', 0.6443514227867126, 'ml-ops', 0), ('pylons/colander', 0.63074791431427, 'util', 1), ('jsonpickle/jsonpickle', 0.6082868576049805, 'data', 1), ('pyeve/cerberus', 0.6078689098358154, 'data', 0), ('keon/algorithms', 0.6064568161964417, 'util', 0), ('omry/omegaconf', 0.5967031121253967, 'util', 1), ('mkdocstrings/griffe', 0.5799145102500916, 'util', 0), ('lk-geimfari/mimesis', 0.5751269459724426, 'data', 1), ('yukinarit/pyserde', 0.5727947354316711, 'util', 3), ('tiangolo/sqlmodel', 0.5694354176521301, 'data', 1), ('instagram/libcst', 0.5643008351325989, 'util', 0), ('pytoolz/toolz', 0.563791811466217, 'util', 0), ('unionai-oss/pandera', 0.5636017322540283, 'pandas', 1), ('jazzband/tablib', 0.5585665106773376, 'data', 0), ('saulpw/visidata', 0.5557106137275696, 'term', 2), ('plotly/dash', 0.5536272525787354, 'viz', 0), ('fabiocaccamo/python-benedict', 0.5445603728294373, 'util', 4), ('eleutherai/pyfra', 0.5400282144546509, 'ml', 0), ('krzjoa/awesome-python-data-science', 0.5375770330429077, 'study', 0), ('falconry/falcon', 0.5320534706115723, 'web', 0), ('wesm/pydata-book', 0.5320025682449341, 'study', 0), ('malloydata/malloy-py', 0.5316958427429199, 'data', 0), ('roniemartinez/dude', 0.5273708701133728, 'util', 0), ('imageio/imageio', 0.5272129774093628, 'util', 0), ('ploomber/ploomber', 0.5260776877403259, 'ml-ops', 0), ('facebook/pyre-check', 0.5255442261695862, 'typing', 0), ('man-group/dtale', 0.5247855186462402, 'viz', 0), ('atsushisakai/pythonrobotics', 0.5244603753089905, 'sim', 0), ('amaargiru/pyroad', 0.5243930220603943, 'study', 0), ('scikit-mobility/scikit-mobility', 0.5235087275505066, 'gis', 0), ('1200wd/bitcoinlib', 0.5217608213424683, 'crypto', 0), ('fastai/fastcore', 0.5195226669311523, 'util', 1), ('kellyjonbrazil/jc', 0.5191496014595032, 'util', 3), ('uqfoundation/dill', 0.5188406705856323, 'data', 0), ('rhettbull/osxphotos', 0.5156180262565613, 'util', 0), ('brokenloop/jsontopydantic', 0.5140889286994934, 'util', 0), ('geopandas/geopandas', 0.5138459801673889, 'gis', 0), ('aswinnnn/pyscan', 0.5133723616600037, 'security', 0), ('fsspec/filesystem_spec', 0.5112189054489136, 'util', 0), ('pyjanitor-devs/pyjanitor', 0.51002436876297, 'pandas', 0), ('crunch-io/lazycsv', 0.5088388323783875, 'perf', 1), ('erotemic/ubelt', 0.5053083896636963, 'util', 0), ('macbre/sql-metadata', 0.5047857761383057, 'data', 0), ('simonw/datasette', 0.5009411573410034, 'data', 2), ('mitmproxy/pdoc', 0.5004577040672302, 'util', 0)]",8,3.0,,1.6,6,3,127,0,10,7,10,6.0,1.0,90.0,0.2,23
1284,data,https://github.com/qdrant/qdrant-haystack,['haystack'],,[],[],,,,qdrant/qdrant-haystack,qdrant-haystack,35,9,3,Python,,An integration of Qdrant ANN vector database backend with Haystack ,qdrant,2023-11-28,2023-01-31,52,0.6730769230769231,https://avatars.githubusercontent.com/u/73504361?v=4,An integration of Qdrant ANN vector database backend with Haystack ,[],['haystack'],2023-11-14,"[('qdrant/qdrant-client', 0.6654171943664551, 'util', 0), ('qdrant/vector-db-benchmark', 0.5743999481201172, 'perf', 0), ('qdrant/qdrant', 0.5697789788246155, 'data', 0), ('facebookresearch/faiss', 0.518367350101471, 'ml', 0), ('pinecone-io/pinecone-python-client', 0.5180879831314087, 'data', 0), ('jina-ai/vectordb', 0.5077526569366455, 'data', 0)]",6,4.0,,1.13,13,4,12,2,15,16,15,13.0,6.0,90.0,0.5,23
1762,term,https://github.com/tconbeer/textual-textarea,['textual'],,[],[],,,,tconbeer/textual-textarea,textual-textarea,10,3,1,Python,,A text area (multi-line input) with syntax highlighting and autocomplete for Textual,tconbeer,2023-12-25,2023-05-19,36,0.2734375,,A text area (multi-line input) with syntax highlighting and autocomplete for Textual,[],['textual'],2024-01-12,[],4,2.0,,2.46,67,59,8,0,30,46,30,67.0,32.0,90.0,0.5,23
1536,study,https://github.com/gerdm/prml,[],,[],[],,,,gerdm/prml,prml,1635,410,32,Jupyter Notebook,,"Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop",gerdm,2024-01-13,2018-11-23,270,6.042766631467793,,"Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop","['bayesian-statistics', 'machine-learning', 'pattern-recognition', 'prml']","['bayesian-statistics', 'machine-learning', 'pattern-recognition', 'prml']",2022-07-25,"[('probml/pyprobml', 0.6533145308494568, 'ml', 1), ('brandon-rhodes/python-patterns', 0.5963156819343567, 'util', 0), ('bayesianmodelingandcomputationinpython/bookcode_edition1', 0.5888375639915466, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5584812760353088, 'study', 0), ('ageron/handson-ml2', 0.5364505648612976, 'ml', 0), ('mynameisfiber/high_performance_python_2e', 0.5304524302482605, 'study', 0), ('rasbt/mlxtend', 0.5303501486778259, 'ml', 1), ('wesm/pydata-book', 0.5239573121070862, 'study', 0), ('pymc-devs/pymc3', 0.5029430985450745, 'ml', 0), ('scikit-learn/scikit-learn', 0.5011841058731079, 'ml', 1)]",3,1.0,,0.0,0,0,63,18,0,0,0,0.0,0.0,90.0,0.0,22
1012,finance,https://github.com/quandl/quandl-python,[],,[],[],,,,quandl/quandl-python,quandl-python,1343,391,131,Python,,,quandl,2024-01-09,2013-03-24,566,2.3715943491422804,https://avatars.githubusercontent.com/u/1659378?v=4,quandl/quandl-python,"['api-client', 'data-frame', 'dataset', 'quandl', 'retrieve-data']","['api-client', 'data-frame', 'dataset', 'quandl', 'retrieve-data']",2021-12-08,"[('cuemacro/findatapy', 0.5631470084190369, 'finance', 1)]",36,2.0,,0.0,0,0,132,26,0,2,2,0.0,0.0,90.0,0.0,22
593,data,https://github.com/uber/fiber,[],,[],[],,,,uber/fiber,fiber,1041,117,21,Python,https://uber.github.io/fiber/,Distributed Computing for AI Made Simple,uber,2024-01-14,2020-01-07,212,4.910377358490566,https://avatars.githubusercontent.com/u/538264?v=4,Distributed Computing for AI Made Simple,"['distributed-computing', 'machine-learning', 'multiprocessing', 'sandbox']","['distributed-computing', 'machine-learning', 'multiprocessing', 'sandbox']",2021-03-15,"[('paddlepaddle/paddle', 0.69061678647995, 'ml-dl', 1), ('horovod/horovod', 0.6222132444381714, 'ml-ops', 1), ('alpa-projects/alpa', 0.5923050045967102, 'ml-dl', 2), ('ray-project/ray', 0.5900027751922607, 'ml-ops', 1), ('tensorflow/tensorflow', 0.5713419914245605, 'ml-dl', 1), ('hpcaitech/colossalai', 0.5713132619857788, 'llm', 1), ('determined-ai/determined', 0.5588817596435547, 'ml-ops', 1), ('jina-ai/jina', 0.5508025884628296, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5499705672264099, 'ml', 1), ('bentoml/bentoml', 0.5481660962104797, 'ml-ops', 1), ('eventual-inc/daft', 0.5396055579185486, 'pandas', 2), ('mlflow/mlflow', 0.5344669818878174, 'ml-ops', 1), ('optuna/optuna', 0.5304052233695984, 'ml', 1), ('mlc-ai/mlc-llm', 0.522170901298523, 'llm', 0), ('fugue-project/fugue', 0.5197455883026123, 'pandas', 2), ('googlecloudplatform/vertex-ai-samples', 0.5184296369552612, 'ml', 0), ('nevronai/metisfl', 0.5169682502746582, 'ml', 1), ('pytorchlightning/pytorch-lightning', 0.512328028678894, 'ml-dl', 1), ('operand/agency', 0.5117772817611694, 'llm', 1), ('polyaxon/polyaxon', 0.5087381601333618, 'ml-ops', 1), ('backtick-se/cowait', 0.5072715282440186, 'util', 0), ('adap/flower', 0.505595326423645, 'ml-ops', 1), ('ml-tooling/opyrator', 0.5049328804016113, 'viz', 1), ('skypilot-org/skypilot', 0.5006906390190125, 'llm', 1)]",5,3.0,,0.0,0,0,49,34,0,0,0,0.0,0.0,90.0,0.0,22
1693,util,https://github.com/getsentry/milksnake,['setuptools'],,[],[],,,,getsentry/milksnake,milksnake,784,36,23,Python,,A setuptools/wheel/cffi extension to embed a binary data in wheels,getsentry,2024-01-12,2017-10-03,330,2.375757575757576,https://avatars.githubusercontent.com/u/1396951?v=4,A setuptools/wheel/cffi extension to embed a binary data in wheels,['tag-production'],"['setuptools', 'tag-production']",2023-04-11,"[('pypa/installer', 0.5740995407104492, 'util', 0)]",8,3.0,,0.02,0,0,76,9,0,1,1,0.0,0.0,90.0,0.0,22
299,nlp,https://github.com/openai/grade-school-math,"['word-problem', 'math', 'dataset']","GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems",[],[],,,,openai/grade-school-math,grade-school-math,744,121,11,Python,,,openai,2024-01-14,2021-10-20,118,6.259615384615385,https://avatars.githubusercontent.com/u/14957082?v=4,"GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems",[],"['dataset', 'math', 'word-problem']",2021-11-19,[],2,1.0,,0.0,10,2,27,26,0,0,0,10.0,2.0,90.0,0.2,22
258,crypto,https://github.com/pmaji/crypto-whale-watching-app,[],,[],[],,,,pmaji/crypto-whale-watching-app,crypto-whale-watching-app,590,142,47,Python,,Python Dash app that tracks whale activity in cryptocurrency markets.,pmaji,2024-01-12,2018-01-23,314,1.8789808917197452,,Python Dash app that tracks whale activity in cryptocurrency markets.,"['bitcoin', 'bitcoin-api', 'bitcoin-price', 'cryptocurrency', 'cryptocurrency-exchanges', 'cryptocurrency-price-ticker', 'cryptocurrency-prices', 'dash', 'ethereum', 'ethereum-blockchain', 'ethereum-price', 'gdax', 'gdax-api', 'gdax-python', 'litecoin', 'litecoin-price', 'plotly', 'plotly-dash']","['bitcoin', 'bitcoin-api', 'bitcoin-price', 'cryptocurrency', 'cryptocurrency-exchanges', 'cryptocurrency-price-ticker', 'cryptocurrency-prices', 'dash', 'ethereum', 'ethereum-blockchain', 'ethereum-price', 'gdax', 'gdax-api', 'gdax-python', 'litecoin', 'litecoin-price', 'plotly', 'plotly-dash']",2023-08-09,"[('1200wd/bitcoinlib', 0.6108453869819641, 'crypto', 3), ('plotly/dash', 0.5946712493896484, 'viz', 3), ('gbeced/basana', 0.553300142288208, 'finance', 1), ('ethereum/web3.py', 0.5342921614646912, 'crypto', 0), ('ccxt/ccxt', 0.5305103063583374, 'crypto', 3), ('primal100/pybitcointools', 0.5292482972145081, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5264788269996643, 'finance', 0), ('bmoscon/cryptofeed', 0.5131617188453674, 'crypto', 3), ('holoviz/panel', 0.5126122236251831, 'viz', 1)]",8,1.0,,0.06,3,1,73,5,0,0,0,3.0,3.0,90.0,1.0,22
189,math,https://github.com/dit/dit,[],,[],[],,,,dit/dit,dit,474,83,25,Python,http://docs.dit.io,Python package for information theory.,dit,2024-01-09,2013-09-29,539,0.8789403973509934,https://avatars.githubusercontent.com/u/3247210?v=4,Python package for information theory.,['information-theory'],['information-theory'],2023-08-30,"[('goldsmith/wikipedia', 0.525985062122345, 'data', 0), ('pytoolz/toolz', 0.5121693015098572, 'util', 0), ('ta-lib/ta-lib-python', 0.5039918422698975, 'finance', 0), ('quantecon/quantecon.py', 0.5037375688552856, 'sim', 0)]",21,3.0,,0.17,2,1,125,5,0,3,3,2.0,0.0,90.0,0.0,22
1555,util,https://github.com/kellyjonbrazil/jello,['jq'],,[],[],,,,kellyjonbrazil/jello,jello,442,19,11,Python,,CLI tool to filter JSON and JSON Lines data with Python syntax. (Similar to jq),kellyjonbrazil,2024-01-14,2020-03-22,201,2.1958836053938966,,CLI tool to filter JSON and JSON Lines data with Python syntax. (Similar to jq),"['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'filter', 'jq', 'json', 'json-lines', 'process', 'query', 'scripting', 'shell-scripting']","['bash', 'bash-scripting', 'cli', 'command-line', 'command-line-interface', 'command-line-tool', 'filter', 'jq', 'json', 'json-lines', 'process', 'query', 'scripting', 'shell-scripting']",2023-04-29,"[('kellyjonbrazil/jc', 0.7774760127067566, 'util', 10), ('kellyjonbrazil/jellex', 0.7509535551071167, 'term', 5), ('brokenloop/jsontopydantic', 0.5349708795547485, 'util', 0), ('google/python-fire', 0.5335484743118286, 'term', 1), ('scikit-hep/awkward-1.0', 0.5297543406486511, 'data', 1), ('tiangolo/sqlmodel', 0.5081574320793152, 'data', 1)]",2,1.0,,0.33,1,1,46,9,2,10,2,1.0,1.0,90.0,1.0,22
573,jupyter,https://github.com/computationalmodelling/nbval,[],,[],[],,,,computationalmodelling/nbval,nbval,425,51,11,Python,,A py.test plugin to validate Jupyter notebooks,computationalmodelling,2024-01-05,2015-04-09,459,0.9244872591671845,https://avatars.githubusercontent.com/u/11869420?v=4,A py.test plugin to validate Jupyter notebooks,"['ipython-notebook', 'jupyter-notebook', 'pytest', 'pytest-plugin', 'testing']","['ipython-notebook', 'jupyter-notebook', 'pytest', 'pytest-plugin', 'testing']",2023-01-11,"[('nteract/testbook', 0.764438271522522, 'jupyter', 2), ('jupyter/notebook', 0.671747624874115, 'jupyter', 1), ('teemu/pytest-sugar', 0.6413612365722656, 'testing', 3), ('ipython/ipykernel', 0.6354666948318481, 'util', 1), ('jupyter-widgets/ipywidgets', 0.6210037469863892, 'jupyter', 0), ('ionelmc/pytest-benchmark', 0.6038401126861572, 'testing', 1), ('pytest-dev/pytest-xdist', 0.5855597257614136, 'testing', 2), ('pytest-dev/pytest-asyncio', 0.5843405723571777, 'testing', 2), ('jupyter/nbformat', 0.5732461214065552, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.5705468654632568, 'jupyter', 1), ('pytest-dev/pytest-cov', 0.5596536993980408, 'testing', 1), ('pytest-dev/pytest-mock', 0.5595971345901489, 'testing', 1), ('mwouts/jupytext', 0.5556824803352356, 'jupyter', 1), ('jupyterlab/jupyterlab', 0.5462405681610107, 'jupyter', 0), ('wolever/parameterized', 0.5427061319351196, 'testing', 0), ('ipython/ipyparallel', 0.5368309617042542, 'perf', 0), ('kiwicom/pytest-recording', 0.535578191280365, 'testing', 2), ('samuelcolvin/dirty-equals', 0.5271663665771484, 'util', 1), ('jupyterlite/jupyterlite', 0.5270730257034302, 'jupyter', 0), ('samuelcolvin/pytest-pretty', 0.5243958234786987, 'testing', 1), ('pypa/virtualenv', 0.523438036441803, 'util', 0), ('taverntesting/tavern', 0.5216328501701355, 'testing', 2), ('voila-dashboards/voila', 0.5194684267044067, 'jupyter', 1), ('jupyter/nbconvert', 0.5174958109855652, 'jupyter', 0), ('maartenbreddels/ipyvolume', 0.5154886841773987, 'jupyter', 1), ('pytest-dev/pytest', 0.5131306648254395, 'testing', 1), ('nbqa-dev/nbqa', 0.5108411312103271, 'jupyter', 1), ('jupyter/nbdime', 0.5095821022987366, 'jupyter', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5067647099494934, 'study', 0)]",33,3.0,,0.0,2,0,107,12,0,2,2,2.0,1.0,90.0,0.5,22
1191,llm,https://github.com/reasoning-machines/pal,[],,[],[],,,,reasoning-machines/pal,pal,405,46,7,Python,https://reasonwithpal.com,PaL: Program-Aided Language Models (ICML 2023),reasoning-machines,2024-01-11,2022-11-18,62,6.472602739726027,https://avatars.githubusercontent.com/u/118758190?v=4,PaL: Program-Aided Language Models (ICML 2023),"['commonsense-reasoning', 'few-shot-learning', 'language-generation', 'language-model', 'large-language-models', 'reasoning']","['commonsense-reasoning', 'few-shot-learning', 'language-generation', 'language-model', 'large-language-models', 'reasoning']",2023-06-30,"[('lupantech/chameleon-llm', 0.672268271446228, 'llm', 2), ('freedomintelligence/llmzoo', 0.6142875552177429, 'llm', 1), ('jonasgeiping/cramming', 0.6135402917861938, 'nlp', 1), ('juncongmoo/pyllama', 0.6026532053947449, 'llm', 0), ('hannibal046/awesome-llm', 0.5971046090126038, 'study', 1), ('ofa-sys/ofa', 0.5922104120254517, 'llm', 0), ('stanfordnlp/dspy', 0.5881893038749695, 'llm', 1), ('eleutherai/lm-evaluation-harness', 0.5877846479415894, 'llm', 1), ('srush/minichain', 0.5743135809898376, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5670881867408752, 'llm', 1), ('guidance-ai/guidance', 0.5653203129768372, 'llm', 1), ('lianjiatech/belle', 0.5578760504722595, 'llm', 0), ('microsoft/autogen', 0.5551219582557678, 'llm', 0), ('openlmlab/moss', 0.5542696714401245, 'llm', 2), ('lm-sys/fastchat', 0.54938805103302, 'llm', 1), ('infinitylogesh/mutate', 0.5475483536720276, 'nlp', 1), ('ai21labs/lm-evaluation', 0.5380016565322876, 'llm', 1), ('kyegomez/tree-of-thoughts', 0.5376133918762207, 'llm', 0), ('nvlabs/prismer', 0.5364494323730469, 'diffusion', 1), ('next-gpt/next-gpt', 0.532139778137207, 'llm', 1), ('yueyu1030/attrprompt', 0.5289052128791809, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5286970734596252, 'llm', 1), ('neulab/prompt2model', 0.5284048318862915, 'llm', 1), ('databrickslabs/dolly', 0.5263434052467346, 'llm', 0), ('llmware-ai/llmware', 0.5255475640296936, 'llm', 1), ('cg123/mergekit', 0.5245123505592346, 'llm', 0), ('young-geng/easylm', 0.5207021832466125, 'llm', 2), ('conceptofmind/toolformer', 0.5196791887283325, 'llm', 1), ('huggingface/text-generation-inference', 0.5183734893798828, 'llm', 0), ('salesforce/blip', 0.512751042842865, 'diffusion', 0), ('paddlepaddle/paddlenlp', 0.5125070214271545, 'llm', 0), ('eth-sri/lmql', 0.5124557018280029, 'llm', 1), ('optimalscale/lmflow', 0.5115619897842407, 'llm', 1), ('likenneth/honest_llama', 0.5100179314613342, 'llm', 1), ('eugeneyan/obsidian-copilot', 0.5085762739181519, 'llm', 1), ('keirp/automatic_prompt_engineer', 0.5067288279533386, 'llm', 1), ('aiwaves-cn/agents', 0.5023298263549805, 'nlp', 1), ('explosion/spacy-models', 0.5003161430358887, 'nlp', 0), ('facebookresearch/shepherd', 0.5001876950263977, 'llm', 1)]",4,1.0,,0.08,0,0,14,7,0,0,0,0.0,0.0,90.0,0.0,22
1044,data,https://github.com/ydataai/ydata-quality,[],,[],[],,,,ydataai/ydata-quality,ydata-quality,396,52,8,Jupyter Notebook,,Data Quality assessment with one line of code,ydataai,2024-01-13,2021-06-24,135,2.917894736842105,https://avatars.githubusercontent.com/u/57689451?v=4,Data Quality assessment with one line of code,"['data', 'machine-learning', 'pandas', 'quality-assessment']","['data', 'machine-learning', 'pandas', 'quality-assessment']",2023-04-05,"[('ydataai/ydata-profiling', 0.6654260754585266, 'pandas', 2), ('cleanlab/cleanlab', 0.583878219127655, 'ml', 0), ('koaning/doubtlab', 0.5491899251937866, 'data', 0), ('great-expectations/great_expectations', 0.5358034372329712, 'ml-ops', 0), ('unionai-oss/pandera', 0.5350058078765869, 'pandas', 1), ('rubik/radon', 0.5341893434524536, 'util', 0)]",10,2.0,,0.02,12,0,31,9,1,5,1,12.0,1.0,90.0,0.1,22
159,jupyter,https://github.com/nteract/testbook,[],,[],[],,,,nteract/testbook,testbook,374,41,17,Python,https://testbook.readthedocs.io,🧪 📗  Unit test your Jupyter Notebooks the right way,nteract,2024-01-04,2020-02-26,204,1.8256624825662482,https://avatars.githubusercontent.com/u/12401040?v=4,🧪 📗  Unit test your Jupyter Notebooks the right way,"['jupyter-notebook', 'nteract', 'pytest', 'testbook', 'unit-testing']","['jupyter-notebook', 'nteract', 'pytest', 'testbook', 'unit-testing']",2022-11-29,"[('computationalmodelling/nbval', 0.764438271522522, 'jupyter', 2), ('jupyter/notebook', 0.6220455765724182, 'jupyter', 1), ('jupyter/nbformat', 0.5974855422973633, 'jupyter', 0), ('samuelcolvin/dirty-equals', 0.5593006014823914, 'util', 2), ('jupyter/nbconvert', 0.5553418397903442, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.5517867207527161, 'jupyter', 0), ('ionelmc/pytest-benchmark', 0.5495516061782837, 'testing', 1), ('ipython/ipykernel', 0.5405094623565674, 'util', 1), ('pytest-dev/pytest-mock', 0.5319708585739136, 'testing', 1), ('jupyterlab/jupyterlab-desktop', 0.5236980319023132, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.5169227719306946, 'jupyter', 0)]",15,5.0,,0.0,0,0,47,14,0,4,4,0.0,0.0,90.0,0.0,22
738,perf,https://github.com/blosc/python-blosc,[],,[],[],,,,blosc/python-blosc,python-blosc,337,117,15,Python,https://www.blosc.org/python-blosc/python-blosc.html,A Python wrapper for the extremely fast Blosc compression library ,blosc,2024-01-12,2010-09-30,695,0.484394250513347,https://avatars.githubusercontent.com/u/6469856?v=4,A Python wrapper for the extremely fast Blosc compression library ,"['blosc', 'compression', 'wrapper']","['blosc', 'compression', 'wrapper']",2023-05-01,"[('zarr-developers/zarr-python', 0.5569503903388977, 'data', 0), ('ultrajson/ultrajson', 0.5216162204742432, 'perf', 0)]",44,8.0,,0.06,1,0,162,9,0,4,4,1.0,0.0,90.0,0.0,22
276,data,https://github.com/airbnb/omniduct,[],,[],[],,,,airbnb/omniduct,omniduct,248,53,29,Python,,"A toolkit providing a uniform interface for connecting to and extracting data from a wide variety of (potentially remote) data stores (including HDFS, Hive, Presto, MySQL, etc).",airbnb,2023-11-14,2017-02-22,361,0.6853533359652586,https://avatars.githubusercontent.com/u/698437?v=4,"A toolkit providing a uniform interface for connecting to and extracting data from a wide variety of (potentially remote) data stores (including HDFS, Hive, Presto, MySQL, etc).",[],[],2023-11-01,"[('airbytehq/airbyte', 0.6164752244949341, 'data', 0), ('simonw/datasette', 0.6034160256385803, 'data', 0), ('aws/aws-sdk-pandas', 0.5810672044754028, 'pandas', 0), ('intake/intake', 0.567048192024231, 'data', 0), ('meltano/meltano', 0.5620505213737488, 'ml-ops', 0), ('databricks/dbt-databricks', 0.5590947866439819, 'data', 0), ('saulpw/visidata', 0.5526487231254578, 'term', 0), ('nasdaq/data-link-python', 0.5350242853164673, 'finance', 0), ('pytables/pytables', 0.5307878851890564, 'data', 0), ('duckdb/dbt-duckdb', 0.5301816463470459, 'data', 0), ('fugue-project/fugue', 0.5275896787643433, 'pandas', 0), ('pynamodb/pynamodb', 0.5225140452384949, 'data', 0), ('hyperqueryhq/whale', 0.5135779976844788, 'data', 0), ('apache/spark', 0.5120821595191956, 'data', 0), ('databrickslabs/dbx', 0.5089248418807983, 'data', 0)]",12,5.0,,0.04,9,1,84,2,1,11,1,9.0,2.0,90.0,0.2,22
1228,util,https://github.com/jaraco/wolframalpha,[],,[],[],,,,jaraco/wolframalpha,wolframalpha,138,26,8,Python,,,jaraco,2024-01-12,2015-11-25,426,0.3232931726907631,,jaraco/wolframalpha,[],[],2023-12-26,[],22,6.0,,0.9,0,0,99,1,0,3,3,0.0,0.0,90.0,0.0,22
1857,llm,https://github.com/fasteval/fasteval,"['evaluation', 'benchmarks']",,[],[],,,,fasteval/fasteval,FastEval,129,16,3,Python,https://fasteval.github.io/FastEval/,Fast & more realistic evaluation of chat language models. Includes leaderboard.,fasteval,2024-01-13,2023-05-07,38,3.3694029850746268,https://avatars.githubusercontent.com/u/141508208?v=4,Fast & more realistic evaluation of chat language models. Includes leaderboard.,"['benchmark', 'evaluation', 'llm']","['benchmark', 'benchmarks', 'evaluation', 'llm']",2023-11-14,"[('lm-sys/fastchat', 0.7203408479690552, 'llm', 1), ('nomic-ai/gpt4all', 0.6797701120376587, 'llm', 0), ('freedomintelligence/llmzoo', 0.6659313440322876, 'llm', 0), ('thudm/chatglm2-6b', 0.6485232710838318, 'llm', 1), ('rcgai/simplyretrieve', 0.6337692141532898, 'llm', 0), ('microsoft/autogen', 0.6213794350624084, 'llm', 0), ('embedchain/embedchain', 0.6101776957511902, 'llm', 1), ('run-llama/rags', 0.6045003533363342, 'llm', 1), ('young-geng/easylm', 0.6013676524162292, 'llm', 0), ('blinkdl/chatrwkv', 0.5970721244812012, 'llm', 0), ('ai21labs/lm-evaluation', 0.5941077470779419, 'llm', 0), ('intel/intel-extension-for-transformers', 0.5938782095909119, 'perf', 0), ('openlmlab/moss', 0.593492329120636, 'llm', 0), ('mlc-ai/web-llm', 0.5912193655967712, 'llm', 1), ('next-gpt/next-gpt', 0.57789546251297, 'llm', 1), ('databrickslabs/dolly', 0.5695880055427551, 'llm', 0), ('rasahq/rasa', 0.5695826411247253, 'llm', 0), ('li-plus/chatglm.cpp', 0.5682762265205383, 'llm', 0), ('chatarena/chatarena', 0.5657923221588135, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5639832019805908, 'nlp', 0), ('hwchase17/langchain', 0.5635910630226135, 'llm', 0), ('minimaxir/simpleaichat', 0.559170663356781, 'llm', 0), ('deepset-ai/haystack', 0.5561326742172241, 'llm', 0), ('confident-ai/deepeval', 0.5521769523620605, 'testing', 2), ('nvidia/nemo', 0.5468846559524536, 'nlp', 0), ('deeppavlov/deeppavlov', 0.5457442402839661, 'nlp', 0), ('cheshire-cat-ai/core', 0.5404486060142517, 'llm', 1), ('openlmlab/leval', 0.5392605662345886, 'llm', 1), ('hiyouga/llama-factory', 0.5391845107078552, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5391843914985657, 'llm', 1), ('guidance-ai/guidance', 0.5377461910247803, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5358782410621643, 'llm', 2), ('langchain-ai/chat-langchain', 0.534351110458374, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5321249961853027, 'llm', 0), ('killianlucas/open-interpreter', 0.5310702919960022, 'llm', 0), ('mit-han-lab/streaming-llm', 0.5244007110595703, 'llm', 0), ('dylanhogg/llmgraph', 0.5239781141281128, 'ml', 1), ('microsoft/lora', 0.5221768021583557, 'llm', 0), ('togethercomputer/openchatkit', 0.519807755947113, 'nlp', 0), ('hannibal046/awesome-llm', 0.5191826820373535, 'study', 0), ('aiwaves-cn/agents', 0.5190715789794922, 'nlp', 1), ('xtekky/gpt4free', 0.5166919827461243, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.514625608921051, 'llm', 0), ('juncongmoo/pyllama', 0.5125579833984375, 'llm', 0), ('paddlepaddle/paddlenlp', 0.507324755191803, 'llm', 1), ('facebookresearch/parlai', 0.5017014145851135, 'nlp', 0), ('lchen001/llmdrift', 0.5002023577690125, 'llm', 0)]",2,0.0,,19.27,6,2,8,2,0,0,0,6.0,3.0,90.0,0.5,22
554,gis,https://github.com/darribas/gds_env,[],,[],[],,,,darribas/gds_env,gds_env,118,41,12,Jupyter Notebook,https://darribas.org/gds_env,A containerised platform for Geographic Data Science,darribas,2023-12-16,2016-08-12,389,0.3028969563623029,,A containerised platform for Geographic Data Science,"['docker', 'geographic-data-science', 'jupyter-lab', 'latex', 'r']","['docker', 'geographic-data-science', 'jupyter-lab', 'latex', 'r']",2023-10-24,"[('backtick-se/cowait', 0.6343125104904175, 'util', 1), ('orchest/orchest', 0.5977623462677002, 'ml-ops', 1), ('opengeos/leafmap', 0.563289999961853, 'gis', 0), ('airbytehq/airbyte', 0.5391685962677002, 'data', 0), ('mamba-org/micromamba-docker', 0.5356658697128296, 'util', 1), ('eventual-inc/daft', 0.5175384879112244, 'pandas', 0), ('domlysz/blendergis', 0.5147411823272705, 'gis', 0), ('multi-py/python-gunicorn', 0.5109146237373352, 'util', 1), ('simonw/datasette', 0.5070315599441528, 'data', 1), ('raphaelquast/eomaps', 0.5032002329826355, 'gis', 0), ('osgeo/grass', 0.502344012260437, 'gis', 0), ('aeternalis-ingenium/fastapi-backend-template', 0.5019727349281311, 'web', 1)]",9,5.0,,1.19,3,1,90,3,3,2,3,3.0,1.0,90.0,0.3,22
1498,llm,https://github.com/titanml/takeoff,"['inference', 'language-model']",A server designed for optimized inference of large language models,[],[],,,,titanml/takeoff,takeoff-community,107,12,7,HTML,https://docs.titanml.co/,"TitanML Takeoff Server is an optimization, compression and deployment platform that makes state of the art machine learning models accessible to everyone.",titanml,2024-01-09,2023-07-31,26,4.092896174863388,https://avatars.githubusercontent.com/u/135022454?v=4,"TitanML Takeoff Server is an optimization, compression and deployment platform that makes state of the art machine learning models accessible to everyone.","['deployment', 'llama', 'llm', 'quantization']","['deployment', 'inference', 'language-model', 'llama', 'llm', 'quantization']",2023-11-17,"[('ml-tooling/opyrator', 0.6359823942184448, 'viz', 1), ('bigscience-workshop/petals', 0.6349102854728699, 'data', 1), ('vllm-project/vllm', 0.5988606214523315, 'llm', 3), ('young-geng/easylm', 0.5864971876144409, 'llm', 2), ('unionai-oss/unionml', 0.56184321641922, 'ml-ops', 0), ('aws/sagemaker-python-sdk', 0.5581379532814026, 'ml', 0), ('huggingface/transformers', 0.5578950047492981, 'nlp', 1), ('bentoml/openllm', 0.5552429556846619, 'ml-ops', 2), ('microsoft/nni', 0.5543978214263916, 'ml', 0), ('bobazooba/xllm', 0.551180899143219, 'llm', 2), ('aiqc/aiqc', 0.550189733505249, 'ml-ops', 0), ('paddlepaddle/paddlenlp', 0.5484669208526611, 'llm', 2), ('mlc-ai/web-llm', 0.548348069190979, 'llm', 2), ('huggingface/datasets', 0.5470577478408813, 'nlp', 0), ('huawei-noah/pretrained-language-model', 0.5469074249267578, 'nlp', 1), ('mlflow/mlflow', 0.5464853048324585, 'ml-ops', 0), ('determined-ai/determined', 0.5414642691612244, 'ml-ops', 0), ('mlc-ai/mlc-llm', 0.5372212529182434, 'llm', 2), ('lm-sys/fastchat', 0.536109447479248, 'llm', 1), ('ludwig-ai/ludwig', 0.5331388711929321, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5318194031715393, 'ml-ops', 0), ('googlecloudplatform/vertex-ai-samples', 0.531377375125885, 'ml', 0), ('alpa-projects/alpa', 0.5302854776382446, 'ml-dl', 1), ('horovod/horovod', 0.529777467250824, 'ml-ops', 0), ('nebuly-ai/nebullvm', 0.5286065936088562, 'perf', 1), ('ray-project/ray', 0.5229300856590271, 'ml-ops', 1), ('squeezeailab/squeezellm', 0.5222111940383911, 'llm', 3), ('kubeflow/fairing', 0.5214179754257202, 'ml-ops', 0), ('ray-project/ray-llm', 0.5190406441688538, 'llm', 1), ('bentoml/bentoml', 0.5187811851501465, 'ml-ops', 0), ('mlc-ai/web-stable-diffusion', 0.5177373886108398, 'diffusion', 0), ('lianjiatech/belle', 0.517549991607666, 'llm', 1), ('tairov/llama2.mojo', 0.5167184472084045, 'llm', 2), ('intel/intel-extension-for-transformers', 0.5163436532020569, 'perf', 0), ('jzhang38/tinyllama', 0.5162729620933533, 'llm', 2), ('deepmind/dm-haiku', 0.5155620574951172, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5151104927062988, 'ml-dl', 0), ('pathwaycom/llm-app', 0.5129966735839844, 'llm', 1), ('zenml-io/zenml', 0.5115346908569336, 'ml-ops', 1), ('microsoft/jarvis', 0.5107346773147583, 'llm', 0), ('ravenscroftj/turbopilot', 0.5088446140289307, 'llm', 1), ('microsoft/onnxruntime', 0.5081208348274231, 'ml', 0), ('tatsu-lab/stanford_alpaca', 0.507792055606842, 'llm', 1), ('selfexplainml/piml-toolbox', 0.5073431134223938, 'ml-interpretability', 0), ('uber/petastorm', 0.5053594708442688, 'data', 0), ('salesforce/xgen', 0.5039275884628296, 'llm', 2), ('databrickslabs/dolly', 0.5038862228393555, 'llm', 0), ('predibase/lorax', 0.50351482629776, 'llm', 2), ('tigerlab-ai/tiger', 0.5030698180198669, 'llm', 1), ('microsoft/lmops', 0.5029642581939697, 'llm', 2), ('argilla-io/argilla', 0.5014408230781555, 'nlp', 1), ('tensorflow/tensorflow', 0.50138258934021, 'ml-dl', 0), ('optimalscale/lmflow', 0.5003492832183838, 'llm', 1)]",5,2.0,,0.62,3,3,6,2,0,0,0,3.0,0.0,90.0,0.0,22
1425,llm,https://github.com/pan-ml/panml,[],,[],[],,,,pan-ml/panml,panml,104,15,4,Python,,PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation.,pan-ml,2024-01-04,2023-05-11,37,2.757575757575758,https://avatars.githubusercontent.com/u/133195194?v=4,PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation.,"['artificial-intelligence', 'machine-learning', 'natural-language-processing', 'open-source', 'prompt-engineering']","['artificial-intelligence', 'machine-learning', 'natural-language-processing', 'open-source', 'prompt-engineering']",2023-07-08,"[('selfexplainml/piml-toolbox', 0.5762995481491089, 'ml-interpretability', 0), ('microsoft/nni', 0.532913088798523, 'ml', 1), ('ofa-sys/ofa', 0.5308709740638733, 'llm', 0), ('microsoft/generative-ai-for-beginners', 0.5278686285018921, 'study', 1), ('microsoft/lmops', 0.5039084553718567, 'llm', 0), ('mlflow/mlflow', 0.5007346868515015, 'ml-ops', 1)]",5,2.0,,1.33,0,0,8,6,25,52,25,0.0,0.0,90.0,0.0,22
1597,study,https://github.com/anyscale/ray-summit-2023-training,[],,[],[],,,,anyscale/ray-summit-2023-training,ray-summit-2023-training,81,27,10,Jupyter Notebook,https://raysummit.anyscale.com/trainings,,anyscale,2024-01-04,2023-08-22,23,3.5217391304347827,https://avatars.githubusercontent.com/u/51251046?v=4,anyscale/ray-summit-2023-training,"['anyscale', 'genai', 'llm', 'llms', 'ray']","['anyscale', 'genai', 'llm', 'llms', 'ray']",2023-10-02,[],7,5.0,,1.1,0,0,5,3,0,0,0,0.0,0.0,90.0,0.0,22
1454,util,https://github.com/conda-forge/feedstocks,"['feedstocks', 'conda']",,[],[],,,,conda-forge/feedstocks,feedstocks,58,41,4,,,"All conda-forge feedstocks, in one convenient place",conda-forge,2023-12-17,2016-01-13,419,0.13814222524668254,https://avatars.githubusercontent.com/u/11897326?v=4,"All conda-forge feedstocks, in one convenient place",[],"['conda', 'feedstocks']",2024-01-14,"[('conda-forge/conda-smithy', 0.8073468208312988, 'util', 0), ('mamba-org/quetz', 0.6082790493965149, 'util', 1), ('conda/conda-pack', 0.5661166906356812, 'util', 1), ('conda/conda-build', 0.5539048314094543, 'util', 1), ('conda-forge/miniforge', 0.5309390425682068, 'util', 0), ('mamba-org/boa', 0.5122072696685791, 'util', 1), ('conda/constructor', 0.507718563079834, 'util', 1)]",13,1.0,,1203.9,0,0,97,0,0,0,0,0.0,0.0,90.0,0.0,22
1178,gamedev,https://github.com/pygamelib/pygamelib,[],,[],[],,,,pygamelib/pygamelib,pygamelib,56,37,4,Python,https://www.pygamelib.org,A (not so) small python library for console (as in terminal) game development. It is developed as a framework to help learn development and python in an entertaining way.,pygamelib,2024-01-11,2019-03-15,254,0.21997755331088664,https://avatars.githubusercontent.com/u/67972986?v=4,A (not so) small python library for console (as in terminal) game development. It is developed as a framework to help learn development and python in an entertaining way.,"['game-development', 'gamedev', 'hacktoberfest2023', 'kids-coding', 'roguelike-library', 'terminal-based']","['game-development', 'gamedev', 'hacktoberfest2023', 'kids-coding', 'roguelike-library', 'terminal-based']",2023-10-21,"[('urwid/urwid', 0.700258731842041, 'term', 0), ('pygame/pygame', 0.6512402892112732, 'gamedev', 2), ('jquast/blessed', 0.6294499635696411, 'term', 0), ('lordmauve/pgzero', 0.6199098229408264, 'gamedev', 0), ('pyglet/pyglet', 0.5976749062538147, 'gamedev', 1), ('pythonarcade/arcade', 0.5959578156471252, 'gamedev', 0), ('xonsh/xonsh', 0.5624502301216125, 'util', 0), ('panda3d/panda3d', 0.5582475662231445, 'gamedev', 2), ('kitao/pyxel', 0.5561051964759827, 'gamedev', 2), ('python/cpython', 0.5532333850860596, 'util', 0), ('pytoolz/toolz', 0.5512012839317322, 'util', 0), ('willmcgugan/textual', 0.539828896522522, 'term', 0), ('eleutherai/pyfra', 0.527156412601471, 'ml', 0), ('pypy/pypy', 0.5232526659965515, 'util', 0), ('r0x0r/pywebview', 0.5211628079414368, 'gui', 0), ('1200wd/bitcoinlib', 0.5186594128608704, 'crypto', 0), ('hoffstadt/dearpygui', 0.5179816484451294, 'gui', 0), ('dylanhogg/awesome-python', 0.5159634947776794, 'study', 0), ('webpy/webpy', 0.5152013301849365, 'web', 0), ('pokepetter/ursina', 0.5143521428108215, 'gamedev', 1), ('evhub/coconut', 0.5140511989593506, 'util', 0), ('amaargiru/pyroad', 0.5083182454109192, 'study', 0), ('pyscript/pyscript-cli', 0.5070560574531555, 'web', 0), ('google/python-fire', 0.5067519545555115, 'term', 0), ('willmcgugan/rich', 0.5059705376625061, 'term', 0)]",28,3.0,,1.23,21,9,59,3,0,2,2,21.0,9.0,90.0,0.4,22
1707,util,https://github.com/stijnwoestenborghs/gradi-mojo,['mojo'],"Implementation of a simple gradient descent problem in Python, Numpy, JAX, C++ (binding with Python) and Mojo.",[],[],,,,stijnwoestenborghs/gradi-mojo,gradi-mojo,29,2,4,C++,,,stijnwoestenborghs,2024-01-04,2023-10-02,17,1.6916666666666667,,"Implementation of a simple gradient descent problem in Python, Numpy, JAX, C++ (binding with Python) and Mojo.",[],['mojo'],2023-12-03,"[('msaelices/py2mojo', 0.5529176592826843, 'util', 1), ('google/jax', 0.5190989971160889, 'ml', 0), ('cma-es/pycma', 0.5154417753219604, 'math', 0)]",4,2.0,,1.13,2,1,3,1,0,0,0,2.0,5.0,90.0,2.5,22
1042,llm,https://github.com/openai/image-gpt,[],"Archived. Code and models from the paper ""Generative Pretraining from Pixels""",[],[],,,,openai/image-gpt,image-gpt,1978,383,82,Python,,,openai,2024-01-12,2020-05-07,194,10.158473954512106,https://avatars.githubusercontent.com/u/14957082?v=4,"Archived. Code and models from the paper ""Generative Pretraining from Pixels""",[],[],2020-12-04,"[('davidadsp/generative_deep_learning_2nd_edition', 0.6263450980186462, 'study', 0), ('ist-daslab/gptq', 0.6169224381446838, 'llm', 0), ('openai/finetune-transformer-lm', 0.6050621867179871, 'llm', 0), ('bigcode-project/starcoder', 0.5846189260482788, 'llm', 0), ('salesforce/codegen', 0.5758861303329468, 'nlp', 0), ('thudm/cogvideo', 0.558464765548706, 'ml', 0), ('pollinations/dance-diffusion', 0.5518842935562134, 'diffusion', 0), ('jerryyli/valhalla-nmt', 0.5397126078605652, 'ml-dl', 0), ('open-mmlab/mmediting', 0.5370244383811951, 'ml', 0), ('salesforce/blip', 0.5261111855506897, 'diffusion', 0), ('nv-tlabs/get3d', 0.5187845826148987, 'ml', 0), ('timothybrooks/instruct-pix2pix', 0.512509286403656, 'diffusion', 0), ('karpathy/mingpt', 0.5077084302902222, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5061761140823364, 'study', 0), ('borisdayma/dalle-mini', 0.5032017827033997, 'diffusion', 0)]",3,0.0,,0.0,0,0,45,38,0,0,0,0.0,0.0,90.0,0.0,21
329,nlp,https://github.com/franck-dernoncourt/neuroner,[],,[],[],,,,franck-dernoncourt/neuroner,NeuroNER,1674,486,81,Python,http://neuroner.com,Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.,franck-dernoncourt,2024-01-13,2017-03-07,360,4.65,,Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.,"['deep-learning', 'machine-learning', 'named-entity-recognition', 'neural-networks', 'nlp', 'tensorflow']","['deep-learning', 'machine-learning', 'named-entity-recognition', 'neural-networks', 'nlp', 'tensorflow']",2019-10-02,"[('flairnlp/flair', 0.7272414565086365, 'nlp', 3), ('allenai/allennlp', 0.5776329040527344, 'nlp', 2), ('deeppavlov/deeppavlov', 0.5672761797904968, 'nlp', 5), ('keras-team/keras-nlp', 0.5547518134117126, 'nlp', 4), ('huggingface/neuralcoref', 0.5318391919136047, 'nlp', 3), ('nvidia/deeplearningexamples', 0.5285832285881042, 'ml-dl', 3), ('microsoft/vert-papers', 0.5278816223144531, 'nlp', 2), ('explosion/spacy', 0.527627170085907, 'nlp', 5), ('huggingface/transformers', 0.5211098790168762, 'nlp', 4), ('rasahq/rasa', 0.5182371735572815, 'llm', 2), ('paddlepaddle/paddlenlp', 0.5083341598510742, 'llm', 1), ('nvidia/nemo', 0.5075932145118713, 'nlp', 2), ('thilinarajapakse/simpletransformers', 0.5060073733329773, 'nlp', 1), ('deepset-ai/farm', 0.5047802329063416, 'nlp', 2), ('explosion/spacy-llm', 0.5040908455848694, 'llm', 3), ('nltk/nltk', 0.5021610856056213, 'nlp', 2)]",7,2.0,,0.0,0,0,83,52,0,0,0,0.0,0.0,90.0,0.0,21
666,perf,https://github.com/markshannon/faster-cpython,[],,[],[],,,,markshannon/faster-cpython,faster-cpython,933,20,84,,,How to make CPython faster.,markshannon,2024-01-10,2020-10-19,171,5.451585976627713,,How to make CPython faster.,[],[],2020-10-28,"[('faster-cpython/tools', 0.8188387751579285, 'perf', 0), ('faster-cpython/ideas', 0.6732155084609985, 'perf', 0), ('brandtbucher/specialist', 0.5744246244430542, 'perf', 0), ('p403n1x87/austin', 0.5521705150604248, 'profiling', 0), ('python/cpython', 0.549221932888031, 'util', 0), ('pypy/pypy', 0.5423987507820129, 'util', 0), ('ipython/ipyparallel', 0.541959285736084, 'perf', 0), ('lcompilers/lpython', 0.5289682149887085, 'util', 0), ('cython/cython', 0.5285276770591736, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5006858706474304, 'perf', 0)]",4,2.0,,0.0,1,1,39,39,0,0,0,1.0,0.0,90.0,0.0,21
1152,util,https://github.com/alex-sherman/unsync,[],,[],[],,,,alex-sherman/unsync,unsync,860,50,21,Python,,Unsynchronize asyncio,alex-sherman,2024-01-13,2018-02-06,312,2.7564102564102564,,Unsynchronize asyncio,[],[],2022-02-06,"[('magicstack/uvloop', 0.6438055634498596, 'util', 0), ('erdewit/nest_asyncio', 0.597553551197052, 'util', 0), ('aio-libs/aiohttp', 0.5807369351387024, 'web', 0), ('timofurrer/awesome-asyncio', 0.5594347715377808, 'study', 0), ('agronholm/anyio', 0.5539295673370361, 'perf', 0), ('tiangolo/asyncer', 0.5449034571647644, 'perf', 0), ('pytest-dev/pytest-asyncio', 0.5358930230140686, 'testing', 0), ('samuelcolvin/arq', 0.5158486366271973, 'data', 0), ('aio-libs/aiokafka', 0.5082889199256897, 'data', 0), ('samuelcolvin/aioaws', 0.507796049118042, 'data', 0), ('noxdafox/pebble', 0.5057518482208252, 'perf', 0)]",11,3.0,,0.0,0,0,72,24,0,1,1,0.0,0.0,90.0,0.0,21
168,nlp,https://github.com/lexpredict/lexpredict-lexnlp,[],,[],[],,,,lexpredict/lexpredict-lexnlp,lexpredict-lexnlp,659,171,51,Jupyter Notebook,,LexNLP by LexPredict,lexpredict,2024-01-06,2017-09-30,330,1.9943795936013835,https://avatars.githubusercontent.com/u/8458599?v=4,LexNLP by LexPredict,"['analytics', 'contracts', 'data', 'law', 'legal', 'legaltech', 'linguistics', 'ml', 'nlp']","['analytics', 'contracts', 'data', 'law', 'legal', 'legaltech', 'linguistics', 'ml', 'nlp']",2023-03-07,"[('nltk/nltk', 0.6562715172767639, 'nlp', 1), ('coastalcph/lex-glue', 0.6552823185920715, 'nlp', 3), ('iclrandd/blackstone', 0.582179069519043, 'nlp', 3), ('explosion/spacy-llm', 0.5600504875183105, 'llm', 1), ('explosion/spacy', 0.5584388971328735, 'nlp', 1), ('sloria/textblob', 0.5583809018135071, 'nlp', 1), ('cgpotts/cs224u', 0.5473800897598267, 'study', 1), ('explosion/spacy-models', 0.5394803285598755, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5384023189544678, 'llm', 1), ('norskregnesentral/skweak', 0.5267290472984314, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.525945782661438, 'study', 1), ('allenai/allennlp', 0.524100661277771, 'nlp', 1), ('infinitylogesh/mutate', 0.5115991234779358, 'nlp', 0)]",9,1.0,,0.04,0,0,77,10,9,2,9,0.0,0.0,90.0,0.0,21
1734,data,https://github.com/mcfunley/pugsql,"['orm', 'hugsql', 'sql']",,[],[],,,,mcfunley/pugsql,pugsql,652,20,10,Python,https://pugsql.org,A HugSQL-inspired database library for Python,mcfunley,2024-01-13,2019-05-19,245,2.658124635993011,,A HugSQL-inspired database library for Python,[],"['hugsql', 'orm', 'sql']",2022-05-27,"[('tiangolo/sqlmodel', 0.6844363808631897, 'data', 1), ('sqlalchemy/sqlalchemy', 0.6739656329154968, 'data', 1), ('ibis-project/ibis', 0.6264197826385498, 'data', 1), ('collerek/ormar', 0.6217651963233948, 'data', 1), ('coleifer/peewee', 0.6096048951148987, 'data', 0), ('andialbrecht/sqlparse', 0.5752284526824951, 'data', 0), ('tobymao/sqlglot', 0.5487765073776245, 'data', 1), ('aio-libs/aiomysql', 0.5428056716918945, 'data', 0), ('qdrant/fastembed', 0.5356664657592773, 'ml', 0), ('nasdaq/data-link-python', 0.5342159867286682, 'finance', 0), ('kayak/pypika', 0.5262505412101746, 'data', 1), ('aio-libs/aiopg', 0.5243105888366699, 'data', 0), ('sdispater/orator', 0.5235227346420288, 'data', 1), ('strawberry-graphql/strawberry', 0.5126366019248962, 'web', 0), ('agronholm/sqlacodegen', 0.5109015703201294, 'data', 0), ('pytoolz/toolz', 0.5106123685836792, 'util', 0), ('sqlalchemy/alembic', 0.5098458528518677, 'data', 1), ('sfu-db/connector-x', 0.5034618377685547, 'data', 1), ('jina-ai/vectordb', 0.5005654096603394, 'data', 0)]",12,1.0,,0.0,2,1,57,20,0,4,4,2.0,0.0,90.0,0.0,21
274,crypto,https://github.com/ethtx/ethtx,[],,[],[],,,,ethtx/ethtx,ethtx,447,74,16,Python,https://www.ethtx.info,Python package with core transaction decoding functions.,ethtx,2024-01-12,2021-06-28,135,3.307610993657505,https://avatars.githubusercontent.com/u/70520035?v=4,Python package with core transaction decoding functions.,[],[],2023-05-17,"[('pytoolz/toolz', 0.5787340402603149, 'util', 0), ('ethtx/ethtx_ce', 0.5777018666267395, 'crypto', 0), ('pmorissette/ffn', 0.5535359978675842, 'finance', 0), ('indygreg/pyoxidizer', 0.5071747303009033, 'util', 0), ('pdm-project/pdm', 0.5046972632408142, 'util', 0), ('pyston/pyston', 0.5028896331787109, 'util', 0)]",6,2.0,,0.08,1,0,31,8,1,15,1,1.0,0.0,90.0,0.0,21
1071,llm,https://github.com/bigscience-workshop/t-zero,[],,[],[],,,,bigscience-workshop/t-zero,t-zero,436,51,24,Python,,Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization),bigscience-workshop,2024-01-10,2021-12-13,111,3.922879177377892,https://avatars.githubusercontent.com/u/82455566?v=4,Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization),[],[],2022-07-29,"[('huggingface/setfit', 0.5152558088302612, 'nlp', 0)]",6,4.0,,0.0,0,0,25,18,0,0,0,0.0,0.0,90.0,0.0,21
833,util,https://github.com/carlospuenteg/file-injector,[],,[],[],,,,carlospuenteg/file-injector,File-Injector,421,24,7,Python,,File Injector is a script that allows you to store any file in an image using steganography,carlospuenteg,2024-01-12,2022-10-22,66,6.337634408602151,,File Injector is a script that allows you to store any file in an image using steganography,"['extraction', 'file', 'file-injection', 'file-injector', 'files', 'image', 'image-manipulation', 'image-processing', 'injection', 'noise', 'numpy', 'photography', 'steganography', 'storage']","['extraction', 'file', 'file-injection', 'file-injector', 'files', 'image', 'image-manipulation', 'image-processing', 'injection', 'noise', 'numpy', 'photography', 'steganography', 'storage']",2022-11-18,[],1,0.0,,0.0,0,0,15,14,0,11,11,0.0,0.0,90.0,0.0,21
1651,nlp,https://github.com/babelscape/rebel,[],,[],[],,,,babelscape/rebel,rebel,382,51,4,Python,,REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).,babelscape,2024-01-11,2021-09-06,125,3.0525114155251143,https://avatars.githubusercontent.com/u/90899893?v=4,REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).,"['entity-linking', 'natural-language-generation', 'natural-language-processing', 'nlp', 'relation-extraction']","['entity-linking', 'natural-language-generation', 'natural-language-processing', 'nlp', 'relation-extraction']",2023-11-09,"[('microsoft/vert-papers', 0.5618175864219666, 'nlp', 2), ('zjunlp/deepke', 0.5535876154899597, 'ml', 2)]",4,0.0,,0.1,2,1,29,2,0,0,0,2.0,2.0,90.0,1.0,21
924,llm,https://github.com/lucidrains/medical-chatgpt,[],,[],[],,,,lucidrains/medical-chatgpt,medical-chatgpt,307,30,32,Python,,"Implementation of ChatGPT, but tailored towards primary care medicine, with the reward being able to collect patient histories in a thorough and efficient manner and come up with a reasonable differential diagnosis",lucidrains,2024-01-04,2022-12-10,59,5.165865384615385,,"Implementation of ChatGPT, but tailored towards primary care medicine, with the reward being able to collect patient histories in a thorough and efficient manner and come up with a reasonable differential diagnosis","['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'medicine', 'transformers']","['artificial-intelligence', 'attention-mechanisms', 'deep-learning', 'medicine', 'transformers']",2023-10-14,"[('oneil512/insight', 0.6099081635475159, 'ml', 0), ('project-monai/monai', 0.5313103795051575, 'ml', 1), ('epfllm/meditron', 0.5020337104797363, 'llm', 0)]",2,1.0,,0.1,0,0,13,3,0,0,0,0.0,0.0,90.0,0.0,21
844,web,https://github.com/conradbez/hstream,[],,[],[],,,,conradbez/hstream,hstream,274,14,7,Python,,Hyper Stream,conradbez,2024-01-04,2022-11-03,64,4.233995584988962,,Hyper Stream,[],[],2023-11-25,[],5,1.0,,0.4,0,0,15,2,0,0,0,0.0,0.0,90.0,0.0,21
1810,llm,https://github.com/alphasecio/langchain-examples,[],,[],[],,,,alphasecio/langchain-examples,langchain-examples,257,68,4,Python,,A collection of apps powered by the LangChain LLM framework.,alphasecio,2024-01-13,2023-05-19,36,7.02734375,,A collection of apps powered by the LangChain LLM framework.,"['genai', 'jupyter-notebook', 'langchain', 'llm', 'notebook', 'openai', 'pinecone', 'streamlit', 'vectordb']","['genai', 'jupyter-notebook', 'langchain', 'llm', 'notebook', 'openai', 'pinecone', 'streamlit', 'vectordb']",2023-09-28,"[('langchain-ai/langgraph', 0.6478251814842224, 'llm', 1), ('pathwaycom/llm-app', 0.6350022554397583, 'llm', 1), ('shishirpatil/gorilla', 0.6303489804267883, 'llm', 1), ('microsoft/semantic-kernel', 0.6260648965835571, 'llm', 2), ('nat/openplayground', 0.6207661628723145, 'llm', 0), ('explosion/spacy-streamlit', 0.6193504929542542, 'nlp', 1), ('lancedb/lancedb', 0.601751446723938, 'data', 1), ('tigerlab-ai/tiger', 0.5991637706756592, 'llm', 1), ('activeloopai/deeplake', 0.5867215991020203, 'ml-ops', 2), ('streamlit/streamlit', 0.5835210084915161, 'viz', 1), ('gradio-app/gradio', 0.5749422907829285, 'viz', 0), ('willmcgugan/textual', 0.570219874382019, 'term', 0), ('kivy/kivy', 0.5670594573020935, 'util', 0), ('microsoft/promptflow', 0.5629417896270752, 'llm', 1), ('cohere-ai/notebooks', 0.5620157718658447, 'llm', 0), ('hegelai/prompttools', 0.5608381628990173, 'llm', 0), ('prefecthq/langchain-prefect', 0.5599607229232788, 'llm', 1), ('chainlit/chainlit', 0.5578954815864563, 'llm', 3), ('gkamradt/langchain-tutorials', 0.5529564023017883, 'study', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5519778728485107, 'template', 0), ('salesforce/xgen', 0.5513124465942383, 'llm', 1), ('run-llama/rags', 0.5506168007850647, 'llm', 3), ('hwchase17/langchain', 0.5499154925346375, 'llm', 1), ('openai/tiktoken', 0.5489485263824463, 'nlp', 0), ('fastai/fastcore', 0.5439481735229492, 'util', 0), ('mlc-ai/web-llm', 0.5424495339393616, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5416777729988098, 'llm', 1), ('bobazooba/xllm', 0.5409016609191895, 'llm', 2), ('berriai/litellm', 0.5404641032218933, 'llm', 3), ('bigscience-workshop/petals', 0.5384784936904907, 'data', 0), ('eugeneyan/open-llms', 0.5366406440734863, 'study', 1), ('hiyouga/llama-efficient-tuning', 0.5362140536308289, 'llm', 1), ('hiyouga/llama-factory', 0.5362140536308289, 'llm', 1), ('aws/graph-notebook', 0.5361274480819702, 'jupyter', 1), ('nebuly-ai/nebullvm', 0.5353783369064331, 'perf', 1), ('embedchain/embedchain', 0.532405436038971, 'llm', 1), ('lianjiatech/belle', 0.5297924876213074, 'llm', 0), ('zilliztech/gptcache', 0.5278271436691284, 'llm', 3), ('nvidia/tensorrt-llm', 0.5265845060348511, 'viz', 0), ('deepset-ai/haystack', 0.5227251648902893, 'llm', 0), ('flet-dev/flet', 0.5201708078384399, 'web', 0), ('google/gin-config', 0.5197805762290955, 'util', 0), ('langchain-ai/langsmith-sdk', 0.5165998935699463, 'llm', 0), ('young-geng/easylm', 0.5165916681289673, 'llm', 0), ('dylanhogg/awesome-python', 0.5161072611808777, 'study', 0), ('alpha-vllm/llama2-accessory', 0.5159311890602112, 'llm', 0), ('agenta-ai/agenta', 0.5152485370635986, 'llm', 2), ('tiangolo/fastapi', 0.5145138502120972, 'web', 0), ('run-llama/llama-hub', 0.5134257674217224, 'data', 1), ('plotly/dash', 0.5120916962623596, 'viz', 0), ('zenodo/zenodo', 0.511971652507782, 'util', 0), ('mito-ds/monorepo', 0.5109399557113647, 'jupyter', 0), ('microsoft/autogen', 0.5106598138809204, 'llm', 0), ('langchain-ai/langsmith-cookbook', 0.5074201822280884, 'llm', 0), ('koaning/calm-notebooks', 0.5073686242103577, 'study', 0), ('cheshire-cat-ai/core', 0.5036561489105225, 'llm', 1), ('vitalik/django-ninja', 0.5026804804801941, 'web', 0), ('facebookresearch/hydra', 0.5024363994598389, 'util', 0), ('holoviz/panel', 0.500730037689209, 'viz', 0), ('h2oai/h2o-llmstudio', 0.5006608366966248, 'llm', 1), ('qdrant/fastembed', 0.5004814267158508, 'ml', 2), ('huggingface/huggingface_hub', 0.5002479553222656, 'ml', 0), ('chroma-core/chroma', 0.5002412796020508, 'data', 1)]",1,0.0,,2.44,1,0,8,4,0,0,0,1.0,0.0,90.0,0.0,21
551,ml,https://github.com/nicolas-chaulet/torch-points3d,[],,[],[],,,,nicolas-chaulet/torch-points3d,torch-points3d,176,40,1,,https://torch-points3d.readthedocs.io/en/latest/,Pytorch framework for doing deep learning on point clouds.,nicolas-chaulet,2024-01-04,2022-01-09,107,1.640479360852197,,Pytorch framework for doing deep learning on point clouds.,[],[],2021-12-10,"[('pytorch/ignite', 0.6167212128639221, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.6138631701469421, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5965690016746521, 'study', 0), ('nvidia/apex', 0.5915487408638, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.580845832824707, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5780234932899475, 'perf', 0), ('skorch-dev/skorch', 0.5687388777732849, 'ml-dl', 0), ('huggingface/accelerate', 0.5572007894515991, 'ml', 0), ('ashleve/lightning-hydra-template', 0.5534338355064392, 'util', 0), ('denys88/rl_games', 0.5515884757041931, 'ml-rl', 0), ('tensorflow/mesh', 0.5492365956306458, 'ml-dl', 0), ('karpathy/micrograd', 0.5409857034683228, 'study', 0), ('lucidrains/imagen-pytorch', 0.5233572125434875, 'ml-dl', 0), ('openai/point-e', 0.5189539790153503, 'util', 0), ('nvlabs/gcvit', 0.5170150995254517, 'diffusion', 0), ('rasbt/machine-learning-book', 0.5115610957145691, 'study', 0), ('ageron/handson-ml2', 0.5103498101234436, 'ml', 0), ('rentruewang/koila', 0.5067731738090515, 'ml', 0), ('xl0/lovely-tensors', 0.5040701031684875, 'ml-dl', 0), ('dmlc/dgl', 0.501939594745636, 'ml-dl', 0), ('hazyresearch/hgcn', 0.5006260871887207, 'ml', 0)]",29,6.0,,0.0,0,0,24,25,0,5,5,0.0,0.0,90.0,0.0,21
1484,util,https://github.com/allrod5/injectable,['dependency-injection'],,[],[],,,,allrod5/injectable,injectable,101,8,5,Python,https://injectable.readthedocs.io,Python Dependency Injection for Humans™,allrod5,2024-01-09,2018-02-04,312,0.3234217749313815,,Python Dependency Injection for Humans™,"['autowired', 'autowiring', 'circular-dependencies', 'dependency-injection', 'for-humans', 'injection', 'ioc', 'lazy-evaluation', 'micro-framework']","['autowired', 'autowiring', 'circular-dependencies', 'dependency-injection', 'for-humans', 'injection', 'ioc', 'lazy-evaluation', 'micro-framework']",2023-01-11,"[('python-injector/injector', 0.6809417605400085, 'util', 1), ('ets-labs/python-dependency-injector', 0.6628846526145935, 'util', 2), ('ivankorobkov/python-inject', 0.640688955783844, 'util', 1), ('pytoolz/toolz', 0.5491871237754822, 'util', 0), ('eleutherai/pyfra', 0.5288636088371277, 'ml', 0), ('pdm-project/pdm', 0.5183610320091248, 'util', 0), ('python-poetry/poetry', 0.5134292244911194, 'util', 0), ('artemyk/dynpy', 0.5095717906951904, 'sim', 0), ('hoffstadt/dearpygui', 0.508715033531189, 'gui', 0), ('micropython/micropython', 0.5082573294639587, 'util', 0), ('google/pyglove', 0.5078258514404297, 'util', 0), ('grahamdumpleton/wrapt', 0.5006911754608154, 'util', 0), ('reloadware/reloadium', 0.5002021193504333, 'profiling', 0)]",3,3.0,,0.0,1,0,72,12,0,5,5,1.0,3.0,90.0,3.0,21
965,data,https://github.com/vmiklos/ged2dot,[],,[],[],,,,vmiklos/ged2dot,ged2dot,93,19,9,Python,https://vmiklos.hu/ged2dot/,GEDCOM to Graphviz converter,vmiklos,2023-11-17,2013-11-01,534,0.17397113842864778,,GEDCOM to Graphviz converter,"['dot', 'gedcom', 'libreoffice']","['dot', 'gedcom', 'libreoffice']",2024-01-01,"[('pydot/pydot', 0.5854193568229675, 'viz', 0), ('pygraphviz/pygraphviz', 0.5002699494361877, 'viz', 0)]",9,2.0,,0.54,7,7,124,0,2,2,2,7.0,2.0,90.0,0.3,21
814,pandas,https://github.com/ddelange/mapply,[],,[],[],,,,ddelange/mapply,mapply,63,3,5,Python,,Sensible multi-core apply function for Pandas,ddelange,2024-01-08,2020-10-26,170,0.3702770780856423,,Sensible multi-core apply function for Pandas,[],[],2024-01-13,"[('jmcarpenter2/swifter', 0.6554047465324402, 'pandas', 0), ('nalepae/pandarallel', 0.6456267833709717, 'pandas', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5479068756103516, 'pandas', 0), ('blaze/blaze', 0.513414204120636, 'pandas', 0)]",2,0.0,,0.4,20,20,39,0,6,8,6,20.0,38.0,90.0,1.9,21
391,pandas,https://github.com/tkrabel/bamboolib,[],,[],[],,,,tkrabel/bamboolib,bamboolib,921,94,32,Jupyter Notebook,https://bamboolib.com,bamboolib - a GUI for pandas DataFrames,tkrabel,2024-01-04,2019-05-29,243,3.7768014059753954,,bamboolib - a GUI for pandas DataFrames,"['jupyter-notebook', 'jupyterlab', 'pandas', 'pandas-dataframes']","['jupyter-notebook', 'jupyterlab', 'pandas', 'pandas-dataframes']",2022-09-27,"[('adamerose/pandasgui', 0.8184458017349243, 'pandas', 1), ('quantopian/qgrid', 0.7011144161224365, 'jupyter', 0), ('lux-org/lux', 0.6735276579856873, 'viz', 1), ('jakevdp/pythondatasciencehandbook', 0.6585032939910889, 'study', 2), ('bloomberg/ipydatagrid', 0.6406834721565247, 'jupyter', 0), ('kanaries/pygwalker', 0.6401150822639465, 'pandas', 1), ('holoviz/panel', 0.620628833770752, 'viz', 0), ('cmudig/autoprofiler', 0.6205199956893921, 'jupyter', 1), ('man-group/dtale', 0.6187593936920166, 'viz', 2), ('jupyter-widgets/ipywidgets', 0.5929217338562012, 'jupyter', 0), ('twopirllc/pandas-ta', 0.5758503675460815, 'finance', 2), ('vizzuhq/ipyvizzu', 0.5739924311637878, 'jupyter', 1), ('beeware/toga', 0.5691761374473572, 'gui', 0), ('mwaskom/seaborn', 0.5646217465400696, 'viz', 1), ('geopandas/geopandas', 0.5592805743217468, 'gis', 1), ('wesm/pydata-book', 0.557572066783905, 'study', 0), ('jupyterlab/jupyterlab-desktop', 0.5493032336235046, 'jupyter', 2), ('jazzband/tablib', 0.5476372241973877, 'data', 0), ('pandas-dev/pandas', 0.5454512238502502, 'pandas', 1), ('jmcarpenter2/swifter', 0.5419985055923462, 'pandas', 1), ('delta-io/delta-rs', 0.5383087396621704, 'pandas', 1), ('aws/graph-notebook', 0.5372664928436279, 'jupyter', 1), ('eleutherai/pyfra', 0.53708815574646, 'ml', 0), ('voila-dashboards/voila', 0.5368069410324097, 'jupyter', 1), ('nalepae/pandarallel', 0.5356969237327576, 'pandas', 1), ('mwouts/jupytext', 0.5347379446029663, 'jupyter', 2), ('jupyter/nbformat', 0.5312812328338623, 'jupyter', 0), ('scikit-learn-contrib/sklearn-pandas', 0.5288722515106201, 'pandas', 0), ('jupyter/notebook', 0.5267034769058228, 'jupyter', 1), ('jmcnamara/xlsxwriter', 0.5247606039047241, 'data', 1), ('zsailer/pandas_flavor', 0.5217926502227783, 'pandas', 1), ('parthjadhav/tkinter-designer', 0.5206194519996643, 'gui', 0), ('ipython/ipyparallel', 0.5092272162437439, 'perf', 0), ('zoomeranalytics/xlwings', 0.5089218616485596, 'data', 0), ('modin-project/modin', 0.5066853761672974, 'perf', 1), ('hoffstadt/dearpygui', 0.5062575936317444, 'gui', 0), ('rsheftel/pandas_market_calendars', 0.5060363411903381, 'finance', 1), ('ipython/ipykernel', 0.5052734017372131, 'util', 1), ('blaze/blaze', 0.5050551295280457, 'pandas', 0), ('dylanhogg/awesome-python', 0.5048226118087769, 'study', 1), ('mementum/bta-lib', 0.5043449401855469, 'finance', 0), ('plotly/plotly.py', 0.5021097660064697, 'viz', 1), ('masoniteframework/masonite', 0.5014203190803528, 'web', 0), ('rapidsai/cudf', 0.5010843276977539, 'pandas', 1)]",4,2.0,,0.0,0,0,56,16,0,0,0,0.0,0.0,90.0,0.0,20
392,perf,https://github.com/klen/py-frameworks-bench,[],,[],[],,,,klen/py-frameworks-bench,py-frameworks-bench,699,86,27,Python,https://klen.github.io/py-frameworks-bench/,Another benchmark for some python frameworks,klen,2024-01-03,2015-04-30,456,1.5304973412574288,,Another benchmark for some python frameworks,"['benchmark', 'python-frameworks']","['benchmark', 'python-frameworks']",2022-03-14,"[('ionelmc/pytest-benchmark', 0.6951150298118591, 'testing', 1), ('locustio/locust', 0.6128438115119934, 'testing', 0), ('fastai/fastcore', 0.6014686226844788, 'util', 0), ('neoteroi/blacksheep', 0.5975432395935059, 'web', 0), ('eleutherai/pyfra', 0.5963848829269409, 'ml', 0), ('lcompilers/lpython', 0.587522566318512, 'util', 0), ('wolever/parameterized', 0.5862182974815369, 'testing', 0), ('pypy/pypy', 0.5855749845504761, 'util', 0), ('pyutils/line_profiler', 0.5825835466384888, 'profiling', 0), ('pyston/pyston', 0.5815452933311462, 'util', 0), ('cython/cython', 0.5801135301589966, 'util', 0), ('pytoolz/toolz', 0.5774570107460022, 'util', 0), ('benfred/py-spy', 0.5661771297454834, 'profiling', 0), ('klen/muffin', 0.5657544732093811, 'web', 0), ('alirn76/panther', 0.564853310585022, 'web', 0), ('sumerc/yappi', 0.5642154216766357, 'profiling', 0), ('p403n1x87/austin', 0.5635471343994141, 'profiling', 0), ('mrdbourke/m1-machine-learning-test', 0.5594583749771118, 'ml', 0), ('pympler/pympler', 0.5578858256340027, 'perf', 0), ('carla-recourse/carla', 0.5568965077400208, 'ml', 1), ('pmorissette/bt', 0.5551998615264893, 'finance', 0), ('qdrant/vector-db-benchmark', 0.5516537427902222, 'perf', 1), ('rubik/radon', 0.5515268445014954, 'util', 0), ('google/gin-config', 0.5510473847389221, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5484030842781067, 'study', 0), ('exaloop/codon', 0.5465443730354309, 'perf', 0), ('grantjenks/python-diskcache', 0.5420731902122498, 'util', 0), ('joblib/joblib', 0.540972888469696, 'util', 0), ('faster-cpython/tools', 0.5376661419868469, 'perf', 0), ('dgilland/cacheout', 0.5362305641174316, 'perf', 0), ('geeogi/async-python-lambda-template', 0.5316784977912903, 'template', 0), ('nvidia/warp', 0.5304505825042725, 'sim', 0), ('bottlepy/bottle', 0.5283790826797485, 'web', 0), ('intel/intel-extension-for-pytorch', 0.5263614654541016, 'perf', 0), ('reloadware/reloadium', 0.5263360142707825, 'profiling', 0), ('python-trio/trio', 0.5250015258789062, 'perf', 0), ('libtcod/python-tcod', 0.5221824049949646, 'gamedev', 0), ('pyinfra-dev/pyinfra', 0.5210394263267517, 'util', 0), ('sfu-db/connector-x', 0.5208754539489746, 'data', 0), ('hyperopt/hyperopt', 0.5207222700119019, 'ml', 0), ('beeware/toga', 0.5207023024559021, 'gui', 0), ('jmcarpenter2/swifter', 0.519839346408844, 'pandas', 0), ('python-cachier/cachier', 0.5169395208358765, 'perf', 0), ('mementum/backtrader', 0.5150114893913269, 'finance', 0), ('nedbat/coveragepy', 0.514492928981781, 'testing', 0), ('hoffstadt/dearpygui', 0.511713981628418, 'gui', 0), ('astral-sh/ruff', 0.5096173882484436, 'util', 0), ('python-restx/flask-restx', 0.5092925429344177, 'web', 0), ('timofurrer/awesome-asyncio', 0.5063012838363647, 'study', 0), ('magicstack/uvloop', 0.5054547190666199, 'util', 0), ('ipython/ipyparallel', 0.5014100074768066, 'perf', 0), ('plasma-umass/scalene', 0.500389039516449, 'profiling', 0)]",10,4.0,,0.0,0,0,106,22,0,2,2,0.0,0.0,90.0,0.0,20
211,time-series,https://github.com/firmai/atspy,[],,[],[],,,,firmai/atspy,atspy,499,89,21,Python,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580631,AtsPy: Automated Time Series Models in Python (by @firmai),firmai,2024-01-05,2020-01-28,209,2.38755980861244,,AtsPy: Automated Time Series Models in Python (by @firmai),"['automated', 'finance', 'forecasting', 'forecasting-models', 'time-series', 'time-series-analysis']","['automated', 'finance', 'forecasting', 'forecasting-models', 'time-series', 'time-series-analysis']",2021-12-18,"[('alkaline-ml/pmdarima', 0.7777550220489502, 'time-series', 3), ('awslabs/gluonts', 0.695907711982727, 'time-series', 2), ('winedarksea/autots', 0.6719325184822083, 'time-series', 2), ('statsmodels/statsmodels', 0.6599208116531372, 'ml', 1), ('unit8co/darts', 0.6426480412483215, 'time-series', 2), ('bashtage/arch', 0.6273349523544312, 'time-series', 3), ('scikit-learn/scikit-learn', 0.6164392232894897, 'ml', 0), ('rjt1990/pyflux', 0.6143859624862671, 'time-series', 1), ('tdameritrade/stumpy', 0.6093729138374329, 'time-series', 1), ('goldmansachs/gs-quant', 0.6063892841339111, 'finance', 0), ('google/temporian', 0.6023882031440735, 'time-series', 1), ('sktime/sktime', 0.5919238924980164, 'time-series', 3), ('uber/orbit', 0.5873233675956726, 'time-series', 2), ('ta-lib/ta-lib-python', 0.5853879451751709, 'finance', 1), ('stan-dev/pystan', 0.5848978757858276, 'ml', 0), ('ranaroussi/quantstats', 0.5767538547515869, 'finance', 1), ('nixtla/statsforecast', 0.5731773972511292, 'time-series', 2), ('cuemacro/finmarketpy', 0.5688939690589905, 'finance', 0), ('crflynn/stochastic', 0.5687724351882935, 'sim', 0), ('featurelabs/featuretools', 0.5671241283416748, 'ml', 0), ('pastas/pastas', 0.5670745372772217, 'time-series', 0), ('eleutherai/pyfra', 0.5660873055458069, 'ml', 0), ('gradio-app/gradio', 0.5611603856086731, 'viz', 0), ('ourownstory/neural_prophet', 0.5586233735084534, 'ml', 2), ('gbeced/pyalgotrade', 0.5548366904258728, 'finance', 0), ('salesforce/merlion', 0.5541254281997681, 'time-series', 2), ('automl/auto-sklearn', 0.5523808598518372, 'ml', 0), ('rasbt/mlxtend', 0.5510342121124268, 'ml', 0), ('google/pyglove', 0.5508860349655151, 'util', 0), ('pmorissette/ffn', 0.5481270551681519, 'finance', 0), ('scikit-mobility/scikit-mobility', 0.5477433800697327, 'gis', 0), ('pycaret/pycaret', 0.5466391444206238, 'ml', 1), ('microsoft/flaml', 0.5425217747688293, 'ml', 0), ('online-ml/river', 0.5391773581504822, 'ml', 0), ('nccr-itmo/fedot', 0.5334661602973938, 'ml-ops', 0), ('pytoolz/toolz', 0.5319315791130066, 'util', 0), ('awslabs/autogluon', 0.53136146068573, 'ml', 2), ('robcarver17/pysystemtrade', 0.5296515226364136, 'finance', 0), ('mljar/mljar-supervised', 0.5243828296661377, 'ml', 0), ('plotly/dash', 0.5219200849533081, 'viz', 1), ('pypy/pypy', 0.5205024480819702, 'util', 0), ('wilsonrljr/sysidentpy', 0.5202825665473938, 'time-series', 1), ('pandas-dev/pandas', 0.5182963013648987, 'pandas', 0), ('dateutil/dateutil', 0.5173526406288147, 'util', 0), ('linkedin/greykite', 0.5171492099761963, 'ml', 0), ('salesforce/deeptime', 0.5146031975746155, 'time-series', 2), ('microprediction/microprediction', 0.5109694004058838, 'time-series', 1), ('shankarpandala/lazypredict', 0.5108960866928101, 'ml', 0), ('facebook/prophet', 0.5104213356971741, 'time-series', 2), ('skops-dev/skops', 0.5083118081092834, 'ml-ops', 0), ('artemyk/dynpy', 0.5067782998085022, 'sim', 0), ('agronholm/apscheduler', 0.5056720972061157, 'util', 0), ('quantopian/pyfolio', 0.5053930282592773, 'finance', 0), ('kernc/backtesting.py', 0.5046423673629761, 'finance', 1), ('hydrosquall/tiingo-python', 0.5044682025909424, 'finance', 1), ('polyaxon/datatile', 0.5035507678985596, 'pandas', 0), ('selfexplainml/piml-toolbox', 0.5030118823051453, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.5014930367469788, 'ml', 0), ('epistasislab/tpot', 0.5008826851844788, 'ml', 0), ('gbeced/basana', 0.5008696913719177, 'finance', 0)]",5,2.0,,0.0,1,0,48,25,0,0,0,1.0,1.0,90.0,1.0,20
1757,diffusion,https://github.com/laion-ai/dalle2-laion,"['text-to-image', 'diffusion']",,[],[],,,,laion-ai/dalle2-laion,dalle2-laion,489,65,23,Python,,Pretrained Dalle2 from laion,laion-ai,2024-01-12,2022-06-26,83,5.871355060034305,https://avatars.githubusercontent.com/u/92627801?v=4,Pretrained Dalle2 from laion,[],"['diffusion', 'text-to-image']",2022-11-09,"[('saharmor/dalle-playground', 0.570446252822876, 'diffusion', 1), ('borisdayma/dalle-mini', 0.5235878229141235, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5131522417068481, 'diffusion', 1), ('huggingface/diffusers', 0.5089276432991028, 'diffusion', 1)]",5,1.0,,0.0,0,0,19,14,0,0,0,0.0,0.0,90.0,0.0,20
1804,util,https://github.com/taylorsmarks/playsound,"['mp3', 'sound']",,[],[],,,,taylorsmarks/playsound,playsound,475,114,13,Python,,"Pure Python, cross platform, single function module with no dependencies for playing sounds.",taylorsmarks,2024-01-09,2016-01-27,417,1.1367521367521367,,"Pure Python, cross platform, single function module with no dependencies for playing sounds.",[],"['mp3', 'sound']",2021-08-06,"[('bastibe/python-soundfile', 0.6413252949714661, 'util', 0), ('spotify/pedalboard', 0.6269357204437256, 'util', 0), ('irmen/pyminiaudio', 0.6150918006896973, 'util', 0), ('quodlibet/mutagen', 0.6006749868392944, 'util', 1), ('uberi/speech_recognition', 0.5477955341339111, 'ml', 0), ('pytoolz/toolz', 0.5312017798423767, 'util', 0), ('asweigart/pyperclip', 0.5268693566322327, 'util', 0)]",8,1.0,,0.0,5,0,97,30,0,0,0,5.0,15.0,90.0,3.0,20
569,gis,https://github.com/developmentseed/label-maker,[],,[],[],,,,developmentseed/label-maker,label-maker,453,114,52,Python,http://devseed.com/label-maker/,Data Preparation for Satellite Machine Learning,developmentseed,2024-01-04,2018-01-10,315,1.4341926729986432,https://avatars.githubusercontent.com/u/92384?v=4,Data Preparation for Satellite Machine Learning,"['computer-vision', 'data-preparation', 'deep-learning', 'keras', 'remote-sensing', 'satellite-imagery']","['computer-vision', 'data-preparation', 'deep-learning', 'keras', 'remote-sensing', 'satellite-imagery']",2020-11-19,"[('azavea/raster-vision', 0.6791407465934753, 'gis', 3), ('microsoft/torchgeo', 0.629837691783905, 'gis', 4), ('plant99/felicette', 0.6269397139549255, 'gis', 1), ('datasystemslab/geotorch', 0.5983750224113464, 'gis', 1), ('remotesensinglab/raster4ml', 0.5611507296562195, 'gis', 1), ('fatiando/verde', 0.5453664660453796, 'gis', 0), ('aleju/imgaug', 0.5313600897789001, 'ml', 1), ('sentinelsat/sentinelsat', 0.5269395112991333, 'gis', 2), ('huggingface/datasets', 0.52162766456604, 'nlp', 2), ('awslabs/autogluon', 0.5088804364204407, 'ml', 2), ('googlecloudplatform/practical-ml-vision-book', 0.505915641784668, 'study', 0), ('sentinel-hub/eo-learn', 0.5026171803474426, 'gis', 0)]",15,6.0,,0.0,0,0,73,38,0,3,3,0.0,0.0,90.0,0.0,20
524,nlp,https://github.com/hazyresearch/fonduer,[],,[],[],,,,hazyresearch/fonduer,fonduer,397,77,28,Python,https://fonduer.readthedocs.io/,A knowledge base construction engine for richly formatted data,hazyresearch,2024-01-04,2018-02-02,312,1.2701096892138939,https://avatars.githubusercontent.com/u/2165246?v=4,A knowledge base construction engine for richly formatted data,"['knowledge-base-construction', 'machine-learning', 'multimodality']","['knowledge-base-construction', 'machine-learning', 'multimodality']",2021-06-23,[],15,5.0,,0.0,0,0,72,31,0,5,5,0.0,0.0,90.0,0.0,20
1482,util,https://github.com/proofit404/dependencies,['dependency-injection'],,[],[],,,,proofit404/dependencies,dependencies,351,17,8,Python,https://proofit404.github.io/dependencies/,Constructor injection designed with OOP in mind.,proofit404,2024-01-06,2016-01-21,418,0.8382804503582395,,Constructor injection designed with OOP in mind.,[],['dependency-injection'],2022-11-01,"[('python-injector/injector', 0.554645836353302, 'util', 1), ('ivankorobkov/python-inject', 0.547492265701294, 'util', 1)]",11,4.0,,0.0,0,0,97,15,0,7,7,0.0,0.0,90.0,0.0,20
248,sim,https://github.com/bilhim/trafficsimulator,[],,[],[],,,,bilhim/trafficsimulator,trafficSimulator,303,118,17,Python,,A microscopic traffic simulation in Python,bilhim,2024-01-12,2021-09-05,125,2.418472063854048,,A microscopic traffic simulation in Python,[],[],2023-06-26,"[('crowddynamics/crowddynamics', 0.5411049723625183, 'sim', 0)]",3,1.0,,0.27,1,0,29,7,0,0,0,1.0,1.0,90.0,1.0,20
1233,llm,https://github.com/conceptofmind/toolformer,"['toolformer', 'language-model']",Open-source implementation of Toolformer: Language Models Can Teach Themselves to Use Tools,[],[],,,,conceptofmind/toolformer,toolformer,296,33,12,Python,,,conceptofmind,2024-01-12,2023-02-17,49,5.971181556195965,,Open-source implementation of Toolformer: Language Models Can Teach Themselves to Use Tools,[],"['language-model', 'toolformer']",2023-03-04,"[('lucidrains/toolformer-pytorch', 0.7667592167854309, 'llm', 2), ('ctlllll/llm-toolmaker', 0.7394540309906006, 'llm', 1), ('openbmb/toolbench', 0.6644108891487122, 'llm', 0), ('lm-sys/fastchat', 0.5984368920326233, 'llm', 1), ('young-geng/easylm', 0.5841966867446899, 'llm', 1), ('salesforce/codet5', 0.5832077264785767, 'nlp', 1), ('guidance-ai/guidance', 0.5727112293243408, 'llm', 1), ('night-chen/toolqa', 0.5704953670501709, 'llm', 0), ('neulab/prompt2model', 0.5687054395675659, 'llm', 1), ('hannibal046/awesome-llm', 0.5643236041069031, 'study', 1), ('openlmlab/moss', 0.5642003417015076, 'llm', 1), ('oobabooga/text-generation-webui', 0.5621170997619629, 'llm', 1), ('hegelai/prompttools', 0.5603080987930298, 'llm', 0), ('thudm/codegeex', 0.5580187439918518, 'llm', 0), ('ai21labs/lm-evaluation', 0.5530425310134888, 'llm', 1), ('lianjiatech/belle', 0.5519489049911499, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5475521087646484, 'llm', 0), ('aiwaves-cn/agents', 0.5445042252540588, 'nlp', 1), ('argilla-io/argilla', 0.5423489809036255, 'nlp', 0), ('agenta-ai/agenta', 0.5377708673477173, 'llm', 0), ('bigscience-workshop/promptsource', 0.5377352237701416, 'nlp', 0), ('tigerlab-ai/tiger', 0.5376387238502502, 'llm', 0), ('juncongmoo/pyllama', 0.5372142195701599, 'llm', 0), ('prefecthq/langchain-prefect', 0.536091685295105, 'llm', 0), ('salesforce/codegen', 0.5336177945137024, 'nlp', 0), ('cg123/mergekit', 0.5336092710494995, 'llm', 0), ('kubeflow/examples', 0.5328210592269897, 'ml-ops', 0), ('nat/openplayground', 0.5319477915763855, 'llm', 1), ('infinitylogesh/mutate', 0.5267210602760315, 'nlp', 1), ('keirp/automatic_prompt_engineer', 0.5263444185256958, 'llm', 1), ('reasoning-machines/pal', 0.5196791887283325, 'llm', 1), ('freedomintelligence/llmzoo', 0.5184057950973511, 'llm', 1), ('hwchase17/langchain', 0.5181885361671448, 'llm', 1), ('salesforce/xgen', 0.5180943608283997, 'llm', 1), ('lupantech/chameleon-llm', 0.5162728428840637, 'llm', 1), ('nomic-ai/gpt4all', 0.5125241875648499, 'llm', 1), ('jalammar/ecco', 0.5121808648109436, 'ml-interpretability', 0), ('tatsu-lab/stanford_alpaca', 0.5121525526046753, 'llm', 1), ('next-gpt/next-gpt', 0.5089335441589355, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5054224729537964, 'llm', 1), ('eleutherai/the-pile', 0.5042285919189453, 'data', 0), ('selfexplainml/piml-toolbox', 0.5030021071434021, 'ml-interpretability', 0), ('hiyouga/llama-factory', 0.5014780759811401, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5014779567718506, 'llm', 1), ('ravenscroftj/turbopilot', 0.5007780194282532, 'llm', 1)]",3,1.0,,0.56,0,0,11,11,0,0,0,0.0,0.0,90.0,0.0,20
939,nlp,https://github.com/ibm/transition-amr-parser,[],,[],[],,,,ibm/transition-amr-parser,transition-amr-parser,218,47,13,Python,,SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch.,ibm,2024-01-10,2019-10-08,225,0.9688888888888889,https://avatars.githubusercontent.com/u/1459110?v=4,SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch.,"['abstract-meaning-representation', 'amr', 'amr-graphs', 'amr-parser', 'amr-parsing', 'machine-learning', 'nlp', 'semantic-parsing']","['abstract-meaning-representation', 'amr', 'amr-graphs', 'amr-parser', 'amr-parsing', 'machine-learning', 'nlp', 'semantic-parsing']",2023-05-09,"[('allenai/allennlp', 0.5920513272285461, 'nlp', 1), ('instagram/libcst', 0.5405070781707764, 'util', 0), ('pytorch/captum', 0.5239638090133667, 'ml-interpretability', 0), ('flairnlp/flair', 0.5182547569274902, 'nlp', 2), ('salesforce/blip', 0.5134626626968384, 'diffusion', 0), ('explosion/spacy', 0.5131350159645081, 'nlp', 2), ('explosion/spacy-llm', 0.5116180777549744, 'llm', 2)]",17,2.0,,1.92,1,0,52,8,0,4,4,1.0,0.0,90.0,0.0,20
936,web,https://github.com/rawheel/fastapi-boilerplate,[],,[],[],,,,rawheel/fastapi-boilerplate,fastapi-boilerplate,190,20,3,Python,,"Dockerized FastAPI boiler plate similar to Django code structure with views, serializers(pydantic) and model( Sqlalchemy ORM) with dockerized database(PostgresSQL) and PgAdmin. 🚀 ",rawheel,2023-12-27,2022-12-28,56,3.341708542713568,,"Dockerized FastAPI boiler plate similar to Django code structure with views, serializers(pydantic) and model( Sqlalchemy ORM) with dockerized database(PostgresSQL) and PgAdmin. 🚀 ","['alembic', 'boilerplate', 'docker', 'docker-compose', 'fastapi', 'fastapi-boilerplate', 'fastapi-sqlalchemy', 'orm', 'poetry-python', 'postgresql', 'pydantic', 'sqlalchemy', 'sqlalchemy-orm']","['alembic', 'boilerplate', 'docker', 'docker-compose', 'fastapi', 'fastapi-boilerplate', 'fastapi-sqlalchemy', 'orm', 'poetry-python', 'postgresql', 'pydantic', 'sqlalchemy', 'sqlalchemy-orm']",2023-08-29,"[('aeternalis-ingenium/fastapi-backend-template', 0.7447752356529236, 'web', 6), ('s3rius/fastapi-template', 0.6903481483459473, 'web', 4), ('asacristani/fastapi-rocket-boilerplate', 0.6740272045135498, 'template', 2), ('tiangolo/full-stack-fastapi-postgresql', 0.6298112869262695, 'template', 3), ('fastai/fastcore', 0.6172206997871399, 'util', 0), ('vitalik/django-ninja', 0.6011227369308472, 'web', 1), ('fastapi-admin/fastapi-admin', 0.5968390107154846, 'web', 1), ('tiangolo/fastapi', 0.5945311188697815, 'web', 2), ('aminalaee/sqladmin', 0.5917928218841553, 'data', 2), ('collerek/ormar', 0.5596618056297302, 'data', 5), ('tiangolo/sqlmodel', 0.5594669580459595, 'data', 3), ('martinheinz/python-project-blueprint', 0.5532102584838867, 'template', 2), ('backtick-se/cowait', 0.5353572964668274, 'util', 1), ('starlite-api/starlite', 0.533804178237915, 'web', 1), ('buuntu/fastapi-react', 0.531681478023529, 'template', 4), ('multi-py/python-gunicorn', 0.5125967264175415, 'util', 1), ('python-restx/flask-restx', 0.5100096464157104, 'web', 0), ('multi-py/python-gunicorn-uvicorn', 0.5090985894203186, 'util', 1), ('ibis-project/ibis', 0.502396821975708, 'data', 2), ('bottlepy/bottle', 0.5016167759895325, 'web', 0), ('willmcgugan/textual', 0.5006387233734131, 'term', 0)]",3,2.0,,0.08,1,0,13,5,1,2,1,1.0,0.0,90.0,0.0,20
1418,llm,https://github.com/night-chen/toolqa,[],,[],[],,,,night-chen/toolqa,ToolQA,178,5,5,Jupyter Notebook,https://arxiv.org/pdf/2306.13304.pdf," ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios. ",night-chen,2024-01-08,2023-06-06,34,5.235294117647059,," ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios. ","['large-language-models', 'natural-language-understanding', 'natural-lauguage-processing', 'question-answering', 'tools']","['large-language-models', 'natural-language-understanding', 'natural-lauguage-processing', 'question-answering', 'tools']",2023-08-19,"[('rlancemartin/auto-evaluator', 0.641534149646759, 'llm', 1), ('deepset-ai/haystack', 0.6367813944816589, 'llm', 2), ('llmware-ai/llmware', 0.6234237551689148, 'llm', 2), ('young-geng/easylm', 0.6132665276527405, 'llm', 1), ('openbmb/toolbench', 0.6098852753639221, 'llm', 0), ('argilla-io/argilla', 0.6075721979141235, 'nlp', 0), ('mooler0410/llmspracticalguide', 0.6017274260520935, 'study', 1), ('hegelai/prompttools', 0.5785123109817505, 'llm', 1), ('nebuly-ai/nebullvm', 0.5734456181526184, 'perf', 1), ('ibm/dromedary', 0.5732406377792358, 'llm', 0), ('conceptofmind/toolformer', 0.5704953670501709, 'llm', 0), ('salesforce/xgen', 0.5676834583282471, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5650957822799683, 'llm', 1), ('explosion/spacy-llm', 0.5622978806495667, 'llm', 1), ('lm-sys/fastchat', 0.5598968267440796, 'llm', 0), ('nomic-ai/gpt4all', 0.5544880032539368, 'llm', 0), ('eleutherai/the-pile', 0.5525684952735901, 'data', 0), ('openlmlab/moss', 0.549114465713501, 'llm', 1), ('whitead/paper-qa', 0.54909348487854, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5460361242294312, 'llm', 0), ('tigerlab-ai/tiger', 0.5423557758331299, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.542137086391449, 'llm', 0), ('agenta-ai/agenta', 0.539746880531311, 'llm', 1), ('defog-ai/sqlcoder', 0.5368439555168152, 'llm', 0), ('confident-ai/deepeval', 0.529060959815979, 'testing', 0), ('salesforce/codet5', 0.525848388671875, 'nlp', 1), ('iryna-kondr/scikit-llm', 0.522339940071106, 'llm', 0), ('bobazooba/xllm', 0.5220770835876465, 'llm', 1), ('pathwaycom/llm-app', 0.5218302607536316, 'llm', 0), ('srush/minichain', 0.5163832306861877, 'llm', 1), ('thudm/chatglm2-6b', 0.5134639143943787, 'llm', 1), ('microsoft/jarvis', 0.5131213068962097, 'llm', 0), ('dylanhogg/llmgraph', 0.5115826725959778, 'ml', 0), ('vllm-project/vllm', 0.5108919143676758, 'llm', 0), ('citadel-ai/langcheck', 0.5104190111160278, 'llm', 0), ('paddlepaddle/rocketqa', 0.5089247226715088, 'nlp', 1), ('deepset-ai/farm', 0.508597195148468, 'nlp', 1), ('aiwaves-cn/agents', 0.5083682537078857, 'nlp', 0), ('ai21labs/lm-evaluation', 0.5064146518707275, 'llm', 0), ('ofa-sys/ofa', 0.5051456093788147, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5044512152671814, 'llm', 0), ('bigscience-workshop/petals', 0.5019873976707458, 'data', 1), ('cg123/mergekit', 0.5008574724197388, 'llm', 0), ('databrickslabs/dolly', 0.5000602006912231, 'llm', 0)]",2,1.0,,0.44,1,0,7,5,0,0,0,1.0,0.0,90.0,0.0,20
529,gis,https://github.com/gdaosu/lod2buildingmodel,[],,[],[],,,,gdaosu/lod2buildingmodel,LOD2BuildingModel,139,28,11,Python,,SAT2LoD2: Automated LoD-2 Model Reconstruction from Satellite-derived DSM and Orthophoto,gdaosu,2024-01-04,2021-08-30,126,1.101925254813137,https://avatars.githubusercontent.com/u/84828009?v=4,SAT2LoD2: Automated LoD-2 Model Reconstruction from Satellite-derived DSM and Orthophoto,[],[],2023-10-10,[],2,2.0,,0.02,1,0,29,3,0,0,0,1.0,2.0,90.0,2.0,20
1905,util,https://github.com/pomponchik/instld,[],,[],[],,,,pomponchik/instld,instld,44,0,2,Python,,The simplest package management,pomponchik,2024-01-18,2023-04-02,43,1.0165016501650166,,The simplest package management,"['context-manager', 'package-manager', 'pip', 'venv']","['context-manager', 'package-manager', 'pip', 'venv']",2024-01-17,"[('mitsuhiko/rye', 0.748336672782898, 'util', 1), ('indygreg/pyoxidizer', 0.7080777883529663, 'util', 1), ('pypa/hatch', 0.6995571255683899, 'util', 1), ('pdm-project/pdm', 0.6940727829933167, 'util', 1), ('python-poetry/poetry', 0.6835312843322754, 'util', 1), ('mamba-org/mamba', 0.6645695567131042, 'util', 1), ('conda/conda', 0.6547331809997559, 'util', 1), ('pypa/pipenv', 0.6500003933906555, 'util', 2), ('pypa/flit', 0.6446828842163086, 'util', 1), ('spack/spack', 0.6380254030227661, 'util', 1), ('pyenv/pyenv', 0.6027212738990784, 'util', 2), ('pypi/warehouse', 0.5979329347610474, 'util', 0), ('thoth-station/micropipenv', 0.5940703749656677, 'util', 1), ('conda/conda-build', 0.5880274772644043, 'util', 0), ('omry/omegaconf', 0.5507912039756775, 'util', 0), ('jazzband/pip-tools', 0.5494071245193481, 'util', 1), ('ofek/pyapp', 0.5397077202796936, 'util', 0), ('google/gin-config', 0.5354675650596619, 'util', 0), ('pypa/setuptools_scm', 0.5312846302986145, 'util', 0), ('pypa/pipx', 0.5103548765182495, 'util', 2), ('pyodide/micropip', 0.5101442337036133, 'util', 0), ('mamba-org/boa', 0.5095345377922058, 'util', 0), ('citadel-ai/langcheck', 0.5082795023918152, 'llm', 0), ('bndr/pipreqs', 0.5049751996994019, 'util', 0), ('shishirpatil/gorilla', 0.5003833174705505, 'llm', 0)]",2,0.0,,6.29,6,6,10,0,2,4,2,6.0,4.0,90.0,0.7,20
1411,ml-interpretability,https://github.com/xplainable/xplainable,[],,[],[],,,,xplainable/xplainable,xplainable,39,4,3,Python,https://www.xplainable.io,Real-time explainable machine learning for business optimisation,xplainable,2024-01-08,2022-09-22,70,0.5515151515151515,https://avatars.githubusercontent.com/u/98626943?v=4,Real-time explainable machine learning for business optimisation,"['auto-ml', 'data-analytics', 'data-science', 'explainable-ai', 'explainable-ml', 'machine-learning', 'machine-learning-algorithms', 'prediction', 'predictions', 'shap', 'statistics', 'xai', 'xplainable']","['auto-ml', 'data-analytics', 'data-science', 'explainable-ai', 'explainable-ml', 'machine-learning', 'machine-learning-algorithms', 'prediction', 'predictions', 'shap', 'statistics', 'xai', 'xplainable']",2023-12-10,"[('interpretml/interpret', 0.6733729839324951, 'ml-interpretability', 4), ('winedarksea/autots', 0.6385115385055542, 'time-series', 1), ('oegedijk/explainerdashboard', 0.627983570098877, 'ml-interpretability', 2), ('seldonio/alibi', 0.6222488880157471, 'ml-interpretability', 2), ('csinva/imodels', 0.6195234656333923, 'ml', 5), ('microsoft/nni', 0.6121291518211365, 'ml', 3), ('mosaicml/composer', 0.5985530614852905, 'ml-dl', 1), ('nccr-itmo/fedot', 0.5892693996429443, 'ml-ops', 1), ('slundberg/shap', 0.5842406153678894, 'ml-interpretability', 2), ('online-ml/river', 0.584074079990387, 'ml', 2), ('bentoml/bentoml', 0.583275318145752, 'ml-ops', 1), ('feast-dev/feast', 0.5823587775230408, 'ml-ops', 2), ('mindsdb/mindsdb', 0.5784251093864441, 'data', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5782299637794495, 'study', 2), ('polyaxon/datatile', 0.5760681629180908, 'pandas', 3), ('google-research/google-research', 0.5756439566612244, 'ml', 1), ('automl/auto-sklearn', 0.5678261518478394, 'ml', 0), ('sktime/sktime', 0.5621213912963867, 'time-series', 2), ('firmai/industry-machine-learning', 0.5596107840538025, 'study', 2), ('mlflow/mlflow', 0.55943363904953, 'ml-ops', 1), ('maif/shapash', 0.551527738571167, 'ml', 3), ('awslabs/autogluon', 0.5499810576438904, 'ml', 2), ('netflix/metaflow', 0.5485888123512268, 'ml-ops', 2), ('polyaxon/polyaxon', 0.5472549796104431, 'ml-ops', 2), ('huggingface/datasets', 0.5465106964111328, 'nlp', 1), ('ml-tooling/opyrator', 0.5456689596176147, 'viz', 1), ('onnx/onnx', 0.5391144156455994, 'ml', 1), ('googlecloudplatform/vertex-ai-samples', 0.53435218334198, 'ml', 1), ('pair-code/lit', 0.5334827303886414, 'ml-interpretability', 1), ('scikit-learn/scikit-learn', 0.5327098965644836, 'ml', 3), ('microsoft/flaml', 0.5324146747589111, 'ml', 2), ('polakowo/vectorbt', 0.532191812992096, 'finance', 2), ('eugeneyan/testing-ml', 0.5317504405975342, 'testing', 1), ('rafiqhasan/auto-tensorflow', 0.5286867618560791, 'ml-dl', 1), ('huggingface/autotrain-advanced', 0.5284545421600342, 'ml', 1), ('shankarpandala/lazypredict', 0.5252819061279297, 'ml', 1), ('hpcaitech/colossalai', 0.5240747928619385, 'llm', 0), ('salesforce/merlion', 0.5224214196205139, 'time-series', 1), ('tensorflow/tensor2tensor', 0.5187652707099915, 'ml', 1), ('microsoft/qlib', 0.5183536410331726, 'finance', 1), ('keras-team/autokeras', 0.5181999802589417, 'ml-dl', 1), ('teamhg-memex/eli5', 0.517871618270874, 'ml', 2), ('ourownstory/neural_prophet', 0.5147477984428406, 'ml', 2), ('ai4finance-foundation/finrl', 0.5115347504615784, 'finance', 0), ('explosion/thinc', 0.5094534754753113, 'ml-dl', 1), ('activeloopai/deeplake', 0.5084322094917297, 'ml-ops', 2), ('marcotcr/lime', 0.5037121176719666, 'ml-interpretability', 0), ('alpa-projects/alpa', 0.5035876035690308, 'ml-dl', 1), ('kubeflow/pipelines', 0.5017673969268799, 'ml-ops', 2), ('google/trax', 0.5001837015151978, 'ml-dl', 1)]",3,1.0,,5.62,32,25,16,1,5,6,5,32.0,2.0,90.0,0.1,20
333,util,https://github.com/clarete/forbiddenfruit,[],,[],[],,,,clarete/forbiddenfruit,forbiddenfruit,794,52,29,Python,https://clarete.li/forbiddenfruit/,Patch built-in python objects,clarete,2024-01-13,2013-04-03,564,1.4056651492159837,,Patch built-in python objects,['monkey-patching'],['monkey-patching'],2022-03-12,"[('grahamdumpleton/wrapt', 0.6830537915229797, 'util', 0)]",15,5.0,,0.0,0,0,131,27,0,0,0,0.0,0.0,90.0,0.0,19
1829,jupyter,https://github.com/koaning/drawdata,[],,[],[],,,,koaning/drawdata,drawdata,579,72,8,Python,https://calmcode.io/labs/drawdata.html,Draw datasets from within Jupyter.,koaning,2024-01-10,2021-04-04,147,3.9311348205625607,,Draw datasets from within Jupyter.,"['data', 'drawdata', 'jupyter']","['data', 'drawdata', 'jupyter']",2022-07-24,"[('jupyterlab/jupyterlab', 0.569868266582489, 'jupyter', 1), ('vizzuhq/ipyvizzu', 0.5688337683677673, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5549478530883789, 'study', 0), ('jupyter/notebook', 0.5532419085502625, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.5521408915519714, 'jupyter', 1), ('bloomberg/ipydatagrid', 0.551867663860321, 'jupyter', 0), ('jupyter/nbformat', 0.5362401008605957, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.5359898805618286, 'jupyter', 0), ('ipython/ipyparallel', 0.5354899168014526, 'perf', 1), ('quantopian/qgrid', 0.5323299765586853, 'jupyter', 0), ('jazzband/tablib', 0.5231077075004578, 'data', 0), ('cmudig/autoprofiler', 0.5204150080680847, 'jupyter', 1), ('ipython/ipykernel', 0.5152633190155029, 'util', 1), ('man-group/dtale', 0.5000766515731812, 'viz', 0)]",3,2.0,,0.0,0,0,34,18,0,0,0,0.0,0.0,90.0,0.0,19
328,ml-dl,https://github.com/facebookresearch/ppuda,[],,[],[],,,,facebookresearch/ppuda,ppuda,479,60,20,Python,,Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021),facebookresearch,2024-01-12,2021-10-21,118,4.034897713598075,https://avatars.githubusercontent.com/u/16943930?v=4,Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021),[],[],2023-07-11,"[('rasbt/deeplearning-models', 0.5405725240707397, 'ml-dl', 0), ('neuralmagic/deepsparse', 0.5350483059883118, 'nlp', 0), ('calculatedcontent/weightwatcher', 0.5242863297462463, 'ml-dl', 0)]",3,1.0,,0.08,0,0,27,6,0,0,0,0.0,0.0,90.0,0.0,19
1018,nlp,https://github.com/prithivirajdamodaran/styleformer,[],,[],[],,,,prithivirajdamodaran/styleformer,Styleformer,462,64,17,Python,,"A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.",prithivirajdamodaran,2024-01-10,2021-06-12,137,3.361746361746362,,"A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.","['active', 'formal-languages', 'informal-sentences', 'nlp', 'passive', 'slang', 'style-transfer', 'text-style', 'text-style-transfer', 'text-style-transfer-benchmark']","['active', 'formal-languages', 'informal-sentences', 'nlp', 'passive', 'slang', 'style-transfer', 'text-style', 'text-style-transfer', 'text-style-transfer-benchmark']",2022-12-27,"[('deepset-ai/farm', 0.5097511410713196, 'nlp', 1), ('alibaba/easynlp', 0.504541277885437, 'nlp', 1), ('yueyu1030/attrprompt', 0.5027515292167664, 'llm', 0)]",4,1.0,,0.0,2,0,32,13,0,1,1,2.0,0.0,90.0,0.0,19
922,ml,https://github.com/google-research/maxvit,[],,[],[],,,,google-research/maxvit,maxvit,403,26,9,Jupyter Notebook,,"[ECCV 2022] Official repository for ""MaxViT: Multi-Axis Vision Transformer"". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling...",google-research,2024-01-14,2022-07-07,81,4.931818181818182,https://avatars.githubusercontent.com/u/43830688?v=4,"[ECCV 2022] Official repository for ""MaxViT: Multi-Axis Vision Transformer"". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling...","['architecture', 'classification', 'cnn', 'computer-vision', 'image', 'image-processing', 'mlp', 'object-detection', 'resnet', 'segmentation', 'transformer', 'transformer-architecture', 'vision-transformer']","['architecture', 'classification', 'cnn', 'computer-vision', 'image', 'image-processing', 'mlp', 'object-detection', 'resnet', 'segmentation', 'transformer', 'transformer-architecture', 'vision-transformer']",2023-06-02,"[('lucidrains/vit-pytorch', 0.6311172246932983, 'ml-dl', 1), ('deci-ai/super-gradients', 0.6301395893096924, 'ml-dl', 2), ('nvlabs/gcvit', 0.6166060566902161, 'diffusion', 2), ('roboflow/notebooks', 0.5931783318519592, 'study', 2), ('microsoft/swin-transformer', 0.5897934436798096, 'ml', 1), ('rwightman/pytorch-image-models', 0.575612485408783, 'ml-dl', 1), ('facebookresearch/vissl', 0.5404923558235168, 'ml', 0), ('roboflow/supervision', 0.5343255996704102, 'ml', 4)]",1,1.0,,0.0,1,0,18,8,0,0,0,1.0,0.0,90.0,0.0,19
1153,util,https://github.com/pyyoshi/cchardet,[],,[],[],,,,pyyoshi/cchardet,cChardet,374,51,12,Python,,universal character encoding detector,pyyoshi,2024-01-12,2012-06-20,605,0.6173072388587597,,universal character encoding detector,[],[],2021-04-28,[],10,2.0,,0.0,3,0,141,33,0,2,2,3.0,5.0,90.0,1.7,19
363,ml-ops,https://github.com/kubeflow/fairing,[],,[],[],,,,kubeflow/fairing,fairing,335,145,40,Jsonnet,,"Python SDK for building, training, and deploying ML models",kubeflow,2024-01-04,2018-09-03,282,1.1873417721518988,https://avatars.githubusercontent.com/u/33164907?v=4,"Python SDK for building, training, and deploying ML models",[],[],2021-08-26,"[('fmind/mlops-python-package', 0.6557989120483398, 'template', 0), ('selfexplainml/piml-toolbox', 0.6489830613136292, 'ml-interpretability', 0), ('gradio-app/gradio', 0.6449424624443054, 'viz', 0), ('huggingface/datasets', 0.6306226253509521, 'nlp', 0), ('skops-dev/skops', 0.6278438568115234, 'ml-ops', 0), ('huggingface/huggingface_hub', 0.624878466129303, 'ml', 0), ('merantix-momentum/squirrel-core', 0.6223782300949097, 'ml', 0), ('featurelabs/featuretools', 0.5992475152015686, 'ml', 0), ('aws/sagemaker-python-sdk', 0.5987374186515808, 'ml', 0), ('ml-tooling/opyrator', 0.5929511189460754, 'viz', 0), ('polyaxon/polyaxon', 0.5774126648902893, 'ml-ops', 0), ('wandb/client', 0.5759884119033813, 'ml', 0), ('amaargiru/pyroad', 0.5755603313446045, 'study', 0), ('milvus-io/pymilvus', 0.5737506151199341, 'util', 0), ('eleutherai/pyfra', 0.5697919726371765, 'ml', 0), ('willmcgugan/textual', 0.5632326006889343, 'term', 0), ('fastai/fastcore', 0.5604775547981262, 'util', 0), ('ashleve/lightning-hydra-template', 0.5602784752845764, 'util', 0), ('ageron/handson-ml2', 0.5555180907249451, 'ml', 0), ('huggingface/transformers', 0.5541255474090576, 'nlp', 0), ('intel/intel-extension-for-pytorch', 0.5540038347244263, 'perf', 0), ('rasbt/machine-learning-book', 0.5534403324127197, 'study', 0), ('microsoft/nni', 0.5502640008926392, 'ml', 0), ('lucidrains/toolformer-pytorch', 0.5491804480552673, 'llm', 0), ('google/temporian', 0.5490253567695618, 'time-series', 0), ('nvidia/deeplearningexamples', 0.548143744468689, 'ml-dl', 0), ('parallel-domain/pd-sdk', 0.5475777983665466, 'data', 0), ('scikit-learn/scikit-learn', 0.5472146272659302, 'ml', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5467697381973267, 'study', 0), ('malloydata/malloy-py', 0.5466133952140808, 'data', 0), ('ploomber/ploomber', 0.5463899970054626, 'ml-ops', 0), ('mlflow/mlflow', 0.545492947101593, 'ml-ops', 0), ('huggingface/exporters', 0.5441210269927979, 'ml', 0), ('falconry/falcon', 0.5438461303710938, 'web', 0), ('pycaret/pycaret', 0.5398439764976501, 'ml', 0), ('reloadware/reloadium', 0.5393650531768799, 'profiling', 0), ('ray-project/ray', 0.5362722873687744, 'ml-ops', 0), ('bentoml/bentoml', 0.5353989005088806, 'ml-ops', 0), ('micropython/micropython', 0.5345472693443298, 'util', 0), ('radiantearth/radiant-mlhub', 0.5332103371620178, 'gis', 0), ('google/gin-config', 0.5331827402114868, 'util', 0), ('evidentlyai/evidently', 0.531782329082489, 'ml-ops', 0), ('uber/petastorm', 0.529427170753479, 'data', 0), ('google/vizier', 0.5291571021080017, 'ml', 0), ('beeware/toga', 0.5289453268051147, 'gui', 0), ('backtick-se/cowait', 0.5283066034317017, 'util', 0), ('dagworks-inc/hamilton', 0.5266019105911255, 'ml-ops', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5263985991477966, 'template', 0), ('epistasislab/tpot', 0.5261349081993103, 'ml', 0), ('pytoolz/toolz', 0.525724470615387, 'util', 0), ('determined-ai/determined', 0.5216547846794128, 'ml-ops', 0), ('titanml/takeoff', 0.5214179754257202, 'llm', 0), ('lk-geimfari/mimesis', 0.5213274955749512, 'data', 0), ('huggingface/evaluate', 0.5212286114692688, 'ml', 0), ('pypa/hatch', 0.5211179256439209, 'util', 0), ('dylanhogg/awesome-python', 0.5207975506782532, 'study', 0), ('unionai-oss/unionml', 0.5197477340698242, 'ml-ops', 0), ('orchest/orchest', 0.5192087292671204, 'ml-ops', 0), ('stability-ai/stability-sdk', 0.5190809369087219, 'diffusion', 0), ('google/pyglove', 0.5187657475471497, 'util', 0), ('pypy/pypy', 0.5187157988548279, 'util', 0), ('pypa/pipenv', 0.5174124836921692, 'util', 0), ('kubeflow-kale/kale', 0.5162668824195862, 'ml-ops', 0), ('openai/openai-python', 0.5159801244735718, 'util', 0), ('pytorch/ignite', 0.5159615874290466, 'ml-dl', 0), ('ddbourgin/numpy-ml', 0.5149458050727844, 'ml', 0), ('polyaxon/datatile', 0.514506459236145, 'pandas', 0), ('scikit-learn-contrib/metric-learn', 0.5143347382545471, 'ml', 0), ('agronholm/apscheduler', 0.5141321420669556, 'util', 0), ('jovianml/opendatasets', 0.5135595202445984, 'data', 0), ('rasbt/mlxtend', 0.5129528045654297, 'ml', 0), ('activeloopai/deeplake', 0.5122445821762085, 'ml-ops', 0), ('goldmansachs/gs-quant', 0.5122407078742981, 'finance', 0), ('csinva/imodels', 0.5098203420639038, 'ml', 0), ('anthropics/anthropic-sdk-python', 0.5095229148864746, 'util', 0), ('rafiqhasan/auto-tensorflow', 0.5086631178855896, 'ml-dl', 0), ('sqlalchemy/mako', 0.5085417628288269, 'template', 0), ('pyston/pyston', 0.508223295211792, 'util', 0), ('mlc-ai/mlc-llm', 0.5076168179512024, 'llm', 0), ('beeware/briefcase', 0.5065484046936035, 'util', 0), ('dask/dask-ml', 0.5057757496833801, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5057130455970764, 'ml', 0), ('astronomer/astro-sdk', 0.5054138898849487, 'ml-ops', 0), ('pallets/flask', 0.5050888657569885, 'web', 0), ('apple/coremltools', 0.5047186613082886, 'ml', 0), ('cohere-ai/cohere-python', 0.5045545697212219, 'util', 0), ('karpathy/micrograd', 0.5037677884101868, 'study', 0), ('pytorch/data', 0.5028988122940063, 'data', 0), ('meltano/meltano', 0.5027660131454468, 'ml-ops', 0), ('open-telemetry/opentelemetry-python', 0.5018121004104614, 'util', 0), ('skorch-dev/skorch', 0.501205325126648, 'ml-dl', 0), ('marshmallow-code/marshmallow', 0.5004829168319702, 'util', 0)]",41,5.0,,0.0,0,0,65,29,0,1,1,0.0,0.0,90.0,0.0,19
1486,crypto,https://github.com/primal100/pybitcointools,[],,[],[],,,,primal100/pybitcointools,pybitcointools,295,149,26,Python,,"Simple, common-sense Bitcoin-themed Python ECC library",primal100,2023-12-26,2017-11-23,322,0.9141212926073484,,"Simple, common-sense Bitcoin-themed Python ECC library",[],[],2023-07-18,"[('1200wd/bitcoinlib', 0.7157860994338989, 'crypto', 0), ('ethereum/web3.py', 0.6811222434043884, 'crypto', 0), ('legrandin/pycryptodome', 0.6471469402313232, 'util', 0), ('pyca/cryptography', 0.6024011373519897, 'util', 0), ('pyston/pyston', 0.5950328707695007, 'util', 0), ('gbeced/pyalgotrade', 0.5933455228805542, 'finance', 0), ('gbeced/basana', 0.5842757821083069, 'finance', 0), ('pytoolz/toolz', 0.5817379951477051, 'util', 0), ('ethereum/py-evm', 0.5760893821716309, 'crypto', 0), ('amzn/ion-python', 0.5686578154563904, 'data', 0), ('pyca/pynacl', 0.56072998046875, 'util', 0), ('pypy/pypy', 0.5500690340995789, 'util', 0), ('pynamodb/pynamodb', 0.5435667634010315, 'data', 0), ('pmaji/crypto-whale-watching-app', 0.5292482972145081, 'crypto', 0), ('man-c/pycoingecko', 0.5288224220275879, 'crypto', 0), ('numerai/example-scripts', 0.5286599397659302, 'finance', 0), ('pyscf/pyscf', 0.5276904106140137, 'sim', 0), ('python/cpython', 0.5267580151557922, 'util', 0), ('eleutherai/pyfra', 0.5250184535980225, 'ml', 0), ('pmorissette/ffn', 0.5242424607276917, 'finance', 0), ('masoniteframework/masonite', 0.5237247347831726, 'web', 0), ('quantopian/zipline', 0.5228780508041382, 'finance', 0), ('adafruit/circuitpython', 0.518639862537384, 'util', 0), ('ccxt/ccxt', 0.5137020945549011, 'crypto', 0), ('libtcod/python-tcod', 0.5110588669776917, 'gamedev', 0), ('pdm-project/pdm', 0.505200207233429, 'util', 0), ('paramiko/paramiko', 0.5015236139297485, 'util', 0)]",30,1.0,,0.42,5,0,75,6,0,0,0,5.0,4.0,90.0,0.8,19
417,pandas,https://github.com/zsailer/pandas_flavor,[],,[],[],,,,zsailer/pandas_flavor,pandas_flavor,289,17,10,Python,https://zsailer.github.io/software/pandas-flavor/,The easy way to write your own flavor of Pandas,zsailer,2024-01-04,2018-01-25,313,0.9212204007285975,https://avatars.githubusercontent.com/u/53411673?v=4,The easy way to write your own flavor of Pandas,['pandas'],['pandas'],2023-07-08,"[('lux-org/lux', 0.5670690536499023, 'viz', 1), ('adamerose/pandasgui', 0.5256001949310303, 'pandas', 1), ('tkrabel/bamboolib', 0.5217926502227783, 'pandas', 1)]",9,3.0,,0.6,1,0,73,6,2,1,2,1.0,0.0,90.0,0.0,19
265,nlp,https://github.com/allenai/s2orc-doc2json,[],,[],[],,,,allenai/s2orc-doc2json,s2orc-doc2json,285,58,7,Python,,"Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON)",allenai,2024-01-12,2020-12-10,163,1.7408376963350785,https://avatars.githubusercontent.com/u/5667695?v=4,"Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON)",[],[],2023-08-22,"[('pdfminer/pdfminer.six', 0.5287355184555054, 'util', 0)]",10,4.0,,0.04,0,0,38,5,0,0,0,0.0,0.0,90.0,0.0,19
1373,llm,https://github.com/extreme-bert/extreme-bert,[],,[],[],,,,extreme-bert/extreme-bert,extreme-bert,283,15,11,Python,https://extreme-bert.github.io/extreme-bert-page,"ExtremeBERT is a toolkit that accelerates the pretraining of customized language models on customized datasets, described in the paper “ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT”.",extreme-bert,2024-01-04,2022-12-01,60,4.661176470588235,https://avatars.githubusercontent.com/u/109327047?v=4,"ExtremeBERT is a toolkit that accelerates the pretraining of customized language models on customized datasets, described in the paper “ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT”.","['bert', 'deep-learning', 'language-model', 'language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'pytorch', 'transformer']","['bert', 'deep-learning', 'language-model', 'language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'pytorch', 'transformer']",2023-01-02,"[('huggingface/transformers', 0.6654086709022522, 'nlp', 9), ('jonasgeiping/cramming', 0.6568642258644104, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.6498593091964722, 'llm', 2), ('explosion/spacy-transformers', 0.6331859230995178, 'llm', 6), ('deepset-ai/farm', 0.6253734827041626, 'nlp', 5), ('graykode/nlp-tutorial', 0.6246617436408997, 'study', 5), ('jina-ai/finetuner', 0.6179499626159668, 'ml', 1), ('bigscience-workshop/megatron-deepspeed', 0.6164913773536682, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6164913773536682, 'llm', 0), ('alibaba/easynlp', 0.614682674407959, 'nlp', 5), ('llmware-ai/llmware', 0.581847071647644, 'llm', 4), ('nvidia/deeplearningexamples', 0.5793726444244385, 'ml-dl', 3), ('maartengr/bertopic', 0.5704385042190552, 'nlp', 3), ('jina-ai/clip-as-service', 0.5639864802360535, 'nlp', 3), ('whu-zqh/chatgpt-vs.-bert', 0.5589087605476379, 'llm', 1), ('qanastek/drbert', 0.5513941645622253, 'llm', 3), ('ddangelov/top2vec', 0.5432024598121643, 'nlp', 1), ('databrickslabs/dolly', 0.5428717136383057, 'llm', 0), ('infinitylogesh/mutate', 0.5343960523605347, 'nlp', 1), ('microsoft/lora', 0.5327245593070984, 'llm', 3), ('google-research/electra', 0.5268334150314331, 'ml-dl', 2), ('lm-sys/fastchat', 0.5264178514480591, 'llm', 1), ('amansrivastava17/embedding-as-service', 0.5209690928459167, 'nlp', 4), ('huggingface/datasets', 0.5209168195724487, 'nlp', 5), ('huawei-noah/pretrained-language-model', 0.5199653506278992, 'nlp', 0), ('young-geng/easylm', 0.5198579430580139, 'llm', 4), ('explosion/thinc', 0.5198476314544678, 'ml-dl', 5), ('freedomintelligence/llmzoo', 0.5171335935592651, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5170273184776306, 'nlp', 0), ('luodian/otter', 0.5146451592445374, 'llm', 2), ('huggingface/autotrain-advanced', 0.5114012360572815, 'ml', 3), ('nvlabs/prismer', 0.5107273459434509, 'diffusion', 1), ('microsoft/unilm', 0.5103119015693665, 'nlp', 1), ('ukplab/sentence-transformers', 0.50993812084198, 'nlp', 0), ('intellabs/fastrag', 0.5088297128677368, 'nlp', 1), ('bytedance/lightseq', 0.5086089968681335, 'nlp', 2), ('plasticityai/magnitude', 0.5061821341514587, 'nlp', 3), ('explosion/spacy-models', 0.5058228969573975, 'nlp', 3), ('allenai/allennlp', 0.5051680207252502, 'nlp', 4), ('openai/finetune-transformer-lm', 0.504263699054718, 'llm', 0), ('openai/clip', 0.5033907890319824, 'ml-dl', 2), ('huggingface/text-generation-inference', 0.5023447871208191, 'llm', 4)]",3,2.0,,0.0,0,0,14,13,0,0,0,0.0,0.0,90.0,0.0,19
1465,llm,https://github.com/microsoft/pycodegpt,['code-generation'],,[],[],,,,microsoft/pycodegpt,PyCodeGPT,219,36,15,Python,,A pre-trained GPT model for Python code completion and generation,microsoft,2024-01-13,2022-03-09,98,2.2153179190751446,https://avatars.githubusercontent.com/u/6154722?v=4,A pre-trained GPT model for Python code completion and generation,[],['code-generation'],2023-01-08,"[('minimaxir/gpt-2-simple', 0.629753589630127, 'llm', 0), ('minimaxir/aitextgen', 0.6111297011375427, 'llm', 0), ('psf/black', 0.5714641213417053, 'util', 0), ('bigcode-project/starcoder', 0.5438132882118225, 'llm', 1), ('dosisod/refurb', 0.5347919464111328, 'util', 0), ('eleutherai/gpt-neo', 0.5271508097648621, 'llm', 0), ('promptslab/promptify', 0.5242052674293518, 'nlp', 0), ('ravenscroftj/turbopilot', 0.524189293384552, 'llm', 0), ('salesforce/codegen', 0.5231809020042419, 'nlp', 0), ('norvig/pytudes', 0.5215215682983398, 'util', 0), ('google/pyglove', 0.520497739315033, 'util', 0), ('thudm/codegeex', 0.5184867978096008, 'llm', 1), ('lianjiatech/belle', 0.5181886553764343, 'llm', 0), ('hannibal046/awesome-llm', 0.51466304063797, 'study', 0), ('python/cpython', 0.5116127133369446, 'util', 0), ('google/latexify_py', 0.5082912445068359, 'util', 0), ('instagram/libcst', 0.5071039795875549, 'util', 0), ('nedbat/coveragepy', 0.506847083568573, 'testing', 0), ('stanfordnlp/dspy', 0.5060772895812988, 'llm', 0), ('exaloop/codon', 0.5030719041824341, 'perf', 0), ('rubik/radon', 0.5025046467781067, 'util', 0), ('grantjenks/blue', 0.5024159550666809, 'util', 0), ('agronholm/sqlacodegen', 0.5004090666770935, 'data', 0)]",6,2.0,,0.0,2,0,23,12,0,2,2,2.0,0.0,90.0,0.0,19
1421,llm,https://github.com/whu-zqh/chatgpt-vs.-bert,[],,[],[],,,,whu-zqh/chatgpt-vs.-bert,ChatGPT-vs.-BERT,190,9,5,Python,https://arxiv.org/abs/2302.10198,🎁[ChatGPT4NLU] A Comparative  Study on ChatGPT and Fine-tuned BERT,whu-zqh,2024-01-04,2023-02-18,49,3.8439306358381504,,🎁[ChatGPT4NLU] A Comparative  Study on ChatGPT and Fine-tuned BERT,"['bert', 'chain-of-thought', 'chatgpt', 'in-context-learning', 'natural-language-understanding']","['bert', 'chain-of-thought', 'chatgpt', 'in-context-learning', 'natural-language-understanding']",2023-04-17,"[('jonasgeiping/cramming', 0.6024624109268188, 'nlp', 0), ('jina-ai/finetuner', 0.577040433883667, 'ml', 1), ('openlmlab/moss', 0.5750217437744141, 'llm', 1), ('bigscience-workshop/megatron-deepspeed', 0.5593957901000977, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5593957901000977, 'llm', 0), ('extreme-bert/extreme-bert', 0.5589087605476379, 'llm', 1), ('killianlucas/open-interpreter', 0.5495890974998474, 'llm', 1), ('maartengr/keybert', 0.529996395111084, 'nlp', 1), ('graykode/nlp-tutorial', 0.5263599753379822, 'study', 1), ('guidance-ai/guidance', 0.5157712697982788, 'llm', 1), ('next-gpt/next-gpt', 0.515221357345581, 'llm', 1), ('fasteval/fasteval', 0.514625608921051, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5144068002700806, 'llm', 1), ('thudm/chatglm2-6b', 0.5137940645217896, 'llm', 0), ('xtekky/gpt4free', 0.5137408971786499, 'llm', 1), ('lm-sys/fastchat', 0.5135443210601807, 'llm', 0), ('run-llama/rags', 0.5091956257820129, 'llm', 1), ('microsoft/autogen', 0.5087406039237976, 'llm', 1)]",3,2.0,,1.23,0,0,11,9,0,0,0,0.0,0.0,90.0,0.0,19
523,ml,https://github.com/hazyresearch/domino,[],Discover slices of data on which your models underperform.,[],[],,,,hazyresearch/domino,domino,131,25,20,Python,,,hazyresearch,2024-01-04,2021-11-29,113,1.1578282828282829,https://avatars.githubusercontent.com/u/2165246?v=4,Discover slices of data on which your models underperform.,[],[],2023-10-30,"[('huggingface/evaluate', 0.5064350366592407, 'ml', 0), ('anthropics/evals', 0.5007730722427368, 'llm', 0)]",9,0.0,,0.33,10,6,26,2,0,3,3,10.0,6.0,90.0,0.6,19
1350,perf,https://github.com/crunch-io/lazycsv,"['csv', 'parser']",lazycsv is a C implementation of a csv parser for python,[],[],,,,crunch-io/lazycsv,lazycsv,118,1,3,C,https://pypi.org/project/lazycsv/,,crunch-io,2024-01-04,2023-03-24,44,2.6474358974358974,https://avatars.githubusercontent.com/u/2966429?v=4,lazycsv is a C implementation of a csv parser for python,[],"['csv', 'parser']",2024-01-10,"[('wireservice/csvkit', 0.561709463596344, 'util', 0), ('dask/fastparquet', 0.5394817590713501, 'data', 0), ('pyston/pyston', 0.5371149182319641, 'util', 0), ('cython/cython', 0.5136982202529907, 'util', 0), ('python-odin/odin', 0.5088388323783875, 'util', 1), ('pytoolz/toolz', 0.5044757127761841, 'util', 0)]",2,1.0,,0.52,0,0,10,0,0,0,0,0.0,0.0,90.0,0.0,19
1653,data,https://github.com/pachyderm/python-pachyderm,[],,[],[],,,,pachyderm/python-pachyderm,python-pachyderm,89,30,25,Python,,Python client for Pachyderm,pachyderm,2024-01-05,2017-02-01,364,0.24393108848864525,https://avatars.githubusercontent.com/u/10432478?v=4,Python client for Pachyderm,"['pachyderm', 'python-bindings']","['pachyderm', 'python-bindings']",2023-08-18,"[('nvidia/cuda-python', 0.5181779861450195, 'ml', 0)]",33,5.0,,0.29,0,0,85,5,3,2,3,0.0,0.0,90.0,0.0,19
1417,data,https://github.com/mitvis/vistext,[],,[],[],,,,mitvis/vistext,vistext,66,3,6,Jupyter Notebook,http://vis.csail.mit.edu/pubs/vistext/,VisText is a benchmark dataset for semantically rich chart captioning.,mitvis,2024-01-07,2023-04-04,43,1.5348837209302326,https://avatars.githubusercontent.com/u/41133679?v=4,VisText is a benchmark dataset for semantically rich chart captioning.,"['captioning', 'captioning-images', 'charts', 'dataset', 't5']","['captioning', 'captioning-images', 'charts', 'dataset', 't5']",2023-10-03,"[('mckinsey/vizro', 0.5145730972290039, 'viz', 0), ('koaning/whatlies', 0.5051085948944092, 'nlp', 0)]",6,2.0,,1.67,2,1,9,3,0,0,0,2.0,1.0,90.0,0.5,19
1342,util,https://github.com/prefecthq/prefect-dask,['dask'],,[],[],,,,prefecthq/prefect-dask,prefect-dask,64,15,12,Python,https://prefecthq.github.io/prefect-dask/,Prefect integrations with the Dask execution framework.,prefecthq,2024-01-13,2022-05-11,89,0.712241653418124,https://avatars.githubusercontent.com/u/39270919?v=4,Prefect integrations with the Dask execution framework.,[],['dask'],2023-11-02,"[('dask/distributed', 0.6549785137176514, 'perf', 1), ('dask/dask-ml', 0.5907831192016602, 'ml', 0), ('fugue-project/fugue', 0.5114060044288635, 'pandas', 1), ('autoviml/auto_ts', 0.5042782425880432, 'time-series', 0)]",14,2.0,,0.4,7,3,20,2,4,7,4,7.0,0.0,90.0,0.0,19
1561,sim,https://github.com/google-research/swirl-lm,"['tpu', 'fluid-dynamics']",Swirl-LM is a computational fluid dynamics simulation framework that is accelerated by the Tensor Processing Unit,[],[],,,,google-research/swirl-lm,swirl-lm,51,7,8,Python,,,google-research,2024-01-12,2022-01-07,107,0.47410358565737054,https://avatars.githubusercontent.com/u/43830688?v=4,Swirl-LM is a computational fluid dynamics simulation framework that is accelerated by the Tensor Processing Unit,[],"['fluid-dynamics', 'tpu']",2024-01-03,[],6,2.0,,0.65,4,2,25,0,2,1,2,4.0,1.0,90.0,0.2,19
1424,testing,https://github.com/vedro-universe/vedro,['testing'],,[],[],,,,vedro-universe/vedro,vedro,30,8,3,Python,https://vedro.io,Pragmatic Testing Framework,vedro-universe,2024-01-08,2015-10-19,432,0.06942148760330578,https://avatars.githubusercontent.com/u/118679807?v=4,Pragmatic Testing Framework,"['e2e-testing', 'testing', 'testing-tools', 'vedro']","['e2e-testing', 'testing', 'testing-tools', 'vedro']",2024-01-13,"[('pytest-dev/pytest-testinfra', 0.526130199432373, 'testing', 2), ('robotframework/robotframework', 0.5252175331115723, 'testing', 1)]",5,0.0,,0.92,16,16,100,0,0,4,4,16.0,12.0,90.0,0.8,19
1403,data,https://github.com/parallel-domain/pd-sdk,['datasets'],The Parallel Domain SDK allows the community to access Parallel Domain's synthetic data as Python objects.,[],[],,,,parallel-domain/pd-sdk,pd-sdk,17,5,4,Python,,,parallel-domain,2024-01-02,2021-05-11,142,0.11971830985915492,https://avatars.githubusercontent.com/u/53447713?v=4,The Parallel Domain SDK allows the community to access Parallel Domain's synthetic data as Python objects.,[],['datasets'],2023-10-31,"[('pytorch/data', 0.6059823036193848, 'data', 0), ('fastai/fastcore', 0.5881737470626831, 'util', 0), ('kubeflow/fairing', 0.5475777983665466, 'ml-ops', 0), ('huggingface/datasets', 0.5329124927520752, 'nlp', 1), ('jovianml/opendatasets', 0.5220646262168884, 'data', 1), ('dask/dask', 0.5014457702636719, 'perf', 0)]",14,2.0,,0.73,33,33,33,2,5,20,5,33.0,0.0,90.0,0.0,19
1662,data,https://github.com/mediawiki-client-tools/wikitools3,"['wikimedia', 'wikipedia']",,[],[],,,,mediawiki-client-tools/wikitools3,wikitools3,4,2,2,Python,,Python package for working with MediaWiki wikis,mediawiki-client-tools,2023-11-06,2021-08-23,127,0.03146067415730337,https://avatars.githubusercontent.com/u/122663498?v=4,Python package for working with MediaWiki wikis,[],"['wikimedia', 'wikipedia']",2023-08-29,"[('mediawiki-client-tools/mediawiki-dump-generator', 0.8232905268669128, 'data', 2), ('goldsmith/wikipedia', 0.708003044128418, 'data', 0), ('harangju/wikinet', 0.6277413368225098, 'data', 0)]",12,5.0,,0.02,5,3,29,5,0,2,2,5.0,7.0,90.0,1.4,19
753,study,https://github.com/jackhidary/quantumcomputingbook,[],,[],[],,,,jackhidary/quantumcomputingbook,quantumcomputingbook,729,201,57,Jupyter Notebook,,Companion site for the textbook Quantum Computing: An Applied Approach,jackhidary,2024-01-04,2019-02-28,256,2.8397328881469117,,Companion site for the textbook Quantum Computing: An Applied Approach,"['cirq', 'google-quantum', 'qiskit', 'quantum', 'quantum-computing', 'quantum-information', 'quantum-information-science', 'quantum-processor', 'quantum-supremacy', 'rigetti', 'sycamore']","['cirq', 'google-quantum', 'qiskit', 'quantum', 'quantum-computing', 'quantum-information', 'quantum-information-science', 'quantum-processor', 'quantum-supremacy', 'rigetti', 'sycamore']",2021-10-14,"[('netket/netket', 0.626657247543335, 'sim', 1), ('cqcl/tket', 0.6081982851028442, 'util', 1), ('qiskit/qiskit', 0.5992211699485779, 'sim', 3), ('quantumlib/cirq', 0.5691633224487305, 'sim', 2), ('cqcl/lambeq', 0.5265811681747437, 'nlp', 0)]",8,1.0,,0.0,1,1,59,27,0,0,0,1.0,0.0,90.0,0.0,18
1181,math,https://github.com/sj001/ai-feynman,"['regression', 'physics']",Implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression,[],[],,,,sj001/ai-feynman,AI-Feynman,567,171,26,Python,,,sj001,2024-01-14,2020-03-08,203,2.7891777933942374,,Implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression,[],"['physics', 'regression']",2021-05-16,[],2,1.0,,0.0,1,0,47,32,0,0,0,1.0,1.0,90.0,1.0,18
374,viz,https://github.com/vhranger/nodevectors,[],,[],[],,,,vhranger/nodevectors,nodevectors,485,58,11,Python,,Fastest network node embeddings in the west,vhranger,2024-01-04,2019-07-25,235,2.0575757575757576,,Fastest network node embeddings in the west,[],[],2021-11-06,"[('rom1504/embedding-reader', 0.53115314245224, 'ml', 0), ('facebookresearch/pytorch-biggraph', 0.5276350975036621, 'ml-dl', 0)]",6,2.0,,0.0,0,0,54,27,0,3,3,0.0,0.0,90.0,0.0,18
479,sim,https://github.com/udst/urbansim,[],,[],[],,,,udst/urbansim,urbansim,457,128,80,Python,https://udst.github.io/urbansim/,Platform for building statistical models of cities and regions,udst,2024-01-10,2013-08-15,545,0.837434554973822,https://avatars.githubusercontent.com/u/5187765?v=4,Platform for building statistical models of cities and regions,[],[],2020-05-11,"[('mcordts/cityscapesscripts', 0.6591488718986511, 'gis', 0), ('spatialucr/geosnap', 0.5915238857269287, 'gis', 0), ('pysal/momepy', 0.5606027245521545, 'gis', 0), ('gregorhd/mapcompare', 0.5511513352394104, 'gis', 0), ('stan-dev/pystan', 0.5362882018089294, 'ml', 0), ('gboeing/street-network-models', 0.5066875219345093, 'sim', 0)]",20,6.0,,0.0,0,0,127,45,0,1,1,0.0,0.0,90.0,0.0,18
812,time-series,https://github.com/salesforce/deeptime,[],,[],[],,,,salesforce/deeptime,DeepTime,317,60,9,Python,,PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023),salesforce,2024-01-13,2022-06-27,83,3.8127147766323026,https://avatars.githubusercontent.com/u/453694?v=4,PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023),"['deep-learning', 'forecasting', 'implicit-neural-representation', 'meta-learning', 'time-series', 'time-series-forecasting', 'time-series-regression']","['deep-learning', 'forecasting', 'implicit-neural-representation', 'meta-learning', 'time-series', 'time-series-forecasting', 'time-series-regression']",2023-08-01,"[('aistream-peelout/flow-forecast', 0.7200793027877808, 'time-series', 5), ('ourownstory/neural_prophet', 0.5901058316230774, 'ml', 3), ('salesforce/merlion', 0.5836126804351807, 'time-series', 2), ('awslabs/gluonts', 0.5721127390861511, 'time-series', 4), ('unit8co/darts', 0.5719814300537109, 'time-series', 3), ('winedarksea/autots', 0.5623159408569336, 'time-series', 3), ('rasbt/machine-learning-book', 0.559136688709259, 'study', 1), ('sktime/sktime', 0.5567091107368469, 'time-series', 3), ('huggingface/transformers', 0.5546357035636902, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5429381728172302, 'ml-dl', 2), ('opengeos/earthformer', 0.5404602289199829, 'gis', 2), ('tensorflow/tensor2tensor', 0.5346342325210571, 'ml', 1), ('alkaline-ml/pmdarima', 0.533848762512207, 'time-series', 2), ('nixtla/statsforecast', 0.5292312502861023, 'time-series', 2), ('rafiqhasan/auto-tensorflow', 0.5278478264808655, 'ml-dl', 0), ('pytorch/ignite', 0.5224276185035706, 'ml-dl', 1), ('awslabs/autogluon', 0.5168805718421936, 'ml', 3), ('firmai/atspy', 0.5146031975746155, 'time-series', 2), ('linkedin/greykite', 0.5127387642860413, 'ml', 0), ('keras-team/autokeras', 0.5080786347389221, 'ml-dl', 1), ('skorch-dev/skorch', 0.506636381149292, 'ml-dl', 0), ('karpathy/micrograd', 0.5016177892684937, 'study', 0), ('arogozhnikov/einops', 0.5012999773025513, 'ml-dl', 1)]",2,0.0,,0.06,2,1,19,6,0,0,0,2.0,1.0,90.0,0.5,18
428,jupyter,https://github.com/chaoleili/jupyterlab_tensorboard,[],,[],[],,,,chaoleili/jupyterlab_tensorboard,jupyterlab_tensorboard,310,36,12,TypeScript,,Tensorboard extension for jupyterlab.,chaoleili,2024-01-12,2018-08-14,285,1.087719298245614,,Tensorboard extension for jupyterlab.,"['jupyterlab', 'jupyterlab-extension', 'tensorboard']","['jupyterlab', 'jupyterlab-extension', 'tensorboard']",2022-07-18,"[('jupyter-widgets/ipywidgets', 0.6089569330215454, 'jupyter', 1), ('mamba-org/gator', 0.5936612486839294, 'jupyter', 1), ('jupyterlab/jupyter-ai', 0.5836908221244812, 'jupyter', 2), ('jupyterlab/jupyterlab', 0.5774864554405212, 'jupyter', 1), ('ipython/ipykernel', 0.5732330679893494, 'util', 0), ('jupyter/notebook', 0.5390121340751648, 'jupyter', 0), ('jupyter/nbformat', 0.5145013928413391, 'jupyter', 0)]",7,3.0,,0.0,1,0,66,18,0,0,0,1.0,1.0,90.0,1.0,18
880,study,https://github.com/amaargiru/pyroad,[],,[],[],,,,amaargiru/pyroad,pyroad,299,37,8,Jupyter Notebook,,Detailed Python developer roadmap,amaargiru,2024-01-12,2022-11-03,64,4.620309050772627,,Detailed Python developer roadmap,"['roadmap', 'tutorial']","['roadmap', 'tutorial']",2023-02-27,"[('eleutherai/pyfra', 0.6402732729911804, 'ml', 0), ('realpython/python-guide', 0.6154986023902893, 'study', 0), ('willmcgugan/textual', 0.6114891171455383, 'term', 0), ('python/cpython', 0.5899056792259216, 'util', 0), ('brandon-rhodes/python-patterns', 0.5854448676109314, 'util', 0), ('samuelcolvin/python-devtools', 0.5811712741851807, 'debug', 0), ('kubeflow/fairing', 0.5755603313446045, 'ml-ops', 0), ('pypa/hatch', 0.5697453618049622, 'util', 0), ('sourcery-ai/sourcery', 0.5690412521362305, 'util', 0), ('norvig/pytudes', 0.5647484064102173, 'util', 0), ('pypa/pipenv', 0.5548535585403442, 'util', 0), ('pytoolz/toolz', 0.5507724285125732, 'util', 0), ('mitmproxy/pdoc', 0.5501058101654053, 'util', 0), ('featurelabs/featuretools', 0.548759937286377, 'ml', 0), ('landscapeio/prospector', 0.5477283596992493, 'util', 0), ('mkdocstrings/griffe', 0.5457209944725037, 'util', 0), ('dosisod/refurb', 0.5455231070518494, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5438991785049438, 'study', 0), ('eugeneyan/python-collab-template', 0.5436649918556213, 'template', 0), ('goldmansachs/gs-quant', 0.5344632863998413, 'finance', 0), ('masoniteframework/masonite', 0.5333690643310547, 'web', 0), ('pypy/pypy', 0.5301415324211121, 'util', 0), ('pyston/pyston', 0.5288054347038269, 'util', 0), ('python-rope/rope', 0.528800904750824, 'util', 0), ('rubik/radon', 0.5273672342300415, 'util', 0), ('python-odin/odin', 0.5243930220603943, 'util', 0), ('google/pyglove', 0.5239276885986328, 'util', 0), ('wesm/pydata-book', 0.5237447023391724, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5223120450973511, 'study', 0), ('microsoft/playwright-python', 0.5205329060554504, 'testing', 0), ('pypa/build', 0.5195523500442505, 'util', 0), ('malloydata/malloy-py', 0.5178565979003906, 'data', 0), ('gradio-app/gradio', 0.5175772905349731, 'viz', 0), ('nedbat/coveragepy', 0.5170186161994934, 'testing', 0), ('requests/toolbelt', 0.5161598324775696, 'util', 0), ('webpy/webpy', 0.5149017572402954, 'web', 0), ('fastai/fastcore', 0.5148802995681763, 'util', 0), ('lordmauve/pgzero', 0.5130576491355896, 'gamedev', 0), ('googleapis/google-api-python-client', 0.5120947957038879, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5113055109977722, 'study', 0), ('python-poetry/poetry', 0.5109917521476746, 'util', 0), ('r0x0r/pywebview', 0.5109246969223022, 'gui', 0), ('pygamelib/pygamelib', 0.5083182454109192, 'gamedev', 0), ('falconry/falcon', 0.5072835087776184, 'web', 0), ('urwid/urwid', 0.5068714618682861, 'term', 0), ('holoviz/holoviz', 0.5063855648040771, 'viz', 0), ('selfexplainml/piml-toolbox', 0.5047166347503662, 'ml-interpretability', 0), ('opengeos/leafmap', 0.503073513507843, 'gis', 0), ('fmind/mlops-python-package', 0.5028426051139832, 'template', 0), ('pallets/flask', 0.5025720596313477, 'web', 0), ('alexmojaki/snoop', 0.5022026896476746, 'debug', 0), ('plotly/dash', 0.5013555884361267, 'viz', 0), ('scikit-mobility/scikit-mobility', 0.5013498067855835, 'gis', 0), ('renpy/renpy', 0.500852644443512, 'viz', 0), ('reloadware/reloadium', 0.5002206563949585, 'profiling', 0)]",1,1.0,,0.08,0,0,15,11,0,0,0,0.0,0.0,90.0,0.0,18
275,crypto,https://github.com/ethtx/ethtx_ce,[],,[],[],,,,ethtx/ethtx_ce,ethtx_ce,264,64,12,Python,https://ethtx.info,Ethereum transaction decoder (community version).,ethtx,2024-01-12,2021-07-26,131,2.0130718954248366,https://avatars.githubusercontent.com/u/70520035?v=4,Ethereum transaction decoder (community version).,[],[],2023-08-08,"[('palkeo/panoramix', 0.6499204635620117, 'crypto', 0), ('ethtx/ethtx', 0.5777018666267395, 'crypto', 0)]",7,2.0,,0.04,0,0,30,5,0,0,0,0.0,0.0,90.0,0.0,18
517,gis,https://github.com/lydorn/polygonization-by-frame-field-learning,[],,[],[],,,,lydorn/polygonization-by-frame-field-learning,Polygonization-by-Frame-Field-Learning,257,56,13,Python,,This repository contains the code for our fast polygonal building extraction from overhead images pipeline.,lydorn,2024-01-04,2020-05-26,192,1.3385416666666667,,This repository contains the code for our fast polygonal building extraction from overhead images pipeline.,"['field', 'frame', 'polygonization', 'remote', 'segmentation', 'sensing']","['field', 'frame', 'polygonization', 'remote', 'segmentation', 'sensing']",2023-10-02,"[('zorzi-s/polyworldpretrainednetwork', 0.6778478026390076, 'gis', 0), ('microsoft/globalmlbuildingfootprints', 0.5449085831642151, 'gis', 0)]",2,1.0,,0.02,2,0,44,3,0,0,0,2.0,2.0,90.0,1.0,18
1401,nlp,https://github.com/facebookresearch/dpr-scale,['retrieval'],,[],[],,,,facebookresearch/dpr-scale,dpr-scale,225,22,18,Python,,Scalable training for dense retrieval models.,facebookresearch,2024-01-11,2021-10-20,118,1.8930288461538463,https://avatars.githubusercontent.com/u/16943930?v=4,Scalable training for dense retrieval models.,[],['retrieval'],2023-05-27,"[('paddlepaddle/rocketqa', 0.6294921636581421, 'nlp', 0), ('castorini/pyserini', 0.5861307978630066, 'ml', 0), ('ai21labs/in-context-ralm', 0.5703505873680115, 'llm', 0), ('intellabs/fastrag', 0.5437898635864258, 'nlp', 0)]",5,3.0,,0.13,0,0,27,8,1,1,1,0.0,0.0,90.0,0.0,18
1566,llm,https://github.com/deep-diver/pingpong,"['lanuage-model', 'contexts']",PingPong is a simple library to manage pings(prompt) and pongs(response). The main purpose of this library is to manage histories and contexts in LLM applied applications.,[],[],,,,deep-diver/pingpong,PingPong,83,8,4,Python,https://pypi.org/project/bingbong/,manage histories of LLM applied applications,deep-diver,2024-01-13,2023-04-11,42,1.9761904761904763,,manage histories of LLM applied applications,[],"['contexts', 'lanuage-model']",2023-11-17,"[('citadel-ai/langcheck', 0.6283354759216309, 'llm', 0), ('hwchase17/langchain', 0.589103639125824, 'llm', 0), ('agenta-ai/agenta', 0.557935893535614, 'llm', 0), ('eugeneyan/open-llms', 0.5374715328216553, 'study', 0), ('confident-ai/deepeval', 0.5305920243263245, 'testing', 0), ('langchain-ai/langgraph', 0.5210304260253906, 'llm', 0), ('ray-project/ray-llm', 0.5193211436271667, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5120090842247009, 'llm', 0), ('ibm/dromedary', 0.510129988193512, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5093730688095093, 'study', 0), ('nat/openplayground', 0.5059070587158203, 'llm', 0), ('pathwaycom/llm-app', 0.5041395425796509, 'llm', 0), ('jina-ai/thinkgpt', 0.5024981498718262, 'llm', 0), ('berriai/litellm', 0.5002941489219666, 'llm', 0)]",2,1.0,,1.21,0,0,9,2,5,7,5,0.0,0.0,90.0,0.0,18
1468,gis,https://github.com/opengeos/earthformer,[],,[],[],,,,opengeos/earthformer,earthformer,73,5,7,Python,https://open.gishub.org/earthformer,A Python package for Earth forecasting transformer,opengeos,2024-01-05,2023-07-31,26,2.7923497267759565,https://avatars.githubusercontent.com/u/129896036?v=4,A Python package for Earth forecasting transformer,"['deep-learning', 'earthformer', 'forecasting', 'geospatial', 'transformer']","['deep-learning', 'earthformer', 'forecasting', 'geospatial', 'transformer']",2023-08-09,"[('alignmentresearch/tuned-lens', 0.6345276832580566, 'ml-interpretability', 0), ('sentinel-hub/eo-learn', 0.5971592664718628, 'gis', 0), ('aistream-peelout/flow-forecast', 0.5866661071777344, 'time-series', 3), ('microsoft/torchgeo', 0.5563389658927917, 'gis', 2), ('amazon-science/earth-forecasting-transformer', 0.5462614893913269, 'gis', 0), ('salesforce/deeptime', 0.5404602289199829, 'time-series', 2), ('unit8co/darts', 0.5380537509918213, 'time-series', 2), ('marella/ctransformers', 0.5367976427078247, 'nlp', 0), ('pytroll/satpy', 0.5178881287574768, 'gis', 0), ('huggingface/transformers', 0.5169755816459656, 'nlp', 2), ('nielsrogge/transformers-tutorials', 0.5140464901924133, 'study', 0), ('ourownstory/neural_prophet', 0.5128765106201172, 'ml', 2), ('nvidia/megatron-lm', 0.507581353187561, 'llm', 0), ('awslabs/gluonts', 0.502416729927063, 'time-series', 2)]",1,1.0,,0.52,0,0,6,5,7,14,7,0.0,0.0,90.0,0.0,18
1512,template,https://github.com/martinheinz/python-project-blueprint,[],,[],[],,,,martinheinz/python-project-blueprint,python-project-blueprint,932,266,41,Makefile,,Blueprint/Boilerplate For Python Projects,martinheinz,2024-01-07,2019-12-26,213,4.36096256684492,,Blueprint/Boilerplate For Python Projects,"['blueprint', 'boilerplate', 'docker', 'kubernetes', 'template']","['blueprint', 'boilerplate', 'docker', 'kubernetes', 'template']",2023-01-06,"[('pypa/hatch', 0.5873016119003296, 'util', 0), ('pyscaffold/pyscaffold', 0.5791087746620178, 'template', 0), ('backtick-se/cowait', 0.5766209363937378, 'util', 2), ('eugeneyan/python-collab-template', 0.5737303495407104, 'template', 0), ('sqlalchemy/mako', 0.5644561648368835, 'template', 0), ('rawheel/fastapi-boilerplate', 0.5532102584838867, 'web', 2), ('ianmiell/shutit', 0.5357551574707031, 'util', 1), ('tedivm/robs_awesome_python_template', 0.5347585082054138, 'template', 0), ('pypa/pipenv', 0.5226168036460876, 'util', 0), ('python-attrs/attrs', 0.521111011505127, 'typing', 1), ('mitmproxy/pdoc', 0.514594554901123, 'util', 0), ('orchest/orchest', 0.5042280554771423, 'ml-ops', 2), ('asacristani/fastapi-rocket-boilerplate', 0.5014850497245789, 'template', 1)]",3,0.0,,0.0,0,0,49,12,0,0,0,0.0,0.0,90.0,0.0,17
628,profiling,https://github.com/csurfer/pyheat,[],,[],[],,,,csurfer/pyheat,pyheat,775,41,12,Python,,pprofile + matplotlib = Python program profiled as an awesome heatmap!,csurfer,2024-01-04,2017-02-04,364,2.1266170129361033,,pprofile + matplotlib = Python program profiled as an awesome heatmap!,"['heatmap', 'matplotlib', 'profiling']","['heatmap', 'matplotlib', 'profiling']",2021-09-18,"[('mwaskom/seaborn', 0.5626926422119141, 'viz', 1), ('matplotlib/basemap', 0.5544516444206238, 'gis', 0), ('matplotlib/matplotlib', 0.547273576259613, 'viz', 1), ('scitools/cartopy', 0.542796790599823, 'gis', 1), ('altair-viz/altair', 0.5379376411437988, 'viz', 0), ('pyutils/line_profiler', 0.5279625654220581, 'profiling', 0), ('benfred/py-spy', 0.5209768414497375, 'profiling', 1), ('pysal/pysal', 0.5142292380332947, 'gis', 0)]",5,2.0,,0.0,0,0,84,28,0,0,0,0.0,0.0,90.0,0.0,17
118,nlp,https://github.com/iclrandd/blackstone,[],,[],[],,,,iclrandd/blackstone,Blackstone,630,100,39,Python,https://research.iclr.co.uk,:black_circle: A spaCy pipeline and model for NLP on unstructured legal text.,iclrandd,2024-01-04,2019-03-25,253,2.4887133182844243,,⚫ A spaCy pipeline and model for NLP on unstructured legal text.,"['caselaw', 'law', 'legaltech', 'nlp', 'spacy-models']","['caselaw', 'law', 'legaltech', 'nlp', 'spacy-models']",2021-01-31,"[('coastalcph/lex-glue', 0.6491376757621765, 'nlp', 2), ('explosion/spacy-models', 0.612856388092041, 'nlp', 2), ('explosion/spacy-stanza', 0.5856739282608032, 'nlp', 1), ('thoppe/the-pile-freelaw', 0.585483193397522, 'data', 0), ('lexpredict/lexpredict-lexnlp', 0.582179069519043, 'nlp', 3), ('explosion/spacy-llm', 0.5293763279914856, 'llm', 1)]",8,2.0,,0.0,0,0,59,36,0,0,0,0.0,0.0,90.0,0.0,17
240,ml,https://github.com/microsoft/focal-transformer,[],,[],[],,,,microsoft/focal-transformer,Focal-Transformer,534,58,17,Python,,"[NeurIPS 2021 Spotlight] Official code for ""Focal Self-attention for Local-Global Interactions in Vision Transformers""",microsoft,2024-01-12,2021-07-10,133,4.002141327623126,https://avatars.githubusercontent.com/u/6154722?v=4,"[NeurIPS 2021 Spotlight] Official code for ""Focal Self-attention for Local-Global Interactions in Vision Transformers""",[],[],2022-03-27,"[('nvlabs/gcvit', 0.5749183893203735, 'diffusion', 0), ('abertsch72/unlimiformer', 0.5153154134750366, 'nlp', 0)]",2,1.0,,0.0,1,0,31,22,0,0,0,1.0,0.0,90.0,0.0,17
1001,viz,https://github.com/cuemacro/chartpy,[],,[],[],,,,cuemacro/chartpy,chartpy,534,99,48,Python,,"Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more",cuemacro,2024-01-04,2016-08-03,390,1.3662280701754386,https://avatars.githubusercontent.com/u/20479975?v=4,"Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more","['bokeh', 'chart', 'matplotlib', 'plotly', 'plotting']","['bokeh', 'chart', 'matplotlib', 'plotly', 'plotting']",2023-10-12,"[('matplotlib/matplotlib', 0.6975510120391846, 'viz', 2), ('holoviz/hvplot', 0.6870236992835999, 'pandas', 1), ('plotly/plotly.py', 0.6865983605384827, 'viz', 1), ('bokeh/bokeh', 0.6531518697738647, 'viz', 2), ('vizzuhq/ipyvizzu', 0.6341903209686279, 'jupyter', 2), ('kanaries/pygwalker', 0.6271337270736694, 'pandas', 2), ('mwaskom/seaborn', 0.6233909130096436, 'viz', 1), ('holoviz/panel', 0.6215455532073975, 'viz', 3), ('nschloe/tikzplotlib', 0.5781739354133606, 'util', 1), ('matplotlib/mplfinance', 0.574644148349762, 'finance', 1), ('federicoceratto/dashing', 0.5717711448669434, 'term', 0), ('scitools/cartopy', 0.5699009299278259, 'gis', 1), ('residentmario/geoplot', 0.5584993958473206, 'gis', 1), ('maartenbreddels/ipyvolume', 0.5550907254219055, 'jupyter', 1), ('holoviz/holoviz', 0.5522693395614624, 'viz', 0), ('man-group/dtale', 0.5520331263542175, 'viz', 0), ('altair-viz/altair', 0.5500401258468628, 'viz', 0), ('plotly/dash', 0.5469133853912354, 'viz', 1), ('holoviz/geoviews', 0.5426660180091858, 'gis', 1), ('has2k1/plotnine', 0.5336882472038269, 'viz', 1), ('lux-org/lux', 0.5205578804016113, 'viz', 0), ('westhealth/pyvis', 0.5167423486709595, 'graph', 0), ('pygraphviz/pygraphviz', 0.5139192342758179, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5052139759063721, 'study', 1), ('graphistry/pygraphistry', 0.5035216212272644, 'data', 0), ('enthought/mayavi', 0.5027343034744263, 'viz', 0), ('jmcnamara/xlsxwriter', 0.500167191028595, 'data', 0)]",1,1.0,,0.02,0,0,91,3,1,2,1,0.0,0.0,90.0,0.0,17
334,ml,https://github.com/mrdbourke/m1-machine-learning-test,[],,[],[],,,,mrdbourke/m1-machine-learning-test,m1-machine-learning-test,477,147,16,Jupyter Notebook,,Code for testing various M1 Chip benchmarks with TensorFlow.,mrdbourke,2024-01-14,2021-11-14,115,4.137546468401487,,Code for testing various M1 Chip benchmarks with TensorFlow.,"['machine-learning', 'metal', 'tensorflow', 'tensorflow-macos']","['machine-learning', 'metal', 'tensorflow', 'tensorflow-macos']",2022-07-16,"[('tlkh/tf-metal-experiments', 0.7584832310676575, 'perf', 1), ('intel/intel-extension-for-pytorch', 0.5805241465568542, 'perf', 1), ('klen/py-frameworks-bench', 0.5594583749771118, 'perf', 0), ('arogozhnikov/einops', 0.556743323802948, 'ml-dl', 1), ('microsoft/onnxruntime', 0.5451663136482239, 'ml', 2), ('ionelmc/pytest-benchmark', 0.5336135625839233, 'testing', 0), ('google/tf-quant-finance', 0.5282031893730164, 'finance', 1), ('determined-ai/determined', 0.5143718719482422, 'ml-ops', 2), ('tlkh/asitop', 0.5042293071746826, 'perf', 0), ('ml-explore/mlx', 0.5017737150192261, 'ml', 0), ('pytorch/pytorch', 0.5010038018226624, 'ml-dl', 1), ('plasma-umass/scalene', 0.5002366900444031, 'profiling', 0)]",2,1.0,,0.0,0,0,26,18,0,0,0,0.0,0.0,90.0,0.0,17
498,study,https://github.com/googlecloudplatform/practical-ml-vision-book,[],,[],[],,,,googlecloudplatform/practical-ml-vision-book,practical-ml-vision-book,425,197,23,Jupyter Notebook,,,googlecloudplatform,2024-01-12,2020-11-18,166,2.5470890410958904,https://avatars.githubusercontent.com/u/2810941?v=4,googlecloudplatform/practical-ml-vision-book,[],[],2023-05-16,"[('googlecloudplatform/vertex-ai-samples', 0.5228831768035889, 'ml', 0), ('developmentseed/label-maker', 0.505915641784668, 'gis', 0), ('googlecloudplatform/dataflow-geobeam', 0.504852831363678, 'gis', 0), ('google/automl', 0.5014958381652832, 'ml', 0)]",6,1.0,,0.02,0,0,38,8,0,0,0,0.0,0.0,90.0,0.0,17
1156,gamedev,https://github.com/bitcraft/pytmx,[],,[],[],,,,bitcraft/pytmx,pytmx,365,76,23,Python,,Python library to read Tiled Map Editor's TMX maps.,bitcraft,2024-01-04,2012-02-22,622,0.5860091743119266,,Python library to read Tiled Map Editor's TMX maps.,[],[],2023-08-18,"[('geopandas/contextily', 0.5879520177841187, 'gis', 0), ('opengeos/leafmap', 0.5061635375022888, 'gis', 0)]",42,2.0,,0.15,3,0,145,5,0,0,0,3.0,0.0,90.0,0.0,17
512,data,https://github.com/jovianml/opendatasets,[],,[],[],,,,jovianml/opendatasets,opendatasets,298,137,14,Python,,"A Python library for downloading datasets from Kaggle, Google Drive, and other online sources.",jovianml,2024-01-09,2020-09-17,175,1.6959349593495936,https://avatars.githubusercontent.com/u/46194244?v=4,"A Python library for downloading datasets from Kaggle, Google Drive, and other online sources.","['data-science', 'datasets', 'machine-learning']","['data-science', 'datasets', 'machine-learning']",2022-11-01,"[('rasbt/mlxtend', 0.6177619695663452, 'ml', 2), ('cuemacro/findatapy', 0.6065632104873657, 'finance', 0), ('nv7-github/googlesearch', 0.5820246338844299, 'util', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5723727941513062, 'ml', 2), ('tensorflow/data-validation', 0.5710594654083252, 'ml-ops', 0), ('krzjoa/awesome-python-data-science', 0.5699965357780457, 'study', 2), ('scikit-learn/scikit-learn', 0.5696676969528198, 'ml', 2), ('pycaret/pycaret', 0.5661379098892212, 'ml', 2), ('firmai/industry-machine-learning', 0.5650241374969482, 'study', 2), ('rasbt/machine-learning-book', 0.5631752610206604, 'study', 1), ('gradio-app/gradio', 0.5612888932228088, 'viz', 2), ('huggingface/evaluate', 0.5602533221244812, 'ml', 1), ('online-ml/river', 0.5532159209251404, 'ml', 2), ('googleapis/google-api-python-client', 0.551531970500946, 'util', 0), ('dylanhogg/awesome-python', 0.5494063496589661, 'study', 2), ('kubeflow-kale/kale', 0.5395414233207703, 'ml-ops', 1), ('ta-lib/ta-lib-python', 0.5373624563217163, 'finance', 0), ('wesm/pydata-book', 0.5370580554008484, 'study', 0), ('erotemic/ubelt', 0.5337167978286743, 'util', 0), ('mattbierbaum/arxiv-public-datasets', 0.5266126990318298, 'data', 0), ('featurelabs/featuretools', 0.5258838534355164, 'ml', 2), ('scrapy/scrapy', 0.5252864360809326, 'data', 0), ('parallel-domain/pd-sdk', 0.5220646262168884, 'data', 1), ('skops-dev/skops', 0.5186297297477722, 'ml-ops', 1), ('polyaxon/datatile', 0.5163763165473938, 'pandas', 1), ('alirezamika/autoscraper', 0.5161617398262024, 'data', 1), ('kubeflow/fairing', 0.5135595202445984, 'ml-ops', 0), ('google/temporian', 0.512967050075531, 'time-series', 0), ('pytorch/data', 0.5112810730934143, 'data', 0), ('imageio/imageio', 0.5112266540527344, 'util', 0), ('dlt-hub/dlt', 0.5093324184417725, 'data', 0), ('pandas-dev/pandas', 0.5063053965568542, 'pandas', 1), ('mito-ds/monorepo', 0.5031294822692871, 'jupyter', 1), ('roniemartinez/dude', 0.5030663013458252, 'util', 0), ('hazyresearch/meerkat', 0.5027737021446228, 'viz', 2), ('merantix-momentum/squirrel-core', 0.5021243095397949, 'ml', 3), ('intake/intake', 0.5019022226333618, 'data', 0), ('probml/pyprobml', 0.5017038583755493, 'ml', 1), ('patchy631/machine-learning', 0.5012773275375366, 'ml', 0), ('radiantearth/radiant-mlhub', 0.5003646016120911, 'gis', 1)]",3,2.0,,0.0,3,0,40,15,0,0,0,3.0,1.0,90.0,0.3,17
384,nlp,https://github.com/kootenpv/contractions,[],,[],[],,,,kootenpv/contractions,contractions,293,38,9,Python,,Fixes contractions such as `you're` to `you are`,kootenpv,2024-01-12,2016-12-25,370,0.7912808641975309,,Fixes contractions such as `you're` to `you are`,[],[],2022-11-15,[],14,6.0,,0.0,0,0,86,14,0,0,0,0.0,0.0,90.0,0.0,17
687,data,https://github.com/paperswithcode/sota-extractor,[],,[],[],,,,paperswithcode/sota-extractor,sota-extractor,279,30,14,Python,,The SOTA extractor pipeline,paperswithcode,2024-01-07,2018-12-07,268,1.0388297872340426,https://avatars.githubusercontent.com/u/40305508?v=4,The SOTA extractor pipeline,[],[],2022-03-09,"[('linealabs/lineapy', 0.5607438087463379, 'jupyter', 0), ('unstructured-io/pipeline-sec-filings', 0.537769615650177, 'data', 0), ('facebookresearch/vissl', 0.5020662546157837, 'ml', 0)]",8,3.0,,0.0,0,0,62,23,0,3,3,0.0,0.0,90.0,0.0,17
1299,llm,https://github.com/salesforce/jaxformer,[],,[],[],,,,salesforce/jaxformer,jaxformer,254,37,7,Python,,Minimal library to train LLMs on TPU in JAX with pjit().,salesforce,2024-01-12,2022-08-29,74,3.4258188824662814,https://avatars.githubusercontent.com/u/453694?v=4,Minimal library to train LLMs on TPU in JAX with pjit().,[],[],2023-07-25,"[('young-geng/easylm', 0.5536481142044067, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5040596723556519, 'llm', 0)]",2,0.0,,0.02,2,1,17,6,0,0,0,2.0,1.0,90.0,0.5,17
459,nlp,https://github.com/yoadtew/zero-shot-image-to-text,[],,[],[],,,,yoadtew/zero-shot-image-to-text,zero-shot-image-to-text,237,36,7,Python,,Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic,yoadtew,2024-01-05,2021-11-26,113,2.086792452830189,,Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic,[],[],2022-09-17,"[('sharonzhou/long_stable_diffusion', 0.5378433465957642, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5140002965927124, 'diffusion', 0)]",6,0.0,,0.0,2,0,26,16,0,0,0,2.0,4.0,90.0,2.0,17
1370,nlp,https://github.com/lingjzhu/charsiug2p,"['phoneme', 'grapheme']",,[],[],,,,lingjzhu/charsiug2p,CharsiuG2P,230,22,10,Jupyter Notebook,,Multilingual G2P in 100 languages,lingjzhu,2024-01-11,2022-01-19,105,2.1727395411605936,,Multilingual G2P in 100 languages,[],"['grapheme', 'phoneme']",2023-05-26,"[('thudm/chatglm2-6b', 0.5563612580299377, 'llm', 0), ('hannibal046/awesome-llm', 0.5186607241630554, 'study', 0), ('next-gpt/next-gpt', 0.5078827738761902, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5040284395217896, 'nlp', 0)]",5,2.0,,0.04,2,0,24,8,0,0,0,2.0,0.0,90.0,0.0,17
1823,util,https://github.com/initialcommit-com/git-story,[],,[],[],,,,initialcommit-com/git-story,git-story,226,8,2,Python,https://initialcommit.com/tools/git-story,"Easily create video animations (.mp4) of your Git commit history, directory from your Git repo.",initialcommit-com,2024-01-05,2022-05-12,89,2.5191082802547773,https://avatars.githubusercontent.com/u/105462693?v=4,"Easily create video animations (.mp4) of your Git commit history, directory from your Git repo.","['animation', 'collaboration', 'git', 'gitcommand', 'gitcommands', 'gitrepo', 'gitrepository', 'gitstory', 'software-development', 'video', 'visualization', 'viz']","['animation', 'collaboration', 'git', 'gitcommand', 'gitcommands', 'gitrepo', 'gitrepository', 'gitstory', 'software-development', 'video', 'visualization', 'viz']",2022-07-20,[],1,1.0,,0.0,1,0,20,18,0,8,8,1.0,0.0,90.0,0.0,17
927,ml,https://github.com/jonasgeiping/breaching,[],,[],[],,,,jonasgeiping/breaching,breaching,225,49,4,Python,,Breaching privacy in federated learning scenarios for vision and text,jonasgeiping,2024-01-13,2022-02-15,102,2.2058823529411766,,Breaching privacy in federated learning scenarios for vision and text,"['decentralized-learning', 'federated-learning', 'machine-learning', 'privacy-audit', 'pytorch', 'security']","['decentralized-learning', 'federated-learning', 'machine-learning', 'privacy-audit', 'pytorch', 'security']",2023-02-09,"[('nevronai/metisfl', 0.6550554037094116, 'ml', 2), ('adap/flower', 0.6508305668830872, 'ml-ops', 3)]",17,2.0,,0.02,0,0,23,11,0,0,0,0.0,0.0,90.0,0.0,17
1133,nlp,https://github.com/amansrivastava17/embedding-as-service,[],,[],[],,,,amansrivastava17/embedding-as-service,embedding-as-service,196,29,11,Python,,One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques ,amansrivastava17,2024-01-04,2019-05-29,243,0.8037492677211482,,One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques ,"['ai', 'albert', 'bert', 'bert-as-service', 'deep-learning', 'embedding', 'embedding-as-service', 'embeddings', 'encoder', 'fasttext', 'glove', 'nlp', 'roberta', 'sentence-encoding', 'tensorflow', 'transformer', 'ulmfit', 'word-embedding', 'word2vec', 'xlnet']","['ai', 'albert', 'bert', 'bert-as-service', 'deep-learning', 'embedding', 'embedding-as-service', 'embeddings', 'encoder', 'fasttext', 'glove', 'nlp', 'roberta', 'sentence-encoding', 'tensorflow', 'transformer', 'ulmfit', 'word-embedding', 'word2vec', 'xlnet']",2022-10-25,"[('jina-ai/clip-as-service', 0.6331066489219666, 'nlp', 4), ('plasticityai/magnitude', 0.6195082068443298, 'nlp', 5), ('google-research/electra', 0.6076592803001404, 'ml-dl', 3), ('jina-ai/finetuner', 0.5800686478614807, 'ml', 1), ('ukplab/sentence-transformers', 0.5738234519958496, 'nlp', 0), ('ddangelov/top2vec', 0.5621562600135803, 'nlp', 1), ('huggingface/text-embeddings-inference', 0.5573744177818298, 'llm', 2), ('llmware-ai/llmware', 0.5347151756286621, 'llm', 4), ('neuml/txtai', 0.5340142250061035, 'nlp', 2), ('muennighoff/sgpt', 0.5335803627967834, 'llm', 0), ('extreme-bert/extreme-bert', 0.5209690928459167, 'llm', 4), ('jina-ai/vectordb', 0.5125251412391663, 'data', 0), ('alibaba/easynlp', 0.5122250318527222, 'nlp', 3), ('deepset-ai/farm', 0.5068530440330505, 'nlp', 4), ('koaning/whatlies', 0.5058193802833557, 'nlp', 2), ('graykode/nlp-tutorial', 0.5044365525245667, 'study', 4)]",12,4.0,,0.0,0,0,56,15,0,2,2,0.0,0.0,90.0,0.0,17
1134,gis,https://github.com/martibosch/detectree,[],,[],[],,,,martibosch/detectree,detectree,171,27,8,Python,https://doi.org/10.21105/joss.02172,Tree detection from aerial imagery in Python,martibosch,2024-01-04,2019-07-31,234,0.7281021897810219,,Tree detection from aerial imagery in Python,"['image-segmentation', 'remote-sensing', 'tree-canopy', 'tree-detection', 'tree-pixels']","['image-segmentation', 'remote-sensing', 'tree-canopy', 'tree-detection', 'tree-pixels']",2022-10-24,[],2,2.0,,0.0,1,1,54,15,0,2,2,1.0,1.0,90.0,1.0,17
1278,sim,https://github.com/elliotwaite/rule-30-and-game-of-life,[],,[],[],,,,elliotwaite/rule-30-and-game-of-life,rule-30-and-game-of-life,156,13,5,Python,https://youtu.be/IK7nBOLYzdE,Generates a 2D animation of Rule 30 (or other rules) being fed into Conway's Game of Life.,elliotwaite,2024-01-10,2019-11-06,220,0.7063389391979301,,Generates a 2D animation of Rule 30 (or other rules) being fed into Conway's Game of Life.,"['cellular-automata', 'conways-game-of-life', 'game-of-life', 'rule-30']","['cellular-automata', 'conways-game-of-life', 'game-of-life', 'rule-30']",2024-01-11,"[('ljvmiranda921/seagull', 0.7149527072906494, 'sim', 3), ('alephalpha/golly', 0.6694435477256775, 'sim', 2)]",2,1.0,,0.06,2,2,51,0,0,0,0,2.0,1.0,90.0,0.5,17
774,gis,https://github.com/ghislainv/forestatrisk,[],,[],[],,,,ghislainv/forestatrisk,forestatrisk,108,26,6,Python,https://ecology.ghislainv.fr/forestatrisk,:package: :snake: Python package to model and forecast the risk of deforestation,ghislainv,2023-11-16,2016-12-01,373,0.2889908256880734,,📦 🐍 Python package to model and forecast the risk of deforestation,"['biodiversity-scenario', 'co2-emissions', 'deforestation', 'deforestation-risk', 'forecasting', 'forest-cover-change', 'ipbes', 'ipcc', 'land-use-change', 'protected-areas', 'redd', 'roads', 'spatial-analysis', 'spatial-autocorrelation', 'spatial-modelling', 'tropical-forests']","['biodiversity-scenario', 'co2-emissions', 'deforestation', 'deforestation-risk', 'forecasting', 'forest-cover-change', 'ipbes', 'ipcc', 'land-use-change', 'protected-areas', 'redd', 'roads', 'spatial-analysis', 'spatial-autocorrelation', 'spatial-modelling', 'tropical-forests']",2023-12-19,[],6,5.0,,0.63,0,0,87,1,0,0,0,0.0,0.0,90.0,0.0,17
268,term,https://github.com/deeplook/sparklines,[],,[],[],,,,deeplook/sparklines,sparklines,102,5,3,Python,,Text-based sparklines for the command line mimicking those of Edward Tuft.,deeplook,2024-01-05,2016-05-17,402,0.2537313432835821,,Text-based sparklines for the command line mimicking those of Edward Tuft.,"['ascii', 'command-line-tool', 'graphs', 'sparkline-graphs', 'sparklines']","['ascii', 'command-line-tool', 'graphs', 'sparkline-graphs', 'sparklines']",2023-10-20,"[('kellyjonbrazil/jc', 0.529207170009613, 'util', 1), ('plotly/plotly.py', 0.5089108943939209, 'viz', 0)]",7,3.0,,0.25,5,4,93,3,0,0,0,5.0,1.0,90.0,0.2,17
917,gis,https://github.com/benbovy/spherely,"['geometric-algorithms', 'geometry']",,[],[],,,,benbovy/spherely,spherely,97,4,6,C++,https://spherely.readthedocs.io/,Manipulation and analysis of geometric objects on the sphere.,benbovy,2024-01-04,2022-11-24,61,1.5717592592592593,,Manipulation and analysis of geometric objects on the sphere.,[],"['geometric-algorithms', 'geometry']",2023-03-20,"[('shapely/shapely', 0.873152494430542, 'gis', 2), ('scikit-geometry/scikit-geometry', 0.5209958553314209, 'gis', 2)]",4,2.0,,0.13,1,0,14,10,0,0,0,1.0,1.0,90.0,1.0,17
1039,finance,https://github.com/numerai/numerai-cli,[],,[],[],,,,numerai/numerai-cli,numerai-cli,90,29,21,Python,,Fully automated submission workflow in the cloud for <$1/mo,numerai,2024-01-11,2019-05-22,244,0.367561260210035,https://avatars.githubusercontent.com/u/15222762?v=4,Fully automated submission workflow in the cloud for <$1/mo,[],[],2023-12-09,"[('prefecthq/server', 0.5039493441581726, 'util', 0)]",14,2.0,,0.52,7,5,57,1,0,0,0,7.0,0.0,90.0,0.0,17
1031,finance,https://github.com/wilsonfreitas/python-bizdays,[],,[],[],,,,wilsonfreitas/python-bizdays,python-bizdays,73,34,9,Jupyter Notebook,http://wilsonfreitas.github.io/python-bizdays/,Business days calculations and utilities,wilsonfreitas,2024-01-13,2013-09-01,543,0.1343676045227452,,Business days calculations and utilities,[],[],2023-12-29,[],7,1.0,,0.27,10,9,126,1,0,0,0,10.0,5.0,90.0,0.5,17
492,ml-dl,https://github.com/hysts/pytorch_image_classification,[],,[],[],,,,hysts/pytorch_image_classification,pytorch_image_classification,1288,296,27,Python,,PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet,hysts,2024-01-13,2017-12-09,320,4.019616584930896,,PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet,"['cifar10', 'computer-vision', 'fashion-mnist', 'imagenet', 'pytorch']","['cifar10', 'computer-vision', 'fashion-mnist', 'imagenet', 'pytorch']",2021-12-12,"[('lucidrains/imagen-pytorch', 0.6431624889373779, 'ml-dl', 0), ('nvlabs/gcvit', 0.6284846067428589, 'diffusion', 1), ('skorch-dev/skorch', 0.619719386100769, 'ml-dl', 1), ('pytorch/ignite', 0.615060031414032, 'ml-dl', 1), ('rwightman/pytorch-image-models', 0.5974335074424744, 'ml-dl', 1), ('intel/intel-extension-for-pytorch', 0.5713286399841309, 'perf', 1), ('nyandwi/modernconvnets', 0.5707684755325317, 'ml-dl', 1), ('huggingface/accelerate', 0.5682789087295532, 'ml', 0), ('nvidia/apex', 0.5612041354179382, 'ml-dl', 0), ('lucidrains/vit-pytorch', 0.5473499298095703, 'ml-dl', 1), ('salesforce/blip', 0.5436573624610901, 'diffusion', 0), ('lightly-ai/lightly', 0.5435891151428223, 'ml', 2), ('roboflow/supervision', 0.538576066493988, 'ml', 2), ('deci-ai/super-gradients', 0.5351361632347107, 'ml-dl', 3), ('lutzroeder/netron', 0.5342903137207031, 'ml', 1), ('rasbt/machine-learning-book', 0.5264284610748291, 'study', 1), ('pytorch/captum', 0.5230705142021179, 'ml-interpretability', 0), ('lucidrains/dalle2-pytorch', 0.519826352596283, 'diffusion', 0), ('pyg-team/pytorch_geometric', 0.5179129242897034, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5127461552619934, 'study', 1), ('keras-team/keras-cv', 0.5079523921012878, 'ml-dl', 1), ('mchong6/jojogan', 0.5051544308662415, 'data', 0), ('mcahny/deep-video-inpainting', 0.5014491677284241, 'ml-dl', 0)]",1,0.0,,0.0,0,0,74,25,0,0,0,0.0,0.0,90.0,0.0,16
781,study,https://github.com/mynameisfiber/high_performance_python_2e,[],,[],[],,,,mynameisfiber/high_performance_python_2e,high_performance_python_2e,371,129,9,Python,,"Code for the book ""High Performance Python 2e"" by Micha Gorelick and Ian Ozsvald with OReilly ",mynameisfiber,2024-01-04,2020-04-12,198,1.8710374639769451,,"Code for the book ""High Performance Python 2e"" by Micha Gorelick and Ian Ozsvald with OReilly ","['code-samples', 'high-performance', 'oreilly', 'oreilly-books']","['code-samples', 'high-performance', 'oreilly', 'oreilly-books']",2023-01-18,"[('fchollet/deep-learning-with-python-notebooks', 0.6630170941352844, 'study', 0), ('wesm/pydata-book', 0.6341950297355652, 'study', 0), ('probml/pyprobml', 0.6130873560905457, 'ml', 0), ('python/cpython', 0.5944276452064514, 'util', 0), ('cohere-ai/notebooks', 0.5679949522018433, 'llm', 0), ('gbeced/pyalgotrade', 0.5649918913841248, 'finance', 0), ('eleutherai/pyfra', 0.5524687170982361, 'ml', 0), ('pypy/pypy', 0.549612820148468, 'util', 0), ('klen/py-frameworks-bench', 0.5484030842781067, 'perf', 0), ('jakevdp/pythondatasciencehandbook', 0.5477574467658997, 'study', 0), ('sympy/sympy', 0.5473185777664185, 'math', 0), ('astral-sh/ruff', 0.542158305644989, 'util', 0), ('pytoolz/toolz', 0.5383166670799255, 'util', 0), ('brandon-rhodes/python-patterns', 0.5350989699363708, 'util', 0), ('gerdm/prml', 0.5304524302482605, 'study', 0), ('ageron/handson-ml2', 0.5303263068199158, 'ml', 0), ('ta-lib/ta-lib-python', 0.5237243175506592, 'finance', 0), ('pyston/pyston', 0.5236924290657043, 'util', 0), ('amaargiru/pyroad', 0.5223120450973511, 'study', 0), ('faster-cpython/tools', 0.5218332409858704, 'perf', 0), ('realpython/python-guide', 0.5209663510322571, 'study', 0), ('google/latexify_py', 0.518977165222168, 'util', 0), ('rubik/radon', 0.5166555643081665, 'util', 0), ('cuemacro/finmarketpy', 0.513410210609436, 'finance', 0), ('scipy/scipy', 0.5127685070037842, 'math', 0), ('google/pytype', 0.5118113160133362, 'typing', 0), ('google/yapf', 0.5080813765525818, 'util', 0), ('renpy/renpy', 0.5067214369773865, 'viz', 0), ('adafruit/circuitpython', 0.5031493902206421, 'util', 0), ('faster-cpython/ideas', 0.5015802979469299, 'perf', 0)]",2,2.0,,0.0,0,0,46,12,0,0,0,0.0,0.0,90.0,0.0,16
462,data,https://github.com/dmarx/psaw,[],,[],[],,,,dmarx/psaw,psaw,358,58,9,Python,,Python Pushshift.io API Wrapper (for comment/submission search),dmarx,2024-01-12,2018-04-15,302,1.1843100189035918,,Python Pushshift.io API Wrapper (for comment/submission search),[],[],2022-07-09,"[('meilisearch/meilisearch-python', 0.5636150240898132, 'data', 0)]",8,3.0,,0.0,0,0,70,18,0,0,0,0.0,0.0,90.0,0.0,16
583,data,https://github.com/tokern/data-lineage,[],,[],[],,,,tokern/data-lineage,data-lineage,291,41,8,Python,https://tokern.io/data-lineage/,Generate and Visualize Data Lineage from query history,tokern,2024-01-04,2020-03-17,202,1.4405940594059405,https://avatars.githubusercontent.com/u/57188591?v=4,Generate and Visualize Data Lineage from query history,"['data-governance', 'data-lineage', 'jupyter', 'postgresql']","['data-governance', 'data-lineage', 'jupyter', 'postgresql']",2023-08-04,"[('airbytehq/airbyte', 0.5245165824890137, 'data', 1), ('man-group/dtale', 0.5029366612434387, 'viz', 0)]",5,0.0,,0.02,0,0,47,5,0,7,7,0.0,0.0,90.0,0.0,16
57,term,https://github.com/click-contrib/click-completion,"['click', 'shell']",,[],[],,,,click-contrib/click-completion,click-completion,281,32,8,Python,,"Add or enhance bash, fish, zsh and powershell completion in Click",click-contrib,2024-01-13,2016-07-23,392,0.71605387695668,https://avatars.githubusercontent.com/u/13245136?v=4,"Add or enhance bash, fish, zsh and powershell completion in Click",[],"['click', 'shell']",2022-05-09,"[('textualize/trogon', 0.5366969704627991, 'term', 1)]",16,4.0,,0.0,0,0,91,21,0,1,1,0.0,0.0,90.0,0.0,16
229,data,https://github.com/microsoft/genalog,[],,[],[],,,,microsoft/genalog,genalog,280,25,12,Jupyter Notebook,https://microsoft.github.io/genalog/,"Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.",microsoft,2024-01-12,2020-06-15,189,1.4803625377643506,https://avatars.githubusercontent.com/u/6154722?v=4,"Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.","['data-generation', 'data-science', 'machine-learning', 'ner', 'ocr-recognition', 'synthetic-data', 'synthetic-data-generation', 'synthetic-images', 'text-alignment']","['data-generation', 'data-science', 'machine-learning', 'ner', 'ocr-recognition', 'synthetic-data', 'synthetic-data-generation', 'synthetic-images', 'text-alignment']",2023-02-14,"[('pyfpdf/fpdf2', 0.5020582675933838, 'util', 0)]",7,1.0,,0.02,1,0,44,11,0,2,2,1.0,0.0,90.0,0.0,16
1262,data,https://github.com/weaviate/semantic-search-through-wikipedia-with-weaviate,['vector-search'],,[],[],,,,weaviate/semantic-search-through-wikipedia-with-weaviate,semantic-search-through-wikipedia-with-weaviate,235,22,8,Python,,Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine,weaviate,2024-01-04,2021-10-26,118,1.9915254237288136,https://avatars.githubusercontent.com/u/37794290?v=4,Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine,[],['vector-search'],2023-05-31,"[('goldsmith/wikipedia', 0.5848309397697449, 'data', 0), ('weaviate/demo-text2vec-openai', 0.55460125207901, 'util', 1), ('harangju/wikinet', 0.5428258776664734, 'data', 0), ('muennighoff/sgpt', 0.5272648334503174, 'llm', 0), ('neuml/txtai', 0.5210995674133301, 'nlp', 1), ('qdrant/vector-db-benchmark', 0.5176312923431396, 'perf', 1)]",2,2.0,,0.12,0,0,27,8,0,0,0,0.0,0.0,90.0,0.0,16
891,gis,https://github.com/kuanb/peartree,[],,[],[],,,,kuanb/peartree,peartree,201,20,13,Python,,peartree: A library for converting transit data into a directed graph for sketch network analysis.,kuanb,2024-01-11,2017-11-12,324,0.6198237885462555,,peartree: A library for converting transit data into a directed graph for sketch network analysis.,"['gis', 'graphs', 'gtfs', 'modeling', 'network-analysis', 'spatial-analysis', 'transit']","['gis', 'graphs', 'gtfs', 'modeling', 'network-analysis', 'spatial-analysis', 'transit']",2021-01-18,"[('networkx/networkx', 0.532261312007904, 'graph', 0), ('graphistry/pygraphistry', 0.5183374285697937, 'data', 1), ('westhealth/pyvis', 0.5152437090873718, 'graph', 0), ('h4kor/graph-force', 0.5150353908538818, 'graph', 0), ('artelys/geonetworkx', 0.5083203911781311, 'gis', 0), ('pygraphviz/pygraphviz', 0.5017895698547363, 'viz', 0)]",5,2.0,,0.0,3,0,75,36,0,2,2,3.0,2.0,90.0,0.7,16
1863,sim,https://github.com/inspirai/timechamber,[],,[],[],,,,inspirai/timechamber,TimeChamber,177,21,8,Python,,A Massively Parallel Large Scale Self-Play Framework,inspirai,2024-01-11,2022-08-17,75,2.3333333333333335,https://avatars.githubusercontent.com/u/44988657?v=4,A Massively Parallel Large Scale Self-Play Framework,"['deep-reinforcement-learning', 'isaac-gym', 'multi-agent', 'reinforcement-learning', 'self-play']","['deep-reinforcement-learning', 'isaac-gym', 'multi-agent', 'reinforcement-learning', 'self-play']",2023-01-09,"[('salesforce/warp-drive', 0.6329183578491211, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.6286770105361938, 'ml-rl', 2), ('thu-ml/tianshou', 0.5735207796096802, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5665203332901001, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5623535513877869, 'ml-rl', 1), ('nvidia-omniverse/isaacgymenvs', 0.56003338098526, 'sim', 0), ('facebookresearch/habitat-lab', 0.5581255555152893, 'sim', 2), ('nvidia-omniverse/omniisaacgymenvs', 0.5575793981552124, 'sim', 0), ('minedojo/voyager', 0.5301423072814941, 'llm', 0), ('pytorch/rl', 0.5206745862960815, 'ml-rl', 1), ('keras-rl/keras-rl', 0.5203887224197388, 'ml-rl', 1), ('operand/agency', 0.5097473859786987, 'llm', 0), ('humancompatibleai/imitation', 0.50629061460495, 'ml-rl', 0), ('google/trax', 0.5059916377067566, 'ml-dl', 2), ('openai/baselines', 0.5040830969810486, 'ml-rl', 0), ('google/dopamine', 0.5013951063156128, 'ml-rl', 0)]",4,2.0,,0.0,0,0,17,12,0,0,0,0.0,0.0,90.0,0.0,16
661,gis,https://github.com/zorzi-s/polyworldpretrainednetwork,[],,[],[],,,,zorzi-s/polyworldpretrainednetwork,PolyWorldPretrainedNetwork,146,27,6,Python,,PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images,zorzi-s,2024-01-10,2022-03-23,96,1.5073746312684366,,PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images,[],[],2022-11-10,"[('lydorn/polygonization-by-frame-field-learning', 0.6778478026390076, 'gis', 0), ('microsoft/globalmlbuildingfootprints', 0.600134015083313, 'gis', 0)]",1,0.0,,0.0,4,1,22,14,0,0,0,4.0,13.0,90.0,3.2,16
1340,llm,https://github.com/prefecthq/langchain-prefect,['langchain'],,[],[],,,,prefecthq/langchain-prefect,langchain-prefect,92,3,3,Python,https://prefecthq.github.io/langchain-prefect/,Tools for using Langchain with Prefect,prefecthq,2024-01-04,2023-03-06,47,1.9515151515151514,https://avatars.githubusercontent.com/u/39270919?v=4,Tools for using Langchain with Prefect,"['langchain', 'large-language-models', 'prefect']","['langchain', 'large-language-models', 'prefect']",2023-06-08,"[('gkamradt/langchain-tutorials', 0.7797976732254028, 'study', 0), ('hannibal046/awesome-llm', 0.6407128572463989, 'study', 0), ('ctlllll/llm-toolmaker', 0.6256077885627747, 'llm', 0), ('langchain-ai/langgraph', 0.6234740018844604, 'llm', 1), ('lianjiatech/belle', 0.6042580604553223, 'llm', 0), ('freedomintelligence/llmzoo', 0.5867060422897339, 'llm', 0), ('ai21labs/lm-evaluation', 0.5636144280433655, 'llm', 0), ('alphasecio/langchain-examples', 0.5599607229232788, 'llm', 1), ('cg123/mergekit', 0.5573033094406128, 'llm', 0), ('guidance-ai/guidance', 0.5558692812919617, 'llm', 0), ('logspace-ai/langflow', 0.548227071762085, 'llm', 2), ('juncongmoo/pyllama', 0.5437901616096497, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5365051627159119, 'llm', 1), ('conceptofmind/toolformer', 0.536091685295105, 'llm', 0), ('salesforce/xgen', 0.5343795418739319, 'llm', 1), ('togethercomputer/redpajama-data', 0.5340298414230347, 'llm', 0), ('hiyouga/llama-factory', 0.5244020819664001, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5244019627571106, 'llm', 1), ('lm-sys/fastchat', 0.5175867080688477, 'llm', 0), ('microsoft/autogen', 0.5145857334136963, 'llm', 0), ('langchain-ai/langsmith-sdk', 0.5083007216453552, 'llm', 0), ('hwchase17/langchain', 0.5060675740242004, 'llm', 1), ('nat/openplayground', 0.5041869282722473, 'llm', 0), ('oobabooga/text-generation-webui', 0.5021174550056458, 'llm', 0), ('infinitylogesh/mutate', 0.50165855884552, 'nlp', 0)]",2,1.0,,1.27,0,0,10,7,3,4,3,0.0,0.0,90.0,0.0,16
1604,term,https://github.com/kellyjonbrazil/jellex,[],,[],[],,,,kellyjonbrazil/jellex,jellex,91,1,2,Python,,TUI to filter JSON and JSON Lines data with Python syntax,kellyjonbrazil,2023-12-28,2021-06-29,135,0.674074074074074,,TUI to filter JSON and JSON Lines data with Python syntax,"['filter', 'json', 'json-lines', 'process', 'query', 'tui']","['filter', 'json', 'json-lines', 'process', 'query', 'tui']",2023-10-24,"[('kellyjonbrazil/jello', 0.7509535551071167, 'util', 5)]",2,0.0,,0.04,1,1,31,3,0,7,7,1.0,1.0,90.0,1.0,16
451,gis,https://github.com/googlecloudplatform/dataflow-geobeam,[],,[],[],,,,googlecloudplatform/dataflow-geobeam,dataflow-geobeam,85,28,11,Python,,,googlecloudplatform,2023-11-24,2021-02-04,155,0.5458715596330275,https://avatars.githubusercontent.com/u/2810941?v=4,googlecloudplatform/dataflow-geobeam,[],[],2023-07-10,"[('googlecloudplatform/practical-ml-vision-book', 0.504852831363678, 'study', 0)]",7,4.0,,0.29,0,0,36,6,0,2,2,0.0,0.0,90.0,0.0,16
234,crypto,https://github.com/blockchainsllc/in3,[],,[],[],,,,blockchainsllc/in3,in3,73,29,13,C,https://in3.readthedocs.io/en/develop/index.html,The IN3 client (written in C).,blockchainsllc,2023-11-19,2019-09-17,228,0.3201754385964912,https://avatars.githubusercontent.com/u/12978006?v=4,The IN3 client (written in C).,"['blockchain', 'crypto-economic', 'ethereum', 'ipfs', 'verify']","['blockchain', 'crypto-economic', 'ethereum', 'ipfs', 'verify']",2022-04-01,[],35,3.0,,0.0,0,0,53,22,0,21,21,0.0,0.0,90.0,0.0,16
1100,template,https://github.com/giswqs/pypackage,[],,[],[],,,,giswqs/pypackage,pypackage,45,16,2,Python,https://giswqs.github.io/pypackage,Cookiecutter template creating a Python package with mkdocs,giswqs,2023-12-16,2020-11-15,167,0.269000853970965,,Cookiecutter template creating a Python package with mkdocs,"['cookiecutter', 'cookiecutter-template', 'mkdocs', 'mkdocs-material', 'template', 'template-project']","['cookiecutter', 'cookiecutter-template', 'mkdocs', 'mkdocs-material', 'template', 'template-project']",2023-07-31,"[('ionelmc/cookiecutter-pylibrary', 0.8761537075042725, 'template', 3), ('lyz-code/cookiecutter-python-project', 0.815499484539032, 'template', 1), ('tedivm/robs_awesome_python_template', 0.7693808078765869, 'template', 1), ('cookiecutter/cookiecutter', 0.7356756329536438, 'template', 1), ('buuntu/fastapi-react', 0.6124856472015381, 'template', 1), ('cjolowicz/cookiecutter-hypermodern-python', 0.5635570883750916, 'template', 0), ('tezromach/python-package-template', 0.5540108680725098, 'template', 2)]",109,5.0,,0.21,0,0,38,6,0,0,0,0.0,0.0,90.0,0.0,16
979,sim,https://github.com/alephalpha/golly,[],,[],[],,,,alephalpha/golly,golly,40,8,5,C++,http://sourceforge.net/projects/golly/,"Golly, a Game of Life simulator (unofficial mirror from SourceForge)",alephalpha,2023-11-24,2018-07-10,290,0.13793103448275862,,"Golly, a Game of Life simulator (unofficial mirror from SourceForge)","['cellular-automata', 'game-of-life']","['cellular-automata', 'game-of-life']",2023-11-04,"[('ljvmiranda921/seagull', 0.7014067769050598, 'sim', 2), ('elliotwaite/rule-30-and-game-of-life', 0.6694435477256775, 'sim', 2), ('projectmesa/mesa', 0.5125412344932556, 'sim', 0), ('lordmauve/pgzero', 0.5084664821624756, 'gamedev', 0), ('pokepetter/ursina', 0.5002817511558533, 'gamedev', 0)]",22,4.0,,0.54,0,0,67,2,0,0,0,0.0,0.0,90.0,0.0,16
1795,ml,https://github.com/hazyresearch/hgcn,[],,[],[],,,,hazyresearch/hgcn,hgcn,526,111,27,Python,,Hyperbolic Graph Convolutional Networks in PyTorch. ,hazyresearch,2024-01-12,2019-09-30,226,2.3259633607075174,https://avatars.githubusercontent.com/u/2165246?v=4,Hyperbolic Graph Convolutional Networks in PyTorch. ,[],[],2020-10-03,"[('pyg-team/pytorch_geometric', 0.6979220509529114, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5537418723106384, 'ml-dl', 0), ('pytorch/ignite', 0.551753580570221, 'ml-dl', 0), ('dmlc/dgl', 0.5476505160331726, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5278249979019165, 'study', 0), ('graphistry/pygraphistry', 0.525396466255188, 'data', 0), ('hamed1375/exphormer', 0.5110289454460144, 'graph', 0), ('tensorflow/mesh', 0.5072011351585388, 'ml-dl', 0), ('nvidia/apex', 0.502190113067627, 'ml-dl', 0), ('nicolas-chaulet/torch-points3d', 0.5006260871887207, 'ml', 0), ('stellargraph/stellargraph', 0.500386118888855, 'graph', 0)]",2,0.0,,0.0,4,0,52,40,0,0,0,4.0,3.0,90.0,0.8,15
495,ml-dl,https://github.com/mcahny/deep-video-inpainting,[],,[],[],,,,mcahny/deep-video-inpainting,Deep-Video-Inpainting,488,93,14,Python,,"Official pytorch implementation for ""Deep Video Inpainting"" (CVPR 2019)",mcahny,2024-01-04,2019-05-22,244,1.9929988331388564,,"Official pytorch implementation for ""Deep Video Inpainting"" (CVPR 2019)",[],[],2020-12-10,"[('researchmm/sttn', 0.6987395882606506, 'ml-dl', 0), ('nvlabs/gcvit', 0.5919110774993896, 'diffusion', 0), ('vt-vl-lab/fgvc', 0.5231187343597412, 'ml-dl', 0), ('nvidia/apex', 0.514928936958313, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5014491677284241, 'ml-dl', 0), ('timothybrooks/instruct-pix2pix', 0.500446081161499, 'diffusion', 0)]",3,2.0,,0.0,0,0,57,38,0,0,0,0.0,0.0,90.0,0.0,15
471,ml-dl,https://github.com/nyandwi/modernconvnets,[],,[],[],,,,nyandwi/modernconvnets,ModernConvNets,323,36,8,Jupyter Notebook,,Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras,nyandwi,2024-01-09,2022-02-10,102,3.144645340751043,,Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras,"['cnns', 'computer-vision', 'convnets', 'convolutional-neural-networks', 'deep-learning-algorithms', 'image-classification', 'keras', 'neural-networks', 'tensorflow']","['cnns', 'computer-vision', 'convnets', 'convolutional-neural-networks', 'deep-learning-algorithms', 'image-classification', 'keras', 'neural-networks', 'tensorflow']",2022-10-05,"[('rwightman/pytorch-image-models', 0.6331978440284729, 'ml-dl', 0), ('tensorflow/tensorflow', 0.6226397752761841, 'ml-dl', 1), ('keras-team/keras', 0.6126129627227783, 'ml-dl', 2), ('onnx/onnx', 0.6126094460487366, 'ml', 2), ('keras-team/keras-cv', 0.6089338064193726, 'ml-dl', 2), ('danielegrattarola/spektral', 0.6024564504623413, 'ml-dl', 2), ('microsoft/onnxruntime', 0.5876009464263916, 'ml', 2), ('roboflow/supervision', 0.5865331292152405, 'ml', 2), ('ddbourgin/numpy-ml', 0.5848450660705566, 'ml', 1), ('lutzroeder/netron', 0.584083616733551, 'ml', 2), ('keras-rl/keras-rl', 0.58319491147995, 'ml-rl', 3), ('deci-ai/super-gradients', 0.5777014493942261, 'ml-dl', 2), ('hysts/pytorch_image_classification', 0.5707684755325317, 'ml-dl', 1), ('lucidrains/imagen-pytorch', 0.5683515667915344, 'ml-dl', 0), ('tensorflow/addons', 0.567768931388855, 'ml', 1), ('neuralmagic/sparseml', 0.567727267742157, 'ml-dl', 4), ('horovod/horovod', 0.5646082162857056, 'ml-ops', 2), ('intel/intel-extension-for-pytorch', 0.564269483089447, 'perf', 0), ('huggingface/datasets', 0.5611058473587036, 'nlp', 2), ('xl0/lovely-tensors', 0.5606785416603088, 'ml-dl', 0), ('pytorchlightning/pytorch-lightning', 0.5557078123092651, 'ml-dl', 0), ('pytorch/ignite', 0.5554874539375305, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5547471046447754, 'ml-dl', 0), ('nvlabs/gcvit', 0.5547017455101013, 'diffusion', 0), ('arogozhnikov/einops', 0.5543664693832397, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5535702705383301, 'ml-rl', 1), ('matterport/mask_rcnn', 0.5529733300209045, 'ml-dl', 2), ('pytorch/pytorch', 0.5467002391815186, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5466137528419495, 'ml-dl', 2), ('keras-team/keras-nlp', 0.5431491136550903, 'nlp', 2), ('neuralmagic/deepsparse', 0.5420705080032349, 'nlp', 1), ('rasbt/deeplearning-models', 0.5375327467918396, 'ml-dl', 0), ('christoschristofidis/awesome-deep-learning', 0.5358662605285645, 'study', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5350536704063416, 'study', 3), ('explosion/thinc', 0.5349946618080139, 'ml-dl', 1), ('huggingface/transformers', 0.5339410305023193, 'nlp', 1), ('datasystemslab/geotorch', 0.5321657061576843, 'gis', 0), ('aistream-peelout/flow-forecast', 0.5294846296310425, 'time-series', 0), ('rasbt/machine-learning-book', 0.5279679298400879, 'study', 1), ('towhee-io/towhee', 0.5275624394416809, 'ml-ops', 1), ('tensorflow/similarity', 0.5236626863479614, 'ml-dl', 1), ('roboflow/notebooks', 0.5212321877479553, 'study', 2), ('determined-ai/determined', 0.5204993486404419, 'ml-ops', 2), ('tensorly/tensorly', 0.5165544748306274, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5134690999984741, 'study', 0), ('fepegar/torchio', 0.5130273699760437, 'ml-dl', 0), ('mosaicml/composer', 0.5126325488090515, 'ml-dl', 1), ('tlkh/tf-metal-experiments', 0.5091290473937988, 'perf', 1), ('skorch-dev/skorch', 0.509011447429657, 'ml-dl', 0), ('alpa-projects/alpa', 0.5051084160804749, 'ml-dl', 0), ('microsoft/semi-supervised-learning', 0.5045065879821777, 'ml', 1), ('blakeblackshear/frigate', 0.5042876601219177, 'util', 1), ('google/tf-quant-finance', 0.501559853553772, 'finance', 1), ('mrdbourke/tensorflow-deep-learning', 0.5007387399673462, 'study', 1)]",1,1.0,,0.0,0,0,23,16,0,0,0,0.0,0.0,90.0,0.0,15
1839,finance,https://github.com/stefmolin/stock-analysis,[],,[],[],,,,stefmolin/stock-analysis,stock-analysis,244,98,5,Python,,Simple to use interfaces for basic technical analysis of stocks.,stefmolin,2024-01-14,2019-01-27,261,0.9338436303991252,,Simple to use interfaces for basic technical analysis of stocks.,"['bitcoin-price', 'stock-analysis', 'stock-data', 'stock-indexes', 'stock-market', 'stock-model', 'stock-prediction', 'stock-price-prediction', 'stock-prices', 'stock-visualizer', 'technical-analysis']","['bitcoin-price', 'stock-analysis', 'stock-data', 'stock-indexes', 'stock-market', 'stock-model', 'stock-prediction', 'stock-price-prediction', 'stock-prices', 'stock-visualizer', 'technical-analysis']",2023-01-22,"[('polakowo/vectorbt', 0.5499805212020874, 'finance', 0), ('ranaroussi/quantstats', 0.5322091579437256, 'finance', 0), ('hydrosquall/tiingo-python', 0.5315351486206055, 'finance', 2), ('ranaroussi/yfinance', 0.5187216401100159, 'finance', 1), ('openbb-finance/openbbterminal', 0.518646776676178, 'finance', 0), ('zvtvz/zvt', 0.5058038830757141, 'finance', 2), ('twopirllc/pandas-ta', 0.5048727989196777, 'finance', 2)]",1,1.0,,0.02,2,2,60,12,0,1,1,2.0,1.0,90.0,0.5,15
1621,data,https://github.com/samuelcolvin/aioaws,[],,[],[],,,,samuelcolvin/aioaws,aioaws,164,13,7,Python,https://pypi.org/project/aioaws/,Asyncio compatible SDK for aws services.,samuelcolvin,2023-12-18,2020-03-25,200,0.8165007112375533,,Asyncio compatible SDK for aws services.,"['asyncio', 'aws', 'python38', 'python39', 's3', 'ses']","['asyncio', 'aws', 'python38', 'python39', 's3', 'ses']",2023-01-11,"[('jordaneremieff/mangum', 0.7040007710456848, 'web', 2), ('aio-libs/aiobotocore', 0.6990792155265808, 'util', 2), ('geeogi/async-python-lambda-template', 0.687824547290802, 'template', 0), ('boto/boto3', 0.6758776307106018, 'util', 1), ('aio-libs/aiohttp', 0.6308304667472839, 'web', 1), ('pallets/quart', 0.5853649377822876, 'web', 1), ('aws/chalice', 0.5785471200942993, 'web', 1), ('timofurrer/awesome-asyncio', 0.5785270929336548, 'study', 1), ('encode/httpx', 0.5720120072364807, 'web', 1), ('aws/aws-lambda-python-runtime-interface-client', 0.5471480488777161, 'util', 0), ('magicstack/uvloop', 0.5443870425224304, 'util', 1), ('aio-libs/aiokafka', 0.5397475957870483, 'data', 1), ('nficano/python-lambda', 0.5388737916946411, 'util', 1), ('samuelcolvin/arq', 0.5388724207878113, 'data', 1), ('alirn76/panther', 0.5380411148071289, 'web', 0), ('pytest-dev/pytest-asyncio', 0.5367757081985474, 'testing', 1), ('encode/uvicorn', 0.5331629514694214, 'web', 1), ('pynamodb/pynamodb', 0.521031379699707, 'data', 1), ('agronholm/anyio', 0.5206239223480225, 'perf', 1), ('terrycain/aioboto3', 0.5090085864067078, 'util', 1), ('alex-sherman/unsync', 0.507796049118042, 'util', 0), ('python-trio/trio', 0.5013687610626221, 'perf', 0)]",5,2.0,,0.0,0,0,46,12,0,3,3,0.0,0.0,90.0,0.0,15
315,util,https://github.com/irmen/pyminiaudio,[],,[],[],,,,irmen/pyminiaudio,pyminiaudio,152,19,6,C,,"python interface to the miniaudio audio playback, recording, decoding and conversion library",irmen,2024-01-04,2019-06-30,239,0.635223880597015,,"python interface to the miniaudio audio playback, recording, decoding and conversion library",[],[],2023-06-13,"[('spotify/pedalboard', 0.7280553579330444, 'util', 0), ('bastibe/python-soundfile', 0.6631749868392944, 'util', 0), ('uberi/speech_recognition', 0.659464955329895, 'ml', 0), ('taylorsmarks/playsound', 0.6150918006896973, 'util', 0), ('quodlibet/mutagen', 0.5700762867927551, 'util', 0), ('kiwicom/pytest-recording', 0.5641629099845886, 'testing', 0), ('pndurette/gtts', 0.5513820648193359, 'util', 0), ('pytoolz/toolz', 0.5377234816551208, 'util', 0), ('nateshmbhat/pyttsx3', 0.536530077457428, 'util', 0), ('pyston/pyston', 0.5274853110313416, 'util', 0), ('imageio/imageio', 0.5260134339332581, 'util', 0), ('pypy/pypy', 0.5216100215911865, 'util', 0), ('urwid/urwid', 0.51679927110672, 'term', 0)]",6,1.0,,0.25,1,0,55,7,4,8,4,1.0,0.0,90.0,0.0,15
464,nlp,https://github.com/infinitylogesh/mutate,[],,[],[],,,,infinitylogesh/mutate,mutate,148,9,5,Python,,A library to synthesize text datasets using Large Language Models (LLM),infinitylogesh,2024-01-10,2021-12-29,108,1.3595800524934383,,A library to synthesize text datasets using Large Language Models (LLM),"['data-augmentation', 'data-labeling', 'language-model', 'nlp-library', 'text-generation']","['data-augmentation', 'data-labeling', 'language-model', 'nlp-library', 'text-generation']",2023-01-17,"[('huggingface/text-generation-inference', 0.6840097904205322, 'llm', 0), ('cg123/mergekit', 0.6713061332702637, 'llm', 0), ('explosion/spacy-llm', 0.6443644762039185, 'llm', 0), ('salesforce/xgen', 0.621751070022583, 'llm', 1), ('eleutherai/the-pile', 0.6196421980857849, 'data', 0), ('togethercomputer/redpajama-data', 0.6091659069061279, 'llm', 0), ('paddlepaddle/paddlenlp', 0.6022129654884338, 'llm', 0), ('databrickslabs/dolly', 0.5938010811805725, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5922251343727112, 'llm', 1), ('hannibal046/awesome-llm', 0.5907807350158691, 'study', 1), ('minimaxir/textgenrnn', 0.5876234769821167, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5839410424232483, 'llm', 1), ('freedomintelligence/llmzoo', 0.5752876996994019, 'llm', 1), ('allenai/allennlp', 0.5738117098808289, 'nlp', 0), ('lianjiatech/belle', 0.5713385939598083, 'llm', 0), ('argilla-io/argilla', 0.5702595114707947, 'nlp', 0), ('young-geng/easylm', 0.566120445728302, 'llm', 1), ('bytedance/lightseq', 0.562773585319519, 'nlp', 0), ('llmware-ai/llmware', 0.5625592470169067, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5620525479316711, 'llm', 1), ('openlmlab/moss', 0.5608090758323669, 'llm', 2), ('princeton-nlp/alce', 0.5599058866500854, 'llm', 0), ('juncongmoo/pyllama', 0.5594502687454224, 'llm', 0), ('bigscience-workshop/biomedical', 0.5588589310646057, 'data', 0), ('yueyu1030/attrprompt', 0.5573945045471191, 'llm', 0), ('microsoft/lora', 0.5570184588432312, 'llm', 1), ('makcedward/nlpaug', 0.5548282265663147, 'nlp', 0), ('bobazooba/xllm', 0.5542972087860107, 'llm', 0), ('aiwaves-cn/agents', 0.5541864037513733, 'nlp', 1), ('google-research/electra', 0.553767204284668, 'ml-dl', 0), ('srush/minichain', 0.5535112023353577, 'llm', 0), ('ai21labs/lm-evaluation', 0.5527203679084778, 'llm', 1), ('lm-sys/fastchat', 0.5509554743766785, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5493302345275879, 'study', 0), ('explosion/spacy-models', 0.5492627620697021, 'nlp', 0), ('huggingface/datasets', 0.5476288199424744, 'nlp', 0), ('reasoning-machines/pal', 0.5475483536720276, 'llm', 1), ('next-gpt/next-gpt', 0.5463948845863342, 'llm', 0), ('rasahq/rasa', 0.5458459258079529, 'llm', 0), ('thudm/chatglm2-6b', 0.5451502203941345, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5444034934043884, 'llm', 1), ('hiyouga/llama-factory', 0.5444034337997437, 'llm', 1), ('alibaba/easynlp', 0.5399907231330872, 'nlp', 0), ('neuml/txtai', 0.5397577285766602, 'nlp', 1), ('squeezeailab/squeezellm', 0.5389288067817688, 'llm', 1), ('norskregnesentral/skweak', 0.5382066965103149, 'nlp', 1), ('tigerlab-ai/tiger', 0.5381948947906494, 'llm', 1), ('deepset-ai/haystack', 0.5364488959312439, 'llm', 1), ('nltk/nltk', 0.5343964695930481, 'nlp', 0), ('extreme-bert/extreme-bert', 0.5343960523605347, 'llm', 1), ('huggingface/text-embeddings-inference', 0.531061053276062, 'llm', 0), ('microsoft/unilm', 0.5307291746139526, 'nlp', 0), ('nomic-ai/gpt4all', 0.5304908752441406, 'llm', 1), ('facebookresearch/seamless_communication', 0.5298645496368408, 'nlp', 0), ('koaning/embetter', 0.5295093059539795, 'data', 0), ('deepset-ai/farm', 0.5289068818092346, 'nlp', 1), ('microsoft/autogen', 0.5268688201904297, 'llm', 0), ('conceptofmind/toolformer', 0.5267210602760315, 'llm', 1), ('intellabs/fastrag', 0.526527464389801, 'nlp', 0), ('optimalscale/lmflow', 0.524427056312561, 'llm', 1), ('ofa-sys/ofa', 0.514673113822937, 'llm', 0), ('jonasgeiping/cramming', 0.5127670168876648, 'nlp', 1), ('google/sentencepiece', 0.5126572847366333, 'nlp', 0), ('jbesomi/texthero', 0.5125301480293274, 'nlp', 0), ('yizhongw/self-instruct', 0.5120193362236023, 'llm', 1), ('lexpredict/lexpredict-lexnlp', 0.5115991234779358, 'nlp', 0), ('flairnlp/flair', 0.5080118775367737, 'nlp', 0), ('lucidrains/dalle2-pytorch', 0.5058514475822449, 'diffusion', 0), ('nebuly-ai/nebullvm', 0.5052860379219055, 'perf', 0), ('prefecthq/langchain-prefect', 0.50165855884552, 'llm', 0)]",2,1.0,,0.0,1,0,25,12,0,0,0,1.0,1.0,90.0,1.0,15
1037,finance,https://github.com/numerai/numerox,[],,[],[],,,,numerai/numerox,numerox,135,36,30,Python,,Numerai tournament toolbox written in Python,numerai,2024-01-06,2017-10-18,327,0.4117647058823529,https://avatars.githubusercontent.com/u/15222762?v=4,Numerai tournament toolbox written in Python,['numerai'],['numerai'],2020-12-08,"[('nuitka/nuitka', 0.5074256062507629, 'util', 0)]",15,3.0,,0.0,0,0,76,38,0,8,8,0.0,0.0,90.0,0.0,15
1052,util,https://github.com/xl0/lovely-numpy,[],,[],[],,,,xl0/lovely-numpy,lovely-numpy,60,3,4,Jupyter Notebook,https://xl0.github.io/lovely-numpy,"NumPy arrays, ready for human consumption",xl0,2023-10-31,2022-11-17,62,0.9567198177676538,,"NumPy arrays, ready for human consumption","['deep-learning', 'numpy', 'statistics', 'visualization']","['deep-learning', 'numpy', 'statistics', 'visualization']",2023-10-31,"[('xl0/lovely-tensors', 0.7344928979873657, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.5750322937965393, 'ml', 0), ('luispedro/mahotas', 0.5749441981315613, 'viz', 1), ('numpy/numpy', 0.5545908808708191, 'math', 1), ('pytorch/pytorch', 0.5318784713745117, 'ml-dl', 2), ('blaze/blaze', 0.5316489934921265, 'pandas', 0), ('gradio-app/gradio', 0.5306854248046875, 'viz', 1), ('ageron/handson-ml2', 0.52412348985672, 'ml', 0), ('ml-explore/mlx', 0.5240765810012817, 'ml', 1), ('pyqtgraph/pyqtgraph', 0.5235827565193176, 'viz', 2), ('huggingface/datasets', 0.519147515296936, 'nlp', 2), ('lightly-ai/lightly', 0.5120965242385864, 'ml', 1), ('scikit-learn/scikit-learn', 0.5066837072372437, 'ml', 1), ('google/tensorstore', 0.5052934288978577, 'data', 0), ('cupy/cupy', 0.5024915933609009, 'math', 1)]",2,1.0,,0.12,0,0,14,2,0,8,8,0.0,0.0,90.0,0.0,15
916,gis,https://github.com/radiantearth/radiant-mlhub,[],,[],[],,,,radiantearth/radiant-mlhub,radiant-mlhub,50,8,5,Python,https://radiant-mlhub.readthedocs.io/,A Python client for the Radiant MLHub API (https://mlhub.earth).,radiantearth,2024-01-04,2020-10-13,172,0.29069767441860467,https://avatars.githubusercontent.com/u/25801078?v=4,A Python client for the Radiant MLHub API (https://mlhub.earth).,"['machine-learning', 'python-client', 'satellite-imagery', 'stac']","['machine-learning', 'python-client', 'satellite-imagery', 'stac']",2023-02-13,"[('sentinel-hub/sentinelhub-py', 0.6164925694465637, 'gis', 1), ('huggingface/huggingface_hub', 0.6118897199630737, 'ml', 1), ('cloudsen12/easystac', 0.6044291853904724, 'gis', 1), ('pytroll/satpy', 0.5896352529525757, 'gis', 0), ('sentinel-hub/eo-learn', 0.5761727094650269, 'gis', 1), ('googleapis/google-api-python-client', 0.5603718757629395, 'util', 0), ('aws/sagemaker-python-sdk', 0.5445782542228699, 'ml', 1), ('kubeflow/fairing', 0.5332103371620178, 'ml-ops', 0), ('encode/httpx', 0.5330476760864258, 'web', 0), ('weecology/deepforest', 0.5279979705810547, 'gis', 0), ('hugapi/hug', 0.5193449854850769, 'util', 0), ('ml-tooling/opyrator', 0.5183095932006836, 'viz', 1), ('google/vizier', 0.5126688480377197, 'ml', 1), ('falconry/falcon', 0.5079211592674255, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5078471899032593, 'data', 0), ('python-pillow/pillow', 0.5047886967658997, 'util', 0), ('giswqs/geemap', 0.5028256773948669, 'gis', 0), ('spotify/voyager', 0.5025808811187744, 'ml', 1), ('jovianml/opendatasets', 0.5003646016120911, 'data', 1)]",10,3.0,,0.02,1,0,40,11,0,6,6,1.0,0.0,90.0,0.0,15
1079,ml-ops,https://github.com/getindata/kedro-kubeflow,[],,[],[],,,,getindata/kedro-kubeflow,kedro-kubeflow,42,20,11,Python,https://kedro-kubeflow.readthedocs.io,Kedro Plugin to support running workflows on Kubeflow Pipelines,getindata,2023-11-04,2020-12-18,162,0.2583479789103691,https://avatars.githubusercontent.com/u/9497597?v=4,Kedro Plugin to support running workflows on Kubeflow Pipelines,"['ai-pipelines', 'kedro', 'kedro-kubeflow', 'kedro-plugin', 'kubeflow', 'kubeflow-pipelines', 'machinelearning', 'mlops']","['ai-pipelines', 'kedro', 'kedro-kubeflow', 'kedro-plugin', 'kubeflow', 'kubeflow-pipelines', 'machinelearning', 'mlops']",2023-06-01,"[('kubeflow/pipelines', 0.7283107042312622, 'ml-ops', 3), ('bodywork-ml/bodywork-core', 0.6706922650337219, 'ml-ops', 1), ('kedro-org/kedro', 0.660918653011322, 'ml-ops', 2), ('flyteorg/flyte', 0.6279534697532654, 'ml-ops', 1), ('kubeflow-kale/kale', 0.5977193117141724, 'ml-ops', 2), ('zenml-io/zenml', 0.5848532915115356, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5841188430786133, 'ml-ops', 1), ('allegroai/clearml', 0.5791431069374084, 'ml-ops', 2), ('kedro-org/kedro-viz', 0.578177273273468, 'ml-ops', 2), ('orchest/orchest', 0.5747969746589661, 'ml-ops', 0), ('apache/airflow', 0.5596566200256348, 'ml-ops', 1), ('prefecthq/prefect', 0.5550775527954102, 'ml-ops', 0), ('mage-ai/mage-ai', 0.5537773370742798, 'ml-ops', 0), ('astronomer/astro-sdk', 0.541987419128418, 'ml-ops', 0), ('kestra-io/kestra', 0.5321413278579712, 'ml-ops', 0)]",15,2.0,,0.06,4,0,37,8,1,9,1,4.0,2.0,90.0,0.5,15
563,sim,https://github.com/gboeing/pynamical,[],,[],[],,,,gboeing/pynamical,pynamical,606,113,32,Python,https://geoffboeing.com/publications/nonlinear-chaos-fractals-prediction/,"Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals.",gboeing,2024-01-04,2014-09-28,487,1.2436235708003518,,"Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals.","['animation', 'bifurcation-diagram', 'chaos', 'cobweb-plot', 'fractal', 'fractals', 'ipynb', 'logistic', 'math', 'matplotlib', 'modeling', 'nonlinear', 'numba', 'numpy', 'pandas', 'phase-diagram', 'physics', 'systems', 'visualization']","['animation', 'bifurcation-diagram', 'chaos', 'cobweb-plot', 'fractal', 'fractals', 'ipynb', 'logistic', 'math', 'matplotlib', 'modeling', 'nonlinear', 'numba', 'numpy', 'pandas', 'phase-diagram', 'physics', 'systems', 'visualization']",2022-05-24,"[('artemyk/dynpy', 0.5693354606628418, 'sim', 0), ('viblo/pymunk', 0.5553194880485535, 'sim', 0), ('pysal/pysal', 0.5425774455070496, 'gis', 0), ('has2k1/plotnine', 0.5370927453041077, 'viz', 0), ('altair-viz/altair', 0.5344410538673401, 'viz', 1), ('projectmesa/mesa', 0.530546247959137, 'sim', 0), ('marcomusy/vedo', 0.5265044569969177, 'viz', 2), ('plotly/plotly.py', 0.5207886099815369, 'viz', 1), ('pyglet/pyglet', 0.5189253091812134, 'gamedev', 0), ('dfki-ric/pytransform3d', 0.5125102400779724, 'math', 2), ('crflynn/stochastic', 0.509087860584259, 'sim', 0), ('albahnsen/pycircular', 0.503804087638855, 'math', 0)]",1,1.0,,0.0,0,0,113,20,0,1,1,0.0,0.0,90.0,0.0,14
470,nlp,https://github.com/google-research/byt5,[],,[],[],,,,google-research/byt5,byt5,451,27,12,Python,,,google-research,2024-01-04,2021-05-26,139,3.2247191011235956,https://avatars.githubusercontent.com/u/43830688?v=4,google-research/byt5,[],[],2023-06-07,"[('google-research/t5x', 0.8195183277130127, 'ml', 0), ('google-research/google-research', 0.6097835898399353, 'ml', 0)]",2,0.0,,0.0,0,0,32,20,0,0,0,0.0,0.0,90.0,0.0,14
497,ml-dl,https://github.com/researchmm/sttn,[],,[],[],,,,researchmm/sttn,STTN,414,73,19,Jupyter Notebook,https://arxiv.org/abs/2007.10247,[ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting,researchmm,2024-01-11,2020-07-10,185,2.23094688221709,https://avatars.githubusercontent.com/u/49016198?v=4,[ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting,"['completing-videos', 'spatial-temporal', 'transformer', 'video-inpainting']","['completing-videos', 'spatial-temporal', 'transformer', 'video-inpainting']",2021-07-26,"[('mcahny/deep-video-inpainting', 0.6987395882606506, 'ml-dl', 0), ('vt-vl-lab/fgvc', 0.6461269855499268, 'ml-dl', 0), ('zulko/moviepy', 0.5262514352798462, 'util', 0), ('facebookresearch/augly', 0.5215305089950562, 'data', 0)]",2,1.0,,0.0,1,0,43,30,0,0,0,1.0,0.0,90.0,0.0,14
686,util,https://github.com/paperswithcode/axcell,[],,[],[],,,,paperswithcode/axcell,axcell,370,57,14,Python,,Tools for extracting tables and results from Machine Learning papers,paperswithcode,2024-01-07,2019-06-27,239,1.5435041716328963,https://avatars.githubusercontent.com/u/40305508?v=4,Tools for extracting tables and results from Machine Learning papers,[],[],2021-06-23,"[('camelot-dev/camelot', 0.577544093132019, 'util', 0), ('huggingface/evaluate', 0.5434747338294983, 'ml', 0), ('lean-dojo/leandojo', 0.5234335660934448, 'math', 0), ('mljar/mljar-supervised', 0.5180116891860962, 'ml', 0), ('tensorflow/data-validation', 0.5049012899398804, 'ml-ops', 0)]",7,2.0,,0.0,0,0,55,31,0,0,0,0.0,0.0,90.0,0.0,14
930,web,https://github.com/dmontagu/fastapi_client,[],,[],[],,,,dmontagu/fastapi_client,fastapi_client,321,42,8,Python,,FastAPI client generator,dmontagu,2024-01-04,2019-08-03,234,1.3692870201096892,,FastAPI client generator,[],[],2021-02-11,"[('koxudaxi/fastapi-code-generator', 0.6843157410621643, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.6545476913452148, 'template', 0), ('fastapi-users/fastapi-users', 0.6202594637870789, 'web', 0), ('s3rius/fastapi-template', 0.6021080613136292, 'web', 0), ('tiangolo/fastapi', 0.5745565891265869, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5413647890090942, 'template', 0), ('awtkns/fastapi-crudrouter', 0.5253075361251831, 'web', 0), ('zhanymkanov/fastapi-best-practices', 0.5196253061294556, 'study', 0), ('samuelcolvin/fastui', 0.5091067552566528, 'gui', 0), ('long2ice/fastapi-cache', 0.5032126307487488, 'web', 0)]",8,2.0,,0.0,0,0,54,36,0,0,0,0.0,0.0,90.0,0.0,14
976,nlp,https://github.com/yixinl7/brio,[],,[],[],,,,yixinl7/brio,BRIO,306,42,2,Python,,ACL 2022: BRIO: Bringing Order to Abstractive Summarization,yixinl7,2024-01-09,2022-03-15,98,3.122448979591837,,ACL 2022: BRIO: Bringing Order to Abstractive Summarization,"['nlp', 'text-summarization']","['nlp', 'text-summarization']",2023-05-23,[],1,0.0,,0.02,0,0,22,8,0,0,0,0.0,0.0,90.0,0.0,14
570,gis,https://github.com/developmentseed/geolambda,[],,[],[],,,,developmentseed/geolambda,geolambda,295,87,49,Dockerfile,,Create and deploy Geospatial AWS Lambda functions,developmentseed,2024-01-12,2017-05-02,352,0.8380681818181818,https://avatars.githubusercontent.com/u/92384?v=4,Create and deploy Geospatial AWS Lambda functions,[],[],2021-02-16,"[('nficano/python-lambda', 0.6138771176338196, 'util', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.5933845043182373, 'util', 0), ('jordaneremieff/mangum', 0.5730166435241699, 'web', 0), ('geeogi/async-python-lambda-template', 0.5692357420921326, 'template', 0), ('rpgreen/apilogs', 0.5092775225639343, 'util', 0), ('giswqs/aws-open-data-geo', 0.507233738899231, 'gis', 0), ('aws/chalice', 0.5061563849449158, 'web', 0)]",6,3.0,,0.0,0,0,82,35,0,1,1,0.0,0.0,90.0,0.0,14
242,ml,https://github.com/carla-recourse/carla,[],,[],[],,,,carla-recourse/carla,CARLA,260,57,6,Python,,CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms,carla-recourse,2024-01-04,2020-12-09,163,1.5867480383609416,https://avatars.githubusercontent.com/u/88393731?v=4,CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms,"['artificial-intelligence', 'benchmark', 'benchmarking', 'counterfactual', 'counterfactual-explanations', 'counterfactuals', 'explainability', 'explainable-ai', 'explainable-ml', 'machine-learning', 'pytorch', 'recourse', 'tensorflow', 'tensorflow2']","['artificial-intelligence', 'benchmark', 'benchmarking', 'counterfactual', 'counterfactual-explanations', 'counterfactuals', 'explainability', 'explainable-ai', 'explainable-ml', 'machine-learning', 'pytorch', 'recourse', 'tensorflow', 'tensorflow2']",2023-02-22,"[('seldonio/alibi', 0.6956292986869812, 'ml-interpretability', 2), ('teamhg-memex/eli5', 0.6284937262535095, 'ml', 1), ('rafiqhasan/auto-tensorflow', 0.5945912003517151, 'ml-dl', 2), ('reloadware/reloadium', 0.5905767679214478, 'profiling', 1), ('klen/py-frameworks-bench', 0.5568965077400208, 'perf', 1), ('oegedijk/explainerdashboard', 0.5436306595802307, 'ml-interpretability', 0), ('tensorflow/lucid', 0.5387822389602661, 'ml-interpretability', 2), ('koaning/human-learn', 0.5305505394935608, 'data', 2), ('rasbt/mlxtend', 0.5272185802459717, 'ml', 1), ('maif/shapash', 0.5270743370056152, 'ml', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5270025730133057, 'study', 2), ('pytoolz/toolz', 0.5241925716400146, 'util', 0), ('tensorflow/data-validation', 0.5232628583908081, 'ml-ops', 0), ('csinva/imodels', 0.5205970406532288, 'ml', 4), ('polyaxon/datatile', 0.5203560590744019, 'pandas', 3), ('huggingface/evaluate', 0.5139058232307434, 'ml', 1), ('mckinsey/causalnex', 0.5116320252418518, 'math', 1), ('pyutils/line_profiler', 0.5099355578422546, 'profiling', 0), ('interpretml/interpret', 0.5091050863265991, 'ml-interpretability', 5), ('rasbt/machine-learning-book', 0.5052952766418457, 'study', 2), ('allenai/allennlp', 0.5022084712982178, 'nlp', 1), ('pypy/pypy', 0.5005764365196228, 'util', 0)]",7,1.0,,0.08,0,0,38,11,0,0,0,0.0,0.0,90.0,0.0,14
247,util,https://github.com/rpgreen/apilogs,[],,[],[],,,,rpgreen/apilogs,apilogs,252,19,10,Python,,Easy logging and debugging for Amazon API Gateway and AWS Lambda Serverless APIs,rpgreen,2024-01-03,2016-09-07,385,0.6530914476119956,,Easy logging and debugging for Amazon API Gateway and AWS Lambda Serverless APIs,"['api', 'api-gateway', 'aws', 'aws-apigateway', 'aws-lambda', 'cloudwatch-logs', 'gateway', 'lambda', 'logging']","['api', 'api-gateway', 'aws', 'aws-apigateway', 'aws-lambda', 'cloudwatch-logs', 'gateway', 'lambda', 'logging']",2019-11-13,"[('nficano/python-lambda', 0.635263204574585, 'util', 2), ('aws/chalice', 0.6253986954689026, 'web', 4), ('jordaneremieff/mangum', 0.595600962638855, 'web', 4), ('jorgebastida/awslogs', 0.5721848607063293, 'util', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.5472556948661804, 'util', 0), ('localstack/localstack', 0.5111103653907776, 'util', 1), ('developmentseed/geolambda', 0.5092775225639343, 'gis', 0)]",23,4.0,,0.0,0,0,89,51,0,0,0,0.0,0.0,90.0,0.0,14
1581,data,https://github.com/brettkromkamp/topic-db,['knowledge-graph'],,[],[],,,,brettkromkamp/topic-db,topic-db,245,13,9,Python,,TopicDB is a topic maps-based semantic graph store (using SQLite for persistence),brettkromkamp,2024-01-03,2016-12-21,370,0.6606317411402157,,TopicDB is a topic maps-based semantic graph store (using SQLite for persistence),"['graph-database', 'knowledge-base', 'knowledge-graph', 'knowledge-management', 'linked-data', 'semantic-web', 'sqlite3', 'sqlite3-database', 'topic-map']","['graph-database', 'knowledge-base', 'knowledge-graph', 'knowledge-management', 'linked-data', 'semantic-web', 'sqlite3', 'sqlite3-database', 'topic-map']",2023-08-15,"[('rare-technologies/gensim', 0.539949893951416, 'nlp', 0)]",3,1.0,,0.02,0,0,86,5,0,1,1,0.0,0.0,90.0,0.0,14
1683,util,https://github.com/pycqa/eradicate,"['linting', 'styling']",,[],[],,,,pycqa/eradicate,eradicate,192,24,5,Python,https://pypi.python.org/pypi/eradicate,Removes commented-out code from Python files,pycqa,2024-01-12,2012-12-23,579,0.331442663378545,https://avatars.githubusercontent.com/u/8749848?v=4,Removes commented-out code from Python files,[],"['linting', 'styling']",2023-06-12,"[('pycqa/autoflake', 0.5524262189865112, 'util', 0), ('landscapeio/prospector', 0.5313110947608948, 'util', 2)]",13,1.0,,0.17,0,0,135,7,2,2,2,0.0,0.0,90.0,0.0,14
414,ml-dl,https://github.com/rafiqhasan/auto-tensorflow,[],,[],[],,,,rafiqhasan/auto-tensorflow,auto-tensorflow,179,39,13,Python,,Build Low Code Automated Tensorflow explainable models in just 3 lines of code. Library created by: Hasan Rafiq - https://www.linkedin.com/in/sam04/,rafiqhasan,2023-11-26,2021-07-05,134,1.334398296059638,,Build Low Code Automated Tensorflow explainable models in just 3 lines of code. Library created by: Hasan Rafiq - https://www.linkedin.com/in/sam04/,"['auto-tensorflow', 'automl', 'autotensorflow', 'deeplearning', 'machine-learning', 'machinelearning', 'neural-networks', 'tensorflow', 'tfx']","['auto-tensorflow', 'automl', 'autotensorflow', 'deeplearning', 'machine-learning', 'machinelearning', 'neural-networks', 'tensorflow', 'tfx']",2022-12-09,"[('keras-team/autokeras', 0.6616485714912415, 'ml-dl', 3), ('ggerganov/ggml', 0.6585282683372498, 'ml', 1), ('arogozhnikov/einops', 0.6536889672279358, 'ml-dl', 1), ('karpathy/micrograd', 0.634564220905304, 'study', 0), ('ludwig-ai/ludwig', 0.6223335266113281, 'ml-ops', 3), ('microsoft/nni', 0.6200402975082397, 'ml', 3), ('tensorly/tensorly', 0.6096346974372864, 'ml-dl', 2), ('carla-recourse/carla', 0.5945912003517151, 'ml', 2), ('nvidia/deeplearningexamples', 0.5845487713813782, 'ml-dl', 1), ('neuralmagic/sparseml', 0.5831721425056458, 'ml-dl', 2), ('huggingface/transformers', 0.5827978253364563, 'nlp', 2), ('oegedijk/explainerdashboard', 0.5813491940498352, 'ml-interpretability', 0), ('nvidia/tensorrt-llm', 0.5734004378318787, 'viz', 0), ('pytorch/pytorch', 0.5693333745002747, 'ml-dl', 1), ('tensorflow/lucid', 0.5636606216430664, 'ml-interpretability', 2), ('interpretml/interpret', 0.5614568591117859, 'ml-interpretability', 1), ('microsoft/flaml', 0.5583081841468811, 'ml', 2), ('optimalscale/lmflow', 0.5579131841659546, 'llm', 0), ('shankarpandala/lazypredict', 0.5576849579811096, 'ml', 2), ('huggingface/exporters', 0.556242823600769, 'ml', 2), ('pytorch/ignite', 0.5545974373817444, 'ml-dl', 1), ('google/tf-quant-finance', 0.5538135766983032, 'finance', 1), ('keras-team/keras-nlp', 0.5517141819000244, 'nlp', 2), ('pytorchlightning/pytorch-lightning', 0.5506039261817932, 'ml-dl', 1), ('seldonio/alibi', 0.5504202842712402, 'ml-interpretability', 1), ('nccr-itmo/fedot', 0.5481418371200562, 'ml-ops', 2), ('tensorflow/similarity', 0.5478165745735168, 'ml-dl', 2), ('ashleve/lightning-hydra-template', 0.5461589694023132, 'util', 0), ('mosaicml/composer', 0.5446411967277527, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5420622825622559, 'perf', 1), ('google/gin-config', 0.5411630868911743, 'util', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5406836867332458, 'study', 0), ('selfexplainml/piml-toolbox', 0.5389483571052551, 'ml-interpretability', 0), ('explosion/thinc', 0.5384374856948853, 'ml-dl', 2), ('graykode/nlp-tutorial', 0.5383457541465759, 'study', 1), ('rasbt/machine-learning-book', 0.5379175543785095, 'study', 2), ('xl0/lovely-tensors', 0.5368985533714294, 'ml-dl', 0), ('zacwellmer/worldmodels', 0.5352241396903992, 'ml-rl', 0), ('featurelabs/featuretools', 0.5342097878456116, 'ml', 2), ('tensorflow/mesh', 0.5327334403991699, 'ml-dl', 0), ('tatsu-lab/stanford_alpaca', 0.5314496755599976, 'llm', 0), ('tensorlayer/tensorlayer', 0.5306537747383118, 'ml-rl', 1), ('teamhg-memex/eli5', 0.5304438471794128, 'ml', 1), ('tensorflow/addons', 0.530368983745575, 'ml', 2), ('tensorflow/tensorflow', 0.5291599035263062, 'ml-dl', 2), ('horovod/horovod', 0.5287712216377258, 'ml-ops', 4), ('xplainable/xplainable', 0.5286867618560791, 'ml-interpretability', 1), ('salesforce/deeptime', 0.5278478264808655, 'time-series', 0), ('d2l-ai/d2l-en', 0.5260531306266785, 'study', 2), ('lutzroeder/netron', 0.5258046388626099, 'ml', 4), ('ray-project/ray', 0.5257235169410706, 'ml-ops', 3), ('csinva/imodels', 0.5232248902320862, 'ml', 1), ('ageron/handson-ml2', 0.5208608508110046, 'ml', 0), ('mlc-ai/mlc-llm', 0.5193679928779602, 'llm', 0), ('patrick-kidger/torchtyping', 0.5187631845474243, 'typing', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5184751152992249, 'study', 1), ('activeloopai/deeplake', 0.5172686576843262, 'ml-ops', 2), ('alpa-projects/alpa', 0.5152245759963989, 'ml-dl', 1), ('pytorch/captum', 0.5135285258293152, 'ml-interpretability', 0), ('aws/sagemaker-python-sdk', 0.512946367263794, 'ml', 2), ('cdpierse/transformers-interpret', 0.5105558633804321, 'ml-interpretability', 1), ('awslabs/autogluon', 0.5091307163238525, 'ml', 2), ('kubeflow/fairing', 0.5086631178855896, 'ml-ops', 0), ('guardrails-ai/guardrails', 0.5081257224082947, 'llm', 0), ('mrdbourke/pytorch-deep-learning', 0.5060444474220276, 'study', 1), ('epistasislab/tpot', 0.5045480132102966, 'ml', 2), ('googlecloudplatform/vertex-ai-samples', 0.5013206601142883, 'ml', 0), ('eleutherai/gpt-neo', 0.5005360841751099, 'llm', 0)]",1,1.0,,0.0,0,0,31,13,0,3,3,0.0,0.0,90.0,0.0,14
964,data,https://github.com/nickreynke/python-gedcom,[],,[],[],,,,nickreynke/python-gedcom,python-gedcom,142,37,17,Python,https://nickreynke.github.io/python-gedcom/gedcom/index.html,"Python module for parsing, analyzing, and manipulating GEDCOM files",nickreynke,2024-01-06,2018-01-09,316,0.44936708860759494,,"Python module for parsing, analyzing, and manipulating GEDCOM files","['gedcom', 'gedcom-parser', 'parser']","['gedcom', 'gedcom-parser', 'parser']",2021-06-03,"[('pytoolz/toolz', 0.5138238072395325, 'util', 0), ('pympler/pympler', 0.5089355111122131, 'perf', 0)]",16,3.0,,0.0,1,0,73,32,0,2,2,1.0,0.0,90.0,0.0,14
1323,web,https://github.com/fourthbrain/fastapi-for-machine-learning-live-demo,"['text-to-image', 'fastapi']",,[],[],,,,fourthbrain/fastapi-for-machine-learning-live-demo,FastAPI-for-Machine-Learning-Live-Demo,136,42,8,Python,,"This repository contains the files to build your very own AI image generation web application!  Outlined are the core components of the FastAPI web framework, and application leverage the newly-released Stable Diffusion text-to-image deep learning model.",fourthbrain,2024-01-07,2022-12-15,58,2.316301703163017,https://avatars.githubusercontent.com/u/72572922?v=4,"This repository contains the files to build your very own AI image generation web application!  Outlined are the core components of the FastAPI web framework, and application leverage the newly-released Stable Diffusion text-to-image deep learning model.",[],"['fastapi', 'text-to-image']",2023-04-12,"[('saharmor/dalle-playground', 0.6557318568229675, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6511092185974121, 'diffusion', 0), ('invoke-ai/invokeai', 0.6325607895851135, 'diffusion', 0), ('lucidrains/deep-daze', 0.625627875328064, 'ml', 1), ('lucidrains/imagen-pytorch', 0.6227165460586548, 'ml-dl', 1), ('lucidrains/dalle2-pytorch', 0.6031858325004578, 'diffusion', 1), ('open-mmlab/mmediting', 0.5617620944976807, 'ml', 0), ('albumentations-team/albumentations', 0.5520427823066711, 'ml-dl', 0), ('carson-katri/dream-textures', 0.5474333763122559, 'diffusion', 0), ('jina-ai/jina', 0.5461900234222412, 'ml', 1), ('nateraw/stable-diffusion-videos', 0.5448333024978638, 'diffusion', 0), ('rom1504/img2dataset', 0.5232915878295898, 'data', 0), ('facebookresearch/mmf', 0.5202192068099976, 'ml-dl', 0), ('activeloopai/deeplake', 0.5164815783500671, 'ml-ops', 0), ('sharonzhou/long_stable_diffusion', 0.513484537601471, 'diffusion', 0), ('thereforegames/unprompted', 0.5091450810432434, 'diffusion', 0), ('alibaba/easynlp', 0.5053616762161255, 'nlp', 0), ('stability-ai/stability-sdk', 0.5016722679138184, 'diffusion', 0), ('microsoft/generative-ai-for-beginners', 0.5012496113777161, 'study', 0), ('albarji/mixture-of-diffusers', 0.5000970959663391, 'diffusion', 0)]",2,1.0,,0.02,0,0,13,9,0,0,0,0.0,0.0,90.0,0.0,14
1202,util,https://github.com/kuimono/openapi-schema-pydantic,[],,[],[],,,,kuimono/openapi-schema-pydantic,openapi-schema-pydantic,100,16,4,Python,,OpenAPI (v3) specification schema as pydantic class ,kuimono,2024-01-14,2020-05-14,193,0.5162241887905604,,OpenAPI (v3) specification schema as pydantic class ,"['openapi3', 'pydantic']","['openapi3', 'pydantic']",2022-06-29,"[('koxudaxi/fastapi-code-generator', 0.6038326025009155, 'web', 1), ('openai/openai-python', 0.52321857213974, 'util', 0)]",7,3.0,,0.0,0,0,45,19,0,3,3,0.0,0.0,90.0,0.0,14
1138,study,https://github.com/nomic-ai/semantic-search-app-template,[],,[],[],,,,nomic-ai/semantic-search-app-template,semantic-search-app-template,96,19,6,Python,,"Tutorial and template for a semantic search app powered by the Atlas Embedding Database, Langchain,  OpenAI and FastAPI",nomic-ai,2024-01-13,2023-03-20,45,2.1265822784810124,https://avatars.githubusercontent.com/u/102670180?v=4,"Tutorial and template for a semantic search app powered by the Atlas Embedding Database, Langchain,  OpenAI and FastAPI","['fastapi', 'openai', 'react', 'semantic-search', 'tutorial']","['fastapi', 'openai', 'react', 'semantic-search', 'tutorial']",2023-09-12,"[('neuml/txtai', 0.6002048254013062, 'nlp', 1), ('freedmand/semantra', 0.5121299624443054, 'ml', 1), ('zilliztech/gptcache', 0.5088302493095398, 'llm', 2), ('qdrant/fastembed', 0.5012847781181335, 'ml', 1), ('chroma-core/chroma', 0.5006744265556335, 'data', 0), ('paddlepaddle/paddlenlp', 0.5003646016120911, 'llm', 0)]",2,1.0,,0.38,0,0,10,4,0,0,0,0.0,0.0,90.0,0.0,14
1602,sim,https://github.com/whitead/molcloud,"['rna', 'molecules']",,[],[],,,,whitead/molcloud,molcloud,87,15,2,Python,,Make a bunch of molecules,whitead,2024-01-04,2022-07-01,82,1.0536332179930796,,Make a bunch of molecules,[],"['molecules', 'rna']",2022-07-30,[],3,2.0,,0.0,1,0,19,18,0,3,3,1.0,0.0,90.0,0.0,14
846,util,https://github.com/backtick-se/cowait,[],,[],[],,,,backtick-se/cowait,cowait,53,5,9,Python,https://cowait.io,Containerized distributed programming framework for Python,backtick-se,2023-07-09,2019-09-18,227,0.23260188087774294,https://avatars.githubusercontent.com/u/51236421?v=4,Containerized distributed programming framework for Python,"['dask', 'data-engineering', 'data-science', 'docker', 'kubernetes', 'spark', 'task-scheduler', 'workflow-engine']","['dask', 'data-engineering', 'data-science', 'docker', 'kubernetes', 'spark', 'task-scheduler', 'workflow-engine']",2022-09-22,"[('eventual-inc/daft', 0.7438012361526489, 'pandas', 2), ('fugue-project/fugue', 0.6694428324699402, 'pandas', 2), ('orchest/orchest', 0.6478020548820496, 'ml-ops', 3), ('darribas/gds_env', 0.6343125104904175, 'gis', 1), ('flyteorg/flyte', 0.6116994023323059, 'ml-ops', 2), ('pyinfra-dev/pyinfra', 0.5932263135910034, 'util', 0), ('kestra-io/kestra', 0.5885079503059387, 'ml-ops', 2), ('dagworks-inc/hamilton', 0.5880764722824097, 'ml-ops', 2), ('merantix-momentum/squirrel-core', 0.5777786374092102, 'ml', 1), ('martinheinz/python-project-blueprint', 0.5766209363937378, 'template', 2), ('fastai/fastcore', 0.5709444284439087, 'util', 0), ('lithops-cloud/lithops', 0.5676038861274719, 'ml-ops', 1), ('aws/chalice', 0.5667223334312439, 'web', 0), ('pallets/flask', 0.5663134455680847, 'web', 0), ('willmcgugan/textual', 0.5645186305046082, 'term', 0), ('boto/boto3', 0.5623748302459717, 'util', 0), ('eleutherai/pyfra', 0.5622064471244812, 'ml', 0), ('dask/distributed', 0.56135493516922, 'perf', 1), ('spotify/luigi', 0.5577235221862793, 'ml-ops', 0), ('horovod/horovod', 0.5568536520004272, 'ml-ops', 1), ('dagster-io/dagster', 0.5563982725143433, 'ml-ops', 2), ('falconry/falcon', 0.5538949370384216, 'web', 0), ('dask/dask', 0.5500026941299438, 'perf', 1), ('pypa/pipenv', 0.5493590235710144, 'util', 0), ('bodywork-ml/bodywork-core', 0.547731876373291, 'ml-ops', 2), ('nficano/python-lambda', 0.5466134548187256, 'util', 0), ('multi-py/python-gunicorn-uvicorn', 0.5445891618728638, 'util', 1), ('kubeflow-kale/kale', 0.5415117740631104, 'ml-ops', 0), ('ianmiell/shutit', 0.5379471182823181, 'util', 1), ('airtai/faststream', 0.5363417267799377, 'perf', 0), ('masoniteframework/masonite', 0.5356951951980591, 'web', 0), ('rawheel/fastapi-boilerplate', 0.5353572964668274, 'web', 1), ('pypa/hatch', 0.5353484153747559, 'util', 0), ('fmind/mlops-python-package', 0.5339037179946899, 'template', 0), ('polyaxon/datatile', 0.533811628818512, 'pandas', 3), ('aeternalis-ingenium/fastapi-backend-template', 0.5334479212760925, 'web', 1), ('airbytehq/airbyte', 0.5309221148490906, 'data', 1), ('kubeflow/fairing', 0.5283066034317017, 'ml-ops', 0), ('polyaxon/polyaxon', 0.5269318222999573, 'ml-ops', 2), ('ploomber/ploomber', 0.5248901844024658, 'ml-ops', 2), ('klen/muffin', 0.5236403942108154, 'web', 0), ('pallets/quart', 0.5232675671577454, 'web', 0), ('gefyrahq/gefyra', 0.5228185653686523, 'util', 2), ('eventlet/eventlet', 0.519990086555481, 'perf', 0), ('apache/airflow', 0.5194593667984009, 'ml-ops', 3), ('bottlepy/bottle', 0.5192388892173767, 'web', 0), ('cython/cython', 0.5182483792304993, 'util', 0), ('multi-py/python-gunicorn', 0.5146100521087646, 'util', 1), ('py4j/py4j', 0.5144885182380676, 'util', 0), ('google/gin-config', 0.5143940448760986, 'util', 0), ('dylanhogg/awesome-python', 0.5124981999397278, 'study', 1), ('ipython/ipyparallel', 0.5108761787414551, 'perf', 0), ('ethereum/py-evm', 0.5091575384140015, 'crypto', 0), ('multi-py/python-uvicorn', 0.5087990760803223, 'util', 1), ('uber/fiber', 0.5072715282440186, 'data', 0), ('skypilot-org/skypilot', 0.5072562098503113, 'llm', 1), ('avaiga/taipy', 0.5056573152542114, 'data', 1), ('hi-primus/optimus', 0.5054327249526978, 'ml-ops', 3), ('python-trio/trio', 0.5035473704338074, 'perf', 0), ('python-restx/flask-restx', 0.5034772157669067, 'web', 0), ('netflix/metaflow', 0.5032603144645691, 'ml-ops', 2), ('agronholm/apscheduler', 0.502835750579834, 'util', 0), ('pypy/pypy', 0.5022944808006287, 'util', 0), ('ets-labs/python-dependency-injector', 0.500067412853241, 'util', 0)]",10,2.0,,0.0,0,0,53,16,0,10,10,0.0,0.0,90.0,0.0,14
1490,math,https://github.com/andgoldschmidt/derivative,[],,[],[],,,,andgoldschmidt/derivative,derivative,48,7,5,Python,https://derivative.readthedocs.io/en/latest/,Optimal numerical differentiation of noisy time series data in python.,andgoldschmidt,2023-12-03,2019-02-06,259,0.18471687740516768,,Optimal numerical differentiation of noisy time series data in python.,"['differentiation', 'experimental-data', 'numerical-differentiation']","['differentiation', 'experimental-data', 'numerical-differentiation']",2023-08-29,"[('rjt1990/pyflux', 0.5164141654968262, 'time-series', 0), ('hips/autograd', 0.5045579075813293, 'ml', 0)]",4,2.0,,0.48,2,2,60,5,2,1,2,2.0,0.0,90.0,0.0,14
673,util,https://github.com/markhershey/arxiv-dl,[],,[],[],,,,markhershey/arxiv-dl,arxiv-dl,26,5,3,Python,https://pypi.org/project/arxiv-dl/,"Command-line ArXiv & CVF (CVPR, ICCV, WACV) Paper Downloader",markhershey,2023-12-27,2021-01-21,157,0.16485507246376813,,"Command-line ArXiv & CVF (CVPR, ICCV, WACV) Paper Downloader","['arxiv', 'command-line-tool', 'cvpr', 'downloader', 'paper', 'paper-with-code', 'research-paper']","['arxiv', 'command-line-tool', 'cvpr', 'downloader', 'paper', 'paper-with-code', 'research-paper']",2023-11-02,[],3,0.0,,0.23,3,1,36,2,2,3,2,3.0,5.0,90.0,1.7,14
659,study,https://github.com/rasbt/stat453-deep-learning-ss20,[],,[],[],,,,rasbt/stat453-deep-learning-ss20,stat453-deep-learning-ss20,540,159,37,Jupyter Notebook,http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/,STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020),rasbt,2024-01-04,2020-01-20,210,2.5696804894629506,,STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020),[],[],2020-05-01,"[('rasbt/stat451-machine-learning-fs20', 0.7537368535995483, 'study', 0), ('atcold/nyu-dlsp21', 0.6361130475997925, 'study', 0), ('udlbook/udlbook', 0.5738182663917542, 'study', 0), ('xl0/lovely-tensors', 0.5372262597084045, 'ml-dl', 0), ('d2l-ai/d2l-en', 0.5362268090248108, 'study', 0), ('mrdbourke/pytorch-deep-learning', 0.5198850631713867, 'study', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5113897919654846, 'study', 0), ('udacity/deep-learning-v2-pytorch', 0.5104526281356812, 'study', 0), ('tatsu-lab/stanford_alpaca', 0.5087552666664124, 'llm', 0), ('christoschristofidis/awesome-deep-learning', 0.506165087223053, 'study', 0), ('nvidia/deeplearningexamples', 0.5055111050605774, 'ml-dl', 0), ('calculatedcontent/weightwatcher', 0.505051851272583, 'ml-dl', 0)]",1,1.0,,0.0,0,0,48,45,0,0,0,0.0,0.0,90.0,0.0,13
302,util,https://github.com/airbnb/ottr,[],,[],[],,,,airbnb/ottr,ottr,264,32,9,Python,,Serverless Public Key Infrastructure Framework,airbnb,2024-01-12,2021-08-27,126,2.0857787810383748,https://avatars.githubusercontent.com/u/698437?v=4,Serverless Public Key Infrastructure Framework,[],[],2022-01-04,[],2,1.0,,0.0,0,0,29,25,0,0,0,0.0,0.0,90.0,0.0,13
338,perf,https://github.com/tlkh/tf-metal-experiments,[],,[],[],,,,tlkh/tf-metal-experiments,tf-metal-experiments,256,32,17,Jupyter Notebook,,TensorFlow Metal Backend on Apple Silicon Experiments (just for fun),tlkh,2024-01-04,2021-10-26,118,2.169491525423729,,TensorFlow Metal Backend on Apple Silicon Experiments (just for fun),"['benchmark', 'bert', 'deep-learning', 'gpu', 'm1', 'm1-max', 'tensorflow']","['benchmark', 'bert', 'deep-learning', 'gpu', 'm1', 'm1-max', 'tensorflow']",2021-11-15,"[('mrdbourke/m1-machine-learning-test', 0.7584832310676575, 'ml', 1), ('microsoft/onnxruntime', 0.6584108471870422, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.6522815227508545, 'perf', 1), ('arogozhnikov/einops', 0.6099873781204224, 'ml-dl', 2), ('ml-explore/mlx', 0.6006742119789124, 'ml', 0), ('ashleve/lightning-hydra-template', 0.5975523591041565, 'util', 1), ('determined-ai/determined', 0.5877756476402283, 'ml-ops', 2), ('google/tf-quant-finance', 0.5859279036521912, 'finance', 2), ('neuralmagic/deepsparse', 0.5730476379394531, 'nlp', 0), ('tensorflow/addons', 0.5677783489227295, 'ml', 2), ('pytorch/ignite', 0.5673794746398926, 'ml-dl', 1), ('keras-team/keras', 0.565044641494751, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5584723949432373, 'ml-dl', 2), ('huggingface/transformers', 0.5560042858123779, 'nlp', 3), ('rasbt/machine-learning-book', 0.5555431246757507, 'study', 1), ('xl0/lovely-tensors', 0.5548020005226135, 'ml-dl', 1), ('horovod/horovod', 0.5538284182548523, 'ml-ops', 2), ('tlkh/asitop', 0.540374219417572, 'perf', 2), ('ageron/handson-ml2', 0.5388534665107727, 'ml', 0), ('keras-rl/keras-rl', 0.5308326482772827, 'ml-rl', 1), ('alpa-projects/alpa', 0.5292550921440125, 'ml-dl', 1), ('tensorlayer/tensorlayer', 0.5288784503936768, 'ml-rl', 2), ('explosion/thinc', 0.5282965302467346, 'ml-dl', 2), ('microsoft/deepspeed', 0.5253063440322876, 'ml-dl', 2), ('huggingface/datasets', 0.5242385268211365, 'nlp', 2), ('pytorch/pytorch', 0.5232517123222351, 'ml-dl', 2), ('tensorflow/tensorflow', 0.5225579142570496, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.5217053890228271, 'ml', 1), ('onnx/onnx', 0.5157482028007507, 'ml', 2), ('skorch-dev/skorch', 0.5154433250427246, 'ml-dl', 0), ('google/trax', 0.5128339529037476, 'ml-dl', 1), ('intel/scikit-learn-intelex', 0.5120803713798523, 'perf', 1), ('wandb/client', 0.5100532174110413, 'ml', 2), ('apache/incubator-mxnet', 0.5093386769294739, 'ml-dl', 0), ('nyandwi/modernconvnets', 0.5091290473937988, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5083010792732239, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.508011519908905, 'study', 2), ('huggingface/optimum', 0.5074736475944519, 'ml', 0), ('aimhubio/aim', 0.501725971698761, 'ml-ops', 1)]",2,1.0,,0.0,0,0,27,26,0,0,0,0.0,0.0,90.0,0.0,13
1187,llm,https://github.com/anthropics/evals,[],Model-Written Evaluation Datasets,[],[],,,,anthropics/evals,evals,184,16,6,,,,anthropics,2024-01-11,2022-12-12,59,3.111111111111111,https://avatars.githubusercontent.com/u/76263028?v=4,Model-Written Evaluation Datasets,[],[],2023-01-03,"[('huggingface/evaluate', 0.7150794267654419, 'ml', 0), ('ai21labs/lm-evaluation', 0.6497257947921753, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5547993183135986, 'llm', 0), ('openlmlab/leval', 0.5348479747772217, 'llm', 0), ('confident-ai/deepeval', 0.5211471915245056, 'testing', 0), ('openai/evals', 0.512044370174408, 'llm', 0), ('bigscience-workshop/biomedical', 0.5112786889076233, 'data', 0), ('selfexplainml/piml-toolbox', 0.5009229183197021, 'ml-interpretability', 0), ('hazyresearch/domino', 0.5007730722427368, 'ml', 0)]",3,0.0,,0.0,0,0,13,12,0,0,0,0.0,0.0,90.0,0.0,13
977,template,https://github.com/janetech-inc/fast-api-admin-template,[],,[],[],,,,janetech-inc/fast-api-admin-template,fast-api-admin-template,111,12,4,JavaScript,, A test driven micro-service template to build and deploy a fast-api service with admin feature.,janetech-inc,2023-12-22,2023-02-15,49,2.226361031518625,https://avatars.githubusercontent.com/u/52669296?v=4, A test driven micro-service template to build and deploy a fast-api service with admin feature.,[],[],2023-03-01,"[('ajndkr/lanarky', 0.6117317080497742, 'llm', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5484818816184998, 'template', 0), ('unionai-oss/unionml', 0.5197975635528564, 'ml-ops', 0), ('starlite-api/starlite', 0.504593551158905, 'web', 0)]",1,1.0,,0.23,1,0,11,11,0,0,0,1.0,0.0,90.0,0.0,13
737,ml-ops,https://github.com/aiqc/aiqc,[],,[],[],,,,aiqc/aiqc,AIQC,96,21,5,Python,,End-to-end deep learning on your desktop or server.,aiqc,2023-10-09,2020-12-02,164,0.5823223570190641,,End-to-end deep learning on your desktop or server.,[],[],2023-08-09,"[('tensorflow/tensorflow', 0.6452828049659729, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.6063768267631531, 'ml-dl', 0), ('keras-team/keras', 0.6046340465545654, 'ml-dl', 0), ('alpa-projects/alpa', 0.5878965854644775, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5876568555831909, 'ml', 0), ('koaning/human-learn', 0.5812904238700867, 'data', 0), ('mosaicml/composer', 0.5669575333595276, 'ml-dl', 0), ('mlflow/mlflow', 0.5631386637687683, 'ml-ops', 0), ('onnx/onnx', 0.5626868605613708, 'ml', 0), ('apache/incubator-mxnet', 0.560641884803772, 'ml-dl', 0), ('bigscience-workshop/petals', 0.5569496154785156, 'data', 0), ('neuralmagic/deepsparse', 0.556576669216156, 'nlp', 0), ('huggingface/datasets', 0.5560346841812134, 'nlp', 0), ('microsoft/deepspeed', 0.55571448802948, 'ml-dl', 0), ('google/trax', 0.555395245552063, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5551923513412476, 'ml', 0), ('explosion/thinc', 0.5513455867767334, 'ml-dl', 0), ('microsoft/jarvis', 0.5510006546974182, 'llm', 0), ('determined-ai/determined', 0.5507001280784607, 'ml-ops', 0), ('titanml/takeoff', 0.550189733505249, 'llm', 0), ('uber/petastorm', 0.5461896061897278, 'data', 0), ('rasbt/deeplearning-models', 0.5450917482376099, 'ml-dl', 0), ('mlc-ai/web-stable-diffusion', 0.5372481346130371, 'diffusion', 0), ('ml-tooling/opyrator', 0.532943844795227, 'viz', 0), ('horovod/horovod', 0.5296958684921265, 'ml-ops', 0), ('deepmind/dm-haiku', 0.5235515236854553, 'ml-dl', 0), ('huggingface/transformers', 0.5214504599571228, 'nlp', 0), ('karpathy/micrograd', 0.5214496850967407, 'study', 0), ('neuralmagic/sparseml', 0.5212861895561218, 'ml-dl', 0), ('paddlepaddle/paddle', 0.5197136998176575, 'ml-dl', 0), ('christoschristofidis/awesome-deep-learning', 0.5192874670028687, 'study', 0), ('rasbt/machine-learning-book', 0.519234299659729, 'study', 0), ('adap/flower', 0.5167785882949829, 'ml-ops', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5146724581718445, 'ml', 0), ('ddbourgin/numpy-ml', 0.5140572190284729, 'ml', 0), ('mlc-ai/web-llm', 0.5105668306350708, 'llm', 0), ('salesforce/warp-drive', 0.509090006351471, 'ml-rl', 0), ('rom1504/img2dataset', 0.5084776282310486, 'data', 0), ('datasystemslab/geotorch', 0.5082912445068359, 'gis', 0), ('microsoft/semi-supervised-learning', 0.5059452652931213, 'ml', 0), ('unity-technologies/ml-agents', 0.5059369802474976, 'ml-rl', 0), ('dmlc/dgl', 0.5034000873565674, 'ml-dl', 0), ('microsoft/nni', 0.5022141337394714, 'ml', 0), ('pytorch/ignite', 0.5004318356513977, 'ml-dl', 0), ('deepchecks/deepchecks', 0.5002310872077942, 'data', 0)]",8,2.0,,0.12,0,0,38,5,0,0,0,0.0,0.0,90.0,0.0,13
1051,ml-dl,https://github.com/xl0/lovely-jax,['jax'],,[],[],,,,xl0/lovely-jax,lovely-jax,85,3,3,Jupyter Notebook,https://xl0.github.io/lovely-jax,JAX Arrays for human consumption,xl0,2024-01-10,2022-11-08,64,1.328125,,JAX Arrays for human consumption,[],['jax'],2023-09-18,[],2,1.0,,0.13,0,0,14,4,0,1,1,0.0,0.0,90.0,0.0,13
1745,template,https://github.com/fmind/mlops-python-package,[],,[],[],,,,fmind/mlops-python-package,mlops-python-package,83,10,4,Python,,"Kickstart your MLOps initiative with a flexible, robust, and productive Python package.",fmind,2024-01-09,2023-06-23,31,2.6289592760180995,,"Kickstart your MLOps initiative with a flexible, robust, and productive Python package.","['ai', 'ml', 'mlops', 'package']","['ai', 'ml', 'mlops', 'package']",2023-06-23,"[('polyaxon/polyaxon', 0.6891217231750488, 'ml-ops', 2), ('kubeflow/fairing', 0.6557989120483398, 'ml-ops', 0), ('skops-dev/skops', 0.6394261121749878, 'ml-ops', 1), ('zenml-io/zenml', 0.6134677529335022, 'ml-ops', 3), ('unionai-oss/unionml', 0.5882454514503479, 'ml-ops', 1), ('merantix-momentum/squirrel-core', 0.5865539312362671, 'ml', 2), ('allegroai/clearml', 0.5833768844604492, 'ml-ops', 2), ('zenml-io/mlstacks', 0.5667558312416077, 'ml-ops', 2), ('netflix/metaflow', 0.5633413195610046, 'ml-ops', 3), ('avaiga/taipy', 0.5603728890419006, 'data', 1), ('evidentlyai/evidently', 0.5556315779685974, 'ml-ops', 1), ('bentoml/bentoml', 0.5531289577484131, 'ml-ops', 2), ('backtick-se/cowait', 0.5339037179946899, 'util', 0), ('reloadware/reloadium', 0.5241882801055908, 'profiling', 1), ('pypa/pipenv', 0.5217861533164978, 'util', 0), ('mlflow/mlflow', 0.5197017788887024, 'ml-ops', 2), ('gradio-app/gradio', 0.5189513564109802, 'viz', 0), ('cheshire-cat-ai/core', 0.5158709287643433, 'llm', 1), ('willmcgugan/textual', 0.5143515467643738, 'term', 0), ('featurelabs/featuretools', 0.5135179758071899, 'ml', 0), ('orchest/orchest', 0.5126034617424011, 'ml-ops', 0), ('ploomber/ploomber', 0.5116260051727295, 'ml-ops', 1), ('microsoft/nni', 0.5072845816612244, 'ml', 1), ('ml-tooling/opyrator', 0.50709468126297, 'viz', 0), ('ray-project/ray', 0.5060107111930847, 'ml-ops', 0), ('wandb/client', 0.5048151612281799, 'ml', 1), ('salesforce/logai', 0.5032975077629089, 'util', 1), ('selfexplainml/piml-toolbox', 0.5029227137565613, 'ml-interpretability', 0), ('amaargiru/pyroad', 0.5028426051139832, 'study', 0), ('aimhubio/aim', 0.5028169751167297, 'ml-ops', 3)]",1,1.0,,0.02,0,0,7,7,0,0,0,0.0,0.0,90.0,0.0,13
1360,study,https://github.com/ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book,[],,[],[],,,,ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book,Machine-Learning-for-High-Risk-Applications-Book,82,20,6,Jupyter Notebook,,Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications,ml-for-high-risk-apps-book,2023-12-23,2022-10-07,68,1.1958333333333333,https://avatars.githubusercontent.com/u/92960961?v=4,Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications,"['deep-learning', 'explainable-ai', 'interpretable-machine-learning', 'machine-learning', 'oreilly', 'oreilly-books', 'responsible-ai', 'security', 'trustworthy-ai']","['deep-learning', 'explainable-ai', 'interpretable-machine-learning', 'machine-learning', 'oreilly', 'oreilly-books', 'responsible-ai', 'security', 'trustworthy-ai']",2023-05-23,"[('csinva/imodels', 0.6214880347251892, 'ml', 2), ('rasbt/machine-learning-book', 0.5862606167793274, 'study', 2), ('patchy631/machine-learning', 0.5826952457427979, 'ml', 0), ('rasbt/stat451-machine-learning-fs20', 0.5810298323631287, 'study', 0), ('probml/pyprobml', 0.576326847076416, 'ml', 1), ('d2l-ai/d2l-en', 0.5661432147026062, 'study', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5615020394325256, 'study', 2), ('tensorlayer/tensorlayer', 0.5567829608917236, 'ml-rl', 1), ('tensorflow/tensorflow', 0.5544643402099609, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5539207458496094, 'ml-dl', 1), ('seldonio/alibi', 0.545021116733551, 'ml-interpretability', 1), ('tensorflow/lucid', 0.5438190698623657, 'ml-interpretability', 1), ('tensorflow/tensor2tensor', 0.5430309772491455, 'ml', 2), ('interpretml/interpret', 0.542955756187439, 'ml-interpretability', 3), ('maif/shapash', 0.5307745337486267, 'ml', 1), ('google-research/language', 0.5293827652931213, 'nlp', 1), ('explosion/thinc', 0.5284711122512817, 'ml-dl', 2), ('tigerlab-ai/tiger', 0.5272374749183655, 'llm', 0), ('carla-recourse/carla', 0.5270025730133057, 'ml', 2), ('christoschristofidis/awesome-deep-learning', 0.5266667008399963, 'study', 2), ('davidadsp/generative_deep_learning_2nd_edition', 0.5259917974472046, 'study', 2), ('teamhg-memex/eli5', 0.5232833623886108, 'ml', 1), ('firmai/industry-machine-learning', 0.5232796669006348, 'study', 1), ('googlecloudplatform/vertex-ai-samples', 0.5220873951911926, 'ml', 0), ('oegedijk/explainerdashboard', 0.52154141664505, 'ml-interpretability', 0), ('mlflow/mlflow', 0.5185167193412781, 'ml-ops', 1), ('rafiqhasan/auto-tensorflow', 0.5184751152992249, 'ml-dl', 1), ('udlbook/udlbook', 0.5133402347564697, 'study', 1), ('pycaret/pycaret', 0.5132032632827759, 'ml', 1), ('google/trax', 0.512615442276001, 'ml-dl', 2), ('rasbt/stat453-deep-learning-ss20', 0.5113897919654846, 'study', 0), ('google-research/google-research', 0.5109012126922607, 'ml', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5104230642318726, 'study', 0), ('borealisai/advertorch', 0.5085124969482422, 'ml', 2), ('unity-technologies/ml-agents', 0.5079880356788635, 'ml-rl', 2)]",2,2.0,,1.23,0,0,15,8,0,0,0,0.0,0.0,90.0,0.0,13
959,math,https://github.com/albahnsen/pycircular,[],,[],[],1.0,,,albahnsen/pycircular,pycircular,81,4,5,Python,,pycircular is a Python module for circular data analysis,albahnsen,2023-12-01,2022-09-12,72,1.1227722772277229,,pycircular is a Python module for circular data analysis,[],[],2023-01-21,"[('pysal/pysal', 0.581803023815155, 'gis', 0), ('scikit-geometry/scikit-geometry', 0.5635026693344116, 'gis', 0), ('scitools/cartopy', 0.5550414323806763, 'gis', 0), ('altair-viz/altair', 0.5373349189758301, 'viz', 0), ('has2k1/plotnine', 0.5327418446540833, 'viz', 0), ('pandas-dev/pandas', 0.5248360633850098, 'pandas', 0), ('wesm/pydata-book', 0.5135537981987, 'study', 0), ('earthlab/earthpy', 0.5108852982521057, 'gis', 0), ('scitools/iris', 0.5095266103744507, 'gis', 0), ('gboeing/pynamical', 0.503804087638855, 'sim', 0), ('enthought/mayavi', 0.5026959776878357, 'viz', 0), ('marcomusy/vedo', 0.5011388063430786, 'viz', 0), ('kanaries/pygwalker', 0.5010564923286438, 'pandas', 0), ('eleutherai/pyfra', 0.5009065270423889, 'ml', 0)]",5,0.0,,0.0,1,0,16,12,1,1,1,1.0,1.0,90.0,1.0,13
1589,util,https://github.com/msaelices/py2mojo,['mojo'],,[],[],,,,msaelices/py2mojo,py2mojo,67,7,2,Python,,Automated Python to Mojo code translation,msaelices,2024-01-12,2023-09-08,20,3.2569444444444446,,Automated Python to Mojo code translation,[],['mojo'],2023-09-23,"[('stijnwoestenborghs/gradi-mojo', 0.5529176592826843, 'util', 1), ('hhatto/autopep8', 0.5481264591217041, 'util', 0), ('lsh/shims', 0.5381791591644287, 'util', 1), ('google/latexify_py', 0.5220133662223816, 'util', 0), ('lynet101/mojo_community-lib', 0.509608805179596, 'util', 1)]",1,0.0,,1.12,0,0,4,4,0,0,0,0.0,0.0,90.0,0.0,13
1551,study,https://github.com/mdmzfzl/neetcode-solutions,[],,[],[],,,,mdmzfzl/neetcode-solutions,NeetCode-Solutions,59,11,2,C++,,"My solutions in C++, Python and Rust for problems on NeetCode.io",mdmzfzl,2024-01-04,2023-06-26,31,1.8944954128440368,,"My solutions in C++, Python and Rust for problems on NeetCode.io","['blind75', 'cpp', 'data-structures', 'data-structures-and-algorithms', 'interview-questions', 'leetcode', 'leetcode-solutions', 'neetcode', 'neetcode150', 'rust', 'rust-lang']","['blind75', 'cpp', 'data-structures', 'data-structures-and-algorithms', 'interview-questions', 'leetcode', 'leetcode-solutions', 'neetcode', 'neetcode150', 'rust', 'rust-lang']",2023-10-27,"[('neetcode-gh/leetcode', 0.6274089217185974, 'study', 3), ('astral-sh/ruff', 0.519241452217102, 'util', 1), ('aswinnnn/pyscan', 0.5120133757591248, 'security', 1), ('rustpython/rustpython', 0.5018560886383057, 'util', 1)]",2,0.0,,2.77,0,0,7,3,0,0,0,0.0,0.0,90.0,0.0,13
769,sim,https://github.com/activitysim/populationsim,[],,[],[],,,,activitysim/populationsim,populationsim,49,37,10,Jupyter Notebook,https://activitysim.github.io/populationsim,An Open Platform for Population Synthesis,activitysim,2023-11-21,2017-02-14,363,0.1349862258953168,https://avatars.githubusercontent.com/u/25851945?v=4,An Open Platform for Population Synthesis,"['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'population-synthesis']","['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'population-synthesis']",2021-11-19,"[('humanoidagents/humanoidagents', 0.5014925003051758, 'sim', 0)]",9,1.0,,0.0,4,1,84,26,0,1,1,4.0,4.0,90.0,1.0,13
1722,sim,https://github.com/roban/cosmolopy,"['cosmology', 'astronomy']",,[],[],,,,roban/cosmolopy,CosmoloPy,44,29,7,HTML,http://roban.github.com/CosmoloPy/,a basic numpy/scipy-based cosmology package for python,roban,2023-08-30,2009-08-09,755,0.058256099867599775,,a basic numpy/scipy-based cosmology package for python,[],"['astronomy', 'cosmology']",2023-06-07,"[('numpy/numpy', 0.7280952334403992, 'math', 0), ('scipy/scipy', 0.6138890385627747, 'math', 0), ('scitools/iris', 0.5999904274940491, 'gis', 0), ('cosmicpython/book', 0.5579319000244141, 'study', 0), ('enthought/mayavi', 0.5520604252815247, 'viz', 0), ('cupy/cupy', 0.5431826710700989, 'math', 0), ('marcomusy/vedo', 0.537321925163269, 'viz', 0), ('cloudsen12/easystac', 0.5210418701171875, 'gis', 0), ('astropy/astropy', 0.5209611654281616, 'sim', 1), ('jakevdp/pythondatasciencehandbook', 0.5194147229194641, 'study', 0), ('blaze/blaze', 0.5100215673446655, 'pandas', 0), ('scikit-learn-contrib/metric-learn', 0.500042200088501, 'ml', 0)]",10,3.0,,0.0,0,0,176,7,0,1,1,0.0,0.0,90.0,0.0,13
1579,data,https://github.com/accenture/cymple,"['cypher', 'neo4j']",,[],[],,,,accenture/cymple,Cymple,37,5,7,Python,,Cymple - a productivity tool for creating Cypher queries in Python,accenture,2024-01-06,2022-03-31,95,0.38656716417910447,https://avatars.githubusercontent.com/u/10454368?v=4,Cymple - a productivity tool for creating Cypher queries in Python,"['cypher', 'neo4j', 'nodes-2022', 'query-builder']","['cypher', 'neo4j', 'nodes-2022', 'query-builder']",2023-08-30,"[('neo4j/neo4j-python-driver', 0.6172598600387573, 'data', 2), ('sqlalchemy/sqlalchemy', 0.5566608309745789, 'data', 0), ('qdrant/qdrant-client', 0.5485904812812805, 'util', 0), ('graphql-python/graphene', 0.5159372091293335, 'web', 0), ('aws/graph-notebook', 0.5142837166786194, 'jupyter', 1)]",5,1.0,,0.83,0,0,22,5,0,6,6,0.0,0.0,90.0,0.0,13
1007,finance,https://github.com/mementum/bta-lib,[],,[],[],,,,mementum/bta-lib,bta-lib,426,102,26,Python,,Technical Analysis library in pandas for backtesting algotrading and quantitative analysis,mementum,2024-01-13,2020-01-31,208,2.0424657534246577,,Technical Analysis library in pandas for backtesting algotrading and quantitative analysis,[],[],2020-03-11,"[('twopirllc/pandas-ta', 0.6815423369407654, 'finance', 0), ('cuemacro/finmarketpy', 0.5935547947883606, 'finance', 0), ('jmcarpenter2/swifter', 0.5637902021408081, 'pandas', 0), ('eleutherai/pyfra', 0.5443049073219299, 'ml', 0), ('wesm/pydata-book', 0.5407599806785583, 'study', 0), ('mementum/backtrader', 0.5395269989967346, 'finance', 0), ('ta-lib/ta-lib-python', 0.5354213714599609, 'finance', 0), ('goldmansachs/gs-quant', 0.5344659090042114, 'finance', 0), ('nalepae/pandarallel', 0.5342207551002502, 'pandas', 0), ('lux-org/lux', 0.5323396921157837, 'viz', 0), ('alkaline-ml/pmdarima', 0.526504635810852, 'time-series', 0), ('unionai-oss/pandera', 0.5138059258460999, 'pandas', 0), ('ydataai/ydata-profiling', 0.5118176937103271, 'pandas', 0), ('rapidsai/cudf', 0.5101376175880432, 'pandas', 0), ('ranaroussi/quantstats', 0.5088109970092773, 'finance', 0), ('jakevdp/pythondatasciencehandbook', 0.5062484741210938, 'study', 0), ('tkrabel/bamboolib', 0.5043449401855469, 'pandas', 0), ('mito-ds/monorepo', 0.5002267360687256, 'jupyter', 0), ('adamerose/pandasgui', 0.5000632405281067, 'pandas', 0)]",1,0.0,,0.0,1,0,48,47,0,2,2,1.0,0.0,90.0,0.0,12
1085,util,https://github.com/mnooner256/pyqrcode,[],,[],[],,,,mnooner256/pyqrcode,pyqrcode,399,73,17,Python,,Python 3 module to generate QR Codes,mnooner256,2024-01-04,2013-06-07,555,0.718179480586269,,Python 3 module to generate QR Codes,[],[],2016-06-20,"[('heuer/segno', 0.7471798658370972, 'util', 0), ('pyscf/pyscf', 0.5908879637718201, 'sim', 0), ('cqcl/lambeq', 0.5321901440620422, 'nlp', 0), ('google/latexify_py', 0.527988612651825, 'util', 0), ('hhatto/autopep8', 0.5037751793861389, 'util', 0)]",8,3.0,,0.0,0,0,129,92,0,0,0,0.0,0.0,90.0,0.0,12
339,term,https://github.com/federicoceratto/dashing,[],,[],[],,,,federicoceratto/dashing,dashing,378,31,10,Python,https://dashing.readthedocs.io/en/latest/,Terminal dashboards for Python,federicoceratto,2024-01-12,2017-06-03,347,1.0879934210526316,,Terminal dashboards for Python,"['charts', 'dashboard', 'gauges', 'terminal', 'terminal-based']","['charts', 'dashboard', 'gauges', 'terminal', 'terminal-based']",2020-09-06,"[('plotly/dash', 0.6700859069824219, 'viz', 0), ('holoviz/panel', 0.6392130851745605, 'viz', 0), ('plotly/plotly.py', 0.6343661546707153, 'viz', 1), ('rapidsai/jupyterlab-nvdashboard', 0.6266454458236694, 'jupyter', 0), ('datapane/datapane', 0.6180770993232727, 'viz', 1), ('vizzuhq/ipyvizzu', 0.5991188883781433, 'jupyter', 1), ('man-group/dtale', 0.5763976573944092, 'viz', 0), ('cuemacro/chartpy', 0.5717711448669434, 'viz', 0), ('kanaries/pygwalker', 0.569820761680603, 'pandas', 0), ('bokeh/bokeh', 0.5679104924201965, 'viz', 0), ('urwid/urwid', 0.5645572543144226, 'term', 0), ('jquast/blessed', 0.5442036986351013, 'term', 1), ('matplotlib/matplotlib', 0.539638102054596, 'viz', 0), ('holoviz/holoviz', 0.5380876064300537, 'viz', 0), ('willmcgugan/rich', 0.5353392958641052, 'term', 1), ('mwaskom/seaborn', 0.528854489326477, 'viz', 0), ('adamerose/pandasgui', 0.5236207246780396, 'pandas', 0), ('goldmansachs/gs-quant', 0.5140957832336426, 'finance', 0), ('xonsh/xonsh', 0.5140331387519836, 'util', 1), ('willmcgugan/textual', 0.5119619369506836, 'term', 1), ('artelys/geonetworkx', 0.5115811228752136, 'gis', 0), ('has2k1/plotnine', 0.5077523589134216, 'viz', 0), ('holoviz/hvplot', 0.5051881670951843, 'pandas', 0), ('lux-org/lux', 0.5034831762313843, 'viz', 0), ('holoviz/geoviews', 0.5021084547042847, 'gis', 0), ('residentmario/geoplot', 0.5019742846488953, 'gis', 0), ('wesm/pydata-book', 0.5012456178665161, 'study', 0), ('ranaroussi/quantstats', 0.5010005235671997, 'finance', 0)]",2,2.0,,0.0,0,0,81,41,0,0,0,0.0,0.0,90.0,0.0,12
658,study,https://github.com/rasbt/stat451-machine-learning-fs20,[],,[],[],,,,rasbt/stat451-machine-learning-fs20,stat451-machine-learning-fs20,360,188,19,Jupyter Notebook,,STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020),rasbt,2024-01-04,2020-08-06,181,1.9811320754716981,,STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020),[],[],2020-12-03,"[('rasbt/stat453-deep-learning-ss20', 0.7537368535995483, 'study', 0), ('patchy631/machine-learning', 0.5887393355369568, 'ml', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5810298323631287, 'study', 0), ('huggingface/evaluate', 0.5357676148414612, 'ml', 0), ('probml/pyprobml', 0.5270578861236572, 'ml', 0), ('firmai/industry-machine-learning', 0.5267770886421204, 'study', 0), ('scikit-learn/scikit-learn', 0.5173183083534241, 'ml', 0), ('ageron/handson-ml2', 0.5153390169143677, 'ml', 0)]",2,1.0,,0.0,0,0,42,38,0,0,0,0.0,0.0,90.0,0.0,12
1397,ml-dl,https://github.com/blackhc/toma,[],,[],[],,,,blackhc/toma,toma,342,9,10,Python,,Helps you write algorithms in PyTorch that adapt to the available (CUDA) memory,blackhc,2024-01-04,2020-04-08,198,1.7198275862068966,,Helps you write algorithms in PyTorch that adapt to the available (CUDA) memory,"['data-science', 'gpu', 'machine-learning', 'pytorch']","['data-science', 'gpu', 'machine-learning', 'pytorch']",2021-04-17,"[('rentruewang/koila', 0.6982141137123108, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.6170483231544495, 'perf', 2), ('cvxgrp/pymde', 0.588315486907959, 'ml', 3), ('huggingface/accelerate', 0.5751364827156067, 'ml', 0), ('tensorflow/addons', 0.5717829465866089, 'ml', 1), ('pytorch/ignite', 0.5623881220817566, 'ml-dl', 2), ('pytorch/torchrec', 0.5616428256034851, 'ml-dl', 2), ('xl0/lovely-tensors', 0.5582937002182007, 'ml-dl', 1), ('pytorch/data', 0.5554980635643005, 'data', 0), ('joblib/joblib', 0.5492387413978577, 'util', 0), ('ashleve/lightning-hydra-template', 0.545914351940155, 'util', 1), ('plasma-umass/scalene', 0.545809268951416, 'profiling', 1), ('arogozhnikov/einops', 0.5450114011764526, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5438900589942932, 'study', 2), ('huggingface/datasets', 0.5416107177734375, 'nlp', 2), ('google/tf-quant-finance', 0.5406495928764343, 'finance', 1), ('mosaicml/composer', 0.5379732251167297, 'ml-dl', 2), ('nvidia/apex', 0.5342817902565002, 'ml-dl', 0), ('cupy/cupy', 0.5264122486114502, 'math', 1), ('nvidia/tensorrt-llm', 0.5219219923019409, 'viz', 1), ('ddbourgin/numpy-ml', 0.521062433719635, 'ml', 1), ('pytorchlightning/pytorch-lightning', 0.5127522945404053, 'ml-dl', 3), ('explosion/thinc', 0.512549638748169, 'ml-dl', 2), ('allenai/allennlp', 0.5094995498657227, 'nlp', 2), ('pytorch/captum', 0.5078623294830322, 'ml-interpretability', 0), ('neuralmagic/deepsparse', 0.5067216753959656, 'nlp', 0), ('microsoft/deepspeed', 0.5066729784011841, 'ml-dl', 3), ('denys88/rl_games', 0.5061429738998413, 'ml-rl', 1), ('isl-org/open3d', 0.5056933164596558, 'sim', 3), ('rasbt/machine-learning-book', 0.5006940960884094, 'study', 2)]",1,1.0,,0.0,0,0,46,33,0,0,0,0.0,0.0,90.0,0.0,12
33,gis,https://github.com/jasonrig/address-net,[],,[],[],,,,jasonrig/address-net,address-net,186,80,13,Python,,A package to structure Australian addresses,jasonrig,2024-01-04,2018-12-05,268,0.691817215727949,,A package to structure Australian addresses,"['address-parser', 'deep-learning', 'machine-learning', 'rnn']","['address-parser', 'deep-learning', 'machine-learning', 'rnn']",2020-09-09,"[('graal-research/deepparse', 0.7487471699714661, 'gis', 1)]",2,2.0,,0.0,2,0,62,41,0,0,0,2.0,1.0,90.0,0.5,12
1277,sim,https://github.com/ljvmiranda921/seagull,[],,[],[],,,,ljvmiranda921/seagull,seagull,168,28,9,Python,https://pyseagull.readthedocs.io/en/latest/index.html#,A Python Library for Conway's Game of Life,ljvmiranda921,2024-01-04,2019-05-02,247,0.6782006920415224,,A Python Library for Conway's Game of Life,"['artificial-life', 'artificial-life-algorithms', 'biology', 'cellular-automata', 'conways-game-of-life', 'game-of-life', 'mathematics', 'simulation-framework']","['artificial-life', 'artificial-life-algorithms', 'biology', 'cellular-automata', 'conways-game-of-life', 'game-of-life', 'mathematics', 'simulation-framework']",2020-11-08,"[('elliotwaite/rule-30-and-game-of-life', 0.7149527072906494, 'sim', 3), ('alephalpha/golly', 0.7014067769050598, 'sim', 2), ('projectmesa/mesa', 0.5834044218063354, 'sim', 1), ('pokepetter/ursina', 0.5359201431274414, 'gamedev', 0), ('lordmauve/pgzero', 0.5329146981239319, 'gamedev', 0), ('openlenia/lenia-tutorial', 0.5293267965316772, 'sim', 0), ('artemyk/dynpy', 0.5030413269996643, 'sim', 0), ('pythonarcade/arcade', 0.5029021501541138, 'gamedev', 0), ('sympy/sympy', 0.5007748603820801, 'math', 0)]",9,2.0,,0.0,0,0,57,39,0,1,1,0.0,0.0,90.0,0.0,12
231,template,https://github.com/crmne/cookiecutter-modern-datascience,[],,[],[],,,,crmne/cookiecutter-modern-datascience,cookiecutter-modern-datascience,163,33,4,Python,,Start a data science project with modern tools,crmne,2024-01-05,2020-07-06,186,0.8756715272448197,,Start a data science project with modern tools,"['cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'datascience']","['cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'datascience']",2023-08-10,"[('drivendata/cookiecutter-data-science', 0.7191076874732971, 'template', 3), ('lyz-code/cookiecutter-python-project', 0.6067662835121155, 'template', 1), ('buuntu/fastapi-react', 0.5824753046035767, 'template', 1), ('cookiecutter/cookiecutter', 0.5783246755599976, 'template', 1), ('tedivm/robs_awesome_python_template', 0.5504110455513, 'template', 1), ('ionelmc/cookiecutter-pylibrary', 0.5333271026611328, 'template', 2)]",2,1.0,,0.02,0,0,43,5,0,0,0,0.0,0.0,90.0,0.0,12
1393,nlp,https://github.com/sebischair/lbl2vec,[],,[],[],,,,sebischair/lbl2vec,Lbl2Vec,156,25,6,Python,https://wwwmatthes.in.tum.de/pages/naimi84squl1/Lbl2Vec-An-Embedding-based-Approach-for-Unsupervised-Document-Retrieval-on-Predefined-Topics,"Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.",sebischair,2024-01-08,2021-07-18,132,1.1792656587473003,https://avatars.githubusercontent.com/u/11438939?v=4,"Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.","['document-embeddings', 'label-embeddings', 'machine-learning', 'natural-language-processing', 'nlp', 'text-classification', 'unsupervised-classification', 'unsupervised-document-retrieval', 'word-embeddings']","['document-embeddings', 'label-embeddings', 'machine-learning', 'natural-language-processing', 'nlp', 'text-classification', 'unsupervised-classification', 'unsupervised-document-retrieval', 'word-embeddings']",2023-01-18,"[('ddangelov/top2vec', 0.8003798723220825, 'nlp', 1), ('plasticityai/magnitude', 0.5967115163803101, 'nlp', 4), ('maartengr/bertopic', 0.5893922448158264, 'nlp', 2), ('koaning/whatlies', 0.5767074823379517, 'nlp', 1), ('chroma-core/chroma', 0.5661131143569946, 'data', 0), ('rare-technologies/gensim', 0.5351875424385071, 'nlp', 4), ('koaning/embetter', 0.5337308645248413, 'data', 0), ('neuml/txtai', 0.5315554738044739, 'nlp', 2), ('huggingface/text-embeddings-inference', 0.5274394154548645, 'llm', 0), ('paddlepaddle/paddlenlp', 0.514652669429779, 'llm', 1), ('ai21labs/in-context-ralm', 0.5090823173522949, 'llm', 0), ('muennighoff/sgpt', 0.5040941834449768, 'llm', 0), ('flairnlp/flair', 0.5040925145149231, 'nlp', 4), ('qdrant/fastembed', 0.5038244128227234, 'ml', 0)]",1,1.0,,0.0,0,0,30,12,0,1,1,0.0,0.0,90.0,0.0,12
1828,util,https://github.com/koaning/clumper,['fluent'],,[],[],,,,koaning/clumper,clumper,144,14,3,Python,https://koaning.github.io/clumper/,A small python library that can clump lists of data together.,koaning,2024-01-04,2020-07-25,183,0.7850467289719626,,A small python library that can clump lists of data together.,[],['fluent'],2021-10-11,"[('pytables/pytables', 0.5409725904464722, 'data', 0), ('saulpw/visidata', 0.5350844264030457, 'term', 0), ('linealabs/lineapy', 0.5260019302368164, 'jupyter', 0), ('fluentpython/example-code-2e', 0.5001460909843445, 'study', 0)]",5,3.0,,0.0,0,0,42,28,0,0,0,0.0,0.0,90.0,0.0,12
839,gis,https://github.com/remotesensinglab/raster4ml,[],,[],[],,,,remotesensinglab/raster4ml,raster4ml,115,14,4,Python,https://raster4ml.readthedocs.io,A geospatial raster processing library for machine learning,remotesensinglab,2024-01-04,2022-07-11,81,1.4172535211267605,,A geospatial raster processing library for machine learning,"['agriculture-research', 'data-science', 'geospatial-data', 'machine-learning', 'remote-sensing', 'vegetation', 'vegetation-index']","['agriculture-research', 'data-science', 'geospatial-data', 'machine-learning', 'remote-sensing', 'vegetation', 'vegetation-index']",2022-11-01,"[('osgeo/grass', 0.6746719479560852, 'gis', 3), ('osgeo/gdal', 0.6307851076126099, 'gis', 2), ('microsoft/torchgeo', 0.6221429705619812, 'gis', 1), ('azavea/raster-vision', 0.5898652076721191, 'gis', 2), ('developmentseed/label-maker', 0.5611507296562195, 'gis', 1), ('perrygeo/python-rasterstats', 0.5588922500610352, 'gis', 0), ('fatiando/verde', 0.5404156446456909, 'gis', 1), ('earthlab/earthpy', 0.5156380534172058, 'gis', 0), ('kornia/kornia', 0.5086722373962402, 'ml-dl', 1), ('sentinel-hub/eo-learn', 0.5058966279029846, 'gis', 1), ('opengeos/segment-geospatial', 0.5023316144943237, 'gis', 1)]",1,1.0,,0.0,1,0,18,15,0,1,1,1.0,0.0,90.0,0.0,12
1283,llm,https://github.com/larsbaunwall/bricky,['haystack'],,[],[],,,,larsbaunwall/bricky,bricky,94,18,6,Python,,Haystack/OpenAI based chatbot curating a custom knowledgebase,larsbaunwall,2024-01-08,2023-01-29,52,1.7978142076502732,,Haystack/OpenAI based chatbot curating a custom knowledgebase,"['ai', 'haystack', 'nextjs', 'openai']","['ai', 'haystack', 'nextjs', 'openai']",2023-03-30,"[('rcgai/simplyretrieve', 0.6707216501235962, 'llm', 0), ('embedchain/embedchain', 0.634949803352356, 'llm', 1), ('togethercomputer/openchatkit', 0.6274131536483765, 'nlp', 0), ('run-llama/rags', 0.5981244444847107, 'llm', 1), ('cheshire-cat-ai/core', 0.598059892654419, 'llm', 1), ('lm-sys/fastchat', 0.5944631099700928, 'llm', 0), ('rasahq/rasa', 0.5906454920768738, 'llm', 0), ('deeppavlov/deeppavlov', 0.5805360078811646, 'nlp', 1), ('prefecthq/marvin', 0.577139675617218, 'nlp', 2), ('krohling/bondai', 0.5706357359886169, 'llm', 0), ('minimaxir/simpleaichat', 0.5494028925895691, 'llm', 1), ('nomic-ai/gpt4all', 0.5481459498405457, 'llm', 0), ('laion-ai/open-assistant', 0.5381844639778137, 'llm', 2), ('langchain-ai/chat-langchain', 0.5356094837188721, 'llm', 0), ('chatarena/chatarena', 0.5276904106140137, 'llm', 1), ('openai/openai-cookbook', 0.527281641960144, 'ml', 1), ('openai/openai-python', 0.5239132642745972, 'util', 1), ('mindsdb/mindsdb', 0.5211383700370789, 'data', 1), ('salesforce/logai', 0.5161862969398499, 'util', 1), ('pathwaycom/llm-app', 0.5143710374832153, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5141124725341797, 'llm', 2), ('deepset-ai/haystack', 0.5118600130081177, 'llm', 2), ('shishirpatil/gorilla', 0.5044655799865723, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5015654563903809, 'llm', 0)]",2,1.0,,0.31,0,0,12,10,0,0,0,0.0,0.0,90.0,0.0,12
1236,llm,https://github.com/zrrskywalker/llama-adapter,"['instruction-tuning', 'llama', 'language-model']",,[],[],,,,zrrskywalker/llama-adapter,LLaMA-Adapter,66,5,3,,,Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters,zrrskywalker,2024-01-05,2023-06-14,32,2.008695652173913,,Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters,[],"['instruction-tuning', 'language-model', 'llama']",2023-06-14,"[('tloen/alpaca-lora', 0.7604994773864746, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.684531569480896, 'llm', 0), ('microsoft/llama-2-onnx', 0.6588950157165527, 'llm', 2), ('facebookresearch/llama-recipes', 0.6457222700119019, 'llm', 2), ('jzhang38/tinyllama', 0.6362742185592651, 'llm', 2), ('hiyouga/llama-factory', 0.6037132740020752, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.6037131547927856, 'llm', 3), ('run-llama/llama-lab', 0.5806187391281128, 'llm', 2), ('lightning-ai/lit-llama', 0.5725643038749695, 'llm', 2), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5689181685447693, 'llm', 2), ('facebookresearch/llama', 0.5667337775230408, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5344659686088562, 'llm', 1), ('declare-lab/instruct-eval', 0.5260562896728516, 'llm', 0), ('bentoml/openllm', 0.5195848345756531, 'ml-ops', 1), ('bigscience-workshop/petals', 0.5049505829811096, 'data', 1), ('karpathy/llama2.c', 0.5048466920852661, 'llm', 2)]",1,1.0,,0.08,0,0,7,7,0,0,0,0.0,0.0,90.0,0.0,12
1587,util,https://github.com/lynet101/mojo_community-lib,['mojo'],,[],[],,,,lynet101/mojo_community-lib,Mojo_community-lib,45,3,5,Python,,A community driven mojo lib,lynet101,2023-12-08,2023-09-09,20,2.202797202797203,,A community driven mojo lib,[],['mojo'],2023-10-02,"[('lsh/shims', 0.7806389927864075, 'util', 1), ('msaelices/py2mojo', 0.509608805179596, 'util', 1)]",3,0.0,,1.63,0,0,4,3,0,0,0,0.0,0.0,90.0,0.0,12
1588,util,https://github.com/lsh/shims,['mojo'],,[],[],,,,lsh/shims,shims,38,2,2,,,Utils for mojo projects,lsh,2023-12-10,2023-09-12,20,1.9,,Utils for mojo projects,[],['mojo'],2023-10-02,"[('lynet101/mojo_community-lib', 0.7806389927864075, 'util', 1), ('msaelices/py2mojo', 0.5381791591644287, 'util', 1)]",1,1.0,,0.13,0,0,4,3,0,0,0,0.0,0.0,90.0,0.0,12
574,term,https://github.com/matthewdeanmartin/terminaltables,[],,[],[],,,,matthewdeanmartin/terminaltables,terminaltables,35,5,1,Python,https://robpol86.github.io/terminaltables,Generate simple tables in terminals from a nested list of strings.,matthewdeanmartin,2023-12-10,2021-12-04,112,0.3113087674714104,,Generate simple tables in terminals from a nested list of strings.,[],[],2022-01-30,[],10,2.0,,0.0,1,0,26,24,0,7,7,1.0,0.0,90.0,0.0,12
1507,sim,https://github.com/cyrus2d/pyrus2d,[],,[],[],,,,cyrus2d/pyrus2d,Pyrus2D,33,4,1,Python,https://cyrus2d.com/,PYRUS Soccer Simulation 2D base code is the first Python base code (sample team) for RoboCup Soccer 2D Simulator. This project is implemented by members of CYRUS soccer simulation 2D team.,cyrus2d,2024-01-12,2021-05-31,139,0.23716632443531827,https://avatars.githubusercontent.com/u/44771435?v=4,PYRUS Soccer Simulation 2D base code is the first Python base code (sample team) for RoboCup Soccer 2D Simulator. This project is implemented by members of CYRUS soccer simulation 2D team.,"['robocup', 'simulation', 'soccer', 'soccer-simulation']","['robocup', 'simulation', 'soccer', 'soccer-simulation']",2023-07-18,"[('viblo/pymunk', 0.5240238904953003, 'sim', 0)]",3,2.0,,3.85,3,0,32,6,0,0,0,3.0,0.0,90.0,0.0,12
1141,security,https://github.com/snyk/faker-security,[],,[],[],,,,snyk/faker-security,faker-security,30,6,13,Python,,Python Faker provider for security related data,snyk,2024-01-12,2022-03-18,97,0.3074670571010249,https://avatars.githubusercontent.com/u/12959162?v=4,Python Faker provider for security related data,[],[],2023-09-21,"[('joke2k/faker', 0.7436192631721497, 'data', 0), ('legrandin/pycryptodome', 0.5843793153762817, 'util', 0), ('lk-geimfari/mimesis', 0.5424483418464661, 'data', 0), ('nedbat/coveragepy', 0.5369202494621277, 'testing', 0), ('pyeve/cerberus', 0.5294828414916992, 'data', 0), ('pallets/itsdangerous', 0.5056630969047546, 'data', 0), ('getsentry/responses', 0.5025610327720642, 'testing', 0)]",5,1.0,,0.37,0,0,22,4,3,2,3,0.0,0.0,90.0,0.0,12
958,ml-dl,https://github.com/suanrong/sdne,[],,[],[],,,,suanrong/sdne,SDNE,319,123,10,Python,http://www.kdd.org/kdd2016/subtopic/view/structural-deep-network-embedding,This is a implementation of SDNE (Structural Deep Network embedding),suanrong,2024-01-04,2016-11-30,373,0.8532670997325181,,This is a implementation of SDNE (Structural Deep Network embedding),[],[],2021-09-10,[],6,1.0,,0.0,0,0,87,29,0,0,0,0.0,0.0,90.0,0.0,11
1550,testing,https://github.com/eugeneyan/testing-ml,[],,[],[],,,,eugeneyan/testing-ml,testing-ml,218,46,7,Python,https://eugeneyan.com/writing/testing-ml/,"🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.",eugeneyan,2024-01-11,2020-08-30,178,1.2227564102564104,,"🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.","['machine-learning', 'model-evaluation', 'testing']","['machine-learning', 'model-evaluation', 'testing']",2022-09-21,"[('huggingface/evaluate', 0.6167011260986328, 'ml', 1), ('patchy631/machine-learning', 0.5529058575630188, 'ml', 0), ('tensorflow/data-validation', 0.5382910966873169, 'ml-ops', 0), ('teamhg-memex/eli5', 0.5324820280075073, 'ml', 1), ('xplainable/xplainable', 0.5317504405975342, 'ml-interpretability', 1), ('automl/auto-sklearn', 0.528367280960083, 'ml', 0), ('ml-tooling/opyrator', 0.5247636437416077, 'viz', 1), ('seldonio/alibi', 0.5242266654968262, 'ml-interpretability', 1), ('districtdatalabs/yellowbrick', 0.5194308757781982, 'ml', 1), ('shankarpandala/lazypredict', 0.5170032382011414, 'ml', 1), ('nccr-itmo/fedot', 0.5155410766601562, 'ml-ops', 1), ('microsoft/nni', 0.5123538374900818, 'ml', 1), ('giskard-ai/giskard', 0.5097730755805969, 'data', 1)]",2,1.0,,0.0,0,0,41,16,0,0,0,0.0,0.0,90.0,0.0,11
882,graph,https://github.com/h4kor/graph-force,[],,[],[],,,,h4kor/graph-force,graph-force,161,1,10,Rust,https://pypi.org/project/graph-force/,"Python library for embedding large graphs in 2D space, using force-directed layouts.",h4kor,2024-01-09,2022-11-28,61,2.633177570093458,,"Python library for embedding large graphs in 2D space, using force-directed layouts.","['force-directed-graphs', 'graph-algorithms']","['force-directed-graphs', 'graph-algorithms']",2022-11-28,"[('graphistry/pygraphistry', 0.6990000605583191, 'data', 0), ('facebookresearch/pytorch-biggraph', 0.6328504085540771, 'ml-dl', 0), ('westhealth/pyvis', 0.6132168173789978, 'graph', 0), ('artelys/geonetworkx', 0.5840879678726196, 'gis', 0), ('dmlc/dgl', 0.573490560054779, 'ml-dl', 0), ('pygraphviz/pygraphviz', 0.571456253528595, 'viz', 0), ('a-r-j/graphein', 0.5339735150337219, 'sim', 0), ('networkx/networkx', 0.5262413620948792, 'graph', 1), ('kuanb/peartree', 0.5150353908538818, 'gis', 0), ('pyg-team/pytorch_geometric', 0.5138907432556152, 'ml-dl', 0), ('plotly/plotly.py', 0.5063482522964478, 'viz', 0), ('benedekrozemberczki/tigerlily', 0.503591001033783, 'ml-dl', 0)]",2,0.0,,0.0,0,0,14,14,0,0,0,0.0,0.0,90.0,0.0,11
1097,ml,https://github.com/eleutherai/pyfra,[],,[],[],,,,eleutherai/pyfra,pyfra,108,12,4,Python,,Python Research Framework,eleutherai,2024-01-04,2021-04-16,145,0.7419038272816487,https://avatars.githubusercontent.com/u/68924597?v=4,Python Research Framework,[],[],2022-11-03,"[('pytoolz/toolz', 0.6730476021766663, 'util', 0), ('willmcgugan/textual', 0.6594876646995544, 'term', 0), ('python/cpython', 0.6574167013168335, 'util', 0), ('masoniteframework/masonite', 0.6563796401023865, 'web', 0), ('pyston/pyston', 0.650770366191864, 'util', 0), ('holoviz/panel', 0.6469884514808655, 'viz', 0), ('pypy/pypy', 0.6457244753837585, 'util', 0), ('amaargiru/pyroad', 0.6402732729911804, 'study', 0), ('wesm/pydata-book', 0.631885826587677, 'study', 0), ('bottlepy/bottle', 0.6280529499053955, 'web', 0), ('buildbot/buildbot', 0.6259655952453613, 'util', 0), ('pandas-dev/pandas', 0.6219991445541382, 'pandas', 0), ('goldmansachs/gs-quant', 0.6204242706298828, 'finance', 0), ('pallets/flask', 0.6199480295181274, 'web', 0), ('nedbat/coveragepy', 0.6178824305534363, 'testing', 0), ('pympler/pympler', 0.6173771023750305, 'perf', 0), ('pytables/pytables', 0.6154819130897522, 'data', 0), ('webpy/webpy', 0.6089206337928772, 'web', 0), ('requests/toolbelt', 0.6041449308395386, 'util', 0), ('klen/py-frameworks-bench', 0.5963848829269409, 'perf', 0), ('falconry/falcon', 0.5904573202133179, 'web', 0), ('klen/muffin', 0.5880747437477112, 'web', 0), ('scrapy/scrapy', 0.5844635367393494, 'data', 0), ('fastai/fastcore', 0.5840798616409302, 'util', 0), ('stanfordnlp/dspy', 0.5839141607284546, 'llm', 0), ('pypa/hatch', 0.5818159580230713, 'util', 0), ('hoffstadt/dearpygui', 0.5808343887329102, 'gui', 0), ('pylons/pyramid', 0.5796028971672058, 'web', 0), ('google/pyglove', 0.5793092250823975, 'util', 0), ('landscapeio/prospector', 0.5767074227333069, 'util', 0), ('numpy/numpy', 0.5739134550094604, 'math', 0), ('gradio-app/gradio', 0.5723903775215149, 'viz', 0), ('google/gin-config', 0.5706988573074341, 'util', 0), ('wolever/parameterized', 0.5701524615287781, 'testing', 0), ('quantopian/pyfolio', 0.5698432922363281, 'finance', 0), ('kubeflow/fairing', 0.5697919726371765, 'ml-ops', 0), ('agronholm/apscheduler', 0.5697214603424072, 'util', 0), ('dylanhogg/awesome-python', 0.5674682259559631, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.5671376585960388, 'study', 0), ('firmai/atspy', 0.5660873055458069, 'time-series', 0), ('malloydata/malloy-py', 0.5658804178237915, 'data', 0), ('stan-dev/pystan', 0.5657259225845337, 'ml', 0), ('ethereum/web3.py', 0.5655627250671387, 'crypto', 0), ('quantecon/quantecon.py', 0.5649722218513489, 'sim', 0), ('altair-viz/altair', 0.5628342032432556, 'viz', 0), ('backtick-se/cowait', 0.5622064471244812, 'util', 0), ('norvig/pytudes', 0.5598863363265991, 'util', 0), ('rasbt/mlxtend', 0.5588405728340149, 'ml', 0), ('replicate/replicate-python', 0.5586503148078918, 'ml', 0), ('cuemacro/finmarketpy', 0.5586219429969788, 'finance', 0), ('ipython/ipyparallel', 0.5585790276527405, 'perf', 0), ('reloadware/reloadium', 0.5577932596206665, 'profiling', 0), ('dagworks-inc/hamilton', 0.5552157163619995, 'ml-ops', 0), ('plotly/dash', 0.5551019310951233, 'viz', 0), ('cython/cython', 0.5548232793807983, 'util', 0), ('clips/pattern', 0.5541388988494873, 'nlp', 0), ('realpython/python-guide', 0.5539833307266235, 'study', 0), ('ibis-project/ibis', 0.5528563857078552, 'data', 0), ('mynameisfiber/high_performance_python_2e', 0.5524687170982361, 'study', 0), ('mwaskom/seaborn', 0.5522692799568176, 'viz', 0), ('sympy/sympy', 0.5521600246429443, 'math', 0), ('brandon-rhodes/python-patterns', 0.5504909157752991, 'util', 0), ('connorferster/handcalcs', 0.5502340793609619, 'jupyter', 0), ('sqlalchemy/sqlalchemy', 0.5482377409934998, 'data', 0), ('pysal/pysal', 0.547865092754364, 'gis', 0), ('scikit-mobility/scikit-mobility', 0.5473421216011047, 'gis', 0), ('cherrypy/cherrypy', 0.5470208525657654, 'web', 0), ('robcarver17/pysystemtrade', 0.5468427538871765, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5461949110031128, 'study', 0), ('xrudelis/pytrait', 0.5457850098609924, 'util', 0), ('mementum/bta-lib', 0.5443049073219299, 'finance', 0), ('krzjoa/awesome-python-data-science', 0.5438522100448608, 'study', 0), ('pyglet/pyglet', 0.5430480241775513, 'gamedev', 0), ('scikit-learn/scikit-learn', 0.543034553527832, 'ml', 0), ('ta-lib/ta-lib-python', 0.5423515439033508, 'finance', 0), ('python-rope/rope', 0.5410192608833313, 'util', 0), ('urwid/urwid', 0.5409858822822571, 'term', 0), ('eugeneyan/python-collab-template', 0.5403453707695007, 'template', 0), ('python-odin/odin', 0.5400282144546509, 'util', 0), ('gbeced/pyalgotrade', 0.5397642850875854, 'finance', 0), ('imageio/imageio', 0.538760244846344, 'util', 0), ('artemyk/dynpy', 0.538719654083252, 'sim', 0), ('getsentry/responses', 0.5386306047439575, 'testing', 0), ('py-why/dowhy', 0.5379133820533752, 'ml', 0), ('merantix-momentum/squirrel-core', 0.53780198097229, 'ml', 0), ('beeware/toga', 0.5376284718513489, 'gui', 0), ('tkrabel/bamboolib', 0.53708815574646, 'pandas', 0), ('pmorissette/ffn', 0.5368715524673462, 'finance', 0), ('faster-cpython/ideas', 0.5366376042366028, 'perf', 0), ('sqlalchemy/mako', 0.5347551107406616, 'template', 0), ('pyutils/line_profiler', 0.5344709157943726, 'profiling', 0), ('pdm-project/pdm', 0.5340529680252075, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5332986116409302, 'study', 0), ('mementum/backtrader', 0.5332545638084412, 'finance', 0), ('ranaroussi/quantstats', 0.5325418710708618, 'finance', 0), ('dosisod/refurb', 0.5314339399337769, 'util', 0), ('contextlab/hypertools', 0.5310801863670349, 'ml', 0), ('sourcery-ai/sourcery', 0.5296950340270996, 'util', 0), ('adamerose/pandasgui', 0.5291572213172913, 'pandas', 0), ('eventual-inc/daft', 0.5290564894676208, 'pandas', 0), ('lk-geimfari/mimesis', 0.5290538668632507, 'data', 0), ('allrod5/injectable', 0.5288636088371277, 'util', 0), ('lux-org/lux', 0.5287709832191467, 'viz', 0), ('1200wd/bitcoinlib', 0.5283639430999756, 'crypto', 0), ('joblib/joblib', 0.5282385349273682, 'util', 0), ('pyinfra-dev/pyinfra', 0.5274806022644043, 'util', 0), ('pygamelib/pygamelib', 0.527156412601471, 'gamedev', 0), ('geopandas/geopandas', 0.5261996388435364, 'gis', 0), ('tiangolo/sqlmodel', 0.5258613228797913, 'data', 0), ('opengeos/leafmap', 0.525818407535553, 'gis', 0), ('primal100/pybitcointools', 0.5250184535980225, 'crypto', 0), ('featurelabs/featuretools', 0.5247564315795898, 'ml', 0), ('urschrei/pyzotero', 0.5244025588035583, 'util', 0), ('pallets/werkzeug', 0.523544430732727, 'web', 0), ('python-poetry/poetry', 0.5234190821647644, 'util', 0), ('pymc-devs/pymc3', 0.5232195854187012, 'ml', 0), ('pypa/pipenv', 0.5228143930435181, 'util', 0), ('pexpect/pexpect', 0.5216565728187561, 'util', 0), ('microsoft/playwright-python', 0.5212807059288025, 'testing', 0), ('pyscript/pyscript-cli', 0.5200616717338562, 'web', 0), ('selfexplainml/piml-toolbox', 0.5199788808822632, 'ml-interpretability', 0), ('holoviz/holoviz', 0.5192615985870361, 'viz', 0), ('scipy/scipy', 0.5191806554794312, 'math', 0), ('jiffyclub/snakeviz', 0.5175898671150208, 'profiling', 0), ('micropython/micropython', 0.5175337195396423, 'util', 0), ('cohere-ai/notebooks', 0.5172991752624512, 'llm', 0), ('gaogaotiantian/viztracer', 0.5164215564727783, 'profiling', 0), ('wandb/client', 0.5158582925796509, 'ml', 0), ('has2k1/plotnine', 0.5156086683273315, 'viz', 0), ('ets-labs/python-dependency-injector', 0.5152551531791687, 'util', 0), ('roniemartinez/dude', 0.5149586200714111, 'util', 0), ('statsmodels/statsmodels', 0.5143864154815674, 'ml', 0), ('rubik/radon', 0.514139711856842, 'util', 0), ('man-group/dtale', 0.5136914253234863, 'viz', 0), ('zenodo/zenodo', 0.5133705139160156, 'util', 0), ('adafruit/circuitpython', 0.5133049488067627, 'util', 0), ('marcomusy/vedo', 0.5130149126052856, 'viz', 0), ('rstudio/py-shiny', 0.5123597979545593, 'web', 0), ('cobrateam/splinter', 0.5119724273681641, 'testing', 0), ('huggingface/huggingface_hub', 0.5113564133644104, 'ml', 0), ('bokeh/bokeh', 0.5110460519790649, 'viz', 0), ('reflex-dev/reflex', 0.5102131962776184, 'web', 0), ('cosmicpython/book', 0.5096278190612793, 'study', 0), ('erotemic/ubelt', 0.5078774094581604, 'util', 0), ('ageron/handson-ml2', 0.5071380734443665, 'ml', 0), ('google/latexify_py', 0.5066225528717041, 'util', 0), ('residentmario/geoplot', 0.506131649017334, 'gis', 0), ('domokane/financepy', 0.5048255920410156, 'finance', 0), ('pythonspeed/filprofiler', 0.5045285820960999, 'profiling', 0), ('indygreg/pyoxidizer', 0.503706157207489, 'util', 0), ('sumerc/yappi', 0.5033401250839233, 'profiling', 0), ('feincms/feincms', 0.5029860734939575, 'web', 0), ('enthought/mayavi', 0.5028614401817322, 'viz', 0), ('pallets/quart', 0.5026024580001831, 'web', 0), ('r0x0r/pywebview', 0.5016577243804932, 'gui', 0), ('albahnsen/pycircular', 0.5009065270423889, 'math', 0), ('alexmojaki/snoop', 0.5008596777915955, 'debug', 0), ('collerek/ormar', 0.5003811717033386, 'data', 0)]",5,1.0,,0.0,0,0,33,15,0,1,1,0.0,0.0,90.0,0.0,11
1687,perf,https://github.com/faster-cpython/tools,['cpython'],Tools fo Faster CPython project.,[],[],,,,faster-cpython/tools,tools,88,14,22,Python,,,faster-cpython,2024-01-05,2021-04-09,146,0.6003898635477583,https://avatars.githubusercontent.com/u/81193161?v=4,Tools fo Faster CPython project.,[],['cpython'],2023-01-19,"[('markshannon/faster-cpython', 0.8188387751579285, 'perf', 0), ('faster-cpython/ideas', 0.8179801106452942, 'perf', 1), ('python/cpython', 0.7096896171569824, 'util', 1), ('pypy/pypy', 0.6747263669967651, 'util', 1), ('brandtbucher/specialist', 0.6422561407089233, 'perf', 1), ('cython/cython', 0.6359994411468506, 'util', 1), ('p403n1x87/austin', 0.6291638612747192, 'profiling', 0), ('ipython/ipyparallel', 0.6260746717453003, 'perf', 0), ('pyston/pyston', 0.5962938070297241, 'util', 0), ('fastai/fastcore', 0.5940293669700623, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5863217115402222, 'perf', 0), ('pytorch/data', 0.5645531415939331, 'data', 0), ('facebookincubator/cinder', 0.5622638463973999, 'perf', 1), ('scikit-build/scikit-build', 0.5580853819847107, 'ml', 1), ('wesm/pydata-book', 0.5520884990692139, 'study', 0), ('gotcha/ipdb', 0.549821138381958, 'debug', 0), ('hoffstadt/dearpygui', 0.5421075820922852, 'gui', 0), ('klen/py-frameworks-bench', 0.5376661419868469, 'perf', 0), ('fchollet/deep-learning-with-python-notebooks', 0.533972442150116, 'study', 0), ('lcompilers/lpython', 0.5253562927246094, 'util', 0), ('nvidia/apex', 0.5224118828773499, 'ml-dl', 0), ('mynameisfiber/high_performance_python_2e', 0.5218332409858704, 'study', 0), ('exaloop/codon', 0.518226146697998, 'perf', 0), ('wxwidgets/phoenix', 0.5171552300453186, 'gui', 0), ('erotemic/ubelt', 0.5155495405197144, 'util', 0), ('ipython/ipython', 0.5140804052352905, 'util', 0), ('ipython/ipykernel', 0.5114536285400391, 'util', 0), ('tqdm/tqdm', 0.5104278922080994, 'term', 0), ('cohere-ai/notebooks', 0.5094420313835144, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5083655118942261, 'llm', 0), ('rasbt/watermark', 0.507169783115387, 'util', 0), ('pyqtgraph/pyqtgraph', 0.5059671998023987, 'viz', 0), ('adafruit/circuitpython', 0.5050845742225647, 'util', 1), ('agronholm/apscheduler', 0.5040667057037354, 'util', 0), ('pytoolz/toolz', 0.5024548172950745, 'util', 0)]",5,2.0,,0.0,0,0,34,12,0,0,0,0.0,0.0,90.0,0.0,11
1089,graph,https://github.com/hamed1375/exphormer,[],,[],[],,,,hamed1375/exphormer,Exphormer,86,13,0,Python,,Exphormer: Sparse Transformer for Graphs,hamed1375,2024-01-12,2023-03-05,47,1.8187311178247734,,Exphormer: Sparse Transformer for Graphs,[],[],2023-07-20,"[('rampasek/graphgps', 0.6791350841522217, 'graph', 0), ('hazyresearch/hgcn', 0.5110289454460144, 'ml', 0)]",3,0.0,,0.29,0,0,10,6,0,0,0,0.0,0.0,90.0,0.0,11
531,gis,https://github.com/cloudsen12/easystac,[],,[],[],,,,cloudsen12/easystac,easystac,63,2,3,Python,https://easystac.readthedocs.io/,A Python package for simple STAC queries,cloudsen12,2023-09-04,2022-01-20,105,0.595945945945946,https://avatars.githubusercontent.com/u/76630702?v=4,A Python package for simple STAC queries,"['earth-observation', 'gis', 'planetary-computer', 'radiant', 'remote-sensing', 'spatio-temporal', 'spatio-temporal-data', 'stac']","['earth-observation', 'gis', 'planetary-computer', 'radiant', 'remote-sensing', 'spatio-temporal', 'spatio-temporal-data', 'stac']",2022-08-07,"[('radiantearth/radiant-mlhub', 0.6044291853904724, 'gis', 1), ('scitools/iris', 0.6042935252189636, 'gis', 0), ('sentinel-hub/eo-learn', 0.5572369694709778, 'gis', 0), ('pytroll/satpy', 0.5563086271286011, 'gis', 0), ('geopandas/geopandas', 0.534330427646637, 'gis', 1), ('earthlab/earthpy', 0.5297847390174866, 'gis', 0), ('roban/cosmolopy', 0.5210418701171875, 'sim', 0), ('opengeos/leafmap', 0.5067197680473328, 'gis', 1), ('artelys/geonetworkx', 0.5005823373794556, 'gis', 0)]",3,3.0,,0.0,0,0,24,18,0,1,1,0.0,0.0,90.0,0.0,11
400,crypto,https://github.com/dylanhogg/crazy-awesome-crypto,['awesome'],,[],['<hide>'],,,,dylanhogg/crazy-awesome-crypto,crazy-awesome-crypto,60,16,5,Python,https://www.awesomecrypto.xyz/,A list of awesome crypto and blockchain projects,dylanhogg,2024-01-08,2021-09-27,122,0.49122807017543857,,A list of awesome crypto and blockchain projects,"['awesome', 'awesome-list', 'bitcoin', 'blockchain', 'crypto', 'cryptocurrency', 'data', 'data-analysis', 'ethereum', 'github']","['awesome', 'awesome-list', 'bitcoin', 'blockchain', 'crypto', 'cryptocurrency', 'data', 'data-analysis', 'ethereum', 'github']",2023-10-22,"[('dylanhogg/awesome-python', 0.5653820037841797, 'study', 3), ('numerai/example-scripts', 0.5541864633560181, 'finance', 1), ('christoschristofidis/awesome-deep-learning', 0.5464283227920532, 'study', 2), ('1200wd/bitcoinlib', 0.5096782445907593, 'crypto', 1)]",1,1.0,,0.17,0,0,28,3,0,1,1,0.0,0.0,90.0,0.0,11
1565,llm,https://github.com/deep-diver/gradio-chat,['gradio'],,[],[],,,,deep-diver/gradio-chat,gradio-chat,56,7,2,Python,,HuggingChat like UI in Gradio,deep-diver,2024-01-04,2023-05-19,36,1.53125,,HuggingChat like UI in Gradio,[],['gradio'],2023-05-23,[],1,1.0,,0.23,0,0,8,8,0,0,0,0.0,0.0,90.0,0.0,11
1630,template,https://github.com/tedivm/robs_awesome_python_template,[],,[],[],,,,tedivm/robs_awesome_python_template,robs_awesome_python_template,20,4,3,Python,https://blog.tedivm.com/open-source/2023/02/robs-awesome-python-template/,A Highly Configurable Python Project Template for Modern Python Projects,tedivm,2024-01-12,2022-12-11,59,0.3373493975903614,,A Highly Configurable Python Project Template for Modern Python Projects,"['cookiecutter-python', 'cookiecutter-template']","['cookiecutter-python', 'cookiecutter-template']",2023-12-31,"[('lyz-code/cookiecutter-python-project', 0.9047797918319702, 'template', 0), ('cookiecutter/cookiecutter', 0.8344708681106567, 'template', 0), ('ionelmc/cookiecutter-pylibrary', 0.8226386308670044, 'template', 1), ('giswqs/pypackage', 0.7693808078765869, 'template', 1), ('buuntu/fastapi-react', 0.6574198603630066, 'template', 0), ('tezromach/python-package-template', 0.647948682308197, 'template', 0), ('pypa/hatch', 0.582638144493103, 'util', 0), ('cjolowicz/cookiecutter-hypermodern-python', 0.5808916687965393, 'template', 0), ('crmne/cookiecutter-modern-datascience', 0.5504110455513, 'template', 1), ('martinheinz/python-project-blueprint', 0.5347585082054138, 'template', 0), ('psf/requests', 0.5218177437782288, 'web', 0), ('pallets/flask', 0.5124099254608154, 'web', 0), ('s3rius/fastapi-template', 0.5110516548156738, 'web', 1), ('pypa/build', 0.5029564499855042, 'util', 0), ('pdm-project/pdm', 0.5013588070869446, 'util', 0)]",1,1.0,,0.75,1,1,13,0,0,0,0,1.0,0.0,90.0,0.0,11
934,sim,https://github.com/crflynn/stochastic,[],,[],[],,,,crflynn/stochastic,stochastic,388,71,15,Python,http://stochastic.readthedocs.io/en/stable/,Generate realizations of stochastic processes in python.,crflynn,2024-01-04,2017-02-17,362,1.070133963750985,,Generate realizations of stochastic processes in python.,"['probability', 'stochastic', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-simulation-algorithm', 'stochastic-volatility-models']","['probability', 'stochastic', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-simulation-algorithm', 'stochastic-volatility-models']",2022-07-12,"[('pymc-devs/pymc3', 0.6376107335090637, 'ml', 0), ('awslabs/gluonts', 0.5971387624740601, 'time-series', 0), ('probml/pyprobml', 0.5735928416252136, 'ml', 0), ('firmai/atspy', 0.5687724351882935, 'time-series', 0), ('stan-dev/pystan', 0.5423630475997925, 'ml', 0), ('artemyk/dynpy', 0.5386329889297485, 'sim', 0), ('statsmodels/statsmodels', 0.5304479002952576, 'ml', 0), ('scikit-learn/scikit-learn', 0.5197334885597229, 'ml', 0), ('gboeing/pynamical', 0.509087860584259, 'sim', 0), ('online-ml/river', 0.507440447807312, 'ml', 0), ('scipy/scipy', 0.5048527121543884, 'math', 0), ('google/temporian', 0.5019282698631287, 'time-series', 0), ('goldmansachs/gs-quant', 0.5013076663017273, 'finance', 0), ('bashtage/arch', 0.5002689957618713, 'time-series', 0), ('uber/orbit', 0.5002565979957581, 'time-series', 0)]",7,0.0,,0.0,0,0,84,18,0,0,0,0.0,0.0,90.0,0.0,10
778,util,https://github.com/brokenloop/jsontopydantic,[],,[],[],,,,brokenloop/jsontopydantic,jsontopydantic,275,9,4,TypeScript,https://jsontopydantic.com,Web tool for generating Pydantic models from JSON objects,brokenloop,2024-01-12,2020-11-28,165,1.662348877374784,,Web tool for generating Pydantic models from JSON objects,[],[],2022-05-13,"[('developmentseed/geojson-pydantic', 0.7540448904037476, 'gis', 0), ('jsonpickle/jsonpickle', 0.5869114398956299, 'data', 0), ('1rgs/jsonformer', 0.5837192535400391, 'llm', 0), ('marshmallow-code/marshmallow', 0.5406548380851746, 'util', 0), ('kellyjonbrazil/jello', 0.5349708795547485, 'util', 0), ('agronholm/sqlacodegen', 0.5330995321273804, 'data', 0), ('scikit-hep/awkward-1.0', 0.5177565813064575, 'data', 0), ('selfexplainml/piml-toolbox', 0.515333354473114, 'ml-interpretability', 0), ('lk-geimfari/mimesis', 0.5148674845695496, 'data', 0), ('python-odin/odin', 0.5140889286994934, 'util', 0), ('stan-dev/pystan', 0.5074247717857361, 'ml', 0)]",2,0.0,,0.0,0,0,38,20,0,0,0,0.0,0.0,90.0,0.0,10
736,study,https://github.com/koaning/calm-notebooks,"['annoy', 'sklearn', 'jax']",,[],[],,,,koaning/calm-notebooks,calm-notebooks,214,175,9,Jupyter Notebook,https://calmcode.io,notebooks that are used at calmcode.io,koaning,2024-01-01,2020-03-01,204,1.0475524475524476,,notebooks that are used at calmcode.io,[],"['annoy', 'jax', 'sklearn']",2021-10-21,"[('cohere-ai/notebooks', 0.5802757143974304, 'llm', 0), ('huggingface/notebooks', 0.5623078942298889, 'ml', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5319899320602417, 'study', 0), ('alphasecio/langchain-examples', 0.5073686242103577, 'llm', 0), ('jupyter/nbgrader', 0.5042494535446167, 'jupyter', 0)]",1,1.0,,0.0,0,0,47,27,0,0,0,0.0,0.0,90.0,0.0,10
525,nlp,https://github.com/hazyresearch/fonduer-tutorials,[],,[],[],,,,hazyresearch/fonduer-tutorials,fonduer-tutorials,98,26,18,Jupyter Notebook,https://github.com/HazyResearch/fonduer,A collection of simple tutorials for using Fonduer,hazyresearch,2024-01-04,2018-03-23,305,0.32071061243571763,https://avatars.githubusercontent.com/u/2165246?v=4,A collection of simple tutorials for using Fonduer,[],[],2020-05-27,[],6,2.0,,0.0,0,0,71,44,0,2,2,0.0,0.0,90.0,0.0,10
530,ml-dl,https://github.com/benedekrozemberczki/tigerlily,[],,[],[],,,,benedekrozemberczki/tigerlily,tigerlily,96,9,2,Jupyter Notebook,,TigerLily: Finding drug interactions in silico with the Graph.,benedekrozemberczki,2024-01-04,2022-02-28,100,0.9586305278174037,,TigerLily: Finding drug interactions in silico with the Graph.,"['biology', 'ddi', 'deep-learning', 'drug-drug-interaction', 'embedding', 'gradient-boosting', 'graph', 'graph-database', 'graph-embedding', 'graph-machine-learning', 'heterogeneous-graph', 'knowledge-graph', 'machine-learning', 'network-science', 'node', 'node-embedding', 'pharmaceuticals', 'tigergraph', 'unsupervised-learning']","['biology', 'ddi', 'deep-learning', 'drug-drug-interaction', 'embedding', 'gradient-boosting', 'graph', 'graph-database', 'graph-embedding', 'graph-machine-learning', 'heterogeneous-graph', 'knowledge-graph', 'machine-learning', 'network-science', 'node', 'node-embedding', 'pharmaceuticals', 'tigergraph', 'unsupervised-learning']",2022-12-17,"[('a-r-j/graphein', 0.6609140634536743, 'sim', 1), ('stellargraph/stellargraph', 0.6446079015731812, 'graph', 3), ('google-deepmind/materials_discovery', 0.5921242237091064, 'sim', 0), ('pyg-team/pytorch_geometric', 0.561957597732544, 'ml-dl', 1), ('danielegrattarola/spektral', 0.5574583411216736, 'ml-dl', 1), ('dmlc/dgl', 0.5503193140029907, 'ml-dl', 1), ('graphistry/pygraphistry', 0.5483189225196838, 'data', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.5460976362228394, 'study', 2), ('accenture/ampligraph', 0.5214887857437134, 'data', 2), ('awslabs/dgl-ke', 0.50465327501297, 'ml', 2), ('h4kor/graph-force', 0.503591001033783, 'graph', 0)]",1,1.0,,0.0,0,0,23,13,0,1,1,0.0,0.0,90.0,0.0,10
843,profiling,https://github.com/kshitij12345/torchnnprofiler,[],,[],[],,,,kshitij12345/torchnnprofiler,torchnnprofiler,81,4,5,Python,,Context Manager to profile the forward and backward times of PyTorch's nn.Module,kshitij12345,2024-01-10,2022-10-22,66,1.2193548387096773,,Context Manager to profile the forward and backward times of PyTorch's nn.Module,[],[],2022-11-02,"[('pytorch/ignite', 0.6059595346450806, 'ml-dl', 0), ('pytorch/data', 0.5611929297447205, 'data', 0), ('nvidia/apex', 0.5381832718849182, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5366755723953247, 'perf', 0), ('skorch-dev/skorch', 0.5358409881591797, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5249190926551819, 'study', 0), ('nvlabs/gcvit', 0.5133840441703796, 'diffusion', 0), ('pytorch/botorch', 0.5020992159843445, 'ml-dl', 0)]",1,0.0,,0.0,1,0,15,15,0,0,0,1.0,1.0,90.0,1.0,10
526,ml,https://github.com/brohrer/cottonwood,[],,[],[],,,,brohrer/cottonwood,cottonwood,76,13,15,Python,https://end-to-end-machine-learning.teachable.com/p/write-a-neural-network-framework/,A flexible neural network framework for running experiments and trying ideas.,brohrer,2024-01-04,2019-09-29,226,0.33585858585858586,,A flexible neural network framework for running experiments and trying ideas.,[],[],2020-02-02,[],3,2.0,,0.0,0,0,52,48,0,3,3,0.0,0.0,90.0,0.0,10
910,ml-ops,https://github.com/anyscale/airflow-provider-ray,[],,[],[],,,,anyscale/airflow-provider-ray,airflow-provider-ray,41,9,13,Python,,Ray provider for Apache Airflow,anyscale,2024-01-05,2021-03-05,151,0.2704995287464656,https://avatars.githubusercontent.com/u/51251046?v=4,Ray provider for Apache Airflow,[],[],2021-10-03,"[('astronomer/astronomer', 0.5748955011367798, 'ml-ops', 0), ('apache/airflow', 0.521160364151001, 'ml-ops', 0)]",8,4.0,,0.0,0,0,35,28,0,0,0,0.0,0.0,90.0,0.0,10
332,util,https://github.com/gondolav/pyfuncol,[],,[],[],,,,gondolav/pyfuncol,pyfuncol,32,2,3,Python,https://pyfuncol.readthedocs.io/,Functional collections extension functions for Python,gondolav,2022-11-16,2021-12-16,110,0.28903225806451616,https://avatars.githubusercontent.com/u/98323830?v=4,Functional collections extension functions for Python,"['collections', 'extension-functions', 'functional', 'parallel']","['collections', 'extension-functions', 'functional', 'parallel']",2023-03-26,"[('pytoolz/toolz', 0.6170833706855774, 'util', 0), ('suor/funcy', 0.5316035747528076, 'util', 0), ('evhub/coconut', 0.5076735019683838, 'util', 1)]",4,1.0,,0.15,0,0,25,10,0,3,3,0.0,0.0,90.0,0.0,10
373,data,https://github.com/ndrplz/google-drive-downloader,[],,[],[],,,,ndrplz/google-drive-downloader,google-drive-downloader,261,63,13,Python,,Minimal class to download shared files from Google Drive.,ndrplz,2024-01-04,2017-12-08,320,0.8141711229946524,,Minimal class to download shared files from Google Drive.,[],[],2019-02-09,[],5,1.0,,0.0,0,0,74,60,0,0,0,0.0,0.0,90.0,0.0,9
289,template,https://github.com/eugeneyan/python-collab-template,[],,[],[],,,,eugeneyan/python-collab-template,python-collab-template,136,39,5,Python,https://eugeneyan.com/writing/setting-up-python-project-for-automation-and-collaboration/,"🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.",eugeneyan,2024-01-14,2020-06-21,188,0.7223065250379362,,"🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.","['coverage', 'github-actions', 'linting', 'makefile', 'type-checking', 'unit-testing']","['coverage', 'github-actions', 'linting', 'makefile', 'type-checking', 'unit-testing']",2022-07-02,"[('nedbat/coveragepy', 0.6858402490615845, 'testing', 0), ('landscapeio/prospector', 0.6496816873550415, 'util', 1), ('pypa/hatch', 0.613045871257782, 'util', 0), ('pyscaffold/pyscaffold', 0.6021620631217957, 'template', 0), ('psf/black', 0.5849995613098145, 'util', 0), ('martinheinz/python-project-blueprint', 0.5737303495407104, 'template', 0), ('mkdocstrings/griffe', 0.5616798400878906, 'util', 0), ('mitmproxy/pdoc', 0.5599040389060974, 'util', 0), ('sqlalchemy/mako', 0.5552263855934143, 'template', 0), ('python-rope/rope', 0.5535590648651123, 'util', 0), ('facebook/pyre-check', 0.5504177212715149, 'typing', 0), ('wolever/parameterized', 0.5470243692398071, 'testing', 0), ('amaargiru/pyroad', 0.5436649918556213, 'study', 0), ('google/pytype', 0.5415253043174744, 'typing', 0), ('eleutherai/pyfra', 0.5403453707695007, 'ml', 0), ('sourcery-ai/sourcery', 0.5378775596618652, 'util', 0), ('omry/omegaconf', 0.5363547205924988, 'util', 0), ('hhatto/autopep8', 0.5339161157608032, 'util', 0), ('pytest-dev/pytest-testinfra', 0.5332326292991638, 'testing', 0), ('grantjenks/blue', 0.5308860540390015, 'util', 0), ('rubik/radon', 0.5297834873199463, 'util', 0), ('pypa/build', 0.523838460445404, 'util', 0), ('pytoolz/toolz', 0.5230408906936646, 'util', 0), ('tezromach/python-package-template', 0.518621563911438, 'template', 1), ('samuelcolvin/dirty-equals', 0.514961302280426, 'util', 1), ('instagram/fixit', 0.5144226551055908, 'util', 0), ('pdoc3/pdoc', 0.5139718651771545, 'util', 0), ('ionelmc/pytest-benchmark', 0.5136798024177551, 'testing', 0), ('pypa/pipenv', 0.5131263732910156, 'util', 0), ('grahamdumpleton/wrapt', 0.5126464366912842, 'util', 0), ('dosisod/refurb', 0.5078116059303284, 'util', 0), ('pdm-project/pdm', 0.5047013163566589, 'util', 0)]",2,1.0,,0.0,0,0,43,19,0,0,0,0.0,0.0,90.0,0.0,9
1379,llm,https://github.com/ai21labs/lm-evaluation,[],,[],[],,,,ai21labs/lm-evaluation,lm-evaluation,122,13,5,Python,,Evaluation suite for large-scale language models.,ai21labs,2024-01-04,2021-08-05,129,0.9405286343612335,https://avatars.githubusercontent.com/u/33798954?v=4,Evaluation suite for large-scale language models.,"['evaluation-framework', 'language-model']","['evaluation-framework', 'language-model']",2021-08-15,"[('eleutherai/lm-evaluation-harness', 0.7471644282341003, 'llm', 2), ('freedomintelligence/llmzoo', 0.7427138090133667, 'llm', 1), ('lm-sys/fastchat', 0.7379257678985596, 'llm', 1), ('openlmlab/leval', 0.7304577231407166, 'llm', 1), ('hannibal046/awesome-llm', 0.725335955619812, 'study', 1), ('juncongmoo/pyllama', 0.699636697769165, 'llm', 0), ('ctlllll/llm-toolmaker', 0.6955636143684387, 'llm', 1), ('lianjiatech/belle', 0.6819735169410706, 'llm', 0), ('openbmb/toolbench', 0.6778345704078674, 'llm', 0), ('cg123/mergekit', 0.6523741483688354, 'llm', 0), ('anthropics/evals', 0.6497257947921753, 'llm', 0), ('bigscience-workshop/biomedical', 0.6412531137466431, 'data', 0), ('confident-ai/deepeval', 0.622775137424469, 'testing', 2), ('jonasgeiping/cramming', 0.6214629411697388, 'nlp', 1), ('togethercomputer/redpajama-data', 0.60612553358078, 'llm', 0), ('guidance-ai/guidance', 0.6006115674972534, 'llm', 1), ('young-geng/easylm', 0.5989466309547424, 'llm', 1), ('fasteval/fasteval', 0.5941077470779419, 'llm', 0), ('huggingface/evaluate', 0.5928609371185303, 'ml', 0), ('next-gpt/next-gpt', 0.5911809802055359, 'llm', 0), ('salesforce/xgen', 0.5776482224464417, 'llm', 1), ('microsoft/autogen', 0.5751771926879883, 'llm', 0), ('srush/minichain', 0.5739299654960632, 'llm', 0), ('oobabooga/text-generation-webui', 0.5664257407188416, 'llm', 1), ('prefecthq/langchain-prefect', 0.5636144280433655, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5610719919204712, 'llm', 1), ('huggingface/text-generation-inference', 0.5604375004768372, 'llm', 0), ('microsoft/lora', 0.55837482213974, 'llm', 1), ('conceptofmind/toolformer', 0.5530425310134888, 'llm', 1), ('infinitylogesh/mutate', 0.5527203679084778, 'nlp', 1), ('openlmlab/moss', 0.5499148368835449, 'llm', 1), ('eleutherai/the-pile', 0.5482072830200195, 'data', 0), ('bigscience-workshop/megatron-deepspeed', 0.5460460186004639, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5460460186004639, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5453725457191467, 'llm', 0), ('neulab/prompt2model', 0.5453250408172607, 'llm', 1), ('openai/evals', 0.5421918034553528, 'llm', 1), ('princeton-nlp/alce', 0.5408934950828552, 'llm', 0), ('hiyouga/llama-factory', 0.5385951995849609, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5385950803756714, 'llm', 1), ('reasoning-machines/pal', 0.5380016565322876, 'llm', 1), ('facebookresearch/shepherd', 0.5377524495124817, 'llm', 1), ('databrickslabs/dolly', 0.5367728471755981, 'llm', 0), ('yizhongw/self-instruct', 0.5341971516609192, 'llm', 1), ('llmware-ai/llmware', 0.5320602655410767, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5313429236412048, 'llm', 0), ('thudm/chatglm2-6b', 0.5304546356201172, 'llm', 0), ('ai21labs/in-context-ralm', 0.5293609499931335, 'llm', 1), ('jalammar/ecco', 0.5280724167823792, 'ml-interpretability', 0), ('mit-han-lab/streaming-llm', 0.5273094773292542, 'llm', 0), ('optimalscale/lmflow', 0.5266632437705994, 'llm', 1), ('agenta-ai/agenta', 0.5219810009002686, 'llm', 0), ('citadel-ai/langcheck', 0.518677294254303, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5184262990951538, 'llm', 1), ('yueyu1030/attrprompt', 0.5179035067558289, 'llm', 0), ('hazyresearch/h3', 0.5161812901496887, 'llm', 0), ('nvlabs/prismer', 0.5158585906028748, 'diffusion', 1), ('thudm/chatglm-6b', 0.5155556201934814, 'llm', 1), ('lupantech/chameleon-llm', 0.5124858617782593, 'llm', 1), ('hpcaitech/energonai', 0.5110083818435669, 'ml', 0), ('cstankonrad/long_llama', 0.5108537077903748, 'llm', 1), ('explosion/spacy-models', 0.5086509585380554, 'nlp', 0), ('night-chen/toolqa', 0.5064146518707275, 'llm', 0), ('lvwerra/trl', 0.5060834288597107, 'llm', 0), ('facebookresearch/seamless_communication', 0.505820631980896, 'nlp', 0), ('1rgs/jsonformer', 0.5037403702735901, 'llm', 0), ('guardrails-ai/guardrails', 0.5022191405296326, 'llm', 0), ('bytedance/lightseq', 0.5000201463699341, 'nlp', 0)]",3,1.0,,0.0,0,0,30,29,0,0,0,0.0,0.0,90.0,0.0,9
1398,diffusion,https://github.com/pollinations/dance-diffusion,['audio'],,[],[],,,,pollinations/dance-diffusion,dance-diffusion,55,11,2,Jupyter Notebook,,Tools to train a generative model on arbitrary audio samples,pollinations,2023-12-31,2022-09-28,69,0.787321063394683,https://avatars.githubusercontent.com/u/86964862?v=4,Tools to train a generative model on arbitrary audio samples,[],['audio'],2022-09-29,"[('suno-ai/bark', 0.6367303729057312, 'ml', 1), ('facebookresearch/audiocraft', 0.5851640105247498, 'util', 1), ('openai/image-gpt', 0.5518842935562134, 'llm', 0), ('huggingface/diffusers', 0.5509036779403687, 'diffusion', 0)]",4,1.0,,0.0,0,0,16,16,0,0,0,0.0,0.0,90.0,0.0,9
1377,nlp,https://github.com/amazon-science/dq-bart,[],,[],[],,,,amazon-science/dq-bart,dq-bart,48,11,3,Python,,DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (ACL 2022),amazon-science,2023-12-05,2022-03-15,98,0.4897959183673469,https://avatars.githubusercontent.com/u/70298811?v=4,DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (ACL 2022),[],[],2022-12-27,"[('hazyresearch/safari', 0.5790391564369202, 'ml', 0), ('bytedance/lightseq', 0.564974844455719, 'nlp', 0), ('artidoro/qlora', 0.5504317283630371, 'llm', 0), ('predibase/llm_distillation_playbook', 0.5063695311546326, 'llm', 0)]",4,2.0,,0.0,0,0,22,13,0,0,0,0.0,0.0,90.0,0.0,9
970,sim,https://github.com/glpcc/pokerpy,[],,[],[],,,,glpcc/pokerpy,PokerPy,43,4,1,C++,,Texas Hold'em Poker Probability Calculator in Python,glpcc,2023-07-08,2022-12-11,59,0.7253012048192771,,Texas Hold'em Poker Probability Calculator in Python,"['cpp', 'fast', 'performance', 'poker', 'pybind11', 'texas-holdem']","['cpp', 'fast', 'performance', 'poker', 'pybind11', 'texas-holdem']",2023-02-10,[],2,1.0,,0.83,0,0,13,11,0,0,0,0.0,0.0,90.0,0.0,9
1065,nlp,https://github.com/airi-institute/probing_framework,[],,[],[],,,,airi-institute/probing_framework,Probing_framework,22,10,1,Python,,Framework for probing tasks,airi-institute,2023-12-22,2022-01-21,105,0.2083897158322057,https://avatars.githubusercontent.com/u/92741417?v=4,Framework for probing tasks,"['multilinguality', 'natural-language-processing', 'probing', 'transformers', 'universal-dependencies']","['multilinguality', 'natural-language-processing', 'probing', 'transformers', 'universal-dependencies']",2023-08-28,"[('keirp/automatic_prompt_engineer', 0.5204843282699585, 'llm', 0), ('openbmb/toolbench', 0.5175856351852417, 'llm', 0)]",7,0.0,,1.77,8,4,24,5,0,0,0,8.0,1.0,90.0,0.1,9
1633,util,https://github.com/multi-py/python-gunicorn-uvicorn,[],,[],[],,,,multi-py/python-gunicorn-uvicorn,python-gunicorn-uvicorn,15,1,1,Shell,,Multiarchitecture Docker Containers for Python using Gunicorn and Uvicorn,multi-py,2023-10-24,2021-10-31,117,0.1278928136419001,https://avatars.githubusercontent.com/u/92491059?v=4,Multiarchitecture Docker Containers for Python using Gunicorn and Uvicorn,"['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn', 'uvicorn']","['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn', 'uvicorn']",2023-12-20,"[('multi-py/python-gunicorn', 0.9625324010848999, 'util', 7), ('multi-py/python-uvicorn', 0.9309157729148865, 'util', 6), ('multi-py/python-celery', 0.7121555209159851, 'util', 6), ('backtick-se/cowait', 0.5445891618728638, 'util', 1), ('rawheel/fastapi-boilerplate', 0.5090985894203186, 'web', 1)]",2,1.0,,0.48,0,0,27,1,0,0,0,0.0,0.0,90.0,0.0,9
1634,util,https://github.com/multi-py/python-uvicorn,[],,[],[],,,,multi-py/python-uvicorn,python-uvicorn,14,0,1,Shell,https://blog.tedivm.com,Multiarchitecture Docker Containers for Python and Uvicorn,multi-py,2023-08-21,2021-10-05,121,0.11570247933884298,https://avatars.githubusercontent.com/u/92491059?v=4,Multiarchitecture Docker Containers for Python and Uvicorn,"['amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'uvicorn']","['amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'uvicorn']",2023-12-20,"[('multi-py/python-gunicorn-uvicorn', 0.9309157729148865, 'util', 6), ('multi-py/python-gunicorn', 0.8768712878227234, 'util', 5), ('multi-py/python-celery', 0.7029248476028442, 'util', 5), ('backtick-se/cowait', 0.5087990760803223, 'util', 1)]",2,1.0,,0.48,0,0,28,1,0,0,0,0.0,0.0,90.0,0.0,9
1660,data,https://github.com/unstructured-io/pipeline-paddleocr,"['unstructured', 'pdf', 'pipeline', 'ocr']",,[],[],,,,unstructured-io/pipeline-paddleocr,pipeline-paddleocr,14,5,13,Jupyter Notebook,,Pipeline for converting PDFs to raw text with PaddleOCR,unstructured-io,2023-12-25,2022-12-08,59,0.23444976076555024,https://avatars.githubusercontent.com/u/108372208?v=4,Pipeline for converting PDFs to raw text with PaddleOCR,[],"['ocr', 'pdf', 'pipeline', 'unstructured']",2023-06-29,"[('camelot-dev/camelot', 0.5457990169525146, 'util', 0), ('py-pdf/pypdf2', 0.5316691398620605, 'util', 1), ('pyfpdf/fpdf2', 0.531453549861908, 'util', 1), ('pdfminer/pdfminer.six', 0.5148302316665649, 'util', 1), ('rapidai/rapidocr', 0.5100756287574768, 'data', 1)]",10,2.0,,0.25,0,0,13,7,0,0,0,0.0,0.0,90.0,0.0,9
1623,template,https://github.com/lyz-code/cookiecutter-python-project,['cookiecutter'],,[],[],,,,lyz-code/cookiecutter-python-project,cookiecutter-python-project,13,1,3,CSS,https://lyz-code.github.io/cookiecutter-python-project,Cookiecutter template to generate python projects following the best practices gathered over the time.,lyz-code,2024-01-12,2020-10-16,171,0.07577019150707744,,Cookiecutter template to generate python projects following the best practices gathered over the time.,[],['cookiecutter'],2023-03-15,"[('tedivm/robs_awesome_python_template', 0.9047797918319702, 'template', 0), ('ionelmc/cookiecutter-pylibrary', 0.8550078272819519, 'template', 1), ('cookiecutter/cookiecutter', 0.8542928099632263, 'template', 1), ('giswqs/pypackage', 0.815499484539032, 'template', 1), ('buuntu/fastapi-react', 0.6677143573760986, 'template', 1), ('cjolowicz/cookiecutter-hypermodern-python', 0.6242104768753052, 'template', 0), ('crmne/cookiecutter-modern-datascience', 0.6067662835121155, 'template', 1), ('tezromach/python-package-template', 0.5960633754730225, 'template', 1), ('psf/requests', 0.5095129013061523, 'web', 0)]",2,1.0,,0.08,0,0,39,10,0,4,4,0.0,0.0,90.0,0.0,9
912,ml-ops,https://github.com/anyscale/prefect-anyscale,[],,[],[],,,,anyscale/prefect-anyscale,prefect-anyscale,8,2,3,Python,,Prefect integration with Anyscale,anyscale,2023-10-02,2022-11-07,64,0.12472160356347439,https://avatars.githubusercontent.com/u/51251046?v=4,Prefect integration with Anyscale,[],[],2023-10-11,[],2,1.0,,0.21,0,0,14,3,2,8,2,0.0,0.0,90.0,0.0,9
1347,ml-rl,https://github.com/zacwellmer/worldmodels,['agent-based-modeling'],,[],[],,,,zacwellmer/worldmodels,WorldModels,259,29,5,Jupyter Notebook,,World Models with TensorFlow 2,zacwellmer,2024-01-11,2020-04-09,198,1.3033788641265276,,World Models with TensorFlow 2,[],['agent-based-modeling'],2021-06-09,"[('projectmesa/mesa', 0.5980151891708374, 'sim', 1), ('operand/agency', 0.5862823128700256, 'llm', 0), ('tensorflow/mesh', 0.5606265068054199, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.5352241396903992, 'ml-dl', 0), ('geekan/metagpt', 0.5185655355453491, 'llm', 0), ('aiwaves-cn/agents', 0.5116593241691589, 'nlp', 0), ('unity-technologies/ml-agents', 0.5110092163085938, 'ml-rl', 0)]",1,0.0,,0.0,0,0,46,32,0,0,0,0.0,0.0,90.0,0.0,8
239,sim,https://github.com/nv-tlabs/gamegan_code,[],,[],[],,,,nv-tlabs/gamegan_code,GameGAN_code,212,36,11,Python,,Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020),nv-tlabs,2024-01-09,2020-12-11,163,1.2960698689956331,https://avatars.githubusercontent.com/u/49653101?v=4,Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020),[],[],2021-11-11,[],2,0.0,,0.0,0,0,38,26,0,0,0,0.0,0.0,90.0,0.0,8
724,gis,https://github.com/bowenc0221/boundary-iou-api,[],,[],[],,,,bowenc0221/boundary-iou-api,boundary-iou-api,198,22,8,Python,,Boundary IoU API (Beta version),bowenc0221,2024-01-13,2021-03-29,148,1.3365477338476375,,Boundary IoU API (Beta version),[],[],2021-04-05,[],2,0.0,,0.0,0,0,34,34,0,0,0,0.0,0.0,90.0,0.0,8
455,nlp,https://github.com/coastalcph/lex-glue,[],,[],[],,,,coastalcph/lex-glue,lex-glue,149,29,6,Python,,LexGLUE: A Benchmark Dataset for Legal Language Understanding in English,coastalcph,2024-01-07,2021-09-27,122,1.2198830409356725,https://avatars.githubusercontent.com/u/6862219?v=4,LexGLUE: A Benchmark Dataset for Legal Language Understanding in English,"['benchmark', 'lawtech', 'legal', 'legaltech', 'nlp']","['benchmark', 'lawtech', 'legal', 'legaltech', 'nlp']",2022-11-04,"[('lexpredict/lexpredict-lexnlp', 0.6552823185920715, 'nlp', 3), ('iclrandd/blackstone', 0.6491376757621765, 'nlp', 2), ('thoppe/the-pile-freelaw', 0.6147865653038025, 'data', 0), ('hazyresearch/legalbench', 0.5063652992248535, 'llm', 2)]",2,0.0,,0.0,0,0,28,15,0,0,0,0.0,0.0,90.0,0.0,8
461,data,https://github.com/psycoguana/subredditmediadownloader,[],,[],[],,,,psycoguana/subredditmediadownloader,SubredditMediaDownloader,122,9,3,Python,,Simple Python script to download images and videos from public subreddits without using Reddit's API 😎,psycoguana,2024-01-10,2022-02-18,101,1.20112517580872,,Simple Python script to download images and videos from public subreddits without using Reddit's API 😎,[],[],2023-01-17,"[('pytube/pytube', 0.5245307087898254, 'util', 0)]",2,0.0,,0.0,0,0,23,12,0,0,0,0.0,0.0,90.0,0.0,8
885,sim,https://github.com/crowdbotp/socialways,[],,[],[],,,,crowdbotp/socialways,socialways,116,45,9,Python,,Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs (CVPR 2019),crowdbotp,2024-01-04,2019-04-23,249,0.46586345381526106,https://avatars.githubusercontent.com/u/70031889?v=4,Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs (CVPR 2019),"['crowd-simulation', 'gan', 'generative-adversarial-network', 'human-trajectory-prediction', 'info-gan', 'pedestrian', 'pedestrian-trajectories', 'prediction-model', 'self-driving-car', 'social-gan', 'social-navigation', 'social-robots', 'social-ways', 'trajectory-forecasting', 'trajectory-prediction']","['crowd-simulation', 'gan', 'generative-adversarial-network', 'human-trajectory-prediction', 'info-gan', 'pedestrian', 'pedestrian-trajectories', 'prediction-model', 'self-driving-car', 'social-gan', 'social-navigation', 'social-robots', 'social-ways', 'trajectory-forecasting', 'trajectory-prediction']",2020-03-20,[],3,2.0,,0.0,0,0,58,46,0,0,0,0.0,0.0,90.0,0.0,8
662,gis,https://github.com/zorzi-s/projectregularization,[],,[],[],,,,zorzi-s/projectregularization,projectRegularization,101,12,2,Python,,Regularization of Building Boundaries using Adversarial and Regularized losses,zorzi-s,2024-01-04,2021-05-18,141,0.7163120567375887,,Regularization of Building Boundaries using Adversarial and Regularized losses,[],[],2023-09-13,"[('cleverhans-lab/cleverhans', 0.5036975741386414, 'ml', 0)]",1,0.0,,0.02,0,0,32,4,0,0,0,0.0,0.0,90.0,0.0,8
705,gis,https://github.com/artelys/geonetworkx,[],,[],[],,,,artelys/geonetworkx,geonetworkx,31,1,7,Python,,Python tools for geographic graphs,artelys,2023-07-20,2019-10-24,222,0.13919178960872355,https://avatars.githubusercontent.com/u/17453408?v=4,Python tools for geographic graphs,[],[],2021-06-28,"[('geopandas/geopandas', 0.7633013725280762, 'gis', 0), ('holoviz/geoviews', 0.663130521774292, 'gis', 0), ('residentmario/geoplot', 0.6383180022239685, 'gis', 0), ('graphistry/pygraphistry', 0.6271733045578003, 'data', 0), ('pysal/pysal', 0.625034749507904, 'gis', 0), ('opengeos/leafmap', 0.6138547658920288, 'gis', 0), ('toblerity/rtree', 0.597081184387207, 'gis', 0), ('westhealth/pyvis', 0.5923200249671936, 'graph', 0), ('pygraphviz/pygraphviz', 0.5921590328216553, 'viz', 0), ('raphaelquast/eomaps', 0.585459291934967, 'gis', 0), ('h4kor/graph-force', 0.5840879678726196, 'graph', 0), ('plotly/plotly.py', 0.5817664861679077, 'viz', 0), ('earthlab/earthpy', 0.5808680057525635, 'gis', 0), ('networkx/networkx', 0.5718642473220825, 'graph', 0), ('pyproj4/pyproj', 0.5713775753974915, 'gis', 0), ('has2k1/plotnine', 0.569690465927124, 'viz', 0), ('gregorhd/mapcompare', 0.5692752599716187, 'gis', 0), ('altair-viz/altair', 0.5582142472267151, 'viz', 0), ('scitools/iris', 0.5512532591819763, 'gis', 0), ('scitools/cartopy', 0.5421754121780396, 'gis', 0), ('holoviz/holoviz', 0.5396808385848999, 'viz', 0), ('makepath/xarray-spatial', 0.5338029265403748, 'gis', 0), ('dfki-ric/pytransform3d', 0.5281858444213867, 'math', 0), ('mwaskom/seaborn', 0.5267704129219055, 'viz', 0), ('scikit-geometry/scikit-geometry', 0.5264812707901001, 'gis', 0), ('openeventdata/mordecai', 0.5222339630126953, 'gis', 0), ('holoviz/hvplot', 0.5222033858299255, 'pandas', 0), ('contextlab/hypertools', 0.5169349908828735, 'ml', 0), ('federicoceratto/dashing', 0.5115811228752136, 'term', 0), ('enthought/mayavi', 0.5101572275161743, 'viz', 0), ('kuanb/peartree', 0.5083203911781311, 'gis', 0), ('matplotlib/matplotlib', 0.5047987103462219, 'viz', 0), ('scikit-mobility/scikit-mobility', 0.5012951493263245, 'gis', 0), ('cloudsen12/easystac', 0.5005823373794556, 'gis', 0)]",6,1.0,,0.0,0,0,51,31,0,2,2,0.0,0.0,90.0,0.0,8
682,ml-dl,https://github.com/jerryyli/valhalla-nmt,[],,[],[],,,,jerryyli/valhalla-nmt,valhalla-nmt,25,4,1,Python,,"Code repository for CVPR 2022 paper ""VALHALLA: Visual Hallucination for Machine Translation""",jerryyli,2024-01-04,2022-03-22,97,0.25773195876288657,,"Code repository for CVPR 2022 paper ""VALHALLA: Visual Hallucination for Machine Translation""","['computer-vision', 'machine-translation', 'multimodal-learning', 'natural-language-processing']","['computer-vision', 'machine-translation', 'multimodal-learning', 'natural-language-processing']",2023-02-19,"[('salesforce/blip', 0.5753607749938965, 'diffusion', 0), ('nvlabs/prismer', 0.5498813390731812, 'diffusion', 0), ('openai/image-gpt', 0.5397126078605652, 'llm', 0)]",3,1.0,,0.02,0,0,22,11,0,1,1,0.0,0.0,90.0,0.0,8
37,math,https://github.com/jszymon/pacal,[],,[],[],,,,jszymon/pacal,pacal,22,8,7,Python,,PaCAL - ProbAbilistic CALculator,jszymon,2023-07-14,2014-08-04,495,0.044431621465666475,,PaCAL - ProbAbilistic CALculator,[],[],2022-11-02,[],8,1.0,,0.0,0,0,115,15,0,0,0,0.0,0.0,90.0,0.0,8
1163,llm,https://github.com/qanastek/drbert,[],,[],[],,,,qanastek/drbert,DrBERT,13,1,1,Python,https://drbert.univ-avignon.fr/,DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains,qanastek,2023-11-09,2023-01-05,55,0.23333333333333334,,DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains,"['bert', 'biomedical', 'french', 'learning', 'machine', 'machine-learning', 'medical', 'ml', 'nlp', 'nlp-machine-learning', 'taln', 'text']","['bert', 'biomedical', 'french', 'learning', 'machine', 'machine-learning', 'medical', 'ml', 'nlp', 'nlp-machine-learning', 'taln', 'text']",2023-11-13,"[('bigscience-workshop/biomedical', 0.6520806550979614, 'data', 0), ('jonasgeiping/cramming', 0.5827434659004211, 'nlp', 1), ('microsoft/unilm', 0.5781573057174683, 'nlp', 1), ('epfllm/meditron', 0.5632989406585693, 'llm', 1), ('deepset-ai/farm', 0.5561661720275879, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5513941645622253, 'llm', 3), ('explosion/spacy-models', 0.5431317090988159, 'nlp', 2), ('openai/finetune-transformer-lm', 0.5415375828742981, 'llm', 0), ('alibaba/easynlp', 0.5349388718605042, 'nlp', 3), ('thudm/glm-130b', 0.5260924696922302, 'llm', 0), ('google-research/electra', 0.5233448147773743, 'ml-dl', 1), ('maartengr/bertopic', 0.51610267162323, 'nlp', 3), ('explosion/spacy-transformers', 0.5139819979667664, 'llm', 3), ('jina-ai/finetuner', 0.5117612481117249, 'ml', 1), ('llmware-ai/llmware', 0.5081828832626343, 'llm', 3), ('flairnlp/flair', 0.5062006711959839, 'nlp', 2), ('explosion/spacy-llm', 0.5015144348144531, 'llm', 2)]",2,1.0,,0.37,0,0,12,2,0,0,0,0.0,0.0,90.0,0.0,8
1504,util,https://github.com/evdcush/fart,[],Fart is focused on making text banners for use in code documentation.,[],[],,,,evdcush/fart,fart,10,0,2,Python,,fart on your code,evdcush,2024-01-03,2020-03-23,201,0.04971590909090909,,fart on your code,"['art', 'ascii-art', 'figlet', 'figlet-fonts', 'smells-good']","['art', 'ascii-art', 'figlet', 'figlet-fonts', 'smells-good']",2024-01-03,[],1,0.0,,0.12,0,0,46,0,0,0,0,0.0,0.0,90.0,0.0,8
181,sim,https://github.com/artemyk/dynpy,[],,[],[],,,,artemyk/dynpy,dynpy,7,5,3,Python,https://dynpy.readthedocs.io/,Dynamical systems for Python,artemyk,2023-08-29,2014-09-12,489,0.014298220017508025,,Dynamical systems for Python,[],[],2023-11-29,"[('pytoolz/toolz', 0.6178411841392517, 'util', 0), ('sympy/sympy', 0.6068984270095825, 'math', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.583624005317688, 'study', 0), ('gboeing/pynamical', 0.5693354606628418, 'sim', 0), ('pyston/pyston', 0.5571711659431458, 'util', 0), ('pytransitions/transitions', 0.55495285987854, 'util', 0), ('wilsonrljr/sysidentpy', 0.5530008673667908, 'time-series', 0), ('projectmesa/mesa', 0.5512898564338684, 'sim', 0), ('micropython/micropython', 0.5470327734947205, 'util', 0), ('python/cpython', 0.5442706346511841, 'util', 0), ('pytorch/pytorch', 0.5410839319229126, 'ml-dl', 0), ('eleutherai/pyfra', 0.538719654083252, 'ml', 0), ('crflynn/stochastic', 0.5386329889297485, 'sim', 0), ('google/jax', 0.5380663871765137, 'ml', 0), ('pypy/pypy', 0.5257759690284729, 'util', 0), ('pymc-devs/pymc3', 0.5243752002716064, 'ml', 0), ('infer-actively/pymdp', 0.5208140015602112, 'ml', 0), ('google/pyglove', 0.5151891708374023, 'util', 0), ('hgrecco/pint', 0.5099499225616455, 'util', 0), ('allrod5/injectable', 0.5095717906951904, 'util', 0), ('firmai/atspy', 0.5067782998085022, 'time-series', 0), ('pyomo/pyomo', 0.5054152011871338, 'math', 0), ('agronholm/apscheduler', 0.5037860870361328, 'util', 0), ('ljvmiranda921/seagull', 0.5030413269996643, 'sim', 0)]",5,0.0,,0.21,0,0,114,2,0,0,0,0.0,0.0,90.0,0.0,8
1637,util,https://github.com/multi-py/python-gunicorn,[],,[],[],,,,multi-py/python-gunicorn,python-gunicorn,4,0,1,Shell,https://blog.tedivm.com, Multiarchitecture Docker Containers for Python and Gunicorn ,multi-py,2022-07-08,2021-10-30,117,0.0340632603406326,https://avatars.githubusercontent.com/u/92491059?v=4, Multiarchitecture Docker Containers for Python and Gunicorn ,"['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn']","['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn']",2023-11-18,"[('multi-py/python-gunicorn-uvicorn', 0.9625324010848999, 'util', 7), ('multi-py/python-uvicorn', 0.8768712878227234, 'util', 5), ('multi-py/python-celery', 0.7504483461380005, 'util', 6), ('backtick-se/cowait', 0.5146100521087646, 'util', 1), ('rawheel/fastapi-boilerplate', 0.5125967264175415, 'web', 1), ('darribas/gds_env', 0.5109146237373352, 'gis', 1)]",2,1.0,,0.27,0,0,27,2,0,0,0,0.0,0.0,90.0,0.0,8
304,util,https://github.com/xrudelis/pytrait,[],,[],[],,,,xrudelis/pytrait,pytrait,133,3,5,Python,,Traits for Python3,xrudelis,2024-01-08,2021-11-21,114,1.16375,,Traits for Python3,[],[],2021-11-27,"[('pytoolz/toolz', 0.5742577314376831, 'util', 0), ('facebook/pyre-check', 0.5590521693229675, 'typing', 0), ('landscapeio/prospector', 0.5579221248626709, 'util', 0), ('instagram/monkeytype', 0.5478743314743042, 'typing', 0), ('google/pytype', 0.5462278127670288, 'typing', 0), ('eleutherai/pyfra', 0.5457850098609924, 'ml', 0), ('python/cpython', 0.5432665944099426, 'util', 0), ('pyston/pyston', 0.5349142551422119, 'util', 0), ('pyutils/line_profiler', 0.5251006484031677, 'profiling', 0), ('pypy/pypy', 0.517668604850769, 'util', 0), ('python-rope/rope', 0.5170307159423828, 'util', 0), ('faif/python-patterns', 0.516368567943573, 'util', 0), ('pythonspeed/filprofiler', 0.5162791013717651, 'profiling', 0), ('rasbt/mlxtend', 0.5159803628921509, 'ml', 0), ('sumerc/yappi', 0.5159697532653809, 'profiling', 0), ('marshmallow-code/marshmallow', 0.5146151185035706, 'util', 0), ('sqlalchemy/mako', 0.505092203617096, 'template', 0), ('brandon-rhodes/python-patterns', 0.5037622451782227, 'util', 0), ('norvig/pytudes', 0.5027519464492798, 'util', 0), ('python-attrs/attrs', 0.5021693110466003, 'typing', 0), ('pympler/pympler', 0.5008642077445984, 'perf', 0)]",2,0.0,,0.0,0,0,26,26,0,0,0,0.0,0.0,90.0,0.0,7
202,crypto,https://github.com/nerolation/ethereum-datafarm,[],,[],[],,,,nerolation/ethereum-datafarm,ethereum-datafarm,58,11,2,Python,,Scrap blockchain data from the public API of Etherscan.io,nerolation,2024-01-01,2021-03-13,150,0.3855650522317189,,Scrap blockchain data from the public API of Etherscan.io,[],[],2023-02-25,[],1,1.0,,0.02,0,0,35,11,0,0,0,0.0,0.0,90.0,0.0,7
385,nlp,https://github.com/ferdinandzhong/punctuator,[],,[],[],,,,ferdinandzhong/punctuator,punctuator,47,7,3,Python,,A small seq2seq punctuator tool based on DistilBERT,ferdinandzhong,2024-01-12,2020-11-19,166,0.2819194515852613,,A small seq2seq punctuator tool based on DistilBERT,"['bert', 'bert-ner', 'chinese-nlp', 'deep-learning', 'nlp', 'punctuation', 'pytorch', 'seq2seq']","['bert', 'bert-ner', 'chinese-nlp', 'deep-learning', 'nlp', 'punctuation', 'pytorch', 'seq2seq']",2022-09-28,"[('bytedance/lightseq', 0.5860391855239868, 'nlp', 1), ('allenai/allennlp', 0.5085445046424866, 'nlp', 3), ('cqcl/lambeq', 0.5024779438972473, 'nlp', 0)]",4,0.0,,0.0,0,0,38,16,0,2,2,0.0,0.0,90.0,0.0,7
886,sim,https://github.com/crowddynamics/crowddynamics,[],,[],[],,,,crowddynamics/crowddynamics,crowddynamics,32,9,9,Python,https://jaantollander.com/post/how-to-implement-continuous-time-multi-agent-crowd-simulation/,Continuous-time multi-agent crowd simulation engine implemented in Python using Numba and Numpy for performance.,crowddynamics,2023-12-07,2016-03-22,410,0.07804878048780488,https://avatars.githubusercontent.com/u/80580011?v=4,Continuous-time multi-agent crowd simulation engine implemented in Python using Numba and Numpy for performance.,"['continuous-time', 'crowd-dynamics', 'crowd-simulation', 'multi-agent']","['continuous-time', 'crowd-dynamics', 'crowd-simulation', 'multi-agent']",2020-01-02,"[('google-deepmind/concordia', 0.5502240657806396, 'sim', 1), ('projectmesa/mesa', 0.5487060546875, 'sim', 0), ('bilhim/trafficsimulator', 0.5411049723625183, 'sim', 0)]",7,2.0,,0.0,0,0,95,49,0,0,0,0.0,0.0,90.0,0.0,7
500,gis,https://github.com/gregorhd/mapcompare,[],,[],[],,,,gregorhd/mapcompare,mapcompare,30,0,2,Python,,Comparison of Python packages and libraries for visualising geospatial vector data: applications for Smarter Cities.,gregorhd,2023-11-13,2021-05-21,140,0.21341463414634146,,Comparison of Python packages and libraries for visualising geospatial vector data: applications for Smarter Cities.,"['comparison', 'data-visualisation', 'data-viz', 'interactive-visualisations', 'sample-visualisation', 'urban-data-science', 'visualisation-libraries']","['comparison', 'data-visualisation', 'data-viz', 'interactive-visualisations', 'sample-visualisation', 'urban-data-science', 'visualisation-libraries']",2022-12-03,"[('residentmario/geoplot', 0.7236021161079407, 'gis', 0), ('earthlab/earthpy', 0.6392421722412109, 'gis', 0), ('raphaelquast/eomaps', 0.625028669834137, 'gis', 0), ('holoviz/geoviews', 0.624692440032959, 'gis', 0), ('giswqs/geemap', 0.6100561022758484, 'gis', 0), ('opengeos/leafmap', 0.6006041169166565, 'gis', 0), ('scitools/iris', 0.6005646586418152, 'gis', 0), ('altair-viz/altair', 0.5779780149459839, 'viz', 0), ('mwaskom/seaborn', 0.573523998260498, 'viz', 0), ('artelys/geonetworkx', 0.5692752599716187, 'gis', 0), ('contextlab/hypertools', 0.565951406955719, 'ml', 0), ('holoviz/holoviz', 0.5640184879302979, 'viz', 0), ('geopandas/geopandas', 0.5594103932380676, 'gis', 0), ('marceloprates/prettymaps', 0.557414710521698, 'viz', 0), ('mcordts/cityscapesscripts', 0.5555253624916077, 'gis', 0), ('udst/urbansim', 0.5511513352394104, 'sim', 0), ('man-group/dtale', 0.5462871789932251, 'viz', 0), ('spatialucr/geosnap', 0.54576176404953, 'gis', 0), ('pyqtgraph/pyqtgraph', 0.5434110760688782, 'viz', 0), ('enthought/mayavi', 0.5386293530464172, 'viz', 0), ('scitools/cartopy', 0.5211345553398132, 'gis', 0), ('makepath/xarray-spatial', 0.5138509273529053, 'gis', 0), ('toblerity/rtree', 0.5135495662689209, 'gis', 0), ('bokeh/bokeh', 0.5074924826622009, 'viz', 1), ('mckinsey/vizro', 0.506889283657074, 'viz', 0), ('matplotlib/matplotlib', 0.5066385269165039, 'viz', 0)]",1,1.0,,0.0,0,0,32,14,0,1,1,0.0,0.0,90.0,0.0,7
1264,util,https://github.com/weaviate/demo-text2vec-openai,[],,[],[],,,,weaviate/demo-text2vec-openai,DEMO-text2vec-openai,29,5,4,Python,,This repository contains an example of how to use the Weaviate vector search engine's text2vec-openai module,weaviate,2023-09-07,2022-01-26,104,0.276566757493188,https://avatars.githubusercontent.com/u/37794290?v=4,This repository contains an example of how to use the Weaviate vector search engine's text2vec-openai module,"['gpt-3', 'openai', 'vector-search', 'weaviate']","['gpt-3', 'openai', 'vector-search', 'weaviate']",2022-03-17,"[('minimaxir/gpt-2-simple', 0.555767834186554, 'llm', 1), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.55460125207901, 'data', 1), ('marqo-ai/marqo', 0.5455718040466309, 'ml', 1), ('muennighoff/sgpt', 0.5382280945777893, 'llm', 0), ('weaviate/weaviate-python-client', 0.5271196365356445, 'util', 2), ('kagisearch/vectordb', 0.5153085589408875, 'data', 0), ('qdrant/vector-db-benchmark', 0.5073652267456055, 'perf', 1), ('chroma-core/chroma', 0.5036654472351074, 'data', 0)]",3,2.0,,0.0,0,0,24,22,0,0,0,0.0,0.0,90.0,0.0,7
1636,util,https://github.com/multi-py/python-celery,[],,[],[],,,,multi-py/python-celery,python-celery,5,1,1,Shell,https://blog.tedivm.com/,Multiarchitecture Docker Containers for Celery,multi-py,2023-07-30,2022-03-24,96,0.051698670605613,https://avatars.githubusercontent.com/u/92491059?v=4,Multiarchitecture Docker Containers for Celery,"['alpine', 'alpine-linux', 'amd64', 'arm64', 'armv7', 'celery', 'celerybeat', 'docker', 'ghcr']","['alpine', 'alpine-linux', 'amd64', 'arm64', 'armv7', 'celery', 'celerybeat', 'docker', 'ghcr']",2023-11-22,"[('multi-py/python-gunicorn', 0.7504483461380005, 'util', 6), ('multi-py/python-gunicorn-uvicorn', 0.7121555209159851, 'util', 6), ('multi-py/python-uvicorn', 0.7029248476028442, 'util', 5)]",1,1.0,,0.35,0,0,22,2,0,0,0,0.0,0.0,90.0,0.0,7
1098,ml-interpretability,https://github.com/eleutherai/knowledge-neurons,[],,[],[],,,,eleutherai/knowledge-neurons,knowledge-neurons,126,18,4,Python,,A library for finding knowledge neurons in pretrained transformer models.,eleutherai,2024-01-04,2021-07-28,130,0.962882096069869,https://avatars.githubusercontent.com/u/68924597?v=4,A library for finding knowledge neurons in pretrained transformer models.,"['interpretability', 'transformers']","['interpretability', 'transformers']",2021-08-11,"[('alignmentresearch/tuned-lens', 0.6871256828308105, 'ml-interpretability', 1), ('cdpierse/transformers-interpret', 0.6161298155784607, 'ml-interpretability', 2), ('huggingface/transformers', 0.6015819907188416, 'nlp', 0), ('nvidia/megatron-lm', 0.5722100138664246, 'llm', 0), ('lvwerra/trl', 0.5679098963737488, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.548306405544281, 'llm', 0), ('microsoft/megatron-deepspeed', 0.548306405544281, 'llm', 0), ('karpathy/mingpt', 0.5461640954017639, 'llm', 0), ('ist-daslab/gptq', 0.53592449426651, 'llm', 0), ('huggingface/optimum', 0.5159322619438171, 'ml', 1), ('apple/ml-ane-transformers', 0.5135902762413025, 'ml', 0), ('thilinarajapakse/simpletransformers', 0.5122392177581787, 'nlp', 1)]",1,0.0,,0.0,0,0,30,30,0,0,0,0.0,0.0,90.0,0.0,6
564,sim,https://github.com/gboeing/street-network-models,[],,[],[],,,,gboeing/street-network-models,street-network-models,72,9,4,Python,https://osf.io/f2dqc,Street network models and indicators for every urban area in the world,gboeing,2023-12-10,2020-04-13,198,0.36337418889689976,,Street network models and indicators for every urban area in the world,[],[],2021-03-05,"[('pysal/momepy', 0.5618022680282593, 'gis', 0), ('gboeing/osmnx', 0.5225948691368103, 'gis', 0), ('udst/urbansim', 0.5066875219345093, 'sim', 0)]",1,1.0,,0.0,0,0,46,35,0,0,0,0.0,0.0,90.0,0.0,6
925,ml,https://github.com/pgniewko/forward_forward_vhts,[],,[],[],,,,pgniewko/forward_forward_vhts,forward_forward_vhts,33,5,1,Jupyter Notebook,,The Forward-Forward Algorithm for Drug Discovery,pgniewko,2024-01-12,2022-12-29,56,0.5818639798488665,,The Forward-Forward Algorithm for Drug Discovery,[],[],2022-12-30,[],1,1.0,,0.0,0,0,13,13,0,0,0,0.0,0.0,90.0,0.0,6
1033,math,https://github.com/mimecorg/fraqtive,[],,[],[],,,,mimecorg/fraqtive,fraqtive,30,7,4,C++,https://fraqtive.mimec.org/,Generator of the Mandelbrot family fractals.,mimecorg,2023-10-22,2018-03-09,307,0.09753831862517418,,Generator of the Mandelbrot family fractals.,"['fractal', 'mandelbrot', 'qt']","['fractal', 'mandelbrot', 'qt']",2023-03-06,[],2,0.0,,0.02,0,0,71,10,1,2,1,0.0,0.0,90.0,0.0,6
1095,data,https://github.com/thoppe/the-pile-freelaw,[],,[],[],,,,thoppe/the-pile-freelaw,The-Pile-FreeLaw,5,3,3,Python,,"Download, parse, and filter data from Court Listener, part of the FreeLaw projects. Data-ready for The-Pile.",thoppe,2024-01-13,2020-09-11,176,0.02831715210355987,,"Download, parse, and filter data from Court Listener, part of the FreeLaw projects. Data-ready for The-Pile.",['datasets'],['datasets'],2023-06-03,"[('coastalcph/lex-glue', 0.6147865653038025, 'nlp', 0), ('iclrandd/blackstone', 0.585483193397522, 'nlp', 0)]",2,1.0,,0.0,0,0,41,8,0,0,0,0.0,0.0,90.0,0.0,6
148,graph,https://github.com/guyallard/markov_clustering,[],,[],[],,,,guyallard/markov_clustering,markov_clustering,156,36,9,Python,,markov clustering in python,guyallard,2024-01-05,2017-09-27,330,0.47150259067357514,,markov clustering in python,"['clustering', 'markov-clustering', 'networks']","['clustering', 'markov-clustering', 'networks']",2018-12-11,"[('pymc-devs/pymc3', 0.5610766410827637, 'ml', 0), ('infer-actively/pymdp', 0.5361882448196411, 'ml', 0), ('scikit-learn/scikit-learn', 0.5356784462928772, 'ml', 0), ('scikit-learn-contrib/metric-learn', 0.5289827585220337, 'ml', 0), ('rasbt/mlxtend', 0.5017951130867004, 'ml', 0)]",3,0.0,,0.0,0,0,77,62,0,0,0,0.0,0.0,90.0,0.0,5
1833,data,https://github.com/koaning/scikit-partial,"['sklearn', 'online-learning']",Offers a pipeline that can run partial_fit. This allows of online learning on an entire pipeline.,[],[],,,,koaning/scikit-partial,scikit-partial,35,1,2,Python,,Pipeline components that support partial_fit.,koaning,2024-01-04,2022-05-16,89,0.3926282051282051,,Pipeline components that support partial_fit.,[],"['online-learning', 'sklearn']",2022-05-16,"[('koaning/scikit-lego', 0.6257511377334595, 'ml', 0), ('orchest/orchest', 0.5157948732376099, 'ml-ops', 0), ('linealabs/lineapy', 0.5030436515808105, 'jupyter', 0)]",1,1.0,,0.0,0,0,20,20,0,0,0,0.0,0.0,90.0,0.0,5
1252,sim,https://github.com/causalsim/unbiased-trace-driven-simulation,[],,[],[],,,,causalsim/unbiased-trace-driven-simulation,Unbiased-Trace-Driven-Simulation,31,6,0,Python,,,causalsim,2023-11-28,2022-09-19,71,0.4357429718875502,https://avatars.githubusercontent.com/u/113940717?v=4,causalsim/Unbiased-Trace-Driven-Simulation,[],[],2023-04-15,[],2,0.0,,0.12,0,0,16,9,0,0,0,0.0,0.0,90.0,0.0,5
1463,finance,https://github.com/mega-barrel/yfin-etl,[],,[],[],,,,mega-barrel/yfin-etl,yfin-etl,6,2,1,Python,,Yahoo Finance ETL script,mega-barrel,2023-12-10,2023-06-27,31,0.1935483870967742,,Yahoo Finance ETL script,"['pytest', 'sqlite3', 'yahoo-finance']","['pytest', 'sqlite3', 'yahoo-finance']",2023-07-21,"[('ranaroussi/yfinance', 0.535876452922821, 'finance', 1)]",1,1.0,,0.96,0,0,7,6,0,0,0,0.0,0.0,90.0,0.0,5
744,gis,https://github.com/edomel/boundaryvt,[],,[],[],,,,edomel/boundaryvt,BoundaryVT,1,0,2,Python,,,edomel,2022-12-19,2022-07-29,78,0.012727272727272728,,edomel/BoundaryVT,[],[],2023-02-24,[],4,1.0,,0.08,0,0,18,11,0,0,0,0.0,0.0,90.0,0.0,5
1547,perf,https://github.com/baruchel/tco,"['optimization', 'perf']",,[],[],,,,baruchel/tco,tco,223,3,19,Python,,Tail Call Optimization for Python,baruchel,2023-12-01,2015-07-12,446,0.49967989756722153,,Tail Call Optimization for Python,[],"['optimization', 'perf']",2016-10-12,"[('hyperopt/hyperopt', 0.5423157215118408, 'ml', 0), ('joblib/joblib', 0.5076671838760376, 'util', 0)]",1,0.0,,0.0,0,0,104,88,0,0,0,0.0,0.0,90.0,0.0,4
663,gis,https://github.com/zorzi-s/maprepair,[],,[],[],,,,zorzi-s/maprepair,MapRepair,22,3,2,Python,,Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images,zorzi-s,2023-10-18,2020-07-30,182,0.12040656763096169,,Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images,[],[],2023-05-03,"[('lydorn/mapalignment', 0.6764458417892456, 'gis', 0)]",1,0.0,,0.02,0,0,42,9,0,0,0,0.0,0.0,90.0,0.0,4
911,study,https://github.com/anyscale/rl-course,[],,[],[],,,,anyscale/rl-course,rl-course,21,2,5,Jupyter Notebook,,,anyscale,2023-10-09,2022-03-01,100,0.21,https://avatars.githubusercontent.com/u/51251046?v=4,anyscale/rl-course,[],[],2023-01-02,[],2,0.0,,0.0,1,0,23,13,0,0,0,1.0,0.0,90.0,0.0,4
696,data,https://github.com/harangju/wikinet,[],,[],[],,,,harangju/wikinet,wikinet,10,5,3,Jupyter Notebook,,Python library for exploring networks of hyperlinked Wikipedia articles,harangju,2022-12-16,2019-08-05,234,0.04270896888346553,,Python library for exploring networks of hyperlinked Wikipedia articles,[],[],2022-01-25,"[('goldsmith/wikipedia', 0.7525506615638733, 'data', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.6572245359420776, 'data', 0), ('mediawiki-client-tools/wikitools3', 0.6277413368225098, 'data', 0), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5428258776664734, 'data', 0), ('castorini/pyserini', 0.5286003351211548, 'ml', 0)]",8,0.0,,0.0,0,0,54,24,0,0,0,0.0,0.0,90.0,0.0,4
483,gis,https://github.com/scisco/area,['geometry'],,[],[],,,,scisco/area,area,95,21,3,Python,,Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python,scisco,2023-11-27,2015-11-25,426,0.22255689424364122,,Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python,[],['geometry'],2018-10-31,[],3,0.0,,0.0,0,0,99,63,0,0,0,0.0,0.0,90.0,0.0,3
988,time-series,https://github.com/zackeskin/pycausality,[],,[],[],,,,zackeskin/pycausality,PyCausality,85,25,6,Python,,Calculate predictive causality between time series using information-theoretic techniques,zackeskin,2023-12-13,2018-07-26,287,0.2954319761668322,,Calculate predictive causality between time series using information-theoretic techniques,[],[],2018-12-24,[],1,0.0,,0.0,0,0,67,62,0,0,0,0.0,0.0,90.0,0.0,3
1143,template,https://github.com/geeogi/async-python-lambda-template,[],,[],[],,,,geeogi/async-python-lambda-template,async-python-lambda-template,69,7,4,Python,,"Build a high-performance Python function in AWS lambda using asyncio, aiohttp and aiobotocore.",geeogi,2023-11-30,2020-06-27,187,0.36814024390243905,,"Build a high-performance Python function in AWS lambda using asyncio, aiohttp and aiobotocore.",[],[],2020-07-01,"[('samuelcolvin/aioaws', 0.687824547290802, 'data', 0), ('jordaneremieff/mangum', 0.6858481168746948, 'web', 0), ('nficano/python-lambda', 0.6575234532356262, 'util', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.6352989673614502, 'util', 0), ('alirn76/panther', 0.6337937712669373, 'web', 0), ('aws/chalice', 0.6311405301094055, 'web', 0), ('pynamodb/pynamodb', 0.6284517049789429, 'data', 0), ('samuelcolvin/arq', 0.6250224113464355, 'data', 0), ('aio-libs/aiobotocore', 0.6242508292198181, 'util', 0), ('amzn/ion-python', 0.6108747720718384, 'data', 0), ('aio-libs/aiohttp', 0.5915722250938416, 'web', 0), ('boto/boto3', 0.5832446813583374, 'util', 0), ('pallets/quart', 0.5774820446968079, 'web', 0), ('awslabs/python-deequ', 0.5767794251441956, 'ml', 0), ('neoteroi/blacksheep', 0.5758123397827148, 'web', 0), ('developmentseed/geolambda', 0.5692357420921326, 'gis', 0), ('python-trio/trio', 0.5684411525726318, 'perf', 0), ('timofurrer/awesome-asyncio', 0.5508684515953064, 'study', 0), ('terrycain/aioboto3', 0.5498723387718201, 'util', 0), ('magicstack/uvloop', 0.5491867065429688, 'util', 0), ('klen/py-frameworks-bench', 0.5316784977912903, 'perf', 0), ('aws/aws-sdk-pandas', 0.5274588465690613, 'pandas', 0), ('airtai/faststream', 0.5201819539070129, 'perf', 0), ('joblib/joblib', 0.5088009834289551, 'util', 0), ('encode/httpx', 0.5077229142189026, 'web', 0), ('hyperopt/hyperopt', 0.5066941976547241, 'ml', 0), ('fastai/fastcore', 0.504927396774292, 'util', 0)]",1,0.0,,0.0,0,0,43,43,0,0,0,0.0,0.0,90.0,0.0,3
518,gis,https://github.com/lydorn/mapalignment,[],,[],[],,,,lydorn/mapalignment,mapalignment,64,13,4,Python,,Aligning and Updating Cadaster Maps with Remote Sensing Images,lydorn,2024-01-08,2018-09-05,281,0.22706538266599088,,Aligning and Updating Cadaster Maps with Remote Sensing Images,[],[],2020-09-03,"[('zorzi-s/maprepair', 0.6764458417892456, 'gis', 0)]",2,0.0,,0.0,0,0,65,41,0,0,0,0.0,0.0,90.0,0.0,3
1846,sim,https://github.com/openlenia/lenia-tutorial,"['conway', 'cgol']",Lenia Tutorial,[],[],,,,openlenia/lenia-tutorial,Lenia-Tutorial,33,4,2,Jupyter Notebook,,,openlenia,2023-11-30,2021-07-15,132,0.24865446716899892,https://avatars.githubusercontent.com/u/84374939?v=4,Lenia Tutorial,[],"['cgol', 'conway']",2021-07-26,"[('ljvmiranda921/seagull', 0.5293267965316772, 'sim', 0)]",1,0.0,,0.0,0,0,30,30,0,0,0,0.0,0.0,90.0,0.0,2