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1,109 | llm | https://github.com/juncongmoo/pyllama | [] | null | [] | ['pyllama'] | null | null | Start 2023-04-13 | juncongmoo/pyllama | pyllama | 2,732 | 309 | 36 | Python | null | LLaMA: Open and Efficient Foundation Language Models | juncongmoo | 2024-01-14 | 2023-02-28 | 48 | 56.916667 | null | 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 | null | 0.83 | 7 | 2 | 11 | 9 | 1 | 1 | 1 | 7 | 7 | 90 | 1 | 48 |
677 | util | https://github.com/spotify/basic-pitch | [] | null | [] | [] | null | null | null | spotify/basic-pitch | basic-pitch | 2,654 | 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 | 29.164835 | 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 | null | 0.67 | 14 | 5 | 21 | 4 | 3 | 2 | 3 | 14 | 18 | 90 | 1.3 | 48 |
850 | jupyter | https://github.com/jupyter/nbdime | [] | null | [] | [] | null | null | null | jupyter/nbdime | nbdime | 2,562 | 164 | 42 | TypeScript | http://nbdime.readthedocs.io | Tools for diffing and merging of Jupyter notebooks. | jupyter | 2024-01-13 | 2015-11-16 | 428 | 5.983984 | 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 | null | 2.46 | 47 | 36 | 99 | 2 | 6 | 11 | 6 | 47 | 182 | 90 | 3.9 | 48 |
1,159 | util | https://github.com/whylabs/whylogs | [] | null | [] | [] | null | null | null | whylabs/whylogs | whylogs | 2,444 | 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 | 13.53481 | 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 | null | 5.04 | 64 | 57 | 42 | 0 | 54 | 43 | 54 | 64 | 16 | 90 | 0.2 | 48 |
914 | profiling | https://github.com/pyutils/line_profiler | [] | null | [] | [] | null | null | null | pyutils/line_profiler | line_profiler | 2,304 | 112 | 14 | Python | null | Line-by-line profiling for Python | pyutils | 2024-01-14 | 2019-12-10 | 216 | 10.666667 | 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 | null | 2.12 | 20 | 13 | 50 | 1 | 4 | 5 | 4 | 20 | 44 | 90 | 2.2 | 48 |
1,073 | ml | https://github.com/google-research/t5x | [] | null | [] | [] | null | null | null | google-research/t5x | t5x | 2,278 | 275 | 36 | Python | null | null | google-research | 2024-01-13 | 2021-11-01 | 117 | 19.446341 | 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 | null | 4.54 | 81 | 45 | 27 | 0 | 0 | 0 | 0 | 81 | 25 | 90 | 0.3 | 48 |
1,395 | llm | https://github.com/civitai/sd_civitai_extension | [] | null | [] | [] | null | null | null | civitai/sd_civitai_extension | sd_civitai_extension | 2,139 | 405 | 73 | Python | null | 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 | null | 1 | 24 | 3 | 13 | 1 | 0 | 0 | 0 | 24 | 20 | 90 | 0.8 | 48 |
1,327 | llm | https://github.com/young-geng/easylm | [] | null | [] | [] | null | null | null | young-geng/easylm | EasyLM | 2,093 | 209 | 36 | Python | null | 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 | 33.758065 | null | 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), 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799 | web | https://github.com/python-restx/flask-restx | [] | null | [] | [] | null | null | null | python-restx/flask-restx | flask-restx | 2,020 | 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 | 9.541161 | 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 | null | 0.56 | 34 | 18 | 49 | 1 | 4 | 4 | 4 | 34 | 47 | 90 | 1.4 | 48 |
1,758 | ml | https://github.com/rom1504/clip-retrieval | [] | null | [] | [] | null | null | null | rom1504/clip-retrieval | clip-retrieval | 1,917 | 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 | 13.876939 | null | 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 | null | 1.08 | 123 | 69 | 32 | 0 | 9 | 33 | 9 | 123 | 80 | 90 | 0.7 | 48 |
1,050 | util | https://github.com/home-assistant/supervisor | [] | null | [] | [] | null | null | null | home-assistant/supervisor | supervisor | 1,559 | 553 | 85 | Python | https://home-assistant.io/hassio/ | :house_with_garden: Home Assistant Supervisor | home-assistant | 2024-01-12 | 2017-03-14 | 359 | 4.342618 | 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 | null | 8.54 | 239 | 205 | 83 | 0 | 37 | 61 | 37 | 239 | 452 | 90 | 1.9 | 48 |
714 | math | https://github.com/facebookresearch/theseus | [] | null | [] | [] | null | null | null | facebookresearch/theseus | theseus | 1,523 | 116 | 29 | Python | null | A library for differentiable nonlinear optimization | facebookresearch | 2024-01-12 | 2021-11-18 | 114 | 13.276463 | 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 | null | 2 | 24 | 17 | 26 | 1 | 3 | 5 | 3 | 24 | 67 | 90 | 2.8 | 48 |
632 | perf | https://github.com/dask/distributed | [] | null | [] | [] | null | null | null | dask/distributed | distributed | 1,513 | 706 | 56 | Python | https://distributed.dask.org | A distributed task scheduler for Dask | dask | 2024-01-13 | 2015-09-13 | 437 | 3.45998 | 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 | null | 10.19 | 243 | 164 | 101 | 0 | 0 | 26 | 26 | 242 | 567 | 90 | 2.3 | 48 |
1,652 | llm | https://github.com/farizrahman4u/loopgpt | [] | Re-implementation of Auto-GPT as a python package, written with modularity and extensibility in mind. | [] | [] | null | null | null | farizrahman4u/loopgpt | loopgpt | 1,339 | 128 | 34 | Python | null | Modular Auto-GPT Framework | farizrahman4u | 2024-01-12 | 2023-04-14 | 41 | 32.209622 | null | 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 | null | 5.17 | 1 | 0 | 9 | 3 | 7 | 9 | 7 | 1 | 1 | 90 | 1 | 48 |
1,241 | ml | https://github.com/visual-layer/fastdup | [] | null | [] | [] | null | null | null | visual-layer/fastdup | fastdup | 1,302 | 67 | 20 | Python | null | 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 | 14.489666 | 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 | null | 12.88 | 28 | 18 | 20 | 0 | 61 | 83 | 61 | 28 | 31 | 90 | 1.1 | 48 |
971 | ml | https://github.com/google/vizier | [] | null | [] | [] | null | null | null | google/vizier | vizier | 1,138 | 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 | 11.172511 | 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 | null | 9.52 | 99 | 94 | 23 | 0 | 15 | 17 | 15 | 99 | 25 | 90 | 0.3 | 48 |
1,573 | data | https://github.com/pathwaycom/pathway | ['llmops'] | null | [] | [] | null | null | null | pathwaycom/pathway | pathway | 1,079 | 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 | 17.606061 | 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 | null | 1 | 1 | 1 | 14 | 0 | 22 | 19 | 22 | 1 | 2 | 90 | 2 | 48 |
1,895 | time-series | https://github.com/google/temporian | ['feature-engineering', 'temporal-data'] | null | [] | [] | 1 | null | null | 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 | 8.240741 | 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 | null | 36.23 | 60 | 56 | 12 | 0 | 7 | 9 | 7 | 60 | 122 | 90 | 2 | 48 |
491 | ml-dl | https://github.com/rasbt/deeplearning-models | [] | null | [] | [] | null | null | null | rasbt/deeplearning-models | deeplearning-models | 16,133 | 3,949 | 599 | Jupyter Notebook | null | A collection of various deep learning architectures, models, and tips | rasbt | 2024-01-14 | 2019-06-05 | 242 | 66.43 | null | 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 | null | 0.08 | 0 | 0 | 56 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 47 |
24 | ml-rl | https://github.com/google/dopamine | [] | null | [] | [] | null | null | null | google/dopamine | dopamine | 10,288 | 1,410 | 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 | 35.757696 | 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 | null | 0.19 | 5 | 2 | 67 | 2 | 0 | 0 | 0 | 5 | 3 | 90 | 0.6 | 47 |
1,107 | ml | https://github.com/twitter/the-algorithm-ml | [] | null | [] | [] | null | null | null | twitter/the-algorithm-ml | the-algorithm-ml | 9,797 | 2,242 | 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 | 221.938511 | https://avatars.githubusercontent.com/u/50278?v=4 | Source code for Twitter's Recommendation Algorithm | [] | [] | 2023-04-06 | [] | 0 | -1 | 23 | 1 | 2 | 2 | 10 | 9 | 0 | 0 | 0 | 2 | 0 | 90 | 0 | 47 |
996 | finance | https://github.com/ta-lib/ta-lib-python | [] | null | [] | [] | null | null | null | ta-lib/ta-lib-python | ta-lib-python | 8,658 | 1,703 | 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 | 13.996767 | 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 | null | 0.58 | 25 | 10 | 144 | 0 | 0 | 2 | 2 | 25 | 47 | 90 | 1.9 | 47 |
838 | time-series | https://github.com/blue-yonder/tsfresh | [] | null | [] | [] | null | null | null | blue-yonder/tsfresh | tsfresh | 7,953 | 1,199 | 167 | Jupyter Notebook | http://tsfresh.readthedocs.io | Automatic extraction of relevant features from time series: | blue-yonder | 2024-01-13 | 2016-10-26 | 378 | 20.992081 | 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 | null | 0.48 | 6 | 2 | 88 | 3 | 1 | 4 | 1 | 6 | 6 | 90 | 1 | 47 |
1,128 | ml | https://github.com/scikit-learn-contrib/imbalanced-learn | [] | null | [] | [] | null | null | null | scikit-learn-contrib/imbalanced-learn | imbalanced-learn | 6,603 | 1,274 | 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 | 13.381876 | 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 | null | 0.83 | 14 | 10 | 115 | 3 | 1 | 3 | 1 | 14 | 13 | 90 | 0.9 | 47 |
421 | util | https://github.com/tebelorg/rpa-python | [] | null | [] | [] | null | null | null | tebelorg/rpa-python | RPA-Python | 4,316 | 648 | 103 | Python | null | Python package for doing RPA | tebelorg | 2024-01-13 | 2019-03-30 | 252 | 17.097906 | 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 | null | 0.44 | 25 | 23 | 58 | 1 | 2 | 11 | 2 | 25 | 79 | 90 | 3.2 | 47 |
1,800 | ml | https://github.com/nv-tlabs/get3d | ['generative-model', '3d'] | Generative Model of High Quality 3D Textured Shapes Learned from Images | [] | [] | null | null | null | nv-tlabs/get3d | GET3D | 4,002 | 364 | 142 | Python | null | null | nv-tlabs | 2024-01-13 | 2022-09-08 | 72 | 55.037328 | 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 | null | 0.08 | 12 | 6 | 16 | 3 | 0 | 0 | 0 | 12 | 20 | 90 | 1.7 | 47 |
610 | testing | https://github.com/spulec/freezegun | [] | null | [] | [] | null | null | null | spulec/freezegun | freezegun | 3,890 | 267 | 34 | Python | null | Let your Python tests travel through time | spulec | 2024-01-12 | 2012-12-11 | 581 | 6.695353 | null | 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 | null | 0.42 | 56 | 21 | 135 | 0 | 3 | 5 | 3 | 56 | 50 | 90 | 0.9 | 47 |
441 | gis | https://github.com/shapely/shapely | ['geometric-algorithms', 'geometry'] | null | [] | [] | 1 | null | null | shapely/shapely | shapely | 3,549 | 554 | 88 | Python | https://shapely.readthedocs.io/en/stable/ | Manipulation and analysis of geometric objects | shapely | 2024-01-12 | 2011-12-31 | 630 | 5.629504 | 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 | null | 1.4 | 67 | 20 | 147 | 0 | 2 | 8 | 2 | 67 | 123 | 90 | 1.8 | 47 |
657 | util | https://github.com/zeromq/pyzmq | [] | null | [] | [] | null | null | null | zeromq/pyzmq | pyzmq | 3,498 | 666 | 103 | Python | http://zguide.zeromq.org/py:all | PyZMQ: Python bindings for zeromq | zeromq | 2024-01-13 | 2010-07-21 | 705 | 4.955677 | 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 | null | 2.38 | 23 | 17 | 164 | 0 | 0 | 6 | 6 | 23 | 37 | 90 | 1.6 | 47 |
249 | web | https://github.com/websocket-client/websocket-client | [] | null | [] | [] | null | null | null | websocket-client/websocket-client | websocket-client | 3,381 | 805 | 86 | Python | https://github.com/websocket-client/websocket-client | WebSocket client for Python | websocket-client | 2024-01-12 | 2010-12-28 | 683 | 4.95022 | 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 | null | 1.31 | 23 | 15 | 159 | 0 | 10 | 6 | 10 | 23 | 28 | 90 | 1.2 | 47 |
94 | web | https://github.com/unbit/uwsgi | [] | null | [] | [] | null | null | null | unbit/uwsgi | uwsgi | 3,374 | 681 | 111 | C | http://projects.unbit.it/uwsgi | uWSGI application server container | unbit | 2024-01-13 | 2011-10-09 | 642 | 5.253114 | null | uWSGI application server container | [] | [] | 2023-12-26 | [] | 359 | 5 | null | 0.48 | 54 | 24 | 149 | 1 | 0 | 10 | 10 | 54 | 88 | 90 | 1.6 | 47 |
618 | util | https://github.com/more-itertools/more-itertools | [] | null | [] | [] | null | null | null | more-itertools/more-itertools | more-itertools | 3,314 | 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 | 5.399907 | 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 | null | 3.12 | 41 | 33 | 143 | 0 | 6 | 4 | 6 | 41 | 42 | 90 | 1 | 47 |
1,499 | ml-rl | https://github.com/deepmind/acme | [] | null | [] | [] | 1 | null | null | deepmind/acme | acme | 3,302 | 410 | 83 | Python | null | A library of reinforcement learning components and agents | deepmind | 2024-01-12 | 2020-05-01 | 195 | 16.883857 | 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 | null | 0.6 | 9 | 2 | 45 | 0 | 0 | 3 | 3 | 9 | 6 | 90 | 0.7 | 47 |
718 | util | https://github.com/ashleve/lightning-hydra-template | [] | null | [] | [] | null | null | null | ashleve/lightning-hydra-template | lightning-hydra-template | 3,296 | 545 | 25 | Python | null | PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡ | ashleve | 2024-01-14 | 2020-11-04 | 168 | 19.519459 | null | 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 | null | 0.56 | 19 | 4 | 39 | 4 | 7 | 4 | 7 | 19 | 6 | 90 | 0.3 | 47 |
1,003 | finance | https://github.com/matplotlib/mplfinance | [] | null | [] | [] | null | null | null | matplotlib/mplfinance | mplfinance | 3,155 | 589 | 85 | Python | https://pypi.org/project/mplfinance/ | Financial Markets Data Visualization using Matplotlib | matplotlib | 2024-01-13 | 2019-12-05 | 216 | 14.558339 | 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 | null | 0.98 | 17 | 8 | 50 | 6 | 1 | 3 | 1 | 17 | 41 | 90 | 2.4 | 47 |
474 | gis | https://github.com/holoviz/datashader | [] | null | [] | [] | 1 | null | null | holoviz/datashader | datashader | 3,127 | 366 | 91 | Python | http://datashader.org | Quickly and accurately render even the largest data. | holoviz | 2024-01-12 | 2015-12-23 | 422 | 7.394932 | 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 | null | 1.88 | 44 | 19 | 98 | 0 | 5 | 13 | 5 | 44 | 38 | 90 | 0.9 | 47 |
1,210 | llm | https://github.com/freedomintelligence/llmzoo | ['language-model'] | null | [] | [] | null | null | null | freedomintelligence/llmzoo | LLMZoo | 2,786 | 189 | 50 | Python | null | ⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ | freedomintelligence | 2024-01-12 | 2023-04-01 | 43 | 64.151316 | 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 | null | 3.96 | 2 | 1 | 10 | 6 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 47 |
863 | profiling | https://github.com/reloadware/reloadium | [] | null | [] | [] | 1 | null | null | reloadware/reloadium | reloadium | 2,621 | 57 | 25 | Python | https://reloadium.io | Hot Reloading, Profiling and AI debugging for Python | reloadware | 2024-01-14 | 2022-01-15 | 106 | 24.626846 | 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 | null | 0.27 | 14 | 11 | 24 | 0 | 0 | 3 | 3 | 14 | 19 | 90 | 1.4 | 47 |
307 | util | https://github.com/legrandin/pycryptodome | [] | null | [] | [] | null | null | null | legrandin/pycryptodome | pycryptodome | 2,582 | 468 | 63 | C | https://www.pycryptodome.org | A self-contained cryptographic library for Python | legrandin | 2024-01-14 | 2014-05-02 | 508 | 5.076966 | null | 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 | null | 2.42 | 27 | 20 | 118 | 0 | 10 | 12 | 10 | 27 | 46 | 90 | 1.7 | 47 |
130 | viz | https://github.com/holoviz/holoviews | [] | null | [] | [] | null | null | null | holoviz/holoviews | holoviews | 2,550 | 386 | 58 | Python | https://holoviews.org | With Holoviews, your data visualizes itself. | holoviz | 2024-01-13 | 2014-05-07 | 507 | 5.021097 | 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 | null | 5.21 | 176 | 82 | 118 | 1 | 8 | 41 | 8 | 174 | 226 | 90 | 1.3 | 47 |
1,731 | testing | https://github.com/kevin1024/vcrpy | [] | null | [] | [] | null | null | null | kevin1024/vcrpy | vcrpy | 2,547 | 363 | 38 | Python | null | Automatically mock your HTTP interactions to simplify and speed up testing | kevin1024 | 2024-01-12 | 2012-05-29 | 609 | 4.182266 | null | 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 | null | 2.52 | 66 | 40 | 142 | 0 | 5 | 5 | 5 | 66 | 125 | 90 | 1.9 | 47 |
867 | perf | https://github.com/ipython/ipyparallel | [] | null | [] | [] | null | null | null | ipython/ipyparallel | ipyparallel | 2,518 | 1,051 | 121 | Jupyter Notebook | https://ipyparallel.readthedocs.io/ | IPython Parallel: Interactive Parallel Computing in Python | ipython | 2024-01-13 | 2015-04-09 | 459 | 5.477315 | 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 | null | 1.63 | 21 | 12 | 107 | 0 | 0 | 7 | 7 | 21 | 36 | 90 | 1.7 | 47 |
439 | gis | https://github.com/rasterio/rasterio | [] | null | [] | [] | 1 | null | null | rasterio/rasterio | rasterio | 2,074 | 521 | 147 | Python | https://rasterio.readthedocs.io/ | Rasterio reads and writes geospatial raster datasets | rasterio | 2024-01-13 | 2013-11-04 | 534 | 3.882856 | 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 | null | 2.65 | 101 | 80 | 124 | 0 | 8 | 17 | 8 | 101 | 134 | 90 | 1.3 | 47 |
116 | perf | https://github.com/h5py/h5py | ['hdf5'] | null | [] | [] | null | null | null | h5py/h5py | h5py | 1,965 | 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 | 3.316056 | 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 | null | 2.65 | 49 | 27 | 138 | 0 | 3 | 4 | 3 | 49 | 164 | 90 | 3.3 | 47 |
1,234 | llm | https://github.com/lucidrains/toolformer-pytorch | ['toolformer', 'language-model'] | null | [] | [] | null | null | null | lucidrains/toolformer-pytorch | toolformer-pytorch | 1,802 | 111 | 38 | Python | null | Implementation of Toolformer, Language Models That Can Use Tools, by MetaAI | lucidrains | 2024-01-14 | 2023-02-10 | 50 | 35.632768 | null | 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), 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1,093 | 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 | [] | [] | null | null | null | eleutherai/pythia | pythia | 1,801 | 117 | 29 | Jupyter Notebook | null | The hub for EleutherAI's work on interpretability and learning dynamics | eleutherai | 2024-01-13 | 2021-12-25 | 109 | 16.458225 | 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 | null | 3.37 | 30 | 23 | 25 | 0 | 0 | 0 | 0 | 30 | 36 | 90 | 1.2 | 47 |
1,116 | web | https://github.com/cherrypy/cherrypy | [] | null | [] | [] | null | null | null | cherrypy/cherrypy | cherrypy | 1,748 | 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 | 4.322148 | 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 | null | 0.5 | 22 | 15 | 94 | 0 | 0 | 17 | 17 | 22 | 54 | 90 | 2.5 | 47 |
1,824 | llm | https://github.com/noahshinn/reflexion | [] | null | [] | [] | null | null | null | noahshinn/reflexion | reflexion | 1,697 | 160 | 29 | Python | null | [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning | noahshinn | 2024-01-14 | 2023-03-22 | 44 | 37.83121 | null | [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 | null | 3.15 | 13 | 9 | 10 | 2 | 0 | 0 | 0 | 13 | 16 | 90 | 1.2 | 47 |
427 | jupyter | https://github.com/jupyter-lsp/jupyterlab-lsp | [] | null | [] | [] | null | null | null | jupyter-lsp/jupyterlab-lsp | jupyterlab-lsp | 1,663 | 134 | 20 | TypeScript | null | Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol | jupyter-lsp | 2024-01-13 | 2019-08-17 | 232 | 7.154886 | 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 | null | 5.02 | 49 | 34 | 54 | 2 | 12 | 12 | 12 | 49 | 75 | 90 | 1.5 | 47 |
1,075 | util | https://github.com/rhettbull/osxphotos | [] | null | [] | [] | null | null | null | rhettbull/osxphotos | osxphotos | 1,521 | 85 | 21 | Python | null | 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 | 6.30373 | null | 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 | null | 6.46 | 129 | 99 | 56 | 0 | 50 | 98 | 50 | 129 | 231 | 90 | 1.8 | 47 |
1,559 | nlp | https://github.com/marella/ctransformers | [] | null | [] | [] | null | null | null | marella/ctransformers | ctransformers | 1,510 | 118 | 18 | C | null | Python bindings for the Transformer models implemented in C/C++ using GGML library. | marella | 2024-01-14 | 2023-05-14 | 37 | 40.498084 | null | 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 | null | 2.77 | 56 | 10 | 8 | 4 | 30 | 46 | 30 | 56 | 72 | 90 | 1.3 | 47 |
402 | perf | https://github.com/agronholm/anyio | [] | null | [] | [] | null | null | null | agronholm/anyio | anyio | 1,482 | 117 | 27 | Python | null | High level asynchronous concurrency and networking framework that works on top of either trio or asyncio | agronholm | 2024-01-13 | 2018-08-19 | 284 | 5.213065 | null | 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 | null | 2.98 | 56 | 48 | 66 | 0 | 3 | 9 | 3 | 56 | 200 | 90 | 3.6 | 47 |
621 | data | https://github.com/zarr-developers/zarr-python | [] | null | [] | [] | null | null | null | zarr-developers/zarr-python | zarr-python | 1,274 | 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 | 3.004717 | 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 | null | 2.67 | 148 | 88 | 98 | 0 | 11 | 11 | 11 | 148 | 242 | 90 | 1.6 | 47 |
764 | data | https://github.com/google/tensorstore | [] | null | [] | [] | null | null | null | google/tensorstore | tensorstore | 1,248 | 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 | 6.235546 | 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 | null | 8.33 | 18 | 10 | 46 | 0 | 0 | 14 | 14 | 18 | 54 | 90 | 3 | 47 |
572 | util | https://github.com/fsspec/filesystem_spec | [] | null | [] | [] | null | null | null | fsspec/filesystem_spec | filesystem_spec | 723 | 308 | 20 | Python | null | A specification that python filesystems should adhere to. | fsspec | 2024-01-14 | 2018-04-23 | 301 | 2.400854 | 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 | null | 3.63 | 124 | 87 | 70 | 0 | 0 | 13 | 13 | 124 | 296 | 90 | 2.4 | 47 |
1,324 | util | https://github.com/anthropics/anthropic-sdk-python | ['sdk', 'language-model', 'api'] | SDK providing access to Anthropic's safety-first language model APIs | [] | [] | null | null | null | anthropics/anthropic-sdk-python | anthropic-sdk-python | 584 | 65 | 42 | Python | null | null | anthropics | 2024-01-13 | 2023-01-17 | 54 | 10.814815 | 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 | null | 4.38 | 120 | 119 | 12 | 0 | 42 | 43 | 42 | 120 | 57 | 90 | 0.5 | 47 |
1,733 | ml | https://github.com/qdrant/fastembed | ['vectordb'] | null | [] | [] | null | null | null | 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 | null | 5.75 | 70 | 46 | 6 | 1 | 3 | 20 | 3 | 70 | 125 | 90 | 1.8 | 47 |
1,606 | llm | https://github.com/opengvlab/omniquant | [] | null | [] | [] | null | null | null | opengvlab/omniquant | OmniQuant | 457 | 36 | 13 | Python | null | OmniQuant is a simple and powerful quantization technique for LLMs. | opengvlab | 2024-01-12 | 2023-08-22 | 23 | 19.869565 | 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 | null | 0.63 | 38 | 29 | 5 | 1 | 1 | 2 | 1 | 38 | 99 | 90 | 2.6 | 47 |
782 | study | https://github.com/wesm/pydata-book | [] | null | [] | [] | null | null | null | wesm/pydata-book | pydata-book | 20,766 | 14,692 | 1,476 | Jupyter Notebook | null | 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 | 34.356417 | null | 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 | null | 0.06 | 6 | 3 | 140 | 9 | 0 | 0 | 0 | 6 | 4 | 90 | 0.7 | 46 |
187 | ml | https://github.com/harisiqbal88/plotneuralnet | ['diagrams', 'latex'] | null | [] | [] | null | null | null | harisiqbal88/plotneuralnet | PlotNeuralNet | 20,540 | 2,735 | 229 | TeX | null | Latex code for making neural networks diagrams | harisiqbal88 | 2024-01-13 | 2018-07-24 | 288 | 71.319444 | null | 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 | null | 0 | 0 | 0 | 67 | 39 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 46 |
145 | llm | https://github.com/openai/gpt-2 | [] | null | [] | [] | null | null | null | openai/gpt-2 | gpt-2 | 20,469 | 5,247 | 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 | 78.987321 | 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 | null | 0 | 3 | 1 | 60 | 38 | 0 | 0 | 0 | 3 | 1 | 90 | 0.3 | 46 |
149 | data | https://github.com/twintproject/twint | [] | null | [] | [] | null | null | null | twintproject/twint | twint | 15,365 | 2,744 | 322 | Python | null | 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 | 44.352577 | 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 | null | 0 | 1 | 1 | 80 | 35 | 0 | 4 | 4 | 1 | 0 | 90 | 0 | 46 |
1,058 | ml | https://github.com/ddbourgin/numpy-ml | [] | null | [] | [] | null | null | null | ddbourgin/numpy-ml | numpy-ml | 14,370 | 3,641 | 452 | Python | https://numpy-ml.readthedocs.io/ | Machine learning, in numpy | ddbourgin | 2024-01-14 | 2019-04-06 | 251 | 57.153409 | null | 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, 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218 | ml | https://github.com/spotify/annoy | [] | null | [] | [] | 1 | null | null | spotify/annoy | annoy | 12,337 | 1,171 | 322 | C++ | null | Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk | spotify | 2024-01-13 | 2013-04-01 | 565 | 21.829879 | 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 | null | 0.6 | 3 | 0 | 131 | 5 | 2 | 3 | 2 | 3 | 3 | 90 | 1 | 46 |
1,006 | finance | https://github.com/mementum/backtrader | [] | null | [] | [] | 1 | null | null | mementum/backtrader | backtrader | 12,322 | 3,618 | 601 | Python | https://www.backtrader.com | Python Backtesting library for trading strategies | mementum | 2024-01-14 | 2015-01-10 | 472 | 26.08225 | null | 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 | null | 0.31 | 6 | 3 | 110 | 9 | 0 | 15 | 15 | 6 | 1 | 90 | 0.2 | 46 |
111 | ml-interpretability | https://github.com/marcotcr/lime | ['interpretable-ml'] | null | [] | [] | null | null | null | marcotcr/lime | lime | 11,075 | 1,798 | 264 | JavaScript | null | Lime: Explaining the predictions of any machine learning classifier | marcotcr | 2024-01-13 | 2016-03-15 | 411 | 26.946472 | null | 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 | null | 0 | 9 | 2 | 95 | 30 | 0 | 2 | 2 | 9 | 8 | 90 | 0.9 | 46 |
746 | study | https://github.com/karpathy/nn-zero-to-hero | [] | null | [] | [] | null | null | null | karpathy/nn-zero-to-hero | nn-zero-to-hero | 9,163 | 1,068 | 259 | Jupyter Notebook | null | Neural Networks: Zero to Hero | karpathy | 2024-01-13 | 2022-09-08 | 72 | 126.013752 | null | 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 | null | 0 | 4 | 3 | 16 | 12 | 0 | 0 | 0 | 4 | 3 | 90 | 0.8 | 46 |
230 | template | https://github.com/drivendata/cookiecutter-data-science | [] | null | [] | [] | 1 | null | null | drivendata/cookiecutter-data-science | cookiecutter-data-science | 7,324 | 2,282 | 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 | 17.009954 | null | 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 | null | 0.02 | 23 | 11 | 100 | 4 | 0 | 0 | 0 | 24 | 22 | 90 | 0.9 | 46 |
453 | ml-rl | https://github.com/tensorlayer/tensorlayer | [] | null | [] | [] | null | null | null | tensorlayer/tensorlayer | TensorLayer | 7,264 | 1,636 | 461 | Python | http://tensorlayerx.com | Deep Learning and Reinforcement Learning Library for Scientists and Engineers | tensorlayer | 2024-01-12 | 2016-06-07 | 399 | 18.205514 | 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', 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1,462 | util | https://github.com/hugapi/hug | [] | null | [] | [] | null | null | null | hugapi/hug | hug | 6,756 | 389 | 161 | Python | null | 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 | 15.162552 | 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 | null | 0.08 | 1 | 0 | 103 | 7 | 0 | 7 | 7 | 1 | 1 | 90 | 1 | 46 |
1,117 | web | https://github.com/webpy/webpy | [] | null | [] | [] | null | null | null | webpy/webpy | webpy | 5,856 | 1,325 | 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 | 7.318693 | 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 | null | 0.17 | 14 | 7 | 186 | 0 | 1 | 1 | 1 | 14 | 31 | 90 | 2.2 | 46 |
757 | ml-dl | https://github.com/xpixelgroup/basicsr | [] | null | [] | [] | null | null | null | xpixelgroup/basicsr | BasicSR | 5,775 | 1,036 | 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 | null | 0.06 | 32 | 2 | 70 | 12 | 0 | 6 | 6 | 32 | 25 | 90 | 0.8 | 46 |
1,646 | util | https://github.com/prompt-toolkit/ptpython | ['repl', 'cli'] | null | [] | [] | 1 | null | null | prompt-toolkit/ptpython | ptpython | 4,969 | 316 | 66 | Python | null | A better Python REPL | prompt-toolkit | 2024-01-13 | 2014-09-29 | 487 | 10.200293 | 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 | null | 0.52 | 24 | 13 | 113 | 1 | 2 | 4 | 2 | 24 | 30 | 90 | 1.2 | 46 |
636 | util | https://github.com/pycqa/pycodestyle | [] | null | [] | [] | null | null | null | pycqa/pycodestyle | pycodestyle | 4,941 | 809 | 117 | Python | https://pycodestyle.pycqa.org | Simple Python style checker in one Python file | pycqa | 2024-01-13 | 2009-10-02 | 747 | 6.609402 | 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 | null | 0.88 | 24 | 21 | 174 | 0 | 0 | 3 | 3 | 24 | 35 | 90 | 1.5 | 46 |
1,268 | perf | https://github.com/ultrajson/ultrajson | [] | null | [] | [] | null | null | null | ultrajson/ultrajson | ultrajson | 4,180 | 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 | 6.199153 | 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 | null | 0.9 | 11 | 10 | 157 | 0 | 2 | 2 | 2 | 11 | 34 | 90 | 3.1 | 46 |
59 | gamedev | https://github.com/panda3d/panda3d | [] | null | [] | [] | null | null | null | panda3d/panda3d | panda3d | 4,140 | 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 | 7.678855 | 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 | null | 5.98 | 71 | 28 | 125 | 0 | 1 | 2 | 1 | 71 | 121 | 90 | 1.7 | 46 |
1,154 | data | https://github.com/mongodb/mongo-python-driver | [] | null | [] | [] | null | null | null | mongodb/mongo-python-driver | mongo-python-driver | 3,982 | 1,192 | 240 | Python | https://pymongo.readthedocs.io | PyMongo - the Official MongoDB Python driver | mongodb | 2024-01-13 | 2009-01-15 | 784 | 5.074458 | 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 | null | 5.9 | 94 | 87 | 183 | 0 | 6 | 10 | 6 | 92 | 109 | 90 | 1.2 | 46 |
775 | diffusion | https://github.com/jina-ai/discoart | [] | null | [] | [] | null | null | null | jina-ai/discoart | discoart | 3,834 | 254 | 34 | Python | null | 🪩 Create Disco Diffusion artworks in one line | jina-ai | 2024-01-13 | 2022-06-30 | 82 | 46.352332 | 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 | null | 0.06 | 2 | 2 | 19 | 8 | 1 | 77 | 1 | 2 | 3 | 90 | 1.5 | 46 |
692 | util | https://github.com/joblib/joblib | [] | null | [] | [] | null | null | null | joblib/joblib | joblib | 3,544 | 436 | 61 | Python | http://joblib.readthedocs.org | Computing with Python functions. | joblib | 2024-01-13 | 2010-05-07 | 716 | 4.945774 | 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 | null | 1.27 | 46 | 21 | 167 | 1 | 3 | 6 | 3 | 46 | 103 | 90 | 2.2 | 46 |
419 | ml-rl | https://github.com/facebookresearch/reagent | [] | null | [] | [] | null | null | null | facebookresearch/reagent | ReAgent | 3,495 | 534 | 152 | Python | https://reagent.ai | A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.) | facebookresearch | 2024-01-14 | 2017-07-27 | 339 | 10.288057 | 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 | null | 1.31 | 1 | 0 | 79 | 0 | 0 | 0 | 0 | 1 | 2 | 90 | 2 | 46 |
139 | ml-interpretability | https://github.com/pair-code/lit | [] | null | [] | [] | 1 | null | null | pair-code/lit | lit | 3,273 | 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 | 17.885246 | 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 | null | 5.25 | 34 | 16 | 42 | 2 | 2 | 3 | 2 | 33 | 13 | 90 | 0.4 | 46 |
1,200 | ml | https://github.com/huggingface/notebooks | [] | null | [] | [] | null | null | null | huggingface/notebooks | notebooks | 3,012 | 1,308 | 73 | Jupyter Notebook | null | Notebooks using the Hugging Face libraries 🤗 | huggingface | 2024-01-14 | 2020-06-15 | 189 | 15.924471 | 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 | null | 4.69 | 35 | 12 | 44 | 0 | 0 | 0 | 0 | 35 | 49 | 90 | 1.4 | 46 |
1,717 | util | https://github.com/jendrikseipp/vulture | ['code-quality'] | null | [] | [] | null | null | null | jendrikseipp/vulture | vulture | 2,874 | 175 | 26 | Python | null | Find dead Python code | jendrikseipp | 2024-01-13 | 2017-03-06 | 360 | 7.980167 | null | 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 | null | 0.62 | 17 | 10 | 84 | 0 | 4 | 7 | 4 | 17 | 29 | 90 | 1.7 | 46 |
99 | data | https://github.com/zoomeranalytics/xlwings | [] | null | [] | [] | null | null | null | zoomeranalytics/xlwings | xlwings | 2,773 | 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 | 5.382973 | 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 | null | 3.02 | 47 | 27 | 120 | 0 | 17 | 16 | 17 | 48 | 84 | 90 | 1.8 | 46 |
1,437 | util | https://github.com/lxml/lxml | ['xml'] | null | [] | [] | null | null | null | lxml/lxml | lxml | 2,512 | 586 | 80 | Python | https://lxml.de/ | The lxml XML toolkit for Python | lxml | 2024-01-14 | 2011-02-11 | 676 | 3.712838 | 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 | null | 5.48 | 19 | 16 | 157 | 0 | 9 | 11 | 9 | 19 | 26 | 90 | 1.4 | 46 |
1,067 | nlp | https://github.com/bigscience-workshop/promptsource | [] | null | [] | [] | null | null | null | bigscience-workshop/promptsource | promptsource | 2,325 | 320 | 28 | Python | null | Toolkit for creating, sharing and using natural language prompts. | bigscience-workshop | 2024-01-14 | 2021-05-19 | 140 | 16.506085 | 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 | null | 0.13 | 11 | 11 | 32 | 3 | 0 | 2 | 2 | 11 | 10 | 90 | 0.9 | 46 |
1,367 | sim | https://github.com/rdkit/rdkit | ['chemistry'] | null | [] | [] | null | null | null | rdkit/rdkit | rdkit | 2,305 | 808 | 85 | HTML | null | The official sources for the RDKit library | rdkit | 2024-01-12 | 2013-05-12 | 559 | 4.121328 | 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 | null | 6.33 | 220 | 142 | 130 | 0 | 11 | 16 | 11 | 220 | 304 | 90 | 1.4 | 46 |
377 | ml-interpretability | https://github.com/oegedijk/explainerdashboard | [] | null | [] | [] | null | null | null | oegedijk/explainerdashboard | explainerdashboard | 2,123 | 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 | 9.569221 | null | 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 | null | 1.04 | 14 | 5 | 51 | 1 | 6 | 19 | 6 | 14 | 22 | 90 | 1.6 | 46 |
1,136 | util | https://github.com/libaudioflux/audioflux | [] | null | [] | [] | null | null | null | libaudioflux/audioflux | audioFlux | 1,957 | 95 | 26 | C | https://audioflux.top | A library for audio and music analysis, feature extraction. | libaudioflux | 2024-01-13 | 2023-01-16 | 54 | 36.145119 | 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 | null | 1.33 | 6 | 3 | 12 | 1 | 8 | 8 | 8 | 6 | 4 | 90 | 0.7 | 46 |
955 | gis | https://github.com/azavea/raster-vision | [] | null | [] | [] | null | null | null | azavea/raster-vision | raster-vision | 1,956 | 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 | 5.363102 | 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 | null | 4.73 | 77 | 56 | 85 | 0 | 6 | 3 | 6 | 77 | 80 | 90 | 1 | 46 |
940 | nlp | https://github.com/alibaba/easynlp | [] | null | [] | [] | null | null | null | alibaba/easynlp | EasyNLP | 1,872 | 238 | 37 | Python | null | EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit | alibaba | 2024-01-13 | 2022-04-06 | 94 | 19.73494 | 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 | null | 1.33 | 11 | 5 | 22 | 0 | 0 | 1 | 1 | 11 | 4 | 90 | 0.4 | 46 |
1,135 | math | https://github.com/pyomo/pyomo | [] | null | [] | [] | null | null | null | pyomo/pyomo | pyomo | 1,749 | 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 | 4.366262 | 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 | null | 48.38 | 186 | 132 | 93 | 0 | 5 | 8 | 5 | 186 | 238 | 90 | 1.3 | 46 |
660 | ml | https://github.com/huggingface/evaluate | [] | null | [] | [] | null | null | null | huggingface/evaluate | evaluate | 1,673 | 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 | 17.453055 | 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 | null | 0.46 | 51 | 12 | 22 | 1 | 1 | 5 | 1 | 51 | 51 | 90 | 1 | 46 |
853 | jupyter | https://github.com/jupyter/nbconvert | [] | null | [] | [] | null | null | null | jupyter/nbconvert | nbconvert | 1,610 | 547 | 51 | Python | https://nbconvert.readthedocs.io/ | Jupyter Notebook Conversion | jupyter | 2024-01-12 | 2015-04-09 | 459 | 3.502175 | 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 | null | 1.9 | 67 | 31 | 107 | 0 | 24 | 10 | 24 | 67 | 63 | 90 | 0.9 | 46 |
1,076 | ml | https://github.com/kubeflow/katib | [] | null | [] | [] | null | null | null | kubeflow/katib | katib | 1,391 | 394 | 67 | Go | null | Repository for hyperparameter tuning | kubeflow | 2024-01-13 | 2018-04-03 | 304 | 4.575658 | 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 | null | 1.79 | 53 | 26 | 70 | 0 | 2 | 4 | 2 | 53 | 125 | 90 | 2.4 | 46 |
926 | nlp | https://github.com/jonasgeiping/cramming | [] | null | [] | [] | null | null | null | jonasgeiping/cramming | cramming | 1,191 | 90 | 21 | Python | null | Cramming the training of a (BERT-type) language model into limited compute. | jonasgeiping | 2024-01-11 | 2022-12-29 | 56 | 21 | null | 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 | null | 1.08 | 6 | 6 | 13 | 4 | 2 | 2 | 2 | 6 | 20 | 90 | 3.3 | 46 |
1,266 | perf | https://github.com/intel/intel-extension-for-pytorch | [] | null | [] | [] | null | null | null | intel/intel-extension-for-pytorch | intel-extension-for-pytorch | 1,150 | 167 | 34 | Python | null | A Python package for extending the official PyTorch that can easily obtain performance on Intel platform | intel | 2024-01-14 | 2020-04-15 | 197 | 5.812274 | 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, 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694 | perf | https://github.com/intel/scikit-learn-intelex | [] | null | [] | [] | null | null | null | intel/scikit-learn-intelex | scikit-learn-intelex | 1,105 | 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 | 3.863636 | 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 | null | 5.44 | 136 | 105 | 66 | 0 | 6 | 5 | 6 | 136 | 532 | 90 | 3.9 | 46 |
1,727 | llm | https://github.com/truera/trulens | ['evaluation'] | null | [] | [] | null | null | null | truera/trulens | trulens | 1,042 | 83 | 12 | Jupyter Notebook | https://www.trulens.org/ | Evaluation and Tracking for LLM Experiments | truera | 2024-01-13 | 2020-11-02 | 169 | 6.160473 | 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 | null | 10.73 | 306 | 286 | 39 | 0 | 23 | 10 | 23 | 306 | 461 | 90 | 1.5 | 46 |
1,893 | util | https://github.com/ofek/pyapp | ['installer', 'bundle', 'packaging'] | null | [] | [] | null | null | null | ofek/pyapp | pyapp | 883 | 17 | 6 | Rust | https://ofek.dev/pyapp/ | Runtime installer for Python applications | ofek | 2024-01-13 | 2023-05-07 | 38 | 23.063433 | null | 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 | null | 1.75 | 21 | 11 | 8 | 0 | 15 | 23 | 15 | 21 | 24 | 90 | 1.1 | 46 |