lambdaofgod
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Add new SentenceTransformer model.
Browse files- 0_WordEmbeddings/pytorch_model.bin +3 -0
- 0_WordEmbeddings/tokenize_fn.pkl +3 -0
- 0_WordEmbeddings/whitespacetokenizer_config.json +1 -0
- 0_WordEmbeddings/wordembedding_config.json +5 -0
- 1_WordWeights/config.json +0 -0
- 2_Pooling/config.json +7 -0
- README.md +60 -0
- config_sentence_transformers.json +7 -0
- modules.json +20 -0
0_WordEmbeddings/pytorch_model.bin
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0_WordEmbeddings/tokenize_fn.pkl
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version https://git-lfs.github.com/spec/v1
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0_WordEmbeddings/whitespacetokenizer_config.json
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{"vocab": ["neural", "ordinary", "differential", "equations", "jump", "stochastic", "speaker", "recognition", "from", "raw", "waveform", "with", "sincnet", "towards", "real", "-", "time", "unsupervised", "monocular", "depth", "estimation", "on", "cpu", "single", "policy", "transfer", "in", "reinforcement", "learning", ":", "cooperative", "multi", "goal", "stage", "agent", "hierarchical", "skill", "discovery", "to", "other", "agents", "path", "based", "reasoning", "over", "heterogeneous", "networks", "for", "recommendation", "via", "bidirectional", "modeling", "densely", "connected", "convolutional", "image", "super", "resolution", "using", "deep", "fcos", "fully", "one", "object", "detection", "yolov4", "optimal", "speed", "and", "accuracy", "of", "openai", "gym", "challenging", "robotics", "environments", "request", "research", "segnet", "a", "encoder", "decoder", "architecture", "segmentation", "tacotron", "end", "speech", "synthesis", "accelerating", "prototype", "drug", "conditional", "diversity", "bayesian", "optimization", "attributed", "graphs", "search", "graph", "scalable", "weight", "normalization", "simple", "reparameterization", "accelerate", "training", "generalized", "zero", "shot", "complete", "distribution", "denoising", "network", "trained", "feature", "losses", ".", "fine", "tuning", "handwriting", "systems", "temporal", "dropout", "historical", "documents", "few", "labeled", "data", "pre", "high", "resource", "improves", "low", "text", "translation", "u", "net", "biomedical", "gating", "revisited", "layer", "rnns", "that", "can", "be", "sequential", "framework", "scene", "generation", "gans", "rethinking", "self", "attention", "transformer", "models", "bert", "contextual", "augmentation", "unet", "++:", "nested", "medical", "where", "look", "the", "pancreas", "recurrent", "residual", "(", "r2u", ")", "cross", "lingual", "knowledge", "alignment", "ssd", "multibox", "detector", "momentum", "contrast", "visual", "representation", 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"compositionality", "products", "node2vec", "cognition", "coded", "almost", "medoids", "delayed", "axiomatic", "portable", "circle", "9", "submodular", "5d", "fighting", "cartoongan", "cartoonization", "width", "biaffine", "matrices", "actionable", "intriguing", "failing", "coordconv", "upside", "coverage", "sketching", "lprnet", "iterated", "openspiel", "factual", "ways", "thresholding", "naive", "nlg", "wrong", "crowds", "probe", "elmo", "swish", "summarisation", "cram", "arxiv", "trigger", "mapper", "drawings", "transportation", "inversion", "emg", "codesearchnet", "pu", "persian", "informativeness", "spans", "profiling", "did", "intention", "formation", "touchdown", "streetlearn", "shareable", "segments", "vietnamese", "encodings", "rain", "++", "assumptions", "unmixing", "amodal", "facts", "cyclical", "censored", "zoneout", "epidemic", "move", "particular", "classical", "ratios", "shapenet", "unique", "capacities", "ibn", "iid", "nuscenes", "leave", "zen", "chain", "seismic", "edvr", "song", "pop", "beat", "normality", "credibility", "crowdsourced", "gendered", "pronouns", "seq", "adapters", "segan", "arena", "panoramic", "protocol", "rlcard", "card", "essential", "terms", "centroids", "textcaps", "memorization", "infusing", "finetuning", "discretized", "combating", "promp", "some", "considerations", "unfolding", "central", "parity", "odyssey", "significance", "finnish", "epidemiological", "command", "inputs", "dirichlet", "blocks", "cylindrical", "reprogramming", "ice", "mnasnet", "disguised", "stories", "tokenization", "spelling", "summarizing", "anywhere", "discriminating", "slate", "grasping", "tsetlin", "propositional", "clauses", "resynthesis", "diagnostics", "discover", "delete", "political", "fuse", "myocardial", "acceptable", "created", "abnormal", "aesthetics", "multigrid", "advertising", "lxmert", "mimicking", "modifications", "introspective", "typology", "dictionaries", "competitions", "11", "bitcoin", "metadata", "svms", "multilinear", "composable", "kronecker", "glove", "pacgan", "genre", "nuclear", "mixtures", "dof", "relocalization", "linknet", "indirect", "configuration", "richer", "artgan", "artwork", "polish", "abductive", "distilbert", "cheaper", "lighter", "fragments", "pirm", "books", "movies", "wrapper", "guiding", "sided", "tensornetwork", "hessian", "assignments", "15", "l2", "minimizing", "dutch", "encouraging", "fitting", "synergies", "sg", "useful", "used", "tough", "styled", "svd", "variants", "suspended", "dispersion", "unconditional", "rolling", "unrolling", "authorship", "championship", "manual", "negation", "share", "correlational", "authentication", "genres", "pc", "6dof", "ternary", "mentions", "communities", "infarction", "while", "nuclei", "demystifying", "obfuscation", "compute", "differences", "bits", "shrinking", "proofs", "facebook", "submission", "bleu", "robustifying", "corruptions", "competing", "risks", "romanian", "posts", "reconstructing", "undersampled", "take", "cr", "configurable", "vq", "grams", "annealing", "atlas", "surprising", "protocols", "stacks", "gains", "there", "deepwalk", "represent", "libraries", "cover", "cities", "humor", "literature", "index", "isolation", "corrupted", "meme", "recognize", "enforcing", "polysemous", "continually", "trec", "put", "prioritization", "fix", "phenomena", "centered", "reranking", "normals", "phoneme", "reset", "poker", "probabilities", "branched", "simplex", "additional", "pvanet", "subtraction", "interleaved", "optimistic", "conjunctive", "timed", "increased", "hyperband", "verb", "conditionally", "extrapolation", "implementing", "spike", "perform", "analogy", "concentration", "travelling", "salesman", "normalising", "comprehend", "lectures", "re3", "fastmri", "atrial", "superpixels", "annular", "netvlad", "hosted", "isic", "poly", "avoiding", "known", "stem", "thorough", "daily", "sphereface", "multiobjective", "p", "costly", "occurrence", "rezero", "noun", "lens", "discretization", "supertagging", "xlnet", "seesawfacenets", "connectionist", "20", "forcing", "pcrnet", "decompositions", "es", "fixations", "plastic", "relaxed", "limit", "marco", "performing", "outliers", "being", "dilemmas", "syntactically", "unfairness", "trackers", "infilling", "prover", "affordance", "omics", "typed", "svdd", "tumour", "crosslingual", "rescaling", "prices", "zoom", "wifi", "biasing", "prototyping", "lost", "everyone", "revenge", "optimizations", "fairseq", "tests", "promise", "eunn", "hash", "return", "attending", "tcav", "pico", "pan", "isolating", "id", "acceptability", "analogies", "alleviating", "stylized", "distinct", "framing", "chains", "inherent", "retouching", "referential", "github", "emoji2vec", "electrocardiography", "wav2vec", "permuted", "flownet3d", "item2vec", "liquid", "plda", "musegan", "accompaniment", "salt", "ssim", "decipherment", "pointgoal", "intersectional", "winograd", "core", "thumbs", "lab", "undirected", "viewpoints", "ncrf", "cardinality", "stabilization", "children", "traveling", "autoencoded", "jiant", "controllers", "inferencing", "outlook", "genomic", "struc2vec", "postprocessing", "spice", "grade", "foveation", "activated", "edinburgh", "terminology", "remove", "shadows", "ghost", "pointhop", "highlight", "snes", "microphone", "captured", "stitching", "gpr", "disfluency", "epidemiology", "lipschitzlr", "board", "coresets", "transliteration", "execute", "centermask", "unity", "demosaicking", "mcqa", "bsds500", "smac", "montezuma", "pepper", "humanitarian", "deception", "seperation", "deblocking", "overlapped", "bitext", "acrobot", "100k"], "stop_words": [], "do_lower_case": true}
|
0_WordEmbeddings/wordembedding_config.json
ADDED
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{
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"tokenizer_class": "mlutil.sentence_transformers_utils.CustomTokenizer",
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"update_embeddings": false,
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+
"max_seq_length": 1000000
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}
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1_WordWeights/config.json
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2_Pooling/config.json
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{
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"word_embedding_dimension": 200,
|
3 |
+
"pooling_mode_cls_token": false,
|
4 |
+
"pooling_mode_mean_tokens": true,
|
5 |
+
"pooling_mode_max_tokens": false,
|
6 |
+
"pooling_mode_mean_sqrt_len_tokens": false
|
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+
}
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README.md
ADDED
@@ -0,0 +1,60 @@
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1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
tags:
|
4 |
+
- sentence-transformers
|
5 |
+
- feature-extraction
|
6 |
+
- sentence-similarity
|
7 |
+
|
8 |
+
---
|
9 |
+
|
10 |
+
# lambdaofgod/query-readme-nbow-nbow-mnrl
|
11 |
+
|
12 |
+
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
13 |
+
|
14 |
+
<!--- Describe your model here -->
|
15 |
+
|
16 |
+
## Usage (Sentence-Transformers)
|
17 |
+
|
18 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
19 |
+
|
20 |
+
```
|
21 |
+
pip install -U sentence-transformers
|
22 |
+
```
|
23 |
+
|
24 |
+
Then you can use the model like this:
|
25 |
+
|
26 |
+
```python
|
27 |
+
from sentence_transformers import SentenceTransformer
|
28 |
+
sentences = ["This is an example sentence", "Each sentence is converted"]
|
29 |
+
|
30 |
+
model = SentenceTransformer('lambdaofgod/query-readme-nbow-nbow-mnrl')
|
31 |
+
embeddings = model.encode(sentences)
|
32 |
+
print(embeddings)
|
33 |
+
```
|
34 |
+
|
35 |
+
|
36 |
+
|
37 |
+
## Evaluation Results
|
38 |
+
|
39 |
+
<!--- Describe how your model was evaluated -->
|
40 |
+
|
41 |
+
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=lambdaofgod/query-readme-nbow-nbow-mnrl)
|
42 |
+
|
43 |
+
|
44 |
+
|
45 |
+
## Full Model Architecture
|
46 |
+
```
|
47 |
+
SentenceTransformer(
|
48 |
+
(0): WordEmbeddings(
|
49 |
+
(emb_layer): Embedding(4395, 200)
|
50 |
+
)
|
51 |
+
(1): WordWeights(
|
52 |
+
(emb_layer): Embedding(4395, 1)
|
53 |
+
)
|
54 |
+
(2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
|
55 |
+
)
|
56 |
+
```
|
57 |
+
|
58 |
+
## Citing & Authors
|
59 |
+
|
60 |
+
<!--- Describe where people can find more information -->
|
config_sentence_transformers.json
ADDED
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.20.0",
|
5 |
+
"pytorch": "1.10.0+cu111"
|
6 |
+
}
|
7 |
+
}
|
modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
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|
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|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "0_WordEmbeddings",
|
6 |
+
"type": "sentence_transformers.models.WordEmbeddings"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_WordWeights",
|
12 |
+
"type": "sentence_transformers.models.WordWeights"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Pooling",
|
18 |
+
"type": "sentence_transformers.models.Pooling"
|
19 |
+
}
|
20 |
+
]
|