models, tools
Browse files- models/universal-sentence-encoder-4/.gitattributes +34 -0
- models/universal-sentence-encoder-4/README.md +708 -0
- models/universal-sentence-encoder-4/source.txt +1 -0
- models/universal-sentence-encoder-large-5-onnx/.gitattributes +35 -0
- models/universal-sentence-encoder-large-5-onnx/README.md +50 -0
- models/universal-sentence-encoder-large-5-onnx/model.onnx +3 -0
- models/universal-sentence-encoder-large-5-onnx/source.txt +1 -0
- models/universal-sentence-encoder-large-5/.gitattributes +34 -0
- models/universal-sentence-encoder-large-5/README.md +707 -0
- models/universal-sentence-encoder-large-5/source.txt +2 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-en-base-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-en-large-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-base-br-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-base-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-preprocess-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-preprocess-v2.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-large-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-large-v2.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-large-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-large-v2.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-qa-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-qa-v2.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-qa-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-qa-v2.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-universal-sentence-encoder-v1.tar.gz +3 -0
- models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-universal-sentence-encoder-v2.tar.gz +3 -0
- models/universal-sentence-encoder-multilingual-3-onnx-quantized/.gitattributes +35 -0
- models/universal-sentence-encoder-multilingual-3-onnx-quantized/README.md +71 -0
- models/universal-sentence-encoder-multilingual-3-onnx-quantized/embedding_model_quantized.onnx +3 -0
- models/universal-sentence-encoder-multilingual-3-onnx-quantized/source.txt +1 -0
- models/universal-sentence-encoder-multilingual-3-onnx/.gitattributes +35 -0
- models/universal-sentence-encoder-multilingual-3-onnx/README.md +65 -0
- models/universal-sentence-encoder-multilingual-3-onnx/model.onnx +3 -0
- models/universal-sentence-encoder-multilingual-3-onnx/source.txt +1 -0
- models/universal-sentence-encoder-multilingual-3/.gitattributes +34 -0
- models/universal-sentence-encoder-multilingual-3/README.md +707 -0
- models/universal-sentence-encoder-multilingual-3/source.txt +2 -0
- models/universal-sentence-encoder-multilingual-3/universal-sentence-encoder-tensorflow2-multilingual-large-v1.tar.gz +3 -0
- models/universal-sentence-encoder-multilingual-3/universal-sentence-encoder-tensorflow2-multilingual-large-v2.tar.gz +3 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/.gitattributes +35 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/README.md +69 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/architecture.py +159 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/model.onnx +3 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/model.pt +3 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/requirements.txt +7 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/source.txt +1 -0
- models/universal-sentence-encoder-multilingual-large-3-pytorch/tokenizer.py +51 -0
- tools/muse_tf2pt.zip +3 -0
models/universal-sentence-encoder-4/.gitattributes
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models/universal-sentence-encoder-4/README.md
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| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
model-index:
|
| 5 |
+
- name: universal-sentence-encoder-4
|
| 6 |
+
results:
|
| 7 |
+
- task:
|
| 8 |
+
type: Classification
|
| 9 |
+
dataset:
|
| 10 |
+
type: mteb/amazon_counterfactual
|
| 11 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
| 12 |
+
config: en
|
| 13 |
+
split: test
|
| 14 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 15 |
+
metrics:
|
| 16 |
+
- type: accuracy
|
| 17 |
+
value: 70.67164179104478
|
| 18 |
+
- type: ap
|
| 19 |
+
value: 32.834763426716584
|
| 20 |
+
- type: f1
|
| 21 |
+
value: 64.42714654873818
|
| 22 |
+
- task:
|
| 23 |
+
type: Classification
|
| 24 |
+
dataset:
|
| 25 |
+
type: mteb/amazon_polarity
|
| 26 |
+
name: MTEB AmazonPolarityClassification
|
| 27 |
+
config: default
|
| 28 |
+
split: test
|
| 29 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
| 30 |
+
metrics:
|
| 31 |
+
- type: accuracy
|
| 32 |
+
value: 67.73207500000001
|
| 33 |
+
- type: ap
|
| 34 |
+
value: 62.47524029220297
|
| 35 |
+
- type: f1
|
| 36 |
+
value: 67.48570902687877
|
| 37 |
+
- task:
|
| 38 |
+
type: Classification
|
| 39 |
+
dataset:
|
| 40 |
+
type: mteb/amazon_reviews_multi
|
| 41 |
+
name: MTEB AmazonReviewsClassification (en)
|
| 42 |
+
config: en
|
| 43 |
+
split: test
|
| 44 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 45 |
+
metrics:
|
| 46 |
+
- type: accuracy
|
| 47 |
+
value: 32.62
|
| 48 |
+
- type: f1
|
| 49 |
+
value: 32.13548057908922
|
| 50 |
+
- task:
|
| 51 |
+
type: Clustering
|
| 52 |
+
dataset:
|
| 53 |
+
type: mteb/arxiv-clustering-p2p
|
| 54 |
+
name: MTEB ArxivClusteringP2P
|
| 55 |
+
config: default
|
| 56 |
+
split: test
|
| 57 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
| 58 |
+
metrics:
|
| 59 |
+
- type: v_measure
|
| 60 |
+
value: 35.12555128655114
|
| 61 |
+
- task:
|
| 62 |
+
type: Clustering
|
| 63 |
+
dataset:
|
| 64 |
+
type: mteb/arxiv-clustering-s2s
|
| 65 |
+
name: MTEB ArxivClusteringS2S
|
| 66 |
+
config: default
|
| 67 |
+
split: test
|
| 68 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
| 69 |
+
metrics:
|
| 70 |
+
- type: v_measure
|
| 71 |
+
value: 23.456590839508902
|
| 72 |
+
- task:
|
| 73 |
+
type: STS
|
| 74 |
+
dataset:
|
| 75 |
+
type: mteb/biosses-sts
|
| 76 |
+
name: MTEB BIOSSES
|
| 77 |
+
config: default
|
| 78 |
+
split: test
|
| 79 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
| 80 |
+
metrics:
|
| 81 |
+
- type: cos_sim_pearson
|
| 82 |
+
value: 64.7357059982867
|
| 83 |
+
- type: cos_sim_spearman
|
| 84 |
+
value: 63.37975740377988
|
| 85 |
+
- type: euclidean_pearson
|
| 86 |
+
value: 63.49896800825232
|
| 87 |
+
- type: euclidean_spearman
|
| 88 |
+
value: 63.37975740377988
|
| 89 |
+
- type: manhattan_pearson
|
| 90 |
+
value: 64.00838198208166
|
| 91 |
+
- type: manhattan_spearman
|
| 92 |
+
value: 63.31710537380123
|
| 93 |
+
- task:
|
| 94 |
+
type: Classification
|
| 95 |
+
dataset:
|
| 96 |
+
type: mteb/banking77
|
| 97 |
+
name: MTEB Banking77Classification
|
| 98 |
+
config: default
|
| 99 |
+
split: test
|
| 100 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
| 101 |
+
metrics:
|
| 102 |
+
- type: accuracy
|
| 103 |
+
value: 74.12012987012989
|
| 104 |
+
- type: f1
|
| 105 |
+
value: 73.23976030012078
|
| 106 |
+
- task:
|
| 107 |
+
type: Clustering
|
| 108 |
+
dataset:
|
| 109 |
+
type: mteb/biorxiv-clustering-p2p
|
| 110 |
+
name: MTEB BiorxivClusteringP2P
|
| 111 |
+
config: default
|
| 112 |
+
split: test
|
| 113 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
| 114 |
+
metrics:
|
| 115 |
+
- type: v_measure
|
| 116 |
+
value: 31.169576856541603
|
| 117 |
+
- task:
|
| 118 |
+
type: Clustering
|
| 119 |
+
dataset:
|
| 120 |
+
type: mteb/biorxiv-clustering-s2s
|
| 121 |
+
name: MTEB BiorxivClusteringS2S
|
| 122 |
+
config: default
|
| 123 |
+
split: test
|
| 124 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
| 125 |
+
metrics:
|
| 126 |
+
- type: v_measure
|
| 127 |
+
value: 18.81055418061209
|
| 128 |
+
- task:
|
| 129 |
+
type: Classification
|
| 130 |
+
dataset:
|
| 131 |
+
type: mteb/emotion
|
| 132 |
+
name: MTEB EmotionClassification
|
| 133 |
+
config: default
|
| 134 |
+
split: test
|
| 135 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 136 |
+
metrics:
|
| 137 |
+
- type: accuracy
|
| 138 |
+
value: 38.64000000000001
|
| 139 |
+
- type: f1
|
| 140 |
+
value: 35.09699868913662
|
| 141 |
+
- task:
|
| 142 |
+
type: Classification
|
| 143 |
+
dataset:
|
| 144 |
+
type: mteb/imdb
|
| 145 |
+
name: MTEB ImdbClassification
|
| 146 |
+
config: default
|
| 147 |
+
split: test
|
| 148 |
+
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
| 149 |
+
metrics:
|
| 150 |
+
- type: accuracy
|
| 151 |
+
value: 68.43239999999999
|
| 152 |
+
- type: ap
|
| 153 |
+
value: 62.76719976357937
|
| 154 |
+
- type: f1
|
| 155 |
+
value: 68.3208799558774
|
| 156 |
+
- task:
|
| 157 |
+
type: Classification
|
| 158 |
+
dataset:
|
| 159 |
+
type: mteb/mtop_domain
|
| 160 |
+
name: MTEB MTOPDomainClassification (en)
|
| 161 |
+
config: en
|
| 162 |
+
split: test
|
| 163 |
+
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
| 164 |
+
metrics:
|
| 165 |
+
- type: accuracy
|
| 166 |
+
value: 90.11627906976744
|
| 167 |
+
- type: f1
|
| 168 |
+
value: 89.69218132313695
|
| 169 |
+
- task:
|
| 170 |
+
type: Classification
|
| 171 |
+
dataset:
|
| 172 |
+
type: mteb/mtop_intent
|
| 173 |
+
name: MTEB MTOPIntentClassification (en)
|
| 174 |
+
config: en
|
| 175 |
+
split: test
|
| 176 |
+
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
| 177 |
+
metrics:
|
| 178 |
+
- type: accuracy
|
| 179 |
+
value: 64.9954400364797
|
| 180 |
+
- type: f1
|
| 181 |
+
value: 46.61477433032086
|
| 182 |
+
- task:
|
| 183 |
+
type: Classification
|
| 184 |
+
dataset:
|
| 185 |
+
type: mteb/amazon_massive_intent
|
| 186 |
+
name: MTEB MassiveIntentClassification (en)
|
| 187 |
+
config: en
|
| 188 |
+
split: test
|
| 189 |
+
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
| 190 |
+
metrics:
|
| 191 |
+
- type: accuracy
|
| 192 |
+
value: 67.23268325487558
|
| 193 |
+
- type: f1
|
| 194 |
+
value: 64.41484453213448
|
| 195 |
+
- task:
|
| 196 |
+
type: Classification
|
| 197 |
+
dataset:
|
| 198 |
+
type: mteb/amazon_massive_scenario
|
| 199 |
+
name: MTEB MassiveScenarioClassification (en)
|
| 200 |
+
config: en
|
| 201 |
+
split: test
|
| 202 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
| 203 |
+
metrics:
|
| 204 |
+
- type: accuracy
|
| 205 |
+
value: 72.78749159381303
|
| 206 |
+
- type: f1
|
| 207 |
+
value: 71.69036260308698
|
| 208 |
+
- task:
|
| 209 |
+
type: Clustering
|
| 210 |
+
dataset:
|
| 211 |
+
type: mteb/medrxiv-clustering-p2p
|
| 212 |
+
name: MTEB MedrxivClusteringP2P
|
| 213 |
+
config: default
|
| 214 |
+
split: test
|
| 215 |
+
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
| 216 |
+
metrics:
|
| 217 |
+
- type: v_measure
|
| 218 |
+
value: 28.88138682114816
|
| 219 |
+
- task:
|
| 220 |
+
type: Clustering
|
| 221 |
+
dataset:
|
| 222 |
+
type: mteb/medrxiv-clustering-s2s
|
| 223 |
+
name: MTEB MedrxivClusteringS2S
|
| 224 |
+
config: default
|
| 225 |
+
split: test
|
| 226 |
+
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
| 227 |
+
metrics:
|
| 228 |
+
- type: v_measure
|
| 229 |
+
value: 23.311493283906064
|
| 230 |
+
- task:
|
| 231 |
+
type: Clustering
|
| 232 |
+
dataset:
|
| 233 |
+
type: mteb/reddit-clustering
|
| 234 |
+
name: MTEB RedditClustering
|
| 235 |
+
config: default
|
| 236 |
+
split: test
|
| 237 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
| 238 |
+
metrics:
|
| 239 |
+
- type: v_measure
|
| 240 |
+
value: 49.71559043766936
|
| 241 |
+
- task:
|
| 242 |
+
type: Clustering
|
| 243 |
+
dataset:
|
| 244 |
+
type: mteb/reddit-clustering-p2p
|
| 245 |
+
name: MTEB RedditClusteringP2P
|
| 246 |
+
config: default
|
| 247 |
+
split: test
|
| 248 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
| 249 |
+
metrics:
|
| 250 |
+
- type: v_measure
|
| 251 |
+
value: 57.91704617095672
|
| 252 |
+
- task:
|
| 253 |
+
type: STS
|
| 254 |
+
dataset:
|
| 255 |
+
type: mteb/sickr-sts
|
| 256 |
+
name: MTEB SICK-R
|
| 257 |
+
config: default
|
| 258 |
+
split: test
|
| 259 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
| 260 |
+
metrics:
|
| 261 |
+
- type: cos_sim_pearson
|
| 262 |
+
value: 77.38041457279672
|
| 263 |
+
- type: cos_sim_spearman
|
| 264 |
+
value: 69.79282361714223
|
| 265 |
+
- type: euclidean_pearson
|
| 266 |
+
value: 74.02315074475364
|
| 267 |
+
- type: euclidean_spearman
|
| 268 |
+
value: 69.79282304260158
|
| 269 |
+
- type: manhattan_pearson
|
| 270 |
+
value: 73.01688608657159
|
| 271 |
+
- type: manhattan_spearman
|
| 272 |
+
value: 68.22940563625058
|
| 273 |
+
- task:
|
| 274 |
+
type: STS
|
| 275 |
+
dataset:
|
| 276 |
+
type: mteb/sts12-sts
|
| 277 |
+
name: MTEB STS12
|
| 278 |
+
config: default
|
| 279 |
+
split: test
|
| 280 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
| 281 |
+
metrics:
|
| 282 |
+
- type: cos_sim_pearson
|
| 283 |
+
value: 72.58420052741107
|
| 284 |
+
- type: cos_sim_spearman
|
| 285 |
+
value: 67.05792005966953
|
| 286 |
+
- type: euclidean_pearson
|
| 287 |
+
value: 68.35629372749483
|
| 288 |
+
- type: euclidean_spearman
|
| 289 |
+
value: 67.05773819854602
|
| 290 |
+
- type: manhattan_pearson
|
| 291 |
+
value: 67.12625747442266
|
| 292 |
+
- type: manhattan_spearman
|
| 293 |
+
value: 65.7252617197503
|
| 294 |
+
- task:
|
| 295 |
+
type: STS
|
| 296 |
+
dataset:
|
| 297 |
+
type: mteb/sts13-sts
|
| 298 |
+
name: MTEB STS13
|
| 299 |
+
config: default
|
| 300 |
+
split: test
|
| 301 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
| 302 |
+
metrics:
|
| 303 |
+
- type: cos_sim_pearson
|
| 304 |
+
value: 69.55548148750847
|
| 305 |
+
- type: cos_sim_spearman
|
| 306 |
+
value: 71.54484814980987
|
| 307 |
+
- type: euclidean_pearson
|
| 308 |
+
value: 71.22072782671158
|
| 309 |
+
- type: euclidean_spearman
|
| 310 |
+
value: 71.54484814980987
|
| 311 |
+
- type: manhattan_pearson
|
| 312 |
+
value: 70.3490839338159
|
| 313 |
+
- type: manhattan_spearman
|
| 314 |
+
value: 70.76414952692804
|
| 315 |
+
- task:
|
| 316 |
+
type: STS
|
| 317 |
+
dataset:
|
| 318 |
+
type: mteb/sts14-sts
|
| 319 |
+
name: MTEB STS14
|
| 320 |
+
config: default
|
| 321 |
+
split: test
|
| 322 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
| 323 |
+
metrics:
|
| 324 |
+
- type: cos_sim_pearson
|
| 325 |
+
value: 73.74818660795891
|
| 326 |
+
- type: cos_sim_spearman
|
| 327 |
+
value: 70.59138342620027
|
| 328 |
+
- type: euclidean_pearson
|
| 329 |
+
value: 72.60887534657319
|
| 330 |
+
- type: euclidean_spearman
|
| 331 |
+
value: 70.5913727471932
|
| 332 |
+
- type: manhattan_pearson
|
| 333 |
+
value: 71.95704368086712
|
| 334 |
+
- type: manhattan_spearman
|
| 335 |
+
value: 69.58620240967204
|
| 336 |
+
- task:
|
| 337 |
+
type: STS
|
| 338 |
+
dataset:
|
| 339 |
+
type: mteb/sts15-sts
|
| 340 |
+
name: MTEB STS15
|
| 341 |
+
config: default
|
| 342 |
+
split: test
|
| 343 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
| 344 |
+
metrics:
|
| 345 |
+
- type: cos_sim_pearson
|
| 346 |
+
value: 79.6360473168554
|
| 347 |
+
- type: cos_sim_spearman
|
| 348 |
+
value: 80.26596000690931
|
| 349 |
+
- type: euclidean_pearson
|
| 350 |
+
value: 79.82176999472074
|
| 351 |
+
- type: euclidean_spearman
|
| 352 |
+
value: 80.26596000690931
|
| 353 |
+
- type: manhattan_pearson
|
| 354 |
+
value: 78.94486463380255
|
| 355 |
+
- type: manhattan_spearman
|
| 356 |
+
value: 79.20674341072848
|
| 357 |
+
- task:
|
| 358 |
+
type: STS
|
| 359 |
+
dataset:
|
| 360 |
+
type: mteb/sts16-sts
|
| 361 |
+
name: MTEB STS16
|
| 362 |
+
config: default
|
| 363 |
+
split: test
|
| 364 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
| 365 |
+
metrics:
|
| 366 |
+
- type: cos_sim_pearson
|
| 367 |
+
value: 74.84322872565855
|
| 368 |
+
- type: cos_sim_spearman
|
| 369 |
+
value: 75.75894131062728
|
| 370 |
+
- type: euclidean_pearson
|
| 371 |
+
value: 75.5333191548161
|
| 372 |
+
- type: euclidean_spearman
|
| 373 |
+
value: 75.75894090191032
|
| 374 |
+
- type: manhattan_pearson
|
| 375 |
+
value: 74.96648591478875
|
| 376 |
+
- type: manhattan_spearman
|
| 377 |
+
value: 75.07346800275856
|
| 378 |
+
- task:
|
| 379 |
+
type: STS
|
| 380 |
+
dataset:
|
| 381 |
+
type: mteb/sts17-crosslingual-sts
|
| 382 |
+
name: MTEB STS17 (en-en)
|
| 383 |
+
config: en-en
|
| 384 |
+
split: test
|
| 385 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
| 386 |
+
metrics:
|
| 387 |
+
- type: cos_sim_pearson
|
| 388 |
+
value: 83.79136005773537
|
| 389 |
+
- type: cos_sim_spearman
|
| 390 |
+
value: 84.94411992446793
|
| 391 |
+
- type: euclidean_pearson
|
| 392 |
+
value: 83.60843297866558
|
| 393 |
+
- type: euclidean_spearman
|
| 394 |
+
value: 84.94411992446793
|
| 395 |
+
- type: manhattan_pearson
|
| 396 |
+
value: 82.81698401022742
|
| 397 |
+
- type: manhattan_spearman
|
| 398 |
+
value: 84.02022263657062
|
| 399 |
+
- task:
|
| 400 |
+
type: STS
|
| 401 |
+
dataset:
|
| 402 |
+
type: mteb/sts22-crosslingual-sts
|
| 403 |
+
name: MTEB STS22 (en)
|
| 404 |
+
config: en
|
| 405 |
+
split: test
|
| 406 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
| 407 |
+
metrics:
|
| 408 |
+
- type: cos_sim_pearson
|
| 409 |
+
value: 60.02909344132073
|
| 410 |
+
- type: cos_sim_spearman
|
| 411 |
+
value: 60.000369508382704
|
| 412 |
+
- type: euclidean_pearson
|
| 413 |
+
value: 61.54466129341342
|
| 414 |
+
- type: euclidean_spearman
|
| 415 |
+
value: 60.000369508382704
|
| 416 |
+
- type: manhattan_pearson
|
| 417 |
+
value: 58.76127065249476
|
| 418 |
+
- type: manhattan_spearman
|
| 419 |
+
value: 58.08063159428285
|
| 420 |
+
- task:
|
| 421 |
+
type: STS
|
| 422 |
+
dataset:
|
| 423 |
+
type: mteb/stsbenchmark-sts
|
| 424 |
+
name: MTEB STSBenchmark
|
| 425 |
+
config: default
|
| 426 |
+
split: test
|
| 427 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
| 428 |
+
metrics:
|
| 429 |
+
- type: cos_sim_pearson
|
| 430 |
+
value: 78.72737325889034
|
| 431 |
+
- type: cos_sim_spearman
|
| 432 |
+
value: 77.08420739421672
|
| 433 |
+
- type: euclidean_pearson
|
| 434 |
+
value: 77.85606384422326
|
| 435 |
+
- type: euclidean_spearman
|
| 436 |
+
value: 77.08420739421672
|
| 437 |
+
- type: manhattan_pearson
|
| 438 |
+
value: 76.63643674764234
|
| 439 |
+
- type: manhattan_spearman
|
| 440 |
+
value: 75.68141928725497
|
| 441 |
+
- task:
|
| 442 |
+
type: PairClassification
|
| 443 |
+
dataset:
|
| 444 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
| 445 |
+
name: MTEB SprintDuplicateQuestions
|
| 446 |
+
config: default
|
| 447 |
+
split: test
|
| 448 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 449 |
+
metrics:
|
| 450 |
+
- type: cos_sim_accuracy
|
| 451 |
+
value: 99.61980198019802
|
| 452 |
+
- type: cos_sim_ap
|
| 453 |
+
value: 86.36827719686161
|
| 454 |
+
- type: cos_sim_f1
|
| 455 |
+
value: 80.35625927758537
|
| 456 |
+
- type: cos_sim_precision
|
| 457 |
+
value: 79.52987267384917
|
| 458 |
+
- type: cos_sim_recall
|
| 459 |
+
value: 81.2
|
| 460 |
+
- type: dot_accuracy
|
| 461 |
+
value: 99.61980198019802
|
| 462 |
+
- type: dot_ap
|
| 463 |
+
value: 86.36827719686161
|
| 464 |
+
- type: dot_f1
|
| 465 |
+
value: 80.35625927758537
|
| 466 |
+
- type: dot_precision
|
| 467 |
+
value: 79.52987267384917
|
| 468 |
+
- type: dot_recall
|
| 469 |
+
value: 81.2
|
| 470 |
+
- type: euclidean_accuracy
|
| 471 |
+
value: 99.61980198019802
|
| 472 |
+
- type: euclidean_ap
|
| 473 |
+
value: 86.3682771543572
|
| 474 |
+
- type: euclidean_f1
|
| 475 |
+
value: 80.35625927758537
|
| 476 |
+
- type: euclidean_precision
|
| 477 |
+
value: 79.52987267384917
|
| 478 |
+
- type: euclidean_recall
|
| 479 |
+
value: 81.2
|
| 480 |
+
- type: manhattan_accuracy
|
| 481 |
+
value: 99.63564356435643
|
| 482 |
+
- type: manhattan_ap
|
| 483 |
+
value: 87.12233265654545
|
| 484 |
+
- type: manhattan_f1
|
| 485 |
+
value: 80.78920041536864
|
| 486 |
+
- type: manhattan_precision
|
| 487 |
+
value: 84.01727861771057
|
| 488 |
+
- type: manhattan_recall
|
| 489 |
+
value: 77.8
|
| 490 |
+
- type: max_accuracy
|
| 491 |
+
value: 99.63564356435643
|
| 492 |
+
- type: max_ap
|
| 493 |
+
value: 87.12233265654545
|
| 494 |
+
- type: max_f1
|
| 495 |
+
value: 80.78920041536864
|
| 496 |
+
- task:
|
| 497 |
+
type: Clustering
|
| 498 |
+
dataset:
|
| 499 |
+
type: mteb/stackexchange-clustering
|
| 500 |
+
name: MTEB StackExchangeClustering
|
| 501 |
+
config: default
|
| 502 |
+
split: test
|
| 503 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
| 504 |
+
metrics:
|
| 505 |
+
- type: v_measure
|
| 506 |
+
value: 54.64344160961463
|
| 507 |
+
- task:
|
| 508 |
+
type: Clustering
|
| 509 |
+
dataset:
|
| 510 |
+
type: mteb/stackexchange-clustering-p2p
|
| 511 |
+
name: MTEB StackExchangeClusteringP2P
|
| 512 |
+
config: default
|
| 513 |
+
split: test
|
| 514 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
| 515 |
+
metrics:
|
| 516 |
+
- type: v_measure
|
| 517 |
+
value: 31.57666192425415
|
| 518 |
+
- task:
|
| 519 |
+
type: Summarization
|
| 520 |
+
dataset:
|
| 521 |
+
type: mteb/summeval
|
| 522 |
+
name: MTEB SummEval
|
| 523 |
+
config: default
|
| 524 |
+
split: test
|
| 525 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 526 |
+
metrics:
|
| 527 |
+
- type: cos_sim_pearson
|
| 528 |
+
value: 29.77545231337259
|
| 529 |
+
- type: cos_sim_spearman
|
| 530 |
+
value: 29.420072483158698
|
| 531 |
+
- type: dot_pearson
|
| 532 |
+
value: 29.775453888622426
|
| 533 |
+
- type: dot_spearman
|
| 534 |
+
value: 29.420072483158698
|
| 535 |
+
- task:
|
| 536 |
+
type: Classification
|
| 537 |
+
dataset:
|
| 538 |
+
type: mteb/toxic_conversations_50k
|
| 539 |
+
name: MTEB ToxicConversationsClassification
|
| 540 |
+
config: default
|
| 541 |
+
split: test
|
| 542 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
| 543 |
+
metrics:
|
| 544 |
+
- type: accuracy
|
| 545 |
+
value: 68.98920000000001
|
| 546 |
+
- type: ap
|
| 547 |
+
value: 12.803310864930856
|
| 548 |
+
- type: f1
|
| 549 |
+
value: 52.67881359079218
|
| 550 |
+
- task:
|
| 551 |
+
type: Classification
|
| 552 |
+
dataset:
|
| 553 |
+
type: mteb/tweet_sentiment_extraction
|
| 554 |
+
name: MTEB TweetSentimentExtractionClassification
|
| 555 |
+
config: default
|
| 556 |
+
split: test
|
| 557 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
| 558 |
+
metrics:
|
| 559 |
+
- type: accuracy
|
| 560 |
+
value: 57.00622524052065
|
| 561 |
+
- type: f1
|
| 562 |
+
value: 57.2232294145327
|
| 563 |
+
- task:
|
| 564 |
+
type: Clustering
|
| 565 |
+
dataset:
|
| 566 |
+
type: mteb/twentynewsgroups-clustering
|
| 567 |
+
name: MTEB TwentyNewsgroupsClustering
|
| 568 |
+
config: default
|
| 569 |
+
split: test
|
| 570 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
| 571 |
+
metrics:
|
| 572 |
+
- type: v_measure
|
| 573 |
+
value: 38.23134611732841
|
| 574 |
+
- task:
|
| 575 |
+
type: PairClassification
|
| 576 |
+
dataset:
|
| 577 |
+
type: mteb/twittersemeval2015-pairclassification
|
| 578 |
+
name: MTEB TwitterSemEval2015
|
| 579 |
+
config: default
|
| 580 |
+
split: test
|
| 581 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 582 |
+
metrics:
|
| 583 |
+
- type: cos_sim_accuracy
|
| 584 |
+
value: 82.25546879656673
|
| 585 |
+
- type: cos_sim_ap
|
| 586 |
+
value: 60.64429023120581
|
| 587 |
+
- type: cos_sim_f1
|
| 588 |
+
value: 57.75789091788608
|
| 589 |
+
- type: cos_sim_precision
|
| 590 |
+
value: 53.31547220361688
|
| 591 |
+
- type: cos_sim_recall
|
| 592 |
+
value: 63.007915567282325
|
| 593 |
+
- type: dot_accuracy
|
| 594 |
+
value: 82.25546879656673
|
| 595 |
+
- type: dot_ap
|
| 596 |
+
value: 60.64428864700383
|
| 597 |
+
- type: dot_f1
|
| 598 |
+
value: 57.75789091788608
|
| 599 |
+
- type: dot_precision
|
| 600 |
+
value: 53.31547220361688
|
| 601 |
+
- type: dot_recall
|
| 602 |
+
value: 63.007915567282325
|
| 603 |
+
- type: euclidean_accuracy
|
| 604 |
+
value: 82.25546879656673
|
| 605 |
+
- type: euclidean_ap
|
| 606 |
+
value: 60.64429357965402
|
| 607 |
+
- type: euclidean_f1
|
| 608 |
+
value: 57.75789091788608
|
| 609 |
+
- type: euclidean_precision
|
| 610 |
+
value: 53.31547220361688
|
| 611 |
+
- type: euclidean_recall
|
| 612 |
+
value: 63.007915567282325
|
| 613 |
+
- type: manhattan_accuracy
|
| 614 |
+
value: 82.14221851344102
|
| 615 |
+
- type: manhattan_ap
|
| 616 |
+
value: 59.542389876094134
|
| 617 |
+
- type: manhattan_f1
|
| 618 |
+
value: 56.935892792466504
|
| 619 |
+
- type: manhattan_precision
|
| 620 |
+
value: 52.48163810371689
|
| 621 |
+
- type: manhattan_recall
|
| 622 |
+
value: 62.21635883905014
|
| 623 |
+
- type: max_accuracy
|
| 624 |
+
value: 82.25546879656673
|
| 625 |
+
- type: max_ap
|
| 626 |
+
value: 60.64429357965402
|
| 627 |
+
- type: max_f1
|
| 628 |
+
value: 57.75789091788608
|
| 629 |
+
- task:
|
| 630 |
+
type: PairClassification
|
| 631 |
+
dataset:
|
| 632 |
+
type: mteb/twitterurlcorpus-pairclassification
|
| 633 |
+
name: MTEB TwitterURLCorpus
|
| 634 |
+
config: default
|
| 635 |
+
split: test
|
| 636 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 637 |
+
metrics:
|
| 638 |
+
- type: cos_sim_accuracy
|
| 639 |
+
value: 86.99305312997244
|
| 640 |
+
- type: cos_sim_ap
|
| 641 |
+
value: 81.6822772881566
|
| 642 |
+
- type: cos_sim_f1
|
| 643 |
+
value: 73.70381406436233
|
| 644 |
+
- type: cos_sim_precision
|
| 645 |
+
value: 71.38528138528139
|
| 646 |
+
- type: cos_sim_recall
|
| 647 |
+
value: 76.17801047120419
|
| 648 |
+
- type: dot_accuracy
|
| 649 |
+
value: 86.99305312997244
|
| 650 |
+
- type: dot_ap
|
| 651 |
+
value: 81.6822783032573
|
| 652 |
+
- type: dot_f1
|
| 653 |
+
value: 73.70381406436233
|
| 654 |
+
- type: dot_precision
|
| 655 |
+
value: 71.38528138528139
|
| 656 |
+
- type: dot_recall
|
| 657 |
+
value: 76.17801047120419
|
| 658 |
+
- type: euclidean_accuracy
|
| 659 |
+
value: 86.99305312997244
|
| 660 |
+
- type: euclidean_ap
|
| 661 |
+
value: 81.68228020882522
|
| 662 |
+
- type: euclidean_f1
|
| 663 |
+
value: 73.70381406436233
|
| 664 |
+
- type: euclidean_precision
|
| 665 |
+
value: 71.38528138528139
|
| 666 |
+
- type: euclidean_recall
|
| 667 |
+
value: 76.17801047120419
|
| 668 |
+
- type: manhattan_accuracy
|
| 669 |
+
value: 86.84557767687352
|
| 670 |
+
- type: manhattan_ap
|
| 671 |
+
value: 81.3912803407899
|
| 672 |
+
- type: manhattan_f1
|
| 673 |
+
value: 73.48302793631723
|
| 674 |
+
- type: manhattan_precision
|
| 675 |
+
value: 71.71650542362944
|
| 676 |
+
- type: manhattan_recall
|
| 677 |
+
value: 75.33877425315676
|
| 678 |
+
- type: max_accuracy
|
| 679 |
+
value: 86.99305312997244
|
| 680 |
+
- type: max_ap
|
| 681 |
+
value: 81.68228020882522
|
| 682 |
+
- type: max_f1
|
| 683 |
+
value: 73.70381406436233
|
| 684 |
+
---
|
| 685 |
+
|
| 686 |
+
This is a part of the [MTEB test](https://huggingface.co/spaces/mteb/leaderboard).
|
| 687 |
+
|
| 688 |
+
```
|
| 689 |
+
# !pip install tensorflow_text
|
| 690 |
+
|
| 691 |
+
import tensorflow_hub as hub
|
| 692 |
+
from tensorflow_text import SentencepieceTokenizer
|
| 693 |
+
import tensorflow as tf
|
| 694 |
+
|
| 695 |
+
embedder=hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3")
|
| 696 |
+
|
| 697 |
+
class USE():
|
| 698 |
+
def encode(self, sentences, batch_size=32, **kwargs):
|
| 699 |
+
embeddings = []
|
| 700 |
+
for i in range(0, len(sentences), batch_size):
|
| 701 |
+
batch_sentences = sentences[i:i+batch_size]
|
| 702 |
+
batch_embeddings = embedder(batch_sentences)
|
| 703 |
+
embeddings.extend(batch_embeddings)
|
| 704 |
+
return embeddings
|
| 705 |
+
|
| 706 |
+
|
| 707 |
+
model = USE()
|
| 708 |
+
```
|
models/universal-sentence-encoder-4/source.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/vprelovac/universal-sentence-encoder-4
|
models/universal-sentence-encoder-large-5-onnx/.gitattributes
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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models/universal-sentence-encoder-large-5-onnx/README.md
ADDED
|
@@ -0,0 +1,50 @@
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| 1 |
+
---
|
| 2 |
+
language: en
|
| 3 |
+
tags:
|
| 4 |
+
- feature-extraction
|
| 5 |
+
- onnx
|
| 6 |
+
- use
|
| 7 |
+
- text-embedding
|
| 8 |
+
- tensorflow-hub
|
| 9 |
+
license: apache-2.0
|
| 10 |
+
inference: false
|
| 11 |
+
widget:
|
| 12 |
+
- text: Thank goodness ONNX is available, it is lots faster!
|
| 13 |
+
---
|
| 14 |
+
### Universal Sentence Encoder Large v5
|
| 15 |
+
|
| 16 |
+
ONNX version of [https://tfhub.dev/google/universal-sentence-encoder-large/5](https://tfhub.dev/google/universal-sentence-encoder-large/5)
|
| 17 |
+
|
| 18 |
+
The original TFHub version of the model is referenced in other models here E.g. [https://huggingface.co/vprelovac/universal-sentence-encoder-large-5](https://huggingface.co/vprelovac/universal-sentence-encoder-large-5)
|
| 19 |
+
|
| 20 |
+
### Overview
|
| 21 |
+
|
| 22 |
+
See overview and license details at [https://tfhub.dev/google/universal-sentence-encoder-large/5](https://tfhub.dev/google/universal-sentence-encoder-large/5)
|
| 23 |
+
|
| 24 |
+
This model is a full precision version of the TFHub original, in ONNX format.
|
| 25 |
+
|
| 26 |
+
It uses the [ONNXRuntime Extensions](https://github.com/microsoft/onnxruntime-extensions) to embed the tokenizer within the ONNX model, so no seperate tokenizer is needed, and text is fed directly into the ONNX model.
|
| 27 |
+
|
| 28 |
+
Post-processing (E.g. pooling, normalization) is also implemented within the ONNX model, so no separate processing is necessary.
|
| 29 |
+
|
| 30 |
+
### How to use
|
| 31 |
+
|
| 32 |
+
```python
|
| 33 |
+
import onnxruntime as ort
|
| 34 |
+
from onnxruntime_extensions import get_library_path
|
| 35 |
+
from os import cpu_count
|
| 36 |
+
|
| 37 |
+
sentences = ["hello world"]
|
| 38 |
+
|
| 39 |
+
def load_onnx_model(model_filepath):
|
| 40 |
+
_options = ort.SessionOptions()
|
| 41 |
+
_options.inter_op_num_threads, _options.intra_op_num_threads = cpu_count(), cpu_count()
|
| 42 |
+
_options.register_custom_ops_library(get_library_path())
|
| 43 |
+
_providers = ["CPUExecutionProvider"] # could use ort.get_available_providers()
|
| 44 |
+
return ort.InferenceSession(path_or_bytes=model_filepath, sess_options=_options, providers=_providers)
|
| 45 |
+
|
| 46 |
+
model = load_onnx_model("filepath_for_model_dot_onnx")
|
| 47 |
+
|
| 48 |
+
model_outputs = model.run(output_names=["outputs"], input_feed={"inputs": sentences})[0]
|
| 49 |
+
print(model_outputs)
|
| 50 |
+
```
|
models/universal-sentence-encoder-large-5-onnx/model.onnx
ADDED
|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d267b0955793f866593e49ba58474fb57f5314cf757ec0945127c521c569b22f
|
| 3 |
+
size 589130634
|
models/universal-sentence-encoder-large-5-onnx/source.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/SamLowe/universal-sentence-encoder-large-5-onnx
|
models/universal-sentence-encoder-large-5/.gitattributes
ADDED
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*.7z filter=lfs diff=lfs merge=lfs -text
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| 3 |
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*.bin filter=lfs diff=lfs merge=lfs -text
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| 4 |
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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| 5 |
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*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
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*.ftz filter=lfs diff=lfs merge=lfs -text
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| 7 |
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| 8 |
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| 9 |
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|
| 10 |
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| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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| 12 |
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*.model filter=lfs diff=lfs merge=lfs -text
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| 13 |
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
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| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
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| 16 |
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*.onnx filter=lfs diff=lfs merge=lfs -text
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| 17 |
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*.ot filter=lfs diff=lfs merge=lfs -text
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| 18 |
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*.parquet filter=lfs diff=lfs merge=lfs -text
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| 19 |
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*.pb filter=lfs diff=lfs merge=lfs -text
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| 20 |
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*.pickle filter=lfs diff=lfs merge=lfs -text
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| 21 |
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*.pkl filter=lfs diff=lfs merge=lfs -text
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| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
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| 23 |
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*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
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*.rar filter=lfs diff=lfs merge=lfs -text
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| 25 |
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*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
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| 28 |
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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| 33 |
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
models/universal-sentence-encoder-large-5/README.md
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
model-index:
|
| 5 |
+
- name: universal-sentence-encoder-large-5
|
| 6 |
+
results:
|
| 7 |
+
- task:
|
| 8 |
+
type: Classification
|
| 9 |
+
dataset:
|
| 10 |
+
type: mteb/amazon_counterfactual
|
| 11 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
| 12 |
+
config: en
|
| 13 |
+
split: test
|
| 14 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 15 |
+
metrics:
|
| 16 |
+
- type: accuracy
|
| 17 |
+
value: 76.19402985074628
|
| 18 |
+
- type: ap
|
| 19 |
+
value: 39.249966888759666
|
| 20 |
+
- type: f1
|
| 21 |
+
value: 70.17510532980124
|
| 22 |
+
- task:
|
| 23 |
+
type: Classification
|
| 24 |
+
dataset:
|
| 25 |
+
type: mteb/amazon_polarity
|
| 26 |
+
name: MTEB AmazonPolarityClassification
|
| 27 |
+
config: default
|
| 28 |
+
split: test
|
| 29 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
| 30 |
+
metrics:
|
| 31 |
+
- type: accuracy
|
| 32 |
+
value: 69.6285
|
| 33 |
+
- type: ap
|
| 34 |
+
value: 63.97317997322299
|
| 35 |
+
- type: f1
|
| 36 |
+
value: 69.48624121982243
|
| 37 |
+
- task:
|
| 38 |
+
type: Classification
|
| 39 |
+
dataset:
|
| 40 |
+
type: mteb/amazon_reviews_multi
|
| 41 |
+
name: MTEB AmazonReviewsClassification (en)
|
| 42 |
+
config: en
|
| 43 |
+
split: test
|
| 44 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 45 |
+
metrics:
|
| 46 |
+
- type: accuracy
|
| 47 |
+
value: 35.534
|
| 48 |
+
- type: f1
|
| 49 |
+
value: 34.974303844745194
|
| 50 |
+
- task:
|
| 51 |
+
type: Clustering
|
| 52 |
+
dataset:
|
| 53 |
+
type: mteb/arxiv-clustering-p2p
|
| 54 |
+
name: MTEB ArxivClusteringP2P
|
| 55 |
+
config: default
|
| 56 |
+
split: test
|
| 57 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
| 58 |
+
metrics:
|
| 59 |
+
- type: v_measure
|
| 60 |
+
value: 34.718110225806626
|
| 61 |
+
- task:
|
| 62 |
+
type: Clustering
|
| 63 |
+
dataset:
|
| 64 |
+
type: mteb/arxiv-clustering-s2s
|
| 65 |
+
name: MTEB ArxivClusteringS2S
|
| 66 |
+
config: default
|
| 67 |
+
split: test
|
| 68 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
| 69 |
+
metrics:
|
| 70 |
+
- type: v_measure
|
| 71 |
+
value: 25.267234486849127
|
| 72 |
+
- task:
|
| 73 |
+
type: STS
|
| 74 |
+
dataset:
|
| 75 |
+
type: mteb/biosses-sts
|
| 76 |
+
name: MTEB BIOSSES
|
| 77 |
+
config: default
|
| 78 |
+
split: test
|
| 79 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
| 80 |
+
metrics:
|
| 81 |
+
- type: cos_sim_pearson
|
| 82 |
+
value: 69.65040443392367
|
| 83 |
+
- type: cos_sim_spearman
|
| 84 |
+
value: 69.35579718635816
|
| 85 |
+
- type: euclidean_pearson
|
| 86 |
+
value: 68.74078260783044
|
| 87 |
+
- type: euclidean_spearman
|
| 88 |
+
value: 69.35579718635816
|
| 89 |
+
- type: manhattan_pearson
|
| 90 |
+
value: 68.97023207188357
|
| 91 |
+
- type: manhattan_spearman
|
| 92 |
+
value: 69.2063961917937
|
| 93 |
+
- task:
|
| 94 |
+
type: Classification
|
| 95 |
+
dataset:
|
| 96 |
+
type: mteb/banking77
|
| 97 |
+
name: MTEB Banking77Classification
|
| 98 |
+
config: default
|
| 99 |
+
split: test
|
| 100 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
| 101 |
+
metrics:
|
| 102 |
+
- type: accuracy
|
| 103 |
+
value: 78.12987012987013
|
| 104 |
+
- type: f1
|
| 105 |
+
value: 77.40193921057201
|
| 106 |
+
- task:
|
| 107 |
+
type: Clustering
|
| 108 |
+
dataset:
|
| 109 |
+
type: mteb/biorxiv-clustering-p2p
|
| 110 |
+
name: MTEB BiorxivClusteringP2P
|
| 111 |
+
config: default
|
| 112 |
+
split: test
|
| 113 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
| 114 |
+
metrics:
|
| 115 |
+
- type: v_measure
|
| 116 |
+
value: 28.39184796722482
|
| 117 |
+
- task:
|
| 118 |
+
type: Clustering
|
| 119 |
+
dataset:
|
| 120 |
+
type: mteb/biorxiv-clustering-s2s
|
| 121 |
+
name: MTEB BiorxivClusteringS2S
|
| 122 |
+
config: default
|
| 123 |
+
split: test
|
| 124 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
| 125 |
+
metrics:
|
| 126 |
+
- type: v_measure
|
| 127 |
+
value: 20.5151608432177
|
| 128 |
+
- task:
|
| 129 |
+
type: Classification
|
| 130 |
+
dataset:
|
| 131 |
+
type: mteb/emotion
|
| 132 |
+
name: MTEB EmotionClassification
|
| 133 |
+
config: default
|
| 134 |
+
split: test
|
| 135 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 136 |
+
metrics:
|
| 137 |
+
- type: accuracy
|
| 138 |
+
value: 45.48
|
| 139 |
+
- type: f1
|
| 140 |
+
value: 41.2632839288363
|
| 141 |
+
- task:
|
| 142 |
+
type: Classification
|
| 143 |
+
dataset:
|
| 144 |
+
type: mteb/imdb
|
| 145 |
+
name: MTEB ImdbClassification
|
| 146 |
+
config: default
|
| 147 |
+
split: test
|
| 148 |
+
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
| 149 |
+
metrics:
|
| 150 |
+
- type: accuracy
|
| 151 |
+
value: 64.0552
|
| 152 |
+
- type: ap
|
| 153 |
+
value: 59.25851636836455
|
| 154 |
+
- type: f1
|
| 155 |
+
value: 63.90501571634165
|
| 156 |
+
- task:
|
| 157 |
+
type: Classification
|
| 158 |
+
dataset:
|
| 159 |
+
type: mteb/mtop_domain
|
| 160 |
+
name: MTEB MTOPDomainClassification (en)
|
| 161 |
+
config: en
|
| 162 |
+
split: test
|
| 163 |
+
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
| 164 |
+
metrics:
|
| 165 |
+
- type: accuracy
|
| 166 |
+
value: 92.94117647058823
|
| 167 |
+
- type: f1
|
| 168 |
+
value: 92.7110107115347
|
| 169 |
+
- task:
|
| 170 |
+
type: Classification
|
| 171 |
+
dataset:
|
| 172 |
+
type: mteb/mtop_intent
|
| 173 |
+
name: MTEB MTOPIntentClassification (en)
|
| 174 |
+
config: en
|
| 175 |
+
split: test
|
| 176 |
+
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
| 177 |
+
metrics:
|
| 178 |
+
- type: accuracy
|
| 179 |
+
value: 74.43456452348381
|
| 180 |
+
- type: f1
|
| 181 |
+
value: 52.53178214778298
|
| 182 |
+
- task:
|
| 183 |
+
type: Classification
|
| 184 |
+
dataset:
|
| 185 |
+
type: mteb/amazon_massive_intent
|
| 186 |
+
name: MTEB MassiveIntentClassification (en)
|
| 187 |
+
config: en
|
| 188 |
+
split: test
|
| 189 |
+
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
| 190 |
+
metrics:
|
| 191 |
+
- type: accuracy
|
| 192 |
+
value: 71.68796234028245
|
| 193 |
+
- type: f1
|
| 194 |
+
value: 68.47828954699564
|
| 195 |
+
- task:
|
| 196 |
+
type: Classification
|
| 197 |
+
dataset:
|
| 198 |
+
type: mteb/amazon_massive_scenario
|
| 199 |
+
name: MTEB MassiveScenarioClassification (en)
|
| 200 |
+
config: en
|
| 201 |
+
split: test
|
| 202 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
| 203 |
+
metrics:
|
| 204 |
+
- type: accuracy
|
| 205 |
+
value: 77.20242098184264
|
| 206 |
+
- type: f1
|
| 207 |
+
value: 76.27977367157321
|
| 208 |
+
- task:
|
| 209 |
+
type: Clustering
|
| 210 |
+
dataset:
|
| 211 |
+
type: mteb/medrxiv-clustering-p2p
|
| 212 |
+
name: MTEB MedrxivClusteringP2P
|
| 213 |
+
config: default
|
| 214 |
+
split: test
|
| 215 |
+
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
| 216 |
+
metrics:
|
| 217 |
+
- type: v_measure
|
| 218 |
+
value: 30.266855488757034
|
| 219 |
+
- task:
|
| 220 |
+
type: Clustering
|
| 221 |
+
dataset:
|
| 222 |
+
type: mteb/medrxiv-clustering-s2s
|
| 223 |
+
name: MTEB MedrxivClusteringS2S
|
| 224 |
+
config: default
|
| 225 |
+
split: test
|
| 226 |
+
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
| 227 |
+
metrics:
|
| 228 |
+
- type: v_measure
|
| 229 |
+
value: 24.580327378539057
|
| 230 |
+
- task:
|
| 231 |
+
type: Clustering
|
| 232 |
+
dataset:
|
| 233 |
+
type: mteb/reddit-clustering
|
| 234 |
+
name: MTEB RedditClustering
|
| 235 |
+
config: default
|
| 236 |
+
split: test
|
| 237 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
| 238 |
+
metrics:
|
| 239 |
+
- type: v_measure
|
| 240 |
+
value: 56.928616405043684
|
| 241 |
+
- task:
|
| 242 |
+
type: Clustering
|
| 243 |
+
dataset:
|
| 244 |
+
type: mteb/reddit-clustering-p2p
|
| 245 |
+
name: MTEB RedditClusteringP2P
|
| 246 |
+
config: default
|
| 247 |
+
split: test
|
| 248 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
| 249 |
+
metrics:
|
| 250 |
+
- type: v_measure
|
| 251 |
+
value: 58.94536303256525
|
| 252 |
+
- task:
|
| 253 |
+
type: STS
|
| 254 |
+
dataset:
|
| 255 |
+
type: mteb/sickr-sts
|
| 256 |
+
name: MTEB SICK-R
|
| 257 |
+
config: default
|
| 258 |
+
split: test
|
| 259 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
| 260 |
+
metrics:
|
| 261 |
+
- type: cos_sim_pearson
|
| 262 |
+
value: 82.43899708996477
|
| 263 |
+
- type: cos_sim_spearman
|
| 264 |
+
value: 76.84011555220044
|
| 265 |
+
- type: euclidean_pearson
|
| 266 |
+
value: 79.6116260676631
|
| 267 |
+
- type: euclidean_spearman
|
| 268 |
+
value: 76.84012073472658
|
| 269 |
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- type: manhattan_pearson
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| 270 |
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value: 78.49980966442152
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| 271 |
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- type: manhattan_spearman
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value: 75.49233078465171
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| 273 |
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- task:
|
| 274 |
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type: STS
|
| 275 |
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dataset:
|
| 276 |
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type: mteb/sts12-sts
|
| 277 |
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name: MTEB STS12
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| 278 |
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config: default
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| 279 |
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split: test
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| 280 |
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revision: a0d554a64d88156834ff5ae9920b964011b16384
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metrics:
|
| 282 |
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- type: cos_sim_pearson
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value: 79.8291506264289
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| 284 |
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- type: cos_sim_spearman
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value: 72.49093632759003
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| 286 |
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- type: euclidean_pearson
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value: 75.42130137819414
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- type: euclidean_spearman
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value: 72.49048089395136
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- type: manhattan_pearson
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value: 74.17957476459091
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- type: manhattan_spearman
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value: 71.6143674273714
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| 294 |
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- task:
|
| 295 |
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type: STS
|
| 296 |
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dataset:
|
| 297 |
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type: mteb/sts13-sts
|
| 298 |
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name: MTEB STS13
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| 299 |
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config: default
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| 300 |
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split: test
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| 301 |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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metrics:
|
| 303 |
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- type: cos_sim_pearson
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value: 70.91903439531401
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- type: cos_sim_spearman
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value: 73.65106317244273
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- type: euclidean_pearson
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value: 73.22383725261588
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- type: euclidean_spearman
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value: 73.65106317244273
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- type: manhattan_pearson
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value: 72.98314057093636
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- type: manhattan_spearman
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value: 73.52101907069579
|
| 315 |
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- task:
|
| 316 |
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type: STS
|
| 317 |
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dataset:
|
| 318 |
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type: mteb/sts14-sts
|
| 319 |
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name: MTEB STS14
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| 320 |
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config: default
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| 321 |
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split: test
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| 322 |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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metrics:
|
| 324 |
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- type: cos_sim_pearson
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value: 75.19632733755482
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| 326 |
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- type: cos_sim_spearman
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value: 71.88328402076041
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- type: euclidean_pearson
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value: 74.02395011081532
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- type: euclidean_spearman
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value: 71.88328903479953
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- type: manhattan_pearson
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value: 73.52941749980135
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value: 71.32905921324534
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| 336 |
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|
| 337 |
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type: STS
|
| 338 |
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dataset:
|
| 339 |
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type: mteb/sts15-sts
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| 340 |
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name: MTEB STS15
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| 341 |
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config: default
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split: test
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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metrics:
|
| 345 |
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- type: cos_sim_pearson
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value: 82.42736501667461
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- type: cos_sim_spearman
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value: 82.89997148218205
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- type: euclidean_pearson
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value: 82.3189209945513
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value: 82.89997089267106
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- type: manhattan_pearson
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value: 81.78597437071429
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value: 82.21582873302081
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|
| 358 |
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type: STS
|
| 359 |
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dataset:
|
| 360 |
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type: mteb/sts16-sts
|
| 361 |
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name: MTEB STS16
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| 362 |
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config: default
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split: test
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| 364 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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metrics:
|
| 366 |
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- type: cos_sim_pearson
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| 367 |
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value: 78.44968010602165
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- type: cos_sim_spearman
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value: 79.82626284236876
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| 370 |
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- type: euclidean_pearson
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value: 79.4157474030238
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- type: euclidean_spearman
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value: 79.82626269881543
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- type: manhattan_pearson
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value: 79.13275737559012
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value: 79.4847570398719
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| 378 |
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- task:
|
| 379 |
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type: STS
|
| 380 |
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dataset:
|
| 381 |
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type: mteb/sts17-crosslingual-sts
|
| 382 |
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name: MTEB STS17 (en-en)
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| 383 |
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config: en-en
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| 384 |
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split: test
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
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metrics:
|
| 387 |
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- type: cos_sim_pearson
|
| 388 |
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value: 84.51882547098218
|
| 389 |
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- type: cos_sim_spearman
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| 391 |
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- type: euclidean_pearson
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| 392 |
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value: 84.78417242196153
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| 393 |
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value: 85.19307726106497
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- type: manhattan_pearson
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value: 84.09108278425708
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value: 84.13590986630149
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| 399 |
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- task:
|
| 400 |
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type: STS
|
| 401 |
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dataset:
|
| 402 |
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type: mteb/sts22-crosslingual-sts
|
| 403 |
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name: MTEB STS22 (en)
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| 404 |
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config: en
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| 405 |
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split: test
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| 406 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
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| 407 |
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metrics:
|
| 408 |
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- type: cos_sim_pearson
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| 409 |
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value: 44.814384769251085
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| 410 |
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- type: cos_sim_spearman
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value: 48.43949857027059
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| 412 |
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- type: euclidean_pearson
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value: 47.479132435178855
|
| 414 |
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- type: euclidean_spearman
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| 415 |
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value: 48.43949857027059
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| 416 |
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- type: manhattan_pearson
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| 417 |
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value: 47.16203934707649
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| 418 |
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value: 48.289920897667095
|
| 420 |
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- task:
|
| 421 |
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type: STS
|
| 422 |
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dataset:
|
| 423 |
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type: mteb/stsbenchmark-sts
|
| 424 |
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name: MTEB STSBenchmark
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| 425 |
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config: default
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| 426 |
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split: test
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| 427 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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| 428 |
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metrics:
|
| 429 |
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- type: cos_sim_pearson
|
| 430 |
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value: 81.25646447054616
|
| 431 |
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- type: cos_sim_spearman
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| 432 |
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value: 79.93231051166357
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| 433 |
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- type: euclidean_pearson
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| 434 |
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value: 80.65225742476945
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| 435 |
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| 436 |
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value: 79.93231051166357
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| 437 |
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- type: manhattan_pearson
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value: 79.84341819764376
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- type: manhattan_spearman
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value: 79.07650150491334
|
| 441 |
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- task:
|
| 442 |
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type: PairClassification
|
| 443 |
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dataset:
|
| 444 |
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type: mteb/sprintduplicatequestions-pairclassification
|
| 445 |
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name: MTEB SprintDuplicateQuestions
|
| 446 |
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config: default
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| 447 |
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split: test
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| 448 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 449 |
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metrics:
|
| 450 |
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- type: cos_sim_accuracy
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value: 99.5910891089109
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| 452 |
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- type: cos_sim_ap
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value: 84.37184771930944
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| 454 |
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| 455 |
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value: 78.78787878787878
|
| 456 |
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- type: cos_sim_precision
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value: 80.99260823653644
|
| 458 |
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- type: cos_sim_recall
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| 459 |
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value: 76.7
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| 460 |
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- type: dot_accuracy
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| 461 |
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value: 99.5910891089109
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| 462 |
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value: 84.37184771930944
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| 464 |
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value: 78.78787878787878
|
| 466 |
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- type: dot_precision
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| 467 |
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value: 80.99260823653644
|
| 468 |
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- type: dot_recall
|
| 469 |
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value: 76.7
|
| 470 |
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- type: euclidean_accuracy
|
| 471 |
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value: 99.5910891089109
|
| 472 |
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- type: euclidean_ap
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| 473 |
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value: 84.37185436709098
|
| 474 |
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- type: euclidean_f1
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value: 78.78787878787878
|
| 476 |
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- type: euclidean_precision
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| 477 |
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value: 80.99260823653644
|
| 478 |
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- type: euclidean_recall
|
| 479 |
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value: 76.7
|
| 480 |
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- type: manhattan_accuracy
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| 481 |
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value: 99.6108910891089
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| 482 |
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value: 85.13355467581354
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| 484 |
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value: 80.2788844621514
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| 486 |
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value: 79.96031746031747
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| 488 |
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- type: manhattan_recall
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value: 80.60000000000001
|
| 490 |
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- type: max_accuracy
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value: 99.6108910891089
|
| 492 |
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- type: max_ap
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value: 85.13355467581354
|
| 494 |
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- type: max_f1
|
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value: 80.2788844621514
|
| 496 |
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- task:
|
| 497 |
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type: Clustering
|
| 498 |
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dataset:
|
| 499 |
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type: mteb/stackexchange-clustering
|
| 500 |
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name: MTEB StackExchangeClustering
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| 501 |
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config: default
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| 502 |
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split: test
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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| 504 |
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metrics:
|
| 505 |
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- type: v_measure
|
| 506 |
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value: 60.8469558550317
|
| 507 |
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- task:
|
| 508 |
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type: Clustering
|
| 509 |
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dataset:
|
| 510 |
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type: mteb/stackexchange-clustering-p2p
|
| 511 |
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name: MTEB StackExchangeClusteringP2P
|
| 512 |
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config: default
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| 513 |
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split: test
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| 514 |
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| 515 |
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metrics:
|
| 516 |
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|
| 517 |
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value: 33.14392913702168
|
| 518 |
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|
| 519 |
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type: Summarization
|
| 520 |
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dataset:
|
| 521 |
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type: mteb/summeval
|
| 522 |
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name: MTEB SummEval
|
| 523 |
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config: default
|
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split: test
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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| 526 |
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metrics:
|
| 527 |
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- type: cos_sim_pearson
|
| 528 |
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value: 29.566148619704457
|
| 529 |
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- type: cos_sim_spearman
|
| 530 |
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value: 29.01201818902588
|
| 531 |
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- type: dot_pearson
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value: 29.566149876183374
|
| 533 |
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- type: dot_spearman
|
| 534 |
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value: 29.014046950422795
|
| 535 |
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- task:
|
| 536 |
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type: Classification
|
| 537 |
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dataset:
|
| 538 |
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type: mteb/toxic_conversations_50k
|
| 539 |
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name: MTEB ToxicConversationsClassification
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| 540 |
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config: default
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| 541 |
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split: test
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|
| 543 |
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metrics:
|
| 544 |
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- type: accuracy
|
| 545 |
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value: 70.17420000000001
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| 546 |
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- type: ap
|
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value: 13.49623412034604
|
| 548 |
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- type: f1
|
| 549 |
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value: 53.7079366494688
|
| 550 |
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- task:
|
| 551 |
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type: Classification
|
| 552 |
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dataset:
|
| 553 |
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type: mteb/tweet_sentiment_extraction
|
| 554 |
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name: MTEB TweetSentimentExtractionClassification
|
| 555 |
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config: default
|
| 556 |
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split: test
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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| 558 |
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metrics:
|
| 559 |
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- type: accuracy
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value: 59.309564233163556
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| 561 |
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- type: f1
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value: 59.33623172630094
|
| 563 |
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|
| 564 |
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type: Clustering
|
| 565 |
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dataset:
|
| 566 |
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type: mteb/twentynewsgroups-clustering
|
| 567 |
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name: MTEB TwentyNewsgroupsClustering
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| 568 |
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config: default
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split: test
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revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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| 571 |
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metrics:
|
| 572 |
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- type: v_measure
|
| 573 |
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value: 42.42960819361032
|
| 574 |
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- task:
|
| 575 |
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type: PairClassification
|
| 576 |
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dataset:
|
| 577 |
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type: mteb/twittersemeval2015-pairclassification
|
| 578 |
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name: MTEB TwitterSemEval2015
|
| 579 |
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config: default
|
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split: test
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| 581 |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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metrics:
|
| 583 |
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|
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value: 85.04500208618943
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- type: cos_sim_ap
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value: 70.12785509302904
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value: 65.36573392243496
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- type: cos_sim_precision
|
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value: 61.10601193207894
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- type: cos_sim_recall
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value: 70.26385224274406
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- type: dot_accuracy
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value: 85.04500208618943
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- type: dot_precision
|
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value: 61.10601193207894
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- type: dot_recall
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value: 70.26385224274406
|
| 603 |
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- type: euclidean_accuracy
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value: 85.04500208618943
|
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- type: euclidean_ap
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value: 70.1278575285826
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value: 65.36573392243496
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- type: euclidean_precision
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| 610 |
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value: 61.10601193207894
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- type: euclidean_recall
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value: 70.26385224274406
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- type: manhattan_accuracy
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value: 85.03308100375514
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value: 69.67192372362932
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value: 64.95726495726495
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| 619 |
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- type: manhattan_precision
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value: 61.218771888862946
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| 621 |
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- type: manhattan_recall
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| 622 |
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value: 69.1820580474934
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| 623 |
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- type: max_accuracy
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value: 85.04500208618943
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- type: max_ap
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value: 70.12785837450095
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| 627 |
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- type: max_f1
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| 628 |
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value: 65.36573392243496
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| 629 |
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- task:
|
| 630 |
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type: PairClassification
|
| 631 |
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dataset:
|
| 632 |
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type: mteb/twitterurlcorpus-pairclassification
|
| 633 |
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name: MTEB TwitterURLCorpus
|
| 634 |
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config: default
|
| 635 |
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split: test
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| 636 |
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revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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| 637 |
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metrics:
|
| 638 |
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value: 88.18644002018085
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- type: cos_sim_ap
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value: 84.09120337117118
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value: 76.33478718604302
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- type: cos_sim_precision
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value: 74.59582598471486
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- type: cos_sim_recall
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value: 78.15676008623345
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- type: dot_ap
|
| 651 |
+
value: 84.09120289232122
|
| 652 |
+
- type: dot_f1
|
| 653 |
+
value: 76.33478718604302
|
| 654 |
+
- type: dot_precision
|
| 655 |
+
value: 74.59582598471486
|
| 656 |
+
- type: dot_recall
|
| 657 |
+
value: 78.15676008623345
|
| 658 |
+
- type: euclidean_accuracy
|
| 659 |
+
value: 88.18644002018085
|
| 660 |
+
- type: euclidean_ap
|
| 661 |
+
value: 84.091202102378
|
| 662 |
+
- type: euclidean_f1
|
| 663 |
+
value: 76.33478718604302
|
| 664 |
+
- type: euclidean_precision
|
| 665 |
+
value: 74.59582598471486
|
| 666 |
+
- type: euclidean_recall
|
| 667 |
+
value: 78.15676008623345
|
| 668 |
+
- type: manhattan_accuracy
|
| 669 |
+
value: 88.19032095315714
|
| 670 |
+
- type: manhattan_ap
|
| 671 |
+
value: 84.0865561436236
|
| 672 |
+
- type: manhattan_f1
|
| 673 |
+
value: 76.16665422235496
|
| 674 |
+
- type: manhattan_precision
|
| 675 |
+
value: 73.93100449340484
|
| 676 |
+
- type: manhattan_recall
|
| 677 |
+
value: 78.54173082845703
|
| 678 |
+
- type: max_accuracy
|
| 679 |
+
value: 88.19032095315714
|
| 680 |
+
- type: max_ap
|
| 681 |
+
value: 84.09120337117118
|
| 682 |
+
- type: max_f1
|
| 683 |
+
value: 76.33478718604302
|
| 684 |
+
---
|
| 685 |
+
This is a part of the [MTEB test](https://huggingface.co/spaces/mteb/leaderboard).
|
| 686 |
+
|
| 687 |
+
```
|
| 688 |
+
# !pip install tensorflow_text
|
| 689 |
+
|
| 690 |
+
import tensorflow_hub as hub
|
| 691 |
+
from tensorflow_text import SentencepieceTokenizer
|
| 692 |
+
import tensorflow as tf
|
| 693 |
+
|
| 694 |
+
embedder=hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3")
|
| 695 |
+
|
| 696 |
+
class USE():
|
| 697 |
+
def encode(self, sentences, batch_size=32, **kwargs):
|
| 698 |
+
embeddings = []
|
| 699 |
+
for i in range(0, len(sentences), batch_size):
|
| 700 |
+
batch_sentences = sentences[i:i+batch_size]
|
| 701 |
+
batch_embeddings = embedder(batch_sentences)
|
| 702 |
+
embeddings.extend(batch_embeddings)
|
| 703 |
+
return embeddings
|
| 704 |
+
|
| 705 |
+
|
| 706 |
+
model = USE()
|
| 707 |
+
```
|
models/universal-sentence-encoder-large-5/source.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/vprelovac/universal-sentence-encoder-large-5
|
| 2 |
+
https://www.kaggle.com/models/google/universal-sentence-encoder/tensorFlow2/large/2
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-en-base-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:e00108b8a3c3175e75119c7cef2ac9438bdddbdf85d2b4cb94ec8916848c335c
|
| 3 |
+
size 407834428
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-en-large-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:c2cb1ff589013928944812032608178b3f3165c1bb29cef1a691ef84c7bd8488
|
| 3 |
+
size 1247654717
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-base-br-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
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|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-base-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
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|
| 3 |
+
size 1757445111
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-preprocess-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 3146299
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-cmlm-multilingual-preprocess-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
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size 3147030
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-large-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 3 |
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|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-large-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 3 |
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size 550093654
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-large-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 3 |
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size 318532925
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-large-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 318551042
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-qa-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 326396355
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-multilingual-qa-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 326416648
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-qa-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
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size 555397765
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-qa-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:8cc88f5f0243db2501544299bb15013a2a516b55530093bad5953ba88eed6bef
|
| 3 |
+
size 555384166
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-universal-sentence-encoder-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 962525828
|
models/universal-sentence-encoder-large-5/universal-sentence-encoder-tensorflow2-universal-sentence-encoder-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 962521942
|
models/universal-sentence-encoder-multilingual-3-onnx-quantized/.gitattributes
ADDED
|
@@ -0,0 +1,35 @@
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
models/universal-sentence-encoder-multilingual-3-onnx-quantized/README.md
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
This model is a quantized version of: [**SamLowe/universal-sentence-encoder-multilingual-3-onnx**](https://huggingface.co/SamLowe/universal-sentence-encoder-multilingual-3-onnx)
|
| 2 |
+
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
languages:
|
| 6 |
+
|
| 7 |
+
* en
|
| 8 |
+
* ar
|
| 9 |
+
* zh
|
| 10 |
+
* fr
|
| 11 |
+
* de
|
| 12 |
+
* it
|
| 13 |
+
* ja
|
| 14 |
+
* ko
|
| 15 |
+
* nl
|
| 16 |
+
* pl
|
| 17 |
+
* pt
|
| 18 |
+
* es
|
| 19 |
+
* th
|
| 20 |
+
* tr
|
| 21 |
+
* ru
|
| 22 |
+
tags:
|
| 23 |
+
* feature-extraction
|
| 24 |
+
* onnx
|
| 25 |
+
* use
|
| 26 |
+
* text-embedding
|
| 27 |
+
* tensorflow-hub
|
| 28 |
+
license: apache-2.0
|
| 29 |
+
inference: false
|
| 30 |
+
widget:
|
| 31 |
+
* text: Thank goodness ONNX is available, it is lots faster!
|
| 32 |
+
|
| 33 |
+
---
|
| 34 |
+
|
| 35 |
+
### Universal Sentence Encoder Multilingual v3
|
| 36 |
+
|
| 37 |
+
ONNX version of [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)
|
| 38 |
+
|
| 39 |
+
The original TFHub version of the model is referenced in other models here E.g. [https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3](https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3)
|
| 40 |
+
|
| 41 |
+
### Overview
|
| 42 |
+
|
| 43 |
+
See overview and license details at [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)
|
| 44 |
+
|
| 45 |
+
This model is a full precision version of the TFHub original, in ONNX format.
|
| 46 |
+
|
| 47 |
+
It uses the [ONNXRuntime Extensions](https://github.com/microsoft/onnxruntime-extensions) to embed the tokenizer within the ONNX model, so no seperate tokenizer is needed, and text is fed directly into the ONNX model.
|
| 48 |
+
|
| 49 |
+
Post-processing (E.g. pooling, normalization) is also implemented within the ONNX model, so no separate processing is necessary.
|
| 50 |
+
|
| 51 |
+
### How to use
|
| 52 |
+
|
| 53 |
+
```python
|
| 54 |
+
import onnxruntime as ort
|
| 55 |
+
from onnxruntime_extensions import get_library_path
|
| 56 |
+
from os import cpu_count
|
| 57 |
+
|
| 58 |
+
sentences = ["hello world"]
|
| 59 |
+
|
| 60 |
+
def load_onnx_model(model_filepath):
|
| 61 |
+
_options = ort.SessionOptions()
|
| 62 |
+
_options.inter_op_num_threads, _options.intra_op_num_threads = cpu_count(), cpu_count()
|
| 63 |
+
_options.register_custom_ops_library(get_library_path())
|
| 64 |
+
_providers = ["CPUExecutionProvider"] # could use ort.get_available_providers()
|
| 65 |
+
return ort.InferenceSession(path_or_bytes=model_filepath, sess_options=_options, providers=_providers)
|
| 66 |
+
|
| 67 |
+
model = load_onnx_model("filepath_for_model_dot_onnx")
|
| 68 |
+
|
| 69 |
+
model_outputs = model.run(output_names=["outputs"], input_feed={"inputs": sentences})[0]
|
| 70 |
+
print(model_outputs)
|
| 71 |
+
```
|
models/universal-sentence-encoder-multilingual-3-onnx-quantized/embedding_model_quantized.onnx
ADDED
|
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:b8b05b01f0861094f0475cb288ad1b303ae8aee2ebe96e650d210805d5ca70f7
|
| 3 |
+
size 71794489
|
models/universal-sentence-encoder-multilingual-3-onnx-quantized/source.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/WiseIntelligence/universal-sentence-encoder-multilingual-3-onnx-quantized
|
models/universal-sentence-encoder-multilingual-3-onnx/.gitattributes
ADDED
|
@@ -0,0 +1,35 @@
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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| 4 |
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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| 5 |
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
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| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
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| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
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| 12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
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| 13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
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| 14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
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| 15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
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| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
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| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
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| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
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| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
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| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
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+
*.pth filter=lfs diff=lfs merge=lfs -text
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| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
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| 30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
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| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
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| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
models/universal-sentence-encoder-multilingual-3-onnx/README.md
ADDED
|
@@ -0,0 +1,65 @@
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| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
- ar
|
| 5 |
+
- zh
|
| 6 |
+
- fr
|
| 7 |
+
- de
|
| 8 |
+
- it
|
| 9 |
+
- ja
|
| 10 |
+
- ko
|
| 11 |
+
- nl
|
| 12 |
+
- pl
|
| 13 |
+
- pt
|
| 14 |
+
- es
|
| 15 |
+
- th
|
| 16 |
+
- tr
|
| 17 |
+
- ru
|
| 18 |
+
tags:
|
| 19 |
+
- feature-extraction
|
| 20 |
+
- onnx
|
| 21 |
+
- use
|
| 22 |
+
- text-embedding
|
| 23 |
+
- tensorflow-hub
|
| 24 |
+
license: apache-2.0
|
| 25 |
+
inference: false
|
| 26 |
+
widget:
|
| 27 |
+
- text: Thank goodness ONNX is available, it is lots faster!
|
| 28 |
+
---
|
| 29 |
+
### Universal Sentence Encoder Multilingual v3
|
| 30 |
+
|
| 31 |
+
ONNX version of [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)
|
| 32 |
+
|
| 33 |
+
The original TFHub version of the model is referenced in other models here E.g. [https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3](https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3)
|
| 34 |
+
|
| 35 |
+
### Overview
|
| 36 |
+
|
| 37 |
+
See overview and license details at [https://tfhub.dev/google/universal-sentence-encoder-multilingual/3](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)
|
| 38 |
+
|
| 39 |
+
This model is a full precision version of the TFHub original, in ONNX format.
|
| 40 |
+
|
| 41 |
+
It uses the [ONNXRuntime Extensions](https://github.com/microsoft/onnxruntime-extensions) to embed the tokenizer within the ONNX model, so no seperate tokenizer is needed, and text is fed directly into the ONNX model.
|
| 42 |
+
|
| 43 |
+
Post-processing (E.g. pooling, normalization) is also implemented within the ONNX model, so no separate processing is necessary.
|
| 44 |
+
|
| 45 |
+
### How to use
|
| 46 |
+
|
| 47 |
+
```python
|
| 48 |
+
import onnxruntime as ort
|
| 49 |
+
from onnxruntime_extensions import get_library_path
|
| 50 |
+
from os import cpu_count
|
| 51 |
+
|
| 52 |
+
sentences = ["hello world"]
|
| 53 |
+
|
| 54 |
+
def load_onnx_model(model_filepath):
|
| 55 |
+
_options = ort.SessionOptions()
|
| 56 |
+
_options.inter_op_num_threads, _options.intra_op_num_threads = cpu_count(), cpu_count()
|
| 57 |
+
_options.register_custom_ops_library(get_library_path())
|
| 58 |
+
_providers = ["CPUExecutionProvider"] # could use ort.get_available_providers()
|
| 59 |
+
return ort.InferenceSession(path_or_bytes=model_filepath, sess_options=_options, providers=_providers)
|
| 60 |
+
|
| 61 |
+
model = load_onnx_model("filepath_for_model_dot_onnx")
|
| 62 |
+
|
| 63 |
+
model_outputs = model.run(output_names=["outputs"], input_feed={"inputs": sentences})[0]
|
| 64 |
+
print(model_outputs)
|
| 65 |
+
```
|
models/universal-sentence-encoder-multilingual-3-onnx/model.onnx
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9f1d9e8ce398071fadb44939cdfddab989abdb73c651136bd3d772d5e1b6a944
|
| 3 |
+
size 278451025
|
models/universal-sentence-encoder-multilingual-3-onnx/source.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/SamLowe/universal-sentence-encoder-multilingual-3-onnx
|
models/universal-sentence-encoder-multilingual-3/.gitattributes
ADDED
|
@@ -0,0 +1,34 @@
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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| 3 |
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*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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| 5 |
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*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
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*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
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*.gz filter=lfs diff=lfs merge=lfs -text
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| 8 |
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*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
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*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
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*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
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*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
models/universal-sentence-encoder-multilingual-3/README.md
ADDED
|
@@ -0,0 +1,707 @@
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
model-index:
|
| 5 |
+
- name: universal-sentence-encoder-multilingual-3
|
| 6 |
+
results:
|
| 7 |
+
- task:
|
| 8 |
+
type: Classification
|
| 9 |
+
dataset:
|
| 10 |
+
type: mteb/amazon_counterfactual
|
| 11 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
| 12 |
+
config: en
|
| 13 |
+
split: test
|
| 14 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 15 |
+
metrics:
|
| 16 |
+
- type: accuracy
|
| 17 |
+
value: 69.83582089552239
|
| 18 |
+
- type: ap
|
| 19 |
+
value: 31.78315481798218
|
| 20 |
+
- type: f1
|
| 21 |
+
value: 63.49599891839609
|
| 22 |
+
- task:
|
| 23 |
+
type: Classification
|
| 24 |
+
dataset:
|
| 25 |
+
type: mteb/amazon_polarity
|
| 26 |
+
name: MTEB AmazonPolarityClassification
|
| 27 |
+
config: default
|
| 28 |
+
split: test
|
| 29 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
| 30 |
+
metrics:
|
| 31 |
+
- type: accuracy
|
| 32 |
+
value: 65.24337500000001
|
| 33 |
+
- type: ap
|
| 34 |
+
value: 60.210780218392365
|
| 35 |
+
- type: f1
|
| 36 |
+
value: 64.93158500771304
|
| 37 |
+
- task:
|
| 38 |
+
type: Classification
|
| 39 |
+
dataset:
|
| 40 |
+
type: mteb/amazon_reviews_multi
|
| 41 |
+
name: MTEB AmazonReviewsClassification (en)
|
| 42 |
+
config: en
|
| 43 |
+
split: test
|
| 44 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 45 |
+
metrics:
|
| 46 |
+
- type: accuracy
|
| 47 |
+
value: 33.95400000000001
|
| 48 |
+
- type: f1
|
| 49 |
+
value: 33.54319450350246
|
| 50 |
+
- task:
|
| 51 |
+
type: Clustering
|
| 52 |
+
dataset:
|
| 53 |
+
type: mteb/arxiv-clustering-p2p
|
| 54 |
+
name: MTEB ArxivClusteringP2P
|
| 55 |
+
config: default
|
| 56 |
+
split: test
|
| 57 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
| 58 |
+
metrics:
|
| 59 |
+
- type: v_measure
|
| 60 |
+
value: 33.721026148085606
|
| 61 |
+
- task:
|
| 62 |
+
type: Clustering
|
| 63 |
+
dataset:
|
| 64 |
+
type: mteb/arxiv-clustering-s2s
|
| 65 |
+
name: MTEB ArxivClusteringS2S
|
| 66 |
+
config: default
|
| 67 |
+
split: test
|
| 68 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
| 69 |
+
metrics:
|
| 70 |
+
- type: v_measure
|
| 71 |
+
value: 23.989923856346987
|
| 72 |
+
- task:
|
| 73 |
+
type: STS
|
| 74 |
+
dataset:
|
| 75 |
+
type: mteb/biosses-sts
|
| 76 |
+
name: MTEB BIOSSES
|
| 77 |
+
config: default
|
| 78 |
+
split: test
|
| 79 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
| 80 |
+
metrics:
|
| 81 |
+
- type: cos_sim_pearson
|
| 82 |
+
value: 80.07123979932489
|
| 83 |
+
- type: cos_sim_spearman
|
| 84 |
+
value: 78.19395037690312
|
| 85 |
+
- type: euclidean_pearson
|
| 86 |
+
value: 78.16919457797398
|
| 87 |
+
- type: euclidean_spearman
|
| 88 |
+
value: 78.19395037690312
|
| 89 |
+
- type: manhattan_pearson
|
| 90 |
+
value: 77.91224702148509
|
| 91 |
+
- type: manhattan_spearman
|
| 92 |
+
value: 77.37284733016378
|
| 93 |
+
- task:
|
| 94 |
+
type: Classification
|
| 95 |
+
dataset:
|
| 96 |
+
type: mteb/banking77
|
| 97 |
+
name: MTEB Banking77Classification
|
| 98 |
+
config: default
|
| 99 |
+
split: test
|
| 100 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
| 101 |
+
metrics:
|
| 102 |
+
- type: accuracy
|
| 103 |
+
value: 72.78571428571429
|
| 104 |
+
- type: f1
|
| 105 |
+
value: 71.7761168332973
|
| 106 |
+
- task:
|
| 107 |
+
type: Clustering
|
| 108 |
+
dataset:
|
| 109 |
+
type: mteb/biorxiv-clustering-p2p
|
| 110 |
+
name: MTEB BiorxivClusteringP2P
|
| 111 |
+
config: default
|
| 112 |
+
split: test
|
| 113 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
| 114 |
+
metrics:
|
| 115 |
+
- type: v_measure
|
| 116 |
+
value: 30.883580770801693
|
| 117 |
+
- task:
|
| 118 |
+
type: Clustering
|
| 119 |
+
dataset:
|
| 120 |
+
type: mteb/biorxiv-clustering-s2s
|
| 121 |
+
name: MTEB BiorxivClusteringS2S
|
| 122 |
+
config: default
|
| 123 |
+
split: test
|
| 124 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
| 125 |
+
metrics:
|
| 126 |
+
- type: v_measure
|
| 127 |
+
value: 21.053335896002615
|
| 128 |
+
- task:
|
| 129 |
+
type: Classification
|
| 130 |
+
dataset:
|
| 131 |
+
type: mteb/emotion
|
| 132 |
+
name: MTEB EmotionClassification
|
| 133 |
+
config: default
|
| 134 |
+
split: test
|
| 135 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 136 |
+
metrics:
|
| 137 |
+
- type: accuracy
|
| 138 |
+
value: 35.6
|
| 139 |
+
- type: f1
|
| 140 |
+
value: 32.46907363797937
|
| 141 |
+
- task:
|
| 142 |
+
type: Classification
|
| 143 |
+
dataset:
|
| 144 |
+
type: mteb/imdb
|
| 145 |
+
name: MTEB ImdbClassification
|
| 146 |
+
config: default
|
| 147 |
+
split: test
|
| 148 |
+
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
| 149 |
+
metrics:
|
| 150 |
+
- type: accuracy
|
| 151 |
+
value: 66.32400000000001
|
| 152 |
+
- type: ap
|
| 153 |
+
value: 60.96534738097902
|
| 154 |
+
- type: f1
|
| 155 |
+
value: 66.17106527460737
|
| 156 |
+
- task:
|
| 157 |
+
type: Classification
|
| 158 |
+
dataset:
|
| 159 |
+
type: mteb/mtop_domain
|
| 160 |
+
name: MTEB MTOPDomainClassification (en)
|
| 161 |
+
config: en
|
| 162 |
+
split: test
|
| 163 |
+
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
| 164 |
+
metrics:
|
| 165 |
+
- type: accuracy
|
| 166 |
+
value: 90.40355677154584
|
| 167 |
+
- type: f1
|
| 168 |
+
value: 90.02197700114169
|
| 169 |
+
- task:
|
| 170 |
+
type: Classification
|
| 171 |
+
dataset:
|
| 172 |
+
type: mteb/mtop_intent
|
| 173 |
+
name: MTEB MTOPIntentClassification (en)
|
| 174 |
+
config: en
|
| 175 |
+
split: test
|
| 176 |
+
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
| 177 |
+
metrics:
|
| 178 |
+
- type: accuracy
|
| 179 |
+
value: 62.282261741906076
|
| 180 |
+
- type: f1
|
| 181 |
+
value: 42.66509438922704
|
| 182 |
+
- task:
|
| 183 |
+
type: Classification
|
| 184 |
+
dataset:
|
| 185 |
+
type: mteb/amazon_massive_intent
|
| 186 |
+
name: MTEB MassiveIntentClassification (en)
|
| 187 |
+
config: en
|
| 188 |
+
split: test
|
| 189 |
+
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
| 190 |
+
metrics:
|
| 191 |
+
- type: accuracy
|
| 192 |
+
value: 66.11634162743779
|
| 193 |
+
- type: f1
|
| 194 |
+
value: 63.22561882532336
|
| 195 |
+
- task:
|
| 196 |
+
type: Classification
|
| 197 |
+
dataset:
|
| 198 |
+
type: mteb/amazon_massive_scenario
|
| 199 |
+
name: MTEB MassiveScenarioClassification (en)
|
| 200 |
+
config: en
|
| 201 |
+
split: test
|
| 202 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
| 203 |
+
metrics:
|
| 204 |
+
- type: accuracy
|
| 205 |
+
value: 73.73570948217888
|
| 206 |
+
- type: f1
|
| 207 |
+
value: 72.60926811627525
|
| 208 |
+
- task:
|
| 209 |
+
type: Clustering
|
| 210 |
+
dataset:
|
| 211 |
+
type: mteb/medrxiv-clustering-p2p
|
| 212 |
+
name: MTEB MedrxivClusteringP2P
|
| 213 |
+
config: default
|
| 214 |
+
split: test
|
| 215 |
+
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
| 216 |
+
metrics:
|
| 217 |
+
- type: v_measure
|
| 218 |
+
value: 28.682218411117113
|
| 219 |
+
- task:
|
| 220 |
+
type: Clustering
|
| 221 |
+
dataset:
|
| 222 |
+
type: mteb/medrxiv-clustering-s2s
|
| 223 |
+
name: MTEB MedrxivClusteringS2S
|
| 224 |
+
config: default
|
| 225 |
+
split: test
|
| 226 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
| 227 |
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metrics:
|
| 228 |
+
- type: v_measure
|
| 229 |
+
value: 24.249068423345125
|
| 230 |
+
- task:
|
| 231 |
+
type: Clustering
|
| 232 |
+
dataset:
|
| 233 |
+
type: mteb/reddit-clustering
|
| 234 |
+
name: MTEB RedditClustering
|
| 235 |
+
config: default
|
| 236 |
+
split: test
|
| 237 |
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revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
| 238 |
+
metrics:
|
| 239 |
+
- type: v_measure
|
| 240 |
+
value: 43.81857444082751
|
| 241 |
+
- task:
|
| 242 |
+
type: Clustering
|
| 243 |
+
dataset:
|
| 244 |
+
type: mteb/reddit-clustering-p2p
|
| 245 |
+
name: MTEB RedditClusteringP2P
|
| 246 |
+
config: default
|
| 247 |
+
split: test
|
| 248 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
| 249 |
+
metrics:
|
| 250 |
+
- type: v_measure
|
| 251 |
+
value: 58.37019068224756
|
| 252 |
+
- task:
|
| 253 |
+
type: STS
|
| 254 |
+
dataset:
|
| 255 |
+
type: mteb/sickr-sts
|
| 256 |
+
name: MTEB SICK-R
|
| 257 |
+
config: default
|
| 258 |
+
split: test
|
| 259 |
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revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
| 260 |
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metrics:
|
| 261 |
+
- type: cos_sim_pearson
|
| 262 |
+
value: 80.40531780187507
|
| 263 |
+
- type: cos_sim_spearman
|
| 264 |
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value: 74.43055388885399
|
| 265 |
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- type: euclidean_pearson
|
| 266 |
+
value: 77.56731280002846
|
| 267 |
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- type: euclidean_spearman
|
| 268 |
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value: 74.43055238430316
|
| 269 |
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- type: manhattan_pearson
|
| 270 |
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value: 77.0225674793828
|
| 271 |
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- type: manhattan_spearman
|
| 272 |
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value: 73.0157763009755
|
| 273 |
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- task:
|
| 274 |
+
type: STS
|
| 275 |
+
dataset:
|
| 276 |
+
type: mteb/sts12-sts
|
| 277 |
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name: MTEB STS12
|
| 278 |
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config: default
|
| 279 |
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split: test
|
| 280 |
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revision: a0d554a64d88156834ff5ae9920b964011b16384
|
| 281 |
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metrics:
|
| 282 |
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- type: cos_sim_pearson
|
| 283 |
+
value: 77.89513323602442
|
| 284 |
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- type: cos_sim_spearman
|
| 285 |
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value: 72.57556877265816
|
| 286 |
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- type: euclidean_pearson
|
| 287 |
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value: 73.85821376234655
|
| 288 |
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- type: euclidean_spearman
|
| 289 |
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value: 72.57491371628555
|
| 290 |
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- type: manhattan_pearson
|
| 291 |
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value: 74.09836169887191
|
| 292 |
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- type: manhattan_spearman
|
| 293 |
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value: 72.68258315762111
|
| 294 |
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- task:
|
| 295 |
+
type: STS
|
| 296 |
+
dataset:
|
| 297 |
+
type: mteb/sts13-sts
|
| 298 |
+
name: MTEB STS13
|
| 299 |
+
config: default
|
| 300 |
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split: test
|
| 301 |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
| 302 |
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metrics:
|
| 303 |
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- type: cos_sim_pearson
|
| 304 |
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value: 70.35242787171663
|
| 305 |
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- type: cos_sim_spearman
|
| 306 |
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value: 72.21957543640191
|
| 307 |
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- type: euclidean_pearson
|
| 308 |
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value: 71.69509554469398
|
| 309 |
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- type: euclidean_spearman
|
| 310 |
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value: 72.21957537220672
|
| 311 |
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- type: manhattan_pearson
|
| 312 |
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value: 71.48837487133736
|
| 313 |
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- type: manhattan_spearman
|
| 314 |
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value: 72.10777865383778
|
| 315 |
+
- task:
|
| 316 |
+
type: STS
|
| 317 |
+
dataset:
|
| 318 |
+
type: mteb/sts14-sts
|
| 319 |
+
name: MTEB STS14
|
| 320 |
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config: default
|
| 321 |
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split: test
|
| 322 |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
| 323 |
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metrics:
|
| 324 |
+
- type: cos_sim_pearson
|
| 325 |
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value: 74.16772362444075
|
| 326 |
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- type: cos_sim_spearman
|
| 327 |
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value: 69.97673740601287
|
| 328 |
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- type: euclidean_pearson
|
| 329 |
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value: 72.31559291876557
|
| 330 |
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- type: euclidean_spearman
|
| 331 |
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value: 69.97673740601287
|
| 332 |
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- type: manhattan_pearson
|
| 333 |
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value: 72.24271795357289
|
| 334 |
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- type: manhattan_spearman
|
| 335 |
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value: 69.49367729167946
|
| 336 |
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- task:
|
| 337 |
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type: STS
|
| 338 |
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dataset:
|
| 339 |
+
type: mteb/sts15-sts
|
| 340 |
+
name: MTEB STS15
|
| 341 |
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config: default
|
| 342 |
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split: test
|
| 343 |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
| 344 |
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metrics:
|
| 345 |
+
- type: cos_sim_pearson
|
| 346 |
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value: 81.79182911906513
|
| 347 |
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- type: cos_sim_spearman
|
| 348 |
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value: 82.22437412512745
|
| 349 |
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- type: euclidean_pearson
|
| 350 |
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value: 81.83097544031064
|
| 351 |
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- type: euclidean_spearman
|
| 352 |
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value: 82.22436565654309
|
| 353 |
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- type: manhattan_pearson
|
| 354 |
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value: 81.36464923871902
|
| 355 |
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- type: manhattan_spearman
|
| 356 |
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value: 81.5343779056359
|
| 357 |
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- task:
|
| 358 |
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type: STS
|
| 359 |
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dataset:
|
| 360 |
+
type: mteb/sts16-sts
|
| 361 |
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name: MTEB STS16
|
| 362 |
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config: default
|
| 363 |
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split: test
|
| 364 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
| 365 |
+
metrics:
|
| 366 |
+
- type: cos_sim_pearson
|
| 367 |
+
value: 75.43557616859144
|
| 368 |
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- type: cos_sim_spearman
|
| 369 |
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value: 76.91365659685123
|
| 370 |
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- type: euclidean_pearson
|
| 371 |
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value: 76.56513219626397
|
| 372 |
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- type: euclidean_spearman
|
| 373 |
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value: 76.91365659685123
|
| 374 |
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- type: manhattan_pearson
|
| 375 |
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value: 76.17721999284116
|
| 376 |
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- type: manhattan_spearman
|
| 377 |
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value: 76.35088101292793
|
| 378 |
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- task:
|
| 379 |
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type: STS
|
| 380 |
+
dataset:
|
| 381 |
+
type: mteb/sts17-crosslingual-sts
|
| 382 |
+
name: MTEB STS17 (en-en)
|
| 383 |
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config: en-en
|
| 384 |
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split: test
|
| 385 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
| 386 |
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metrics:
|
| 387 |
+
- type: cos_sim_pearson
|
| 388 |
+
value: 84.94818288101212
|
| 389 |
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- type: cos_sim_spearman
|
| 390 |
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value: 85.2167560886111
|
| 391 |
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- type: euclidean_pearson
|
| 392 |
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value: 85.17622731729108
|
| 393 |
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- type: euclidean_spearman
|
| 394 |
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value: 85.2167560886111
|
| 395 |
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- type: manhattan_pearson
|
| 396 |
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value: 84.8324354792735
|
| 397 |
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- type: manhattan_spearman
|
| 398 |
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value: 84.69897393431455
|
| 399 |
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- task:
|
| 400 |
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type: STS
|
| 401 |
+
dataset:
|
| 402 |
+
type: mteb/sts22-crosslingual-sts
|
| 403 |
+
name: MTEB STS22 (en)
|
| 404 |
+
config: en
|
| 405 |
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split: test
|
| 406 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
| 407 |
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metrics:
|
| 408 |
+
- type: cos_sim_pearson
|
| 409 |
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value: 61.591839696719234
|
| 410 |
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- type: cos_sim_spearman
|
| 411 |
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value: 61.90385583991066
|
| 412 |
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- type: euclidean_pearson
|
| 413 |
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value: 63.49437829898753
|
| 414 |
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- type: euclidean_spearman
|
| 415 |
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value: 61.90385583991066
|
| 416 |
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- type: manhattan_pearson
|
| 417 |
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value: 60.78565195357755
|
| 418 |
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- type: manhattan_spearman
|
| 419 |
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value: 59.802983782193905
|
| 420 |
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- task:
|
| 421 |
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type: STS
|
| 422 |
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dataset:
|
| 423 |
+
type: mteb/stsbenchmark-sts
|
| 424 |
+
name: MTEB STSBenchmark
|
| 425 |
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config: default
|
| 426 |
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split: test
|
| 427 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
| 428 |
+
metrics:
|
| 429 |
+
- type: cos_sim_pearson
|
| 430 |
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value: 81.37748727839765
|
| 431 |
+
- type: cos_sim_spearman
|
| 432 |
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value: 80.28214972273051
|
| 433 |
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- type: euclidean_pearson
|
| 434 |
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value: 81.0274577938496
|
| 435 |
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- type: euclidean_spearman
|
| 436 |
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value: 80.28214972273051
|
| 437 |
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- type: manhattan_pearson
|
| 438 |
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value: 80.18978223617367
|
| 439 |
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- type: manhattan_spearman
|
| 440 |
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value: 79.17781398461948
|
| 441 |
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- task:
|
| 442 |
+
type: PairClassification
|
| 443 |
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dataset:
|
| 444 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
| 445 |
+
name: MTEB SprintDuplicateQuestions
|
| 446 |
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config: default
|
| 447 |
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split: test
|
| 448 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 449 |
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metrics:
|
| 450 |
+
- type: cos_sim_accuracy
|
| 451 |
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value: 99.62475247524752
|
| 452 |
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- type: cos_sim_ap
|
| 453 |
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value: 87.05032305596566
|
| 454 |
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- type: cos_sim_f1
|
| 455 |
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value: 80.25987006496752
|
| 456 |
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- type: cos_sim_precision
|
| 457 |
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value: 80.21978021978022
|
| 458 |
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- type: cos_sim_recall
|
| 459 |
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value: 80.30000000000001
|
| 460 |
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- type: dot_accuracy
|
| 461 |
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value: 99.62475247524752
|
| 462 |
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- type: dot_ap
|
| 463 |
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value: 87.05032305596568
|
| 464 |
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- type: dot_f1
|
| 465 |
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value: 80.25987006496752
|
| 466 |
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- type: dot_precision
|
| 467 |
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value: 80.21978021978022
|
| 468 |
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- type: dot_recall
|
| 469 |
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value: 80.30000000000001
|
| 470 |
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- type: euclidean_accuracy
|
| 471 |
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value: 99.62475247524752
|
| 472 |
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- type: euclidean_ap
|
| 473 |
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value: 87.05031186021066
|
| 474 |
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- type: euclidean_f1
|
| 475 |
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value: 80.25987006496752
|
| 476 |
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- type: euclidean_precision
|
| 477 |
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value: 80.21978021978022
|
| 478 |
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- type: euclidean_recall
|
| 479 |
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value: 80.30000000000001
|
| 480 |
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- type: manhattan_accuracy
|
| 481 |
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value: 99.64554455445544
|
| 482 |
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- type: manhattan_ap
|
| 483 |
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value: 87.773065204317
|
| 484 |
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- type: manhattan_f1
|
| 485 |
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value: 81.32094943240455
|
| 486 |
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- type: manhattan_precision
|
| 487 |
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value: 84.00852878464818
|
| 488 |
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- type: manhattan_recall
|
| 489 |
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value: 78.8
|
| 490 |
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- type: max_accuracy
|
| 491 |
+
value: 99.64554455445544
|
| 492 |
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- type: max_ap
|
| 493 |
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value: 87.773065204317
|
| 494 |
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- type: max_f1
|
| 495 |
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value: 81.32094943240455
|
| 496 |
+
- task:
|
| 497 |
+
type: Clustering
|
| 498 |
+
dataset:
|
| 499 |
+
type: mteb/stackexchange-clustering
|
| 500 |
+
name: MTEB StackExchangeClustering
|
| 501 |
+
config: default
|
| 502 |
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split: test
|
| 503 |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
| 504 |
+
metrics:
|
| 505 |
+
- type: v_measure
|
| 506 |
+
value: 47.82768409586564
|
| 507 |
+
- task:
|
| 508 |
+
type: Clustering
|
| 509 |
+
dataset:
|
| 510 |
+
type: mteb/stackexchange-clustering-p2p
|
| 511 |
+
name: MTEB StackExchangeClusteringP2P
|
| 512 |
+
config: default
|
| 513 |
+
split: test
|
| 514 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
| 515 |
+
metrics:
|
| 516 |
+
- type: v_measure
|
| 517 |
+
value: 33.010238746581386
|
| 518 |
+
- task:
|
| 519 |
+
type: Summarization
|
| 520 |
+
dataset:
|
| 521 |
+
type: mteb/summeval
|
| 522 |
+
name: MTEB SummEval
|
| 523 |
+
config: default
|
| 524 |
+
split: test
|
| 525 |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 526 |
+
metrics:
|
| 527 |
+
- type: cos_sim_pearson
|
| 528 |
+
value: 30.93460869020161
|
| 529 |
+
- type: cos_sim_spearman
|
| 530 |
+
value: 30.793100826598852
|
| 531 |
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- type: dot_pearson
|
| 532 |
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value: 30.934611387819803
|
| 533 |
+
- type: dot_spearman
|
| 534 |
+
value: 30.793100826598852
|
| 535 |
+
- task:
|
| 536 |
+
type: Classification
|
| 537 |
+
dataset:
|
| 538 |
+
type: mteb/toxic_conversations_50k
|
| 539 |
+
name: MTEB ToxicConversationsClassification
|
| 540 |
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config: default
|
| 541 |
+
split: test
|
| 542 |
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revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
| 543 |
+
metrics:
|
| 544 |
+
- type: accuracy
|
| 545 |
+
value: 67.56439999999999
|
| 546 |
+
- type: ap
|
| 547 |
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value: 12.622864890343005
|
| 548 |
+
- type: f1
|
| 549 |
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value: 51.866745839430514
|
| 550 |
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- task:
|
| 551 |
+
type: Classification
|
| 552 |
+
dataset:
|
| 553 |
+
type: mteb/tweet_sentiment_extraction
|
| 554 |
+
name: MTEB TweetSentimentExtractionClassification
|
| 555 |
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config: default
|
| 556 |
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split: test
|
| 557 |
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
| 558 |
+
metrics:
|
| 559 |
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- type: accuracy
|
| 560 |
+
value: 57.2354272778721
|
| 561 |
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- type: f1
|
| 562 |
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value: 57.42332031933637
|
| 563 |
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- task:
|
| 564 |
+
type: Clustering
|
| 565 |
+
dataset:
|
| 566 |
+
type: mteb/twentynewsgroups-clustering
|
| 567 |
+
name: MTEB TwentyNewsgroupsClustering
|
| 568 |
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config: default
|
| 569 |
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split: test
|
| 570 |
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revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
| 571 |
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metrics:
|
| 572 |
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- type: v_measure
|
| 573 |
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value: 37.46769271159515
|
| 574 |
+
- task:
|
| 575 |
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type: PairClassification
|
| 576 |
+
dataset:
|
| 577 |
+
type: mteb/twittersemeval2015-pairclassification
|
| 578 |
+
name: MTEB TwitterSemEval2015
|
| 579 |
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config: default
|
| 580 |
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split: test
|
| 581 |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 582 |
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metrics:
|
| 583 |
+
- type: cos_sim_accuracy
|
| 584 |
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value: 81.53424330929249
|
| 585 |
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- type: cos_sim_ap
|
| 586 |
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value: 57.033163698414114
|
| 587 |
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- type: cos_sim_f1
|
| 588 |
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value: 54.54983148772268
|
| 589 |
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- type: cos_sim_precision
|
| 590 |
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value: 50.154935812306334
|
| 591 |
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- type: cos_sim_recall
|
| 592 |
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value: 59.788918205804755
|
| 593 |
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- type: dot_accuracy
|
| 594 |
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value: 81.53424330929249
|
| 595 |
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- type: dot_ap
|
| 596 |
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value: 57.03315710343203
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| 597 |
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- type: dot_f1
|
| 598 |
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value: 54.54983148772268
|
| 599 |
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- type: dot_precision
|
| 600 |
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value: 50.154935812306334
|
| 601 |
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- type: dot_recall
|
| 602 |
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value: 59.788918205804755
|
| 603 |
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- type: euclidean_accuracy
|
| 604 |
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value: 81.53424330929249
|
| 605 |
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- type: euclidean_ap
|
| 606 |
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value: 57.033158170117446
|
| 607 |
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- type: euclidean_f1
|
| 608 |
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value: 54.54983148772268
|
| 609 |
+
- type: euclidean_precision
|
| 610 |
+
value: 50.154935812306334
|
| 611 |
+
- type: euclidean_recall
|
| 612 |
+
value: 59.788918205804755
|
| 613 |
+
- type: manhattan_accuracy
|
| 614 |
+
value: 81.29582166060678
|
| 615 |
+
- type: manhattan_ap
|
| 616 |
+
value: 55.74973597316332
|
| 617 |
+
- type: manhattan_f1
|
| 618 |
+
value: 53.15203955500617
|
| 619 |
+
- type: manhattan_precision
|
| 620 |
+
value: 50.0
|
| 621 |
+
- type: manhattan_recall
|
| 622 |
+
value: 56.72823218997362
|
| 623 |
+
- type: max_accuracy
|
| 624 |
+
value: 81.53424330929249
|
| 625 |
+
- type: max_ap
|
| 626 |
+
value: 57.033163698414114
|
| 627 |
+
- type: max_f1
|
| 628 |
+
value: 54.54983148772268
|
| 629 |
+
- task:
|
| 630 |
+
type: PairClassification
|
| 631 |
+
dataset:
|
| 632 |
+
type: mteb/twitterurlcorpus-pairclassification
|
| 633 |
+
name: MTEB TwitterURLCorpus
|
| 634 |
+
config: default
|
| 635 |
+
split: test
|
| 636 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 637 |
+
metrics:
|
| 638 |
+
- type: cos_sim_accuracy
|
| 639 |
+
value: 87.27053983777701
|
| 640 |
+
- type: cos_sim_ap
|
| 641 |
+
value: 82.20632443836952
|
| 642 |
+
- type: cos_sim_f1
|
| 643 |
+
value: 74.4764795144158
|
| 644 |
+
- type: cos_sim_precision
|
| 645 |
+
value: 73.40711935387377
|
| 646 |
+
- type: cos_sim_recall
|
| 647 |
+
value: 75.57745611333539
|
| 648 |
+
- type: dot_accuracy
|
| 649 |
+
value: 87.27053983777701
|
| 650 |
+
- type: dot_ap
|
| 651 |
+
value: 82.20632570091198
|
| 652 |
+
- type: dot_f1
|
| 653 |
+
value: 74.4764795144158
|
| 654 |
+
- type: dot_precision
|
| 655 |
+
value: 73.40711935387377
|
| 656 |
+
- type: dot_recall
|
| 657 |
+
value: 75.57745611333539
|
| 658 |
+
- type: euclidean_accuracy
|
| 659 |
+
value: 87.27053983777701
|
| 660 |
+
- type: euclidean_ap
|
| 661 |
+
value: 82.20632379487282
|
| 662 |
+
- type: euclidean_f1
|
| 663 |
+
value: 74.4764795144158
|
| 664 |
+
- type: euclidean_precision
|
| 665 |
+
value: 73.40711935387377
|
| 666 |
+
- type: euclidean_recall
|
| 667 |
+
value: 75.57745611333539
|
| 668 |
+
- type: manhattan_accuracy
|
| 669 |
+
value: 87.18321884581053
|
| 670 |
+
- type: manhattan_ap
|
| 671 |
+
value: 81.95324384723243
|
| 672 |
+
- type: manhattan_f1
|
| 673 |
+
value: 74.28318388132404
|
| 674 |
+
- type: manhattan_precision
|
| 675 |
+
value: 71.32733328608887
|
| 676 |
+
- type: manhattan_recall
|
| 677 |
+
value: 77.49461040960887
|
| 678 |
+
- type: max_accuracy
|
| 679 |
+
value: 87.27053983777701
|
| 680 |
+
- type: max_ap
|
| 681 |
+
value: 82.20632570091198
|
| 682 |
+
- type: max_f1
|
| 683 |
+
value: 74.4764795144158
|
| 684 |
+
---
|
| 685 |
+
This is a part of the [MTEB test](https://huggingface.co/spaces/mteb/leaderboard).
|
| 686 |
+
|
| 687 |
+
```
|
| 688 |
+
# !pip install tensorflow_text
|
| 689 |
+
|
| 690 |
+
import tensorflow_hub as hub
|
| 691 |
+
from tensorflow_text import SentencepieceTokenizer
|
| 692 |
+
import tensorflow as tf
|
| 693 |
+
|
| 694 |
+
embedder=hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3")
|
| 695 |
+
|
| 696 |
+
class USE():
|
| 697 |
+
def encode(self, sentences, batch_size=32, **kwargs):
|
| 698 |
+
embeddings = []
|
| 699 |
+
for i in range(0, len(sentences), batch_size):
|
| 700 |
+
batch_sentences = sentences[i:i+batch_size]
|
| 701 |
+
batch_embeddings = embedder(batch_sentences)
|
| 702 |
+
embeddings.extend(batch_embeddings)
|
| 703 |
+
return embeddings
|
| 704 |
+
|
| 705 |
+
|
| 706 |
+
model = USE()
|
| 707 |
+
```
|
models/universal-sentence-encoder-multilingual-3/source.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3
|
| 2 |
+
https://www.kaggle.com/models/google/universal-sentence-encoder
|
models/universal-sentence-encoder-multilingual-3/universal-sentence-encoder-tensorflow2-multilingual-large-v1.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:982833f2ab2634c611cd59a9dee64f94c5fc01c0051e12c5e0dcfb16f3faa586
|
| 3 |
+
size 318532925
|
models/universal-sentence-encoder-multilingual-3/universal-sentence-encoder-tensorflow2-multilingual-large-v2.tar.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:75818b72e4a0c99cf84389b4df6406af15fda1e8b6c74b8815e21625ed7cc541
|
| 3 |
+
size 318551042
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/.gitattributes
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/README.md
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- ru
|
| 6 |
+
- ar
|
| 7 |
+
- zh
|
| 8 |
+
- fr
|
| 9 |
+
- de
|
| 10 |
+
- it
|
| 11 |
+
- ja
|
| 12 |
+
- ko
|
| 13 |
+
- nl
|
| 14 |
+
- pl
|
| 15 |
+
- pt
|
| 16 |
+
- es
|
| 17 |
+
- th
|
| 18 |
+
- tr
|
| 19 |
+
library_name: sentence-transformers
|
| 20 |
+
pipeline_tag: feature-extraction
|
| 21 |
+
tags:
|
| 22 |
+
- mteb
|
| 23 |
+
- Sentence Transformers
|
| 24 |
+
- sentence-similarity
|
| 25 |
+
- arxiv:1803.11175
|
| 26 |
+
- arxiv:1907.04307
|
| 27 |
+
---
|
| 28 |
+
|
| 29 |
+
# Convert MUSE from TensorFlow to PyTorch
|
| 30 |
+
|
| 31 |
+
This repository contains code to use mUSE (Multilingual Universal Sentence Encoder) transformer model from [TF Hub](https://www.kaggle.com/models/google/universal-sentence-encoder/tensorFlow2/multilingual-large) using **PyTorch**.
|
| 32 |
+
|
| 33 |
+
> [!IMPORTANT]
|
| 34 |
+
> **The PyTorch model can be used not only for inference, but also for additional training and fine-tuning!**
|
| 35 |
+
|
| 36 |
+
Read more about the project: [GitHub](https://github.com/dayyass/muse_tf2pt/tree/main).
|
| 37 |
+
|
| 38 |
+
# Usage
|
| 39 |
+
|
| 40 |
+
The model is available in [HF Models](https://huggingface.co/dayyass/universal-sentence-encoder-multilingual-large-3-pytorch/tree/main) directly through `torch` (*currently, without native support from the `transformers` library*).
|
| 41 |
+
|
| 42 |
+
Model initialization and usage code:
|
| 43 |
+
```python
|
| 44 |
+
import torch
|
| 45 |
+
from functools import partial
|
| 46 |
+
from architecture import MUSE
|
| 47 |
+
from tokenizer import get_tokenizer, tokenize
|
| 48 |
+
|
| 49 |
+
PATH_TO_PT_MODEL = "model.pt"
|
| 50 |
+
PATH_TO_TF_MODEL = "universal-sentence-encoder-multilingual-large-3"
|
| 51 |
+
|
| 52 |
+
tokenizer = get_tokenizer(PATH_TO_TF_MODEL)
|
| 53 |
+
tokenize = partial(tokenize, tokenizer=tokenizer)
|
| 54 |
+
|
| 55 |
+
model_torch = MUSE(
|
| 56 |
+
num_embeddings=128010,
|
| 57 |
+
embedding_dim=512,
|
| 58 |
+
d_model=512,
|
| 59 |
+
num_heads=8,
|
| 60 |
+
)
|
| 61 |
+
model_torch.load_state_dict(
|
| 62 |
+
torch.load(PATH_TO_PT_MODEL)
|
| 63 |
+
)
|
| 64 |
+
|
| 65 |
+
sentence = "Hello, world!"
|
| 66 |
+
res = model_torch(tokenize(sentence))
|
| 67 |
+
```
|
| 68 |
+
> [!NOTE]
|
| 69 |
+
> Currently, the checkpoint of the original TF Hub model is used for tokenization, so it is loaded in the code above.
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/architecture.py
ADDED
|
@@ -0,0 +1,159 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import math
|
| 2 |
+
|
| 3 |
+
import torch
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class PositionalEncoding(torch.nn.Module):
|
| 7 |
+
"""
|
| 8 |
+
https://pytorch.org/tutorials/beginner/transformer_tutorial.html
|
| 9 |
+
"""
|
| 10 |
+
|
| 11 |
+
def __init__(self, d_model: int, max_len: int = 512):
|
| 12 |
+
super().__init__()
|
| 13 |
+
|
| 14 |
+
position = torch.arange(max_len).unsqueeze(1)
|
| 15 |
+
div_term = torch.exp(
|
| 16 |
+
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
|
| 17 |
+
)
|
| 18 |
+
|
| 19 |
+
pe = torch.zeros(max_len, d_model)
|
| 20 |
+
pe[:, : d_model // 2] = torch.sin(position * div_term)
|
| 21 |
+
pe[:, d_model // 2 :] = torch.cos(position * div_term)
|
| 22 |
+
|
| 23 |
+
self.register_buffer("pe", pe)
|
| 24 |
+
|
| 25 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 26 |
+
x = x + self.pe[: x.size(0)]
|
| 27 |
+
return x
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
class MultiheadSelfAttention(torch.nn.Module):
|
| 31 |
+
def __init__(self, embed_dim: int, num_heads: int = 8):
|
| 32 |
+
super().__init__()
|
| 33 |
+
|
| 34 |
+
self.embed_dim = embed_dim
|
| 35 |
+
self.num_heads = num_heads
|
| 36 |
+
|
| 37 |
+
self.query = torch.nn.Linear(
|
| 38 |
+
in_features=embed_dim,
|
| 39 |
+
out_features=embed_dim,
|
| 40 |
+
)
|
| 41 |
+
self.key = torch.nn.Linear(
|
| 42 |
+
in_features=embed_dim,
|
| 43 |
+
out_features=embed_dim,
|
| 44 |
+
)
|
| 45 |
+
self.value = torch.nn.Linear(
|
| 46 |
+
in_features=embed_dim,
|
| 47 |
+
out_features=embed_dim,
|
| 48 |
+
)
|
| 49 |
+
|
| 50 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 51 |
+
q = self.query(x).view(x.shape[0], self.num_heads, -1).transpose(0, 1)
|
| 52 |
+
k = self.key(x).view(x.shape[0], self.num_heads, -1).permute(1, 2, 0)
|
| 53 |
+
v = self.value(x).view(x.shape[0], self.num_heads, -1).transpose(0, 1)
|
| 54 |
+
qk = torch.softmax(
|
| 55 |
+
torch.matmul(q, k) / (self.embed_dim / self.num_heads) ** 0.5,
|
| 56 |
+
dim=-1,
|
| 57 |
+
)
|
| 58 |
+
qkv = torch.matmul(qk, v).transpose(0, 1).reshape(x.shape[0], -1)
|
| 59 |
+
return qkv
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
class Block(torch.nn.Module):
|
| 63 |
+
def __init__(self, d_model: int, num_heads: int = 8, eps: float = 1e-6):
|
| 64 |
+
super().__init__()
|
| 65 |
+
|
| 66 |
+
self.ln1 = torch.nn.LayerNorm(normalized_shape=d_model, eps=eps)
|
| 67 |
+
self.attn = MultiheadSelfAttention(embed_dim=d_model, num_heads=num_heads)
|
| 68 |
+
self.ln2 = torch.nn.LayerNorm(normalized_shape=d_model, eps=eps)
|
| 69 |
+
self.linear1 = torch.nn.Linear(in_features=d_model, out_features=d_model * 4)
|
| 70 |
+
self.linear2 = torch.nn.Linear(in_features=d_model * 4, out_features=d_model)
|
| 71 |
+
|
| 72 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 73 |
+
ln1 = self.ln1(x)
|
| 74 |
+
attn = self.attn(ln1)
|
| 75 |
+
ln2 = self.ln2(x + attn)
|
| 76 |
+
mlp = self.linear2(torch.relu(self.linear1(ln2)))
|
| 77 |
+
return mlp + x + attn
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
class Head(torch.nn.Module):
|
| 81 |
+
def __init__(
|
| 82 |
+
self,
|
| 83 |
+
d_model: int,
|
| 84 |
+
eps: float = 1e-6,
|
| 85 |
+
):
|
| 86 |
+
super().__init__()
|
| 87 |
+
|
| 88 |
+
self.d_model = d_model
|
| 89 |
+
self.eps = eps
|
| 90 |
+
|
| 91 |
+
self.ln = torch.nn.LayerNorm(normalized_shape=d_model, eps=eps)
|
| 92 |
+
self.linear1 = torch.nn.Linear(in_features=d_model, out_features=d_model)
|
| 93 |
+
self.linear2 = torch.nn.Linear(in_features=d_model, out_features=d_model)
|
| 94 |
+
self.tanh_layer = torch.nn.Linear(in_features=d_model * 2, out_features=d_model)
|
| 95 |
+
|
| 96 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 97 |
+
ln = self.ln(x)
|
| 98 |
+
mlp = torch.exp(self.linear2(torch.nn.functional.elu(self.linear1(ln))))
|
| 99 |
+
res = torch.cat(
|
| 100 |
+
[
|
| 101 |
+
ln.sum(dim=0) / ln.shape[0],
|
| 102 |
+
(mlp * ln).sum(dim=0) / mlp.sum(dim=0),
|
| 103 |
+
]
|
| 104 |
+
)
|
| 105 |
+
res = torch.tanh(self.tanh_layer(res))
|
| 106 |
+
res /= (res**2).sum() ** 0.5
|
| 107 |
+
res /= (res**2).sum() ** 0.5
|
| 108 |
+
return res
|
| 109 |
+
|
| 110 |
+
|
| 111 |
+
class MUSE(torch.nn.Module):
|
| 112 |
+
def __init__(
|
| 113 |
+
self,
|
| 114 |
+
num_embeddings: int,
|
| 115 |
+
embedding_dim: int,
|
| 116 |
+
d_model: int,
|
| 117 |
+
num_heads: int,
|
| 118 |
+
eps: float = 1e-6,
|
| 119 |
+
):
|
| 120 |
+
super().__init__()
|
| 121 |
+
|
| 122 |
+
self.num_embeddings = num_embeddings
|
| 123 |
+
self.embedding_dim = embedding_dim
|
| 124 |
+
self.d_model = d_model
|
| 125 |
+
self.num_heads = num_heads
|
| 126 |
+
self.eps = eps
|
| 127 |
+
|
| 128 |
+
self.embedding = torch.nn.Embedding(
|
| 129 |
+
num_embeddings=num_embeddings,
|
| 130 |
+
embedding_dim=embedding_dim,
|
| 131 |
+
)
|
| 132 |
+
self.linear = torch.nn.Linear(
|
| 133 |
+
in_features=embedding_dim,
|
| 134 |
+
out_features=d_model,
|
| 135 |
+
)
|
| 136 |
+
self.pe = PositionalEncoding(
|
| 137 |
+
d_model=d_model,
|
| 138 |
+
max_len=512, # TODO: remove hardcode
|
| 139 |
+
)
|
| 140 |
+
self.block0 = Block(d_model=d_model)
|
| 141 |
+
self.block1 = Block(d_model=d_model)
|
| 142 |
+
self.block2 = Block(d_model=d_model)
|
| 143 |
+
self.block3 = Block(d_model=d_model)
|
| 144 |
+
self.block4 = Block(d_model=d_model)
|
| 145 |
+
self.block5 = Block(d_model=d_model)
|
| 146 |
+
self.head = Head(d_model=d_model)
|
| 147 |
+
|
| 148 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 149 |
+
x = self.embedding(x)
|
| 150 |
+
x = self.linear(x)
|
| 151 |
+
x = self.pe(x)
|
| 152 |
+
x = self.block0(x)
|
| 153 |
+
x = self.block1(x)
|
| 154 |
+
x = self.block2(x)
|
| 155 |
+
x = self.block3(x)
|
| 156 |
+
x = self.block4(x)
|
| 157 |
+
x = self.block5(x)
|
| 158 |
+
x = self.head(x)
|
| 159 |
+
return x
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/model.onnx
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5907cbe35da97e0d7f3aa22629d86cdab9cb96bec0a291eb35ea3d6afc166049
|
| 3 |
+
size 339553121
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/model.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3ce98b598a817095e531ea77fb79c0f59a85c4da75ecfbf8172842fd48b166f5
|
| 3 |
+
size 337845962
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
onnx==1.16.0
|
| 2 |
+
onnxruntime==1.18.0
|
| 3 |
+
onnxruntime_extensions==0.10.1
|
| 4 |
+
tensorflow==2.16.1
|
| 5 |
+
tensorflow-hub==0.16.1
|
| 6 |
+
tensorflow-text==2.16.1
|
| 7 |
+
torch==2.3.0
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/source.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
https://huggingface.co/dayyass/universal-sentence-encoder-multilingual-large-3-pytorch
|
models/universal-sentence-encoder-multilingual-large-3-pytorch/tokenizer.py
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from tensorflow.core.protobuf.saved_model_pb2 import SavedModel
|
| 3 |
+
from tensorflow.python.saved_model.loader_impl import parse_saved_model
|
| 4 |
+
from tensorflow_text.python.ops.sentencepiece_tokenizer import SentencepieceTokenizer
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def _get_tokenizer_from_saved_model(saved_model: SavedModel) -> SentencepieceTokenizer:
|
| 8 |
+
"""
|
| 9 |
+
Get tokenizer from tf SavedModel.
|
| 10 |
+
:param SavedModel saved_model: tf SavedModel.
|
| 11 |
+
:return: tokenizer.
|
| 12 |
+
:rtype: SentencepieceTokenizer
|
| 13 |
+
"""
|
| 14 |
+
|
| 15 |
+
# extract functions that contain SentencePiece somewhere in there
|
| 16 |
+
functions_with_sp = [
|
| 17 |
+
f
|
| 18 |
+
for f in saved_model.meta_graphs[0].graph_def.library.function
|
| 19 |
+
if "tokenizer" in str(f).lower()
|
| 20 |
+
]
|
| 21 |
+
|
| 22 |
+
assert (
|
| 23 |
+
len(functions_with_sp) == 1
|
| 24 |
+
), f"len(functions_with_sp) = {len(functions_with_sp)}"
|
| 25 |
+
|
| 26 |
+
# find SentencePieceOp (contains the model) in the found function
|
| 27 |
+
nodes_with_sp = [
|
| 28 |
+
n for n in functions_with_sp[0].node_def if n.op == "SentencepieceOp"
|
| 29 |
+
]
|
| 30 |
+
|
| 31 |
+
assert len(nodes_with_sp) == 1, f"len(nodes_with_sp) = {len(nodes_with_sp)}"
|
| 32 |
+
|
| 33 |
+
# we can pretty much save the model into a file since it does not change
|
| 34 |
+
model = nodes_with_sp[0].attr["model"].s
|
| 35 |
+
|
| 36 |
+
# instantiate the model
|
| 37 |
+
tokenizer = SentencepieceTokenizer(model)
|
| 38 |
+
|
| 39 |
+
return tokenizer
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def get_tokenizer(model_path: str) -> SentencepieceTokenizer:
|
| 43 |
+
tokenizer = _get_tokenizer_from_saved_model(parse_saved_model(model_path))
|
| 44 |
+
return tokenizer
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
def tokenize(
|
| 48 |
+
sentence: str, # TODO: add batch processing
|
| 49 |
+
tokenizer: SentencepieceTokenizer,
|
| 50 |
+
) -> torch.Tensor:
|
| 51 |
+
return torch.LongTensor([1] + tokenizer.tokenize([sentence]).to_list()[0] + [2])
|
tools/muse_tf2pt.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4aedd865fe2fab33226ec3e2c209c731b2b7999c2700059646fe836d0c473393
|
| 3 |
+
size 3604222
|