Commit
•
2216880
1
Parent(s):
dcb6bfc
Add new SentenceTransformer model with an openvino backend
Browse filesHello!
*This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.*
## Full Model Architecture:
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: OVModelForFeatureExtraction
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer
# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"aliakseilabanau/bge-small-en",
revision=f"refs/pr/{pr_number}",
backend="openvino",
)
# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
```
- 1_Pooling/config.json +10 -0
- README.md +3014 -0
- config.json +31 -0
- config_sentence_transformers.json +10 -0
- modules.json +20 -0
- openvino/openvino_model.bin +3 -0
- openvino/openvino_model.xml +0 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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@@ -0,0 +1,3014 @@
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- mteb
|
| 4 |
+
- sentence transformers
|
| 5 |
+
model-index:
|
| 6 |
+
- name: bge-small-en
|
| 7 |
+
results:
|
| 8 |
+
- task:
|
| 9 |
+
type: Classification
|
| 10 |
+
dataset:
|
| 11 |
+
type: mteb/amazon_counterfactual
|
| 12 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
| 13 |
+
config: en
|
| 14 |
+
split: test
|
| 15 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 16 |
+
metrics:
|
| 17 |
+
- type: accuracy
|
| 18 |
+
value: 74.34328358208955
|
| 19 |
+
- type: ap
|
| 20 |
+
value: 37.59947775195661
|
| 21 |
+
- type: f1
|
| 22 |
+
value: 68.548415491933
|
| 23 |
+
- task:
|
| 24 |
+
type: Classification
|
| 25 |
+
dataset:
|
| 26 |
+
type: mteb/amazon_polarity
|
| 27 |
+
name: MTEB AmazonPolarityClassification
|
| 28 |
+
config: default
|
| 29 |
+
split: test
|
| 30 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
| 31 |
+
metrics:
|
| 32 |
+
- type: accuracy
|
| 33 |
+
value: 93.04527499999999
|
| 34 |
+
- type: ap
|
| 35 |
+
value: 89.60696356772135
|
| 36 |
+
- type: f1
|
| 37 |
+
value: 93.03361469382438
|
| 38 |
+
- task:
|
| 39 |
+
type: Classification
|
| 40 |
+
dataset:
|
| 41 |
+
type: mteb/amazon_reviews_multi
|
| 42 |
+
name: MTEB AmazonReviewsClassification (en)
|
| 43 |
+
config: en
|
| 44 |
+
split: test
|
| 45 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 46 |
+
metrics:
|
| 47 |
+
- type: accuracy
|
| 48 |
+
value: 46.08
|
| 49 |
+
- type: f1
|
| 50 |
+
value: 45.66249835363254
|
| 51 |
+
- task:
|
| 52 |
+
type: Retrieval
|
| 53 |
+
dataset:
|
| 54 |
+
type: arguana
|
| 55 |
+
name: MTEB ArguAna
|
| 56 |
+
config: default
|
| 57 |
+
split: test
|
| 58 |
+
revision: None
|
| 59 |
+
metrics:
|
| 60 |
+
- type: map_at_1
|
| 61 |
+
value: 35.205999999999996
|
| 62 |
+
- type: map_at_10
|
| 63 |
+
value: 50.782000000000004
|
| 64 |
+
- type: map_at_100
|
| 65 |
+
value: 51.547
|
| 66 |
+
- type: map_at_1000
|
| 67 |
+
value: 51.554
|
| 68 |
+
- type: map_at_3
|
| 69 |
+
value: 46.515
|
| 70 |
+
- type: map_at_5
|
| 71 |
+
value: 49.296
|
| 72 |
+
- type: mrr_at_1
|
| 73 |
+
value: 35.632999999999996
|
| 74 |
+
- type: mrr_at_10
|
| 75 |
+
value: 50.958999999999996
|
| 76 |
+
- type: mrr_at_100
|
| 77 |
+
value: 51.724000000000004
|
| 78 |
+
- type: mrr_at_1000
|
| 79 |
+
value: 51.731
|
| 80 |
+
- type: mrr_at_3
|
| 81 |
+
value: 46.669
|
| 82 |
+
- type: mrr_at_5
|
| 83 |
+
value: 49.439
|
| 84 |
+
- type: ndcg_at_1
|
| 85 |
+
value: 35.205999999999996
|
| 86 |
+
- type: ndcg_at_10
|
| 87 |
+
value: 58.835
|
| 88 |
+
- type: ndcg_at_100
|
| 89 |
+
value: 62.095
|
| 90 |
+
- type: ndcg_at_1000
|
| 91 |
+
value: 62.255
|
| 92 |
+
- type: ndcg_at_3
|
| 93 |
+
value: 50.255
|
| 94 |
+
- type: ndcg_at_5
|
| 95 |
+
value: 55.296
|
| 96 |
+
- type: precision_at_1
|
| 97 |
+
value: 35.205999999999996
|
| 98 |
+
- type: precision_at_10
|
| 99 |
+
value: 8.421
|
| 100 |
+
- type: precision_at_100
|
| 101 |
+
value: 0.984
|
| 102 |
+
- type: precision_at_1000
|
| 103 |
+
value: 0.1
|
| 104 |
+
- type: precision_at_3
|
| 105 |
+
value: 20.365
|
| 106 |
+
- type: precision_at_5
|
| 107 |
+
value: 14.680000000000001
|
| 108 |
+
- type: recall_at_1
|
| 109 |
+
value: 35.205999999999996
|
| 110 |
+
- type: recall_at_10
|
| 111 |
+
value: 84.211
|
| 112 |
+
- type: recall_at_100
|
| 113 |
+
value: 98.43499999999999
|
| 114 |
+
- type: recall_at_1000
|
| 115 |
+
value: 99.644
|
| 116 |
+
- type: recall_at_3
|
| 117 |
+
value: 61.095
|
| 118 |
+
- type: recall_at_5
|
| 119 |
+
value: 73.4
|
| 120 |
+
- task:
|
| 121 |
+
type: Clustering
|
| 122 |
+
dataset:
|
| 123 |
+
type: mteb/arxiv-clustering-p2p
|
| 124 |
+
name: MTEB ArxivClusteringP2P
|
| 125 |
+
config: default
|
| 126 |
+
split: test
|
| 127 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
| 128 |
+
metrics:
|
| 129 |
+
- type: v_measure
|
| 130 |
+
value: 47.52644476278646
|
| 131 |
+
- task:
|
| 132 |
+
type: Clustering
|
| 133 |
+
dataset:
|
| 134 |
+
type: mteb/arxiv-clustering-s2s
|
| 135 |
+
name: MTEB ArxivClusteringS2S
|
| 136 |
+
config: default
|
| 137 |
+
split: test
|
| 138 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
| 139 |
+
metrics:
|
| 140 |
+
- type: v_measure
|
| 141 |
+
value: 39.973045724188964
|
| 142 |
+
- task:
|
| 143 |
+
type: Reranking
|
| 144 |
+
dataset:
|
| 145 |
+
type: mteb/askubuntudupquestions-reranking
|
| 146 |
+
name: MTEB AskUbuntuDupQuestions
|
| 147 |
+
config: default
|
| 148 |
+
split: test
|
| 149 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
| 150 |
+
metrics:
|
| 151 |
+
- type: map
|
| 152 |
+
value: 62.28285314871488
|
| 153 |
+
- type: mrr
|
| 154 |
+
value: 74.52743701358659
|
| 155 |
+
- task:
|
| 156 |
+
type: STS
|
| 157 |
+
dataset:
|
| 158 |
+
type: mteb/biosses-sts
|
| 159 |
+
name: MTEB BIOSSES
|
| 160 |
+
config: default
|
| 161 |
+
split: test
|
| 162 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
| 163 |
+
metrics:
|
| 164 |
+
- type: cos_sim_pearson
|
| 165 |
+
value: 80.09041909160327
|
| 166 |
+
- type: cos_sim_spearman
|
| 167 |
+
value: 79.96266537706944
|
| 168 |
+
- type: euclidean_pearson
|
| 169 |
+
value: 79.50774978162241
|
| 170 |
+
- type: euclidean_spearman
|
| 171 |
+
value: 79.9144715078551
|
| 172 |
+
- type: manhattan_pearson
|
| 173 |
+
value: 79.2062139879302
|
| 174 |
+
- type: manhattan_spearman
|
| 175 |
+
value: 79.35000081468212
|
| 176 |
+
- task:
|
| 177 |
+
type: Classification
|
| 178 |
+
dataset:
|
| 179 |
+
type: mteb/banking77
|
| 180 |
+
name: MTEB Banking77Classification
|
| 181 |
+
config: default
|
| 182 |
+
split: test
|
| 183 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
| 184 |
+
metrics:
|
| 185 |
+
- type: accuracy
|
| 186 |
+
value: 85.31493506493506
|
| 187 |
+
- type: f1
|
| 188 |
+
value: 85.2704557977762
|
| 189 |
+
- task:
|
| 190 |
+
type: Clustering
|
| 191 |
+
dataset:
|
| 192 |
+
type: mteb/biorxiv-clustering-p2p
|
| 193 |
+
name: MTEB BiorxivClusteringP2P
|
| 194 |
+
config: default
|
| 195 |
+
split: test
|
| 196 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
| 197 |
+
metrics:
|
| 198 |
+
- type: v_measure
|
| 199 |
+
value: 39.6837242810816
|
| 200 |
+
- task:
|
| 201 |
+
type: Clustering
|
| 202 |
+
dataset:
|
| 203 |
+
type: mteb/biorxiv-clustering-s2s
|
| 204 |
+
name: MTEB BiorxivClusteringS2S
|
| 205 |
+
config: default
|
| 206 |
+
split: test
|
| 207 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
| 208 |
+
metrics:
|
| 209 |
+
- type: v_measure
|
| 210 |
+
value: 35.38881249555897
|
| 211 |
+
- task:
|
| 212 |
+
type: Retrieval
|
| 213 |
+
dataset:
|
| 214 |
+
type: BeIR/cqadupstack
|
| 215 |
+
name: MTEB CQADupstackAndroidRetrieval
|
| 216 |
+
config: default
|
| 217 |
+
split: test
|
| 218 |
+
revision: None
|
| 219 |
+
metrics:
|
| 220 |
+
- type: map_at_1
|
| 221 |
+
value: 27.884999999999998
|
| 222 |
+
- type: map_at_10
|
| 223 |
+
value: 39.574
|
| 224 |
+
- type: map_at_100
|
| 225 |
+
value: 40.993
|
| 226 |
+
- type: map_at_1000
|
| 227 |
+
value: 41.129
|
| 228 |
+
- type: map_at_3
|
| 229 |
+
value: 36.089
|
| 230 |
+
- type: map_at_5
|
| 231 |
+
value: 38.191
|
| 232 |
+
- type: mrr_at_1
|
| 233 |
+
value: 34.477999999999994
|
| 234 |
+
- type: mrr_at_10
|
| 235 |
+
value: 45.411
|
| 236 |
+
- type: mrr_at_100
|
| 237 |
+
value: 46.089999999999996
|
| 238 |
+
- type: mrr_at_1000
|
| 239 |
+
value: 46.147
|
| 240 |
+
- type: mrr_at_3
|
| 241 |
+
value: 42.346000000000004
|
| 242 |
+
- type: mrr_at_5
|
| 243 |
+
value: 44.292
|
| 244 |
+
- type: ndcg_at_1
|
| 245 |
+
value: 34.477999999999994
|
| 246 |
+
- type: ndcg_at_10
|
| 247 |
+
value: 46.123999999999995
|
| 248 |
+
- type: ndcg_at_100
|
| 249 |
+
value: 51.349999999999994
|
| 250 |
+
- type: ndcg_at_1000
|
| 251 |
+
value: 53.578
|
| 252 |
+
- type: ndcg_at_3
|
| 253 |
+
value: 40.824
|
| 254 |
+
- type: ndcg_at_5
|
| 255 |
+
value: 43.571
|
| 256 |
+
- type: precision_at_1
|
| 257 |
+
value: 34.477999999999994
|
| 258 |
+
- type: precision_at_10
|
| 259 |
+
value: 8.841000000000001
|
| 260 |
+
- type: precision_at_100
|
| 261 |
+
value: 1.4460000000000002
|
| 262 |
+
- type: precision_at_1000
|
| 263 |
+
value: 0.192
|
| 264 |
+
- type: precision_at_3
|
| 265 |
+
value: 19.742
|
| 266 |
+
- type: precision_at_5
|
| 267 |
+
value: 14.421000000000001
|
| 268 |
+
- type: recall_at_1
|
| 269 |
+
value: 27.884999999999998
|
| 270 |
+
- type: recall_at_10
|
| 271 |
+
value: 59.087
|
| 272 |
+
- type: recall_at_100
|
| 273 |
+
value: 80.609
|
| 274 |
+
- type: recall_at_1000
|
| 275 |
+
value: 95.054
|
| 276 |
+
- type: recall_at_3
|
| 277 |
+
value: 44.082
|
| 278 |
+
- type: recall_at_5
|
| 279 |
+
value: 51.593999999999994
|
| 280 |
+
- task:
|
| 281 |
+
type: Retrieval
|
| 282 |
+
dataset:
|
| 283 |
+
type: BeIR/cqadupstack
|
| 284 |
+
name: MTEB CQADupstackEnglishRetrieval
|
| 285 |
+
config: default
|
| 286 |
+
split: test
|
| 287 |
+
revision: None
|
| 288 |
+
metrics:
|
| 289 |
+
- type: map_at_1
|
| 290 |
+
value: 30.639
|
| 291 |
+
- type: map_at_10
|
| 292 |
+
value: 40.047
|
| 293 |
+
- type: map_at_100
|
| 294 |
+
value: 41.302
|
| 295 |
+
- type: map_at_1000
|
| 296 |
+
value: 41.425
|
| 297 |
+
- type: map_at_3
|
| 298 |
+
value: 37.406
|
| 299 |
+
- type: map_at_5
|
| 300 |
+
value: 38.934000000000005
|
| 301 |
+
- type: mrr_at_1
|
| 302 |
+
value: 37.707
|
| 303 |
+
- type: mrr_at_10
|
| 304 |
+
value: 46.082
|
| 305 |
+
- type: mrr_at_100
|
| 306 |
+
value: 46.745
|
| 307 |
+
- type: mrr_at_1000
|
| 308 |
+
value: 46.786
|
| 309 |
+
- type: mrr_at_3
|
| 310 |
+
value: 43.980999999999995
|
| 311 |
+
- type: mrr_at_5
|
| 312 |
+
value: 45.287
|
| 313 |
+
- type: ndcg_at_1
|
| 314 |
+
value: 37.707
|
| 315 |
+
- type: ndcg_at_10
|
| 316 |
+
value: 45.525
|
| 317 |
+
- type: ndcg_at_100
|
| 318 |
+
value: 49.976
|
| 319 |
+
- type: ndcg_at_1000
|
| 320 |
+
value: 51.94499999999999
|
| 321 |
+
- type: ndcg_at_3
|
| 322 |
+
value: 41.704
|
| 323 |
+
- type: ndcg_at_5
|
| 324 |
+
value: 43.596000000000004
|
| 325 |
+
- type: precision_at_1
|
| 326 |
+
value: 37.707
|
| 327 |
+
- type: precision_at_10
|
| 328 |
+
value: 8.465
|
| 329 |
+
- type: precision_at_100
|
| 330 |
+
value: 1.375
|
| 331 |
+
- type: precision_at_1000
|
| 332 |
+
value: 0.183
|
| 333 |
+
- type: precision_at_3
|
| 334 |
+
value: 19.979
|
| 335 |
+
- type: precision_at_5
|
| 336 |
+
value: 14.115
|
| 337 |
+
- type: recall_at_1
|
| 338 |
+
value: 30.639
|
| 339 |
+
- type: recall_at_10
|
| 340 |
+
value: 54.775
|
| 341 |
+
- type: recall_at_100
|
| 342 |
+
value: 73.678
|
| 343 |
+
- type: recall_at_1000
|
| 344 |
+
value: 86.142
|
| 345 |
+
- type: recall_at_3
|
| 346 |
+
value: 43.230000000000004
|
| 347 |
+
- type: recall_at_5
|
| 348 |
+
value: 48.622
|
| 349 |
+
- task:
|
| 350 |
+
type: Retrieval
|
| 351 |
+
dataset:
|
| 352 |
+
type: BeIR/cqadupstack
|
| 353 |
+
name: MTEB CQADupstackGamingRetrieval
|
| 354 |
+
config: default
|
| 355 |
+
split: test
|
| 356 |
+
revision: None
|
| 357 |
+
metrics:
|
| 358 |
+
- type: map_at_1
|
| 359 |
+
value: 38.038
|
| 360 |
+
- type: map_at_10
|
| 361 |
+
value: 49.922
|
| 362 |
+
- type: map_at_100
|
| 363 |
+
value: 51.032
|
| 364 |
+
- type: map_at_1000
|
| 365 |
+
value: 51.085
|
| 366 |
+
- type: map_at_3
|
| 367 |
+
value: 46.664
|
| 368 |
+
- type: map_at_5
|
| 369 |
+
value: 48.588
|
| 370 |
+
- type: mrr_at_1
|
| 371 |
+
value: 43.95
|
| 372 |
+
- type: mrr_at_10
|
| 373 |
+
value: 53.566
|
| 374 |
+
- type: mrr_at_100
|
| 375 |
+
value: 54.318999999999996
|
| 376 |
+
- type: mrr_at_1000
|
| 377 |
+
value: 54.348
|
| 378 |
+
- type: mrr_at_3
|
| 379 |
+
value: 51.066
|
| 380 |
+
- type: mrr_at_5
|
| 381 |
+
value: 52.649
|
| 382 |
+
- type: ndcg_at_1
|
| 383 |
+
value: 43.95
|
| 384 |
+
- type: ndcg_at_10
|
| 385 |
+
value: 55.676
|
| 386 |
+
- type: ndcg_at_100
|
| 387 |
+
value: 60.126000000000005
|
| 388 |
+
- type: ndcg_at_1000
|
| 389 |
+
value: 61.208
|
| 390 |
+
- type: ndcg_at_3
|
| 391 |
+
value: 50.20400000000001
|
| 392 |
+
- type: ndcg_at_5
|
| 393 |
+
value: 53.038
|
| 394 |
+
- type: precision_at_1
|
| 395 |
+
value: 43.95
|
| 396 |
+
- type: precision_at_10
|
| 397 |
+
value: 8.953
|
| 398 |
+
- type: precision_at_100
|
| 399 |
+
value: 1.2109999999999999
|
| 400 |
+
- type: precision_at_1000
|
| 401 |
+
value: 0.135
|
| 402 |
+
- type: precision_at_3
|
| 403 |
+
value: 22.256999999999998
|
| 404 |
+
- type: precision_at_5
|
| 405 |
+
value: 15.524
|
| 406 |
+
- type: recall_at_1
|
| 407 |
+
value: 38.038
|
| 408 |
+
- type: recall_at_10
|
| 409 |
+
value: 69.15
|
| 410 |
+
- type: recall_at_100
|
| 411 |
+
value: 88.31599999999999
|
| 412 |
+
- type: recall_at_1000
|
| 413 |
+
value: 95.993
|
| 414 |
+
- type: recall_at_3
|
| 415 |
+
value: 54.663
|
| 416 |
+
- type: recall_at_5
|
| 417 |
+
value: 61.373
|
| 418 |
+
- task:
|
| 419 |
+
type: Retrieval
|
| 420 |
+
dataset:
|
| 421 |
+
type: BeIR/cqadupstack
|
| 422 |
+
name: MTEB CQADupstackGisRetrieval
|
| 423 |
+
config: default
|
| 424 |
+
split: test
|
| 425 |
+
revision: None
|
| 426 |
+
metrics:
|
| 427 |
+
- type: map_at_1
|
| 428 |
+
value: 24.872
|
| 429 |
+
- type: map_at_10
|
| 430 |
+
value: 32.912
|
| 431 |
+
- type: map_at_100
|
| 432 |
+
value: 33.972
|
| 433 |
+
- type: map_at_1000
|
| 434 |
+
value: 34.046
|
| 435 |
+
- type: map_at_3
|
| 436 |
+
value: 30.361
|
| 437 |
+
- type: map_at_5
|
| 438 |
+
value: 31.704
|
| 439 |
+
- type: mrr_at_1
|
| 440 |
+
value: 26.779999999999998
|
| 441 |
+
- type: mrr_at_10
|
| 442 |
+
value: 34.812
|
| 443 |
+
- type: mrr_at_100
|
| 444 |
+
value: 35.754999999999995
|
| 445 |
+
- type: mrr_at_1000
|
| 446 |
+
value: 35.809000000000005
|
| 447 |
+
- type: mrr_at_3
|
| 448 |
+
value: 32.335
|
| 449 |
+
- type: mrr_at_5
|
| 450 |
+
value: 33.64
|
| 451 |
+
- type: ndcg_at_1
|
| 452 |
+
value: 26.779999999999998
|
| 453 |
+
- type: ndcg_at_10
|
| 454 |
+
value: 37.623
|
| 455 |
+
- type: ndcg_at_100
|
| 456 |
+
value: 42.924
|
| 457 |
+
- type: ndcg_at_1000
|
| 458 |
+
value: 44.856
|
| 459 |
+
- type: ndcg_at_3
|
| 460 |
+
value: 32.574
|
| 461 |
+
- type: ndcg_at_5
|
| 462 |
+
value: 34.842
|
| 463 |
+
- type: precision_at_1
|
| 464 |
+
value: 26.779999999999998
|
| 465 |
+
- type: precision_at_10
|
| 466 |
+
value: 5.729
|
| 467 |
+
- type: precision_at_100
|
| 468 |
+
value: 0.886
|
| 469 |
+
- type: precision_at_1000
|
| 470 |
+
value: 0.109
|
| 471 |
+
- type: precision_at_3
|
| 472 |
+
value: 13.559
|
| 473 |
+
- type: precision_at_5
|
| 474 |
+
value: 9.469
|
| 475 |
+
- type: recall_at_1
|
| 476 |
+
value: 24.872
|
| 477 |
+
- type: recall_at_10
|
| 478 |
+
value: 50.400999999999996
|
| 479 |
+
- type: recall_at_100
|
| 480 |
+
value: 74.954
|
| 481 |
+
- type: recall_at_1000
|
| 482 |
+
value: 89.56
|
| 483 |
+
- type: recall_at_3
|
| 484 |
+
value: 36.726
|
| 485 |
+
- type: recall_at_5
|
| 486 |
+
value: 42.138999999999996
|
| 487 |
+
- task:
|
| 488 |
+
type: Retrieval
|
| 489 |
+
dataset:
|
| 490 |
+
type: BeIR/cqadupstack
|
| 491 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
| 492 |
+
config: default
|
| 493 |
+
split: test
|
| 494 |
+
revision: None
|
| 495 |
+
metrics:
|
| 496 |
+
- type: map_at_1
|
| 497 |
+
value: 16.803
|
| 498 |
+
- type: map_at_10
|
| 499 |
+
value: 24.348
|
| 500 |
+
- type: map_at_100
|
| 501 |
+
value: 25.56
|
| 502 |
+
- type: map_at_1000
|
| 503 |
+
value: 25.668000000000003
|
| 504 |
+
- type: map_at_3
|
| 505 |
+
value: 21.811
|
| 506 |
+
- type: map_at_5
|
| 507 |
+
value: 23.287
|
| 508 |
+
- type: mrr_at_1
|
| 509 |
+
value: 20.771
|
| 510 |
+
- type: mrr_at_10
|
| 511 |
+
value: 28.961
|
| 512 |
+
- type: mrr_at_100
|
| 513 |
+
value: 29.979
|
| 514 |
+
- type: mrr_at_1000
|
| 515 |
+
value: 30.046
|
| 516 |
+
- type: mrr_at_3
|
| 517 |
+
value: 26.555
|
| 518 |
+
- type: mrr_at_5
|
| 519 |
+
value: 28.060000000000002
|
| 520 |
+
- type: ndcg_at_1
|
| 521 |
+
value: 20.771
|
| 522 |
+
- type: ndcg_at_10
|
| 523 |
+
value: 29.335
|
| 524 |
+
- type: ndcg_at_100
|
| 525 |
+
value: 35.188
|
| 526 |
+
- type: ndcg_at_1000
|
| 527 |
+
value: 37.812
|
| 528 |
+
- type: ndcg_at_3
|
| 529 |
+
value: 24.83
|
| 530 |
+
- type: ndcg_at_5
|
| 531 |
+
value: 27.119
|
| 532 |
+
- type: precision_at_1
|
| 533 |
+
value: 20.771
|
| 534 |
+
- type: precision_at_10
|
| 535 |
+
value: 5.4350000000000005
|
| 536 |
+
- type: precision_at_100
|
| 537 |
+
value: 0.9480000000000001
|
| 538 |
+
- type: precision_at_1000
|
| 539 |
+
value: 0.13
|
| 540 |
+
- type: precision_at_3
|
| 541 |
+
value: 11.982
|
| 542 |
+
- type: precision_at_5
|
| 543 |
+
value: 8.831
|
| 544 |
+
- type: recall_at_1
|
| 545 |
+
value: 16.803
|
| 546 |
+
- type: recall_at_10
|
| 547 |
+
value: 40.039
|
| 548 |
+
- type: recall_at_100
|
| 549 |
+
value: 65.83200000000001
|
| 550 |
+
- type: recall_at_1000
|
| 551 |
+
value: 84.478
|
| 552 |
+
- type: recall_at_3
|
| 553 |
+
value: 27.682000000000002
|
| 554 |
+
- type: recall_at_5
|
| 555 |
+
value: 33.535
|
| 556 |
+
- task:
|
| 557 |
+
type: Retrieval
|
| 558 |
+
dataset:
|
| 559 |
+
type: BeIR/cqadupstack
|
| 560 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
| 561 |
+
config: default
|
| 562 |
+
split: test
|
| 563 |
+
revision: None
|
| 564 |
+
metrics:
|
| 565 |
+
- type: map_at_1
|
| 566 |
+
value: 28.345
|
| 567 |
+
- type: map_at_10
|
| 568 |
+
value: 37.757000000000005
|
| 569 |
+
- type: map_at_100
|
| 570 |
+
value: 39.141
|
| 571 |
+
- type: map_at_1000
|
| 572 |
+
value: 39.262
|
| 573 |
+
- type: map_at_3
|
| 574 |
+
value: 35.183
|
| 575 |
+
- type: map_at_5
|
| 576 |
+
value: 36.592
|
| 577 |
+
- type: mrr_at_1
|
| 578 |
+
value: 34.649
|
| 579 |
+
- type: mrr_at_10
|
| 580 |
+
value: 43.586999999999996
|
| 581 |
+
- type: mrr_at_100
|
| 582 |
+
value: 44.481
|
| 583 |
+
- type: mrr_at_1000
|
| 584 |
+
value: 44.542
|
| 585 |
+
- type: mrr_at_3
|
| 586 |
+
value: 41.29
|
| 587 |
+
- type: mrr_at_5
|
| 588 |
+
value: 42.642
|
| 589 |
+
- type: ndcg_at_1
|
| 590 |
+
value: 34.649
|
| 591 |
+
- type: ndcg_at_10
|
| 592 |
+
value: 43.161
|
| 593 |
+
- type: ndcg_at_100
|
| 594 |
+
value: 48.734
|
| 595 |
+
- type: ndcg_at_1000
|
| 596 |
+
value: 51.046
|
| 597 |
+
- type: ndcg_at_3
|
| 598 |
+
value: 39.118
|
| 599 |
+
- type: ndcg_at_5
|
| 600 |
+
value: 41.022
|
| 601 |
+
- type: precision_at_1
|
| 602 |
+
value: 34.649
|
| 603 |
+
- type: precision_at_10
|
| 604 |
+
value: 7.603
|
| 605 |
+
- type: precision_at_100
|
| 606 |
+
value: 1.209
|
| 607 |
+
- type: precision_at_1000
|
| 608 |
+
value: 0.157
|
| 609 |
+
- type: precision_at_3
|
| 610 |
+
value: 18.319
|
| 611 |
+
- type: precision_at_5
|
| 612 |
+
value: 12.839
|
| 613 |
+
- type: recall_at_1
|
| 614 |
+
value: 28.345
|
| 615 |
+
- type: recall_at_10
|
| 616 |
+
value: 53.367
|
| 617 |
+
- type: recall_at_100
|
| 618 |
+
value: 76.453
|
| 619 |
+
- type: recall_at_1000
|
| 620 |
+
value: 91.82000000000001
|
| 621 |
+
- type: recall_at_3
|
| 622 |
+
value: 41.636
|
| 623 |
+
- type: recall_at_5
|
| 624 |
+
value: 46.760000000000005
|
| 625 |
+
- task:
|
| 626 |
+
type: Retrieval
|
| 627 |
+
dataset:
|
| 628 |
+
type: BeIR/cqadupstack
|
| 629 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
| 630 |
+
config: default
|
| 631 |
+
split: test
|
| 632 |
+
revision: None
|
| 633 |
+
metrics:
|
| 634 |
+
- type: map_at_1
|
| 635 |
+
value: 22.419
|
| 636 |
+
- type: map_at_10
|
| 637 |
+
value: 31.716
|
| 638 |
+
- type: map_at_100
|
| 639 |
+
value: 33.152
|
| 640 |
+
- type: map_at_1000
|
| 641 |
+
value: 33.267
|
| 642 |
+
- type: map_at_3
|
| 643 |
+
value: 28.74
|
| 644 |
+
- type: map_at_5
|
| 645 |
+
value: 30.48
|
| 646 |
+
- type: mrr_at_1
|
| 647 |
+
value: 28.310999999999996
|
| 648 |
+
- type: mrr_at_10
|
| 649 |
+
value: 37.039
|
| 650 |
+
- type: mrr_at_100
|
| 651 |
+
value: 38.09
|
| 652 |
+
- type: mrr_at_1000
|
| 653 |
+
value: 38.145
|
| 654 |
+
- type: mrr_at_3
|
| 655 |
+
value: 34.437
|
| 656 |
+
- type: mrr_at_5
|
| 657 |
+
value: 36.024
|
| 658 |
+
- type: ndcg_at_1
|
| 659 |
+
value: 28.310999999999996
|
| 660 |
+
- type: ndcg_at_10
|
| 661 |
+
value: 37.41
|
| 662 |
+
- type: ndcg_at_100
|
| 663 |
+
value: 43.647999999999996
|
| 664 |
+
- type: ndcg_at_1000
|
| 665 |
+
value: 46.007
|
| 666 |
+
- type: ndcg_at_3
|
| 667 |
+
value: 32.509
|
| 668 |
+
- type: ndcg_at_5
|
| 669 |
+
value: 34.943999999999996
|
| 670 |
+
- type: precision_at_1
|
| 671 |
+
value: 28.310999999999996
|
| 672 |
+
- type: precision_at_10
|
| 673 |
+
value: 6.963
|
| 674 |
+
- type: precision_at_100
|
| 675 |
+
value: 1.1860000000000002
|
| 676 |
+
- type: precision_at_1000
|
| 677 |
+
value: 0.154
|
| 678 |
+
- type: precision_at_3
|
| 679 |
+
value: 15.867999999999999
|
| 680 |
+
- type: precision_at_5
|
| 681 |
+
value: 11.507000000000001
|
| 682 |
+
- type: recall_at_1
|
| 683 |
+
value: 22.419
|
| 684 |
+
- type: recall_at_10
|
| 685 |
+
value: 49.28
|
| 686 |
+
- type: recall_at_100
|
| 687 |
+
value: 75.802
|
| 688 |
+
- type: recall_at_1000
|
| 689 |
+
value: 92.032
|
| 690 |
+
- type: recall_at_3
|
| 691 |
+
value: 35.399
|
| 692 |
+
- type: recall_at_5
|
| 693 |
+
value: 42.027
|
| 694 |
+
- task:
|
| 695 |
+
type: Retrieval
|
| 696 |
+
dataset:
|
| 697 |
+
type: BeIR/cqadupstack
|
| 698 |
+
name: MTEB CQADupstackRetrieval
|
| 699 |
+
config: default
|
| 700 |
+
split: test
|
| 701 |
+
revision: None
|
| 702 |
+
metrics:
|
| 703 |
+
- type: map_at_1
|
| 704 |
+
value: 24.669249999999998
|
| 705 |
+
- type: map_at_10
|
| 706 |
+
value: 33.332583333333325
|
| 707 |
+
- type: map_at_100
|
| 708 |
+
value: 34.557833333333335
|
| 709 |
+
- type: map_at_1000
|
| 710 |
+
value: 34.67141666666666
|
| 711 |
+
- type: map_at_3
|
| 712 |
+
value: 30.663166666666662
|
| 713 |
+
- type: map_at_5
|
| 714 |
+
value: 32.14883333333333
|
| 715 |
+
- type: mrr_at_1
|
| 716 |
+
value: 29.193833333333334
|
| 717 |
+
- type: mrr_at_10
|
| 718 |
+
value: 37.47625
|
| 719 |
+
- type: mrr_at_100
|
| 720 |
+
value: 38.3545
|
| 721 |
+
- type: mrr_at_1000
|
| 722 |
+
value: 38.413166666666676
|
| 723 |
+
- type: mrr_at_3
|
| 724 |
+
value: 35.06741666666667
|
| 725 |
+
- type: mrr_at_5
|
| 726 |
+
value: 36.450666666666656
|
| 727 |
+
- type: ndcg_at_1
|
| 728 |
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value: 29.193833333333334
|
| 729 |
+
- type: ndcg_at_10
|
| 730 |
+
value: 38.505416666666676
|
| 731 |
+
- type: ndcg_at_100
|
| 732 |
+
value: 43.81125
|
| 733 |
+
- type: ndcg_at_1000
|
| 734 |
+
value: 46.09558333333333
|
| 735 |
+
- type: ndcg_at_3
|
| 736 |
+
value: 33.90916666666667
|
| 737 |
+
- type: ndcg_at_5
|
| 738 |
+
value: 36.07666666666666
|
| 739 |
+
- type: precision_at_1
|
| 740 |
+
value: 29.193833333333334
|
| 741 |
+
- type: precision_at_10
|
| 742 |
+
value: 6.7251666666666665
|
| 743 |
+
- type: precision_at_100
|
| 744 |
+
value: 1.1058333333333332
|
| 745 |
+
- type: precision_at_1000
|
| 746 |
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value: 0.14833333333333332
|
| 747 |
+
- type: precision_at_3
|
| 748 |
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value: 15.554166666666665
|
| 749 |
+
- type: precision_at_5
|
| 750 |
+
value: 11.079250000000002
|
| 751 |
+
- type: recall_at_1
|
| 752 |
+
value: 24.669249999999998
|
| 753 |
+
- type: recall_at_10
|
| 754 |
+
value: 49.75583333333332
|
| 755 |
+
- type: recall_at_100
|
| 756 |
+
value: 73.06908333333332
|
| 757 |
+
- type: recall_at_1000
|
| 758 |
+
value: 88.91316666666667
|
| 759 |
+
- type: recall_at_3
|
| 760 |
+
value: 36.913250000000005
|
| 761 |
+
- type: recall_at_5
|
| 762 |
+
value: 42.48641666666666
|
| 763 |
+
- task:
|
| 764 |
+
type: Retrieval
|
| 765 |
+
dataset:
|
| 766 |
+
type: BeIR/cqadupstack
|
| 767 |
+
name: MTEB CQADupstackStatsRetrieval
|
| 768 |
+
config: default
|
| 769 |
+
split: test
|
| 770 |
+
revision: None
|
| 771 |
+
metrics:
|
| 772 |
+
- type: map_at_1
|
| 773 |
+
value: 24.044999999999998
|
| 774 |
+
- type: map_at_10
|
| 775 |
+
value: 30.349999999999998
|
| 776 |
+
- type: map_at_100
|
| 777 |
+
value: 31.273
|
| 778 |
+
- type: map_at_1000
|
| 779 |
+
value: 31.362000000000002
|
| 780 |
+
- type: map_at_3
|
| 781 |
+
value: 28.508
|
| 782 |
+
- type: map_at_5
|
| 783 |
+
value: 29.369
|
| 784 |
+
- type: mrr_at_1
|
| 785 |
+
value: 26.994
|
| 786 |
+
- type: mrr_at_10
|
| 787 |
+
value: 33.12
|
| 788 |
+
- type: mrr_at_100
|
| 789 |
+
value: 33.904
|
| 790 |
+
- type: mrr_at_1000
|
| 791 |
+
value: 33.967000000000006
|
| 792 |
+
- type: mrr_at_3
|
| 793 |
+
value: 31.365
|
| 794 |
+
- type: mrr_at_5
|
| 795 |
+
value: 32.124
|
| 796 |
+
- type: ndcg_at_1
|
| 797 |
+
value: 26.994
|
| 798 |
+
- type: ndcg_at_10
|
| 799 |
+
value: 34.214
|
| 800 |
+
- type: ndcg_at_100
|
| 801 |
+
value: 38.681
|
| 802 |
+
- type: ndcg_at_1000
|
| 803 |
+
value: 40.926
|
| 804 |
+
- type: ndcg_at_3
|
| 805 |
+
value: 30.725
|
| 806 |
+
- type: ndcg_at_5
|
| 807 |
+
value: 31.967000000000002
|
| 808 |
+
- type: precision_at_1
|
| 809 |
+
value: 26.994
|
| 810 |
+
- type: precision_at_10
|
| 811 |
+
value: 5.215
|
| 812 |
+
- type: precision_at_100
|
| 813 |
+
value: 0.807
|
| 814 |
+
- type: precision_at_1000
|
| 815 |
+
value: 0.108
|
| 816 |
+
- type: precision_at_3
|
| 817 |
+
value: 12.986
|
| 818 |
+
- type: precision_at_5
|
| 819 |
+
value: 8.712
|
| 820 |
+
- type: recall_at_1
|
| 821 |
+
value: 24.044999999999998
|
| 822 |
+
- type: recall_at_10
|
| 823 |
+
value: 43.456
|
| 824 |
+
- type: recall_at_100
|
| 825 |
+
value: 63.675000000000004
|
| 826 |
+
- type: recall_at_1000
|
| 827 |
+
value: 80.05499999999999
|
| 828 |
+
- type: recall_at_3
|
| 829 |
+
value: 33.561
|
| 830 |
+
- type: recall_at_5
|
| 831 |
+
value: 36.767
|
| 832 |
+
- task:
|
| 833 |
+
type: Retrieval
|
| 834 |
+
dataset:
|
| 835 |
+
type: BeIR/cqadupstack
|
| 836 |
+
name: MTEB CQADupstackTexRetrieval
|
| 837 |
+
config: default
|
| 838 |
+
split: test
|
| 839 |
+
revision: None
|
| 840 |
+
metrics:
|
| 841 |
+
- type: map_at_1
|
| 842 |
+
value: 15.672
|
| 843 |
+
- type: map_at_10
|
| 844 |
+
value: 22.641
|
| 845 |
+
- type: map_at_100
|
| 846 |
+
value: 23.75
|
| 847 |
+
- type: map_at_1000
|
| 848 |
+
value: 23.877000000000002
|
| 849 |
+
- type: map_at_3
|
| 850 |
+
value: 20.219
|
| 851 |
+
- type: map_at_5
|
| 852 |
+
value: 21.648
|
| 853 |
+
- type: mrr_at_1
|
| 854 |
+
value: 18.823
|
| 855 |
+
- type: mrr_at_10
|
| 856 |
+
value: 26.101999999999997
|
| 857 |
+
- type: mrr_at_100
|
| 858 |
+
value: 27.038
|
| 859 |
+
- type: mrr_at_1000
|
| 860 |
+
value: 27.118
|
| 861 |
+
- type: mrr_at_3
|
| 862 |
+
value: 23.669
|
| 863 |
+
- type: mrr_at_5
|
| 864 |
+
value: 25.173000000000002
|
| 865 |
+
- type: ndcg_at_1
|
| 866 |
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value: 18.823
|
| 867 |
+
- type: ndcg_at_10
|
| 868 |
+
value: 27.176000000000002
|
| 869 |
+
- type: ndcg_at_100
|
| 870 |
+
value: 32.42
|
| 871 |
+
- type: ndcg_at_1000
|
| 872 |
+
value: 35.413
|
| 873 |
+
- type: ndcg_at_3
|
| 874 |
+
value: 22.756999999999998
|
| 875 |
+
- type: ndcg_at_5
|
| 876 |
+
value: 25.032
|
| 877 |
+
- type: precision_at_1
|
| 878 |
+
value: 18.823
|
| 879 |
+
- type: precision_at_10
|
| 880 |
+
value: 5.034000000000001
|
| 881 |
+
- type: precision_at_100
|
| 882 |
+
value: 0.895
|
| 883 |
+
- type: precision_at_1000
|
| 884 |
+
value: 0.132
|
| 885 |
+
- type: precision_at_3
|
| 886 |
+
value: 10.771
|
| 887 |
+
- type: precision_at_5
|
| 888 |
+
value: 8.1
|
| 889 |
+
- type: recall_at_1
|
| 890 |
+
value: 15.672
|
| 891 |
+
- type: recall_at_10
|
| 892 |
+
value: 37.296
|
| 893 |
+
- type: recall_at_100
|
| 894 |
+
value: 60.863
|
| 895 |
+
- type: recall_at_1000
|
| 896 |
+
value: 82.234
|
| 897 |
+
- type: recall_at_3
|
| 898 |
+
value: 25.330000000000002
|
| 899 |
+
- type: recall_at_5
|
| 900 |
+
value: 30.964000000000002
|
| 901 |
+
- task:
|
| 902 |
+
type: Retrieval
|
| 903 |
+
dataset:
|
| 904 |
+
type: BeIR/cqadupstack
|
| 905 |
+
name: MTEB CQADupstackUnixRetrieval
|
| 906 |
+
config: default
|
| 907 |
+
split: test
|
| 908 |
+
revision: None
|
| 909 |
+
metrics:
|
| 910 |
+
- type: map_at_1
|
| 911 |
+
value: 24.633
|
| 912 |
+
- type: map_at_10
|
| 913 |
+
value: 32.858
|
| 914 |
+
- type: map_at_100
|
| 915 |
+
value: 34.038000000000004
|
| 916 |
+
- type: map_at_1000
|
| 917 |
+
value: 34.141
|
| 918 |
+
- type: map_at_3
|
| 919 |
+
value: 30.209000000000003
|
| 920 |
+
- type: map_at_5
|
| 921 |
+
value: 31.567
|
| 922 |
+
- type: mrr_at_1
|
| 923 |
+
value: 28.358
|
| 924 |
+
- type: mrr_at_10
|
| 925 |
+
value: 36.433
|
| 926 |
+
- type: mrr_at_100
|
| 927 |
+
value: 37.352000000000004
|
| 928 |
+
- type: mrr_at_1000
|
| 929 |
+
value: 37.41
|
| 930 |
+
- type: mrr_at_3
|
| 931 |
+
value: 34.033
|
| 932 |
+
- type: mrr_at_5
|
| 933 |
+
value: 35.246
|
| 934 |
+
- type: ndcg_at_1
|
| 935 |
+
value: 28.358
|
| 936 |
+
- type: ndcg_at_10
|
| 937 |
+
value: 37.973
|
| 938 |
+
- type: ndcg_at_100
|
| 939 |
+
value: 43.411
|
| 940 |
+
- type: ndcg_at_1000
|
| 941 |
+
value: 45.747
|
| 942 |
+
- type: ndcg_at_3
|
| 943 |
+
value: 32.934999999999995
|
| 944 |
+
- type: ndcg_at_5
|
| 945 |
+
value: 35.013
|
| 946 |
+
- type: precision_at_1
|
| 947 |
+
value: 28.358
|
| 948 |
+
- type: precision_at_10
|
| 949 |
+
value: 6.418
|
| 950 |
+
- type: precision_at_100
|
| 951 |
+
value: 1.02
|
| 952 |
+
- type: precision_at_1000
|
| 953 |
+
value: 0.133
|
| 954 |
+
- type: precision_at_3
|
| 955 |
+
value: 14.677000000000001
|
| 956 |
+
- type: precision_at_5
|
| 957 |
+
value: 10.335999999999999
|
| 958 |
+
- type: recall_at_1
|
| 959 |
+
value: 24.633
|
| 960 |
+
- type: recall_at_10
|
| 961 |
+
value: 50.048
|
| 962 |
+
- type: recall_at_100
|
| 963 |
+
value: 73.821
|
| 964 |
+
- type: recall_at_1000
|
| 965 |
+
value: 90.046
|
| 966 |
+
- type: recall_at_3
|
| 967 |
+
value: 36.284
|
| 968 |
+
- type: recall_at_5
|
| 969 |
+
value: 41.370000000000005
|
| 970 |
+
- task:
|
| 971 |
+
type: Retrieval
|
| 972 |
+
dataset:
|
| 973 |
+
type: BeIR/cqadupstack
|
| 974 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
| 975 |
+
config: default
|
| 976 |
+
split: test
|
| 977 |
+
revision: None
|
| 978 |
+
metrics:
|
| 979 |
+
- type: map_at_1
|
| 980 |
+
value: 23.133
|
| 981 |
+
- type: map_at_10
|
| 982 |
+
value: 31.491999999999997
|
| 983 |
+
- type: map_at_100
|
| 984 |
+
value: 33.062000000000005
|
| 985 |
+
- type: map_at_1000
|
| 986 |
+
value: 33.256
|
| 987 |
+
- type: map_at_3
|
| 988 |
+
value: 28.886
|
| 989 |
+
- type: map_at_5
|
| 990 |
+
value: 30.262
|
| 991 |
+
- type: mrr_at_1
|
| 992 |
+
value: 28.063
|
| 993 |
+
- type: mrr_at_10
|
| 994 |
+
value: 36.144
|
| 995 |
+
- type: mrr_at_100
|
| 996 |
+
value: 37.14
|
| 997 |
+
- type: mrr_at_1000
|
| 998 |
+
value: 37.191
|
| 999 |
+
- type: mrr_at_3
|
| 1000 |
+
value: 33.762
|
| 1001 |
+
- type: mrr_at_5
|
| 1002 |
+
value: 34.997
|
| 1003 |
+
- type: ndcg_at_1
|
| 1004 |
+
value: 28.063
|
| 1005 |
+
- type: ndcg_at_10
|
| 1006 |
+
value: 36.951
|
| 1007 |
+
- type: ndcg_at_100
|
| 1008 |
+
value: 43.287
|
| 1009 |
+
- type: ndcg_at_1000
|
| 1010 |
+
value: 45.777
|
| 1011 |
+
- type: ndcg_at_3
|
| 1012 |
+
value: 32.786
|
| 1013 |
+
- type: ndcg_at_5
|
| 1014 |
+
value: 34.65
|
| 1015 |
+
- type: precision_at_1
|
| 1016 |
+
value: 28.063
|
| 1017 |
+
- type: precision_at_10
|
| 1018 |
+
value: 7.055
|
| 1019 |
+
- type: precision_at_100
|
| 1020 |
+
value: 1.476
|
| 1021 |
+
- type: precision_at_1000
|
| 1022 |
+
value: 0.22899999999999998
|
| 1023 |
+
- type: precision_at_3
|
| 1024 |
+
value: 15.481
|
| 1025 |
+
- type: precision_at_5
|
| 1026 |
+
value: 11.186
|
| 1027 |
+
- type: recall_at_1
|
| 1028 |
+
value: 23.133
|
| 1029 |
+
- type: recall_at_10
|
| 1030 |
+
value: 47.285
|
| 1031 |
+
- type: recall_at_100
|
| 1032 |
+
value: 76.176
|
| 1033 |
+
- type: recall_at_1000
|
| 1034 |
+
value: 92.176
|
| 1035 |
+
- type: recall_at_3
|
| 1036 |
+
value: 35.223
|
| 1037 |
+
- type: recall_at_5
|
| 1038 |
+
value: 40.142
|
| 1039 |
+
- task:
|
| 1040 |
+
type: Retrieval
|
| 1041 |
+
dataset:
|
| 1042 |
+
type: BeIR/cqadupstack
|
| 1043 |
+
name: MTEB CQADupstackWordpressRetrieval
|
| 1044 |
+
config: default
|
| 1045 |
+
split: test
|
| 1046 |
+
revision: None
|
| 1047 |
+
metrics:
|
| 1048 |
+
- type: map_at_1
|
| 1049 |
+
value: 19.547
|
| 1050 |
+
- type: map_at_10
|
| 1051 |
+
value: 26.374
|
| 1052 |
+
- type: map_at_100
|
| 1053 |
+
value: 27.419
|
| 1054 |
+
- type: map_at_1000
|
| 1055 |
+
value: 27.539
|
| 1056 |
+
- type: map_at_3
|
| 1057 |
+
value: 23.882
|
| 1058 |
+
- type: map_at_5
|
| 1059 |
+
value: 25.163999999999998
|
| 1060 |
+
- type: mrr_at_1
|
| 1061 |
+
value: 21.442
|
| 1062 |
+
- type: mrr_at_10
|
| 1063 |
+
value: 28.458
|
| 1064 |
+
- type: mrr_at_100
|
| 1065 |
+
value: 29.360999999999997
|
| 1066 |
+
- type: mrr_at_1000
|
| 1067 |
+
value: 29.448999999999998
|
| 1068 |
+
- type: mrr_at_3
|
| 1069 |
+
value: 25.97
|
| 1070 |
+
- type: mrr_at_5
|
| 1071 |
+
value: 27.273999999999997
|
| 1072 |
+
- type: ndcg_at_1
|
| 1073 |
+
value: 21.442
|
| 1074 |
+
- type: ndcg_at_10
|
| 1075 |
+
value: 30.897000000000002
|
| 1076 |
+
- type: ndcg_at_100
|
| 1077 |
+
value: 35.99
|
| 1078 |
+
- type: ndcg_at_1000
|
| 1079 |
+
value: 38.832
|
| 1080 |
+
- type: ndcg_at_3
|
| 1081 |
+
value: 25.944
|
| 1082 |
+
- type: ndcg_at_5
|
| 1083 |
+
value: 28.126
|
| 1084 |
+
- type: precision_at_1
|
| 1085 |
+
value: 21.442
|
| 1086 |
+
- type: precision_at_10
|
| 1087 |
+
value: 4.9910000000000005
|
| 1088 |
+
- type: precision_at_100
|
| 1089 |
+
value: 0.8109999999999999
|
| 1090 |
+
- type: precision_at_1000
|
| 1091 |
+
value: 0.11800000000000001
|
| 1092 |
+
- type: precision_at_3
|
| 1093 |
+
value: 11.029
|
| 1094 |
+
- type: precision_at_5
|
| 1095 |
+
value: 7.911
|
| 1096 |
+
- type: recall_at_1
|
| 1097 |
+
value: 19.547
|
| 1098 |
+
- type: recall_at_10
|
| 1099 |
+
value: 42.886
|
| 1100 |
+
- type: recall_at_100
|
| 1101 |
+
value: 66.64999999999999
|
| 1102 |
+
- type: recall_at_1000
|
| 1103 |
+
value: 87.368
|
| 1104 |
+
- type: recall_at_3
|
| 1105 |
+
value: 29.143
|
| 1106 |
+
- type: recall_at_5
|
| 1107 |
+
value: 34.544000000000004
|
| 1108 |
+
- task:
|
| 1109 |
+
type: Retrieval
|
| 1110 |
+
dataset:
|
| 1111 |
+
type: climate-fever
|
| 1112 |
+
name: MTEB ClimateFEVER
|
| 1113 |
+
config: default
|
| 1114 |
+
split: test
|
| 1115 |
+
revision: None
|
| 1116 |
+
metrics:
|
| 1117 |
+
- type: map_at_1
|
| 1118 |
+
value: 15.572
|
| 1119 |
+
- type: map_at_10
|
| 1120 |
+
value: 25.312
|
| 1121 |
+
- type: map_at_100
|
| 1122 |
+
value: 27.062
|
| 1123 |
+
- type: map_at_1000
|
| 1124 |
+
value: 27.253
|
| 1125 |
+
- type: map_at_3
|
| 1126 |
+
value: 21.601
|
| 1127 |
+
- type: map_at_5
|
| 1128 |
+
value: 23.473
|
| 1129 |
+
- type: mrr_at_1
|
| 1130 |
+
value: 34.984
|
| 1131 |
+
- type: mrr_at_10
|
| 1132 |
+
value: 46.406
|
| 1133 |
+
- type: mrr_at_100
|
| 1134 |
+
value: 47.179
|
| 1135 |
+
- type: mrr_at_1000
|
| 1136 |
+
value: 47.21
|
| 1137 |
+
- type: mrr_at_3
|
| 1138 |
+
value: 43.485
|
| 1139 |
+
- type: mrr_at_5
|
| 1140 |
+
value: 45.322
|
| 1141 |
+
- type: ndcg_at_1
|
| 1142 |
+
value: 34.984
|
| 1143 |
+
- type: ndcg_at_10
|
| 1144 |
+
value: 34.344
|
| 1145 |
+
- type: ndcg_at_100
|
| 1146 |
+
value: 41.015
|
| 1147 |
+
- type: ndcg_at_1000
|
| 1148 |
+
value: 44.366
|
| 1149 |
+
- type: ndcg_at_3
|
| 1150 |
+
value: 29.119
|
| 1151 |
+
- type: ndcg_at_5
|
| 1152 |
+
value: 30.825999999999997
|
| 1153 |
+
- type: precision_at_1
|
| 1154 |
+
value: 34.984
|
| 1155 |
+
- type: precision_at_10
|
| 1156 |
+
value: 10.358
|
| 1157 |
+
- type: precision_at_100
|
| 1158 |
+
value: 1.762
|
| 1159 |
+
- type: precision_at_1000
|
| 1160 |
+
value: 0.23900000000000002
|
| 1161 |
+
- type: precision_at_3
|
| 1162 |
+
value: 21.368000000000002
|
| 1163 |
+
- type: precision_at_5
|
| 1164 |
+
value: 15.948
|
| 1165 |
+
- type: recall_at_1
|
| 1166 |
+
value: 15.572
|
| 1167 |
+
- type: recall_at_10
|
| 1168 |
+
value: 39.367999999999995
|
| 1169 |
+
- type: recall_at_100
|
| 1170 |
+
value: 62.183
|
| 1171 |
+
- type: recall_at_1000
|
| 1172 |
+
value: 80.92200000000001
|
| 1173 |
+
- type: recall_at_3
|
| 1174 |
+
value: 26.131999999999998
|
| 1175 |
+
- type: recall_at_5
|
| 1176 |
+
value: 31.635999999999996
|
| 1177 |
+
- task:
|
| 1178 |
+
type: Retrieval
|
| 1179 |
+
dataset:
|
| 1180 |
+
type: dbpedia-entity
|
| 1181 |
+
name: MTEB DBPedia
|
| 1182 |
+
config: default
|
| 1183 |
+
split: test
|
| 1184 |
+
revision: None
|
| 1185 |
+
metrics:
|
| 1186 |
+
- type: map_at_1
|
| 1187 |
+
value: 8.848
|
| 1188 |
+
- type: map_at_10
|
| 1189 |
+
value: 19.25
|
| 1190 |
+
- type: map_at_100
|
| 1191 |
+
value: 27.193
|
| 1192 |
+
- type: map_at_1000
|
| 1193 |
+
value: 28.721999999999998
|
| 1194 |
+
- type: map_at_3
|
| 1195 |
+
value: 13.968
|
| 1196 |
+
- type: map_at_5
|
| 1197 |
+
value: 16.283
|
| 1198 |
+
- type: mrr_at_1
|
| 1199 |
+
value: 68.75
|
| 1200 |
+
- type: mrr_at_10
|
| 1201 |
+
value: 76.25
|
| 1202 |
+
- type: mrr_at_100
|
| 1203 |
+
value: 76.534
|
| 1204 |
+
- type: mrr_at_1000
|
| 1205 |
+
value: 76.53999999999999
|
| 1206 |
+
- type: mrr_at_3
|
| 1207 |
+
value: 74.667
|
| 1208 |
+
- type: mrr_at_5
|
| 1209 |
+
value: 75.86699999999999
|
| 1210 |
+
- type: ndcg_at_1
|
| 1211 |
+
value: 56.00000000000001
|
| 1212 |
+
- type: ndcg_at_10
|
| 1213 |
+
value: 41.426
|
| 1214 |
+
- type: ndcg_at_100
|
| 1215 |
+
value: 45.660000000000004
|
| 1216 |
+
- type: ndcg_at_1000
|
| 1217 |
+
value: 53.02
|
| 1218 |
+
- type: ndcg_at_3
|
| 1219 |
+
value: 46.581
|
| 1220 |
+
- type: ndcg_at_5
|
| 1221 |
+
value: 43.836999999999996
|
| 1222 |
+
- type: precision_at_1
|
| 1223 |
+
value: 68.75
|
| 1224 |
+
- type: precision_at_10
|
| 1225 |
+
value: 32.800000000000004
|
| 1226 |
+
- type: precision_at_100
|
| 1227 |
+
value: 10.440000000000001
|
| 1228 |
+
- type: precision_at_1000
|
| 1229 |
+
value: 1.9980000000000002
|
| 1230 |
+
- type: precision_at_3
|
| 1231 |
+
value: 49.667
|
| 1232 |
+
- type: precision_at_5
|
| 1233 |
+
value: 42.25
|
| 1234 |
+
- type: recall_at_1
|
| 1235 |
+
value: 8.848
|
| 1236 |
+
- type: recall_at_10
|
| 1237 |
+
value: 24.467
|
| 1238 |
+
- type: recall_at_100
|
| 1239 |
+
value: 51.344
|
| 1240 |
+
- type: recall_at_1000
|
| 1241 |
+
value: 75.235
|
| 1242 |
+
- type: recall_at_3
|
| 1243 |
+
value: 15.329
|
| 1244 |
+
- type: recall_at_5
|
| 1245 |
+
value: 18.892999999999997
|
| 1246 |
+
- task:
|
| 1247 |
+
type: Classification
|
| 1248 |
+
dataset:
|
| 1249 |
+
type: mteb/emotion
|
| 1250 |
+
name: MTEB EmotionClassification
|
| 1251 |
+
config: default
|
| 1252 |
+
split: test
|
| 1253 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 1254 |
+
metrics:
|
| 1255 |
+
- type: accuracy
|
| 1256 |
+
value: 48.95
|
| 1257 |
+
- type: f1
|
| 1258 |
+
value: 43.44563593360779
|
| 1259 |
+
- task:
|
| 1260 |
+
type: Retrieval
|
| 1261 |
+
dataset:
|
| 1262 |
+
type: fever
|
| 1263 |
+
name: MTEB FEVER
|
| 1264 |
+
config: default
|
| 1265 |
+
split: test
|
| 1266 |
+
revision: None
|
| 1267 |
+
metrics:
|
| 1268 |
+
- type: map_at_1
|
| 1269 |
+
value: 78.036
|
| 1270 |
+
- type: map_at_10
|
| 1271 |
+
value: 85.639
|
| 1272 |
+
- type: map_at_100
|
| 1273 |
+
value: 85.815
|
| 1274 |
+
- type: map_at_1000
|
| 1275 |
+
value: 85.829
|
| 1276 |
+
- type: map_at_3
|
| 1277 |
+
value: 84.795
|
| 1278 |
+
- type: map_at_5
|
| 1279 |
+
value: 85.336
|
| 1280 |
+
- type: mrr_at_1
|
| 1281 |
+
value: 84.353
|
| 1282 |
+
- type: mrr_at_10
|
| 1283 |
+
value: 90.582
|
| 1284 |
+
- type: mrr_at_100
|
| 1285 |
+
value: 90.617
|
| 1286 |
+
- type: mrr_at_1000
|
| 1287 |
+
value: 90.617
|
| 1288 |
+
- type: mrr_at_3
|
| 1289 |
+
value: 90.132
|
| 1290 |
+
- type: mrr_at_5
|
| 1291 |
+
value: 90.447
|
| 1292 |
+
- type: ndcg_at_1
|
| 1293 |
+
value: 84.353
|
| 1294 |
+
- type: ndcg_at_10
|
| 1295 |
+
value: 89.003
|
| 1296 |
+
- type: ndcg_at_100
|
| 1297 |
+
value: 89.60000000000001
|
| 1298 |
+
- type: ndcg_at_1000
|
| 1299 |
+
value: 89.836
|
| 1300 |
+
- type: ndcg_at_3
|
| 1301 |
+
value: 87.81400000000001
|
| 1302 |
+
- type: ndcg_at_5
|
| 1303 |
+
value: 88.478
|
| 1304 |
+
- type: precision_at_1
|
| 1305 |
+
value: 84.353
|
| 1306 |
+
- type: precision_at_10
|
| 1307 |
+
value: 10.482
|
| 1308 |
+
- type: precision_at_100
|
| 1309 |
+
value: 1.099
|
| 1310 |
+
- type: precision_at_1000
|
| 1311 |
+
value: 0.11399999999999999
|
| 1312 |
+
- type: precision_at_3
|
| 1313 |
+
value: 33.257999999999996
|
| 1314 |
+
- type: precision_at_5
|
| 1315 |
+
value: 20.465
|
| 1316 |
+
- type: recall_at_1
|
| 1317 |
+
value: 78.036
|
| 1318 |
+
- type: recall_at_10
|
| 1319 |
+
value: 94.517
|
| 1320 |
+
- type: recall_at_100
|
| 1321 |
+
value: 96.828
|
| 1322 |
+
- type: recall_at_1000
|
| 1323 |
+
value: 98.261
|
| 1324 |
+
- type: recall_at_3
|
| 1325 |
+
value: 91.12
|
| 1326 |
+
- type: recall_at_5
|
| 1327 |
+
value: 92.946
|
| 1328 |
+
- task:
|
| 1329 |
+
type: Retrieval
|
| 1330 |
+
dataset:
|
| 1331 |
+
type: fiqa
|
| 1332 |
+
name: MTEB FiQA2018
|
| 1333 |
+
config: default
|
| 1334 |
+
split: test
|
| 1335 |
+
revision: None
|
| 1336 |
+
metrics:
|
| 1337 |
+
- type: map_at_1
|
| 1338 |
+
value: 20.191
|
| 1339 |
+
- type: map_at_10
|
| 1340 |
+
value: 32.369
|
| 1341 |
+
- type: map_at_100
|
| 1342 |
+
value: 34.123999999999995
|
| 1343 |
+
- type: map_at_1000
|
| 1344 |
+
value: 34.317
|
| 1345 |
+
- type: map_at_3
|
| 1346 |
+
value: 28.71
|
| 1347 |
+
- type: map_at_5
|
| 1348 |
+
value: 30.607
|
| 1349 |
+
- type: mrr_at_1
|
| 1350 |
+
value: 40.894999999999996
|
| 1351 |
+
- type: mrr_at_10
|
| 1352 |
+
value: 48.842
|
| 1353 |
+
- type: mrr_at_100
|
| 1354 |
+
value: 49.599
|
| 1355 |
+
- type: mrr_at_1000
|
| 1356 |
+
value: 49.647000000000006
|
| 1357 |
+
- type: mrr_at_3
|
| 1358 |
+
value: 46.785
|
| 1359 |
+
- type: mrr_at_5
|
| 1360 |
+
value: 47.672
|
| 1361 |
+
- type: ndcg_at_1
|
| 1362 |
+
value: 40.894999999999996
|
| 1363 |
+
- type: ndcg_at_10
|
| 1364 |
+
value: 39.872
|
| 1365 |
+
- type: ndcg_at_100
|
| 1366 |
+
value: 46.126
|
| 1367 |
+
- type: ndcg_at_1000
|
| 1368 |
+
value: 49.476
|
| 1369 |
+
- type: ndcg_at_3
|
| 1370 |
+
value: 37.153000000000006
|
| 1371 |
+
- type: ndcg_at_5
|
| 1372 |
+
value: 37.433
|
| 1373 |
+
- type: precision_at_1
|
| 1374 |
+
value: 40.894999999999996
|
| 1375 |
+
- type: precision_at_10
|
| 1376 |
+
value: 10.818
|
| 1377 |
+
- type: precision_at_100
|
| 1378 |
+
value: 1.73
|
| 1379 |
+
- type: precision_at_1000
|
| 1380 |
+
value: 0.231
|
| 1381 |
+
- type: precision_at_3
|
| 1382 |
+
value: 25.051000000000002
|
| 1383 |
+
- type: precision_at_5
|
| 1384 |
+
value: 17.531
|
| 1385 |
+
- type: recall_at_1
|
| 1386 |
+
value: 20.191
|
| 1387 |
+
- type: recall_at_10
|
| 1388 |
+
value: 45.768
|
| 1389 |
+
- type: recall_at_100
|
| 1390 |
+
value: 68.82000000000001
|
| 1391 |
+
- type: recall_at_1000
|
| 1392 |
+
value: 89.133
|
| 1393 |
+
- type: recall_at_3
|
| 1394 |
+
value: 33.296
|
| 1395 |
+
- type: recall_at_5
|
| 1396 |
+
value: 38.022
|
| 1397 |
+
- task:
|
| 1398 |
+
type: Retrieval
|
| 1399 |
+
dataset:
|
| 1400 |
+
type: hotpotqa
|
| 1401 |
+
name: MTEB HotpotQA
|
| 1402 |
+
config: default
|
| 1403 |
+
split: test
|
| 1404 |
+
revision: None
|
| 1405 |
+
metrics:
|
| 1406 |
+
- type: map_at_1
|
| 1407 |
+
value: 39.257
|
| 1408 |
+
- type: map_at_10
|
| 1409 |
+
value: 61.467000000000006
|
| 1410 |
+
- type: map_at_100
|
| 1411 |
+
value: 62.364
|
| 1412 |
+
- type: map_at_1000
|
| 1413 |
+
value: 62.424
|
| 1414 |
+
- type: map_at_3
|
| 1415 |
+
value: 58.228
|
| 1416 |
+
- type: map_at_5
|
| 1417 |
+
value: 60.283
|
| 1418 |
+
- type: mrr_at_1
|
| 1419 |
+
value: 78.515
|
| 1420 |
+
- type: mrr_at_10
|
| 1421 |
+
value: 84.191
|
| 1422 |
+
- type: mrr_at_100
|
| 1423 |
+
value: 84.378
|
| 1424 |
+
- type: mrr_at_1000
|
| 1425 |
+
value: 84.385
|
| 1426 |
+
- type: mrr_at_3
|
| 1427 |
+
value: 83.284
|
| 1428 |
+
- type: mrr_at_5
|
| 1429 |
+
value: 83.856
|
| 1430 |
+
- type: ndcg_at_1
|
| 1431 |
+
value: 78.515
|
| 1432 |
+
- type: ndcg_at_10
|
| 1433 |
+
value: 69.78999999999999
|
| 1434 |
+
- type: ndcg_at_100
|
| 1435 |
+
value: 72.886
|
| 1436 |
+
- type: ndcg_at_1000
|
| 1437 |
+
value: 74.015
|
| 1438 |
+
- type: ndcg_at_3
|
| 1439 |
+
value: 65.23
|
| 1440 |
+
- type: ndcg_at_5
|
| 1441 |
+
value: 67.80199999999999
|
| 1442 |
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- type: precision_at_1
|
| 1443 |
+
value: 78.515
|
| 1444 |
+
- type: precision_at_10
|
| 1445 |
+
value: 14.519000000000002
|
| 1446 |
+
- type: precision_at_100
|
| 1447 |
+
value: 1.694
|
| 1448 |
+
- type: precision_at_1000
|
| 1449 |
+
value: 0.184
|
| 1450 |
+
- type: precision_at_3
|
| 1451 |
+
value: 41.702
|
| 1452 |
+
- type: precision_at_5
|
| 1453 |
+
value: 27.046999999999997
|
| 1454 |
+
- type: recall_at_1
|
| 1455 |
+
value: 39.257
|
| 1456 |
+
- type: recall_at_10
|
| 1457 |
+
value: 72.59299999999999
|
| 1458 |
+
- type: recall_at_100
|
| 1459 |
+
value: 84.679
|
| 1460 |
+
- type: recall_at_1000
|
| 1461 |
+
value: 92.12
|
| 1462 |
+
- type: recall_at_3
|
| 1463 |
+
value: 62.552
|
| 1464 |
+
- type: recall_at_5
|
| 1465 |
+
value: 67.616
|
| 1466 |
+
- task:
|
| 1467 |
+
type: Classification
|
| 1468 |
+
dataset:
|
| 1469 |
+
type: mteb/imdb
|
| 1470 |
+
name: MTEB ImdbClassification
|
| 1471 |
+
config: default
|
| 1472 |
+
split: test
|
| 1473 |
+
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
| 1474 |
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metrics:
|
| 1475 |
+
- type: accuracy
|
| 1476 |
+
value: 91.5152
|
| 1477 |
+
- type: ap
|
| 1478 |
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value: 87.64584669595709
|
| 1479 |
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- type: f1
|
| 1480 |
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value: 91.50605576428437
|
| 1481 |
+
- task:
|
| 1482 |
+
type: Retrieval
|
| 1483 |
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dataset:
|
| 1484 |
+
type: msmarco
|
| 1485 |
+
name: MTEB MSMARCO
|
| 1486 |
+
config: default
|
| 1487 |
+
split: dev
|
| 1488 |
+
revision: None
|
| 1489 |
+
metrics:
|
| 1490 |
+
- type: map_at_1
|
| 1491 |
+
value: 21.926000000000002
|
| 1492 |
+
- type: map_at_10
|
| 1493 |
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value: 34.049
|
| 1494 |
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- type: map_at_100
|
| 1495 |
+
value: 35.213
|
| 1496 |
+
- type: map_at_1000
|
| 1497 |
+
value: 35.265
|
| 1498 |
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- type: map_at_3
|
| 1499 |
+
value: 30.309
|
| 1500 |
+
- type: map_at_5
|
| 1501 |
+
value: 32.407000000000004
|
| 1502 |
+
- type: mrr_at_1
|
| 1503 |
+
value: 22.55
|
| 1504 |
+
- type: mrr_at_10
|
| 1505 |
+
value: 34.657
|
| 1506 |
+
- type: mrr_at_100
|
| 1507 |
+
value: 35.760999999999996
|
| 1508 |
+
- type: mrr_at_1000
|
| 1509 |
+
value: 35.807
|
| 1510 |
+
- type: mrr_at_3
|
| 1511 |
+
value: 30.989
|
| 1512 |
+
- type: mrr_at_5
|
| 1513 |
+
value: 33.039
|
| 1514 |
+
- type: ndcg_at_1
|
| 1515 |
+
value: 22.55
|
| 1516 |
+
- type: ndcg_at_10
|
| 1517 |
+
value: 40.842
|
| 1518 |
+
- type: ndcg_at_100
|
| 1519 |
+
value: 46.436
|
| 1520 |
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- type: ndcg_at_1000
|
| 1521 |
+
value: 47.721999999999994
|
| 1522 |
+
- type: ndcg_at_3
|
| 1523 |
+
value: 33.209
|
| 1524 |
+
- type: ndcg_at_5
|
| 1525 |
+
value: 36.943
|
| 1526 |
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- type: precision_at_1
|
| 1527 |
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value: 22.55
|
| 1528 |
+
- type: precision_at_10
|
| 1529 |
+
value: 6.447
|
| 1530 |
+
- type: precision_at_100
|
| 1531 |
+
value: 0.9249999999999999
|
| 1532 |
+
- type: precision_at_1000
|
| 1533 |
+
value: 0.104
|
| 1534 |
+
- type: precision_at_3
|
| 1535 |
+
value: 14.136000000000001
|
| 1536 |
+
- type: precision_at_5
|
| 1537 |
+
value: 10.381
|
| 1538 |
+
- type: recall_at_1
|
| 1539 |
+
value: 21.926000000000002
|
| 1540 |
+
- type: recall_at_10
|
| 1541 |
+
value: 61.724999999999994
|
| 1542 |
+
- type: recall_at_100
|
| 1543 |
+
value: 87.604
|
| 1544 |
+
- type: recall_at_1000
|
| 1545 |
+
value: 97.421
|
| 1546 |
+
- type: recall_at_3
|
| 1547 |
+
value: 40.944
|
| 1548 |
+
- type: recall_at_5
|
| 1549 |
+
value: 49.915
|
| 1550 |
+
- task:
|
| 1551 |
+
type: Classification
|
| 1552 |
+
dataset:
|
| 1553 |
+
type: mteb/mtop_domain
|
| 1554 |
+
name: MTEB MTOPDomainClassification (en)
|
| 1555 |
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config: en
|
| 1556 |
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split: test
|
| 1557 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
| 1558 |
+
metrics:
|
| 1559 |
+
- type: accuracy
|
| 1560 |
+
value: 93.54765161878704
|
| 1561 |
+
- type: f1
|
| 1562 |
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value: 93.3298945415573
|
| 1563 |
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- task:
|
| 1564 |
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type: Classification
|
| 1565 |
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dataset:
|
| 1566 |
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type: mteb/mtop_intent
|
| 1567 |
+
name: MTEB MTOPIntentClassification (en)
|
| 1568 |
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config: en
|
| 1569 |
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split: test
|
| 1570 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
| 1571 |
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metrics:
|
| 1572 |
+
- type: accuracy
|
| 1573 |
+
value: 75.71591427268582
|
| 1574 |
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- type: f1
|
| 1575 |
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value: 59.32113870474471
|
| 1576 |
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- task:
|
| 1577 |
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type: Classification
|
| 1578 |
+
dataset:
|
| 1579 |
+
type: mteb/amazon_massive_intent
|
| 1580 |
+
name: MTEB MassiveIntentClassification (en)
|
| 1581 |
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config: en
|
| 1582 |
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split: test
|
| 1583 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
| 1584 |
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metrics:
|
| 1585 |
+
- type: accuracy
|
| 1586 |
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value: 75.83053127101547
|
| 1587 |
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- type: f1
|
| 1588 |
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value: 73.60757944876475
|
| 1589 |
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- task:
|
| 1590 |
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type: Classification
|
| 1591 |
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dataset:
|
| 1592 |
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type: mteb/amazon_massive_scenario
|
| 1593 |
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name: MTEB MassiveScenarioClassification (en)
|
| 1594 |
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config: en
|
| 1595 |
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split: test
|
| 1596 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
| 1597 |
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metrics:
|
| 1598 |
+
- type: accuracy
|
| 1599 |
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value: 78.72562205783457
|
| 1600 |
+
- type: f1
|
| 1601 |
+
value: 78.63761662505502
|
| 1602 |
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- task:
|
| 1603 |
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type: Clustering
|
| 1604 |
+
dataset:
|
| 1605 |
+
type: mteb/medrxiv-clustering-p2p
|
| 1606 |
+
name: MTEB MedrxivClusteringP2P
|
| 1607 |
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config: default
|
| 1608 |
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split: test
|
| 1609 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
| 1610 |
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metrics:
|
| 1611 |
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- type: v_measure
|
| 1612 |
+
value: 33.37935633767996
|
| 1613 |
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- task:
|
| 1614 |
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type: Clustering
|
| 1615 |
+
dataset:
|
| 1616 |
+
type: mteb/medrxiv-clustering-s2s
|
| 1617 |
+
name: MTEB MedrxivClusteringS2S
|
| 1618 |
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config: default
|
| 1619 |
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split: test
|
| 1620 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
| 1621 |
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metrics:
|
| 1622 |
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- type: v_measure
|
| 1623 |
+
value: 31.55270546130387
|
| 1624 |
+
- task:
|
| 1625 |
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type: Reranking
|
| 1626 |
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dataset:
|
| 1627 |
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type: mteb/mind_small
|
| 1628 |
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name: MTEB MindSmallReranking
|
| 1629 |
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config: default
|
| 1630 |
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split: test
|
| 1631 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
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| 1632 |
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metrics:
|
| 1633 |
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- type: map
|
| 1634 |
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value: 30.462692753143834
|
| 1635 |
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- type: mrr
|
| 1636 |
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value: 31.497569753511563
|
| 1637 |
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- task:
|
| 1638 |
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type: Retrieval
|
| 1639 |
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dataset:
|
| 1640 |
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type: nfcorpus
|
| 1641 |
+
name: MTEB NFCorpus
|
| 1642 |
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config: default
|
| 1643 |
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split: test
|
| 1644 |
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revision: None
|
| 1645 |
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metrics:
|
| 1646 |
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- type: map_at_1
|
| 1647 |
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value: 5.646
|
| 1648 |
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- type: map_at_10
|
| 1649 |
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value: 12.498
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| 1650 |
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- type: map_at_100
|
| 1651 |
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value: 15.486
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| 1652 |
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- type: map_at_1000
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| 1653 |
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value: 16.805999999999997
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| 1654 |
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|
| 1655 |
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value: 9.325
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| 1656 |
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| 1657 |
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value: 10.751
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| 1658 |
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|
| 1659 |
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value: 43.034
|
| 1660 |
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|
| 1661 |
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value: 52.662
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| 1662 |
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- type: mrr_at_100
|
| 1663 |
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value: 53.189
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| 1664 |
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- type: mrr_at_1000
|
| 1665 |
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value: 53.25
|
| 1666 |
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|
| 1667 |
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value: 50.929
|
| 1668 |
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- type: mrr_at_5
|
| 1669 |
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value: 51.92
|
| 1670 |
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- type: ndcg_at_1
|
| 1671 |
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value: 41.796
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| 1672 |
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| 1673 |
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value: 33.477000000000004
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| 1674 |
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- type: ndcg_at_100
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| 1675 |
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value: 29.996000000000002
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| 1676 |
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- type: ndcg_at_1000
|
| 1677 |
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value: 38.864
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| 1678 |
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- type: ndcg_at_3
|
| 1679 |
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value: 38.940000000000005
|
| 1680 |
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- type: ndcg_at_5
|
| 1681 |
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value: 36.689
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| 1682 |
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- type: precision_at_1
|
| 1683 |
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value: 43.034
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| 1684 |
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- type: precision_at_10
|
| 1685 |
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value: 24.799
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| 1686 |
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- type: precision_at_100
|
| 1687 |
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value: 7.432999999999999
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| 1688 |
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- type: precision_at_1000
|
| 1689 |
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value: 1.9929999999999999
|
| 1690 |
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- type: precision_at_3
|
| 1691 |
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value: 36.842000000000006
|
| 1692 |
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- type: precision_at_5
|
| 1693 |
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value: 32.135999999999996
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| 1694 |
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- type: recall_at_1
|
| 1695 |
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value: 5.646
|
| 1696 |
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- type: recall_at_10
|
| 1697 |
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value: 15.963
|
| 1698 |
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- type: recall_at_100
|
| 1699 |
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value: 29.492
|
| 1700 |
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- type: recall_at_1000
|
| 1701 |
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value: 61.711000000000006
|
| 1702 |
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- type: recall_at_3
|
| 1703 |
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value: 10.585
|
| 1704 |
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- type: recall_at_5
|
| 1705 |
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value: 12.753999999999998
|
| 1706 |
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- task:
|
| 1707 |
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type: Retrieval
|
| 1708 |
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dataset:
|
| 1709 |
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type: nq
|
| 1710 |
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name: MTEB NQ
|
| 1711 |
+
config: default
|
| 1712 |
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split: test
|
| 1713 |
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revision: None
|
| 1714 |
+
metrics:
|
| 1715 |
+
- type: map_at_1
|
| 1716 |
+
value: 27.602
|
| 1717 |
+
- type: map_at_10
|
| 1718 |
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value: 41.545
|
| 1719 |
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- type: map_at_100
|
| 1720 |
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value: 42.644999999999996
|
| 1721 |
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- type: map_at_1000
|
| 1722 |
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value: 42.685
|
| 1723 |
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- type: map_at_3
|
| 1724 |
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value: 37.261
|
| 1725 |
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- type: map_at_5
|
| 1726 |
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value: 39.706
|
| 1727 |
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- type: mrr_at_1
|
| 1728 |
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value: 31.141000000000002
|
| 1729 |
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- type: mrr_at_10
|
| 1730 |
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value: 44.139
|
| 1731 |
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- type: mrr_at_100
|
| 1732 |
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value: 44.997
|
| 1733 |
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- type: mrr_at_1000
|
| 1734 |
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value: 45.025999999999996
|
| 1735 |
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- type: mrr_at_3
|
| 1736 |
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value: 40.503
|
| 1737 |
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- type: mrr_at_5
|
| 1738 |
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value: 42.64
|
| 1739 |
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- type: ndcg_at_1
|
| 1740 |
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value: 31.141000000000002
|
| 1741 |
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- type: ndcg_at_10
|
| 1742 |
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value: 48.995
|
| 1743 |
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- type: ndcg_at_100
|
| 1744 |
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value: 53.788000000000004
|
| 1745 |
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- type: ndcg_at_1000
|
| 1746 |
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value: 54.730000000000004
|
| 1747 |
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- type: ndcg_at_3
|
| 1748 |
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value: 40.844
|
| 1749 |
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- type: ndcg_at_5
|
| 1750 |
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value: 44.955
|
| 1751 |
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- type: precision_at_1
|
| 1752 |
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value: 31.141000000000002
|
| 1753 |
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- type: precision_at_10
|
| 1754 |
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value: 8.233
|
| 1755 |
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- type: precision_at_100
|
| 1756 |
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value: 1.093
|
| 1757 |
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- type: precision_at_1000
|
| 1758 |
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value: 0.11800000000000001
|
| 1759 |
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- type: precision_at_3
|
| 1760 |
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value: 18.579
|
| 1761 |
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- type: precision_at_5
|
| 1762 |
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value: 13.533999999999999
|
| 1763 |
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- type: recall_at_1
|
| 1764 |
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value: 27.602
|
| 1765 |
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- type: recall_at_10
|
| 1766 |
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value: 69.216
|
| 1767 |
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- type: recall_at_100
|
| 1768 |
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value: 90.252
|
| 1769 |
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- type: recall_at_1000
|
| 1770 |
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value: 97.27
|
| 1771 |
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- type: recall_at_3
|
| 1772 |
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value: 47.987
|
| 1773 |
+
- type: recall_at_5
|
| 1774 |
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value: 57.438
|
| 1775 |
+
- task:
|
| 1776 |
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type: Retrieval
|
| 1777 |
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dataset:
|
| 1778 |
+
type: quora
|
| 1779 |
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name: MTEB QuoraRetrieval
|
| 1780 |
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config: default
|
| 1781 |
+
split: test
|
| 1782 |
+
revision: None
|
| 1783 |
+
metrics:
|
| 1784 |
+
- type: map_at_1
|
| 1785 |
+
value: 70.949
|
| 1786 |
+
- type: map_at_10
|
| 1787 |
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value: 84.89999999999999
|
| 1788 |
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- type: map_at_100
|
| 1789 |
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value: 85.531
|
| 1790 |
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- type: map_at_1000
|
| 1791 |
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value: 85.548
|
| 1792 |
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- type: map_at_3
|
| 1793 |
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value: 82.027
|
| 1794 |
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- type: map_at_5
|
| 1795 |
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value: 83.853
|
| 1796 |
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|
| 1797 |
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value: 81.69999999999999
|
| 1798 |
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- type: mrr_at_10
|
| 1799 |
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value: 87.813
|
| 1800 |
+
- type: mrr_at_100
|
| 1801 |
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value: 87.917
|
| 1802 |
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- type: mrr_at_1000
|
| 1803 |
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value: 87.91799999999999
|
| 1804 |
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- type: mrr_at_3
|
| 1805 |
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value: 86.938
|
| 1806 |
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- type: mrr_at_5
|
| 1807 |
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value: 87.53999999999999
|
| 1808 |
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- type: ndcg_at_1
|
| 1809 |
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value: 81.75
|
| 1810 |
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- type: ndcg_at_10
|
| 1811 |
+
value: 88.55499999999999
|
| 1812 |
+
- type: ndcg_at_100
|
| 1813 |
+
value: 89.765
|
| 1814 |
+
- type: ndcg_at_1000
|
| 1815 |
+
value: 89.871
|
| 1816 |
+
- type: ndcg_at_3
|
| 1817 |
+
value: 85.905
|
| 1818 |
+
- type: ndcg_at_5
|
| 1819 |
+
value: 87.41
|
| 1820 |
+
- type: precision_at_1
|
| 1821 |
+
value: 81.75
|
| 1822 |
+
- type: precision_at_10
|
| 1823 |
+
value: 13.403
|
| 1824 |
+
- type: precision_at_100
|
| 1825 |
+
value: 1.528
|
| 1826 |
+
- type: precision_at_1000
|
| 1827 |
+
value: 0.157
|
| 1828 |
+
- type: precision_at_3
|
| 1829 |
+
value: 37.597
|
| 1830 |
+
- type: precision_at_5
|
| 1831 |
+
value: 24.69
|
| 1832 |
+
- type: recall_at_1
|
| 1833 |
+
value: 70.949
|
| 1834 |
+
- type: recall_at_10
|
| 1835 |
+
value: 95.423
|
| 1836 |
+
- type: recall_at_100
|
| 1837 |
+
value: 99.509
|
| 1838 |
+
- type: recall_at_1000
|
| 1839 |
+
value: 99.982
|
| 1840 |
+
- type: recall_at_3
|
| 1841 |
+
value: 87.717
|
| 1842 |
+
- type: recall_at_5
|
| 1843 |
+
value: 92.032
|
| 1844 |
+
- task:
|
| 1845 |
+
type: Clustering
|
| 1846 |
+
dataset:
|
| 1847 |
+
type: mteb/reddit-clustering
|
| 1848 |
+
name: MTEB RedditClustering
|
| 1849 |
+
config: default
|
| 1850 |
+
split: test
|
| 1851 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
| 1852 |
+
metrics:
|
| 1853 |
+
- type: v_measure
|
| 1854 |
+
value: 51.76962893449579
|
| 1855 |
+
- task:
|
| 1856 |
+
type: Clustering
|
| 1857 |
+
dataset:
|
| 1858 |
+
type: mteb/reddit-clustering-p2p
|
| 1859 |
+
name: MTEB RedditClusteringP2P
|
| 1860 |
+
config: default
|
| 1861 |
+
split: test
|
| 1862 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
| 1863 |
+
metrics:
|
| 1864 |
+
- type: v_measure
|
| 1865 |
+
value: 62.32897690686379
|
| 1866 |
+
- task:
|
| 1867 |
+
type: Retrieval
|
| 1868 |
+
dataset:
|
| 1869 |
+
type: scidocs
|
| 1870 |
+
name: MTEB SCIDOCS
|
| 1871 |
+
config: default
|
| 1872 |
+
split: test
|
| 1873 |
+
revision: None
|
| 1874 |
+
metrics:
|
| 1875 |
+
- type: map_at_1
|
| 1876 |
+
value: 4.478
|
| 1877 |
+
- type: map_at_10
|
| 1878 |
+
value: 11.994
|
| 1879 |
+
- type: map_at_100
|
| 1880 |
+
value: 13.977
|
| 1881 |
+
- type: map_at_1000
|
| 1882 |
+
value: 14.295
|
| 1883 |
+
- type: map_at_3
|
| 1884 |
+
value: 8.408999999999999
|
| 1885 |
+
- type: map_at_5
|
| 1886 |
+
value: 10.024
|
| 1887 |
+
- type: mrr_at_1
|
| 1888 |
+
value: 22.1
|
| 1889 |
+
- type: mrr_at_10
|
| 1890 |
+
value: 33.526
|
| 1891 |
+
- type: mrr_at_100
|
| 1892 |
+
value: 34.577000000000005
|
| 1893 |
+
- type: mrr_at_1000
|
| 1894 |
+
value: 34.632000000000005
|
| 1895 |
+
- type: mrr_at_3
|
| 1896 |
+
value: 30.217
|
| 1897 |
+
- type: mrr_at_5
|
| 1898 |
+
value: 31.962000000000003
|
| 1899 |
+
- type: ndcg_at_1
|
| 1900 |
+
value: 22.1
|
| 1901 |
+
- type: ndcg_at_10
|
| 1902 |
+
value: 20.191
|
| 1903 |
+
- type: ndcg_at_100
|
| 1904 |
+
value: 27.954
|
| 1905 |
+
- type: ndcg_at_1000
|
| 1906 |
+
value: 33.491
|
| 1907 |
+
- type: ndcg_at_3
|
| 1908 |
+
value: 18.787000000000003
|
| 1909 |
+
- type: ndcg_at_5
|
| 1910 |
+
value: 16.378999999999998
|
| 1911 |
+
- type: precision_at_1
|
| 1912 |
+
value: 22.1
|
| 1913 |
+
- type: precision_at_10
|
| 1914 |
+
value: 10.69
|
| 1915 |
+
- type: precision_at_100
|
| 1916 |
+
value: 2.1919999999999997
|
| 1917 |
+
- type: precision_at_1000
|
| 1918 |
+
value: 0.35200000000000004
|
| 1919 |
+
- type: precision_at_3
|
| 1920 |
+
value: 17.732999999999997
|
| 1921 |
+
- type: precision_at_5
|
| 1922 |
+
value: 14.499999999999998
|
| 1923 |
+
- type: recall_at_1
|
| 1924 |
+
value: 4.478
|
| 1925 |
+
- type: recall_at_10
|
| 1926 |
+
value: 21.657
|
| 1927 |
+
- type: recall_at_100
|
| 1928 |
+
value: 44.54
|
| 1929 |
+
- type: recall_at_1000
|
| 1930 |
+
value: 71.542
|
| 1931 |
+
- type: recall_at_3
|
| 1932 |
+
value: 10.778
|
| 1933 |
+
- type: recall_at_5
|
| 1934 |
+
value: 14.687
|
| 1935 |
+
- task:
|
| 1936 |
+
type: STS
|
| 1937 |
+
dataset:
|
| 1938 |
+
type: mteb/sickr-sts
|
| 1939 |
+
name: MTEB SICK-R
|
| 1940 |
+
config: default
|
| 1941 |
+
split: test
|
| 1942 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
| 1943 |
+
metrics:
|
| 1944 |
+
- type: cos_sim_pearson
|
| 1945 |
+
value: 82.82325259156718
|
| 1946 |
+
- type: cos_sim_spearman
|
| 1947 |
+
value: 79.2463589100662
|
| 1948 |
+
- type: euclidean_pearson
|
| 1949 |
+
value: 80.48318380496771
|
| 1950 |
+
- type: euclidean_spearman
|
| 1951 |
+
value: 79.34451935199979
|
| 1952 |
+
- type: manhattan_pearson
|
| 1953 |
+
value: 80.39041824178759
|
| 1954 |
+
- type: manhattan_spearman
|
| 1955 |
+
value: 79.23002892700211
|
| 1956 |
+
- task:
|
| 1957 |
+
type: STS
|
| 1958 |
+
dataset:
|
| 1959 |
+
type: mteb/sts12-sts
|
| 1960 |
+
name: MTEB STS12
|
| 1961 |
+
config: default
|
| 1962 |
+
split: test
|
| 1963 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
| 1964 |
+
metrics:
|
| 1965 |
+
- type: cos_sim_pearson
|
| 1966 |
+
value: 85.74130231431258
|
| 1967 |
+
- type: cos_sim_spearman
|
| 1968 |
+
value: 78.36856568042397
|
| 1969 |
+
- type: euclidean_pearson
|
| 1970 |
+
value: 82.48301631890303
|
| 1971 |
+
- type: euclidean_spearman
|
| 1972 |
+
value: 78.28376980722732
|
| 1973 |
+
- type: manhattan_pearson
|
| 1974 |
+
value: 82.43552075450525
|
| 1975 |
+
- type: manhattan_spearman
|
| 1976 |
+
value: 78.22702443947126
|
| 1977 |
+
- task:
|
| 1978 |
+
type: STS
|
| 1979 |
+
dataset:
|
| 1980 |
+
type: mteb/sts13-sts
|
| 1981 |
+
name: MTEB STS13
|
| 1982 |
+
config: default
|
| 1983 |
+
split: test
|
| 1984 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
| 1985 |
+
metrics:
|
| 1986 |
+
- type: cos_sim_pearson
|
| 1987 |
+
value: 79.96138619461459
|
| 1988 |
+
- type: cos_sim_spearman
|
| 1989 |
+
value: 81.85436343502379
|
| 1990 |
+
- type: euclidean_pearson
|
| 1991 |
+
value: 81.82895226665367
|
| 1992 |
+
- type: euclidean_spearman
|
| 1993 |
+
value: 82.22707349602916
|
| 1994 |
+
- type: manhattan_pearson
|
| 1995 |
+
value: 81.66303369445873
|
| 1996 |
+
- type: manhattan_spearman
|
| 1997 |
+
value: 82.05030197179455
|
| 1998 |
+
- task:
|
| 1999 |
+
type: STS
|
| 2000 |
+
dataset:
|
| 2001 |
+
type: mteb/sts14-sts
|
| 2002 |
+
name: MTEB STS14
|
| 2003 |
+
config: default
|
| 2004 |
+
split: test
|
| 2005 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
| 2006 |
+
metrics:
|
| 2007 |
+
- type: cos_sim_pearson
|
| 2008 |
+
value: 80.05481244198648
|
| 2009 |
+
- type: cos_sim_spearman
|
| 2010 |
+
value: 80.85052504637808
|
| 2011 |
+
- type: euclidean_pearson
|
| 2012 |
+
value: 80.86728419744497
|
| 2013 |
+
- type: euclidean_spearman
|
| 2014 |
+
value: 81.033786401512
|
| 2015 |
+
- type: manhattan_pearson
|
| 2016 |
+
value: 80.90107531061103
|
| 2017 |
+
- type: manhattan_spearman
|
| 2018 |
+
value: 81.11374116827795
|
| 2019 |
+
- task:
|
| 2020 |
+
type: STS
|
| 2021 |
+
dataset:
|
| 2022 |
+
type: mteb/sts15-sts
|
| 2023 |
+
name: MTEB STS15
|
| 2024 |
+
config: default
|
| 2025 |
+
split: test
|
| 2026 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
| 2027 |
+
metrics:
|
| 2028 |
+
- type: cos_sim_pearson
|
| 2029 |
+
value: 84.615220756399
|
| 2030 |
+
- type: cos_sim_spearman
|
| 2031 |
+
value: 86.46858500002092
|
| 2032 |
+
- type: euclidean_pearson
|
| 2033 |
+
value: 86.08307800247586
|
| 2034 |
+
- type: euclidean_spearman
|
| 2035 |
+
value: 86.72691443870013
|
| 2036 |
+
- type: manhattan_pearson
|
| 2037 |
+
value: 85.96155594487269
|
| 2038 |
+
- type: manhattan_spearman
|
| 2039 |
+
value: 86.605909505275
|
| 2040 |
+
- task:
|
| 2041 |
+
type: STS
|
| 2042 |
+
dataset:
|
| 2043 |
+
type: mteb/sts16-sts
|
| 2044 |
+
name: MTEB STS16
|
| 2045 |
+
config: default
|
| 2046 |
+
split: test
|
| 2047 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
| 2048 |
+
metrics:
|
| 2049 |
+
- type: cos_sim_pearson
|
| 2050 |
+
value: 82.14363913634436
|
| 2051 |
+
- type: cos_sim_spearman
|
| 2052 |
+
value: 84.48430226487102
|
| 2053 |
+
- type: euclidean_pearson
|
| 2054 |
+
value: 83.75303424801902
|
| 2055 |
+
- type: euclidean_spearman
|
| 2056 |
+
value: 84.56762380734538
|
| 2057 |
+
- type: manhattan_pearson
|
| 2058 |
+
value: 83.6135447165928
|
| 2059 |
+
- type: manhattan_spearman
|
| 2060 |
+
value: 84.39898212616731
|
| 2061 |
+
- task:
|
| 2062 |
+
type: STS
|
| 2063 |
+
dataset:
|
| 2064 |
+
type: mteb/sts17-crosslingual-sts
|
| 2065 |
+
name: MTEB STS17 (en-en)
|
| 2066 |
+
config: en-en
|
| 2067 |
+
split: test
|
| 2068 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
| 2069 |
+
metrics:
|
| 2070 |
+
- type: cos_sim_pearson
|
| 2071 |
+
value: 85.09909252554525
|
| 2072 |
+
- type: cos_sim_spearman
|
| 2073 |
+
value: 85.70951402743276
|
| 2074 |
+
- type: euclidean_pearson
|
| 2075 |
+
value: 87.1991936239908
|
| 2076 |
+
- type: euclidean_spearman
|
| 2077 |
+
value: 86.07745840612071
|
| 2078 |
+
- type: manhattan_pearson
|
| 2079 |
+
value: 87.25039137549952
|
| 2080 |
+
- type: manhattan_spearman
|
| 2081 |
+
value: 85.99938746659761
|
| 2082 |
+
- task:
|
| 2083 |
+
type: STS
|
| 2084 |
+
dataset:
|
| 2085 |
+
type: mteb/sts22-crosslingual-sts
|
| 2086 |
+
name: MTEB STS22 (en)
|
| 2087 |
+
config: en
|
| 2088 |
+
split: test
|
| 2089 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
| 2090 |
+
metrics:
|
| 2091 |
+
- type: cos_sim_pearson
|
| 2092 |
+
value: 63.529332093413615
|
| 2093 |
+
- type: cos_sim_spearman
|
| 2094 |
+
value: 65.38177340147439
|
| 2095 |
+
- type: euclidean_pearson
|
| 2096 |
+
value: 66.35278011412136
|
| 2097 |
+
- type: euclidean_spearman
|
| 2098 |
+
value: 65.47147267032997
|
| 2099 |
+
- type: manhattan_pearson
|
| 2100 |
+
value: 66.71804682408693
|
| 2101 |
+
- type: manhattan_spearman
|
| 2102 |
+
value: 65.67406521423597
|
| 2103 |
+
- task:
|
| 2104 |
+
type: STS
|
| 2105 |
+
dataset:
|
| 2106 |
+
type: mteb/stsbenchmark-sts
|
| 2107 |
+
name: MTEB STSBenchmark
|
| 2108 |
+
config: default
|
| 2109 |
+
split: test
|
| 2110 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
| 2111 |
+
metrics:
|
| 2112 |
+
- type: cos_sim_pearson
|
| 2113 |
+
value: 82.45802942885662
|
| 2114 |
+
- type: cos_sim_spearman
|
| 2115 |
+
value: 84.8853341842566
|
| 2116 |
+
- type: euclidean_pearson
|
| 2117 |
+
value: 84.60915021096707
|
| 2118 |
+
- type: euclidean_spearman
|
| 2119 |
+
value: 85.11181242913666
|
| 2120 |
+
- type: manhattan_pearson
|
| 2121 |
+
value: 84.38600521210364
|
| 2122 |
+
- type: manhattan_spearman
|
| 2123 |
+
value: 84.89045417981723
|
| 2124 |
+
- task:
|
| 2125 |
+
type: Reranking
|
| 2126 |
+
dataset:
|
| 2127 |
+
type: mteb/scidocs-reranking
|
| 2128 |
+
name: MTEB SciDocsRR
|
| 2129 |
+
config: default
|
| 2130 |
+
split: test
|
| 2131 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
| 2132 |
+
metrics:
|
| 2133 |
+
- type: map
|
| 2134 |
+
value: 85.92793380635129
|
| 2135 |
+
- type: mrr
|
| 2136 |
+
value: 95.85834191226348
|
| 2137 |
+
- task:
|
| 2138 |
+
type: Retrieval
|
| 2139 |
+
dataset:
|
| 2140 |
+
type: scifact
|
| 2141 |
+
name: MTEB SciFact
|
| 2142 |
+
config: default
|
| 2143 |
+
split: test
|
| 2144 |
+
revision: None
|
| 2145 |
+
metrics:
|
| 2146 |
+
- type: map_at_1
|
| 2147 |
+
value: 55.74400000000001
|
| 2148 |
+
- type: map_at_10
|
| 2149 |
+
value: 65.455
|
| 2150 |
+
- type: map_at_100
|
| 2151 |
+
value: 66.106
|
| 2152 |
+
- type: map_at_1000
|
| 2153 |
+
value: 66.129
|
| 2154 |
+
- type: map_at_3
|
| 2155 |
+
value: 62.719
|
| 2156 |
+
- type: map_at_5
|
| 2157 |
+
value: 64.441
|
| 2158 |
+
- type: mrr_at_1
|
| 2159 |
+
value: 58.667
|
| 2160 |
+
- type: mrr_at_10
|
| 2161 |
+
value: 66.776
|
| 2162 |
+
- type: mrr_at_100
|
| 2163 |
+
value: 67.363
|
| 2164 |
+
- type: mrr_at_1000
|
| 2165 |
+
value: 67.384
|
| 2166 |
+
- type: mrr_at_3
|
| 2167 |
+
value: 64.889
|
| 2168 |
+
- type: mrr_at_5
|
| 2169 |
+
value: 66.122
|
| 2170 |
+
- type: ndcg_at_1
|
| 2171 |
+
value: 58.667
|
| 2172 |
+
- type: ndcg_at_10
|
| 2173 |
+
value: 69.904
|
| 2174 |
+
- type: ndcg_at_100
|
| 2175 |
+
value: 72.807
|
| 2176 |
+
- type: ndcg_at_1000
|
| 2177 |
+
value: 73.423
|
| 2178 |
+
- type: ndcg_at_3
|
| 2179 |
+
value: 65.405
|
| 2180 |
+
- type: ndcg_at_5
|
| 2181 |
+
value: 67.86999999999999
|
| 2182 |
+
- type: precision_at_1
|
| 2183 |
+
value: 58.667
|
| 2184 |
+
- type: precision_at_10
|
| 2185 |
+
value: 9.3
|
| 2186 |
+
- type: precision_at_100
|
| 2187 |
+
value: 1.08
|
| 2188 |
+
- type: precision_at_1000
|
| 2189 |
+
value: 0.11299999999999999
|
| 2190 |
+
- type: precision_at_3
|
| 2191 |
+
value: 25.444
|
| 2192 |
+
- type: precision_at_5
|
| 2193 |
+
value: 17
|
| 2194 |
+
- type: recall_at_1
|
| 2195 |
+
value: 55.74400000000001
|
| 2196 |
+
- type: recall_at_10
|
| 2197 |
+
value: 82.122
|
| 2198 |
+
- type: recall_at_100
|
| 2199 |
+
value: 95.167
|
| 2200 |
+
- type: recall_at_1000
|
| 2201 |
+
value: 100
|
| 2202 |
+
- type: recall_at_3
|
| 2203 |
+
value: 70.14399999999999
|
| 2204 |
+
- type: recall_at_5
|
| 2205 |
+
value: 76.417
|
| 2206 |
+
- task:
|
| 2207 |
+
type: PairClassification
|
| 2208 |
+
dataset:
|
| 2209 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
| 2210 |
+
name: MTEB SprintDuplicateQuestions
|
| 2211 |
+
config: default
|
| 2212 |
+
split: test
|
| 2213 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 2214 |
+
metrics:
|
| 2215 |
+
- type: cos_sim_accuracy
|
| 2216 |
+
value: 99.86534653465347
|
| 2217 |
+
- type: cos_sim_ap
|
| 2218 |
+
value: 96.54142419791388
|
| 2219 |
+
- type: cos_sim_f1
|
| 2220 |
+
value: 93.07535641547861
|
| 2221 |
+
- type: cos_sim_precision
|
| 2222 |
+
value: 94.81327800829875
|
| 2223 |
+
- type: cos_sim_recall
|
| 2224 |
+
value: 91.4
|
| 2225 |
+
- type: dot_accuracy
|
| 2226 |
+
value: 99.86435643564356
|
| 2227 |
+
- type: dot_ap
|
| 2228 |
+
value: 96.53682260449868
|
| 2229 |
+
- type: dot_f1
|
| 2230 |
+
value: 92.98515104966718
|
| 2231 |
+
- type: dot_precision
|
| 2232 |
+
value: 95.27806925498426
|
| 2233 |
+
- type: dot_recall
|
| 2234 |
+
value: 90.8
|
| 2235 |
+
- type: euclidean_accuracy
|
| 2236 |
+
value: 99.86336633663366
|
| 2237 |
+
- type: euclidean_ap
|
| 2238 |
+
value: 96.5228676185697
|
| 2239 |
+
- type: euclidean_f1
|
| 2240 |
+
value: 92.9735234215886
|
| 2241 |
+
- type: euclidean_precision
|
| 2242 |
+
value: 94.70954356846472
|
| 2243 |
+
- type: euclidean_recall
|
| 2244 |
+
value: 91.3
|
| 2245 |
+
- type: manhattan_accuracy
|
| 2246 |
+
value: 99.85841584158416
|
| 2247 |
+
- type: manhattan_ap
|
| 2248 |
+
value: 96.50392760934032
|
| 2249 |
+
- type: manhattan_f1
|
| 2250 |
+
value: 92.84642321160581
|
| 2251 |
+
- type: manhattan_precision
|
| 2252 |
+
value: 92.8928928928929
|
| 2253 |
+
- type: manhattan_recall
|
| 2254 |
+
value: 92.80000000000001
|
| 2255 |
+
- type: max_accuracy
|
| 2256 |
+
value: 99.86534653465347
|
| 2257 |
+
- type: max_ap
|
| 2258 |
+
value: 96.54142419791388
|
| 2259 |
+
- type: max_f1
|
| 2260 |
+
value: 93.07535641547861
|
| 2261 |
+
- task:
|
| 2262 |
+
type: Clustering
|
| 2263 |
+
dataset:
|
| 2264 |
+
type: mteb/stackexchange-clustering
|
| 2265 |
+
name: MTEB StackExchangeClustering
|
| 2266 |
+
config: default
|
| 2267 |
+
split: test
|
| 2268 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
| 2269 |
+
metrics:
|
| 2270 |
+
- type: v_measure
|
| 2271 |
+
value: 61.08285408766616
|
| 2272 |
+
- task:
|
| 2273 |
+
type: Clustering
|
| 2274 |
+
dataset:
|
| 2275 |
+
type: mteb/stackexchange-clustering-p2p
|
| 2276 |
+
name: MTEB StackExchangeClusteringP2P
|
| 2277 |
+
config: default
|
| 2278 |
+
split: test
|
| 2279 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
| 2280 |
+
metrics:
|
| 2281 |
+
- type: v_measure
|
| 2282 |
+
value: 35.640675309010604
|
| 2283 |
+
- task:
|
| 2284 |
+
type: Reranking
|
| 2285 |
+
dataset:
|
| 2286 |
+
type: mteb/stackoverflowdupquestions-reranking
|
| 2287 |
+
name: MTEB StackOverflowDupQuestions
|
| 2288 |
+
config: default
|
| 2289 |
+
split: test
|
| 2290 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
| 2291 |
+
metrics:
|
| 2292 |
+
- type: map
|
| 2293 |
+
value: 53.20333913710715
|
| 2294 |
+
- type: mrr
|
| 2295 |
+
value: 54.088813555725324
|
| 2296 |
+
- task:
|
| 2297 |
+
type: Summarization
|
| 2298 |
+
dataset:
|
| 2299 |
+
type: mteb/summeval
|
| 2300 |
+
name: MTEB SummEval
|
| 2301 |
+
config: default
|
| 2302 |
+
split: test
|
| 2303 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 2304 |
+
metrics:
|
| 2305 |
+
- type: cos_sim_pearson
|
| 2306 |
+
value: 30.79465221925075
|
| 2307 |
+
- type: cos_sim_spearman
|
| 2308 |
+
value: 30.530816059163634
|
| 2309 |
+
- type: dot_pearson
|
| 2310 |
+
value: 31.364837244718043
|
| 2311 |
+
- type: dot_spearman
|
| 2312 |
+
value: 30.79726823684003
|
| 2313 |
+
- task:
|
| 2314 |
+
type: Retrieval
|
| 2315 |
+
dataset:
|
| 2316 |
+
type: trec-covid
|
| 2317 |
+
name: MTEB TRECCOVID
|
| 2318 |
+
config: default
|
| 2319 |
+
split: test
|
| 2320 |
+
revision: None
|
| 2321 |
+
metrics:
|
| 2322 |
+
- type: map_at_1
|
| 2323 |
+
value: 0.22599999999999998
|
| 2324 |
+
- type: map_at_10
|
| 2325 |
+
value: 1.735
|
| 2326 |
+
- type: map_at_100
|
| 2327 |
+
value: 8.978
|
| 2328 |
+
- type: map_at_1000
|
| 2329 |
+
value: 20.851
|
| 2330 |
+
- type: map_at_3
|
| 2331 |
+
value: 0.613
|
| 2332 |
+
- type: map_at_5
|
| 2333 |
+
value: 0.964
|
| 2334 |
+
- type: mrr_at_1
|
| 2335 |
+
value: 88
|
| 2336 |
+
- type: mrr_at_10
|
| 2337 |
+
value: 92.867
|
| 2338 |
+
- type: mrr_at_100
|
| 2339 |
+
value: 92.867
|
| 2340 |
+
- type: mrr_at_1000
|
| 2341 |
+
value: 92.867
|
| 2342 |
+
- type: mrr_at_3
|
| 2343 |
+
value: 92.667
|
| 2344 |
+
- type: mrr_at_5
|
| 2345 |
+
value: 92.667
|
| 2346 |
+
- type: ndcg_at_1
|
| 2347 |
+
value: 82
|
| 2348 |
+
- type: ndcg_at_10
|
| 2349 |
+
value: 73.164
|
| 2350 |
+
- type: ndcg_at_100
|
| 2351 |
+
value: 51.878
|
| 2352 |
+
- type: ndcg_at_1000
|
| 2353 |
+
value: 44.864
|
| 2354 |
+
- type: ndcg_at_3
|
| 2355 |
+
value: 79.184
|
| 2356 |
+
- type: ndcg_at_5
|
| 2357 |
+
value: 76.39
|
| 2358 |
+
- type: precision_at_1
|
| 2359 |
+
value: 88
|
| 2360 |
+
- type: precision_at_10
|
| 2361 |
+
value: 76.2
|
| 2362 |
+
- type: precision_at_100
|
| 2363 |
+
value: 52.459999999999994
|
| 2364 |
+
- type: precision_at_1000
|
| 2365 |
+
value: 19.692
|
| 2366 |
+
- type: precision_at_3
|
| 2367 |
+
value: 82.667
|
| 2368 |
+
- type: precision_at_5
|
| 2369 |
+
value: 80
|
| 2370 |
+
- type: recall_at_1
|
| 2371 |
+
value: 0.22599999999999998
|
| 2372 |
+
- type: recall_at_10
|
| 2373 |
+
value: 1.942
|
| 2374 |
+
- type: recall_at_100
|
| 2375 |
+
value: 12.342
|
| 2376 |
+
- type: recall_at_1000
|
| 2377 |
+
value: 41.42
|
| 2378 |
+
- type: recall_at_3
|
| 2379 |
+
value: 0.637
|
| 2380 |
+
- type: recall_at_5
|
| 2381 |
+
value: 1.034
|
| 2382 |
+
- task:
|
| 2383 |
+
type: Retrieval
|
| 2384 |
+
dataset:
|
| 2385 |
+
type: webis-touche2020
|
| 2386 |
+
name: MTEB Touche2020
|
| 2387 |
+
config: default
|
| 2388 |
+
split: test
|
| 2389 |
+
revision: None
|
| 2390 |
+
metrics:
|
| 2391 |
+
- type: map_at_1
|
| 2392 |
+
value: 3.567
|
| 2393 |
+
- type: map_at_10
|
| 2394 |
+
value: 13.116
|
| 2395 |
+
- type: map_at_100
|
| 2396 |
+
value: 19.39
|
| 2397 |
+
- type: map_at_1000
|
| 2398 |
+
value: 20.988
|
| 2399 |
+
- type: map_at_3
|
| 2400 |
+
value: 7.109
|
| 2401 |
+
- type: map_at_5
|
| 2402 |
+
value: 9.950000000000001
|
| 2403 |
+
- type: mrr_at_1
|
| 2404 |
+
value: 42.857
|
| 2405 |
+
- type: mrr_at_10
|
| 2406 |
+
value: 57.404999999999994
|
| 2407 |
+
- type: mrr_at_100
|
| 2408 |
+
value: 58.021
|
| 2409 |
+
- type: mrr_at_1000
|
| 2410 |
+
value: 58.021
|
| 2411 |
+
- type: mrr_at_3
|
| 2412 |
+
value: 54.762
|
| 2413 |
+
- type: mrr_at_5
|
| 2414 |
+
value: 56.19
|
| 2415 |
+
- type: ndcg_at_1
|
| 2416 |
+
value: 38.775999999999996
|
| 2417 |
+
- type: ndcg_at_10
|
| 2418 |
+
value: 30.359
|
| 2419 |
+
- type: ndcg_at_100
|
| 2420 |
+
value: 41.284
|
| 2421 |
+
- type: ndcg_at_1000
|
| 2422 |
+
value: 52.30200000000001
|
| 2423 |
+
- type: ndcg_at_3
|
| 2424 |
+
value: 36.744
|
| 2425 |
+
- type: ndcg_at_5
|
| 2426 |
+
value: 34.326
|
| 2427 |
+
- type: precision_at_1
|
| 2428 |
+
value: 42.857
|
| 2429 |
+
- type: precision_at_10
|
| 2430 |
+
value: 26.122
|
| 2431 |
+
- type: precision_at_100
|
| 2432 |
+
value: 8.082
|
| 2433 |
+
- type: precision_at_1000
|
| 2434 |
+
value: 1.559
|
| 2435 |
+
- type: precision_at_3
|
| 2436 |
+
value: 40.136
|
| 2437 |
+
- type: precision_at_5
|
| 2438 |
+
value: 35.510000000000005
|
| 2439 |
+
- type: recall_at_1
|
| 2440 |
+
value: 3.567
|
| 2441 |
+
- type: recall_at_10
|
| 2442 |
+
value: 19.045
|
| 2443 |
+
- type: recall_at_100
|
| 2444 |
+
value: 49.979
|
| 2445 |
+
- type: recall_at_1000
|
| 2446 |
+
value: 84.206
|
| 2447 |
+
- type: recall_at_3
|
| 2448 |
+
value: 8.52
|
| 2449 |
+
- type: recall_at_5
|
| 2450 |
+
value: 13.103000000000002
|
| 2451 |
+
- task:
|
| 2452 |
+
type: Classification
|
| 2453 |
+
dataset:
|
| 2454 |
+
type: mteb/toxic_conversations_50k
|
| 2455 |
+
name: MTEB ToxicConversationsClassification
|
| 2456 |
+
config: default
|
| 2457 |
+
split: test
|
| 2458 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
| 2459 |
+
metrics:
|
| 2460 |
+
- type: accuracy
|
| 2461 |
+
value: 68.8394
|
| 2462 |
+
- type: ap
|
| 2463 |
+
value: 13.454399712443099
|
| 2464 |
+
- type: f1
|
| 2465 |
+
value: 53.04963076364322
|
| 2466 |
+
- task:
|
| 2467 |
+
type: Classification
|
| 2468 |
+
dataset:
|
| 2469 |
+
type: mteb/tweet_sentiment_extraction
|
| 2470 |
+
name: MTEB TweetSentimentExtractionClassification
|
| 2471 |
+
config: default
|
| 2472 |
+
split: test
|
| 2473 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
| 2474 |
+
metrics:
|
| 2475 |
+
- type: accuracy
|
| 2476 |
+
value: 60.546123372948514
|
| 2477 |
+
- type: f1
|
| 2478 |
+
value: 60.86952793277713
|
| 2479 |
+
- task:
|
| 2480 |
+
type: Clustering
|
| 2481 |
+
dataset:
|
| 2482 |
+
type: mteb/twentynewsgroups-clustering
|
| 2483 |
+
name: MTEB TwentyNewsgroupsClustering
|
| 2484 |
+
config: default
|
| 2485 |
+
split: test
|
| 2486 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
| 2487 |
+
metrics:
|
| 2488 |
+
- type: v_measure
|
| 2489 |
+
value: 49.10042955060234
|
| 2490 |
+
- task:
|
| 2491 |
+
type: PairClassification
|
| 2492 |
+
dataset:
|
| 2493 |
+
type: mteb/twittersemeval2015-pairclassification
|
| 2494 |
+
name: MTEB TwitterSemEval2015
|
| 2495 |
+
config: default
|
| 2496 |
+
split: test
|
| 2497 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 2498 |
+
metrics:
|
| 2499 |
+
- type: cos_sim_accuracy
|
| 2500 |
+
value: 85.03308100375514
|
| 2501 |
+
- type: cos_sim_ap
|
| 2502 |
+
value: 71.08284605869684
|
| 2503 |
+
- type: cos_sim_f1
|
| 2504 |
+
value: 65.42539436255494
|
| 2505 |
+
- type: cos_sim_precision
|
| 2506 |
+
value: 64.14807302231237
|
| 2507 |
+
- type: cos_sim_recall
|
| 2508 |
+
value: 66.75461741424802
|
| 2509 |
+
- type: dot_accuracy
|
| 2510 |
+
value: 84.68736961316088
|
| 2511 |
+
- type: dot_ap
|
| 2512 |
+
value: 69.20524036530992
|
| 2513 |
+
- type: dot_f1
|
| 2514 |
+
value: 63.54893953365829
|
| 2515 |
+
- type: dot_precision
|
| 2516 |
+
value: 63.45698500394633
|
| 2517 |
+
- type: dot_recall
|
| 2518 |
+
value: 63.641160949868066
|
| 2519 |
+
- type: euclidean_accuracy
|
| 2520 |
+
value: 85.07480479227513
|
| 2521 |
+
- type: euclidean_ap
|
| 2522 |
+
value: 71.14592761009864
|
| 2523 |
+
- type: euclidean_f1
|
| 2524 |
+
value: 65.43814432989691
|
| 2525 |
+
- type: euclidean_precision
|
| 2526 |
+
value: 63.95465994962216
|
| 2527 |
+
- type: euclidean_recall
|
| 2528 |
+
value: 66.99208443271768
|
| 2529 |
+
- type: manhattan_accuracy
|
| 2530 |
+
value: 85.06288370984085
|
| 2531 |
+
- type: manhattan_ap
|
| 2532 |
+
value: 71.07289742593868
|
| 2533 |
+
- type: manhattan_f1
|
| 2534 |
+
value: 65.37585421412301
|
| 2535 |
+
- type: manhattan_precision
|
| 2536 |
+
value: 62.816147859922175
|
| 2537 |
+
- type: manhattan_recall
|
| 2538 |
+
value: 68.15303430079156
|
| 2539 |
+
- type: max_accuracy
|
| 2540 |
+
value: 85.07480479227513
|
| 2541 |
+
- type: max_ap
|
| 2542 |
+
value: 71.14592761009864
|
| 2543 |
+
- type: max_f1
|
| 2544 |
+
value: 65.43814432989691
|
| 2545 |
+
- task:
|
| 2546 |
+
type: PairClassification
|
| 2547 |
+
dataset:
|
| 2548 |
+
type: mteb/twitterurlcorpus-pairclassification
|
| 2549 |
+
name: MTEB TwitterURLCorpus
|
| 2550 |
+
config: default
|
| 2551 |
+
split: test
|
| 2552 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 2553 |
+
metrics:
|
| 2554 |
+
- type: cos_sim_accuracy
|
| 2555 |
+
value: 87.79058485659952
|
| 2556 |
+
- type: cos_sim_ap
|
| 2557 |
+
value: 83.7183187008759
|
| 2558 |
+
- type: cos_sim_f1
|
| 2559 |
+
value: 75.86921142180798
|
| 2560 |
+
- type: cos_sim_precision
|
| 2561 |
+
value: 73.00683371298405
|
| 2562 |
+
- type: cos_sim_recall
|
| 2563 |
+
value: 78.96519864490298
|
| 2564 |
+
- type: dot_accuracy
|
| 2565 |
+
value: 87.0085768618776
|
| 2566 |
+
- type: dot_ap
|
| 2567 |
+
value: 81.87467488474279
|
| 2568 |
+
- type: dot_f1
|
| 2569 |
+
value: 74.04188363990559
|
| 2570 |
+
- type: dot_precision
|
| 2571 |
+
value: 72.10507114191901
|
| 2572 |
+
- type: dot_recall
|
| 2573 |
+
value: 76.08561749307053
|
| 2574 |
+
- type: euclidean_accuracy
|
| 2575 |
+
value: 87.8332751193387
|
| 2576 |
+
- type: euclidean_ap
|
| 2577 |
+
value: 83.83585648120315
|
| 2578 |
+
- type: euclidean_f1
|
| 2579 |
+
value: 76.02582177042369
|
| 2580 |
+
- type: euclidean_precision
|
| 2581 |
+
value: 73.36388371759989
|
| 2582 |
+
- type: euclidean_recall
|
| 2583 |
+
value: 78.88820449645827
|
| 2584 |
+
- type: manhattan_accuracy
|
| 2585 |
+
value: 87.87208444910156
|
| 2586 |
+
- type: manhattan_ap
|
| 2587 |
+
value: 83.8101950642973
|
| 2588 |
+
- type: manhattan_f1
|
| 2589 |
+
value: 75.90454195535027
|
| 2590 |
+
- type: manhattan_precision
|
| 2591 |
+
value: 72.44419564761039
|
| 2592 |
+
- type: manhattan_recall
|
| 2593 |
+
value: 79.71204188481676
|
| 2594 |
+
- type: max_accuracy
|
| 2595 |
+
value: 87.87208444910156
|
| 2596 |
+
- type: max_ap
|
| 2597 |
+
value: 83.83585648120315
|
| 2598 |
+
- type: max_f1
|
| 2599 |
+
value: 76.02582177042369
|
| 2600 |
+
license: mit
|
| 2601 |
+
language:
|
| 2602 |
+
- en
|
| 2603 |
+
---
|
| 2604 |
+
|
| 2605 |
+
|
| 2606 |
+
**Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
|
| 2607 |
+
|
| 2608 |
+
<h1 align="center">FlagEmbedding</h1>
|
| 2609 |
+
|
| 2610 |
+
|
| 2611 |
+
<h4 align="center">
|
| 2612 |
+
<p>
|
| 2613 |
+
<a href=#model-list>Model List</a> |
|
| 2614 |
+
<a href=#frequently-asked-questions>FAQ</a> |
|
| 2615 |
+
<a href=#usage>Usage</a> |
|
| 2616 |
+
<a href="#evaluation">Evaluation</a> |
|
| 2617 |
+
<a href="#train">Train</a> |
|
| 2618 |
+
<a href="#citation">Citation</a> |
|
| 2619 |
+
<a href="#license">License</a>
|
| 2620 |
+
<p>
|
| 2621 |
+
</h4>
|
| 2622 |
+
|
| 2623 |
+
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
|
| 2624 |
+
|
| 2625 |
+
|
| 2626 |
+
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
|
| 2627 |
+
|
| 2628 |
+
FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:
|
| 2629 |
+
|
| 2630 |
+
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
|
| 2631 |
+
- **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
|
| 2632 |
+
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
|
| 2633 |
+
|
| 2634 |
+
|
| 2635 |
+
## News
|
| 2636 |
+
|
| 2637 |
+
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
|
| 2638 |
+
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
|
| 2639 |
+
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
|
| 2640 |
+
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
|
| 2641 |
+
- 09/12/2023: New models:
|
| 2642 |
+
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
|
| 2643 |
+
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
|
| 2644 |
+
|
| 2645 |
+
|
| 2646 |
+
<details>
|
| 2647 |
+
<summary>More</summary>
|
| 2648 |
+
<!-- ### More -->
|
| 2649 |
+
|
| 2650 |
+
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
|
| 2651 |
+
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
|
| 2652 |
+
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
|
| 2653 |
+
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
|
| 2654 |
+
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
|
| 2655 |
+
|
| 2656 |
+
</details>
|
| 2657 |
+
|
| 2658 |
+
|
| 2659 |
+
## Model List
|
| 2660 |
+
|
| 2661 |
+
`bge` is short for `BAAI general embedding`.
|
| 2662 |
+
|
| 2663 |
+
| Model | Language | | Description | query instruction for retrieval [1] |
|
| 2664 |
+
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
|
| 2665 |
+
| [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |
|
| 2666 |
+
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
|
| 2667 |
+
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
| 2668 |
+
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
| 2669 |
+
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
| 2670 |
+
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
| 2671 |
+
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
| 2672 |
+
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
| 2673 |
+
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
| 2674 |
+
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
| 2675 |
+
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
|
| 2676 |
+
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
|
| 2677 |
+
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
|
| 2678 |
+
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
|
| 2679 |
+
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
|
| 2680 |
+
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
|
| 2681 |
+
|
| 2682 |
+
|
| 2683 |
+
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
|
| 2684 |
+
|
| 2685 |
+
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
|
| 2686 |
+
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
|
| 2687 |
+
|
| 2688 |
+
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
|
| 2689 |
+
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
|
| 2690 |
+
|
| 2691 |
+
|
| 2692 |
+
## Frequently asked questions
|
| 2693 |
+
|
| 2694 |
+
<details>
|
| 2695 |
+
<summary>1. How to fine-tune bge embedding model?</summary>
|
| 2696 |
+
|
| 2697 |
+
<!-- ### How to fine-tune bge embedding model? -->
|
| 2698 |
+
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
|
| 2699 |
+
Some suggestions:
|
| 2700 |
+
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
|
| 2701 |
+
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
|
| 2702 |
+
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
|
| 2703 |
+
|
| 2704 |
+
|
| 2705 |
+
</details>
|
| 2706 |
+
|
| 2707 |
+
<details>
|
| 2708 |
+
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
|
| 2709 |
+
|
| 2710 |
+
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
|
| 2711 |
+
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
|
| 2712 |
+
|
| 2713 |
+
Since we finetune the models by contrastive learning with a temperature of 0.01,
|
| 2714 |
+
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
|
| 2715 |
+
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
|
| 2716 |
+
|
| 2717 |
+
For downstream tasks, such as passage retrieval or semantic similarity,
|
| 2718 |
+
**what matters is the relative order of the scores, not the absolute value.**
|
| 2719 |
+
If you need to filter similar sentences based on a similarity threshold,
|
| 2720 |
+
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
|
| 2721 |
+
|
| 2722 |
+
</details>
|
| 2723 |
+
|
| 2724 |
+
<details>
|
| 2725 |
+
<summary>3. When does the query instruction need to be used</summary>
|
| 2726 |
+
|
| 2727 |
+
<!-- ### When does the query instruction need to be used -->
|
| 2728 |
+
|
| 2729 |
+
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
|
| 2730 |
+
No instruction only has a slight degradation in retrieval performance compared with using instruction.
|
| 2731 |
+
So you can generate embedding without instruction in all cases for convenience.
|
| 2732 |
+
|
| 2733 |
+
For a retrieval task that uses short queries to find long related documents,
|
| 2734 |
+
it is recommended to add instructions for these short queries.
|
| 2735 |
+
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
|
| 2736 |
+
In all cases, the documents/passages do not need to add the instruction.
|
| 2737 |
+
|
| 2738 |
+
</details>
|
| 2739 |
+
|
| 2740 |
+
|
| 2741 |
+
## Usage
|
| 2742 |
+
|
| 2743 |
+
### Usage for Embedding Model
|
| 2744 |
+
|
| 2745 |
+
Here are some examples for using `bge` models with
|
| 2746 |
+
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
|
| 2747 |
+
|
| 2748 |
+
#### Using FlagEmbedding
|
| 2749 |
+
```
|
| 2750 |
+
pip install -U FlagEmbedding
|
| 2751 |
+
```
|
| 2752 |
+
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
|
| 2753 |
+
|
| 2754 |
+
```python
|
| 2755 |
+
from FlagEmbedding import FlagModel
|
| 2756 |
+
sentences_1 = ["样例数据-1", "样例数据-2"]
|
| 2757 |
+
sentences_2 = ["样例数据-3", "样例数据-4"]
|
| 2758 |
+
model = FlagModel('BAAI/bge-large-zh-v1.5',
|
| 2759 |
+
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
|
| 2760 |
+
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
| 2761 |
+
embeddings_1 = model.encode(sentences_1)
|
| 2762 |
+
embeddings_2 = model.encode(sentences_2)
|
| 2763 |
+
similarity = embeddings_1 @ embeddings_2.T
|
| 2764 |
+
print(similarity)
|
| 2765 |
+
|
| 2766 |
+
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
|
| 2767 |
+
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
|
| 2768 |
+
queries = ['query_1', 'query_2']
|
| 2769 |
+
passages = ["样例文档-1", "样例文档-2"]
|
| 2770 |
+
q_embeddings = model.encode_queries(queries)
|
| 2771 |
+
p_embeddings = model.encode(passages)
|
| 2772 |
+
scores = q_embeddings @ p_embeddings.T
|
| 2773 |
+
```
|
| 2774 |
+
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
|
| 2775 |
+
|
| 2776 |
+
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
|
| 2777 |
+
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
|
| 2778 |
+
|
| 2779 |
+
|
| 2780 |
+
#### Using Sentence-Transformers
|
| 2781 |
+
|
| 2782 |
+
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
|
| 2783 |
+
|
| 2784 |
+
```
|
| 2785 |
+
pip install -U sentence-transformers
|
| 2786 |
+
```
|
| 2787 |
+
```python
|
| 2788 |
+
from sentence_transformers import SentenceTransformer
|
| 2789 |
+
sentences_1 = ["样例数据-1", "样例数据-2"]
|
| 2790 |
+
sentences_2 = ["样例数据-3", "样例数据-4"]
|
| 2791 |
+
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
|
| 2792 |
+
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
|
| 2793 |
+
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
|
| 2794 |
+
similarity = embeddings_1 @ embeddings_2.T
|
| 2795 |
+
print(similarity)
|
| 2796 |
+
```
|
| 2797 |
+
For s2p(short query to long passage) retrieval task,
|
| 2798 |
+
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
|
| 2799 |
+
But the instruction is not needed for passages.
|
| 2800 |
+
```python
|
| 2801 |
+
from sentence_transformers import SentenceTransformer
|
| 2802 |
+
queries = ['query_1', 'query_2']
|
| 2803 |
+
passages = ["样例文档-1", "样例文档-2"]
|
| 2804 |
+
instruction = "为这个句子生成表示以用于检索相关文章："
|
| 2805 |
+
|
| 2806 |
+
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
|
| 2807 |
+
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
|
| 2808 |
+
p_embeddings = model.encode(passages, normalize_embeddings=True)
|
| 2809 |
+
scores = q_embeddings @ p_embeddings.T
|
| 2810 |
+
```
|
| 2811 |
+
|
| 2812 |
+
#### Using Langchain
|
| 2813 |
+
|
| 2814 |
+
You can use `bge` in langchain like this:
|
| 2815 |
+
```python
|
| 2816 |
+
from langchain.embeddings import HuggingFaceBgeEmbeddings
|
| 2817 |
+
model_name = "BAAI/bge-large-en-v1.5"
|
| 2818 |
+
model_kwargs = {'device': 'cuda'}
|
| 2819 |
+
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
|
| 2820 |
+
model = HuggingFaceBgeEmbeddings(
|
| 2821 |
+
model_name=model_name,
|
| 2822 |
+
model_kwargs=model_kwargs,
|
| 2823 |
+
encode_kwargs=encode_kwargs,
|
| 2824 |
+
query_instruction="为这个句子生成表示以用于检索相关文章："
|
| 2825 |
+
)
|
| 2826 |
+
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
|
| 2827 |
+
```
|
| 2828 |
+
|
| 2829 |
+
|
| 2830 |
+
#### Using HuggingFace Transformers
|
| 2831 |
+
|
| 2832 |
+
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
|
| 2833 |
+
|
| 2834 |
+
```python
|
| 2835 |
+
from transformers import AutoTokenizer, AutoModel
|
| 2836 |
+
import torch
|
| 2837 |
+
# Sentences we want sentence embeddings for
|
| 2838 |
+
sentences = ["样例数据-1", "样例数据-2"]
|
| 2839 |
+
|
| 2840 |
+
# Load model from HuggingFace Hub
|
| 2841 |
+
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
|
| 2842 |
+
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
|
| 2843 |
+
model.eval()
|
| 2844 |
+
|
| 2845 |
+
# Tokenize sentences
|
| 2846 |
+
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
| 2847 |
+
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
|
| 2848 |
+
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
|
| 2849 |
+
|
| 2850 |
+
# Compute token embeddings
|
| 2851 |
+
with torch.no_grad():
|
| 2852 |
+
model_output = model(**encoded_input)
|
| 2853 |
+
# Perform pooling. In this case, cls pooling.
|
| 2854 |
+
sentence_embeddings = model_output[0][:, 0]
|
| 2855 |
+
# normalize embeddings
|
| 2856 |
+
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
|
| 2857 |
+
print("Sentence embeddings:", sentence_embeddings)
|
| 2858 |
+
```
|
| 2859 |
+
|
| 2860 |
+
### Usage for Reranker
|
| 2861 |
+
|
| 2862 |
+
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
|
| 2863 |
+
You can get a relevance score by inputting query and passage to the reranker.
|
| 2864 |
+
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
|
| 2865 |
+
|
| 2866 |
+
|
| 2867 |
+
#### Using FlagEmbedding
|
| 2868 |
+
```
|
| 2869 |
+
pip install -U FlagEmbedding
|
| 2870 |
+
```
|
| 2871 |
+
|
| 2872 |
+
Get relevance scores (higher scores indicate more relevance):
|
| 2873 |
+
```python
|
| 2874 |
+
from FlagEmbedding import FlagReranker
|
| 2875 |
+
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
| 2876 |
+
|
| 2877 |
+
score = reranker.compute_score(['query', 'passage'])
|
| 2878 |
+
print(score)
|
| 2879 |
+
|
| 2880 |
+
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
|
| 2881 |
+
print(scores)
|
| 2882 |
+
```
|
| 2883 |
+
|
| 2884 |
+
|
| 2885 |
+
#### Using Huggingface transformers
|
| 2886 |
+
|
| 2887 |
+
```python
|
| 2888 |
+
import torch
|
| 2889 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
| 2890 |
+
|
| 2891 |
+
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
|
| 2892 |
+
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
|
| 2893 |
+
model.eval()
|
| 2894 |
+
|
| 2895 |
+
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
|
| 2896 |
+
with torch.no_grad():
|
| 2897 |
+
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
|
| 2898 |
+
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
|
| 2899 |
+
print(scores)
|
| 2900 |
+
```
|
| 2901 |
+
|
| 2902 |
+
## Evaluation
|
| 2903 |
+
|
| 2904 |
+
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
|
| 2905 |
+
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
|
| 2906 |
+
|
| 2907 |
+
- **MTEB**:
|
| 2908 |
+
|
| 2909 |
+
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|
| 2910 |
+
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 2911 |
+
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
|
| 2912 |
+
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
|
| 2913 |
+
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
|
| 2914 |
+
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
|
| 2915 |
+
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
|
| 2916 |
+
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
|
| 2917 |
+
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
|
| 2918 |
+
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
|
| 2919 |
+
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
|
| 2920 |
+
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
|
| 2921 |
+
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
|
| 2922 |
+
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
|
| 2923 |
+
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
|
| 2924 |
+
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
|
| 2925 |
+
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
|
| 2926 |
+
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
|
| 2927 |
+
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
|
| 2928 |
+
|
| 2929 |
+
|
| 2930 |
+
|
| 2931 |
+
- **C-MTEB**:
|
| 2932 |
+
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
|
| 2933 |
+
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
|
| 2934 |
+
|
| 2935 |
+
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|
| 2936 |
+
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|
| 2937 |
+
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
|
| 2938 |
+
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
|
| 2939 |
+
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
|
| 2940 |
+
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
|
| 2941 |
+
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
|
| 2942 |
+
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
|
| 2943 |
+
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
|
| 2944 |
+
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
|
| 2945 |
+
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
|
| 2946 |
+
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
|
| 2947 |
+
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
|
| 2948 |
+
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
|
| 2949 |
+
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
|
| 2950 |
+
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
|
| 2951 |
+
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
|
| 2952 |
+
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
|
| 2953 |
+
|
| 2954 |
+
|
| 2955 |
+
- **Reranking**:
|
| 2956 |
+
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
|
| 2957 |
+
|
| 2958 |
+
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|
| 2959 |
+
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|
| 2960 |
+
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
|
| 2961 |
+
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
|
| 2962 |
+
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
|
| 2963 |
+
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
|
| 2964 |
+
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
|
| 2965 |
+
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
|
| 2966 |
+
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
|
| 2967 |
+
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
|
| 2968 |
+
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
|
| 2969 |
+
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
|
| 2970 |
+
|
| 2971 |
+
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
|
| 2972 |
+
|
| 2973 |
+
## Train
|
| 2974 |
+
|
| 2975 |
+
### BAAI Embedding
|
| 2976 |
+
|
| 2977 |
+
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
|
| 2978 |
+
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
|
| 2979 |
+
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
|
| 2980 |
+
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
|
| 2981 |
+
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
|
| 2982 |
+
|
| 2983 |
+
|
| 2984 |
+
|
| 2985 |
+
### BGE Reranker
|
| 2986 |
+
|
| 2987 |
+
Cross-encoder will perform full-attention over the input pair,
|
| 2988 |
+
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
|
| 2989 |
+
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
|
| 2990 |
+
We train the cross-encoder on a multilingual pair data,
|
| 2991 |
+
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
|
| 2992 |
+
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
|
| 2993 |
+
|
| 2994 |
+
|
| 2995 |
+
|
| 2996 |
+
|
| 2997 |
+
## Citation
|
| 2998 |
+
|
| 2999 |
+
If you find this repository useful, please consider giving a star :star: and citation
|
| 3000 |
+
|
| 3001 |
+
```
|
| 3002 |
+
@misc{bge_embedding,
|
| 3003 |
+
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
|
| 3004 |
+
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
|
| 3005 |
+
year={2023},
|
| 3006 |
+
eprint={2309.07597},
|
| 3007 |
+
archivePrefix={arXiv},
|
| 3008 |
+
primaryClass={cs.CL}
|
| 3009 |
+
}
|
| 3010 |
+
```
|
| 3011 |
+
|
| 3012 |
+
## License
|
| 3013 |
+
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
|
| 3014 |
+
|
config.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "BAAI/bge-small-en",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"classifier_dropout": null,
|
| 8 |
+
"hidden_act": "gelu",
|
| 9 |
+
"hidden_dropout_prob": 0.1,
|
| 10 |
+
"hidden_size": 384,
|
| 11 |
+
"id2label": {
|
| 12 |
+
"0": "LABEL_0"
|
| 13 |
+
},
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 1536,
|
| 16 |
+
"label2id": {
|
| 17 |
+
"LABEL_0": 0
|
| 18 |
+
},
|
| 19 |
+
"layer_norm_eps": 1e-12,
|
| 20 |
+
"max_position_embeddings": 512,
|
| 21 |
+
"model_type": "bert",
|
| 22 |
+
"num_attention_heads": 12,
|
| 23 |
+
"num_hidden_layers": 12,
|
| 24 |
+
"pad_token_id": 0,
|
| 25 |
+
"position_embedding_type": "absolute",
|
| 26 |
+
"torch_dtype": "float32",
|
| 27 |
+
"transformers_version": "4.44.2",
|
| 28 |
+
"type_vocab_size": 2,
|
| 29 |
+
"use_cache": true,
|
| 30 |
+
"vocab_size": 30522
|
| 31 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
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| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.3.0",
|
| 4 |
+
"transformers": "4.44.2",
|
| 5 |
+
"pytorch": "2.5.1+cpu"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
modules.json
ADDED
|
@@ -0,0 +1,20 @@
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|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"idx": 2,
|
| 16 |
+
"name": "2",
|
| 17 |
+
"path": "2_Normalize",
|
| 18 |
+
"type": "sentence_transformers.models.Normalize"
|
| 19 |
+
}
|
| 20 |
+
]
|
openvino/openvino_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:edeeffb0e749faee0457e5b456274f4362a7963905ee17dee5d3d3336997b534
|
| 3 |
+
size 132852896
|
openvino/openvino_model.xml
ADDED
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The diff for this file is too large to render.
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|
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": true
|
| 4 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
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|
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|
| 1 |
+
{
|
| 2 |
+
"cls_token": {
|
| 3 |
+
"content": "[CLS]",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"mask_token": {
|
| 10 |
+
"content": "[MASK]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "[PAD]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"sep_token": {
|
| 24 |
+
"content": "[SEP]",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"unk_token": {
|
| 31 |
+
"content": "[UNK]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
}
|
| 37 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,57 @@
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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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|
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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 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[PAD]",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"100": {
|
| 12 |
+
"content": "[UNK]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"101": {
|
| 20 |
+
"content": "[CLS]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"102": {
|
| 28 |
+
"content": "[SEP]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"103": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_basic_tokenize": true,
|
| 47 |
+
"do_lower_case": true,
|
| 48 |
+
"mask_token": "[MASK]",
|
| 49 |
+
"model_max_length": 512,
|
| 50 |
+
"never_split": null,
|
| 51 |
+
"pad_token": "[PAD]",
|
| 52 |
+
"sep_token": "[SEP]",
|
| 53 |
+
"strip_accents": null,
|
| 54 |
+
"tokenize_chinese_chars": true,
|
| 55 |
+
"tokenizer_class": "BertTokenizer",
|
| 56 |
+
"unk_token": "[UNK]"
|
| 57 |
+
}
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|