README.md
| 1 | --- |
| 2 | tags: |
| 3 | - sentence-transformers |
| 4 | - feature-extraction |
| 5 | - sentence-similarity |
| 6 | - transformers |
| 7 | - mteb |
| 8 | model-index: |
| 9 | - name: bge-large-en-v1.5 |
| 10 | results: |
| 11 | - task: |
| 12 | type: Classification |
| 13 | dataset: |
| 14 | type: mteb/amazon_counterfactual |
| 15 | name: MTEB AmazonCounterfactualClassification (en) |
| 16 | config: en |
| 17 | split: test |
| 18 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 19 | metrics: |
| 20 | - type: accuracy |
| 21 | value: 75.8507462686567 |
| 22 | - type: ap |
| 23 | value: 38.566457320228245 |
| 24 | - type: f1 |
| 25 | value: 69.69386648043475 |
| 26 | - task: |
| 27 | type: Classification |
| 28 | dataset: |
| 29 | type: mteb/amazon_polarity |
| 30 | name: MTEB AmazonPolarityClassification |
| 31 | config: default |
| 32 | split: test |
| 33 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 34 | metrics: |
| 35 | - type: accuracy |
| 36 | value: 92.416675 |
| 37 | - type: ap |
| 38 | value: 89.1928861155922 |
| 39 | - type: f1 |
| 40 | value: 92.39477019574215 |
| 41 | - task: |
| 42 | type: Classification |
| 43 | dataset: |
| 44 | type: mteb/amazon_reviews_multi |
| 45 | name: MTEB AmazonReviewsClassification (en) |
| 46 | config: en |
| 47 | split: test |
| 48 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 49 | metrics: |
| 50 | - type: accuracy |
| 51 | value: 48.175999999999995 |
| 52 | - type: f1 |
| 53 | value: 47.80712792870253 |
| 54 | - task: |
| 55 | type: Retrieval |
| 56 | dataset: |
| 57 | type: arguana |
| 58 | name: MTEB ArguAna |
| 59 | config: default |
| 60 | split: test |
| 61 | revision: None |
| 62 | metrics: |
| 63 | - type: map_at_1 |
| 64 | value: 40.184999999999995 |
| 65 | - type: map_at_10 |
| 66 | value: 55.654 |
| 67 | - type: map_at_100 |
| 68 | value: 56.25 |
| 69 | - type: map_at_1000 |
| 70 | value: 56.255 |
| 71 | - type: map_at_3 |
| 72 | value: 51.742999999999995 |
| 73 | - type: map_at_5 |
| 74 | value: 54.129000000000005 |
| 75 | - type: mrr_at_1 |
| 76 | value: 40.967 |
| 77 | - type: mrr_at_10 |
| 78 | value: 55.96 |
| 79 | - type: mrr_at_100 |
| 80 | value: 56.54900000000001 |
| 81 | - type: mrr_at_1000 |
| 82 | value: 56.554 |
| 83 | - type: mrr_at_3 |
| 84 | value: 51.980000000000004 |
| 85 | - type: mrr_at_5 |
| 86 | value: 54.44 |
| 87 | - type: ndcg_at_1 |
| 88 | value: 40.184999999999995 |
| 89 | - type: ndcg_at_10 |
| 90 | value: 63.542 |
| 91 | - type: ndcg_at_100 |
| 92 | value: 65.96499999999999 |
| 93 | - type: ndcg_at_1000 |
| 94 | value: 66.08699999999999 |
| 95 | - type: ndcg_at_3 |
| 96 | value: 55.582 |
| 97 | - type: ndcg_at_5 |
| 98 | value: 59.855000000000004 |
| 99 | - type: precision_at_1 |
| 100 | value: 40.184999999999995 |
| 101 | - type: precision_at_10 |
| 102 | value: 8.841000000000001 |
| 103 | - type: precision_at_100 |
| 104 | value: 0.987 |
| 105 | - type: precision_at_1000 |
| 106 | value: 0.1 |
| 107 | - type: precision_at_3 |
| 108 | value: 22.238 |
| 109 | - type: precision_at_5 |
| 110 | value: 15.405 |
| 111 | - type: recall_at_1 |
| 112 | value: 40.184999999999995 |
| 113 | - type: recall_at_10 |
| 114 | value: 88.407 |
| 115 | - type: recall_at_100 |
| 116 | value: 98.72 |
| 117 | - type: recall_at_1000 |
| 118 | value: 99.644 |
| 119 | - type: recall_at_3 |
| 120 | value: 66.714 |
| 121 | - type: recall_at_5 |
| 122 | value: 77.027 |
| 123 | - task: |
| 124 | type: Clustering |
| 125 | dataset: |
| 126 | type: mteb/arxiv-clustering-p2p |
| 127 | name: MTEB ArxivClusteringP2P |
| 128 | config: default |
| 129 | split: test |
| 130 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 131 | metrics: |
| 132 | - type: v_measure |
| 133 | value: 48.567077926750066 |
| 134 | - task: |
| 135 | type: Clustering |
| 136 | dataset: |
| 137 | type: mteb/arxiv-clustering-s2s |
| 138 | name: MTEB ArxivClusteringS2S |
| 139 | config: default |
| 140 | split: test |
| 141 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 142 | metrics: |
| 143 | - type: v_measure |
| 144 | value: 43.19453389182364 |
| 145 | - task: |
| 146 | type: Reranking |
| 147 | dataset: |
| 148 | type: mteb/askubuntudupquestions-reranking |
| 149 | name: MTEB AskUbuntuDupQuestions |
| 150 | config: default |
| 151 | split: test |
| 152 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 153 | metrics: |
| 154 | - type: map |
| 155 | value: 64.46555939623092 |
| 156 | - type: mrr |
| 157 | value: 77.82361605768807 |
| 158 | - task: |
| 159 | type: STS |
| 160 | dataset: |
| 161 | type: mteb/biosses-sts |
| 162 | name: MTEB BIOSSES |
| 163 | config: default |
| 164 | split: test |
| 165 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 166 | metrics: |
| 167 | - type: cos_sim_pearson |
| 168 | value: 84.9554128814735 |
| 169 | - type: cos_sim_spearman |
| 170 | value: 84.65373612172036 |
| 171 | - type: euclidean_pearson |
| 172 | value: 83.2905059954138 |
| 173 | - type: euclidean_spearman |
| 174 | value: 84.52240782811128 |
| 175 | - type: manhattan_pearson |
| 176 | value: 82.99533802997436 |
| 177 | - type: manhattan_spearman |
| 178 | value: 84.20673798475734 |
| 179 | - task: |
| 180 | type: Classification |
| 181 | dataset: |
| 182 | type: mteb/banking77 |
| 183 | name: MTEB Banking77Classification |
| 184 | config: default |
| 185 | split: test |
| 186 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 187 | metrics: |
| 188 | - type: accuracy |
| 189 | value: 87.78896103896103 |
| 190 | - type: f1 |
| 191 | value: 87.77189310964883 |
| 192 | - task: |
| 193 | type: Clustering |
| 194 | dataset: |
| 195 | type: mteb/biorxiv-clustering-p2p |
| 196 | name: MTEB BiorxivClusteringP2P |
| 197 | config: default |
| 198 | split: test |
| 199 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 200 | metrics: |
| 201 | - type: v_measure |
| 202 | value: 39.714538337650495 |
| 203 | - task: |
| 204 | type: Clustering |
| 205 | dataset: |
| 206 | type: mteb/biorxiv-clustering-s2s |
| 207 | name: MTEB BiorxivClusteringS2S |
| 208 | config: default |
| 209 | split: test |
| 210 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 211 | metrics: |
| 212 | - type: v_measure |
| 213 | value: 36.90108349284447 |
| 214 | - task: |
| 215 | type: Retrieval |
| 216 | dataset: |
| 217 | type: BeIR/cqadupstack |
| 218 | name: MTEB CQADupstackAndroidRetrieval |
| 219 | config: default |
| 220 | split: test |
| 221 | revision: None |
| 222 | metrics: |
| 223 | - type: map_at_1 |
| 224 | value: 32.795 |
| 225 | - type: map_at_10 |
| 226 | value: 43.669000000000004 |
| 227 | - type: map_at_100 |
| 228 | value: 45.151 |
| 229 | - type: map_at_1000 |
| 230 | value: 45.278 |
| 231 | - type: map_at_3 |
| 232 | value: 40.006 |
| 233 | - type: map_at_5 |
| 234 | value: 42.059999999999995 |
| 235 | - type: mrr_at_1 |
| 236 | value: 39.771 |
| 237 | - type: mrr_at_10 |
| 238 | value: 49.826 |
| 239 | - type: mrr_at_100 |
| 240 | value: 50.504000000000005 |
| 241 | - type: mrr_at_1000 |
| 242 | value: 50.549 |
| 243 | - type: mrr_at_3 |
| 244 | value: 47.115 |
| 245 | - type: mrr_at_5 |
| 246 | value: 48.832 |
| 247 | - type: ndcg_at_1 |
| 248 | value: 39.771 |
| 249 | - type: ndcg_at_10 |
| 250 | value: 50.217999999999996 |
| 251 | - type: ndcg_at_100 |
| 252 | value: 55.454 |
| 253 | - type: ndcg_at_1000 |
| 254 | value: 57.37 |
| 255 | - type: ndcg_at_3 |
| 256 | value: 44.885000000000005 |
| 257 | - type: ndcg_at_5 |
| 258 | value: 47.419 |
| 259 | - type: precision_at_1 |
| 260 | value: 39.771 |
| 261 | - type: precision_at_10 |
| 262 | value: 9.642000000000001 |
| 263 | - type: precision_at_100 |
| 264 | value: 1.538 |
| 265 | - type: precision_at_1000 |
| 266 | value: 0.198 |
| 267 | - type: precision_at_3 |
| 268 | value: 21.268 |
| 269 | - type: precision_at_5 |
| 270 | value: 15.536 |
| 271 | - type: recall_at_1 |
| 272 | value: 32.795 |
| 273 | - type: recall_at_10 |
| 274 | value: 62.580999999999996 |
| 275 | - type: recall_at_100 |
| 276 | value: 84.438 |
| 277 | - type: recall_at_1000 |
| 278 | value: 96.492 |
| 279 | - type: recall_at_3 |
| 280 | value: 47.071000000000005 |
| 281 | - type: recall_at_5 |
| 282 | value: 54.079 |
| 283 | - task: |
| 284 | type: Retrieval |
| 285 | dataset: |
| 286 | type: BeIR/cqadupstack |
| 287 | name: MTEB CQADupstackEnglishRetrieval |
| 288 | config: default |
| 289 | split: test |
| 290 | revision: None |
| 291 | metrics: |
| 292 | - type: map_at_1 |
| 293 | value: 32.671 |
| 294 | - type: map_at_10 |
| 295 | value: 43.334 |
| 296 | - type: map_at_100 |
| 297 | value: 44.566 |
| 298 | - type: map_at_1000 |
| 299 | value: 44.702999999999996 |
| 300 | - type: map_at_3 |
| 301 | value: 40.343 |
| 302 | - type: map_at_5 |
| 303 | value: 41.983 |
| 304 | - type: mrr_at_1 |
| 305 | value: 40.764 |
| 306 | - type: mrr_at_10 |
| 307 | value: 49.382 |
| 308 | - type: mrr_at_100 |
| 309 | value: 49.988 |
| 310 | - type: mrr_at_1000 |
| 311 | value: 50.03300000000001 |
| 312 | - type: mrr_at_3 |
| 313 | value: 47.293 |
| 314 | - type: mrr_at_5 |
| 315 | value: 48.51 |
| 316 | - type: ndcg_at_1 |
| 317 | value: 40.764 |
| 318 | - type: ndcg_at_10 |
| 319 | value: 49.039 |
| 320 | - type: ndcg_at_100 |
| 321 | value: 53.259 |
| 322 | - type: ndcg_at_1000 |
| 323 | value: 55.253 |
| 324 | - type: ndcg_at_3 |
| 325 | value: 45.091 |
| 326 | - type: ndcg_at_5 |
| 327 | value: 46.839999999999996 |
| 328 | - type: precision_at_1 |
| 329 | value: 40.764 |
| 330 | - type: precision_at_10 |
| 331 | value: 9.191 |
| 332 | - type: precision_at_100 |
| 333 | value: 1.476 |
| 334 | - type: precision_at_1000 |
| 335 | value: 0.19499999999999998 |
| 336 | - type: precision_at_3 |
| 337 | value: 21.72 |
| 338 | - type: precision_at_5 |
| 339 | value: 15.299 |
| 340 | - type: recall_at_1 |
| 341 | value: 32.671 |
| 342 | - type: recall_at_10 |
| 343 | value: 58.816 |
| 344 | - type: recall_at_100 |
| 345 | value: 76.654 |
| 346 | - type: recall_at_1000 |
| 347 | value: 89.05999999999999 |
| 348 | - type: recall_at_3 |
| 349 | value: 46.743 |
| 350 | - type: recall_at_5 |
| 351 | value: 51.783 |
| 352 | - task: |
| 353 | type: Retrieval |
| 354 | dataset: |
| 355 | type: BeIR/cqadupstack |
| 356 | name: MTEB CQADupstackGamingRetrieval |
| 357 | config: default |
| 358 | split: test |
| 359 | revision: None |
| 360 | metrics: |
| 361 | - type: map_at_1 |
| 362 | value: 40.328 |
| 363 | - type: map_at_10 |
| 364 | value: 53.32599999999999 |
| 365 | - type: map_at_100 |
| 366 | value: 54.37499999999999 |
| 367 | - type: map_at_1000 |
| 368 | value: 54.429 |
| 369 | - type: map_at_3 |
| 370 | value: 49.902 |
| 371 | - type: map_at_5 |
| 372 | value: 52.002 |
| 373 | - type: mrr_at_1 |
| 374 | value: 46.332 |
| 375 | - type: mrr_at_10 |
| 376 | value: 56.858 |
| 377 | - type: mrr_at_100 |
| 378 | value: 57.522 |
| 379 | - type: mrr_at_1000 |
| 380 | value: 57.54899999999999 |
| 381 | - type: mrr_at_3 |
| 382 | value: 54.472 |
| 383 | - type: mrr_at_5 |
| 384 | value: 55.996 |
| 385 | - type: ndcg_at_1 |
| 386 | value: 46.332 |
| 387 | - type: ndcg_at_10 |
| 388 | value: 59.313 |
| 389 | - type: ndcg_at_100 |
| 390 | value: 63.266999999999996 |
| 391 | - type: ndcg_at_1000 |
| 392 | value: 64.36 |
| 393 | - type: ndcg_at_3 |
| 394 | value: 53.815000000000005 |
| 395 | - type: ndcg_at_5 |
| 396 | value: 56.814 |
| 397 | - type: precision_at_1 |
| 398 | value: 46.332 |
| 399 | - type: precision_at_10 |
| 400 | value: 9.53 |
| 401 | - type: precision_at_100 |
| 402 | value: 1.238 |
| 403 | - type: precision_at_1000 |
| 404 | value: 0.13699999999999998 |
| 405 | - type: precision_at_3 |
| 406 | value: 24.054000000000002 |
| 407 | - type: precision_at_5 |
| 408 | value: 16.589000000000002 |
| 409 | - type: recall_at_1 |
| 410 | value: 40.328 |
| 411 | - type: recall_at_10 |
| 412 | value: 73.421 |
| 413 | - type: recall_at_100 |
| 414 | value: 90.059 |
| 415 | - type: recall_at_1000 |
| 416 | value: 97.81 |
| 417 | - type: recall_at_3 |
| 418 | value: 59.009 |
| 419 | - type: recall_at_5 |
| 420 | value: 66.352 |
| 421 | - task: |
| 422 | type: Retrieval |
| 423 | dataset: |
| 424 | type: BeIR/cqadupstack |
| 425 | name: MTEB CQADupstackGisRetrieval |
| 426 | config: default |
| 427 | split: test |
| 428 | revision: None |
| 429 | metrics: |
| 430 | - type: map_at_1 |
| 431 | value: 27.424 |
| 432 | - type: map_at_10 |
| 433 | value: 36.332 |
| 434 | - type: map_at_100 |
| 435 | value: 37.347 |
| 436 | - type: map_at_1000 |
| 437 | value: 37.422 |
| 438 | - type: map_at_3 |
| 439 | value: 33.743 |
| 440 | - type: map_at_5 |
| 441 | value: 35.176 |
| 442 | - type: mrr_at_1 |
| 443 | value: 29.153000000000002 |
| 444 | - type: mrr_at_10 |
| 445 | value: 38.233 |
| 446 | - type: mrr_at_100 |
| 447 | value: 39.109 |
| 448 | - type: mrr_at_1000 |
| 449 | value: 39.164 |
| 450 | - type: mrr_at_3 |
| 451 | value: 35.876000000000005 |
| 452 | - type: mrr_at_5 |
| 453 | value: 37.169000000000004 |
| 454 | - type: ndcg_at_1 |
| 455 | value: 29.153000000000002 |
| 456 | - type: ndcg_at_10 |
| 457 | value: 41.439 |
| 458 | - type: ndcg_at_100 |
| 459 | value: 46.42 |
| 460 | - type: ndcg_at_1000 |
| 461 | value: 48.242000000000004 |
| 462 | - type: ndcg_at_3 |
| 463 | value: 36.362 |
| 464 | - type: ndcg_at_5 |
| 465 | value: 38.743 |
| 466 | - type: precision_at_1 |
| 467 | value: 29.153000000000002 |
| 468 | - type: precision_at_10 |
| 469 | value: 6.315999999999999 |
| 470 | - type: precision_at_100 |
| 471 | value: 0.927 |
| 472 | - type: precision_at_1000 |
| 473 | value: 0.11199999999999999 |
| 474 | - type: precision_at_3 |
| 475 | value: 15.443000000000001 |
| 476 | - type: precision_at_5 |
| 477 | value: 10.644 |
| 478 | - type: recall_at_1 |
| 479 | value: 27.424 |
| 480 | - type: recall_at_10 |
| 481 | value: 55.364000000000004 |
| 482 | - type: recall_at_100 |
| 483 | value: 78.211 |
| 484 | - type: recall_at_1000 |
| 485 | value: 91.74600000000001 |
| 486 | - type: recall_at_3 |
| 487 | value: 41.379 |
| 488 | - type: recall_at_5 |
| 489 | value: 47.14 |
| 490 | - task: |
| 491 | type: Retrieval |
| 492 | dataset: |
| 493 | type: BeIR/cqadupstack |
| 494 | name: MTEB CQADupstackMathematicaRetrieval |
| 495 | config: default |
| 496 | split: test |
| 497 | revision: None |
| 498 | metrics: |
| 499 | - type: map_at_1 |
| 500 | value: 19.601 |
| 501 | - type: map_at_10 |
| 502 | value: 27.826 |
| 503 | - type: map_at_100 |
| 504 | value: 29.017 |
| 505 | - type: map_at_1000 |
| 506 | value: 29.137 |
| 507 | - type: map_at_3 |
| 508 | value: 25.125999999999998 |
| 509 | - type: map_at_5 |
| 510 | value: 26.765 |
| 511 | - type: mrr_at_1 |
| 512 | value: 24.005000000000003 |
| 513 | - type: mrr_at_10 |
| 514 | value: 32.716 |
| 515 | - type: mrr_at_100 |
| 516 | value: 33.631 |
| 517 | - type: mrr_at_1000 |
| 518 | value: 33.694 |
| 519 | - type: mrr_at_3 |
| 520 | value: 29.934 |
| 521 | - type: mrr_at_5 |
| 522 | value: 31.630999999999997 |
| 523 | - type: ndcg_at_1 |
| 524 | value: 24.005000000000003 |
| 525 | - type: ndcg_at_10 |
| 526 | value: 33.158 |
| 527 | - type: ndcg_at_100 |
| 528 | value: 38.739000000000004 |
| 529 | - type: ndcg_at_1000 |
| 530 | value: 41.495 |
| 531 | - type: ndcg_at_3 |
| 532 | value: 28.185 |
| 533 | - type: ndcg_at_5 |
| 534 | value: 30.796 |
| 535 | - type: precision_at_1 |
| 536 | value: 24.005000000000003 |
| 537 | - type: precision_at_10 |
| 538 | value: 5.908 |
| 539 | - type: precision_at_100 |
| 540 | value: 1.005 |
| 541 | - type: precision_at_1000 |
| 542 | value: 0.13899999999999998 |
| 543 | - type: precision_at_3 |
| 544 | value: 13.391 |
| 545 | - type: precision_at_5 |
| 546 | value: 9.876 |
| 547 | - type: recall_at_1 |
| 548 | value: 19.601 |
| 549 | - type: recall_at_10 |
| 550 | value: 44.746 |
| 551 | - type: recall_at_100 |
| 552 | value: 68.82300000000001 |
| 553 | - type: recall_at_1000 |
| 554 | value: 88.215 |
| 555 | - type: recall_at_3 |
| 556 | value: 31.239 |
| 557 | - type: recall_at_5 |
| 558 | value: 37.695 |
| 559 | - task: |
| 560 | type: Retrieval |
| 561 | dataset: |
| 562 | type: BeIR/cqadupstack |
| 563 | name: MTEB CQADupstackPhysicsRetrieval |
| 564 | config: default |
| 565 | split: test |
| 566 | revision: None |
| 567 | metrics: |
| 568 | - type: map_at_1 |
| 569 | value: 30.130000000000003 |
| 570 | - type: map_at_10 |
| 571 | value: 40.96 |
| 572 | - type: map_at_100 |
| 573 | value: 42.282 |
| 574 | - type: map_at_1000 |
| 575 | value: 42.392 |
| 576 | - type: map_at_3 |
| 577 | value: 37.889 |
| 578 | - type: map_at_5 |
| 579 | value: 39.661 |
| 580 | - type: mrr_at_1 |
| 581 | value: 36.958999999999996 |
| 582 | - type: mrr_at_10 |
| 583 | value: 46.835 |
| 584 | - type: mrr_at_100 |
| 585 | value: 47.644 |
| 586 | - type: mrr_at_1000 |
| 587 | value: 47.688 |
| 588 | - type: mrr_at_3 |
| 589 | value: 44.562000000000005 |
| 590 | - type: mrr_at_5 |
| 591 | value: 45.938 |
| 592 | - type: ndcg_at_1 |
| 593 | value: 36.958999999999996 |
| 594 | - type: ndcg_at_10 |
| 595 | value: 47.06 |
| 596 | - type: ndcg_at_100 |
| 597 | value: 52.345 |
| 598 | - type: ndcg_at_1000 |
| 599 | value: 54.35 |
| 600 | - type: ndcg_at_3 |
| 601 | value: 42.301 |
| 602 | - type: ndcg_at_5 |
| 603 | value: 44.635999999999996 |
| 604 | - type: precision_at_1 |
| 605 | value: 36.958999999999996 |
| 606 | - type: precision_at_10 |
| 607 | value: 8.479000000000001 |
| 608 | - type: precision_at_100 |
| 609 | value: 1.284 |
| 610 | - type: precision_at_1000 |
| 611 | value: 0.163 |
| 612 | - type: precision_at_3 |
| 613 | value: 20.244 |
| 614 | - type: precision_at_5 |
| 615 | value: 14.224999999999998 |
| 616 | - type: recall_at_1 |
| 617 | value: 30.130000000000003 |
| 618 | - type: recall_at_10 |
| 619 | value: 59.27 |
| 620 | - type: recall_at_100 |
| 621 | value: 81.195 |
| 622 | - type: recall_at_1000 |
| 623 | value: 94.21199999999999 |
| 624 | - type: recall_at_3 |
| 625 | value: 45.885 |
| 626 | - type: recall_at_5 |
| 627 | value: 52.016 |
| 628 | - task: |
| 629 | type: Retrieval |
| 630 | dataset: |
| 631 | type: BeIR/cqadupstack |
| 632 | name: MTEB CQADupstackProgrammersRetrieval |
| 633 | config: default |
| 634 | split: test |
| 635 | revision: None |
| 636 | metrics: |
| 637 | - type: map_at_1 |
| 638 | value: 26.169999999999998 |
| 639 | - type: map_at_10 |
| 640 | value: 36.451 |
| 641 | - type: map_at_100 |
| 642 | value: 37.791000000000004 |
| 643 | - type: map_at_1000 |
| 644 | value: 37.897 |
| 645 | - type: map_at_3 |
| 646 | value: 33.109 |
| 647 | - type: map_at_5 |
| 648 | value: 34.937000000000005 |
| 649 | - type: mrr_at_1 |
| 650 | value: 32.877 |
| 651 | - type: mrr_at_10 |
| 652 | value: 42.368 |
| 653 | - type: mrr_at_100 |
| 654 | value: 43.201 |
| 655 | - type: mrr_at_1000 |
| 656 | value: 43.259 |
| 657 | - type: mrr_at_3 |
| 658 | value: 39.763999999999996 |
| 659 | - type: mrr_at_5 |
| 660 | value: 41.260000000000005 |
| 661 | - type: ndcg_at_1 |
| 662 | value: 32.877 |
| 663 | - type: ndcg_at_10 |
| 664 | value: 42.659000000000006 |
| 665 | - type: ndcg_at_100 |
| 666 | value: 48.161 |
| 667 | - type: ndcg_at_1000 |
| 668 | value: 50.345 |
| 669 | - type: ndcg_at_3 |
| 670 | value: 37.302 |
| 671 | - type: ndcg_at_5 |
| 672 | value: 39.722 |
| 673 | - type: precision_at_1 |
| 674 | value: 32.877 |
| 675 | - type: precision_at_10 |
| 676 | value: 7.9 |
| 677 | - type: precision_at_100 |
| 678 | value: 1.236 |
| 679 | - type: precision_at_1000 |
| 680 | value: 0.158 |
| 681 | - type: precision_at_3 |
| 682 | value: 17.846 |
| 683 | - type: precision_at_5 |
| 684 | value: 12.9 |
| 685 | - type: recall_at_1 |
| 686 | value: 26.169999999999998 |
| 687 | - type: recall_at_10 |
| 688 | value: 55.35 |
| 689 | - type: recall_at_100 |
| 690 | value: 78.755 |
| 691 | - type: recall_at_1000 |
| 692 | value: 93.518 |
| 693 | - type: recall_at_3 |
| 694 | value: 40.176 |
| 695 | - type: recall_at_5 |
| 696 | value: 46.589000000000006 |
| 697 | - task: |
| 698 | type: Retrieval |
| 699 | dataset: |
| 700 | type: BeIR/cqadupstack |
| 701 | name: MTEB CQADupstackRetrieval |
| 702 | config: default |
| 703 | split: test |
| 704 | revision: None |
| 705 | metrics: |
| 706 | - type: map_at_1 |
| 707 | value: 27.15516666666667 |
| 708 | - type: map_at_10 |
| 709 | value: 36.65741666666667 |
| 710 | - type: map_at_100 |
| 711 | value: 37.84991666666666 |
| 712 | - type: map_at_1000 |
| 713 | value: 37.96316666666667 |
| 714 | - type: map_at_3 |
| 715 | value: 33.74974999999999 |
| 716 | - type: map_at_5 |
| 717 | value: 35.3765 |
| 718 | - type: mrr_at_1 |
| 719 | value: 32.08233333333334 |
| 720 | - type: mrr_at_10 |
| 721 | value: 41.033833333333334 |
| 722 | - type: mrr_at_100 |
| 723 | value: 41.84524999999999 |
| 724 | - type: mrr_at_1000 |
| 725 | value: 41.89983333333333 |
| 726 | - type: mrr_at_3 |
| 727 | value: 38.62008333333333 |
| 728 | - type: mrr_at_5 |
| 729 | value: 40.03441666666666 |
| 730 | - type: ndcg_at_1 |
| 731 | value: 32.08233333333334 |
| 732 | - type: ndcg_at_10 |
| 733 | value: 42.229 |
| 734 | - type: ndcg_at_100 |
| 735 | value: 47.26716666666667 |
| 736 | - type: ndcg_at_1000 |
| 737 | value: 49.43466666666667 |
| 738 | - type: ndcg_at_3 |
| 739 | value: 37.36408333333333 |
| 740 | - type: ndcg_at_5 |
| 741 | value: 39.6715 |
| 742 | - type: precision_at_1 |
| 743 | value: 32.08233333333334 |
| 744 | - type: precision_at_10 |
| 745 | value: 7.382583333333334 |
| 746 | - type: precision_at_100 |
| 747 | value: 1.16625 |
| 748 | - type: precision_at_1000 |
| 749 | value: 0.15408333333333332 |
| 750 | - type: precision_at_3 |
| 751 | value: 17.218 |
| 752 | - type: precision_at_5 |
| 753 | value: 12.21875 |
| 754 | - type: recall_at_1 |
| 755 | value: 27.15516666666667 |
| 756 | - type: recall_at_10 |
| 757 | value: 54.36683333333333 |
| 758 | - type: recall_at_100 |
| 759 | value: 76.37183333333333 |
| 760 | - type: recall_at_1000 |
| 761 | value: 91.26183333333333 |
| 762 | - type: recall_at_3 |
| 763 | value: 40.769916666666674 |
| 764 | - type: recall_at_5 |
| 765 | value: 46.702333333333335 |
| 766 | - task: |
| 767 | type: Retrieval |
| 768 | dataset: |
| 769 | type: BeIR/cqadupstack |
| 770 | name: MTEB CQADupstackStatsRetrieval |
| 771 | config: default |
| 772 | split: test |
| 773 | revision: None |
| 774 | metrics: |
| 775 | - type: map_at_1 |
| 776 | value: 25.749 |
| 777 | - type: map_at_10 |
| 778 | value: 33.001999999999995 |
| 779 | - type: map_at_100 |
| 780 | value: 33.891 |
| 781 | - type: map_at_1000 |
| 782 | value: 33.993 |
| 783 | - type: map_at_3 |
| 784 | value: 30.703999999999997 |
| 785 | - type: map_at_5 |
| 786 | value: 31.959 |
| 787 | - type: mrr_at_1 |
| 788 | value: 28.834 |
| 789 | - type: mrr_at_10 |
| 790 | value: 35.955 |
| 791 | - type: mrr_at_100 |
| 792 | value: 36.709 |
| 793 | - type: mrr_at_1000 |
| 794 | value: 36.779 |
| 795 | - type: mrr_at_3 |
| 796 | value: 33.947 |
| 797 | - type: mrr_at_5 |
| 798 | value: 35.089 |
| 799 | - type: ndcg_at_1 |
| 800 | value: 28.834 |
| 801 | - type: ndcg_at_10 |
| 802 | value: 37.329 |
| 803 | - type: ndcg_at_100 |
| 804 | value: 41.79 |
| 805 | - type: ndcg_at_1000 |
| 806 | value: 44.169000000000004 |
| 807 | - type: ndcg_at_3 |
| 808 | value: 33.184999999999995 |
| 809 | - type: ndcg_at_5 |
| 810 | value: 35.107 |
| 811 | - type: precision_at_1 |
| 812 | value: 28.834 |
| 813 | - type: precision_at_10 |
| 814 | value: 5.7669999999999995 |
| 815 | - type: precision_at_100 |
| 816 | value: 0.876 |
| 817 | - type: precision_at_1000 |
| 818 | value: 0.11399999999999999 |
| 819 | - type: precision_at_3 |
| 820 | value: 14.213000000000001 |
| 821 | - type: precision_at_5 |
| 822 | value: 9.754999999999999 |
| 823 | - type: recall_at_1 |
| 824 | value: 25.749 |
| 825 | - type: recall_at_10 |
| 826 | value: 47.791 |
| 827 | - type: recall_at_100 |
| 828 | value: 68.255 |
| 829 | - type: recall_at_1000 |
| 830 | value: 85.749 |
| 831 | - type: recall_at_3 |
| 832 | value: 36.199 |
| 833 | - type: recall_at_5 |
| 834 | value: 41.071999999999996 |
| 835 | - task: |
| 836 | type: Retrieval |
| 837 | dataset: |
| 838 | type: BeIR/cqadupstack |
| 839 | name: MTEB CQADupstackTexRetrieval |
| 840 | config: default |
| 841 | split: test |
| 842 | revision: None |
| 843 | metrics: |
| 844 | - type: map_at_1 |
| 845 | value: 17.777 |
| 846 | - type: map_at_10 |
| 847 | value: 25.201 |
| 848 | - type: map_at_100 |
| 849 | value: 26.423999999999996 |
| 850 | - type: map_at_1000 |
| 851 | value: 26.544 |
| 852 | - type: map_at_3 |
| 853 | value: 22.869 |
| 854 | - type: map_at_5 |
| 855 | value: 24.023 |
| 856 | - type: mrr_at_1 |
| 857 | value: 21.473 |
| 858 | - type: mrr_at_10 |
| 859 | value: 29.12 |
| 860 | - type: mrr_at_100 |
| 861 | value: 30.144 |
| 862 | - type: mrr_at_1000 |
| 863 | value: 30.215999999999998 |
| 864 | - type: mrr_at_3 |
| 865 | value: 26.933 |
| 866 | - type: mrr_at_5 |
| 867 | value: 28.051 |
| 868 | - type: ndcg_at_1 |
| 869 | value: 21.473 |
| 870 | - type: ndcg_at_10 |
| 871 | value: 30.003 |
| 872 | - type: ndcg_at_100 |
| 873 | value: 35.766 |
| 874 | - type: ndcg_at_1000 |
| 875 | value: 38.501000000000005 |
| 876 | - type: ndcg_at_3 |
| 877 | value: 25.773000000000003 |
| 878 | - type: ndcg_at_5 |
| 879 | value: 27.462999999999997 |
| 880 | - type: precision_at_1 |
| 881 | value: 21.473 |
| 882 | - type: precision_at_10 |
| 883 | value: 5.482 |
| 884 | - type: precision_at_100 |
| 885 | value: 0.975 |
| 886 | - type: precision_at_1000 |
| 887 | value: 0.13799999999999998 |
| 888 | - type: precision_at_3 |
| 889 | value: 12.205 |
| 890 | - type: precision_at_5 |
| 891 | value: 8.692 |
| 892 | - type: recall_at_1 |
| 893 | value: 17.777 |
| 894 | - type: recall_at_10 |
| 895 | value: 40.582 |
| 896 | - type: recall_at_100 |
| 897 | value: 66.305 |
| 898 | - type: recall_at_1000 |
| 899 | value: 85.636 |
| 900 | - type: recall_at_3 |
| 901 | value: 28.687 |
| 902 | - type: recall_at_5 |
| 903 | value: 33.089 |
| 904 | - task: |
| 905 | type: Retrieval |
| 906 | dataset: |
| 907 | type: BeIR/cqadupstack |
| 908 | name: MTEB CQADupstackUnixRetrieval |
| 909 | config: default |
| 910 | split: test |
| 911 | revision: None |
| 912 | metrics: |
| 913 | - type: map_at_1 |
| 914 | value: 26.677 |
| 915 | - type: map_at_10 |
| 916 | value: 36.309000000000005 |
| 917 | - type: map_at_100 |
| 918 | value: 37.403999999999996 |
| 919 | - type: map_at_1000 |
| 920 | value: 37.496 |
| 921 | - type: map_at_3 |
| 922 | value: 33.382 |
| 923 | - type: map_at_5 |
| 924 | value: 34.98 |
| 925 | - type: mrr_at_1 |
| 926 | value: 31.343 |
| 927 | - type: mrr_at_10 |
| 928 | value: 40.549 |
| 929 | - type: mrr_at_100 |
| 930 | value: 41.342 |
| 931 | - type: mrr_at_1000 |
| 932 | value: 41.397 |
| 933 | - type: mrr_at_3 |
| 934 | value: 38.029 |
| 935 | - type: mrr_at_5 |
| 936 | value: 39.451 |
| 937 | - type: ndcg_at_1 |
| 938 | value: 31.343 |
| 939 | - type: ndcg_at_10 |
| 940 | value: 42.1 |
| 941 | - type: ndcg_at_100 |
| 942 | value: 47.089999999999996 |
| 943 | - type: ndcg_at_1000 |
| 944 | value: 49.222 |
| 945 | - type: ndcg_at_3 |
| 946 | value: 36.836999999999996 |
| 947 | - type: ndcg_at_5 |
| 948 | value: 39.21 |
| 949 | - type: precision_at_1 |
| 950 | value: 31.343 |
| 951 | - type: precision_at_10 |
| 952 | value: 7.164 |
| 953 | - type: precision_at_100 |
| 954 | value: 1.0959999999999999 |
| 955 | - type: precision_at_1000 |
| 956 | value: 0.13899999999999998 |
| 957 | - type: precision_at_3 |
| 958 | value: 16.915 |
| 959 | - type: precision_at_5 |
| 960 | value: 11.940000000000001 |
| 961 | - type: recall_at_1 |
| 962 | value: 26.677 |
| 963 | - type: recall_at_10 |
| 964 | value: 55.54599999999999 |
| 965 | - type: recall_at_100 |
| 966 | value: 77.094 |
| 967 | - type: recall_at_1000 |
| 968 | value: 92.01 |
| 969 | - type: recall_at_3 |
| 970 | value: 41.191 |
| 971 | - type: recall_at_5 |
| 972 | value: 47.006 |
| 973 | - task: |
| 974 | type: Retrieval |
| 975 | dataset: |
| 976 | type: BeIR/cqadupstack |
| 977 | name: MTEB CQADupstackWebmastersRetrieval |
| 978 | config: default |
| 979 | split: test |
| 980 | revision: None |
| 981 | metrics: |
| 982 | - type: map_at_1 |
| 983 | value: 24.501 |
| 984 | - type: map_at_10 |
| 985 | value: 33.102 |
| 986 | - type: map_at_100 |
| 987 | value: 34.676 |
| 988 | - type: map_at_1000 |
| 989 | value: 34.888000000000005 |
| 990 | - type: map_at_3 |
| 991 | value: 29.944 |
| 992 | - type: map_at_5 |
| 993 | value: 31.613999999999997 |
| 994 | - type: mrr_at_1 |
| 995 | value: 29.447000000000003 |
| 996 | - type: mrr_at_10 |
| 997 | value: 37.996 |
| 998 | - type: mrr_at_100 |
| 999 | value: 38.946 |
| 1000 | - type: mrr_at_1000 |
| 1001 | value: 38.995000000000005 |
| 1002 | - type: mrr_at_3 |
| 1003 | value: 35.079 |
| 1004 | - type: mrr_at_5 |
| 1005 | value: 36.69 |
| 1006 | - type: ndcg_at_1 |
| 1007 | value: 29.447000000000003 |
| 1008 | - type: ndcg_at_10 |
| 1009 | value: 39.232 |
| 1010 | - type: ndcg_at_100 |
| 1011 | value: 45.247 |
| 1012 | - type: ndcg_at_1000 |
| 1013 | value: 47.613 |
| 1014 | - type: ndcg_at_3 |
| 1015 | value: 33.922999999999995 |
| 1016 | - type: ndcg_at_5 |
| 1017 | value: 36.284 |
| 1018 | - type: precision_at_1 |
| 1019 | value: 29.447000000000003 |
| 1020 | - type: precision_at_10 |
| 1021 | value: 7.648000000000001 |
| 1022 | - type: precision_at_100 |
| 1023 | value: 1.516 |
| 1024 | - type: precision_at_1000 |
| 1025 | value: 0.23900000000000002 |
| 1026 | - type: precision_at_3 |
| 1027 | value: 16.008 |
| 1028 | - type: precision_at_5 |
| 1029 | value: 11.779 |
| 1030 | - type: recall_at_1 |
| 1031 | value: 24.501 |
| 1032 | - type: recall_at_10 |
| 1033 | value: 51.18899999999999 |
| 1034 | - type: recall_at_100 |
| 1035 | value: 78.437 |
| 1036 | - type: recall_at_1000 |
| 1037 | value: 92.842 |
| 1038 | - type: recall_at_3 |
| 1039 | value: 35.808 |
| 1040 | - type: recall_at_5 |
| 1041 | value: 42.197 |
| 1042 | - task: |
| 1043 | type: Retrieval |
| 1044 | dataset: |
| 1045 | type: BeIR/cqadupstack |
| 1046 | name: MTEB CQADupstackWordpressRetrieval |
| 1047 | config: default |
| 1048 | split: test |
| 1049 | revision: None |
| 1050 | metrics: |
| 1051 | - type: map_at_1 |
| 1052 | value: 22.039 |
| 1053 | - type: map_at_10 |
| 1054 | value: 30.377 |
| 1055 | - type: map_at_100 |
| 1056 | value: 31.275 |
| 1057 | - type: map_at_1000 |
| 1058 | value: 31.379 |
| 1059 | - type: map_at_3 |
| 1060 | value: 27.98 |
| 1061 | - type: map_at_5 |
| 1062 | value: 29.358 |
| 1063 | - type: mrr_at_1 |
| 1064 | value: 24.03 |
| 1065 | - type: mrr_at_10 |
| 1066 | value: 32.568000000000005 |
| 1067 | - type: mrr_at_100 |
| 1068 | value: 33.403 |
| 1069 | - type: mrr_at_1000 |
| 1070 | value: 33.475 |
| 1071 | - type: mrr_at_3 |
| 1072 | value: 30.436999999999998 |
| 1073 | - type: mrr_at_5 |
| 1074 | value: 31.796000000000003 |
| 1075 | - type: ndcg_at_1 |
| 1076 | value: 24.03 |
| 1077 | - type: ndcg_at_10 |
| 1078 | value: 35.198 |
| 1079 | - type: ndcg_at_100 |
| 1080 | value: 39.668 |
| 1081 | - type: ndcg_at_1000 |
| 1082 | value: 42.296 |
| 1083 | - type: ndcg_at_3 |
| 1084 | value: 30.709999999999997 |
| 1085 | - type: ndcg_at_5 |
| 1086 | value: 33.024 |
| 1087 | - type: precision_at_1 |
| 1088 | value: 24.03 |
| 1089 | - type: precision_at_10 |
| 1090 | value: 5.564 |
| 1091 | - type: precision_at_100 |
| 1092 | value: 0.828 |
| 1093 | - type: precision_at_1000 |
| 1094 | value: 0.117 |
| 1095 | - type: precision_at_3 |
| 1096 | value: 13.309000000000001 |
| 1097 | - type: precision_at_5 |
| 1098 | value: 9.39 |
| 1099 | - type: recall_at_1 |
| 1100 | value: 22.039 |
| 1101 | - type: recall_at_10 |
| 1102 | value: 47.746 |
| 1103 | - type: recall_at_100 |
| 1104 | value: 68.23599999999999 |
| 1105 | - type: recall_at_1000 |
| 1106 | value: 87.852 |
| 1107 | - type: recall_at_3 |
| 1108 | value: 35.852000000000004 |
| 1109 | - type: recall_at_5 |
| 1110 | value: 41.410000000000004 |
| 1111 | - task: |
| 1112 | type: Retrieval |
| 1113 | dataset: |
| 1114 | type: climate-fever |
| 1115 | name: MTEB ClimateFEVER |
| 1116 | config: default |
| 1117 | split: test |
| 1118 | revision: None |
| 1119 | metrics: |
| 1120 | - type: map_at_1 |
| 1121 | value: 15.692999999999998 |
| 1122 | - type: map_at_10 |
| 1123 | value: 26.903 |
| 1124 | - type: map_at_100 |
| 1125 | value: 28.987000000000002 |
| 1126 | - type: map_at_1000 |
| 1127 | value: 29.176999999999996 |
| 1128 | - type: map_at_3 |
| 1129 | value: 22.137 |
| 1130 | - type: map_at_5 |
| 1131 | value: 24.758 |
| 1132 | - type: mrr_at_1 |
| 1133 | value: 35.57 |
| 1134 | - type: mrr_at_10 |
| 1135 | value: 47.821999999999996 |
| 1136 | - type: mrr_at_100 |
| 1137 | value: 48.608000000000004 |
| 1138 | - type: mrr_at_1000 |
| 1139 | value: 48.638999999999996 |
| 1140 | - type: mrr_at_3 |
| 1141 | value: 44.452000000000005 |
| 1142 | - type: mrr_at_5 |
| 1143 | value: 46.546 |
| 1144 | - type: ndcg_at_1 |
| 1145 | value: 35.57 |
| 1146 | - type: ndcg_at_10 |
| 1147 | value: 36.567 |
| 1148 | - type: ndcg_at_100 |
| 1149 | value: 44.085 |
| 1150 | - type: ndcg_at_1000 |
| 1151 | value: 47.24 |
| 1152 | - type: ndcg_at_3 |
| 1153 | value: 29.964000000000002 |
| 1154 | - type: ndcg_at_5 |
| 1155 | value: 32.511 |
| 1156 | - type: precision_at_1 |
| 1157 | value: 35.57 |
| 1158 | - type: precision_at_10 |
| 1159 | value: 11.485 |
| 1160 | - type: precision_at_100 |
| 1161 | value: 1.9619999999999997 |
| 1162 | - type: precision_at_1000 |
| 1163 | value: 0.256 |
| 1164 | - type: precision_at_3 |
| 1165 | value: 22.237000000000002 |
| 1166 | - type: precision_at_5 |
| 1167 | value: 17.471999999999998 |
| 1168 | - type: recall_at_1 |
| 1169 | value: 15.692999999999998 |
| 1170 | - type: recall_at_10 |
| 1171 | value: 43.056 |
| 1172 | - type: recall_at_100 |
| 1173 | value: 68.628 |
| 1174 | - type: recall_at_1000 |
| 1175 | value: 86.075 |
| 1176 | - type: recall_at_3 |
| 1177 | value: 26.918999999999997 |
| 1178 | - type: recall_at_5 |
| 1179 | value: 34.14 |
| 1180 | - task: |
| 1181 | type: Retrieval |
| 1182 | dataset: |
| 1183 | type: dbpedia-entity |
| 1184 | name: MTEB DBPedia |
| 1185 | config: default |
| 1186 | split: test |
| 1187 | revision: None |
| 1188 | metrics: |
| 1189 | - type: map_at_1 |
| 1190 | value: 9.53 |
| 1191 | - type: map_at_10 |
| 1192 | value: 20.951 |
| 1193 | - type: map_at_100 |
| 1194 | value: 30.136000000000003 |
| 1195 | - type: map_at_1000 |
| 1196 | value: 31.801000000000002 |
| 1197 | - type: map_at_3 |
| 1198 | value: 15.021 |
| 1199 | - type: map_at_5 |
| 1200 | value: 17.471999999999998 |
| 1201 | - type: mrr_at_1 |
| 1202 | value: 71.0 |
| 1203 | - type: mrr_at_10 |
| 1204 | value: 79.176 |
| 1205 | - type: mrr_at_100 |
| 1206 | value: 79.418 |
| 1207 | - type: mrr_at_1000 |
| 1208 | value: 79.426 |
| 1209 | - type: mrr_at_3 |
| 1210 | value: 78.125 |
| 1211 | - type: mrr_at_5 |
| 1212 | value: 78.61200000000001 |
| 1213 | - type: ndcg_at_1 |
| 1214 | value: 58.5 |
| 1215 | - type: ndcg_at_10 |
| 1216 | value: 44.106 |
| 1217 | - type: ndcg_at_100 |
| 1218 | value: 49.268 |
| 1219 | - type: ndcg_at_1000 |
| 1220 | value: 56.711999999999996 |
| 1221 | - type: ndcg_at_3 |
| 1222 | value: 48.934 |
| 1223 | - type: ndcg_at_5 |
| 1224 | value: 45.826 |
| 1225 | - type: precision_at_1 |
| 1226 | value: 71.0 |
| 1227 | - type: precision_at_10 |
| 1228 | value: 35.0 |
| 1229 | - type: precision_at_100 |
| 1230 | value: 11.360000000000001 |
| 1231 | - type: precision_at_1000 |
| 1232 | value: 2.046 |
| 1233 | - type: precision_at_3 |
| 1234 | value: 52.833 |
| 1235 | - type: precision_at_5 |
| 1236 | value: 44.15 |
| 1237 | - type: recall_at_1 |
| 1238 | value: 9.53 |
| 1239 | - type: recall_at_10 |
| 1240 | value: 26.811 |
| 1241 | - type: recall_at_100 |
| 1242 | value: 55.916999999999994 |
| 1243 | - type: recall_at_1000 |
| 1244 | value: 79.973 |
| 1245 | - type: recall_at_3 |
| 1246 | value: 16.413 |
| 1247 | - type: recall_at_5 |
| 1248 | value: 19.980999999999998 |
| 1249 | - task: |
| 1250 | type: Classification |
| 1251 | dataset: |
| 1252 | type: mteb/emotion |
| 1253 | name: MTEB EmotionClassification |
| 1254 | config: default |
| 1255 | split: test |
| 1256 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 1257 | metrics: |
| 1258 | - type: accuracy |
| 1259 | value: 51.519999999999996 |
| 1260 | - type: f1 |
| 1261 | value: 46.36601294761231 |
| 1262 | - task: |
| 1263 | type: Retrieval |
| 1264 | dataset: |
| 1265 | type: fever |
| 1266 | name: MTEB FEVER |
| 1267 | config: default |
| 1268 | split: test |
| 1269 | revision: None |
| 1270 | metrics: |
| 1271 | - type: map_at_1 |
| 1272 | value: 74.413 |
| 1273 | - type: map_at_10 |
| 1274 | value: 83.414 |
| 1275 | - type: map_at_100 |
| 1276 | value: 83.621 |
| 1277 | - type: map_at_1000 |
| 1278 | value: 83.635 |
| 1279 | - type: map_at_3 |
| 1280 | value: 82.337 |
| 1281 | - type: map_at_5 |
| 1282 | value: 83.039 |
| 1283 | - type: mrr_at_1 |
| 1284 | value: 80.19800000000001 |
| 1285 | - type: mrr_at_10 |
| 1286 | value: 87.715 |
| 1287 | - type: mrr_at_100 |
| 1288 | value: 87.778 |
| 1289 | - type: mrr_at_1000 |
| 1290 | value: 87.779 |
| 1291 | - type: mrr_at_3 |
| 1292 | value: 87.106 |
| 1293 | - type: mrr_at_5 |
| 1294 | value: 87.555 |
| 1295 | - type: ndcg_at_1 |
| 1296 | value: 80.19800000000001 |
| 1297 | - type: ndcg_at_10 |
| 1298 | value: 87.182 |
| 1299 | - type: ndcg_at_100 |
| 1300 | value: 87.90299999999999 |
| 1301 | - type: ndcg_at_1000 |
| 1302 | value: 88.143 |
| 1303 | - type: ndcg_at_3 |
| 1304 | value: 85.60600000000001 |
| 1305 | - type: ndcg_at_5 |
| 1306 | value: 86.541 |
| 1307 | - type: precision_at_1 |
| 1308 | value: 80.19800000000001 |
| 1309 | - type: precision_at_10 |
| 1310 | value: 10.531 |
| 1311 | - type: precision_at_100 |
| 1312 | value: 1.113 |
| 1313 | - type: precision_at_1000 |
| 1314 | value: 0.11499999999999999 |
| 1315 | - type: precision_at_3 |
| 1316 | value: 32.933 |
| 1317 | - type: precision_at_5 |
| 1318 | value: 20.429 |
| 1319 | - type: recall_at_1 |
| 1320 | value: 74.413 |
| 1321 | - type: recall_at_10 |
| 1322 | value: 94.363 |
| 1323 | - type: recall_at_100 |
| 1324 | value: 97.165 |
| 1325 | - type: recall_at_1000 |
| 1326 | value: 98.668 |
| 1327 | - type: recall_at_3 |
| 1328 | value: 90.108 |
| 1329 | - type: recall_at_5 |
| 1330 | value: 92.52 |
| 1331 | - task: |
| 1332 | type: Retrieval |
| 1333 | dataset: |
| 1334 | type: fiqa |
| 1335 | name: MTEB FiQA2018 |
| 1336 | config: default |
| 1337 | split: test |
| 1338 | revision: None |
| 1339 | metrics: |
| 1340 | - type: map_at_1 |
| 1341 | value: 22.701 |
| 1342 | - type: map_at_10 |
| 1343 | value: 37.122 |
| 1344 | - type: map_at_100 |
| 1345 | value: 39.178000000000004 |
| 1346 | - type: map_at_1000 |
| 1347 | value: 39.326 |
| 1348 | - type: map_at_3 |
| 1349 | value: 32.971000000000004 |
| 1350 | - type: map_at_5 |
| 1351 | value: 35.332 |
| 1352 | - type: mrr_at_1 |
| 1353 | value: 44.753 |
| 1354 | - type: mrr_at_10 |
| 1355 | value: 53.452 |
| 1356 | - type: mrr_at_100 |
| 1357 | value: 54.198 |
| 1358 | - type: mrr_at_1000 |
| 1359 | value: 54.225 |
| 1360 | - type: mrr_at_3 |
| 1361 | value: 50.952 |
| 1362 | - type: mrr_at_5 |
| 1363 | value: 52.464 |
| 1364 | - type: ndcg_at_1 |
| 1365 | value: 44.753 |
| 1366 | - type: ndcg_at_10 |
| 1367 | value: 45.021 |
| 1368 | - type: ndcg_at_100 |
| 1369 | value: 52.028 |
| 1370 | - type: ndcg_at_1000 |
| 1371 | value: 54.596000000000004 |
| 1372 | - type: ndcg_at_3 |
| 1373 | value: 41.622 |
| 1374 | - type: ndcg_at_5 |
| 1375 | value: 42.736000000000004 |
| 1376 | - type: precision_at_1 |
| 1377 | value: 44.753 |
| 1378 | - type: precision_at_10 |
| 1379 | value: 12.284 |
| 1380 | - type: precision_at_100 |
| 1381 | value: 1.955 |
| 1382 | - type: precision_at_1000 |
| 1383 | value: 0.243 |
| 1384 | - type: precision_at_3 |
| 1385 | value: 27.828999999999997 |
| 1386 | - type: precision_at_5 |
| 1387 | value: 20.061999999999998 |
| 1388 | - type: recall_at_1 |
| 1389 | value: 22.701 |
| 1390 | - type: recall_at_10 |
| 1391 | value: 51.432 |
| 1392 | - type: recall_at_100 |
| 1393 | value: 77.009 |
| 1394 | - type: recall_at_1000 |
| 1395 | value: 92.511 |
| 1396 | - type: recall_at_3 |
| 1397 | value: 37.919000000000004 |
| 1398 | - type: recall_at_5 |
| 1399 | value: 44.131 |
| 1400 | - task: |
| 1401 | type: Retrieval |
| 1402 | dataset: |
| 1403 | type: hotpotqa |
| 1404 | name: MTEB HotpotQA |
| 1405 | config: default |
| 1406 | split: test |
| 1407 | revision: None |
| 1408 | metrics: |
| 1409 | - type: map_at_1 |
| 1410 | value: 40.189 |
| 1411 | - type: map_at_10 |
| 1412 | value: 66.24600000000001 |
| 1413 | - type: map_at_100 |
| 1414 | value: 67.098 |
| 1415 | - type: map_at_1000 |
| 1416 | value: 67.149 |
| 1417 | - type: map_at_3 |
| 1418 | value: 62.684 |
| 1419 | - type: map_at_5 |
| 1420 | value: 64.974 |
| 1421 | - type: mrr_at_1 |
| 1422 | value: 80.378 |
| 1423 | - type: mrr_at_10 |
| 1424 | value: 86.127 |
| 1425 | - type: mrr_at_100 |
| 1426 | value: 86.29299999999999 |
| 1427 | - type: mrr_at_1000 |
| 1428 | value: 86.297 |
| 1429 | - type: mrr_at_3 |
| 1430 | value: 85.31400000000001 |
| 1431 | - type: mrr_at_5 |
| 1432 | value: 85.858 |
| 1433 | - type: ndcg_at_1 |
| 1434 | value: 80.378 |
| 1435 | - type: ndcg_at_10 |
| 1436 | value: 74.101 |
| 1437 | - type: ndcg_at_100 |
| 1438 | value: 76.993 |
| 1439 | - type: ndcg_at_1000 |
| 1440 | value: 77.948 |
| 1441 | - type: ndcg_at_3 |
| 1442 | value: 69.232 |
| 1443 | - type: ndcg_at_5 |
| 1444 | value: 72.04599999999999 |
| 1445 | - type: precision_at_1 |
| 1446 | value: 80.378 |
| 1447 | - type: precision_at_10 |
| 1448 | value: 15.595999999999998 |
| 1449 | - type: precision_at_100 |
| 1450 | value: 1.7840000000000003 |
| 1451 | - type: precision_at_1000 |
| 1452 | value: 0.191 |
| 1453 | - type: precision_at_3 |
| 1454 | value: 44.884 |
| 1455 | - type: precision_at_5 |
| 1456 | value: 29.145 |
| 1457 | - type: recall_at_1 |
| 1458 | value: 40.189 |
| 1459 | - type: recall_at_10 |
| 1460 | value: 77.981 |
| 1461 | - type: recall_at_100 |
| 1462 | value: 89.21 |
| 1463 | - type: recall_at_1000 |
| 1464 | value: 95.48299999999999 |
| 1465 | - type: recall_at_3 |
| 1466 | value: 67.326 |
| 1467 | - type: recall_at_5 |
| 1468 | value: 72.863 |
| 1469 | - task: |
| 1470 | type: Classification |
| 1471 | dataset: |
| 1472 | type: mteb/imdb |
| 1473 | name: MTEB ImdbClassification |
| 1474 | config: default |
| 1475 | split: test |
| 1476 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 1477 | metrics: |
| 1478 | - type: accuracy |
| 1479 | value: 92.84599999999999 |
| 1480 | - type: ap |
| 1481 | value: 89.4710787567357 |
| 1482 | - type: f1 |
| 1483 | value: 92.83752676932258 |
| 1484 | - task: |
| 1485 | type: Retrieval |
| 1486 | dataset: |
| 1487 | type: msmarco |
| 1488 | name: MTEB MSMARCO |
| 1489 | config: default |
| 1490 | split: dev |
| 1491 | revision: None |
| 1492 | metrics: |
| 1493 | - type: map_at_1 |
| 1494 | value: 23.132 |
| 1495 | - type: map_at_10 |
| 1496 | value: 35.543 |
| 1497 | - type: map_at_100 |
| 1498 | value: 36.702 |
| 1499 | - type: map_at_1000 |
| 1500 | value: 36.748999999999995 |
| 1501 | - type: map_at_3 |
| 1502 | value: 31.737 |
| 1503 | - type: map_at_5 |
| 1504 | value: 33.927 |
| 1505 | - type: mrr_at_1 |
| 1506 | value: 23.782 |
| 1507 | - type: mrr_at_10 |
| 1508 | value: 36.204 |
| 1509 | - type: mrr_at_100 |
| 1510 | value: 37.29 |
| 1511 | - type: mrr_at_1000 |
| 1512 | value: 37.330999999999996 |
| 1513 | - type: mrr_at_3 |
| 1514 | value: 32.458999999999996 |
| 1515 | - type: mrr_at_5 |
| 1516 | value: 34.631 |
| 1517 | - type: ndcg_at_1 |
| 1518 | value: 23.782 |
| 1519 | - type: ndcg_at_10 |
| 1520 | value: 42.492999999999995 |
| 1521 | - type: ndcg_at_100 |
| 1522 | value: 47.985 |
| 1523 | - type: ndcg_at_1000 |
| 1524 | value: 49.141 |
| 1525 | - type: ndcg_at_3 |
| 1526 | value: 34.748000000000005 |
| 1527 | - type: ndcg_at_5 |
| 1528 | value: 38.651 |
| 1529 | - type: precision_at_1 |
| 1530 | value: 23.782 |
| 1531 | - type: precision_at_10 |
| 1532 | value: 6.665 |
| 1533 | - type: precision_at_100 |
| 1534 | value: 0.941 |
| 1535 | - type: precision_at_1000 |
| 1536 | value: 0.104 |
| 1537 | - type: precision_at_3 |
| 1538 | value: 14.776 |
| 1539 | - type: precision_at_5 |
| 1540 | value: 10.84 |
| 1541 | - type: recall_at_1 |
| 1542 | value: 23.132 |
| 1543 | - type: recall_at_10 |
| 1544 | value: 63.794 |
| 1545 | - type: recall_at_100 |
| 1546 | value: 89.027 |
| 1547 | - type: recall_at_1000 |
| 1548 | value: 97.807 |
| 1549 | - type: recall_at_3 |
| 1550 | value: 42.765 |
| 1551 | - type: recall_at_5 |
| 1552 | value: 52.11 |
| 1553 | - task: |
| 1554 | type: Classification |
| 1555 | dataset: |
| 1556 | type: mteb/mtop_domain |
| 1557 | name: MTEB MTOPDomainClassification (en) |
| 1558 | config: en |
| 1559 | split: test |
| 1560 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1561 | metrics: |
| 1562 | - type: accuracy |
| 1563 | value: 94.59188326493388 |
| 1564 | - type: f1 |
| 1565 | value: 94.3842594786827 |
| 1566 | - task: |
| 1567 | type: Classification |
| 1568 | dataset: |
| 1569 | type: mteb/mtop_intent |
| 1570 | name: MTEB MTOPIntentClassification (en) |
| 1571 | config: en |
| 1572 | split: test |
| 1573 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1574 | metrics: |
| 1575 | - type: accuracy |
| 1576 | value: 79.49384404924761 |
| 1577 | - type: f1 |
| 1578 | value: 59.7580539534629 |
| 1579 | - task: |
| 1580 | type: Classification |
| 1581 | dataset: |
| 1582 | type: mteb/amazon_massive_intent |
| 1583 | name: MTEB MassiveIntentClassification (en) |
| 1584 | config: en |
| 1585 | split: test |
| 1586 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1587 | metrics: |
| 1588 | - type: accuracy |
| 1589 | value: 77.56220578345663 |
| 1590 | - type: f1 |
| 1591 | value: 75.27228165561478 |
| 1592 | - task: |
| 1593 | type: Classification |
| 1594 | dataset: |
| 1595 | type: mteb/amazon_massive_scenario |
| 1596 | name: MTEB MassiveScenarioClassification (en) |
| 1597 | config: en |
| 1598 | split: test |
| 1599 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1600 | metrics: |
| 1601 | - type: accuracy |
| 1602 | value: 80.53463349024884 |
| 1603 | - type: f1 |
| 1604 | value: 80.4893958236536 |
| 1605 | - task: |
| 1606 | type: Clustering |
| 1607 | dataset: |
| 1608 | type: mteb/medrxiv-clustering-p2p |
| 1609 | name: MTEB MedrxivClusteringP2P |
| 1610 | config: default |
| 1611 | split: test |
| 1612 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 1613 | metrics: |
| 1614 | - type: v_measure |
| 1615 | value: 32.56100273484962 |
| 1616 | - task: |
| 1617 | type: Clustering |
| 1618 | dataset: |
| 1619 | type: mteb/medrxiv-clustering-s2s |
| 1620 | name: MTEB MedrxivClusteringS2S |
| 1621 | config: default |
| 1622 | split: test |
| 1623 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 1624 | metrics: |
| 1625 | - type: v_measure |
| 1626 | value: 31.470380028839607 |
| 1627 | - task: |
| 1628 | type: Reranking |
| 1629 | dataset: |
| 1630 | type: mteb/mind_small |
| 1631 | name: MTEB MindSmallReranking |
| 1632 | config: default |
| 1633 | split: test |
| 1634 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 1635 | metrics: |
| 1636 | - type: map |
| 1637 | value: 32.06102792457849 |
| 1638 | - type: mrr |
| 1639 | value: 33.30709199672238 |
| 1640 | - task: |
| 1641 | type: Retrieval |
| 1642 | dataset: |
| 1643 | type: nfcorpus |
| 1644 | name: MTEB NFCorpus |
| 1645 | config: default |
| 1646 | split: test |
| 1647 | revision: None |
| 1648 | metrics: |
| 1649 | - type: map_at_1 |
| 1650 | value: 6.776999999999999 |
| 1651 | - type: map_at_10 |
| 1652 | value: 14.924000000000001 |
| 1653 | - type: map_at_100 |
| 1654 | value: 18.955 |
| 1655 | - type: map_at_1000 |
| 1656 | value: 20.538999999999998 |
| 1657 | - type: map_at_3 |
| 1658 | value: 10.982 |
| 1659 | - type: map_at_5 |
| 1660 | value: 12.679000000000002 |
| 1661 | - type: mrr_at_1 |
| 1662 | value: 47.988 |
| 1663 | - type: mrr_at_10 |
| 1664 | value: 57.232000000000006 |
| 1665 | - type: mrr_at_100 |
| 1666 | value: 57.818999999999996 |
| 1667 | - type: mrr_at_1000 |
| 1668 | value: 57.847 |
| 1669 | - type: mrr_at_3 |
| 1670 | value: 54.901999999999994 |
| 1671 | - type: mrr_at_5 |
| 1672 | value: 56.481 |
| 1673 | - type: ndcg_at_1 |
| 1674 | value: 46.594 |
| 1675 | - type: ndcg_at_10 |
| 1676 | value: 38.129000000000005 |
| 1677 | - type: ndcg_at_100 |
| 1678 | value: 35.54 |
| 1679 | - type: ndcg_at_1000 |
| 1680 | value: 44.172 |
| 1681 | - type: ndcg_at_3 |
| 1682 | value: 43.025999999999996 |
| 1683 | - type: ndcg_at_5 |
| 1684 | value: 41.052 |
| 1685 | - type: precision_at_1 |
| 1686 | value: 47.988 |
| 1687 | - type: precision_at_10 |
| 1688 | value: 28.111000000000004 |
| 1689 | - type: precision_at_100 |
| 1690 | value: 8.929 |
| 1691 | - type: precision_at_1000 |
| 1692 | value: 2.185 |
| 1693 | - type: precision_at_3 |
| 1694 | value: 40.144000000000005 |
| 1695 | - type: precision_at_5 |
| 1696 | value: 35.232 |
| 1697 | - type: recall_at_1 |
| 1698 | value: 6.776999999999999 |
| 1699 | - type: recall_at_10 |
| 1700 | value: 19.289 |
| 1701 | - type: recall_at_100 |
| 1702 | value: 36.359 |
| 1703 | - type: recall_at_1000 |
| 1704 | value: 67.54 |
| 1705 | - type: recall_at_3 |
| 1706 | value: 11.869 |
| 1707 | - type: recall_at_5 |
| 1708 | value: 14.999 |
| 1709 | - task: |
| 1710 | type: Retrieval |
| 1711 | dataset: |
| 1712 | type: nq |
| 1713 | name: MTEB NQ |
| 1714 | config: default |
| 1715 | split: test |
| 1716 | revision: None |
| 1717 | metrics: |
| 1718 | - type: map_at_1 |
| 1719 | value: 31.108000000000004 |
| 1720 | - type: map_at_10 |
| 1721 | value: 47.126000000000005 |
| 1722 | - type: map_at_100 |
| 1723 | value: 48.171 |
| 1724 | - type: map_at_1000 |
| 1725 | value: 48.199 |
| 1726 | - type: map_at_3 |
| 1727 | value: 42.734 |
| 1728 | - type: map_at_5 |
| 1729 | value: 45.362 |
| 1730 | - type: mrr_at_1 |
| 1731 | value: 34.936 |
| 1732 | - type: mrr_at_10 |
| 1733 | value: 49.571 |
| 1734 | - type: mrr_at_100 |
| 1735 | value: 50.345 |
| 1736 | - type: mrr_at_1000 |
| 1737 | value: 50.363 |
| 1738 | - type: mrr_at_3 |
| 1739 | value: 45.959 |
| 1740 | - type: mrr_at_5 |
| 1741 | value: 48.165 |
| 1742 | - type: ndcg_at_1 |
| 1743 | value: 34.936 |
| 1744 | - type: ndcg_at_10 |
| 1745 | value: 55.028999999999996 |
| 1746 | - type: ndcg_at_100 |
| 1747 | value: 59.244 |
| 1748 | - type: ndcg_at_1000 |
| 1749 | value: 59.861 |
| 1750 | - type: ndcg_at_3 |
| 1751 | value: 46.872 |
| 1752 | - type: ndcg_at_5 |
| 1753 | value: 51.217999999999996 |
| 1754 | - type: precision_at_1 |
| 1755 | value: 34.936 |
| 1756 | - type: precision_at_10 |
| 1757 | value: 9.099 |
| 1758 | - type: precision_at_100 |
| 1759 | value: 1.145 |
| 1760 | - type: precision_at_1000 |
| 1761 | value: 0.12 |
| 1762 | - type: precision_at_3 |
| 1763 | value: 21.456 |
| 1764 | - type: precision_at_5 |
| 1765 | value: 15.411 |
| 1766 | - type: recall_at_1 |
| 1767 | value: 31.108000000000004 |
| 1768 | - type: recall_at_10 |
| 1769 | value: 76.53999999999999 |
| 1770 | - type: recall_at_100 |
| 1771 | value: 94.39 |
| 1772 | - type: recall_at_1000 |
| 1773 | value: 98.947 |
| 1774 | - type: recall_at_3 |
| 1775 | value: 55.572 |
| 1776 | - type: recall_at_5 |
| 1777 | value: 65.525 |
| 1778 | - task: |
| 1779 | type: Retrieval |
| 1780 | dataset: |
| 1781 | type: quora |
| 1782 | name: MTEB QuoraRetrieval |
| 1783 | config: default |
| 1784 | split: test |
| 1785 | revision: None |
| 1786 | metrics: |
| 1787 | - type: map_at_1 |
| 1788 | value: 71.56400000000001 |
| 1789 | - type: map_at_10 |
| 1790 | value: 85.482 |
| 1791 | - type: map_at_100 |
| 1792 | value: 86.114 |
| 1793 | - type: map_at_1000 |
| 1794 | value: 86.13 |
| 1795 | - type: map_at_3 |
| 1796 | value: 82.607 |
| 1797 | - type: map_at_5 |
| 1798 | value: 84.405 |
| 1799 | - type: mrr_at_1 |
| 1800 | value: 82.42 |
| 1801 | - type: mrr_at_10 |
| 1802 | value: 88.304 |
| 1803 | - type: mrr_at_100 |
| 1804 | value: 88.399 |
| 1805 | - type: mrr_at_1000 |
| 1806 | value: 88.399 |
| 1807 | - type: mrr_at_3 |
| 1808 | value: 87.37 |
| 1809 | - type: mrr_at_5 |
| 1810 | value: 88.024 |
| 1811 | - type: ndcg_at_1 |
| 1812 | value: 82.45 |
| 1813 | - type: ndcg_at_10 |
| 1814 | value: 89.06500000000001 |
| 1815 | - type: ndcg_at_100 |
| 1816 | value: 90.232 |
| 1817 | - type: ndcg_at_1000 |
| 1818 | value: 90.305 |
| 1819 | - type: ndcg_at_3 |
| 1820 | value: 86.375 |
| 1821 | - type: ndcg_at_5 |
| 1822 | value: 87.85300000000001 |
| 1823 | - type: precision_at_1 |
| 1824 | value: 82.45 |
| 1825 | - type: precision_at_10 |
| 1826 | value: 13.486999999999998 |
| 1827 | - type: precision_at_100 |
| 1828 | value: 1.534 |
| 1829 | - type: precision_at_1000 |
| 1830 | value: 0.157 |
| 1831 | - type: precision_at_3 |
| 1832 | value: 37.813 |
| 1833 | - type: precision_at_5 |
| 1834 | value: 24.773999999999997 |
| 1835 | - type: recall_at_1 |
| 1836 | value: 71.56400000000001 |
| 1837 | - type: recall_at_10 |
| 1838 | value: 95.812 |
| 1839 | - type: recall_at_100 |
| 1840 | value: 99.7 |
| 1841 | - type: recall_at_1000 |
| 1842 | value: 99.979 |
| 1843 | - type: recall_at_3 |
| 1844 | value: 87.966 |
| 1845 | - type: recall_at_5 |
| 1846 | value: 92.268 |
| 1847 | - task: |
| 1848 | type: Clustering |
| 1849 | dataset: |
| 1850 | type: mteb/reddit-clustering |
| 1851 | name: MTEB RedditClustering |
| 1852 | config: default |
| 1853 | split: test |
| 1854 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 1855 | metrics: |
| 1856 | - type: v_measure |
| 1857 | value: 57.241876648614145 |
| 1858 | - task: |
| 1859 | type: Clustering |
| 1860 | dataset: |
| 1861 | type: mteb/reddit-clustering-p2p |
| 1862 | name: MTEB RedditClusteringP2P |
| 1863 | config: default |
| 1864 | split: test |
| 1865 | revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| 1866 | metrics: |
| 1867 | - type: v_measure |
| 1868 | value: 64.66212576446223 |
| 1869 | - task: |
| 1870 | type: Retrieval |
| 1871 | dataset: |
| 1872 | type: scidocs |
| 1873 | name: MTEB SCIDOCS |
| 1874 | config: default |
| 1875 | split: test |
| 1876 | revision: None |
| 1877 | metrics: |
| 1878 | - type: map_at_1 |
| 1879 | value: 5.308 |
| 1880 | - type: map_at_10 |
| 1881 | value: 13.803 |
| 1882 | - type: map_at_100 |
| 1883 | value: 16.176 |
| 1884 | - type: map_at_1000 |
| 1885 | value: 16.561 |
| 1886 | - type: map_at_3 |
| 1887 | value: 9.761000000000001 |
| 1888 | - type: map_at_5 |
| 1889 | value: 11.802 |
| 1890 | - type: mrr_at_1 |
| 1891 | value: 26.200000000000003 |
| 1892 | - type: mrr_at_10 |
| 1893 | value: 37.621 |
| 1894 | - type: mrr_at_100 |
| 1895 | value: 38.767 |
| 1896 | - type: mrr_at_1000 |
| 1897 | value: 38.815 |
| 1898 | - type: mrr_at_3 |
| 1899 | value: 34.117 |
| 1900 | - type: mrr_at_5 |
| 1901 | value: 36.107 |
| 1902 | - type: ndcg_at_1 |
| 1903 | value: 26.200000000000003 |
| 1904 | - type: ndcg_at_10 |
| 1905 | value: 22.64 |
| 1906 | - type: ndcg_at_100 |
| 1907 | value: 31.567 |
| 1908 | - type: ndcg_at_1000 |
| 1909 | value: 37.623 |
| 1910 | - type: ndcg_at_3 |
| 1911 | value: 21.435000000000002 |
| 1912 | - type: ndcg_at_5 |
| 1913 | value: 18.87 |
| 1914 | - type: precision_at_1 |
| 1915 | value: 26.200000000000003 |
| 1916 | - type: precision_at_10 |
| 1917 | value: 11.74 |
| 1918 | - type: precision_at_100 |
| 1919 | value: 2.465 |
| 1920 | - type: precision_at_1000 |
| 1921 | value: 0.391 |
| 1922 | - type: precision_at_3 |
| 1923 | value: 20.033 |
| 1924 | - type: precision_at_5 |
| 1925 | value: 16.64 |
| 1926 | - type: recall_at_1 |
| 1927 | value: 5.308 |
| 1928 | - type: recall_at_10 |
| 1929 | value: 23.794999999999998 |
| 1930 | - type: recall_at_100 |
| 1931 | value: 50.015 |
| 1932 | - type: recall_at_1000 |
| 1933 | value: 79.283 |
| 1934 | - type: recall_at_3 |
| 1935 | value: 12.178 |
| 1936 | - type: recall_at_5 |
| 1937 | value: 16.882 |
| 1938 | - task: |
| 1939 | type: STS |
| 1940 | dataset: |
| 1941 | type: mteb/sickr-sts |
| 1942 | name: MTEB SICK-R |
| 1943 | config: default |
| 1944 | split: test |
| 1945 | revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| 1946 | metrics: |
| 1947 | - type: cos_sim_pearson |
| 1948 | value: 84.93231134675553 |
| 1949 | - type: cos_sim_spearman |
| 1950 | value: 81.68319292603205 |
| 1951 | - type: euclidean_pearson |
| 1952 | value: 81.8396814380367 |
| 1953 | - type: euclidean_spearman |
| 1954 | value: 81.24641903349945 |
| 1955 | - type: manhattan_pearson |
| 1956 | value: 81.84698799204274 |
| 1957 | - type: manhattan_spearman |
| 1958 | value: 81.24269997904105 |
| 1959 | - task: |
| 1960 | type: STS |
| 1961 | dataset: |
| 1962 | type: mteb/sts12-sts |
| 1963 | name: MTEB STS12 |
| 1964 | config: default |
| 1965 | split: test |
| 1966 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 1967 | metrics: |
| 1968 | - type: cos_sim_pearson |
| 1969 | value: 86.73241671587446 |
| 1970 | - type: cos_sim_spearman |
| 1971 | value: 79.05091082971826 |
| 1972 | - type: euclidean_pearson |
| 1973 | value: 83.91146869578044 |
| 1974 | - type: euclidean_spearman |
| 1975 | value: 79.87978465370936 |
| 1976 | - type: manhattan_pearson |
| 1977 | value: 83.90888338917678 |
| 1978 | - type: manhattan_spearman |
| 1979 | value: 79.87482848584241 |
| 1980 | - task: |
| 1981 | type: STS |
| 1982 | dataset: |
| 1983 | type: mteb/sts13-sts |
| 1984 | name: MTEB STS13 |
| 1985 | config: default |
| 1986 | split: test |
| 1987 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 1988 | metrics: |
| 1989 | - type: cos_sim_pearson |
| 1990 | value: 85.14970731146177 |
| 1991 | - type: cos_sim_spearman |
| 1992 | value: 86.37363490084627 |
| 1993 | - type: euclidean_pearson |
| 1994 | value: 83.02154218530433 |
| 1995 | - type: euclidean_spearman |
| 1996 | value: 83.80258761957367 |
| 1997 | - type: manhattan_pearson |
| 1998 | value: 83.01664495119347 |
| 1999 | - type: manhattan_spearman |
| 2000 | value: 83.77567458007952 |
| 2001 | - task: |
| 2002 | type: STS |
| 2003 | dataset: |
| 2004 | type: mteb/sts14-sts |
| 2005 | name: MTEB STS14 |
| 2006 | config: default |
| 2007 | split: test |
| 2008 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 2009 | metrics: |
| 2010 | - type: cos_sim_pearson |
| 2011 | value: 83.40474139886784 |
| 2012 | - type: cos_sim_spearman |
| 2013 | value: 82.77768789165984 |
| 2014 | - type: euclidean_pearson |
| 2015 | value: 80.7065877443695 |
| 2016 | - type: euclidean_spearman |
| 2017 | value: 81.375940662505 |
| 2018 | - type: manhattan_pearson |
| 2019 | value: 80.6507552270278 |
| 2020 | - type: manhattan_spearman |
| 2021 | value: 81.32782179098741 |
| 2022 | - task: |
| 2023 | type: STS |
| 2024 | dataset: |
| 2025 | type: mteb/sts15-sts |
| 2026 | name: MTEB STS15 |
| 2027 | config: default |
| 2028 | split: test |
| 2029 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 2030 | metrics: |
| 2031 | - type: cos_sim_pearson |
| 2032 | value: 87.08585968722274 |
| 2033 | - type: cos_sim_spearman |
| 2034 | value: 88.03110031451399 |
| 2035 | - type: euclidean_pearson |
| 2036 | value: 85.74012019602384 |
| 2037 | - type: euclidean_spearman |
| 2038 | value: 86.13592849438209 |
| 2039 | - type: manhattan_pearson |
| 2040 | value: 85.74404842369206 |
| 2041 | - type: manhattan_spearman |
| 2042 | value: 86.14492318960154 |
| 2043 | - task: |
| 2044 | type: STS |
| 2045 | dataset: |
| 2046 | type: mteb/sts16-sts |
| 2047 | name: MTEB STS16 |
| 2048 | config: default |
| 2049 | split: test |
| 2050 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 2051 | metrics: |
| 2052 | - type: cos_sim_pearson |
| 2053 | value: 84.95069052788875 |
| 2054 | - type: cos_sim_spearman |
| 2055 | value: 86.4867991595147 |
| 2056 | - type: euclidean_pearson |
| 2057 | value: 84.31013325754635 |
| 2058 | - type: euclidean_spearman |
| 2059 | value: 85.01529258006482 |
| 2060 | - type: manhattan_pearson |
| 2061 | value: 84.26995570085374 |
| 2062 | - type: manhattan_spearman |
| 2063 | value: 84.96982104986162 |
| 2064 | - task: |
| 2065 | type: STS |
| 2066 | dataset: |
| 2067 | type: mteb/sts17-crosslingual-sts |
| 2068 | name: MTEB STS17 (en-en) |
| 2069 | config: en-en |
| 2070 | split: test |
| 2071 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2072 | metrics: |
| 2073 | - type: cos_sim_pearson |
| 2074 | value: 87.54617647971897 |
| 2075 | - type: cos_sim_spearman |
| 2076 | value: 87.49834181751034 |
| 2077 | - type: euclidean_pearson |
| 2078 | value: 86.01015322577122 |
| 2079 | - type: euclidean_spearman |
| 2080 | value: 84.63362652063199 |
| 2081 | - type: manhattan_pearson |
| 2082 | value: 86.13807574475706 |
| 2083 | - type: manhattan_spearman |
| 2084 | value: 84.7772370721132 |
| 2085 | - task: |
| 2086 | type: STS |
| 2087 | dataset: |
| 2088 | type: mteb/sts22-crosslingual-sts |
| 2089 | name: MTEB STS22 (en) |
| 2090 | config: en |
| 2091 | split: test |
| 2092 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 2093 | metrics: |
| 2094 | - type: cos_sim_pearson |
| 2095 | value: 67.20047755786615 |
| 2096 | - type: cos_sim_spearman |
| 2097 | value: 67.05324077987636 |
| 2098 | - type: euclidean_pearson |
| 2099 | value: 66.91930642976601 |
| 2100 | - type: euclidean_spearman |
| 2101 | value: 65.21491856099105 |
| 2102 | - type: manhattan_pearson |
| 2103 | value: 66.78756851976624 |
| 2104 | - type: manhattan_spearman |
| 2105 | value: 65.12356257740728 |
| 2106 | - task: |
| 2107 | type: STS |
| 2108 | dataset: |
| 2109 | type: mteb/stsbenchmark-sts |
| 2110 | name: MTEB STSBenchmark |
| 2111 | config: default |
| 2112 | split: test |
| 2113 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 2114 | metrics: |
| 2115 | - type: cos_sim_pearson |
| 2116 | value: 86.19852871539686 |
| 2117 | - type: cos_sim_spearman |
| 2118 | value: 87.5161895296395 |
| 2119 | - type: euclidean_pearson |
| 2120 | value: 84.59848645207485 |
| 2121 | - type: euclidean_spearman |
| 2122 | value: 85.26427328757919 |
| 2123 | - type: manhattan_pearson |
| 2124 | value: 84.59747366996524 |
| 2125 | - type: manhattan_spearman |
| 2126 | value: 85.24045855146915 |
| 2127 | - task: |
| 2128 | type: Reranking |
| 2129 | dataset: |
| 2130 | type: mteb/scidocs-reranking |
| 2131 | name: MTEB SciDocsRR |
| 2132 | config: default |
| 2133 | split: test |
| 2134 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 2135 | metrics: |
| 2136 | - type: map |
| 2137 | value: 87.63320317811032 |
| 2138 | - type: mrr |
| 2139 | value: 96.26242947321379 |
| 2140 | - task: |
| 2141 | type: Retrieval |
| 2142 | dataset: |
| 2143 | type: scifact |
| 2144 | name: MTEB SciFact |
| 2145 | config: default |
| 2146 | split: test |
| 2147 | revision: None |
| 2148 | metrics: |
| 2149 | - type: map_at_1 |
| 2150 | value: 60.928000000000004 |
| 2151 | - type: map_at_10 |
| 2152 | value: 70.112 |
| 2153 | - type: map_at_100 |
| 2154 | value: 70.59299999999999 |
| 2155 | - type: map_at_1000 |
| 2156 | value: 70.623 |
| 2157 | - type: map_at_3 |
| 2158 | value: 66.846 |
| 2159 | - type: map_at_5 |
| 2160 | value: 68.447 |
| 2161 | - type: mrr_at_1 |
| 2162 | value: 64.0 |
| 2163 | - type: mrr_at_10 |
| 2164 | value: 71.212 |
| 2165 | - type: mrr_at_100 |
| 2166 | value: 71.616 |
| 2167 | - type: mrr_at_1000 |
| 2168 | value: 71.64500000000001 |
| 2169 | - type: mrr_at_3 |
| 2170 | value: 68.77799999999999 |
| 2171 | - type: mrr_at_5 |
| 2172 | value: 70.094 |
| 2173 | - type: ndcg_at_1 |
| 2174 | value: 64.0 |
| 2175 | - type: ndcg_at_10 |
| 2176 | value: 74.607 |
| 2177 | - type: ndcg_at_100 |
| 2178 | value: 76.416 |
| 2179 | - type: ndcg_at_1000 |
| 2180 | value: 77.102 |
| 2181 | - type: ndcg_at_3 |
| 2182 | value: 69.126 |
| 2183 | - type: ndcg_at_5 |
| 2184 | value: 71.41300000000001 |
| 2185 | - type: precision_at_1 |
| 2186 | value: 64.0 |
| 2187 | - type: precision_at_10 |
| 2188 | value: 9.933 |
| 2189 | - type: precision_at_100 |
| 2190 | value: 1.077 |
| 2191 | - type: precision_at_1000 |
| 2192 | value: 0.11299999999999999 |
| 2193 | - type: precision_at_3 |
| 2194 | value: 26.556 |
| 2195 | - type: precision_at_5 |
| 2196 | value: 17.467 |
| 2197 | - type: recall_at_1 |
| 2198 | value: 60.928000000000004 |
| 2199 | - type: recall_at_10 |
| 2200 | value: 87.322 |
| 2201 | - type: recall_at_100 |
| 2202 | value: 94.833 |
| 2203 | - type: recall_at_1000 |
| 2204 | value: 100.0 |
| 2205 | - type: recall_at_3 |
| 2206 | value: 72.628 |
| 2207 | - type: recall_at_5 |
| 2208 | value: 78.428 |
| 2209 | - task: |
| 2210 | type: PairClassification |
| 2211 | dataset: |
| 2212 | type: mteb/sprintduplicatequestions-pairclassification |
| 2213 | name: MTEB SprintDuplicateQuestions |
| 2214 | config: default |
| 2215 | split: test |
| 2216 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 2217 | metrics: |
| 2218 | - type: cos_sim_accuracy |
| 2219 | value: 99.86237623762376 |
| 2220 | - type: cos_sim_ap |
| 2221 | value: 96.72586477206649 |
| 2222 | - type: cos_sim_f1 |
| 2223 | value: 93.01858362631845 |
| 2224 | - type: cos_sim_precision |
| 2225 | value: 93.4409687184662 |
| 2226 | - type: cos_sim_recall |
| 2227 | value: 92.60000000000001 |
| 2228 | - type: dot_accuracy |
| 2229 | value: 99.78019801980199 |
| 2230 | - type: dot_ap |
| 2231 | value: 93.72748205246228 |
| 2232 | - type: dot_f1 |
| 2233 | value: 89.04109589041096 |
| 2234 | - type: dot_precision |
| 2235 | value: 87.16475095785441 |
| 2236 | - type: dot_recall |
| 2237 | value: 91.0 |
| 2238 | - type: euclidean_accuracy |
| 2239 | value: 99.85445544554456 |
| 2240 | - type: euclidean_ap |
| 2241 | value: 96.6661459876145 |
| 2242 | - type: euclidean_f1 |
| 2243 | value: 92.58337481333997 |
| 2244 | - type: euclidean_precision |
| 2245 | value: 92.17046580773042 |
| 2246 | - type: euclidean_recall |
| 2247 | value: 93.0 |
| 2248 | - type: manhattan_accuracy |
| 2249 | value: 99.85445544554456 |
| 2250 | - type: manhattan_ap |
| 2251 | value: 96.6883549244056 |
| 2252 | - type: manhattan_f1 |
| 2253 | value: 92.57598405580468 |
| 2254 | - type: manhattan_precision |
| 2255 | value: 92.25422045680239 |
| 2256 | - type: manhattan_recall |
| 2257 | value: 92.9 |
| 2258 | - type: max_accuracy |
| 2259 | value: 99.86237623762376 |
| 2260 | - type: max_ap |
| 2261 | value: 96.72586477206649 |
| 2262 | - type: max_f1 |
| 2263 | value: 93.01858362631845 |
| 2264 | - task: |
| 2265 | type: Clustering |
| 2266 | dataset: |
| 2267 | type: mteb/stackexchange-clustering |
| 2268 | name: MTEB StackExchangeClustering |
| 2269 | config: default |
| 2270 | split: test |
| 2271 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 2272 | metrics: |
| 2273 | - type: v_measure |
| 2274 | value: 66.39930057069995 |
| 2275 | - task: |
| 2276 | type: Clustering |
| 2277 | dataset: |
| 2278 | type: mteb/stackexchange-clustering-p2p |
| 2279 | name: MTEB StackExchangeClusteringP2P |
| 2280 | config: default |
| 2281 | split: test |
| 2282 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 2283 | metrics: |
| 2284 | - type: v_measure |
| 2285 | value: 34.96398659903402 |
| 2286 | - task: |
| 2287 | type: Reranking |
| 2288 | dataset: |
| 2289 | type: mteb/stackoverflowdupquestions-reranking |
| 2290 | name: MTEB StackOverflowDupQuestions |
| 2291 | config: default |
| 2292 | split: test |
| 2293 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 2294 | metrics: |
| 2295 | - type: map |
| 2296 | value: 55.946944700355395 |
| 2297 | - type: mrr |
| 2298 | value: 56.97151398438164 |
| 2299 | - task: |
| 2300 | type: Summarization |
| 2301 | dataset: |
| 2302 | type: mteb/summeval |
| 2303 | name: MTEB SummEval |
| 2304 | config: default |
| 2305 | split: test |
| 2306 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 2307 | metrics: |
| 2308 | - type: cos_sim_pearson |
| 2309 | value: 31.541657650692905 |
| 2310 | - type: cos_sim_spearman |
| 2311 | value: 31.605804192286303 |
| 2312 | - type: dot_pearson |
| 2313 | value: 28.26905996736398 |
| 2314 | - type: dot_spearman |
| 2315 | value: 27.864801765851187 |
| 2316 | - task: |
| 2317 | type: Retrieval |
| 2318 | dataset: |
| 2319 | type: trec-covid |
| 2320 | name: MTEB TRECCOVID |
| 2321 | config: default |
| 2322 | split: test |
| 2323 | revision: None |
| 2324 | metrics: |
| 2325 | - type: map_at_1 |
| 2326 | value: 0.22599999999999998 |
| 2327 | - type: map_at_10 |
| 2328 | value: 1.8870000000000002 |
| 2329 | - type: map_at_100 |
| 2330 | value: 9.78 |
| 2331 | - type: map_at_1000 |
| 2332 | value: 22.514 |
| 2333 | - type: map_at_3 |
| 2334 | value: 0.6669999999999999 |
| 2335 | - type: map_at_5 |
| 2336 | value: 1.077 |
| 2337 | - type: mrr_at_1 |
| 2338 | value: 82.0 |
| 2339 | - type: mrr_at_10 |
| 2340 | value: 89.86699999999999 |
| 2341 | - type: mrr_at_100 |
| 2342 | value: 89.86699999999999 |
| 2343 | - type: mrr_at_1000 |
| 2344 | value: 89.86699999999999 |
| 2345 | - type: mrr_at_3 |
| 2346 | value: 89.667 |
| 2347 | - type: mrr_at_5 |
| 2348 | value: 89.667 |
| 2349 | - type: ndcg_at_1 |
| 2350 | value: 79.0 |
| 2351 | - type: ndcg_at_10 |
| 2352 | value: 74.818 |
| 2353 | - type: ndcg_at_100 |
| 2354 | value: 53.715999999999994 |
| 2355 | - type: ndcg_at_1000 |
| 2356 | value: 47.082 |
| 2357 | - type: ndcg_at_3 |
| 2358 | value: 82.134 |
| 2359 | - type: ndcg_at_5 |
| 2360 | value: 79.81899999999999 |
| 2361 | - type: precision_at_1 |
| 2362 | value: 82.0 |
| 2363 | - type: precision_at_10 |
| 2364 | value: 78.0 |
| 2365 | - type: precision_at_100 |
| 2366 | value: 54.48 |
| 2367 | - type: precision_at_1000 |
| 2368 | value: 20.518 |
| 2369 | - type: precision_at_3 |
| 2370 | value: 87.333 |
| 2371 | - type: precision_at_5 |
| 2372 | value: 85.2 |
| 2373 | - type: recall_at_1 |
| 2374 | value: 0.22599999999999998 |
| 2375 | - type: recall_at_10 |
| 2376 | value: 2.072 |
| 2377 | - type: recall_at_100 |
| 2378 | value: 13.013 |
| 2379 | - type: recall_at_1000 |
| 2380 | value: 43.462 |
| 2381 | - type: recall_at_3 |
| 2382 | value: 0.695 |
| 2383 | - type: recall_at_5 |
| 2384 | value: 1.139 |
| 2385 | - task: |
| 2386 | type: Retrieval |
| 2387 | dataset: |
| 2388 | type: webis-touche2020 |
| 2389 | name: MTEB Touche2020 |
| 2390 | config: default |
| 2391 | split: test |
| 2392 | revision: None |
| 2393 | metrics: |
| 2394 | - type: map_at_1 |
| 2395 | value: 2.328 |
| 2396 | - type: map_at_10 |
| 2397 | value: 9.795 |
| 2398 | - type: map_at_100 |
| 2399 | value: 15.801000000000002 |
| 2400 | - type: map_at_1000 |
| 2401 | value: 17.23 |
| 2402 | - type: map_at_3 |
| 2403 | value: 4.734 |
| 2404 | - type: map_at_5 |
| 2405 | value: 6.644 |
| 2406 | - type: mrr_at_1 |
| 2407 | value: 30.612000000000002 |
| 2408 | - type: mrr_at_10 |
| 2409 | value: 46.902 |
| 2410 | - type: mrr_at_100 |
| 2411 | value: 47.495 |
| 2412 | - type: mrr_at_1000 |
| 2413 | value: 47.495 |
| 2414 | - type: mrr_at_3 |
| 2415 | value: 41.156 |
| 2416 | - type: mrr_at_5 |
| 2417 | value: 44.218 |
| 2418 | - type: ndcg_at_1 |
| 2419 | value: 28.571 |
| 2420 | - type: ndcg_at_10 |
| 2421 | value: 24.806 |
| 2422 | - type: ndcg_at_100 |
| 2423 | value: 36.419000000000004 |
| 2424 | - type: ndcg_at_1000 |
| 2425 | value: 47.272999999999996 |
| 2426 | - type: ndcg_at_3 |
| 2427 | value: 25.666 |
| 2428 | - type: ndcg_at_5 |
| 2429 | value: 25.448999999999998 |
| 2430 | - type: precision_at_1 |
| 2431 | value: 30.612000000000002 |
| 2432 | - type: precision_at_10 |
| 2433 | value: 23.061 |
| 2434 | - type: precision_at_100 |
| 2435 | value: 7.714 |
| 2436 | - type: precision_at_1000 |
| 2437 | value: 1.484 |
| 2438 | - type: precision_at_3 |
| 2439 | value: 26.531 |
| 2440 | - type: precision_at_5 |
| 2441 | value: 26.122 |
| 2442 | - type: recall_at_1 |
| 2443 | value: 2.328 |
| 2444 | - type: recall_at_10 |
| 2445 | value: 16.524 |
| 2446 | - type: recall_at_100 |
| 2447 | value: 47.179 |
| 2448 | - type: recall_at_1000 |
| 2449 | value: 81.22200000000001 |
| 2450 | - type: recall_at_3 |
| 2451 | value: 5.745 |
| 2452 | - type: recall_at_5 |
| 2453 | value: 9.339 |
| 2454 | - task: |
| 2455 | type: Classification |
| 2456 | dataset: |
| 2457 | type: mteb/toxic_conversations_50k |
| 2458 | name: MTEB ToxicConversationsClassification |
| 2459 | config: default |
| 2460 | split: test |
| 2461 | revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| 2462 | metrics: |
| 2463 | - type: accuracy |
| 2464 | value: 70.9142 |
| 2465 | - type: ap |
| 2466 | value: 14.335574772555415 |
| 2467 | - type: f1 |
| 2468 | value: 54.62839595194111 |
| 2469 | - task: |
| 2470 | type: Classification |
| 2471 | dataset: |
| 2472 | type: mteb/tweet_sentiment_extraction |
| 2473 | name: MTEB TweetSentimentExtractionClassification |
| 2474 | config: default |
| 2475 | split: test |
| 2476 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 2477 | metrics: |
| 2478 | - type: accuracy |
| 2479 | value: 59.94340690435768 |
| 2480 | - type: f1 |
| 2481 | value: 60.286487936731916 |
| 2482 | - task: |
| 2483 | type: Clustering |
| 2484 | dataset: |
| 2485 | type: mteb/twentynewsgroups-clustering |
| 2486 | name: MTEB TwentyNewsgroupsClustering |
| 2487 | config: default |
| 2488 | split: test |
| 2489 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 2490 | metrics: |
| 2491 | - type: v_measure |
| 2492 | value: 51.26597708987974 |
| 2493 | - task: |
| 2494 | type: PairClassification |
| 2495 | dataset: |
| 2496 | type: mteb/twittersemeval2015-pairclassification |
| 2497 | name: MTEB TwitterSemEval2015 |
| 2498 | config: default |
| 2499 | split: test |
| 2500 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 2501 | metrics: |
| 2502 | - type: cos_sim_accuracy |
| 2503 | value: 87.48882398521786 |
| 2504 | - type: cos_sim_ap |
| 2505 | value: 79.04326607602204 |
| 2506 | - type: cos_sim_f1 |
| 2507 | value: 71.64566826860633 |
| 2508 | - type: cos_sim_precision |
| 2509 | value: 70.55512918905092 |
| 2510 | - type: cos_sim_recall |
| 2511 | value: 72.77044854881267 |
| 2512 | - type: dot_accuracy |
| 2513 | value: 84.19264469213805 |
| 2514 | - type: dot_ap |
| 2515 | value: 67.96360043562528 |
| 2516 | - type: dot_f1 |
| 2517 | value: 64.06418393006827 |
| 2518 | - type: dot_precision |
| 2519 | value: 58.64941898706424 |
| 2520 | - type: dot_recall |
| 2521 | value: 70.58047493403694 |
| 2522 | - type: euclidean_accuracy |
| 2523 | value: 87.45902127913214 |
| 2524 | - type: euclidean_ap |
| 2525 | value: 78.9742237648272 |
| 2526 | - type: euclidean_f1 |
| 2527 | value: 71.5553235908142 |
| 2528 | - type: euclidean_precision |
| 2529 | value: 70.77955601445535 |
| 2530 | - type: euclidean_recall |
| 2531 | value: 72.34828496042216 |
| 2532 | - type: manhattan_accuracy |
| 2533 | value: 87.41729749061214 |
| 2534 | - type: manhattan_ap |
| 2535 | value: 78.90073137580596 |
| 2536 | - type: manhattan_f1 |
| 2537 | value: 71.3942611553533 |
| 2538 | - type: manhattan_precision |
| 2539 | value: 68.52705653967483 |
| 2540 | - type: manhattan_recall |
| 2541 | value: 74.51187335092348 |
| 2542 | - type: max_accuracy |
| 2543 | value: 87.48882398521786 |
| 2544 | - type: max_ap |
| 2545 | value: 79.04326607602204 |
| 2546 | - type: max_f1 |
| 2547 | value: 71.64566826860633 |
| 2548 | - task: |
| 2549 | type: PairClassification |
| 2550 | dataset: |
| 2551 | type: mteb/twitterurlcorpus-pairclassification |
| 2552 | name: MTEB TwitterURLCorpus |
| 2553 | config: default |
| 2554 | split: test |
| 2555 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 2556 | metrics: |
| 2557 | - type: cos_sim_accuracy |
| 2558 | value: 88.68125897465751 |
| 2559 | - type: cos_sim_ap |
| 2560 | value: 85.6003454431979 |
| 2561 | - type: cos_sim_f1 |
| 2562 | value: 77.6957163958641 |
| 2563 | - type: cos_sim_precision |
| 2564 | value: 73.0110366307807 |
| 2565 | - type: cos_sim_recall |
| 2566 | value: 83.02279026793964 |
| 2567 | - type: dot_accuracy |
| 2568 | value: 87.7672992587418 |
| 2569 | - type: dot_ap |
| 2570 | value: 82.4971301112899 |
| 2571 | - type: dot_f1 |
| 2572 | value: 75.90528233151184 |
| 2573 | - type: dot_precision |
| 2574 | value: 72.0370626469368 |
| 2575 | - type: dot_recall |
| 2576 | value: 80.21250384970742 |
| 2577 | - type: euclidean_accuracy |
| 2578 | value: 88.4503434625684 |
| 2579 | - type: euclidean_ap |
| 2580 | value: 84.91949884748384 |
| 2581 | - type: euclidean_f1 |
| 2582 | value: 76.92365018444684 |
| 2583 | - type: euclidean_precision |
| 2584 | value: 74.53245721712759 |
| 2585 | - type: euclidean_recall |
| 2586 | value: 79.47336002463813 |
| 2587 | - type: manhattan_accuracy |
| 2588 | value: 88.47556952691427 |
| 2589 | - type: manhattan_ap |
| 2590 | value: 84.8963689101517 |
| 2591 | - type: manhattan_f1 |
| 2592 | value: 76.85901249256395 |
| 2593 | - type: manhattan_precision |
| 2594 | value: 74.31693989071039 |
| 2595 | - type: manhattan_recall |
| 2596 | value: 79.58115183246073 |
| 2597 | - type: max_accuracy |
| 2598 | value: 88.68125897465751 |
| 2599 | - type: max_ap |
| 2600 | value: 85.6003454431979 |
| 2601 | - type: max_f1 |
| 2602 | value: 77.6957163958641 |
| 2603 | license: mit |
| 2604 | language: |
| 2605 | - en |
| 2606 | --- |
| 2607 | |
| 2608 | |
| 2609 | <h1 align="center">FlagEmbedding</h1> |
| 2610 | |
| 2611 | |
| 2612 | <h4 align="center"> |
| 2613 | <p> |
| 2614 | <a href=#model-list>Model List</a> | |
| 2615 | <a href=#frequently-asked-questions>FAQ</a> | |
| 2616 | <a href=#usage>Usage</a> | |
| 2617 | <a href="#evaluation">Evaluation</a> | |
| 2618 | <a href="#train">Train</a> | |
| 2619 | <a href="#contact">Contact</a> | |
| 2620 | <a href="#citation">Citation</a> | |
| 2621 | <a href="#license">License</a> |
| 2622 | <p> |
| 2623 | </h4> |
| 2624 | |
| 2625 | For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
| 2626 | |
| 2627 | If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). |
| 2628 | |
| 2629 | |
| 2630 | [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
| 2631 | |
| 2632 | FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: |
| 2633 | |
| 2634 | - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) |
| 2635 | - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) |
| 2636 | - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [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) |
| 2637 | - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
| 2638 | - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) |
| 2639 | |
| 2640 | ## News |
| 2641 | - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). |
| 2642 | It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. |
| 2643 | [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: |
| 2644 | - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: |
| 2645 | - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: |
| 2646 | - 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: |
| 2647 | - 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) |
| 2648 | - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released |
| 2649 | - 09/12/2023: New models: |
| 2650 | - **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. |
| 2651 | - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. |
| 2652 | |
| 2653 | |
| 2654 | <details> |
| 2655 | <summary>More</summary> |
| 2656 | <!-- ### More --> |
| 2657 | |
| 2658 | - 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. |
| 2659 | - 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). |
| 2660 | - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
| 2661 | - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: |
| 2662 | - 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. |
| 2663 | |
| 2664 | </details> |
| 2665 | |
| 2666 | |
| 2667 | ## Model List |
| 2668 | |
| 2669 | `bge` is short for `BAAI general embedding`. |
| 2670 | |
| 2671 | | Model | Language | | Description | query instruction for retrieval [1] | |
| 2672 | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| |
| 2673 | | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | |
| 2674 | | [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) | |
| 2675 | | [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] | | |
| 2676 | | [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] | | |
| 2677 | | [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: ` | |
| 2678 | | [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: ` | |
| 2679 | | [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: ` | |
| 2680 | | [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：` | |
| 2681 | | [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：` | |
| 2682 | | [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：` | |
| 2683 | | [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: ` | |
| 2684 | | [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: ` | |
| 2685 | | [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: ` | |
| 2686 | | [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：` | |
| 2687 | | [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：` | |
| 2688 | | [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：` | |
| 2689 | |
| 2690 | [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. |
| 2691 | |
| 2692 | [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. |
| 2693 | 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. |
| 2694 | |
| 2695 | All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. |
| 2696 | If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . |
| 2697 | |
| 2698 | |
| 2699 | ## Frequently asked questions |
| 2700 | |
| 2701 | <details> |
| 2702 | <summary>1. How to fine-tune bge embedding model?</summary> |
| 2703 | |
| 2704 | <!-- ### How to fine-tune bge embedding model? --> |
| 2705 | Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. |
| 2706 | Some suggestions: |
| 2707 | - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. |
| 2708 | - 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. |
| 2709 | - 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. |
| 2710 | |
| 2711 | |
| 2712 | </details> |
| 2713 | |
| 2714 | <details> |
| 2715 | <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> |
| 2716 | |
| 2717 | <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> |
| 2718 | **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** |
| 2719 | |
| 2720 | Since we finetune the models by contrastive learning with a temperature of 0.01, |
| 2721 | the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. |
| 2722 | So a similarity score greater than 0.5 does not indicate that the two sentences are similar. |
| 2723 | |
| 2724 | For downstream tasks, such as passage retrieval or semantic similarity, |
| 2725 | **what matters is the relative order of the scores, not the absolute value.** |
| 2726 | If you need to filter similar sentences based on a similarity threshold, |
| 2727 | please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). |
| 2728 | |
| 2729 | </details> |
| 2730 | |
| 2731 | <details> |
| 2732 | <summary>3. When does the query instruction need to be used</summary> |
| 2733 | |
| 2734 | <!-- ### When does the query instruction need to be used --> |
| 2735 | |
| 2736 | For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. |
| 2737 | No instruction only has a slight degradation in retrieval performance compared with using instruction. |
| 2738 | So you can generate embedding without instruction in all cases for convenience. |
| 2739 | |
| 2740 | For a retrieval task that uses short queries to find long related documents, |
| 2741 | it is recommended to add instructions for these short queries. |
| 2742 | **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** |
| 2743 | In all cases, the documents/passages do not need to add the instruction. |
| 2744 | |
| 2745 | </details> |
| 2746 | |
| 2747 | |
| 2748 | ## Usage |
| 2749 | |
| 2750 | ### Usage for Embedding Model |
| 2751 | |
| 2752 | Here are some examples for using `bge` models with |
| 2753 | [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
| 2754 | |
| 2755 | #### Using FlagEmbedding |
| 2756 | ``` |
| 2757 | pip install -U FlagEmbedding |
| 2758 | ``` |
| 2759 | 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. |
| 2760 | |
| 2761 | ```python |
| 2762 | from FlagEmbedding import FlagModel |
| 2763 | sentences_1 = ["样例数据-1", "样例数据-2"] |
| 2764 | sentences_2 = ["样例数据-3", "样例数据-4"] |
| 2765 | model = FlagModel('BAAI/bge-large-zh-v1.5', |
| 2766 | query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", |
| 2767 | use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
| 2768 | embeddings_1 = model.encode(sentences_1) |
| 2769 | embeddings_2 = model.encode(sentences_2) |
| 2770 | similarity = embeddings_1 @ embeddings_2.T |
| 2771 | print(similarity) |
| 2772 | |
| 2773 | # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query |
| 2774 | # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
| 2775 | queries = ['query_1', 'query_2'] |
| 2776 | passages = ["样例文档-1", "样例文档-2"] |
| 2777 | q_embeddings = model.encode_queries(queries) |
| 2778 | p_embeddings = model.encode(passages) |
| 2779 | scores = q_embeddings @ p_embeddings.T |
| 2780 | ``` |
| 2781 | For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
| 2782 | |
| 2783 | By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. |
| 2784 | You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. |
| 2785 | |
| 2786 | |
| 2787 | #### Using Sentence-Transformers |
| 2788 | |
| 2789 | You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): |
| 2790 | |
| 2791 | ``` |
| 2792 | pip install -U sentence-transformers |
| 2793 | ``` |
| 2794 | ```python |
| 2795 | from sentence_transformers import SentenceTransformer |
| 2796 | sentences_1 = ["样例数据-1", "样例数据-2"] |
| 2797 | sentences_2 = ["样例数据-3", "样例数据-4"] |
| 2798 | model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
| 2799 | embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) |
| 2800 | embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) |
| 2801 | similarity = embeddings_1 @ embeddings_2.T |
| 2802 | print(similarity) |
| 2803 | ``` |
| 2804 | For s2p(short query to long passage) retrieval task, |
| 2805 | each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
| 2806 | But the instruction is not needed for passages. |
| 2807 | ```python |
| 2808 | from sentence_transformers import SentenceTransformer |
| 2809 | queries = ['query_1', 'query_2'] |
| 2810 | passages = ["样例文档-1", "样例文档-2"] |
| 2811 | instruction = "为这个句子生成表示以用于检索相关文章：" |
| 2812 | |
| 2813 | model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
| 2814 | q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
| 2815 | p_embeddings = model.encode(passages, normalize_embeddings=True) |
| 2816 | scores = q_embeddings @ p_embeddings.T |
| 2817 | ``` |
| 2818 | |
| 2819 | #### Using Langchain |
| 2820 | |
| 2821 | You can use `bge` in langchain like this: |
| 2822 | ```python |
| 2823 | from langchain.embeddings import HuggingFaceBgeEmbeddings |
| 2824 | model_name = "BAAI/bge-large-en-v1.5" |
| 2825 | model_kwargs = {'device': 'cuda'} |
| 2826 | encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
| 2827 | model = HuggingFaceBgeEmbeddings( |
| 2828 | model_name=model_name, |
| 2829 | model_kwargs=model_kwargs, |
| 2830 | encode_kwargs=encode_kwargs, |
| 2831 | query_instruction="为这个句子生成表示以用于检索相关文章：" |
| 2832 | ) |
| 2833 | model.query_instruction = "为这个句子生成表示以用于检索相关文章：" |
| 2834 | ``` |
| 2835 | |
| 2836 | |
| 2837 | #### Using HuggingFace Transformers |
| 2838 | |
| 2839 | 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. |
| 2840 | |
| 2841 | ```python |
| 2842 | from transformers import AutoTokenizer, AutoModel |
| 2843 | import torch |
| 2844 | # Sentences we want sentence embeddings for |
| 2845 | sentences = ["样例数据-1", "样例数据-2"] |
| 2846 | |
| 2847 | # Load model from HuggingFace Hub |
| 2848 | tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') |
| 2849 | model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') |
| 2850 | model.eval() |
| 2851 | |
| 2852 | # Tokenize sentences |
| 2853 | encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
| 2854 | # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
| 2855 | # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
| 2856 | |
| 2857 | # Compute token embeddings |
| 2858 | with torch.no_grad(): |
| 2859 | model_output = model(**encoded_input) |
| 2860 | # Perform pooling. In this case, cls pooling. |
| 2861 | sentence_embeddings = model_output[0][:, 0] |
| 2862 | # normalize embeddings |
| 2863 | sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
| 2864 | print("Sentence embeddings:", sentence_embeddings) |
| 2865 | ``` |
| 2866 | |
| 2867 | #### Usage of the ONNX files |
| 2868 | |
| 2869 | ```python |
| 2870 | from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore |
| 2871 | |
| 2872 | import torch |
| 2873 | from transformers import AutoModel, AutoTokenizer |
| 2874 | |
| 2875 | tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') |
| 2876 | model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") |
| 2877 | model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") |
| 2878 | |
| 2879 | # Sentences we want sentence embeddings for |
| 2880 | sentences = ["样例数据-1", "样例数据-2"] |
| 2881 | |
| 2882 | # Tokenize sentences |
| 2883 | encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
| 2884 | # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
| 2885 | # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
| 2886 | |
| 2887 | model_output_ort = model_ort(**encoded_input) |
| 2888 | # Compute token embeddings |
| 2889 | with torch.no_grad(): |
| 2890 | model_output = model(**encoded_input) |
| 2891 | |
| 2892 | # model_output and model_output_ort are identical |
| 2893 | |
| 2894 | ``` |
| 2895 | |
| 2896 | Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. |
| 2897 | ```python |
| 2898 | import asyncio |
| 2899 | from infinity_emb import AsyncEmbeddingEngine, EngineArgs |
| 2900 | |
| 2901 | sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] |
| 2902 | engine = AsyncEmbeddingEngine.from_args( |
| 2903 | EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" |
| 2904 | )) |
| 2905 | |
| 2906 | async def main(): |
| 2907 | async with engine: |
| 2908 | embeddings, usage = await engine.embed(sentences=sentences) |
| 2909 | asyncio.run(main()) |
| 2910 | ``` |
| 2911 | |
| 2912 | ### Usage for Reranker |
| 2913 | |
| 2914 | Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. |
| 2915 | You can get a relevance score by inputting query and passage to the reranker. |
| 2916 | The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. |
| 2917 | |
| 2918 | |
| 2919 | #### Using FlagEmbedding |
| 2920 | ``` |
| 2921 | pip install -U FlagEmbedding |
| 2922 | ``` |
| 2923 | |
| 2924 | Get relevance scores (higher scores indicate more relevance): |
| 2925 | ```python |
| 2926 | from FlagEmbedding import FlagReranker |
| 2927 | reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
| 2928 | |
| 2929 | score = reranker.compute_score(['query', 'passage']) |
| 2930 | print(score) |
| 2931 | |
| 2932 | 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.']]) |
| 2933 | print(scores) |
| 2934 | ``` |
| 2935 | |
| 2936 | |
| 2937 | #### Using Huggingface transformers |
| 2938 | |
| 2939 | ```python |
| 2940 | import torch |
| 2941 | from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| 2942 | |
| 2943 | tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') |
| 2944 | model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') |
| 2945 | model.eval() |
| 2946 | |
| 2947 | 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.']] |
| 2948 | with torch.no_grad(): |
| 2949 | inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) |
| 2950 | scores = model(**inputs, return_dict=True).logits.view(-1, ).float() |
| 2951 | print(scores) |
| 2952 | ``` |
| 2953 | |
| 2954 | ## Evaluation |
| 2955 | |
| 2956 | `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
| 2957 | For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
| 2958 | |
| 2959 | - **MTEB**: |
| 2960 | |
| 2961 | | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
| 2962 | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| 2963 | | [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 | |
| 2964 | | [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 | |
| 2965 | | [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 | |
| 2966 | | [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 | |
| 2967 | | [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 | |
| 2968 | | [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 | |
| 2969 | | [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 | |
| 2970 | | [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 | |
| 2971 | | [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 | |
| 2972 | | [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 | |
| 2973 | | [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 | |
| 2974 | | [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 | |
| 2975 | | [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 | |
| 2976 | | [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 | |
| 2977 | | [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 | |
| 2978 | | [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 | |
| 2979 | | [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 | |
| 2980 | |
| 2981 | |
| 2982 | |
| 2983 | - **C-MTEB**: |
| 2984 | We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. |
| 2985 | Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
| 2986 | |
| 2987 | | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
| 2988 | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
| 2989 | | [**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 | |
| 2990 | | [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 | |
| 2991 | | [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 | |
| 2992 | | [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 | |
| 2993 | | [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 | |
| 2994 | | [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 | |
| 2995 | | [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 | |
| 2996 | | [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 | |
| 2997 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | |
| 2998 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | |
| 2999 | | [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 | |
| 3000 | | [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 | |
| 3001 | | [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 | |
| 3002 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | |
| 3003 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | |
| 3004 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | |
| 3005 | |
| 3006 | |
| 3007 | - **Reranking**: |
| 3008 | See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. |
| 3009 | |
| 3010 | | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |
| 3011 | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
| 3012 | | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | |
| 3013 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | |
| 3014 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | |
| 3015 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | |
| 3016 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | |
| 3017 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | |
| 3018 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | |
| 3019 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | |
| 3020 | | [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 | |
| 3021 | | [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 | |
| 3022 | |
| 3023 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks |
| 3024 | |
| 3025 | ## Train |
| 3026 | |
| 3027 | ### BAAI Embedding |
| 3028 | |
| 3029 | We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. |
| 3030 | **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** |
| 3031 | We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). |
| 3032 | 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. |
| 3033 | More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
| 3034 | |
| 3035 | |
| 3036 | |
| 3037 | ### BGE Reranker |
| 3038 | |
| 3039 | Cross-encoder will perform full-attention over the input pair, |
| 3040 | which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. |
| 3041 | Therefore, it can be used to re-rank the top-k documents returned by embedding model. |
| 3042 | We train the cross-encoder on a multilingual pair data, |
| 3043 | 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). |
| 3044 | More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
| 3045 | |
| 3046 | |
| 3047 | ## Contact |
| 3048 | If you have any question or suggestion related to this project, feel free to open an issue or pull request. |
| 3049 | You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). |
| 3050 | |
| 3051 | |
| 3052 | ## Citation |
| 3053 | |
| 3054 | If you find this repository useful, please consider giving a star :star: and citation |
| 3055 | |
| 3056 | ``` |
| 3057 | @misc{bge_embedding, |
| 3058 | title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, |
| 3059 | author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, |
| 3060 | year={2023}, |
| 3061 | eprint={2309.07597}, |
| 3062 | archivePrefix={arXiv}, |
| 3063 | primaryClass={cs.CL} |
| 3064 | } |
| 3065 | ``` |
| 3066 | |
| 3067 | ## License |
| 3068 | 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. |
| 3069 | |
| 3070 | |