README.md
| 1 | --- |
| 2 | pipeline_tag: sentence-similarity |
| 3 | tags: |
| 4 | - sentence-transformers |
| 5 | - feature-extraction |
| 6 | - sentence-similarity |
| 7 | - mteb |
| 8 | - arctic |
| 9 | - snowflake-arctic-embed |
| 10 | - transformers.js |
| 11 | model-index: |
| 12 | - name: snowflake-snowflake-arctic-embed-xs |
| 13 | results: |
| 14 | - task: |
| 15 | type: Classification |
| 16 | dataset: |
| 17 | type: mteb/amazon_counterfactual |
| 18 | name: MTEB AmazonCounterfactualClassification (en) |
| 19 | config: en |
| 20 | split: test |
| 21 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 22 | metrics: |
| 23 | - type: accuracy |
| 24 | value: 65.08955223880598 |
| 25 | - type: ap |
| 26 | value: 28.514291209445364 |
| 27 | - type: f1 |
| 28 | value: 59.2604580112738 |
| 29 | - task: |
| 30 | type: Classification |
| 31 | dataset: |
| 32 | type: mteb/amazon_polarity |
| 33 | name: MTEB AmazonPolarityClassification |
| 34 | config: default |
| 35 | split: test |
| 36 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 37 | metrics: |
| 38 | - type: accuracy |
| 39 | value: 70.035375 |
| 40 | - type: ap |
| 41 | value: 64.29444264250405 |
| 42 | - type: f1 |
| 43 | value: 69.78382333907138 |
| 44 | - task: |
| 45 | type: Classification |
| 46 | dataset: |
| 47 | type: mteb/amazon_reviews_multi |
| 48 | name: MTEB AmazonReviewsClassification (en) |
| 49 | config: en |
| 50 | split: test |
| 51 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 52 | metrics: |
| 53 | - type: accuracy |
| 54 | value: 35.343999999999994 |
| 55 | - type: f1 |
| 56 | value: 34.69618251902858 |
| 57 | - task: |
| 58 | type: Retrieval |
| 59 | dataset: |
| 60 | type: mteb/arguana |
| 61 | name: MTEB ArguAna |
| 62 | config: default |
| 63 | split: test |
| 64 | revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| 65 | metrics: |
| 66 | - type: map_at_1 |
| 67 | value: 28.592000000000002 |
| 68 | - type: map_at_10 |
| 69 | value: 43.597 |
| 70 | - type: map_at_100 |
| 71 | value: 44.614 |
| 72 | - type: map_at_1000 |
| 73 | value: 44.624 |
| 74 | - type: map_at_3 |
| 75 | value: 38.928000000000004 |
| 76 | - type: map_at_5 |
| 77 | value: 41.453 |
| 78 | - type: mrr_at_1 |
| 79 | value: 29.232000000000003 |
| 80 | - type: mrr_at_10 |
| 81 | value: 43.829 |
| 82 | - type: mrr_at_100 |
| 83 | value: 44.852 |
| 84 | - type: mrr_at_1000 |
| 85 | value: 44.862 |
| 86 | - type: mrr_at_3 |
| 87 | value: 39.118 |
| 88 | - type: mrr_at_5 |
| 89 | value: 41.703 |
| 90 | - type: ndcg_at_1 |
| 91 | value: 28.592000000000002 |
| 92 | - type: ndcg_at_10 |
| 93 | value: 52.081 |
| 94 | - type: ndcg_at_100 |
| 95 | value: 56.37 |
| 96 | - type: ndcg_at_1000 |
| 97 | value: 56.598000000000006 |
| 98 | - type: ndcg_at_3 |
| 99 | value: 42.42 |
| 100 | - type: ndcg_at_5 |
| 101 | value: 46.965 |
| 102 | - type: precision_at_1 |
| 103 | value: 28.592000000000002 |
| 104 | - type: precision_at_10 |
| 105 | value: 7.922999999999999 |
| 106 | - type: precision_at_100 |
| 107 | value: 0.979 |
| 108 | - type: precision_at_1000 |
| 109 | value: 0.1 |
| 110 | - type: precision_at_3 |
| 111 | value: 17.52 |
| 112 | - type: precision_at_5 |
| 113 | value: 12.717 |
| 114 | - type: recall_at_1 |
| 115 | value: 28.592000000000002 |
| 116 | - type: recall_at_10 |
| 117 | value: 79.232 |
| 118 | - type: recall_at_100 |
| 119 | value: 97.866 |
| 120 | - type: recall_at_1000 |
| 121 | value: 99.57300000000001 |
| 122 | - type: recall_at_3 |
| 123 | value: 52.559999999999995 |
| 124 | - type: recall_at_5 |
| 125 | value: 63.585 |
| 126 | - task: |
| 127 | type: Clustering |
| 128 | dataset: |
| 129 | type: mteb/arxiv-clustering-p2p |
| 130 | name: MTEB ArxivClusteringP2P |
| 131 | config: default |
| 132 | split: test |
| 133 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 134 | metrics: |
| 135 | - type: v_measure |
| 136 | value: 43.50220588953974 |
| 137 | - task: |
| 138 | type: Clustering |
| 139 | dataset: |
| 140 | type: mteb/arxiv-clustering-s2s |
| 141 | name: MTEB ArxivClusteringS2S |
| 142 | config: default |
| 143 | split: test |
| 144 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 145 | metrics: |
| 146 | - type: v_measure |
| 147 | value: 32.08725826118282 |
| 148 | - task: |
| 149 | type: Reranking |
| 150 | dataset: |
| 151 | type: mteb/askubuntudupquestions-reranking |
| 152 | name: MTEB AskUbuntuDupQuestions |
| 153 | config: default |
| 154 | split: test |
| 155 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 156 | metrics: |
| 157 | - type: map |
| 158 | value: 60.25381587694928 |
| 159 | - type: mrr |
| 160 | value: 73.79776194873148 |
| 161 | - task: |
| 162 | type: STS |
| 163 | dataset: |
| 164 | type: mteb/biosses-sts |
| 165 | name: MTEB BIOSSES |
| 166 | config: default |
| 167 | split: test |
| 168 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 169 | metrics: |
| 170 | - type: cos_sim_pearson |
| 171 | value: 85.47489332445278 |
| 172 | - type: cos_sim_spearman |
| 173 | value: 84.05432487336698 |
| 174 | - type: euclidean_pearson |
| 175 | value: 84.5108222177219 |
| 176 | - type: euclidean_spearman |
| 177 | value: 84.05432487336698 |
| 178 | - type: manhattan_pearson |
| 179 | value: 84.20440618321464 |
| 180 | - type: manhattan_spearman |
| 181 | value: 83.9290208134097 |
| 182 | - task: |
| 183 | type: Classification |
| 184 | dataset: |
| 185 | type: mteb/banking77 |
| 186 | name: MTEB Banking77Classification |
| 187 | config: default |
| 188 | split: test |
| 189 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 190 | metrics: |
| 191 | - type: accuracy |
| 192 | value: 76.37337662337663 |
| 193 | - type: f1 |
| 194 | value: 75.33296834885043 |
| 195 | - task: |
| 196 | type: Clustering |
| 197 | dataset: |
| 198 | type: jinaai/big-patent-clustering |
| 199 | name: MTEB BigPatentClustering |
| 200 | config: default |
| 201 | split: test |
| 202 | revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
| 203 | metrics: |
| 204 | - type: v_measure |
| 205 | value: 21.31174373264835 |
| 206 | - task: |
| 207 | type: Clustering |
| 208 | dataset: |
| 209 | type: mteb/biorxiv-clustering-p2p |
| 210 | name: MTEB BiorxivClusteringP2P |
| 211 | config: default |
| 212 | split: test |
| 213 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 214 | metrics: |
| 215 | - type: v_measure |
| 216 | value: 34.481973521597844 |
| 217 | - task: |
| 218 | type: Clustering |
| 219 | dataset: |
| 220 | type: mteb/biorxiv-clustering-s2s |
| 221 | name: MTEB BiorxivClusteringS2S |
| 222 | config: default |
| 223 | split: test |
| 224 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 225 | metrics: |
| 226 | - type: v_measure |
| 227 | value: 26.14094256567341 |
| 228 | - task: |
| 229 | type: Retrieval |
| 230 | dataset: |
| 231 | type: mteb/cqadupstack-android |
| 232 | name: MTEB CQADupstackAndroidRetrieval |
| 233 | config: default |
| 234 | split: test |
| 235 | revision: f46a197baaae43b4f621051089b82a364682dfeb |
| 236 | metrics: |
| 237 | - type: map_at_1 |
| 238 | value: 32.527 |
| 239 | - type: map_at_10 |
| 240 | value: 43.699 |
| 241 | - type: map_at_100 |
| 242 | value: 45.03 |
| 243 | - type: map_at_1000 |
| 244 | value: 45.157000000000004 |
| 245 | - type: map_at_3 |
| 246 | value: 39.943 |
| 247 | - type: map_at_5 |
| 248 | value: 42.324 |
| 249 | - type: mrr_at_1 |
| 250 | value: 39.771 |
| 251 | - type: mrr_at_10 |
| 252 | value: 49.277 |
| 253 | - type: mrr_at_100 |
| 254 | value: 49.956 |
| 255 | - type: mrr_at_1000 |
| 256 | value: 50.005 |
| 257 | - type: mrr_at_3 |
| 258 | value: 46.304 |
| 259 | - type: mrr_at_5 |
| 260 | value: 48.493 |
| 261 | - type: ndcg_at_1 |
| 262 | value: 39.771 |
| 263 | - type: ndcg_at_10 |
| 264 | value: 49.957 |
| 265 | - type: ndcg_at_100 |
| 266 | value: 54.678000000000004 |
| 267 | - type: ndcg_at_1000 |
| 268 | value: 56.751 |
| 269 | - type: ndcg_at_3 |
| 270 | value: 44.608 |
| 271 | - type: ndcg_at_5 |
| 272 | value: 47.687000000000005 |
| 273 | - type: precision_at_1 |
| 274 | value: 39.771 |
| 275 | - type: precision_at_10 |
| 276 | value: 9.557 |
| 277 | - type: precision_at_100 |
| 278 | value: 1.5010000000000001 |
| 279 | - type: precision_at_1000 |
| 280 | value: 0.194 |
| 281 | - type: precision_at_3 |
| 282 | value: 21.173000000000002 |
| 283 | - type: precision_at_5 |
| 284 | value: 15.794 |
| 285 | - type: recall_at_1 |
| 286 | value: 32.527 |
| 287 | - type: recall_at_10 |
| 288 | value: 61.791 |
| 289 | - type: recall_at_100 |
| 290 | value: 81.49300000000001 |
| 291 | - type: recall_at_1000 |
| 292 | value: 95.014 |
| 293 | - type: recall_at_3 |
| 294 | value: 46.605000000000004 |
| 295 | - type: recall_at_5 |
| 296 | value: 54.83 |
| 297 | - task: |
| 298 | type: Retrieval |
| 299 | dataset: |
| 300 | type: mteb/cqadupstack-english |
| 301 | name: MTEB CQADupstackEnglishRetrieval |
| 302 | config: default |
| 303 | split: test |
| 304 | revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
| 305 | metrics: |
| 306 | - type: map_at_1 |
| 307 | value: 29.424 |
| 308 | - type: map_at_10 |
| 309 | value: 38.667 |
| 310 | - type: map_at_100 |
| 311 | value: 39.771 |
| 312 | - type: map_at_1000 |
| 313 | value: 39.899 |
| 314 | - type: map_at_3 |
| 315 | value: 35.91 |
| 316 | - type: map_at_5 |
| 317 | value: 37.45 |
| 318 | - type: mrr_at_1 |
| 319 | value: 36.687999999999995 |
| 320 | - type: mrr_at_10 |
| 321 | value: 44.673 |
| 322 | - type: mrr_at_100 |
| 323 | value: 45.289 |
| 324 | - type: mrr_at_1000 |
| 325 | value: 45.338 |
| 326 | - type: mrr_at_3 |
| 327 | value: 42.601 |
| 328 | - type: mrr_at_5 |
| 329 | value: 43.875 |
| 330 | - type: ndcg_at_1 |
| 331 | value: 36.687999999999995 |
| 332 | - type: ndcg_at_10 |
| 333 | value: 44.013000000000005 |
| 334 | - type: ndcg_at_100 |
| 335 | value: 48.13 |
| 336 | - type: ndcg_at_1000 |
| 337 | value: 50.294000000000004 |
| 338 | - type: ndcg_at_3 |
| 339 | value: 40.056999999999995 |
| 340 | - type: ndcg_at_5 |
| 341 | value: 41.902 |
| 342 | - type: precision_at_1 |
| 343 | value: 36.687999999999995 |
| 344 | - type: precision_at_10 |
| 345 | value: 8.158999999999999 |
| 346 | - type: precision_at_100 |
| 347 | value: 1.321 |
| 348 | - type: precision_at_1000 |
| 349 | value: 0.179 |
| 350 | - type: precision_at_3 |
| 351 | value: 19.045 |
| 352 | - type: precision_at_5 |
| 353 | value: 13.427 |
| 354 | - type: recall_at_1 |
| 355 | value: 29.424 |
| 356 | - type: recall_at_10 |
| 357 | value: 53.08500000000001 |
| 358 | - type: recall_at_100 |
| 359 | value: 70.679 |
| 360 | - type: recall_at_1000 |
| 361 | value: 84.66 |
| 362 | - type: recall_at_3 |
| 363 | value: 41.399 |
| 364 | - type: recall_at_5 |
| 365 | value: 46.632 |
| 366 | - task: |
| 367 | type: Retrieval |
| 368 | dataset: |
| 369 | type: mteb/cqadupstack-gaming |
| 370 | name: MTEB CQADupstackGamingRetrieval |
| 371 | config: default |
| 372 | split: test |
| 373 | revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
| 374 | metrics: |
| 375 | - type: map_at_1 |
| 376 | value: 39.747 |
| 377 | - type: map_at_10 |
| 378 | value: 51.452 |
| 379 | - type: map_at_100 |
| 380 | value: 52.384 |
| 381 | - type: map_at_1000 |
| 382 | value: 52.437 |
| 383 | - type: map_at_3 |
| 384 | value: 48.213 |
| 385 | - type: map_at_5 |
| 386 | value: 50.195 |
| 387 | - type: mrr_at_1 |
| 388 | value: 45.391999999999996 |
| 389 | - type: mrr_at_10 |
| 390 | value: 54.928 |
| 391 | - type: mrr_at_100 |
| 392 | value: 55.532000000000004 |
| 393 | - type: mrr_at_1000 |
| 394 | value: 55.565 |
| 395 | - type: mrr_at_3 |
| 396 | value: 52.456 |
| 397 | - type: mrr_at_5 |
| 398 | value: 54.054 |
| 399 | - type: ndcg_at_1 |
| 400 | value: 45.391999999999996 |
| 401 | - type: ndcg_at_10 |
| 402 | value: 57.055 |
| 403 | - type: ndcg_at_100 |
| 404 | value: 60.751999999999995 |
| 405 | - type: ndcg_at_1000 |
| 406 | value: 61.864 |
| 407 | - type: ndcg_at_3 |
| 408 | value: 51.662 |
| 409 | - type: ndcg_at_5 |
| 410 | value: 54.613 |
| 411 | - type: precision_at_1 |
| 412 | value: 45.391999999999996 |
| 413 | - type: precision_at_10 |
| 414 | value: 9.103 |
| 415 | - type: precision_at_100 |
| 416 | value: 1.1780000000000002 |
| 417 | - type: precision_at_1000 |
| 418 | value: 0.132 |
| 419 | - type: precision_at_3 |
| 420 | value: 22.717000000000002 |
| 421 | - type: precision_at_5 |
| 422 | value: 15.812000000000001 |
| 423 | - type: recall_at_1 |
| 424 | value: 39.747 |
| 425 | - type: recall_at_10 |
| 426 | value: 70.10499999999999 |
| 427 | - type: recall_at_100 |
| 428 | value: 86.23100000000001 |
| 429 | - type: recall_at_1000 |
| 430 | value: 94.025 |
| 431 | - type: recall_at_3 |
| 432 | value: 55.899 |
| 433 | - type: recall_at_5 |
| 434 | value: 63.05500000000001 |
| 435 | - task: |
| 436 | type: Retrieval |
| 437 | dataset: |
| 438 | type: mteb/cqadupstack-gis |
| 439 | name: MTEB CQADupstackGisRetrieval |
| 440 | config: default |
| 441 | split: test |
| 442 | revision: 5003b3064772da1887988e05400cf3806fe491f2 |
| 443 | metrics: |
| 444 | - type: map_at_1 |
| 445 | value: 27.168999999999997 |
| 446 | - type: map_at_10 |
| 447 | value: 34.975 |
| 448 | - type: map_at_100 |
| 449 | value: 35.94 |
| 450 | - type: map_at_1000 |
| 451 | value: 36.021 |
| 452 | - type: map_at_3 |
| 453 | value: 32.35 |
| 454 | - type: map_at_5 |
| 455 | value: 33.831 |
| 456 | - type: mrr_at_1 |
| 457 | value: 28.701 |
| 458 | - type: mrr_at_10 |
| 459 | value: 36.698 |
| 460 | - type: mrr_at_100 |
| 461 | value: 37.546 |
| 462 | - type: mrr_at_1000 |
| 463 | value: 37.613 |
| 464 | - type: mrr_at_3 |
| 465 | value: 34.256 |
| 466 | - type: mrr_at_5 |
| 467 | value: 35.685 |
| 468 | - type: ndcg_at_1 |
| 469 | value: 28.701 |
| 470 | - type: ndcg_at_10 |
| 471 | value: 39.639 |
| 472 | - type: ndcg_at_100 |
| 473 | value: 44.389 |
| 474 | - type: ndcg_at_1000 |
| 475 | value: 46.46 |
| 476 | - type: ndcg_at_3 |
| 477 | value: 34.52 |
| 478 | - type: ndcg_at_5 |
| 479 | value: 37.076 |
| 480 | - type: precision_at_1 |
| 481 | value: 28.701 |
| 482 | - type: precision_at_10 |
| 483 | value: 5.955 |
| 484 | - type: precision_at_100 |
| 485 | value: 0.8880000000000001 |
| 486 | - type: precision_at_1000 |
| 487 | value: 0.109 |
| 488 | - type: precision_at_3 |
| 489 | value: 14.274999999999999 |
| 490 | - type: precision_at_5 |
| 491 | value: 10.011000000000001 |
| 492 | - type: recall_at_1 |
| 493 | value: 27.168999999999997 |
| 494 | - type: recall_at_10 |
| 495 | value: 52.347 |
| 496 | - type: recall_at_100 |
| 497 | value: 74.1 |
| 498 | - type: recall_at_1000 |
| 499 | value: 89.739 |
| 500 | - type: recall_at_3 |
| 501 | value: 38.567 |
| 502 | - type: recall_at_5 |
| 503 | value: 44.767 |
| 504 | - task: |
| 505 | type: Retrieval |
| 506 | dataset: |
| 507 | type: mteb/cqadupstack-mathematica |
| 508 | name: MTEB CQADupstackMathematicaRetrieval |
| 509 | config: default |
| 510 | split: test |
| 511 | revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| 512 | metrics: |
| 513 | - type: map_at_1 |
| 514 | value: 15.872 |
| 515 | - type: map_at_10 |
| 516 | value: 23.153000000000002 |
| 517 | - type: map_at_100 |
| 518 | value: 24.311 |
| 519 | - type: map_at_1000 |
| 520 | value: 24.432000000000002 |
| 521 | - type: map_at_3 |
| 522 | value: 20.707 |
| 523 | - type: map_at_5 |
| 524 | value: 21.921 |
| 525 | - type: mrr_at_1 |
| 526 | value: 19.776 |
| 527 | - type: mrr_at_10 |
| 528 | value: 27.755999999999997 |
| 529 | - type: mrr_at_100 |
| 530 | value: 28.709 |
| 531 | - type: mrr_at_1000 |
| 532 | value: 28.778 |
| 533 | - type: mrr_at_3 |
| 534 | value: 25.186999999999998 |
| 535 | - type: mrr_at_5 |
| 536 | value: 26.43 |
| 537 | - type: ndcg_at_1 |
| 538 | value: 19.776 |
| 539 | - type: ndcg_at_10 |
| 540 | value: 28.288999999999998 |
| 541 | - type: ndcg_at_100 |
| 542 | value: 34.011 |
| 543 | - type: ndcg_at_1000 |
| 544 | value: 36.916 |
| 545 | - type: ndcg_at_3 |
| 546 | value: 23.551 |
| 547 | - type: ndcg_at_5 |
| 548 | value: 25.429000000000002 |
| 549 | - type: precision_at_1 |
| 550 | value: 19.776 |
| 551 | - type: precision_at_10 |
| 552 | value: 5.311 |
| 553 | - type: precision_at_100 |
| 554 | value: 0.9440000000000001 |
| 555 | - type: precision_at_1000 |
| 556 | value: 0.132 |
| 557 | - type: precision_at_3 |
| 558 | value: 11.360000000000001 |
| 559 | - type: precision_at_5 |
| 560 | value: 8.209 |
| 561 | - type: recall_at_1 |
| 562 | value: 15.872 |
| 563 | - type: recall_at_10 |
| 564 | value: 39.726 |
| 565 | - type: recall_at_100 |
| 566 | value: 65.035 |
| 567 | - type: recall_at_1000 |
| 568 | value: 85.846 |
| 569 | - type: recall_at_3 |
| 570 | value: 26.432 |
| 571 | - type: recall_at_5 |
| 572 | value: 31.22 |
| 573 | - task: |
| 574 | type: Retrieval |
| 575 | dataset: |
| 576 | type: mteb/cqadupstack-physics |
| 577 | name: MTEB CQADupstackPhysicsRetrieval |
| 578 | config: default |
| 579 | split: test |
| 580 | revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
| 581 | metrics: |
| 582 | - type: map_at_1 |
| 583 | value: 28.126 |
| 584 | - type: map_at_10 |
| 585 | value: 37.537 |
| 586 | - type: map_at_100 |
| 587 | value: 38.807 |
| 588 | - type: map_at_1000 |
| 589 | value: 38.923 |
| 590 | - type: map_at_3 |
| 591 | value: 34.65 |
| 592 | - type: map_at_5 |
| 593 | value: 36.248000000000005 |
| 594 | - type: mrr_at_1 |
| 595 | value: 34.649 |
| 596 | - type: mrr_at_10 |
| 597 | value: 42.893 |
| 598 | - type: mrr_at_100 |
| 599 | value: 43.721 |
| 600 | - type: mrr_at_1000 |
| 601 | value: 43.775999999999996 |
| 602 | - type: mrr_at_3 |
| 603 | value: 40.488 |
| 604 | - type: mrr_at_5 |
| 605 | value: 41.729 |
| 606 | - type: ndcg_at_1 |
| 607 | value: 34.649 |
| 608 | - type: ndcg_at_10 |
| 609 | value: 43.072 |
| 610 | - type: ndcg_at_100 |
| 611 | value: 48.464 |
| 612 | - type: ndcg_at_1000 |
| 613 | value: 50.724000000000004 |
| 614 | - type: ndcg_at_3 |
| 615 | value: 38.506 |
| 616 | - type: ndcg_at_5 |
| 617 | value: 40.522000000000006 |
| 618 | - type: precision_at_1 |
| 619 | value: 34.649 |
| 620 | - type: precision_at_10 |
| 621 | value: 7.68 |
| 622 | - type: precision_at_100 |
| 623 | value: 1.214 |
| 624 | - type: precision_at_1000 |
| 625 | value: 0.16 |
| 626 | - type: precision_at_3 |
| 627 | value: 18.029999999999998 |
| 628 | - type: precision_at_5 |
| 629 | value: 12.666 |
| 630 | - type: recall_at_1 |
| 631 | value: 28.126 |
| 632 | - type: recall_at_10 |
| 633 | value: 54.396 |
| 634 | - type: recall_at_100 |
| 635 | value: 76.988 |
| 636 | - type: recall_at_1000 |
| 637 | value: 91.85799999999999 |
| 638 | - type: recall_at_3 |
| 639 | value: 41.169 |
| 640 | - type: recall_at_5 |
| 641 | value: 46.658 |
| 642 | - task: |
| 643 | type: Retrieval |
| 644 | dataset: |
| 645 | type: mteb/cqadupstack-programmers |
| 646 | name: MTEB CQADupstackProgrammersRetrieval |
| 647 | config: default |
| 648 | split: test |
| 649 | revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
| 650 | metrics: |
| 651 | - type: map_at_1 |
| 652 | value: 26.68 |
| 653 | - type: map_at_10 |
| 654 | value: 35.702 |
| 655 | - type: map_at_100 |
| 656 | value: 36.864999999999995 |
| 657 | - type: map_at_1000 |
| 658 | value: 36.977 |
| 659 | - type: map_at_3 |
| 660 | value: 32.828 |
| 661 | - type: map_at_5 |
| 662 | value: 34.481 |
| 663 | - type: mrr_at_1 |
| 664 | value: 32.991 |
| 665 | - type: mrr_at_10 |
| 666 | value: 40.993 |
| 667 | - type: mrr_at_100 |
| 668 | value: 41.827 |
| 669 | - type: mrr_at_1000 |
| 670 | value: 41.887 |
| 671 | - type: mrr_at_3 |
| 672 | value: 38.623000000000005 |
| 673 | - type: mrr_at_5 |
| 674 | value: 40.021 |
| 675 | - type: ndcg_at_1 |
| 676 | value: 32.991 |
| 677 | - type: ndcg_at_10 |
| 678 | value: 41.036 |
| 679 | - type: ndcg_at_100 |
| 680 | value: 46.294000000000004 |
| 681 | - type: ndcg_at_1000 |
| 682 | value: 48.644 |
| 683 | - type: ndcg_at_3 |
| 684 | value: 36.419000000000004 |
| 685 | - type: ndcg_at_5 |
| 686 | value: 38.618 |
| 687 | - type: precision_at_1 |
| 688 | value: 32.991 |
| 689 | - type: precision_at_10 |
| 690 | value: 7.385999999999999 |
| 691 | - type: precision_at_100 |
| 692 | value: 1.176 |
| 693 | - type: precision_at_1000 |
| 694 | value: 0.151 |
| 695 | - type: precision_at_3 |
| 696 | value: 17.122999999999998 |
| 697 | - type: precision_at_5 |
| 698 | value: 12.215 |
| 699 | - type: recall_at_1 |
| 700 | value: 26.68 |
| 701 | - type: recall_at_10 |
| 702 | value: 51.644 |
| 703 | - type: recall_at_100 |
| 704 | value: 74.55000000000001 |
| 705 | - type: recall_at_1000 |
| 706 | value: 90.825 |
| 707 | - type: recall_at_3 |
| 708 | value: 38.579 |
| 709 | - type: recall_at_5 |
| 710 | value: 44.512 |
| 711 | - task: |
| 712 | type: Retrieval |
| 713 | dataset: |
| 714 | type: mteb/cqadupstack |
| 715 | name: MTEB CQADupstackRetrieval |
| 716 | config: default |
| 717 | split: test |
| 718 | revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| 719 | metrics: |
| 720 | - type: map_at_1 |
| 721 | value: 26.30825 |
| 722 | - type: map_at_10 |
| 723 | value: 34.97866666666666 |
| 724 | - type: map_at_100 |
| 725 | value: 36.109249999999996 |
| 726 | - type: map_at_1000 |
| 727 | value: 36.22508333333333 |
| 728 | - type: map_at_3 |
| 729 | value: 32.239083333333326 |
| 730 | - type: map_at_5 |
| 731 | value: 33.75933333333334 |
| 732 | - type: mrr_at_1 |
| 733 | value: 31.05308333333333 |
| 734 | - type: mrr_at_10 |
| 735 | value: 39.099833333333336 |
| 736 | - type: mrr_at_100 |
| 737 | value: 39.92008333333334 |
| 738 | - type: mrr_at_1000 |
| 739 | value: 39.980000000000004 |
| 740 | - type: mrr_at_3 |
| 741 | value: 36.75958333333333 |
| 742 | - type: mrr_at_5 |
| 743 | value: 38.086416666666665 |
| 744 | - type: ndcg_at_1 |
| 745 | value: 31.05308333333333 |
| 746 | - type: ndcg_at_10 |
| 747 | value: 40.11558333333334 |
| 748 | - type: ndcg_at_100 |
| 749 | value: 45.05966666666667 |
| 750 | - type: ndcg_at_1000 |
| 751 | value: 47.36516666666667 |
| 752 | - type: ndcg_at_3 |
| 753 | value: 35.490833333333335 |
| 754 | - type: ndcg_at_5 |
| 755 | value: 37.64541666666666 |
| 756 | - type: precision_at_1 |
| 757 | value: 31.05308333333333 |
| 758 | - type: precision_at_10 |
| 759 | value: 6.968416666666666 |
| 760 | - type: precision_at_100 |
| 761 | value: 1.1156666666666666 |
| 762 | - type: precision_at_1000 |
| 763 | value: 0.14950000000000002 |
| 764 | - type: precision_at_3 |
| 765 | value: 16.123 |
| 766 | - type: precision_at_5 |
| 767 | value: 11.451166666666666 |
| 768 | - type: recall_at_1 |
| 769 | value: 26.30825 |
| 770 | - type: recall_at_10 |
| 771 | value: 51.19283333333333 |
| 772 | - type: recall_at_100 |
| 773 | value: 73.0285 |
| 774 | - type: recall_at_1000 |
| 775 | value: 89.11133333333333 |
| 776 | - type: recall_at_3 |
| 777 | value: 38.26208333333333 |
| 778 | - type: recall_at_5 |
| 779 | value: 43.855916666666666 |
| 780 | - task: |
| 781 | type: Retrieval |
| 782 | dataset: |
| 783 | type: mteb/cqadupstack-stats |
| 784 | name: MTEB CQADupstackStatsRetrieval |
| 785 | config: default |
| 786 | split: test |
| 787 | revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
| 788 | metrics: |
| 789 | - type: map_at_1 |
| 790 | value: 23.363999999999997 |
| 791 | - type: map_at_10 |
| 792 | value: 30.606 |
| 793 | - type: map_at_100 |
| 794 | value: 31.491999999999997 |
| 795 | - type: map_at_1000 |
| 796 | value: 31.578 |
| 797 | - type: map_at_3 |
| 798 | value: 28.610000000000003 |
| 799 | - type: map_at_5 |
| 800 | value: 29.602 |
| 801 | - type: mrr_at_1 |
| 802 | value: 26.38 |
| 803 | - type: mrr_at_10 |
| 804 | value: 33.472 |
| 805 | - type: mrr_at_100 |
| 806 | value: 34.299 |
| 807 | - type: mrr_at_1000 |
| 808 | value: 34.361999999999995 |
| 809 | - type: mrr_at_3 |
| 810 | value: 31.696999999999996 |
| 811 | - type: mrr_at_5 |
| 812 | value: 32.503 |
| 813 | - type: ndcg_at_1 |
| 814 | value: 26.38 |
| 815 | - type: ndcg_at_10 |
| 816 | value: 34.772999999999996 |
| 817 | - type: ndcg_at_100 |
| 818 | value: 39.334 |
| 819 | - type: ndcg_at_1000 |
| 820 | value: 41.676 |
| 821 | - type: ndcg_at_3 |
| 822 | value: 31.097 |
| 823 | - type: ndcg_at_5 |
| 824 | value: 32.561 |
| 825 | - type: precision_at_1 |
| 826 | value: 26.38 |
| 827 | - type: precision_at_10 |
| 828 | value: 5.475 |
| 829 | - type: precision_at_100 |
| 830 | value: 0.84 |
| 831 | - type: precision_at_1000 |
| 832 | value: 0.11100000000000002 |
| 833 | - type: precision_at_3 |
| 834 | value: 13.395000000000001 |
| 835 | - type: precision_at_5 |
| 836 | value: 9.11 |
| 837 | - type: recall_at_1 |
| 838 | value: 23.363999999999997 |
| 839 | - type: recall_at_10 |
| 840 | value: 44.656 |
| 841 | - type: recall_at_100 |
| 842 | value: 65.77199999999999 |
| 843 | - type: recall_at_1000 |
| 844 | value: 83.462 |
| 845 | - type: recall_at_3 |
| 846 | value: 34.213 |
| 847 | - type: recall_at_5 |
| 848 | value: 38.091 |
| 849 | - task: |
| 850 | type: Retrieval |
| 851 | dataset: |
| 852 | type: mteb/cqadupstack-tex |
| 853 | name: MTEB CQADupstackTexRetrieval |
| 854 | config: default |
| 855 | split: test |
| 856 | revision: 46989137a86843e03a6195de44b09deda022eec7 |
| 857 | metrics: |
| 858 | - type: map_at_1 |
| 859 | value: 17.971999999999998 |
| 860 | - type: map_at_10 |
| 861 | value: 24.913 |
| 862 | - type: map_at_100 |
| 863 | value: 25.916 |
| 864 | - type: map_at_1000 |
| 865 | value: 26.049 |
| 866 | - type: map_at_3 |
| 867 | value: 22.569 |
| 868 | - type: map_at_5 |
| 869 | value: 23.858999999999998 |
| 870 | - type: mrr_at_1 |
| 871 | value: 21.748 |
| 872 | - type: mrr_at_10 |
| 873 | value: 28.711 |
| 874 | - type: mrr_at_100 |
| 875 | value: 29.535 |
| 876 | - type: mrr_at_1000 |
| 877 | value: 29.621 |
| 878 | - type: mrr_at_3 |
| 879 | value: 26.484999999999996 |
| 880 | - type: mrr_at_5 |
| 881 | value: 27.701999999999998 |
| 882 | - type: ndcg_at_1 |
| 883 | value: 21.748 |
| 884 | - type: ndcg_at_10 |
| 885 | value: 29.412 |
| 886 | - type: ndcg_at_100 |
| 887 | value: 34.204 |
| 888 | - type: ndcg_at_1000 |
| 889 | value: 37.358000000000004 |
| 890 | - type: ndcg_at_3 |
| 891 | value: 25.202 |
| 892 | - type: ndcg_at_5 |
| 893 | value: 27.128000000000004 |
| 894 | - type: precision_at_1 |
| 895 | value: 21.748 |
| 896 | - type: precision_at_10 |
| 897 | value: 5.279 |
| 898 | - type: precision_at_100 |
| 899 | value: 0.902 |
| 900 | - type: precision_at_1000 |
| 901 | value: 0.135 |
| 902 | - type: precision_at_3 |
| 903 | value: 11.551 |
| 904 | - type: precision_at_5 |
| 905 | value: 8.437999999999999 |
| 906 | - type: recall_at_1 |
| 907 | value: 17.971999999999998 |
| 908 | - type: recall_at_10 |
| 909 | value: 39.186 |
| 910 | - type: recall_at_100 |
| 911 | value: 60.785999999999994 |
| 912 | - type: recall_at_1000 |
| 913 | value: 83.372 |
| 914 | - type: recall_at_3 |
| 915 | value: 27.584999999999997 |
| 916 | - type: recall_at_5 |
| 917 | value: 32.448 |
| 918 | - task: |
| 919 | type: Retrieval |
| 920 | dataset: |
| 921 | type: mteb/cqadupstack-unix |
| 922 | name: MTEB CQADupstackUnixRetrieval |
| 923 | config: default |
| 924 | split: test |
| 925 | revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
| 926 | metrics: |
| 927 | - type: map_at_1 |
| 928 | value: 26.684 |
| 929 | - type: map_at_10 |
| 930 | value: 35.188 |
| 931 | - type: map_at_100 |
| 932 | value: 36.379 |
| 933 | - type: map_at_1000 |
| 934 | value: 36.481 |
| 935 | - type: map_at_3 |
| 936 | value: 32.401 |
| 937 | - type: map_at_5 |
| 938 | value: 34.132 |
| 939 | - type: mrr_at_1 |
| 940 | value: 31.063000000000002 |
| 941 | - type: mrr_at_10 |
| 942 | value: 39.104 |
| 943 | - type: mrr_at_100 |
| 944 | value: 40.062999999999995 |
| 945 | - type: mrr_at_1000 |
| 946 | value: 40.119 |
| 947 | - type: mrr_at_3 |
| 948 | value: 36.692 |
| 949 | - type: mrr_at_5 |
| 950 | value: 38.161 |
| 951 | - type: ndcg_at_1 |
| 952 | value: 31.063000000000002 |
| 953 | - type: ndcg_at_10 |
| 954 | value: 40.096 |
| 955 | - type: ndcg_at_100 |
| 956 | value: 45.616 |
| 957 | - type: ndcg_at_1000 |
| 958 | value: 47.869 |
| 959 | - type: ndcg_at_3 |
| 960 | value: 35.256 |
| 961 | - type: ndcg_at_5 |
| 962 | value: 37.826 |
| 963 | - type: precision_at_1 |
| 964 | value: 31.063000000000002 |
| 965 | - type: precision_at_10 |
| 966 | value: 6.622999999999999 |
| 967 | - type: precision_at_100 |
| 968 | value: 1.046 |
| 969 | - type: precision_at_1000 |
| 970 | value: 0.135 |
| 971 | - type: precision_at_3 |
| 972 | value: 15.641 |
| 973 | - type: precision_at_5 |
| 974 | value: 11.231 |
| 975 | - type: recall_at_1 |
| 976 | value: 26.684 |
| 977 | - type: recall_at_10 |
| 978 | value: 51.092999999999996 |
| 979 | - type: recall_at_100 |
| 980 | value: 75.099 |
| 981 | - type: recall_at_1000 |
| 982 | value: 90.644 |
| 983 | - type: recall_at_3 |
| 984 | value: 38.063 |
| 985 | - type: recall_at_5 |
| 986 | value: 44.518 |
| 987 | - task: |
| 988 | type: Retrieval |
| 989 | dataset: |
| 990 | type: mteb/cqadupstack-webmasters |
| 991 | name: MTEB CQADupstackWebmastersRetrieval |
| 992 | config: default |
| 993 | split: test |
| 994 | revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
| 995 | metrics: |
| 996 | - type: map_at_1 |
| 997 | value: 26.249 |
| 998 | - type: map_at_10 |
| 999 | value: 34.694 |
| 1000 | - type: map_at_100 |
| 1001 | value: 36.208 |
| 1002 | - type: map_at_1000 |
| 1003 | value: 36.443 |
| 1004 | - type: map_at_3 |
| 1005 | value: 31.868000000000002 |
| 1006 | - type: map_at_5 |
| 1007 | value: 33.018 |
| 1008 | - type: mrr_at_1 |
| 1009 | value: 31.818 |
| 1010 | - type: mrr_at_10 |
| 1011 | value: 39.416000000000004 |
| 1012 | - type: mrr_at_100 |
| 1013 | value: 40.327 |
| 1014 | - type: mrr_at_1000 |
| 1015 | value: 40.388000000000005 |
| 1016 | - type: mrr_at_3 |
| 1017 | value: 37.120999999999995 |
| 1018 | - type: mrr_at_5 |
| 1019 | value: 38.07 |
| 1020 | - type: ndcg_at_1 |
| 1021 | value: 31.818 |
| 1022 | - type: ndcg_at_10 |
| 1023 | value: 40.405 |
| 1024 | - type: ndcg_at_100 |
| 1025 | value: 45.816 |
| 1026 | - type: ndcg_at_1000 |
| 1027 | value: 48.403 |
| 1028 | - type: ndcg_at_3 |
| 1029 | value: 35.823 |
| 1030 | - type: ndcg_at_5 |
| 1031 | value: 37.191 |
| 1032 | - type: precision_at_1 |
| 1033 | value: 31.818 |
| 1034 | - type: precision_at_10 |
| 1035 | value: 7.806 |
| 1036 | - type: precision_at_100 |
| 1037 | value: 1.518 |
| 1038 | - type: precision_at_1000 |
| 1039 | value: 0.241 |
| 1040 | - type: precision_at_3 |
| 1041 | value: 16.535 |
| 1042 | - type: precision_at_5 |
| 1043 | value: 11.738999999999999 |
| 1044 | - type: recall_at_1 |
| 1045 | value: 26.249 |
| 1046 | - type: recall_at_10 |
| 1047 | value: 50.928 |
| 1048 | - type: recall_at_100 |
| 1049 | value: 75.271 |
| 1050 | - type: recall_at_1000 |
| 1051 | value: 91.535 |
| 1052 | - type: recall_at_3 |
| 1053 | value: 37.322 |
| 1054 | - type: recall_at_5 |
| 1055 | value: 41.318 |
| 1056 | - task: |
| 1057 | type: Retrieval |
| 1058 | dataset: |
| 1059 | type: mteb/cqadupstack-wordpress |
| 1060 | name: MTEB CQADupstackWordpressRetrieval |
| 1061 | config: default |
| 1062 | split: test |
| 1063 | revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| 1064 | metrics: |
| 1065 | - type: map_at_1 |
| 1066 | value: 21.884999999999998 |
| 1067 | - type: map_at_10 |
| 1068 | value: 29.158 |
| 1069 | - type: map_at_100 |
| 1070 | value: 30.208000000000002 |
| 1071 | - type: map_at_1000 |
| 1072 | value: 30.304 |
| 1073 | - type: map_at_3 |
| 1074 | value: 26.82 |
| 1075 | - type: map_at_5 |
| 1076 | value: 28.051 |
| 1077 | - type: mrr_at_1 |
| 1078 | value: 23.66 |
| 1079 | - type: mrr_at_10 |
| 1080 | value: 31.277 |
| 1081 | - type: mrr_at_100 |
| 1082 | value: 32.237 |
| 1083 | - type: mrr_at_1000 |
| 1084 | value: 32.308 |
| 1085 | - type: mrr_at_3 |
| 1086 | value: 29.205 |
| 1087 | - type: mrr_at_5 |
| 1088 | value: 30.314000000000004 |
| 1089 | - type: ndcg_at_1 |
| 1090 | value: 23.66 |
| 1091 | - type: ndcg_at_10 |
| 1092 | value: 33.64 |
| 1093 | - type: ndcg_at_100 |
| 1094 | value: 39.028 |
| 1095 | - type: ndcg_at_1000 |
| 1096 | value: 41.423 |
| 1097 | - type: ndcg_at_3 |
| 1098 | value: 29.189 |
| 1099 | - type: ndcg_at_5 |
| 1100 | value: 31.191999999999997 |
| 1101 | - type: precision_at_1 |
| 1102 | value: 23.66 |
| 1103 | - type: precision_at_10 |
| 1104 | value: 5.287 |
| 1105 | - type: precision_at_100 |
| 1106 | value: 0.86 |
| 1107 | - type: precision_at_1000 |
| 1108 | value: 0.11499999999999999 |
| 1109 | - type: precision_at_3 |
| 1110 | value: 12.631 |
| 1111 | - type: precision_at_5 |
| 1112 | value: 8.762 |
| 1113 | - type: recall_at_1 |
| 1114 | value: 21.884999999999998 |
| 1115 | - type: recall_at_10 |
| 1116 | value: 45.357 |
| 1117 | - type: recall_at_100 |
| 1118 | value: 70.338 |
| 1119 | - type: recall_at_1000 |
| 1120 | value: 88.356 |
| 1121 | - type: recall_at_3 |
| 1122 | value: 33.312000000000005 |
| 1123 | - type: recall_at_5 |
| 1124 | value: 38.222 |
| 1125 | - task: |
| 1126 | type: Retrieval |
| 1127 | dataset: |
| 1128 | type: mteb/climate-fever |
| 1129 | name: MTEB ClimateFEVER |
| 1130 | config: default |
| 1131 | split: test |
| 1132 | revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
| 1133 | metrics: |
| 1134 | - type: map_at_1 |
| 1135 | value: 13.058 |
| 1136 | - type: map_at_10 |
| 1137 | value: 21.549 |
| 1138 | - type: map_at_100 |
| 1139 | value: 23.287 |
| 1140 | - type: map_at_1000 |
| 1141 | value: 23.444000000000003 |
| 1142 | - type: map_at_3 |
| 1143 | value: 18.18 |
| 1144 | - type: map_at_5 |
| 1145 | value: 19.886 |
| 1146 | - type: mrr_at_1 |
| 1147 | value: 28.73 |
| 1148 | - type: mrr_at_10 |
| 1149 | value: 40.014 |
| 1150 | - type: mrr_at_100 |
| 1151 | value: 40.827000000000005 |
| 1152 | - type: mrr_at_1000 |
| 1153 | value: 40.866 |
| 1154 | - type: mrr_at_3 |
| 1155 | value: 36.602000000000004 |
| 1156 | - type: mrr_at_5 |
| 1157 | value: 38.702 |
| 1158 | - type: ndcg_at_1 |
| 1159 | value: 28.73 |
| 1160 | - type: ndcg_at_10 |
| 1161 | value: 29.881 |
| 1162 | - type: ndcg_at_100 |
| 1163 | value: 36.662 |
| 1164 | - type: ndcg_at_1000 |
| 1165 | value: 39.641999999999996 |
| 1166 | - type: ndcg_at_3 |
| 1167 | value: 24.661 |
| 1168 | - type: ndcg_at_5 |
| 1169 | value: 26.548 |
| 1170 | - type: precision_at_1 |
| 1171 | value: 28.73 |
| 1172 | - type: precision_at_10 |
| 1173 | value: 9.094 |
| 1174 | - type: precision_at_100 |
| 1175 | value: 1.6480000000000001 |
| 1176 | - type: precision_at_1000 |
| 1177 | value: 0.22100000000000003 |
| 1178 | - type: precision_at_3 |
| 1179 | value: 17.98 |
| 1180 | - type: precision_at_5 |
| 1181 | value: 13.811000000000002 |
| 1182 | - type: recall_at_1 |
| 1183 | value: 13.058 |
| 1184 | - type: recall_at_10 |
| 1185 | value: 35.458 |
| 1186 | - type: recall_at_100 |
| 1187 | value: 58.719 |
| 1188 | - type: recall_at_1000 |
| 1189 | value: 75.495 |
| 1190 | - type: recall_at_3 |
| 1191 | value: 22.607 |
| 1192 | - type: recall_at_5 |
| 1193 | value: 28.067999999999998 |
| 1194 | - task: |
| 1195 | type: Retrieval |
| 1196 | dataset: |
| 1197 | type: mteb/dbpedia |
| 1198 | name: MTEB DBPedia |
| 1199 | config: default |
| 1200 | split: test |
| 1201 | revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
| 1202 | metrics: |
| 1203 | - type: map_at_1 |
| 1204 | value: 8.811 |
| 1205 | - type: map_at_10 |
| 1206 | value: 19.134999999999998 |
| 1207 | - type: map_at_100 |
| 1208 | value: 26.905 |
| 1209 | - type: map_at_1000 |
| 1210 | value: 28.503 |
| 1211 | - type: map_at_3 |
| 1212 | value: 13.863 |
| 1213 | - type: map_at_5 |
| 1214 | value: 16.062 |
| 1215 | - type: mrr_at_1 |
| 1216 | value: 67 |
| 1217 | - type: mrr_at_10 |
| 1218 | value: 74.607 |
| 1219 | - type: mrr_at_100 |
| 1220 | value: 74.941 |
| 1221 | - type: mrr_at_1000 |
| 1222 | value: 74.954 |
| 1223 | - type: mrr_at_3 |
| 1224 | value: 73.042 |
| 1225 | - type: mrr_at_5 |
| 1226 | value: 73.992 |
| 1227 | - type: ndcg_at_1 |
| 1228 | value: 52.87500000000001 |
| 1229 | - type: ndcg_at_10 |
| 1230 | value: 40.199 |
| 1231 | - type: ndcg_at_100 |
| 1232 | value: 44.901 |
| 1233 | - type: ndcg_at_1000 |
| 1234 | value: 52.239999999999995 |
| 1235 | - type: ndcg_at_3 |
| 1236 | value: 44.983000000000004 |
| 1237 | - type: ndcg_at_5 |
| 1238 | value: 42.137 |
| 1239 | - type: precision_at_1 |
| 1240 | value: 67 |
| 1241 | - type: precision_at_10 |
| 1242 | value: 31.8 |
| 1243 | - type: precision_at_100 |
| 1244 | value: 10.315000000000001 |
| 1245 | - type: precision_at_1000 |
| 1246 | value: 2.0420000000000003 |
| 1247 | - type: precision_at_3 |
| 1248 | value: 48.667 |
| 1249 | - type: precision_at_5 |
| 1250 | value: 40.9 |
| 1251 | - type: recall_at_1 |
| 1252 | value: 8.811 |
| 1253 | - type: recall_at_10 |
| 1254 | value: 24.503 |
| 1255 | - type: recall_at_100 |
| 1256 | value: 51.288999999999994 |
| 1257 | - type: recall_at_1000 |
| 1258 | value: 74.827 |
| 1259 | - type: recall_at_3 |
| 1260 | value: 15.254999999999999 |
| 1261 | - type: recall_at_5 |
| 1262 | value: 18.698999999999998 |
| 1263 | - task: |
| 1264 | type: Classification |
| 1265 | dataset: |
| 1266 | type: mteb/emotion |
| 1267 | name: MTEB EmotionClassification |
| 1268 | config: default |
| 1269 | split: test |
| 1270 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 1271 | metrics: |
| 1272 | - type: accuracy |
| 1273 | value: 41.839999999999996 |
| 1274 | - type: f1 |
| 1275 | value: 37.78718146306379 |
| 1276 | - task: |
| 1277 | type: Retrieval |
| 1278 | dataset: |
| 1279 | type: mteb/fever |
| 1280 | name: MTEB FEVER |
| 1281 | config: default |
| 1282 | split: test |
| 1283 | revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
| 1284 | metrics: |
| 1285 | - type: map_at_1 |
| 1286 | value: 68.47999999999999 |
| 1287 | - type: map_at_10 |
| 1288 | value: 78.782 |
| 1289 | - type: map_at_100 |
| 1290 | value: 79.021 |
| 1291 | - type: map_at_1000 |
| 1292 | value: 79.035 |
| 1293 | - type: map_at_3 |
| 1294 | value: 77.389 |
| 1295 | - type: map_at_5 |
| 1296 | value: 78.347 |
| 1297 | - type: mrr_at_1 |
| 1298 | value: 73.837 |
| 1299 | - type: mrr_at_10 |
| 1300 | value: 83.41499999999999 |
| 1301 | - type: mrr_at_100 |
| 1302 | value: 83.53399999999999 |
| 1303 | - type: mrr_at_1000 |
| 1304 | value: 83.535 |
| 1305 | - type: mrr_at_3 |
| 1306 | value: 82.32300000000001 |
| 1307 | - type: mrr_at_5 |
| 1308 | value: 83.13000000000001 |
| 1309 | - type: ndcg_at_1 |
| 1310 | value: 73.837 |
| 1311 | - type: ndcg_at_10 |
| 1312 | value: 83.404 |
| 1313 | - type: ndcg_at_100 |
| 1314 | value: 84.287 |
| 1315 | - type: ndcg_at_1000 |
| 1316 | value: 84.52199999999999 |
| 1317 | - type: ndcg_at_3 |
| 1318 | value: 81.072 |
| 1319 | - type: ndcg_at_5 |
| 1320 | value: 82.537 |
| 1321 | - type: precision_at_1 |
| 1322 | value: 73.837 |
| 1323 | - type: precision_at_10 |
| 1324 | value: 10.254000000000001 |
| 1325 | - type: precision_at_100 |
| 1326 | value: 1.088 |
| 1327 | - type: precision_at_1000 |
| 1328 | value: 0.11299999999999999 |
| 1329 | - type: precision_at_3 |
| 1330 | value: 31.538 |
| 1331 | - type: precision_at_5 |
| 1332 | value: 19.811 |
| 1333 | - type: recall_at_1 |
| 1334 | value: 68.47999999999999 |
| 1335 | - type: recall_at_10 |
| 1336 | value: 92.98100000000001 |
| 1337 | - type: recall_at_100 |
| 1338 | value: 96.50800000000001 |
| 1339 | - type: recall_at_1000 |
| 1340 | value: 97.925 |
| 1341 | - type: recall_at_3 |
| 1342 | value: 86.764 |
| 1343 | - type: recall_at_5 |
| 1344 | value: 90.39 |
| 1345 | - task: |
| 1346 | type: Retrieval |
| 1347 | dataset: |
| 1348 | type: mteb/fiqa |
| 1349 | name: MTEB FiQA2018 |
| 1350 | config: default |
| 1351 | split: test |
| 1352 | revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
| 1353 | metrics: |
| 1354 | - type: map_at_1 |
| 1355 | value: 16.786 |
| 1356 | - type: map_at_10 |
| 1357 | value: 26.97 |
| 1358 | - type: map_at_100 |
| 1359 | value: 28.488000000000003 |
| 1360 | - type: map_at_1000 |
| 1361 | value: 28.665000000000003 |
| 1362 | - type: map_at_3 |
| 1363 | value: 23.3 |
| 1364 | - type: map_at_5 |
| 1365 | value: 25.249 |
| 1366 | - type: mrr_at_1 |
| 1367 | value: 33.025 |
| 1368 | - type: mrr_at_10 |
| 1369 | value: 41.86 |
| 1370 | - type: mrr_at_100 |
| 1371 | value: 42.673 |
| 1372 | - type: mrr_at_1000 |
| 1373 | value: 42.714 |
| 1374 | - type: mrr_at_3 |
| 1375 | value: 39.403 |
| 1376 | - type: mrr_at_5 |
| 1377 | value: 40.723 |
| 1378 | - type: ndcg_at_1 |
| 1379 | value: 33.025 |
| 1380 | - type: ndcg_at_10 |
| 1381 | value: 34.522999999999996 |
| 1382 | - type: ndcg_at_100 |
| 1383 | value: 40.831 |
| 1384 | - type: ndcg_at_1000 |
| 1385 | value: 44.01 |
| 1386 | - type: ndcg_at_3 |
| 1387 | value: 30.698999999999998 |
| 1388 | - type: ndcg_at_5 |
| 1389 | value: 31.832 |
| 1390 | - type: precision_at_1 |
| 1391 | value: 33.025 |
| 1392 | - type: precision_at_10 |
| 1393 | value: 9.583 |
| 1394 | - type: precision_at_100 |
| 1395 | value: 1.619 |
| 1396 | - type: precision_at_1000 |
| 1397 | value: 0.22100000000000003 |
| 1398 | - type: precision_at_3 |
| 1399 | value: 20.216 |
| 1400 | - type: precision_at_5 |
| 1401 | value: 15.031 |
| 1402 | - type: recall_at_1 |
| 1403 | value: 16.786 |
| 1404 | - type: recall_at_10 |
| 1405 | value: 41.969 |
| 1406 | - type: recall_at_100 |
| 1407 | value: 66.353 |
| 1408 | - type: recall_at_1000 |
| 1409 | value: 85.299 |
| 1410 | - type: recall_at_3 |
| 1411 | value: 28.111000000000004 |
| 1412 | - type: recall_at_5 |
| 1413 | value: 33.645 |
| 1414 | - task: |
| 1415 | type: Retrieval |
| 1416 | dataset: |
| 1417 | type: mteb/hotpotqa |
| 1418 | name: MTEB HotpotQA |
| 1419 | config: default |
| 1420 | split: test |
| 1421 | revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
| 1422 | metrics: |
| 1423 | - type: map_at_1 |
| 1424 | value: 37.346000000000004 |
| 1425 | - type: map_at_10 |
| 1426 | value: 56.184999999999995 |
| 1427 | - type: map_at_100 |
| 1428 | value: 57.062000000000005 |
| 1429 | - type: map_at_1000 |
| 1430 | value: 57.126999999999995 |
| 1431 | - type: map_at_3 |
| 1432 | value: 52.815 |
| 1433 | - type: map_at_5 |
| 1434 | value: 54.893 |
| 1435 | - type: mrr_at_1 |
| 1436 | value: 74.693 |
| 1437 | - type: mrr_at_10 |
| 1438 | value: 81.128 |
| 1439 | - type: mrr_at_100 |
| 1440 | value: 81.356 |
| 1441 | - type: mrr_at_1000 |
| 1442 | value: 81.363 |
| 1443 | - type: mrr_at_3 |
| 1444 | value: 80.05600000000001 |
| 1445 | - type: mrr_at_5 |
| 1446 | value: 80.74 |
| 1447 | - type: ndcg_at_1 |
| 1448 | value: 74.693 |
| 1449 | - type: ndcg_at_10 |
| 1450 | value: 65.249 |
| 1451 | - type: ndcg_at_100 |
| 1452 | value: 68.357 |
| 1453 | - type: ndcg_at_1000 |
| 1454 | value: 69.64200000000001 |
| 1455 | - type: ndcg_at_3 |
| 1456 | value: 60.377 |
| 1457 | - type: ndcg_at_5 |
| 1458 | value: 63.044 |
| 1459 | - type: precision_at_1 |
| 1460 | value: 74.693 |
| 1461 | - type: precision_at_10 |
| 1462 | value: 13.630999999999998 |
| 1463 | - type: precision_at_100 |
| 1464 | value: 1.606 |
| 1465 | - type: precision_at_1000 |
| 1466 | value: 0.178 |
| 1467 | - type: precision_at_3 |
| 1468 | value: 38.222 |
| 1469 | - type: precision_at_5 |
| 1470 | value: 25.040000000000003 |
| 1471 | - type: recall_at_1 |
| 1472 | value: 37.346000000000004 |
| 1473 | - type: recall_at_10 |
| 1474 | value: 68.157 |
| 1475 | - type: recall_at_100 |
| 1476 | value: 80.297 |
| 1477 | - type: recall_at_1000 |
| 1478 | value: 88.832 |
| 1479 | - type: recall_at_3 |
| 1480 | value: 57.333 |
| 1481 | - type: recall_at_5 |
| 1482 | value: 62.6 |
| 1483 | - task: |
| 1484 | type: Classification |
| 1485 | dataset: |
| 1486 | type: mteb/imdb |
| 1487 | name: MTEB ImdbClassification |
| 1488 | config: default |
| 1489 | split: test |
| 1490 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 1491 | metrics: |
| 1492 | - type: accuracy |
| 1493 | value: 62.80240000000001 |
| 1494 | - type: ap |
| 1495 | value: 58.22949464075975 |
| 1496 | - type: f1 |
| 1497 | value: 62.55694937343487 |
| 1498 | - task: |
| 1499 | type: Retrieval |
| 1500 | dataset: |
| 1501 | type: mteb/msmarco |
| 1502 | name: MTEB MSMARCO |
| 1503 | config: default |
| 1504 | split: dev |
| 1505 | revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| 1506 | metrics: |
| 1507 | - type: map_at_1 |
| 1508 | value: 20.918 |
| 1509 | - type: map_at_10 |
| 1510 | value: 32.732 |
| 1511 | - type: map_at_100 |
| 1512 | value: 33.922000000000004 |
| 1513 | - type: map_at_1000 |
| 1514 | value: 33.976 |
| 1515 | - type: map_at_3 |
| 1516 | value: 29.051 |
| 1517 | - type: map_at_5 |
| 1518 | value: 31.101 |
| 1519 | - type: mrr_at_1 |
| 1520 | value: 21.418 |
| 1521 | - type: mrr_at_10 |
| 1522 | value: 33.284000000000006 |
| 1523 | - type: mrr_at_100 |
| 1524 | value: 34.426 |
| 1525 | - type: mrr_at_1000 |
| 1526 | value: 34.473 |
| 1527 | - type: mrr_at_3 |
| 1528 | value: 29.644 |
| 1529 | - type: mrr_at_5 |
| 1530 | value: 31.691000000000003 |
| 1531 | - type: ndcg_at_1 |
| 1532 | value: 21.418 |
| 1533 | - type: ndcg_at_10 |
| 1534 | value: 39.427 |
| 1535 | - type: ndcg_at_100 |
| 1536 | value: 45.190999999999995 |
| 1537 | - type: ndcg_at_1000 |
| 1538 | value: 46.544000000000004 |
| 1539 | - type: ndcg_at_3 |
| 1540 | value: 31.885 |
| 1541 | - type: ndcg_at_5 |
| 1542 | value: 35.555 |
| 1543 | - type: precision_at_1 |
| 1544 | value: 21.418 |
| 1545 | - type: precision_at_10 |
| 1546 | value: 6.254999999999999 |
| 1547 | - type: precision_at_100 |
| 1548 | value: 0.915 |
| 1549 | - type: precision_at_1000 |
| 1550 | value: 0.10300000000000001 |
| 1551 | - type: precision_at_3 |
| 1552 | value: 13.591000000000001 |
| 1553 | - type: precision_at_5 |
| 1554 | value: 10.011000000000001 |
| 1555 | - type: recall_at_1 |
| 1556 | value: 20.918 |
| 1557 | - type: recall_at_10 |
| 1558 | value: 60.074000000000005 |
| 1559 | - type: recall_at_100 |
| 1560 | value: 86.726 |
| 1561 | - type: recall_at_1000 |
| 1562 | value: 97.116 |
| 1563 | - type: recall_at_3 |
| 1564 | value: 39.506 |
| 1565 | - type: recall_at_5 |
| 1566 | value: 48.319 |
| 1567 | - task: |
| 1568 | type: Classification |
| 1569 | dataset: |
| 1570 | type: mteb/mtop_domain |
| 1571 | name: MTEB MTOPDomainClassification (en) |
| 1572 | config: en |
| 1573 | split: test |
| 1574 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1575 | metrics: |
| 1576 | - type: accuracy |
| 1577 | value: 90.79799361605106 |
| 1578 | - type: f1 |
| 1579 | value: 90.0757957511057 |
| 1580 | - task: |
| 1581 | type: Classification |
| 1582 | dataset: |
| 1583 | type: mteb/mtop_intent |
| 1584 | name: MTEB MTOPIntentClassification (en) |
| 1585 | config: en |
| 1586 | split: test |
| 1587 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1588 | metrics: |
| 1589 | - type: accuracy |
| 1590 | value: 58.00501595987233 |
| 1591 | - type: f1 |
| 1592 | value: 39.85731569133947 |
| 1593 | - task: |
| 1594 | type: Classification |
| 1595 | dataset: |
| 1596 | type: masakhane/masakhanews |
| 1597 | name: MTEB MasakhaNEWSClassification (eng) |
| 1598 | config: eng |
| 1599 | split: test |
| 1600 | revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| 1601 | metrics: |
| 1602 | - type: accuracy |
| 1603 | value: 77.10970464135022 |
| 1604 | - type: f1 |
| 1605 | value: 76.12037616356896 |
| 1606 | - task: |
| 1607 | type: Clustering |
| 1608 | dataset: |
| 1609 | type: masakhane/masakhanews |
| 1610 | name: MTEB MasakhaNEWSClusteringP2P (eng) |
| 1611 | config: eng |
| 1612 | split: test |
| 1613 | revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| 1614 | metrics: |
| 1615 | - type: v_measure |
| 1616 | value: 69.81323966287493 |
| 1617 | - task: |
| 1618 | type: Clustering |
| 1619 | dataset: |
| 1620 | type: masakhane/masakhanews |
| 1621 | name: MTEB MasakhaNEWSClusteringS2S (eng) |
| 1622 | config: eng |
| 1623 | split: test |
| 1624 | revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| 1625 | metrics: |
| 1626 | - type: v_measure |
| 1627 | value: 33.112774215788455 |
| 1628 | - task: |
| 1629 | type: Classification |
| 1630 | dataset: |
| 1631 | type: mteb/amazon_massive_intent |
| 1632 | name: MTEB MassiveIntentClassification (en) |
| 1633 | config: en |
| 1634 | split: test |
| 1635 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1636 | metrics: |
| 1637 | - type: accuracy |
| 1638 | value: 63.51042367182246 |
| 1639 | - type: f1 |
| 1640 | value: 60.99310361578824 |
| 1641 | - task: |
| 1642 | type: Classification |
| 1643 | dataset: |
| 1644 | type: mteb/amazon_massive_scenario |
| 1645 | name: MTEB MassiveScenarioClassification (en) |
| 1646 | config: en |
| 1647 | split: test |
| 1648 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1649 | metrics: |
| 1650 | - type: accuracy |
| 1651 | value: 71.0053799596503 |
| 1652 | - type: f1 |
| 1653 | value: 69.7794673003686 |
| 1654 | - task: |
| 1655 | type: Clustering |
| 1656 | dataset: |
| 1657 | type: mteb/medrxiv-clustering-p2p |
| 1658 | name: MTEB MedrxivClusteringP2P |
| 1659 | config: default |
| 1660 | split: test |
| 1661 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 1662 | metrics: |
| 1663 | - type: v_measure |
| 1664 | value: 30.56899174856954 |
| 1665 | - task: |
| 1666 | type: Clustering |
| 1667 | dataset: |
| 1668 | type: mteb/medrxiv-clustering-s2s |
| 1669 | name: MTEB MedrxivClusteringS2S |
| 1670 | config: default |
| 1671 | split: test |
| 1672 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 1673 | metrics: |
| 1674 | - type: v_measure |
| 1675 | value: 26.21848014733929 |
| 1676 | - task: |
| 1677 | type: Reranking |
| 1678 | dataset: |
| 1679 | type: mteb/mind_small |
| 1680 | name: MTEB MindSmallReranking |
| 1681 | config: default |
| 1682 | split: test |
| 1683 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 1684 | metrics: |
| 1685 | - type: map |
| 1686 | value: 30.256308756916646 |
| 1687 | - type: mrr |
| 1688 | value: 31.123872086825656 |
| 1689 | - task: |
| 1690 | type: Retrieval |
| 1691 | dataset: |
| 1692 | type: mteb/nfcorpus |
| 1693 | name: MTEB NFCorpus |
| 1694 | config: default |
| 1695 | split: test |
| 1696 | revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| 1697 | metrics: |
| 1698 | - type: map_at_1 |
| 1699 | value: 5.07 |
| 1700 | - type: map_at_10 |
| 1701 | value: 11.286999999999999 |
| 1702 | - type: map_at_100 |
| 1703 | value: 13.630999999999998 |
| 1704 | - type: map_at_1000 |
| 1705 | value: 14.844 |
| 1706 | - type: map_at_3 |
| 1707 | value: 8.395 |
| 1708 | - type: map_at_5 |
| 1709 | value: 9.721 |
| 1710 | - type: mrr_at_1 |
| 1711 | value: 41.486000000000004 |
| 1712 | - type: mrr_at_10 |
| 1713 | value: 51.041000000000004 |
| 1714 | - type: mrr_at_100 |
| 1715 | value: 51.661 |
| 1716 | - type: mrr_at_1000 |
| 1717 | value: 51.7 |
| 1718 | - type: mrr_at_3 |
| 1719 | value: 49.226 |
| 1720 | - type: mrr_at_5 |
| 1721 | value: 50.526 |
| 1722 | - type: ndcg_at_1 |
| 1723 | value: 39.783 |
| 1724 | - type: ndcg_at_10 |
| 1725 | value: 30.885 |
| 1726 | - type: ndcg_at_100 |
| 1727 | value: 27.459 |
| 1728 | - type: ndcg_at_1000 |
| 1729 | value: 35.988 |
| 1730 | - type: ndcg_at_3 |
| 1731 | value: 36.705 |
| 1732 | - type: ndcg_at_5 |
| 1733 | value: 34.156 |
| 1734 | - type: precision_at_1 |
| 1735 | value: 41.486000000000004 |
| 1736 | - type: precision_at_10 |
| 1737 | value: 22.415 |
| 1738 | - type: precision_at_100 |
| 1739 | value: 6.819999999999999 |
| 1740 | - type: precision_at_1000 |
| 1741 | value: 1.8980000000000001 |
| 1742 | - type: precision_at_3 |
| 1743 | value: 34.572 |
| 1744 | - type: precision_at_5 |
| 1745 | value: 29.287999999999997 |
| 1746 | - type: recall_at_1 |
| 1747 | value: 5.07 |
| 1748 | - type: recall_at_10 |
| 1749 | value: 14.576 |
| 1750 | - type: recall_at_100 |
| 1751 | value: 27.112000000000002 |
| 1752 | - type: recall_at_1000 |
| 1753 | value: 57.995 |
| 1754 | - type: recall_at_3 |
| 1755 | value: 9.242 |
| 1756 | - type: recall_at_5 |
| 1757 | value: 11.668000000000001 |
| 1758 | - task: |
| 1759 | type: Retrieval |
| 1760 | dataset: |
| 1761 | type: mteb/nq |
| 1762 | name: MTEB NQ |
| 1763 | config: default |
| 1764 | split: test |
| 1765 | revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
| 1766 | metrics: |
| 1767 | - type: map_at_1 |
| 1768 | value: 32.263999999999996 |
| 1769 | - type: map_at_10 |
| 1770 | value: 47.219 |
| 1771 | - type: map_at_100 |
| 1772 | value: 48.209999999999994 |
| 1773 | - type: map_at_1000 |
| 1774 | value: 48.24 |
| 1775 | - type: map_at_3 |
| 1776 | value: 42.905 |
| 1777 | - type: map_at_5 |
| 1778 | value: 45.501000000000005 |
| 1779 | - type: mrr_at_1 |
| 1780 | value: 36.153 |
| 1781 | - type: mrr_at_10 |
| 1782 | value: 49.636 |
| 1783 | - type: mrr_at_100 |
| 1784 | value: 50.357 |
| 1785 | - type: mrr_at_1000 |
| 1786 | value: 50.378 |
| 1787 | - type: mrr_at_3 |
| 1788 | value: 46.094 |
| 1789 | - type: mrr_at_5 |
| 1790 | value: 48.233 |
| 1791 | - type: ndcg_at_1 |
| 1792 | value: 36.124 |
| 1793 | - type: ndcg_at_10 |
| 1794 | value: 54.764 |
| 1795 | - type: ndcg_at_100 |
| 1796 | value: 58.867999999999995 |
| 1797 | - type: ndcg_at_1000 |
| 1798 | value: 59.548 |
| 1799 | - type: ndcg_at_3 |
| 1800 | value: 46.717999999999996 |
| 1801 | - type: ndcg_at_5 |
| 1802 | value: 50.981 |
| 1803 | - type: precision_at_1 |
| 1804 | value: 36.124 |
| 1805 | - type: precision_at_10 |
| 1806 | value: 8.931000000000001 |
| 1807 | - type: precision_at_100 |
| 1808 | value: 1.126 |
| 1809 | - type: precision_at_1000 |
| 1810 | value: 0.11900000000000001 |
| 1811 | - type: precision_at_3 |
| 1812 | value: 21.051000000000002 |
| 1813 | - type: precision_at_5 |
| 1814 | value: 15.104000000000001 |
| 1815 | - type: recall_at_1 |
| 1816 | value: 32.263999999999996 |
| 1817 | - type: recall_at_10 |
| 1818 | value: 75.39099999999999 |
| 1819 | - type: recall_at_100 |
| 1820 | value: 93.038 |
| 1821 | - type: recall_at_1000 |
| 1822 | value: 98.006 |
| 1823 | - type: recall_at_3 |
| 1824 | value: 54.562999999999995 |
| 1825 | - type: recall_at_5 |
| 1826 | value: 64.352 |
| 1827 | - task: |
| 1828 | type: Classification |
| 1829 | dataset: |
| 1830 | type: ag_news |
| 1831 | name: MTEB NewsClassification |
| 1832 | config: default |
| 1833 | split: test |
| 1834 | revision: eb185aade064a813bc0b7f42de02595523103ca4 |
| 1835 | metrics: |
| 1836 | - type: accuracy |
| 1837 | value: 77.75 |
| 1838 | - type: f1 |
| 1839 | value: 77.504243291547 |
| 1840 | - task: |
| 1841 | type: PairClassification |
| 1842 | dataset: |
| 1843 | type: GEM/opusparcus |
| 1844 | name: MTEB OpusparcusPC (en) |
| 1845 | config: en |
| 1846 | split: test |
| 1847 | revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
| 1848 | metrics: |
| 1849 | - type: cos_sim_accuracy |
| 1850 | value: 99.89816700610999 |
| 1851 | - type: cos_sim_ap |
| 1852 | value: 100 |
| 1853 | - type: cos_sim_f1 |
| 1854 | value: 99.9490575649516 |
| 1855 | - type: cos_sim_precision |
| 1856 | value: 100 |
| 1857 | - type: cos_sim_recall |
| 1858 | value: 99.89816700610999 |
| 1859 | - type: dot_accuracy |
| 1860 | value: 99.89816700610999 |
| 1861 | - type: dot_ap |
| 1862 | value: 100 |
| 1863 | - type: dot_f1 |
| 1864 | value: 99.9490575649516 |
| 1865 | - type: dot_precision |
| 1866 | value: 100 |
| 1867 | - type: dot_recall |
| 1868 | value: 99.89816700610999 |
| 1869 | - type: euclidean_accuracy |
| 1870 | value: 99.89816700610999 |
| 1871 | - type: euclidean_ap |
| 1872 | value: 100 |
| 1873 | - type: euclidean_f1 |
| 1874 | value: 99.9490575649516 |
| 1875 | - type: euclidean_precision |
| 1876 | value: 100 |
| 1877 | - type: euclidean_recall |
| 1878 | value: 99.89816700610999 |
| 1879 | - type: manhattan_accuracy |
| 1880 | value: 99.89816700610999 |
| 1881 | - type: manhattan_ap |
| 1882 | value: 100 |
| 1883 | - type: manhattan_f1 |
| 1884 | value: 99.9490575649516 |
| 1885 | - type: manhattan_precision |
| 1886 | value: 100 |
| 1887 | - type: manhattan_recall |
| 1888 | value: 99.89816700610999 |
| 1889 | - type: max_accuracy |
| 1890 | value: 99.89816700610999 |
| 1891 | - type: max_ap |
| 1892 | value: 100 |
| 1893 | - type: max_f1 |
| 1894 | value: 99.9490575649516 |
| 1895 | - task: |
| 1896 | type: PairClassification |
| 1897 | dataset: |
| 1898 | type: paws-x |
| 1899 | name: MTEB PawsX (en) |
| 1900 | config: en |
| 1901 | split: test |
| 1902 | revision: 8a04d940a42cd40658986fdd8e3da561533a3646 |
| 1903 | metrics: |
| 1904 | - type: cos_sim_accuracy |
| 1905 | value: 61.75000000000001 |
| 1906 | - type: cos_sim_ap |
| 1907 | value: 57.9482264289061 |
| 1908 | - type: cos_sim_f1 |
| 1909 | value: 62.444061962134256 |
| 1910 | - type: cos_sim_precision |
| 1911 | value: 45.3953953953954 |
| 1912 | - type: cos_sim_recall |
| 1913 | value: 100 |
| 1914 | - type: dot_accuracy |
| 1915 | value: 61.75000000000001 |
| 1916 | - type: dot_ap |
| 1917 | value: 57.94808038610475 |
| 1918 | - type: dot_f1 |
| 1919 | value: 62.444061962134256 |
| 1920 | - type: dot_precision |
| 1921 | value: 45.3953953953954 |
| 1922 | - type: dot_recall |
| 1923 | value: 100 |
| 1924 | - type: euclidean_accuracy |
| 1925 | value: 61.75000000000001 |
| 1926 | - type: euclidean_ap |
| 1927 | value: 57.94808038610475 |
| 1928 | - type: euclidean_f1 |
| 1929 | value: 62.444061962134256 |
| 1930 | - type: euclidean_precision |
| 1931 | value: 45.3953953953954 |
| 1932 | - type: euclidean_recall |
| 1933 | value: 100 |
| 1934 | - type: manhattan_accuracy |
| 1935 | value: 61.7 |
| 1936 | - type: manhattan_ap |
| 1937 | value: 57.996119308184966 |
| 1938 | - type: manhattan_f1 |
| 1939 | value: 62.46078773091669 |
| 1940 | - type: manhattan_precision |
| 1941 | value: 45.66768603465851 |
| 1942 | - type: manhattan_recall |
| 1943 | value: 98.78721058434398 |
| 1944 | - type: max_accuracy |
| 1945 | value: 61.75000000000001 |
| 1946 | - type: max_ap |
| 1947 | value: 57.996119308184966 |
| 1948 | - type: max_f1 |
| 1949 | value: 62.46078773091669 |
| 1950 | - task: |
| 1951 | type: Retrieval |
| 1952 | dataset: |
| 1953 | type: mteb/quora |
| 1954 | name: MTEB QuoraRetrieval |
| 1955 | config: default |
| 1956 | split: test |
| 1957 | revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
| 1958 | metrics: |
| 1959 | - type: map_at_1 |
| 1960 | value: 69.001 |
| 1961 | - type: map_at_10 |
| 1962 | value: 82.573 |
| 1963 | - type: map_at_100 |
| 1964 | value: 83.226 |
| 1965 | - type: map_at_1000 |
| 1966 | value: 83.246 |
| 1967 | - type: map_at_3 |
| 1968 | value: 79.625 |
| 1969 | - type: map_at_5 |
| 1970 | value: 81.491 |
| 1971 | - type: mrr_at_1 |
| 1972 | value: 79.44 |
| 1973 | - type: mrr_at_10 |
| 1974 | value: 85.928 |
| 1975 | - type: mrr_at_100 |
| 1976 | value: 86.05199999999999 |
| 1977 | - type: mrr_at_1000 |
| 1978 | value: 86.054 |
| 1979 | - type: mrr_at_3 |
| 1980 | value: 84.847 |
| 1981 | - type: mrr_at_5 |
| 1982 | value: 85.596 |
| 1983 | - type: ndcg_at_1 |
| 1984 | value: 79.41 |
| 1985 | - type: ndcg_at_10 |
| 1986 | value: 86.568 |
| 1987 | - type: ndcg_at_100 |
| 1988 | value: 87.965 |
| 1989 | - type: ndcg_at_1000 |
| 1990 | value: 88.134 |
| 1991 | - type: ndcg_at_3 |
| 1992 | value: 83.55900000000001 |
| 1993 | - type: ndcg_at_5 |
| 1994 | value: 85.244 |
| 1995 | - type: precision_at_1 |
| 1996 | value: 79.41 |
| 1997 | - type: precision_at_10 |
| 1998 | value: 13.108 |
| 1999 | - type: precision_at_100 |
| 2000 | value: 1.509 |
| 2001 | - type: precision_at_1000 |
| 2002 | value: 0.156 |
| 2003 | - type: precision_at_3 |
| 2004 | value: 36.443 |
| 2005 | - type: precision_at_5 |
| 2006 | value: 24.03 |
| 2007 | - type: recall_at_1 |
| 2008 | value: 69.001 |
| 2009 | - type: recall_at_10 |
| 2010 | value: 94.132 |
| 2011 | - type: recall_at_100 |
| 2012 | value: 99.043 |
| 2013 | - type: recall_at_1000 |
| 2014 | value: 99.878 |
| 2015 | - type: recall_at_3 |
| 2016 | value: 85.492 |
| 2017 | - type: recall_at_5 |
| 2018 | value: 90.226 |
| 2019 | - task: |
| 2020 | type: Clustering |
| 2021 | dataset: |
| 2022 | type: mteb/reddit-clustering |
| 2023 | name: MTEB RedditClustering |
| 2024 | config: default |
| 2025 | split: test |
| 2026 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 2027 | metrics: |
| 2028 | - type: v_measure |
| 2029 | value: 48.3161352736264 |
| 2030 | - task: |
| 2031 | type: Clustering |
| 2032 | dataset: |
| 2033 | type: mteb/reddit-clustering-p2p |
| 2034 | name: MTEB RedditClusteringP2P |
| 2035 | config: default |
| 2036 | split: test |
| 2037 | revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
| 2038 | metrics: |
| 2039 | - type: v_measure |
| 2040 | value: 57.83784484156747 |
| 2041 | - task: |
| 2042 | type: Retrieval |
| 2043 | dataset: |
| 2044 | type: mteb/scidocs |
| 2045 | name: MTEB SCIDOCS |
| 2046 | config: default |
| 2047 | split: test |
| 2048 | revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
| 2049 | metrics: |
| 2050 | - type: map_at_1 |
| 2051 | value: 4.403 |
| 2052 | - type: map_at_10 |
| 2053 | value: 10.922 |
| 2054 | - type: map_at_100 |
| 2055 | value: 12.626000000000001 |
| 2056 | - type: map_at_1000 |
| 2057 | value: 12.883 |
| 2058 | - type: map_at_3 |
| 2059 | value: 7.982 |
| 2060 | - type: map_at_5 |
| 2061 | value: 9.442 |
| 2062 | - type: mrr_at_1 |
| 2063 | value: 21.7 |
| 2064 | - type: mrr_at_10 |
| 2065 | value: 31.653 |
| 2066 | - type: mrr_at_100 |
| 2067 | value: 32.757999999999996 |
| 2068 | - type: mrr_at_1000 |
| 2069 | value: 32.824999999999996 |
| 2070 | - type: mrr_at_3 |
| 2071 | value: 28.266999999999996 |
| 2072 | - type: mrr_at_5 |
| 2073 | value: 30.127 |
| 2074 | - type: ndcg_at_1 |
| 2075 | value: 21.7 |
| 2076 | - type: ndcg_at_10 |
| 2077 | value: 18.355 |
| 2078 | - type: ndcg_at_100 |
| 2079 | value: 25.228 |
| 2080 | - type: ndcg_at_1000 |
| 2081 | value: 30.164 |
| 2082 | - type: ndcg_at_3 |
| 2083 | value: 17.549 |
| 2084 | - type: ndcg_at_5 |
| 2085 | value: 15.260000000000002 |
| 2086 | - type: precision_at_1 |
| 2087 | value: 21.7 |
| 2088 | - type: precision_at_10 |
| 2089 | value: 9.47 |
| 2090 | - type: precision_at_100 |
| 2091 | value: 1.9290000000000003 |
| 2092 | - type: precision_at_1000 |
| 2093 | value: 0.312 |
| 2094 | - type: precision_at_3 |
| 2095 | value: 16.3 |
| 2096 | - type: precision_at_5 |
| 2097 | value: 13.28 |
| 2098 | - type: recall_at_1 |
| 2099 | value: 4.403 |
| 2100 | - type: recall_at_10 |
| 2101 | value: 19.18 |
| 2102 | - type: recall_at_100 |
| 2103 | value: 39.182 |
| 2104 | - type: recall_at_1000 |
| 2105 | value: 63.378 |
| 2106 | - type: recall_at_3 |
| 2107 | value: 9.934999999999999 |
| 2108 | - type: recall_at_5 |
| 2109 | value: 13.459999999999999 |
| 2110 | - task: |
| 2111 | type: STS |
| 2112 | dataset: |
| 2113 | type: mteb/sickr-sts |
| 2114 | name: MTEB SICK-R |
| 2115 | config: default |
| 2116 | split: test |
| 2117 | revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| 2118 | metrics: |
| 2119 | - type: cos_sim_pearson |
| 2120 | value: 76.90841073432534 |
| 2121 | - type: cos_sim_spearman |
| 2122 | value: 69.2566375434526 |
| 2123 | - type: euclidean_pearson |
| 2124 | value: 73.00183878559413 |
| 2125 | - type: euclidean_spearman |
| 2126 | value: 69.25664656235413 |
| 2127 | - type: manhattan_pearson |
| 2128 | value: 72.89594756197533 |
| 2129 | - type: manhattan_spearman |
| 2130 | value: 69.23247111043545 |
| 2131 | - task: |
| 2132 | type: STS |
| 2133 | dataset: |
| 2134 | type: mteb/sts12-sts |
| 2135 | name: MTEB STS12 |
| 2136 | config: default |
| 2137 | split: test |
| 2138 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 2139 | metrics: |
| 2140 | - type: cos_sim_pearson |
| 2141 | value: 69.60878511794063 |
| 2142 | - type: cos_sim_spearman |
| 2143 | value: 65.89916377105551 |
| 2144 | - type: euclidean_pearson |
| 2145 | value: 66.90761876557181 |
| 2146 | - type: euclidean_spearman |
| 2147 | value: 65.89915018368384 |
| 2148 | - type: manhattan_pearson |
| 2149 | value: 66.78502575257721 |
| 2150 | - type: manhattan_spearman |
| 2151 | value: 65.79977053467938 |
| 2152 | - task: |
| 2153 | type: STS |
| 2154 | dataset: |
| 2155 | type: mteb/sts13-sts |
| 2156 | name: MTEB STS13 |
| 2157 | config: default |
| 2158 | split: test |
| 2159 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 2160 | metrics: |
| 2161 | - type: cos_sim_pearson |
| 2162 | value: 77.2869334987418 |
| 2163 | - type: cos_sim_spearman |
| 2164 | value: 77.86961921643416 |
| 2165 | - type: euclidean_pearson |
| 2166 | value: 77.43179820479914 |
| 2167 | - type: euclidean_spearman |
| 2168 | value: 77.86961921643416 |
| 2169 | - type: manhattan_pearson |
| 2170 | value: 77.18900647348373 |
| 2171 | - type: manhattan_spearman |
| 2172 | value: 77.61209060062608 |
| 2173 | - task: |
| 2174 | type: STS |
| 2175 | dataset: |
| 2176 | type: mteb/sts14-sts |
| 2177 | name: MTEB STS14 |
| 2178 | config: default |
| 2179 | split: test |
| 2180 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 2181 | metrics: |
| 2182 | - type: cos_sim_pearson |
| 2183 | value: 76.26453932960364 |
| 2184 | - type: cos_sim_spearman |
| 2185 | value: 72.81574657995401 |
| 2186 | - type: euclidean_pearson |
| 2187 | value: 75.0708953437423 |
| 2188 | - type: euclidean_spearman |
| 2189 | value: 72.81574657995401 |
| 2190 | - type: manhattan_pearson |
| 2191 | value: 74.88396609999512 |
| 2192 | - type: manhattan_spearman |
| 2193 | value: 72.65437562156805 |
| 2194 | - task: |
| 2195 | type: STS |
| 2196 | dataset: |
| 2197 | type: mteb/sts15-sts |
| 2198 | name: MTEB STS15 |
| 2199 | config: default |
| 2200 | split: test |
| 2201 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 2202 | metrics: |
| 2203 | - type: cos_sim_pearson |
| 2204 | value: 82.37827653919395 |
| 2205 | - type: cos_sim_spearman |
| 2206 | value: 83.4885552472602 |
| 2207 | - type: euclidean_pearson |
| 2208 | value: 82.89377087926749 |
| 2209 | - type: euclidean_spearman |
| 2210 | value: 83.4885552472602 |
| 2211 | - type: manhattan_pearson |
| 2212 | value: 82.82440771787735 |
| 2213 | - type: manhattan_spearman |
| 2214 | value: 83.41449537888975 |
| 2215 | - task: |
| 2216 | type: STS |
| 2217 | dataset: |
| 2218 | type: mteb/sts16-sts |
| 2219 | name: MTEB STS16 |
| 2220 | config: default |
| 2221 | split: test |
| 2222 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 2223 | metrics: |
| 2224 | - type: cos_sim_pearson |
| 2225 | value: 78.7995043673964 |
| 2226 | - type: cos_sim_spearman |
| 2227 | value: 80.57804447517638 |
| 2228 | - type: euclidean_pearson |
| 2229 | value: 80.03013884278195 |
| 2230 | - type: euclidean_spearman |
| 2231 | value: 80.57804447517638 |
| 2232 | - type: manhattan_pearson |
| 2233 | value: 80.13406111544424 |
| 2234 | - type: manhattan_spearman |
| 2235 | value: 80.65354602648962 |
| 2236 | - task: |
| 2237 | type: STS |
| 2238 | dataset: |
| 2239 | type: mteb/sts17-crosslingual-sts |
| 2240 | name: MTEB STS17 (en-en) |
| 2241 | config: en-en |
| 2242 | split: test |
| 2243 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2244 | metrics: |
| 2245 | - type: cos_sim_pearson |
| 2246 | value: 83.63565989937278 |
| 2247 | - type: cos_sim_spearman |
| 2248 | value: 84.4948593656943 |
| 2249 | - type: euclidean_pearson |
| 2250 | value: 84.68743074820951 |
| 2251 | - type: euclidean_spearman |
| 2252 | value: 84.4948593656943 |
| 2253 | - type: manhattan_pearson |
| 2254 | value: 84.43639397781811 |
| 2255 | - type: manhattan_spearman |
| 2256 | value: 84.32595552115242 |
| 2257 | - task: |
| 2258 | type: STS |
| 2259 | dataset: |
| 2260 | type: mteb/sts22-crosslingual-sts |
| 2261 | name: MTEB STS22 (en) |
| 2262 | config: en |
| 2263 | split: test |
| 2264 | revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
| 2265 | metrics: |
| 2266 | - type: cos_sim_pearson |
| 2267 | value: 65.06382649277246 |
| 2268 | - type: cos_sim_spearman |
| 2269 | value: 66.28447782018655 |
| 2270 | - type: euclidean_pearson |
| 2271 | value: 67.09895930908392 |
| 2272 | - type: euclidean_spearman |
| 2273 | value: 66.28447782018655 |
| 2274 | - type: manhattan_pearson |
| 2275 | value: 66.96342453888376 |
| 2276 | - type: manhattan_spearman |
| 2277 | value: 66.33876259551842 |
| 2278 | - task: |
| 2279 | type: STS |
| 2280 | dataset: |
| 2281 | type: mteb/stsbenchmark-sts |
| 2282 | name: MTEB STSBenchmark |
| 2283 | config: default |
| 2284 | split: test |
| 2285 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 2286 | metrics: |
| 2287 | - type: cos_sim_pearson |
| 2288 | value: 78.43883428940346 |
| 2289 | - type: cos_sim_spearman |
| 2290 | value: 79.18395553127085 |
| 2291 | - type: euclidean_pearson |
| 2292 | value: 79.22986635457109 |
| 2293 | - type: euclidean_spearman |
| 2294 | value: 79.18395553127085 |
| 2295 | - type: manhattan_pearson |
| 2296 | value: 79.10921229934691 |
| 2297 | - type: manhattan_spearman |
| 2298 | value: 79.02283553930171 |
| 2299 | - task: |
| 2300 | type: STS |
| 2301 | dataset: |
| 2302 | type: PhilipMay/stsb_multi_mt |
| 2303 | name: MTEB STSBenchmarkMultilingualSTS (en) |
| 2304 | config: en |
| 2305 | split: test |
| 2306 | revision: 93d57ef91790589e3ce9c365164337a8a78b7632 |
| 2307 | metrics: |
| 2308 | - type: cos_sim_pearson |
| 2309 | value: 78.43883433444418 |
| 2310 | - type: cos_sim_spearman |
| 2311 | value: 79.18395553127085 |
| 2312 | - type: euclidean_pearson |
| 2313 | value: 79.22986642351681 |
| 2314 | - type: euclidean_spearman |
| 2315 | value: 79.18395553127085 |
| 2316 | - type: manhattan_pearson |
| 2317 | value: 79.10921236746302 |
| 2318 | - type: manhattan_spearman |
| 2319 | value: 79.02283553930171 |
| 2320 | - task: |
| 2321 | type: Reranking |
| 2322 | dataset: |
| 2323 | type: mteb/scidocs-reranking |
| 2324 | name: MTEB SciDocsRR |
| 2325 | config: default |
| 2326 | split: test |
| 2327 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 2328 | metrics: |
| 2329 | - type: map |
| 2330 | value: 76.9361627171417 |
| 2331 | - type: mrr |
| 2332 | value: 93.06577046773126 |
| 2333 | - task: |
| 2334 | type: Retrieval |
| 2335 | dataset: |
| 2336 | type: mteb/scifact |
| 2337 | name: MTEB SciFact |
| 2338 | config: default |
| 2339 | split: test |
| 2340 | revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| 2341 | metrics: |
| 2342 | - type: map_at_1 |
| 2343 | value: 50.693999999999996 |
| 2344 | - type: map_at_10 |
| 2345 | value: 59.784000000000006 |
| 2346 | - type: map_at_100 |
| 2347 | value: 60.443000000000005 |
| 2348 | - type: map_at_1000 |
| 2349 | value: 60.480000000000004 |
| 2350 | - type: map_at_3 |
| 2351 | value: 57.028 |
| 2352 | - type: map_at_5 |
| 2353 | value: 58.306999999999995 |
| 2354 | - type: mrr_at_1 |
| 2355 | value: 53.333 |
| 2356 | - type: mrr_at_10 |
| 2357 | value: 61.565000000000005 |
| 2358 | - type: mrr_at_100 |
| 2359 | value: 62.095 |
| 2360 | - type: mrr_at_1000 |
| 2361 | value: 62.131 |
| 2362 | - type: mrr_at_3 |
| 2363 | value: 59.721999999999994 |
| 2364 | - type: mrr_at_5 |
| 2365 | value: 60.589000000000006 |
| 2366 | - type: ndcg_at_1 |
| 2367 | value: 53.333 |
| 2368 | - type: ndcg_at_10 |
| 2369 | value: 64.512 |
| 2370 | - type: ndcg_at_100 |
| 2371 | value: 67.366 |
| 2372 | - type: ndcg_at_1000 |
| 2373 | value: 68.46799999999999 |
| 2374 | - type: ndcg_at_3 |
| 2375 | value: 59.748999999999995 |
| 2376 | - type: ndcg_at_5 |
| 2377 | value: 61.526 |
| 2378 | - type: precision_at_1 |
| 2379 | value: 53.333 |
| 2380 | - type: precision_at_10 |
| 2381 | value: 8.733 |
| 2382 | - type: precision_at_100 |
| 2383 | value: 1.027 |
| 2384 | - type: precision_at_1000 |
| 2385 | value: 0.11199999999999999 |
| 2386 | - type: precision_at_3 |
| 2387 | value: 23.222 |
| 2388 | - type: precision_at_5 |
| 2389 | value: 15.2 |
| 2390 | - type: recall_at_1 |
| 2391 | value: 50.693999999999996 |
| 2392 | - type: recall_at_10 |
| 2393 | value: 77.333 |
| 2394 | - type: recall_at_100 |
| 2395 | value: 90.10000000000001 |
| 2396 | - type: recall_at_1000 |
| 2397 | value: 99 |
| 2398 | - type: recall_at_3 |
| 2399 | value: 64.39399999999999 |
| 2400 | - type: recall_at_5 |
| 2401 | value: 68.7 |
| 2402 | - task: |
| 2403 | type: PairClassification |
| 2404 | dataset: |
| 2405 | type: mteb/sprintduplicatequestions-pairclassification |
| 2406 | name: MTEB SprintDuplicateQuestions |
| 2407 | config: default |
| 2408 | split: test |
| 2409 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 2410 | metrics: |
| 2411 | - type: cos_sim_accuracy |
| 2412 | value: 99.81386138613861 |
| 2413 | - type: cos_sim_ap |
| 2414 | value: 94.96375600031361 |
| 2415 | - type: cos_sim_f1 |
| 2416 | value: 90.36885245901641 |
| 2417 | - type: cos_sim_precision |
| 2418 | value: 92.64705882352942 |
| 2419 | - type: cos_sim_recall |
| 2420 | value: 88.2 |
| 2421 | - type: dot_accuracy |
| 2422 | value: 99.81386138613861 |
| 2423 | - type: dot_ap |
| 2424 | value: 94.96375600031361 |
| 2425 | - type: dot_f1 |
| 2426 | value: 90.36885245901641 |
| 2427 | - type: dot_precision |
| 2428 | value: 92.64705882352942 |
| 2429 | - type: dot_recall |
| 2430 | value: 88.2 |
| 2431 | - type: euclidean_accuracy |
| 2432 | value: 99.81386138613861 |
| 2433 | - type: euclidean_ap |
| 2434 | value: 94.96375600031361 |
| 2435 | - type: euclidean_f1 |
| 2436 | value: 90.36885245901641 |
| 2437 | - type: euclidean_precision |
| 2438 | value: 92.64705882352942 |
| 2439 | - type: euclidean_recall |
| 2440 | value: 88.2 |
| 2441 | - type: manhattan_accuracy |
| 2442 | value: 99.81287128712871 |
| 2443 | - type: manhattan_ap |
| 2444 | value: 94.92563500640084 |
| 2445 | - type: manhattan_f1 |
| 2446 | value: 90.27277406073082 |
| 2447 | - type: manhattan_precision |
| 2448 | value: 93.00106044538707 |
| 2449 | - type: manhattan_recall |
| 2450 | value: 87.7 |
| 2451 | - type: max_accuracy |
| 2452 | value: 99.81386138613861 |
| 2453 | - type: max_ap |
| 2454 | value: 94.96375600031361 |
| 2455 | - type: max_f1 |
| 2456 | value: 90.36885245901641 |
| 2457 | - task: |
| 2458 | type: Clustering |
| 2459 | dataset: |
| 2460 | type: mteb/stackexchange-clustering |
| 2461 | name: MTEB StackExchangeClustering |
| 2462 | config: default |
| 2463 | split: test |
| 2464 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 2465 | metrics: |
| 2466 | - type: v_measure |
| 2467 | value: 57.486984956276274 |
| 2468 | - task: |
| 2469 | type: Clustering |
| 2470 | dataset: |
| 2471 | type: mteb/stackexchange-clustering-p2p |
| 2472 | name: MTEB StackExchangeClusteringP2P |
| 2473 | config: default |
| 2474 | split: test |
| 2475 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 2476 | metrics: |
| 2477 | - type: v_measure |
| 2478 | value: 34.58453023612073 |
| 2479 | - task: |
| 2480 | type: Reranking |
| 2481 | dataset: |
| 2482 | type: mteb/stackoverflowdupquestions-reranking |
| 2483 | name: MTEB StackOverflowDupQuestions |
| 2484 | config: default |
| 2485 | split: test |
| 2486 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 2487 | metrics: |
| 2488 | - type: map |
| 2489 | value: 50.16317315282306 |
| 2490 | - type: mrr |
| 2491 | value: 50.82617137764197 |
| 2492 | - task: |
| 2493 | type: Summarization |
| 2494 | dataset: |
| 2495 | type: mteb/summeval |
| 2496 | name: MTEB SummEval |
| 2497 | config: default |
| 2498 | split: test |
| 2499 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 2500 | metrics: |
| 2501 | - type: cos_sim_pearson |
| 2502 | value: 30.2927995133324 |
| 2503 | - type: cos_sim_spearman |
| 2504 | value: 30.09648622523191 |
| 2505 | - type: dot_pearson |
| 2506 | value: 30.29279853541771 |
| 2507 | - type: dot_spearman |
| 2508 | value: 30.09648622523191 |
| 2509 | - task: |
| 2510 | type: Retrieval |
| 2511 | dataset: |
| 2512 | type: mteb/trec-covid |
| 2513 | name: MTEB TRECCOVID |
| 2514 | config: default |
| 2515 | split: test |
| 2516 | revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
| 2517 | metrics: |
| 2518 | - type: map_at_1 |
| 2519 | value: 0.23500000000000001 |
| 2520 | - type: map_at_10 |
| 2521 | value: 2.01 |
| 2522 | - type: map_at_100 |
| 2523 | value: 12.064 |
| 2524 | - type: map_at_1000 |
| 2525 | value: 27.437 |
| 2526 | - type: map_at_3 |
| 2527 | value: 0.6649999999999999 |
| 2528 | - type: map_at_5 |
| 2529 | value: 1.0959999999999999 |
| 2530 | - type: mrr_at_1 |
| 2531 | value: 88 |
| 2532 | - type: mrr_at_10 |
| 2533 | value: 92.667 |
| 2534 | - type: mrr_at_100 |
| 2535 | value: 92.667 |
| 2536 | - type: mrr_at_1000 |
| 2537 | value: 92.667 |
| 2538 | - type: mrr_at_3 |
| 2539 | value: 91.667 |
| 2540 | - type: mrr_at_5 |
| 2541 | value: 92.667 |
| 2542 | - type: ndcg_at_1 |
| 2543 | value: 84 |
| 2544 | - type: ndcg_at_10 |
| 2545 | value: 79.431 |
| 2546 | - type: ndcg_at_100 |
| 2547 | value: 60.914 |
| 2548 | - type: ndcg_at_1000 |
| 2549 | value: 52.005 |
| 2550 | - type: ndcg_at_3 |
| 2551 | value: 82.285 |
| 2552 | - type: ndcg_at_5 |
| 2553 | value: 81.565 |
| 2554 | - type: precision_at_1 |
| 2555 | value: 88 |
| 2556 | - type: precision_at_10 |
| 2557 | value: 84.8 |
| 2558 | - type: precision_at_100 |
| 2559 | value: 62.32 |
| 2560 | - type: precision_at_1000 |
| 2561 | value: 23.014000000000003 |
| 2562 | - type: precision_at_3 |
| 2563 | value: 86.667 |
| 2564 | - type: precision_at_5 |
| 2565 | value: 87.2 |
| 2566 | - type: recall_at_1 |
| 2567 | value: 0.23500000000000001 |
| 2568 | - type: recall_at_10 |
| 2569 | value: 2.19 |
| 2570 | - type: recall_at_100 |
| 2571 | value: 14.904 |
| 2572 | - type: recall_at_1000 |
| 2573 | value: 47.875 |
| 2574 | - type: recall_at_3 |
| 2575 | value: 0.695 |
| 2576 | - type: recall_at_5 |
| 2577 | value: 1.165 |
| 2578 | - task: |
| 2579 | type: Retrieval |
| 2580 | dataset: |
| 2581 | type: mteb/touche2020 |
| 2582 | name: MTEB Touche2020 |
| 2583 | config: default |
| 2584 | split: test |
| 2585 | revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
| 2586 | metrics: |
| 2587 | - type: map_at_1 |
| 2588 | value: 3.639 |
| 2589 | - type: map_at_10 |
| 2590 | value: 14.184 |
| 2591 | - type: map_at_100 |
| 2592 | value: 20.61 |
| 2593 | - type: map_at_1000 |
| 2594 | value: 22.377 |
| 2595 | - type: map_at_3 |
| 2596 | value: 9.163 |
| 2597 | - type: map_at_5 |
| 2598 | value: 10.773000000000001 |
| 2599 | - type: mrr_at_1 |
| 2600 | value: 46.939 |
| 2601 | - type: mrr_at_10 |
| 2602 | value: 59.345000000000006 |
| 2603 | - type: mrr_at_100 |
| 2604 | value: 60.07599999999999 |
| 2605 | - type: mrr_at_1000 |
| 2606 | value: 60.07599999999999 |
| 2607 | - type: mrr_at_3 |
| 2608 | value: 55.782 |
| 2609 | - type: mrr_at_5 |
| 2610 | value: 58.231 |
| 2611 | - type: ndcg_at_1 |
| 2612 | value: 41.837 |
| 2613 | - type: ndcg_at_10 |
| 2614 | value: 32.789 |
| 2615 | - type: ndcg_at_100 |
| 2616 | value: 42.232 |
| 2617 | - type: ndcg_at_1000 |
| 2618 | value: 53.900999999999996 |
| 2619 | - type: ndcg_at_3 |
| 2620 | value: 41.963 |
| 2621 | - type: ndcg_at_5 |
| 2622 | value: 35.983 |
| 2623 | - type: precision_at_1 |
| 2624 | value: 46.939 |
| 2625 | - type: precision_at_10 |
| 2626 | value: 28.163 |
| 2627 | - type: precision_at_100 |
| 2628 | value: 8.102 |
| 2629 | - type: precision_at_1000 |
| 2630 | value: 1.59 |
| 2631 | - type: precision_at_3 |
| 2632 | value: 44.897999999999996 |
| 2633 | - type: precision_at_5 |
| 2634 | value: 34.694 |
| 2635 | - type: recall_at_1 |
| 2636 | value: 3.639 |
| 2637 | - type: recall_at_10 |
| 2638 | value: 19.308 |
| 2639 | - type: recall_at_100 |
| 2640 | value: 48.992000000000004 |
| 2641 | - type: recall_at_1000 |
| 2642 | value: 84.59400000000001 |
| 2643 | - type: recall_at_3 |
| 2644 | value: 9.956 |
| 2645 | - type: recall_at_5 |
| 2646 | value: 12.33 |
| 2647 | - task: |
| 2648 | type: Classification |
| 2649 | dataset: |
| 2650 | type: mteb/toxic_conversations_50k |
| 2651 | name: MTEB ToxicConversationsClassification |
| 2652 | config: default |
| 2653 | split: test |
| 2654 | revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| 2655 | metrics: |
| 2656 | - type: accuracy |
| 2657 | value: 64.305 |
| 2658 | - type: ap |
| 2659 | value: 11.330746746072599 |
| 2660 | - type: f1 |
| 2661 | value: 49.290704382387865 |
| 2662 | - task: |
| 2663 | type: Classification |
| 2664 | dataset: |
| 2665 | type: mteb/tweet_sentiment_extraction |
| 2666 | name: MTEB TweetSentimentExtractionClassification |
| 2667 | config: default |
| 2668 | split: test |
| 2669 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 2670 | metrics: |
| 2671 | - type: accuracy |
| 2672 | value: 56.1941143180532 |
| 2673 | - type: f1 |
| 2674 | value: 56.40189765095578 |
| 2675 | - task: |
| 2676 | type: Clustering |
| 2677 | dataset: |
| 2678 | type: mteb/twentynewsgroups-clustering |
| 2679 | name: MTEB TwentyNewsgroupsClustering |
| 2680 | config: default |
| 2681 | split: test |
| 2682 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 2683 | metrics: |
| 2684 | - type: v_measure |
| 2685 | value: 36.28189332526842 |
| 2686 | - task: |
| 2687 | type: PairClassification |
| 2688 | dataset: |
| 2689 | type: mteb/twittersemeval2015-pairclassification |
| 2690 | name: MTEB TwitterSemEval2015 |
| 2691 | config: default |
| 2692 | split: test |
| 2693 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 2694 | metrics: |
| 2695 | - type: cos_sim_accuracy |
| 2696 | value: 83.1912737676581 |
| 2697 | - type: cos_sim_ap |
| 2698 | value: 64.31536990146257 |
| 2699 | - type: cos_sim_f1 |
| 2700 | value: 61.095167030191696 |
| 2701 | - type: cos_sim_precision |
| 2702 | value: 54.074375127006704 |
| 2703 | - type: cos_sim_recall |
| 2704 | value: 70.21108179419525 |
| 2705 | - type: dot_accuracy |
| 2706 | value: 83.1912737676581 |
| 2707 | - type: dot_ap |
| 2708 | value: 64.31539216162541 |
| 2709 | - type: dot_f1 |
| 2710 | value: 61.095167030191696 |
| 2711 | - type: dot_precision |
| 2712 | value: 54.074375127006704 |
| 2713 | - type: dot_recall |
| 2714 | value: 70.21108179419525 |
| 2715 | - type: euclidean_accuracy |
| 2716 | value: 83.1912737676581 |
| 2717 | - type: euclidean_ap |
| 2718 | value: 64.31538391358727 |
| 2719 | - type: euclidean_f1 |
| 2720 | value: 61.095167030191696 |
| 2721 | - type: euclidean_precision |
| 2722 | value: 54.074375127006704 |
| 2723 | - type: euclidean_recall |
| 2724 | value: 70.21108179419525 |
| 2725 | - type: manhattan_accuracy |
| 2726 | value: 83.07206294331525 |
| 2727 | - type: manhattan_ap |
| 2728 | value: 64.14646315556838 |
| 2729 | - type: manhattan_f1 |
| 2730 | value: 61.194029850746254 |
| 2731 | - type: manhattan_precision |
| 2732 | value: 54.166666666666664 |
| 2733 | - type: manhattan_recall |
| 2734 | value: 70.31662269129288 |
| 2735 | - type: max_accuracy |
| 2736 | value: 83.1912737676581 |
| 2737 | - type: max_ap |
| 2738 | value: 64.31539216162541 |
| 2739 | - type: max_f1 |
| 2740 | value: 61.194029850746254 |
| 2741 | - task: |
| 2742 | type: PairClassification |
| 2743 | dataset: |
| 2744 | type: mteb/twitterurlcorpus-pairclassification |
| 2745 | name: MTEB TwitterURLCorpus |
| 2746 | config: default |
| 2747 | split: test |
| 2748 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 2749 | metrics: |
| 2750 | - type: cos_sim_accuracy |
| 2751 | value: 88.38242713548337 |
| 2752 | - type: cos_sim_ap |
| 2753 | value: 84.70041255196017 |
| 2754 | - type: cos_sim_f1 |
| 2755 | value: 77.13222561986515 |
| 2756 | - type: cos_sim_precision |
| 2757 | value: 73.95266690215472 |
| 2758 | - type: cos_sim_recall |
| 2759 | value: 80.59747459193102 |
| 2760 | - type: dot_accuracy |
| 2761 | value: 88.38242713548337 |
| 2762 | - type: dot_ap |
| 2763 | value: 84.7004118720222 |
| 2764 | - type: dot_f1 |
| 2765 | value: 77.13222561986515 |
| 2766 | - type: dot_precision |
| 2767 | value: 73.95266690215472 |
| 2768 | - type: dot_recall |
| 2769 | value: 80.59747459193102 |
| 2770 | - type: euclidean_accuracy |
| 2771 | value: 88.38242713548337 |
| 2772 | - type: euclidean_ap |
| 2773 | value: 84.70041593996575 |
| 2774 | - type: euclidean_f1 |
| 2775 | value: 77.13222561986515 |
| 2776 | - type: euclidean_precision |
| 2777 | value: 73.95266690215472 |
| 2778 | - type: euclidean_recall |
| 2779 | value: 80.59747459193102 |
| 2780 | - type: manhattan_accuracy |
| 2781 | value: 88.36108200411378 |
| 2782 | - type: manhattan_ap |
| 2783 | value: 84.66897701572054 |
| 2784 | - type: manhattan_f1 |
| 2785 | value: 77.00707640360645 |
| 2786 | - type: manhattan_precision |
| 2787 | value: 72.17695778062082 |
| 2788 | - type: manhattan_recall |
| 2789 | value: 82.53002771789343 |
| 2790 | - type: max_accuracy |
| 2791 | value: 88.38242713548337 |
| 2792 | - type: max_ap |
| 2793 | value: 84.70041593996575 |
| 2794 | - type: max_f1 |
| 2795 | value: 77.13222561986515 |
| 2796 | - task: |
| 2797 | type: Clustering |
| 2798 | dataset: |
| 2799 | type: jinaai/cities_wiki_clustering |
| 2800 | name: MTEB WikiCitiesClustering |
| 2801 | config: default |
| 2802 | split: test |
| 2803 | revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
| 2804 | metrics: |
| 2805 | - type: v_measure |
| 2806 | value: 81.46426354153643 |
| 2807 | --- |
| 2808 | <h1 align="center">Snowflake's Arctic-embed-xs</h1> |
| 2809 | <h4 align="center"> |
| 2810 | <p> |
| 2811 | <a href=#news>News</a> | |
| 2812 | <a href=#models>Models</a> | |
| 2813 | <a href=#usage>Usage</a> | |
| 2814 | <a href="#evaluation">Evaluation</a> | |
| 2815 | <a href="#contact">Contact</a> | |
| 2816 | <a href="#faq">FAQ</a> |
| 2817 | <a href="#license">License</a> | |
| 2818 | <a href="#acknowledgement">Acknowledgement</a> |
| 2819 | <p> |
| 2820 | </h4> |
| 2821 | |
| 2822 | |
| 2823 | ## News |
| 2824 | |
| 2825 | 12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! |
| 2826 | |
| 2827 | 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv. |
| 2828 | |
| 2829 | 07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/). |
| 2830 | |
| 2831 | 05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374) |
| 2832 | |
| 2833 | 04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed). |
| 2834 | |
| 2835 | |
| 2836 | ## Models |
| 2837 | |
| 2838 | |
| 2839 | snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance. |
| 2840 | |
| 2841 | |
| 2842 | The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models. |
| 2843 | |
| 2844 | |
| 2845 | The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374). |
| 2846 | |
| 2847 | |
| 2848 | | Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension | |
| 2849 | | ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- | |
| 2850 | | [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 | |
| 2851 | | [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 | |
| 2852 | | [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 | |
| 2853 | | [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 | |
| 2854 | | [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 | |
| 2855 | |
| 2856 | |
| 2857 | Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below. |
| 2858 | |
| 2859 | |
| 2860 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2861 | | ------------------------------------------------------------------ | -------------------------------- | |
| 2862 | | [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
| 2863 | | Google-gecko-text-embedding | 55.7 | |
| 2864 | | text-embedding-3-large | 55.44 | |
| 2865 | | Cohere-embed-english-v3.0 | 55.00 | |
| 2866 | | bge-large-en-v1.5 | 54.29 | |
| 2867 | |
| 2868 | |
| 2869 | ### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) |
| 2870 | |
| 2871 | |
| 2872 | This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers. |
| 2873 | |
| 2874 | |
| 2875 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2876 | | ------------------------------------------------------------------- | -------------------------------- | |
| 2877 | | [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | |
| 2878 | | GIST-all-MiniLM-L6-v2 | 45.12 | |
| 2879 | | gte-tiny | 44.92 | |
| 2880 | | all-MiniLM-L6-v2 | 41.95 | |
| 2881 | | bge-micro-v2 | 42.56 | |
| 2882 | |
| 2883 | |
| 2884 | ### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) |
| 2885 | |
| 2886 | |
| 2887 | Based on the [infloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets. |
| 2888 | |
| 2889 | |
| 2890 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2891 | | ------------------------------------------------------------------ | -------------------------------- | |
| 2892 | | [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | |
| 2893 | | bge-small-en-v1.5 | 51.68 | |
| 2894 | | Cohere-embed-english-light-v3.0 | 51.34 | |
| 2895 | | text-embedding-3-small | 51.08 | |
| 2896 | | e5-small-v2 | 49.04 | |
| 2897 | |
| 2898 | |
| 2899 | ### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) |
| 2900 | |
| 2901 | |
| 2902 | Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference. |
| 2903 | |
| 2904 | |
| 2905 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2906 | | ------------------------------------------------------------------ | -------------------------------- | |
| 2907 | | [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | |
| 2908 | | bge-base-en-v1.5 | 53.25 | |
| 2909 | | nomic-embed-text-v1.5 | 53.25 | |
| 2910 | | GIST-Embedding-v0 | 52.31 | |
| 2911 | | gte-base | 52.31 | |
| 2912 | |
| 2913 | ### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) |
| 2914 | |
| 2915 | |
| 2916 | Based on the [nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192! |
| 2917 | |
| 2918 | |
| 2919 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2920 | | ------------------------------------------------------------------ | -------------------------------- | |
| 2921 | | [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | |
| 2922 | | nomic-embed-text-v1.5 | 53.01 | |
| 2923 | | nomic-embed-text-v1 | 52.81 | |
| 2924 | |
| 2925 | |
| 2926 | |
| 2927 | |
| 2928 | ### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) |
| 2929 | |
| 2930 | |
| 2931 | Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience. |
| 2932 | |
| 2933 | |
| 2934 | | Model Name | MTEB Retrieval Score (NDCG @ 10) | |
| 2935 | | ------------------------------------------------------------------ | -------------------------------- | |
| 2936 | | [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
| 2937 | | UAE-Large-V1 | 54.66 | |
| 2938 | | bge-large-en-v1.5 | 54.29 | |
| 2939 | | mxbai-embed-large-v1 | 54.39 | |
| 2940 | | e5-Large-v2 | 50.56 | |
| 2941 | |
| 2942 | |
| 2943 | ## Usage |
| 2944 | |
| 2945 | ### Using Sentence Transformers |
| 2946 | |
| 2947 | You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. |
| 2948 | |
| 2949 | ```python |
| 2950 | from sentence_transformers import SentenceTransformer |
| 2951 | |
| 2952 | model = SentenceTransformer("Snowflake/snowflake-arctic-embed-xs") |
| 2953 | |
| 2954 | queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
| 2955 | documents = ['The Data Cloud!', 'Mexico City of Course!'] |
| 2956 | |
| 2957 | query_embeddings = model.encode(queries, prompt_name="query") |
| 2958 | document_embeddings = model.encode(documents) |
| 2959 | |
| 2960 | scores = query_embeddings @ document_embeddings.T |
| 2961 | for query, query_scores in zip(queries, scores): |
| 2962 | doc_score_pairs = list(zip(documents, query_scores)) |
| 2963 | doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
| 2964 | # Output passages & scores |
| 2965 | print("Query:", query) |
| 2966 | for document, score in doc_score_pairs: |
| 2967 | print(score, document) |
| 2968 | ``` |
| 2969 | ``` |
| 2970 | Query: what is snowflake? |
| 2971 | 0.57515126 The Data Cloud! |
| 2972 | 0.45798576 Mexico City of Course! |
| 2973 | Query: Where can I get the best tacos? |
| 2974 | 0.5636022 Mexico City of Course! |
| 2975 | 0.5044898 The Data Cloud! |
| 2976 | ``` |
| 2977 | |
| 2978 | ### Using Huggingface transformers |
| 2979 | |
| 2980 | |
| 2981 | You can use the transformers package for a snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query). |
| 2982 | |
| 2983 | |
| 2984 | |
| 2985 | ```python |
| 2986 | import torch |
| 2987 | from transformers import AutoModel, AutoTokenizer |
| 2988 | |
| 2989 | tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-xs') |
| 2990 | model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-xs', add_pooling_layer=False) |
| 2991 | model.eval() |
| 2992 | |
| 2993 | query_prefix = 'Represent this sentence for searching relevant passages: ' |
| 2994 | queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
| 2995 | queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries] |
| 2996 | query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512) |
| 2997 | |
| 2998 | documents = ['The Data Cloud!', 'Mexico City of Course!'] |
| 2999 | document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512) |
| 3000 | |
| 3001 | # Compute token embeddings |
| 3002 | with torch.no_grad(): |
| 3003 | query_embeddings = model(**query_tokens)[0][:, 0] |
| 3004 | document_embeddings = model(**document_tokens)[0][:, 0] |
| 3005 | |
| 3006 | |
| 3007 | # normalize embeddings |
| 3008 | query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1) |
| 3009 | document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1) |
| 3010 | |
| 3011 | scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1)) |
| 3012 | for query, query_scores in zip(queries, scores): |
| 3013 | doc_score_pairs = list(zip(documents, query_scores)) |
| 3014 | doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
| 3015 | #Output passages & scores |
| 3016 | print("Query:", query) |
| 3017 | for document, score in doc_score_pairs: |
| 3018 | print(score, document) |
| 3019 | ``` |
| 3020 | |
| 3021 | ### Using Transformers.js |
| 3022 | |
| 3023 | If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running: |
| 3024 | ```bash |
| 3025 | npm i @xenova/transformers |
| 3026 | ``` |
| 3027 | |
| 3028 | You can then use the model to compute embeddings as follows: |
| 3029 | |
| 3030 | ```js |
| 3031 | import { pipeline, dot } from '@xenova/transformers'; |
| 3032 | |
| 3033 | // Create feature extraction pipeline |
| 3034 | const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-xs', { |
| 3035 | quantized: false, // Comment out this line to use the quantized version |
| 3036 | }); |
| 3037 | |
| 3038 | // Generate sentence embeddings |
| 3039 | const sentences = [ |
| 3040 | 'Represent this sentence for searching relevant passages: Where can I get the best tacos?', |
| 3041 | 'The Data Cloud!', |
| 3042 | 'Mexico City of Course!', |
| 3043 | ] |
| 3044 | const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); |
| 3045 | |
| 3046 | // Compute similarity scores |
| 3047 | const [source_embeddings, ...document_embeddings ] = output.tolist(); |
| 3048 | const similarities = document_embeddings.map(x => dot(source_embeddings, x)); |
| 3049 | console.log(similarities); // [0.5044895661144148, 0.5636021124426508] |
| 3050 | ``` |
| 3051 | |
| 3052 | ## FAQ |
| 3053 | |
| 3054 | |
| 3055 | TBD |
| 3056 | |
| 3057 | |
| 3058 | ## Contact |
| 3059 | |
| 3060 | |
| 3061 | Feel free to open an issue or pull request if you have any questions or suggestions about this project. |
| 3062 | You also can email Daniel Campos(daniel.campos@snowflake.com). |
| 3063 | |
| 3064 | |
| 3065 | ## License |
| 3066 | |
| 3067 | |
| 3068 | Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. |
| 3069 | |
| 3070 | |
| 3071 | ## Acknowledgement |
| 3072 | |
| 3073 | |
| 3074 | We want to thank the open-source community, which has provided the great building blocks upon which we could make our models. |
| 3075 | We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. |
| 3076 | We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. |
| 3077 | We also thank the open-source community for producing the great models we could build on top of and making these releases possible. |
| 3078 | Finally, we thank the researchers who created BEIR and MTEB benchmarks. |
| 3079 | It is largely thanks to their tireless work to define what better looks like that we could improve model performance. |
| 3080 | |
| 3081 | <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=15cd6ef8-397b-4e85-9d74-27ebdc7e9765" /> |