README.md
| 1 | --- |
| 2 | tags: |
| 3 | - mteb |
| 4 | - Sentence Transformers |
| 5 | - sentence-similarity |
| 6 | - sentence-transformers |
| 7 | model-index: |
| 8 | - name: multilingual-e5-base |
| 9 | results: |
| 10 | - task: |
| 11 | type: Classification |
| 12 | dataset: |
| 13 | type: mteb/amazon_counterfactual |
| 14 | name: MTEB AmazonCounterfactualClassification (en) |
| 15 | config: en |
| 16 | split: test |
| 17 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 18 | metrics: |
| 19 | - type: accuracy |
| 20 | value: 78.97014925373135 |
| 21 | - type: ap |
| 22 | value: 43.69351129103008 |
| 23 | - type: f1 |
| 24 | value: 73.38075030070492 |
| 25 | - task: |
| 26 | type: Classification |
| 27 | dataset: |
| 28 | type: mteb/amazon_counterfactual |
| 29 | name: MTEB AmazonCounterfactualClassification (de) |
| 30 | config: de |
| 31 | split: test |
| 32 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 33 | metrics: |
| 34 | - type: accuracy |
| 35 | value: 71.7237687366167 |
| 36 | - type: ap |
| 37 | value: 82.22089859962671 |
| 38 | - type: f1 |
| 39 | value: 69.95532758884401 |
| 40 | - task: |
| 41 | type: Classification |
| 42 | dataset: |
| 43 | type: mteb/amazon_counterfactual |
| 44 | name: MTEB AmazonCounterfactualClassification (en-ext) |
| 45 | config: en-ext |
| 46 | split: test |
| 47 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 48 | metrics: |
| 49 | - type: accuracy |
| 50 | value: 79.65517241379312 |
| 51 | - type: ap |
| 52 | value: 28.507918657094738 |
| 53 | - type: f1 |
| 54 | value: 66.84516013726119 |
| 55 | - task: |
| 56 | type: Classification |
| 57 | dataset: |
| 58 | type: mteb/amazon_counterfactual |
| 59 | name: MTEB AmazonCounterfactualClassification (ja) |
| 60 | config: ja |
| 61 | split: test |
| 62 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 63 | metrics: |
| 64 | - type: accuracy |
| 65 | value: 73.32976445396146 |
| 66 | - type: ap |
| 67 | value: 20.720481637566014 |
| 68 | - type: f1 |
| 69 | value: 59.78002763416003 |
| 70 | - task: |
| 71 | type: Classification |
| 72 | dataset: |
| 73 | type: mteb/amazon_polarity |
| 74 | name: MTEB AmazonPolarityClassification |
| 75 | config: default |
| 76 | split: test |
| 77 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 78 | metrics: |
| 79 | - type: accuracy |
| 80 | value: 90.63775 |
| 81 | - type: ap |
| 82 | value: 87.22277903861716 |
| 83 | - type: f1 |
| 84 | value: 90.60378636386807 |
| 85 | - task: |
| 86 | type: Classification |
| 87 | dataset: |
| 88 | type: mteb/amazon_reviews_multi |
| 89 | name: MTEB AmazonReviewsClassification (en) |
| 90 | config: en |
| 91 | split: test |
| 92 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 93 | metrics: |
| 94 | - type: accuracy |
| 95 | value: 44.546 |
| 96 | - type: f1 |
| 97 | value: 44.05666638370923 |
| 98 | - task: |
| 99 | type: Classification |
| 100 | dataset: |
| 101 | type: mteb/amazon_reviews_multi |
| 102 | name: MTEB AmazonReviewsClassification (de) |
| 103 | config: de |
| 104 | split: test |
| 105 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 106 | metrics: |
| 107 | - type: accuracy |
| 108 | value: 41.828 |
| 109 | - type: f1 |
| 110 | value: 41.2710255644252 |
| 111 | - task: |
| 112 | type: Classification |
| 113 | dataset: |
| 114 | type: mteb/amazon_reviews_multi |
| 115 | name: MTEB AmazonReviewsClassification (es) |
| 116 | config: es |
| 117 | split: test |
| 118 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 119 | metrics: |
| 120 | - type: accuracy |
| 121 | value: 40.534 |
| 122 | - type: f1 |
| 123 | value: 39.820743174270326 |
| 124 | - task: |
| 125 | type: Classification |
| 126 | dataset: |
| 127 | type: mteb/amazon_reviews_multi |
| 128 | name: MTEB AmazonReviewsClassification (fr) |
| 129 | config: fr |
| 130 | split: test |
| 131 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 132 | metrics: |
| 133 | - type: accuracy |
| 134 | value: 39.684 |
| 135 | - type: f1 |
| 136 | value: 39.11052682815307 |
| 137 | - task: |
| 138 | type: Classification |
| 139 | dataset: |
| 140 | type: mteb/amazon_reviews_multi |
| 141 | name: MTEB AmazonReviewsClassification (ja) |
| 142 | config: ja |
| 143 | split: test |
| 144 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 145 | metrics: |
| 146 | - type: accuracy |
| 147 | value: 37.436 |
| 148 | - type: f1 |
| 149 | value: 37.07082931930871 |
| 150 | - task: |
| 151 | type: Classification |
| 152 | dataset: |
| 153 | type: mteb/amazon_reviews_multi |
| 154 | name: MTEB AmazonReviewsClassification (zh) |
| 155 | config: zh |
| 156 | split: test |
| 157 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 158 | metrics: |
| 159 | - type: accuracy |
| 160 | value: 37.226000000000006 |
| 161 | - type: f1 |
| 162 | value: 36.65372077739185 |
| 163 | - task: |
| 164 | type: Retrieval |
| 165 | dataset: |
| 166 | type: arguana |
| 167 | name: MTEB ArguAna |
| 168 | config: default |
| 169 | split: test |
| 170 | revision: None |
| 171 | metrics: |
| 172 | - type: map_at_1 |
| 173 | value: 22.831000000000003 |
| 174 | - type: map_at_10 |
| 175 | value: 36.42 |
| 176 | - type: map_at_100 |
| 177 | value: 37.699 |
| 178 | - type: map_at_1000 |
| 179 | value: 37.724000000000004 |
| 180 | - type: map_at_3 |
| 181 | value: 32.207 |
| 182 | - type: map_at_5 |
| 183 | value: 34.312 |
| 184 | - type: mrr_at_1 |
| 185 | value: 23.257 |
| 186 | - type: mrr_at_10 |
| 187 | value: 36.574 |
| 188 | - type: mrr_at_100 |
| 189 | value: 37.854 |
| 190 | - type: mrr_at_1000 |
| 191 | value: 37.878 |
| 192 | - type: mrr_at_3 |
| 193 | value: 32.385000000000005 |
| 194 | - type: mrr_at_5 |
| 195 | value: 34.48 |
| 196 | - type: ndcg_at_1 |
| 197 | value: 22.831000000000003 |
| 198 | - type: ndcg_at_10 |
| 199 | value: 44.230000000000004 |
| 200 | - type: ndcg_at_100 |
| 201 | value: 49.974000000000004 |
| 202 | - type: ndcg_at_1000 |
| 203 | value: 50.522999999999996 |
| 204 | - type: ndcg_at_3 |
| 205 | value: 35.363 |
| 206 | - type: ndcg_at_5 |
| 207 | value: 39.164 |
| 208 | - type: precision_at_1 |
| 209 | value: 22.831000000000003 |
| 210 | - type: precision_at_10 |
| 211 | value: 6.935 |
| 212 | - type: precision_at_100 |
| 213 | value: 0.9520000000000001 |
| 214 | - type: precision_at_1000 |
| 215 | value: 0.099 |
| 216 | - type: precision_at_3 |
| 217 | value: 14.841 |
| 218 | - type: precision_at_5 |
| 219 | value: 10.754 |
| 220 | - type: recall_at_1 |
| 221 | value: 22.831000000000003 |
| 222 | - type: recall_at_10 |
| 223 | value: 69.346 |
| 224 | - type: recall_at_100 |
| 225 | value: 95.235 |
| 226 | - type: recall_at_1000 |
| 227 | value: 99.36 |
| 228 | - type: recall_at_3 |
| 229 | value: 44.523 |
| 230 | - type: recall_at_5 |
| 231 | value: 53.769999999999996 |
| 232 | - task: |
| 233 | type: Clustering |
| 234 | dataset: |
| 235 | type: mteb/arxiv-clustering-p2p |
| 236 | name: MTEB ArxivClusteringP2P |
| 237 | config: default |
| 238 | split: test |
| 239 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 240 | metrics: |
| 241 | - type: v_measure |
| 242 | value: 40.27789869854063 |
| 243 | - task: |
| 244 | type: Clustering |
| 245 | dataset: |
| 246 | type: mteb/arxiv-clustering-s2s |
| 247 | name: MTEB ArxivClusteringS2S |
| 248 | config: default |
| 249 | split: test |
| 250 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 251 | metrics: |
| 252 | - type: v_measure |
| 253 | value: 35.41979463347428 |
| 254 | - task: |
| 255 | type: Reranking |
| 256 | dataset: |
| 257 | type: mteb/askubuntudupquestions-reranking |
| 258 | name: MTEB AskUbuntuDupQuestions |
| 259 | config: default |
| 260 | split: test |
| 261 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 262 | metrics: |
| 263 | - type: map |
| 264 | value: 58.22752045109304 |
| 265 | - type: mrr |
| 266 | value: 71.51112430198303 |
| 267 | - task: |
| 268 | type: STS |
| 269 | dataset: |
| 270 | type: mteb/biosses-sts |
| 271 | name: MTEB BIOSSES |
| 272 | config: default |
| 273 | split: test |
| 274 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 275 | metrics: |
| 276 | - type: cos_sim_pearson |
| 277 | value: 84.71147646622866 |
| 278 | - type: cos_sim_spearman |
| 279 | value: 85.059167046486 |
| 280 | - type: euclidean_pearson |
| 281 | value: 75.88421613600647 |
| 282 | - type: euclidean_spearman |
| 283 | value: 75.12821787150585 |
| 284 | - type: manhattan_pearson |
| 285 | value: 75.22005646957604 |
| 286 | - type: manhattan_spearman |
| 287 | value: 74.42880434453272 |
| 288 | - task: |
| 289 | type: BitextMining |
| 290 | dataset: |
| 291 | type: mteb/bucc-bitext-mining |
| 292 | name: MTEB BUCC (de-en) |
| 293 | config: de-en |
| 294 | split: test |
| 295 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 296 | metrics: |
| 297 | - type: accuracy |
| 298 | value: 99.23799582463465 |
| 299 | - type: f1 |
| 300 | value: 99.12665274878218 |
| 301 | - type: precision |
| 302 | value: 99.07098121085595 |
| 303 | - type: recall |
| 304 | value: 99.23799582463465 |
| 305 | - task: |
| 306 | type: BitextMining |
| 307 | dataset: |
| 308 | type: mteb/bucc-bitext-mining |
| 309 | name: MTEB BUCC (fr-en) |
| 310 | config: fr-en |
| 311 | split: test |
| 312 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 313 | metrics: |
| 314 | - type: accuracy |
| 315 | value: 97.88685890380806 |
| 316 | - type: f1 |
| 317 | value: 97.59336708489249 |
| 318 | - type: precision |
| 319 | value: 97.44662117543473 |
| 320 | - type: recall |
| 321 | value: 97.88685890380806 |
| 322 | - task: |
| 323 | type: BitextMining |
| 324 | dataset: |
| 325 | type: mteb/bucc-bitext-mining |
| 326 | name: MTEB BUCC (ru-en) |
| 327 | config: ru-en |
| 328 | split: test |
| 329 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 330 | metrics: |
| 331 | - type: accuracy |
| 332 | value: 97.47142362313821 |
| 333 | - type: f1 |
| 334 | value: 97.1989377670015 |
| 335 | - type: precision |
| 336 | value: 97.06384944001847 |
| 337 | - type: recall |
| 338 | value: 97.47142362313821 |
| 339 | - task: |
| 340 | type: BitextMining |
| 341 | dataset: |
| 342 | type: mteb/bucc-bitext-mining |
| 343 | name: MTEB BUCC (zh-en) |
| 344 | config: zh-en |
| 345 | split: test |
| 346 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 347 | metrics: |
| 348 | - type: accuracy |
| 349 | value: 98.4728804634018 |
| 350 | - type: f1 |
| 351 | value: 98.2973494821836 |
| 352 | - type: precision |
| 353 | value: 98.2095839915745 |
| 354 | - type: recall |
| 355 | value: 98.4728804634018 |
| 356 | - task: |
| 357 | type: Classification |
| 358 | dataset: |
| 359 | type: mteb/banking77 |
| 360 | name: MTEB Banking77Classification |
| 361 | config: default |
| 362 | split: test |
| 363 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 364 | metrics: |
| 365 | - type: accuracy |
| 366 | value: 82.74025974025975 |
| 367 | - type: f1 |
| 368 | value: 82.67420447730439 |
| 369 | - task: |
| 370 | type: Clustering |
| 371 | dataset: |
| 372 | type: mteb/biorxiv-clustering-p2p |
| 373 | name: MTEB BiorxivClusteringP2P |
| 374 | config: default |
| 375 | split: test |
| 376 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 377 | metrics: |
| 378 | - type: v_measure |
| 379 | value: 35.0380848063507 |
| 380 | - task: |
| 381 | type: Clustering |
| 382 | dataset: |
| 383 | type: mteb/biorxiv-clustering-s2s |
| 384 | name: MTEB BiorxivClusteringS2S |
| 385 | config: default |
| 386 | split: test |
| 387 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 388 | metrics: |
| 389 | - type: v_measure |
| 390 | value: 29.45956405670166 |
| 391 | - task: |
| 392 | type: Retrieval |
| 393 | dataset: |
| 394 | type: BeIR/cqadupstack |
| 395 | name: MTEB CQADupstackAndroidRetrieval |
| 396 | config: default |
| 397 | split: test |
| 398 | revision: None |
| 399 | metrics: |
| 400 | - type: map_at_1 |
| 401 | value: 32.122 |
| 402 | - type: map_at_10 |
| 403 | value: 42.03 |
| 404 | - type: map_at_100 |
| 405 | value: 43.364000000000004 |
| 406 | - type: map_at_1000 |
| 407 | value: 43.474000000000004 |
| 408 | - type: map_at_3 |
| 409 | value: 38.804 |
| 410 | - type: map_at_5 |
| 411 | value: 40.585 |
| 412 | - type: mrr_at_1 |
| 413 | value: 39.914 |
| 414 | - type: mrr_at_10 |
| 415 | value: 48.227 |
| 416 | - type: mrr_at_100 |
| 417 | value: 49.018 |
| 418 | - type: mrr_at_1000 |
| 419 | value: 49.064 |
| 420 | - type: mrr_at_3 |
| 421 | value: 45.994 |
| 422 | - type: mrr_at_5 |
| 423 | value: 47.396 |
| 424 | - type: ndcg_at_1 |
| 425 | value: 39.914 |
| 426 | - type: ndcg_at_10 |
| 427 | value: 47.825 |
| 428 | - type: ndcg_at_100 |
| 429 | value: 52.852 |
| 430 | - type: ndcg_at_1000 |
| 431 | value: 54.891 |
| 432 | - type: ndcg_at_3 |
| 433 | value: 43.517 |
| 434 | - type: ndcg_at_5 |
| 435 | value: 45.493 |
| 436 | - type: precision_at_1 |
| 437 | value: 39.914 |
| 438 | - type: precision_at_10 |
| 439 | value: 8.956 |
| 440 | - type: precision_at_100 |
| 441 | value: 1.388 |
| 442 | - type: precision_at_1000 |
| 443 | value: 0.182 |
| 444 | - type: precision_at_3 |
| 445 | value: 20.791999999999998 |
| 446 | - type: precision_at_5 |
| 447 | value: 14.821000000000002 |
| 448 | - type: recall_at_1 |
| 449 | value: 32.122 |
| 450 | - type: recall_at_10 |
| 451 | value: 58.294999999999995 |
| 452 | - type: recall_at_100 |
| 453 | value: 79.726 |
| 454 | - type: recall_at_1000 |
| 455 | value: 93.099 |
| 456 | - type: recall_at_3 |
| 457 | value: 45.017 |
| 458 | - type: recall_at_5 |
| 459 | value: 51.002 |
| 460 | - task: |
| 461 | type: Retrieval |
| 462 | dataset: |
| 463 | type: BeIR/cqadupstack |
| 464 | name: MTEB CQADupstackEnglishRetrieval |
| 465 | config: default |
| 466 | split: test |
| 467 | revision: None |
| 468 | metrics: |
| 469 | - type: map_at_1 |
| 470 | value: 29.677999999999997 |
| 471 | - type: map_at_10 |
| 472 | value: 38.684000000000005 |
| 473 | - type: map_at_100 |
| 474 | value: 39.812999999999995 |
| 475 | - type: map_at_1000 |
| 476 | value: 39.945 |
| 477 | - type: map_at_3 |
| 478 | value: 35.831 |
| 479 | - type: map_at_5 |
| 480 | value: 37.446 |
| 481 | - type: mrr_at_1 |
| 482 | value: 37.771 |
| 483 | - type: mrr_at_10 |
| 484 | value: 44.936 |
| 485 | - type: mrr_at_100 |
| 486 | value: 45.583 |
| 487 | - type: mrr_at_1000 |
| 488 | value: 45.634 |
| 489 | - type: mrr_at_3 |
| 490 | value: 42.771 |
| 491 | - type: mrr_at_5 |
| 492 | value: 43.994 |
| 493 | - type: ndcg_at_1 |
| 494 | value: 37.771 |
| 495 | - type: ndcg_at_10 |
| 496 | value: 44.059 |
| 497 | - type: ndcg_at_100 |
| 498 | value: 48.192 |
| 499 | - type: ndcg_at_1000 |
| 500 | value: 50.375 |
| 501 | - type: ndcg_at_3 |
| 502 | value: 40.172000000000004 |
| 503 | - type: ndcg_at_5 |
| 504 | value: 41.899 |
| 505 | - type: precision_at_1 |
| 506 | value: 37.771 |
| 507 | - type: precision_at_10 |
| 508 | value: 8.286999999999999 |
| 509 | - type: precision_at_100 |
| 510 | value: 1.322 |
| 511 | - type: precision_at_1000 |
| 512 | value: 0.178 |
| 513 | - type: precision_at_3 |
| 514 | value: 19.406000000000002 |
| 515 | - type: precision_at_5 |
| 516 | value: 13.745 |
| 517 | - type: recall_at_1 |
| 518 | value: 29.677999999999997 |
| 519 | - type: recall_at_10 |
| 520 | value: 53.071 |
| 521 | - type: recall_at_100 |
| 522 | value: 70.812 |
| 523 | - type: recall_at_1000 |
| 524 | value: 84.841 |
| 525 | - type: recall_at_3 |
| 526 | value: 41.016000000000005 |
| 527 | - type: recall_at_5 |
| 528 | value: 46.22 |
| 529 | - task: |
| 530 | type: Retrieval |
| 531 | dataset: |
| 532 | type: BeIR/cqadupstack |
| 533 | name: MTEB CQADupstackGamingRetrieval |
| 534 | config: default |
| 535 | split: test |
| 536 | revision: None |
| 537 | metrics: |
| 538 | - type: map_at_1 |
| 539 | value: 42.675000000000004 |
| 540 | - type: map_at_10 |
| 541 | value: 53.93599999999999 |
| 542 | - type: map_at_100 |
| 543 | value: 54.806999999999995 |
| 544 | - type: map_at_1000 |
| 545 | value: 54.867 |
| 546 | - type: map_at_3 |
| 547 | value: 50.934000000000005 |
| 548 | - type: map_at_5 |
| 549 | value: 52.583 |
| 550 | - type: mrr_at_1 |
| 551 | value: 48.339 |
| 552 | - type: mrr_at_10 |
| 553 | value: 57.265 |
| 554 | - type: mrr_at_100 |
| 555 | value: 57.873 |
| 556 | - type: mrr_at_1000 |
| 557 | value: 57.906 |
| 558 | - type: mrr_at_3 |
| 559 | value: 55.193000000000005 |
| 560 | - type: mrr_at_5 |
| 561 | value: 56.303000000000004 |
| 562 | - type: ndcg_at_1 |
| 563 | value: 48.339 |
| 564 | - type: ndcg_at_10 |
| 565 | value: 59.19799999999999 |
| 566 | - type: ndcg_at_100 |
| 567 | value: 62.743 |
| 568 | - type: ndcg_at_1000 |
| 569 | value: 63.99399999999999 |
| 570 | - type: ndcg_at_3 |
| 571 | value: 54.367 |
| 572 | - type: ndcg_at_5 |
| 573 | value: 56.548 |
| 574 | - type: precision_at_1 |
| 575 | value: 48.339 |
| 576 | - type: precision_at_10 |
| 577 | value: 9.216000000000001 |
| 578 | - type: precision_at_100 |
| 579 | value: 1.1809999999999998 |
| 580 | - type: precision_at_1000 |
| 581 | value: 0.134 |
| 582 | - type: precision_at_3 |
| 583 | value: 23.72 |
| 584 | - type: precision_at_5 |
| 585 | value: 16.025 |
| 586 | - type: recall_at_1 |
| 587 | value: 42.675000000000004 |
| 588 | - type: recall_at_10 |
| 589 | value: 71.437 |
| 590 | - type: recall_at_100 |
| 591 | value: 86.803 |
| 592 | - type: recall_at_1000 |
| 593 | value: 95.581 |
| 594 | - type: recall_at_3 |
| 595 | value: 58.434 |
| 596 | - type: recall_at_5 |
| 597 | value: 63.754 |
| 598 | - task: |
| 599 | type: Retrieval |
| 600 | dataset: |
| 601 | type: BeIR/cqadupstack |
| 602 | name: MTEB CQADupstackGisRetrieval |
| 603 | config: default |
| 604 | split: test |
| 605 | revision: None |
| 606 | metrics: |
| 607 | - type: map_at_1 |
| 608 | value: 23.518 |
| 609 | - type: map_at_10 |
| 610 | value: 30.648999999999997 |
| 611 | - type: map_at_100 |
| 612 | value: 31.508999999999997 |
| 613 | - type: map_at_1000 |
| 614 | value: 31.604 |
| 615 | - type: map_at_3 |
| 616 | value: 28.247 |
| 617 | - type: map_at_5 |
| 618 | value: 29.65 |
| 619 | - type: mrr_at_1 |
| 620 | value: 25.650000000000002 |
| 621 | - type: mrr_at_10 |
| 622 | value: 32.771 |
| 623 | - type: mrr_at_100 |
| 624 | value: 33.554 |
| 625 | - type: mrr_at_1000 |
| 626 | value: 33.629999999999995 |
| 627 | - type: mrr_at_3 |
| 628 | value: 30.433 |
| 629 | - type: mrr_at_5 |
| 630 | value: 31.812 |
| 631 | - type: ndcg_at_1 |
| 632 | value: 25.650000000000002 |
| 633 | - type: ndcg_at_10 |
| 634 | value: 34.929 |
| 635 | - type: ndcg_at_100 |
| 636 | value: 39.382 |
| 637 | - type: ndcg_at_1000 |
| 638 | value: 41.913 |
| 639 | - type: ndcg_at_3 |
| 640 | value: 30.292 |
| 641 | - type: ndcg_at_5 |
| 642 | value: 32.629999999999995 |
| 643 | - type: precision_at_1 |
| 644 | value: 25.650000000000002 |
| 645 | - type: precision_at_10 |
| 646 | value: 5.311 |
| 647 | - type: precision_at_100 |
| 648 | value: 0.792 |
| 649 | - type: precision_at_1000 |
| 650 | value: 0.105 |
| 651 | - type: precision_at_3 |
| 652 | value: 12.58 |
| 653 | - type: precision_at_5 |
| 654 | value: 8.994 |
| 655 | - type: recall_at_1 |
| 656 | value: 23.518 |
| 657 | - type: recall_at_10 |
| 658 | value: 46.19 |
| 659 | - type: recall_at_100 |
| 660 | value: 67.123 |
| 661 | - type: recall_at_1000 |
| 662 | value: 86.442 |
| 663 | - type: recall_at_3 |
| 664 | value: 33.678000000000004 |
| 665 | - type: recall_at_5 |
| 666 | value: 39.244 |
| 667 | - task: |
| 668 | type: Retrieval |
| 669 | dataset: |
| 670 | type: BeIR/cqadupstack |
| 671 | name: MTEB CQADupstackMathematicaRetrieval |
| 672 | config: default |
| 673 | split: test |
| 674 | revision: None |
| 675 | metrics: |
| 676 | - type: map_at_1 |
| 677 | value: 15.891 |
| 678 | - type: map_at_10 |
| 679 | value: 22.464000000000002 |
| 680 | - type: map_at_100 |
| 681 | value: 23.483 |
| 682 | - type: map_at_1000 |
| 683 | value: 23.613 |
| 684 | - type: map_at_3 |
| 685 | value: 20.080000000000002 |
| 686 | - type: map_at_5 |
| 687 | value: 21.526 |
| 688 | - type: mrr_at_1 |
| 689 | value: 20.025000000000002 |
| 690 | - type: mrr_at_10 |
| 691 | value: 26.712999999999997 |
| 692 | - type: mrr_at_100 |
| 693 | value: 27.650000000000002 |
| 694 | - type: mrr_at_1000 |
| 695 | value: 27.737000000000002 |
| 696 | - type: mrr_at_3 |
| 697 | value: 24.274 |
| 698 | - type: mrr_at_5 |
| 699 | value: 25.711000000000002 |
| 700 | - type: ndcg_at_1 |
| 701 | value: 20.025000000000002 |
| 702 | - type: ndcg_at_10 |
| 703 | value: 27.028999999999996 |
| 704 | - type: ndcg_at_100 |
| 705 | value: 32.064 |
| 706 | - type: ndcg_at_1000 |
| 707 | value: 35.188 |
| 708 | - type: ndcg_at_3 |
| 709 | value: 22.512999999999998 |
| 710 | - type: ndcg_at_5 |
| 711 | value: 24.89 |
| 712 | - type: precision_at_1 |
| 713 | value: 20.025000000000002 |
| 714 | - type: precision_at_10 |
| 715 | value: 4.776 |
| 716 | - type: precision_at_100 |
| 717 | value: 0.8500000000000001 |
| 718 | - type: precision_at_1000 |
| 719 | value: 0.125 |
| 720 | - type: precision_at_3 |
| 721 | value: 10.531 |
| 722 | - type: precision_at_5 |
| 723 | value: 7.811 |
| 724 | - type: recall_at_1 |
| 725 | value: 15.891 |
| 726 | - type: recall_at_10 |
| 727 | value: 37.261 |
| 728 | - type: recall_at_100 |
| 729 | value: 59.12 |
| 730 | - type: recall_at_1000 |
| 731 | value: 81.356 |
| 732 | - type: recall_at_3 |
| 733 | value: 24.741 |
| 734 | - type: recall_at_5 |
| 735 | value: 30.753999999999998 |
| 736 | - task: |
| 737 | type: Retrieval |
| 738 | dataset: |
| 739 | type: BeIR/cqadupstack |
| 740 | name: MTEB CQADupstackPhysicsRetrieval |
| 741 | config: default |
| 742 | split: test |
| 743 | revision: None |
| 744 | metrics: |
| 745 | - type: map_at_1 |
| 746 | value: 27.544 |
| 747 | - type: map_at_10 |
| 748 | value: 36.283 |
| 749 | - type: map_at_100 |
| 750 | value: 37.467 |
| 751 | - type: map_at_1000 |
| 752 | value: 37.574000000000005 |
| 753 | - type: map_at_3 |
| 754 | value: 33.528999999999996 |
| 755 | - type: map_at_5 |
| 756 | value: 35.028999999999996 |
| 757 | - type: mrr_at_1 |
| 758 | value: 34.166999999999994 |
| 759 | - type: mrr_at_10 |
| 760 | value: 41.866 |
| 761 | - type: mrr_at_100 |
| 762 | value: 42.666 |
| 763 | - type: mrr_at_1000 |
| 764 | value: 42.716 |
| 765 | - type: mrr_at_3 |
| 766 | value: 39.541 |
| 767 | - type: mrr_at_5 |
| 768 | value: 40.768 |
| 769 | - type: ndcg_at_1 |
| 770 | value: 34.166999999999994 |
| 771 | - type: ndcg_at_10 |
| 772 | value: 41.577 |
| 773 | - type: ndcg_at_100 |
| 774 | value: 46.687 |
| 775 | - type: ndcg_at_1000 |
| 776 | value: 48.967 |
| 777 | - type: ndcg_at_3 |
| 778 | value: 37.177 |
| 779 | - type: ndcg_at_5 |
| 780 | value: 39.097 |
| 781 | - type: precision_at_1 |
| 782 | value: 34.166999999999994 |
| 783 | - type: precision_at_10 |
| 784 | value: 7.420999999999999 |
| 785 | - type: precision_at_100 |
| 786 | value: 1.165 |
| 787 | - type: precision_at_1000 |
| 788 | value: 0.154 |
| 789 | - type: precision_at_3 |
| 790 | value: 17.291999999999998 |
| 791 | - type: precision_at_5 |
| 792 | value: 12.166 |
| 793 | - type: recall_at_1 |
| 794 | value: 27.544 |
| 795 | - type: recall_at_10 |
| 796 | value: 51.99399999999999 |
| 797 | - type: recall_at_100 |
| 798 | value: 73.738 |
| 799 | - type: recall_at_1000 |
| 800 | value: 89.33 |
| 801 | - type: recall_at_3 |
| 802 | value: 39.179 |
| 803 | - type: recall_at_5 |
| 804 | value: 44.385999999999996 |
| 805 | - task: |
| 806 | type: Retrieval |
| 807 | dataset: |
| 808 | type: BeIR/cqadupstack |
| 809 | name: MTEB CQADupstackProgrammersRetrieval |
| 810 | config: default |
| 811 | split: test |
| 812 | revision: None |
| 813 | metrics: |
| 814 | - type: map_at_1 |
| 815 | value: 26.661 |
| 816 | - type: map_at_10 |
| 817 | value: 35.475 |
| 818 | - type: map_at_100 |
| 819 | value: 36.626999999999995 |
| 820 | - type: map_at_1000 |
| 821 | value: 36.741 |
| 822 | - type: map_at_3 |
| 823 | value: 32.818000000000005 |
| 824 | - type: map_at_5 |
| 825 | value: 34.397 |
| 826 | - type: mrr_at_1 |
| 827 | value: 32.647999999999996 |
| 828 | - type: mrr_at_10 |
| 829 | value: 40.784 |
| 830 | - type: mrr_at_100 |
| 831 | value: 41.602 |
| 832 | - type: mrr_at_1000 |
| 833 | value: 41.661 |
| 834 | - type: mrr_at_3 |
| 835 | value: 38.68 |
| 836 | - type: mrr_at_5 |
| 837 | value: 39.838 |
| 838 | - type: ndcg_at_1 |
| 839 | value: 32.647999999999996 |
| 840 | - type: ndcg_at_10 |
| 841 | value: 40.697 |
| 842 | - type: ndcg_at_100 |
| 843 | value: 45.799 |
| 844 | - type: ndcg_at_1000 |
| 845 | value: 48.235 |
| 846 | - type: ndcg_at_3 |
| 847 | value: 36.516 |
| 848 | - type: ndcg_at_5 |
| 849 | value: 38.515 |
| 850 | - type: precision_at_1 |
| 851 | value: 32.647999999999996 |
| 852 | - type: precision_at_10 |
| 853 | value: 7.202999999999999 |
| 854 | - type: precision_at_100 |
| 855 | value: 1.1360000000000001 |
| 856 | - type: precision_at_1000 |
| 857 | value: 0.151 |
| 858 | - type: precision_at_3 |
| 859 | value: 17.314 |
| 860 | - type: precision_at_5 |
| 861 | value: 12.145999999999999 |
| 862 | - type: recall_at_1 |
| 863 | value: 26.661 |
| 864 | - type: recall_at_10 |
| 865 | value: 50.995000000000005 |
| 866 | - type: recall_at_100 |
| 867 | value: 73.065 |
| 868 | - type: recall_at_1000 |
| 869 | value: 89.781 |
| 870 | - type: recall_at_3 |
| 871 | value: 39.073 |
| 872 | - type: recall_at_5 |
| 873 | value: 44.395 |
| 874 | - task: |
| 875 | type: Retrieval |
| 876 | dataset: |
| 877 | type: BeIR/cqadupstack |
| 878 | name: MTEB CQADupstackRetrieval |
| 879 | config: default |
| 880 | split: test |
| 881 | revision: None |
| 882 | metrics: |
| 883 | - type: map_at_1 |
| 884 | value: 25.946583333333333 |
| 885 | - type: map_at_10 |
| 886 | value: 33.79725 |
| 887 | - type: map_at_100 |
| 888 | value: 34.86408333333333 |
| 889 | - type: map_at_1000 |
| 890 | value: 34.9795 |
| 891 | - type: map_at_3 |
| 892 | value: 31.259999999999998 |
| 893 | - type: map_at_5 |
| 894 | value: 32.71541666666666 |
| 895 | - type: mrr_at_1 |
| 896 | value: 30.863749999999996 |
| 897 | - type: mrr_at_10 |
| 898 | value: 37.99183333333333 |
| 899 | - type: mrr_at_100 |
| 900 | value: 38.790499999999994 |
| 901 | - type: mrr_at_1000 |
| 902 | value: 38.85575000000001 |
| 903 | - type: mrr_at_3 |
| 904 | value: 35.82083333333333 |
| 905 | - type: mrr_at_5 |
| 906 | value: 37.07533333333333 |
| 907 | - type: ndcg_at_1 |
| 908 | value: 30.863749999999996 |
| 909 | - type: ndcg_at_10 |
| 910 | value: 38.52141666666667 |
| 911 | - type: ndcg_at_100 |
| 912 | value: 43.17966666666667 |
| 913 | - type: ndcg_at_1000 |
| 914 | value: 45.64608333333333 |
| 915 | - type: ndcg_at_3 |
| 916 | value: 34.333000000000006 |
| 917 | - type: ndcg_at_5 |
| 918 | value: 36.34975 |
| 919 | - type: precision_at_1 |
| 920 | value: 30.863749999999996 |
| 921 | - type: precision_at_10 |
| 922 | value: 6.598999999999999 |
| 923 | - type: precision_at_100 |
| 924 | value: 1.0502500000000001 |
| 925 | - type: precision_at_1000 |
| 926 | value: 0.14400000000000002 |
| 927 | - type: precision_at_3 |
| 928 | value: 15.557583333333334 |
| 929 | - type: precision_at_5 |
| 930 | value: 11.020000000000001 |
| 931 | - type: recall_at_1 |
| 932 | value: 25.946583333333333 |
| 933 | - type: recall_at_10 |
| 934 | value: 48.36991666666666 |
| 935 | - type: recall_at_100 |
| 936 | value: 69.02408333333334 |
| 937 | - type: recall_at_1000 |
| 938 | value: 86.43858333333331 |
| 939 | - type: recall_at_3 |
| 940 | value: 36.4965 |
| 941 | - type: recall_at_5 |
| 942 | value: 41.76258333333334 |
| 943 | - task: |
| 944 | type: Retrieval |
| 945 | dataset: |
| 946 | type: BeIR/cqadupstack |
| 947 | name: MTEB CQADupstackStatsRetrieval |
| 948 | config: default |
| 949 | split: test |
| 950 | revision: None |
| 951 | metrics: |
| 952 | - type: map_at_1 |
| 953 | value: 22.431 |
| 954 | - type: map_at_10 |
| 955 | value: 28.889 |
| 956 | - type: map_at_100 |
| 957 | value: 29.642000000000003 |
| 958 | - type: map_at_1000 |
| 959 | value: 29.742 |
| 960 | - type: map_at_3 |
| 961 | value: 26.998 |
| 962 | - type: map_at_5 |
| 963 | value: 28.172000000000004 |
| 964 | - type: mrr_at_1 |
| 965 | value: 25.307000000000002 |
| 966 | - type: mrr_at_10 |
| 967 | value: 31.763 |
| 968 | - type: mrr_at_100 |
| 969 | value: 32.443 |
| 970 | - type: mrr_at_1000 |
| 971 | value: 32.531 |
| 972 | - type: mrr_at_3 |
| 973 | value: 29.959000000000003 |
| 974 | - type: mrr_at_5 |
| 975 | value: 31.063000000000002 |
| 976 | - type: ndcg_at_1 |
| 977 | value: 25.307000000000002 |
| 978 | - type: ndcg_at_10 |
| 979 | value: 32.586999999999996 |
| 980 | - type: ndcg_at_100 |
| 981 | value: 36.5 |
| 982 | - type: ndcg_at_1000 |
| 983 | value: 39.133 |
| 984 | - type: ndcg_at_3 |
| 985 | value: 29.25 |
| 986 | - type: ndcg_at_5 |
| 987 | value: 31.023 |
| 988 | - type: precision_at_1 |
| 989 | value: 25.307000000000002 |
| 990 | - type: precision_at_10 |
| 991 | value: 4.954 |
| 992 | - type: precision_at_100 |
| 993 | value: 0.747 |
| 994 | - type: precision_at_1000 |
| 995 | value: 0.104 |
| 996 | - type: precision_at_3 |
| 997 | value: 12.577 |
| 998 | - type: precision_at_5 |
| 999 | value: 8.741999999999999 |
| 1000 | - type: recall_at_1 |
| 1001 | value: 22.431 |
| 1002 | - type: recall_at_10 |
| 1003 | value: 41.134 |
| 1004 | - type: recall_at_100 |
| 1005 | value: 59.28600000000001 |
| 1006 | - type: recall_at_1000 |
| 1007 | value: 78.857 |
| 1008 | - type: recall_at_3 |
| 1009 | value: 31.926 |
| 1010 | - type: recall_at_5 |
| 1011 | value: 36.335 |
| 1012 | - task: |
| 1013 | type: Retrieval |
| 1014 | dataset: |
| 1015 | type: BeIR/cqadupstack |
| 1016 | name: MTEB CQADupstackTexRetrieval |
| 1017 | config: default |
| 1018 | split: test |
| 1019 | revision: None |
| 1020 | metrics: |
| 1021 | - type: map_at_1 |
| 1022 | value: 17.586 |
| 1023 | - type: map_at_10 |
| 1024 | value: 23.304 |
| 1025 | - type: map_at_100 |
| 1026 | value: 24.159 |
| 1027 | - type: map_at_1000 |
| 1028 | value: 24.281 |
| 1029 | - type: map_at_3 |
| 1030 | value: 21.316 |
| 1031 | - type: map_at_5 |
| 1032 | value: 22.383 |
| 1033 | - type: mrr_at_1 |
| 1034 | value: 21.645 |
| 1035 | - type: mrr_at_10 |
| 1036 | value: 27.365000000000002 |
| 1037 | - type: mrr_at_100 |
| 1038 | value: 28.108 |
| 1039 | - type: mrr_at_1000 |
| 1040 | value: 28.192 |
| 1041 | - type: mrr_at_3 |
| 1042 | value: 25.482 |
| 1043 | - type: mrr_at_5 |
| 1044 | value: 26.479999999999997 |
| 1045 | - type: ndcg_at_1 |
| 1046 | value: 21.645 |
| 1047 | - type: ndcg_at_10 |
| 1048 | value: 27.306 |
| 1049 | - type: ndcg_at_100 |
| 1050 | value: 31.496000000000002 |
| 1051 | - type: ndcg_at_1000 |
| 1052 | value: 34.53 |
| 1053 | - type: ndcg_at_3 |
| 1054 | value: 23.73 |
| 1055 | - type: ndcg_at_5 |
| 1056 | value: 25.294 |
| 1057 | - type: precision_at_1 |
| 1058 | value: 21.645 |
| 1059 | - type: precision_at_10 |
| 1060 | value: 4.797 |
| 1061 | - type: precision_at_100 |
| 1062 | value: 0.8059999999999999 |
| 1063 | - type: precision_at_1000 |
| 1064 | value: 0.121 |
| 1065 | - type: precision_at_3 |
| 1066 | value: 10.850999999999999 |
| 1067 | - type: precision_at_5 |
| 1068 | value: 7.736 |
| 1069 | - type: recall_at_1 |
| 1070 | value: 17.586 |
| 1071 | - type: recall_at_10 |
| 1072 | value: 35.481 |
| 1073 | - type: recall_at_100 |
| 1074 | value: 54.534000000000006 |
| 1075 | - type: recall_at_1000 |
| 1076 | value: 76.456 |
| 1077 | - type: recall_at_3 |
| 1078 | value: 25.335 |
| 1079 | - type: recall_at_5 |
| 1080 | value: 29.473 |
| 1081 | - task: |
| 1082 | type: Retrieval |
| 1083 | dataset: |
| 1084 | type: BeIR/cqadupstack |
| 1085 | name: MTEB CQADupstackUnixRetrieval |
| 1086 | config: default |
| 1087 | split: test |
| 1088 | revision: None |
| 1089 | metrics: |
| 1090 | - type: map_at_1 |
| 1091 | value: 25.095 |
| 1092 | - type: map_at_10 |
| 1093 | value: 32.374 |
| 1094 | - type: map_at_100 |
| 1095 | value: 33.537 |
| 1096 | - type: map_at_1000 |
| 1097 | value: 33.634 |
| 1098 | - type: map_at_3 |
| 1099 | value: 30.089 |
| 1100 | - type: map_at_5 |
| 1101 | value: 31.433 |
| 1102 | - type: mrr_at_1 |
| 1103 | value: 29.198 |
| 1104 | - type: mrr_at_10 |
| 1105 | value: 36.01 |
| 1106 | - type: mrr_at_100 |
| 1107 | value: 37.022 |
| 1108 | - type: mrr_at_1000 |
| 1109 | value: 37.083 |
| 1110 | - type: mrr_at_3 |
| 1111 | value: 33.94 |
| 1112 | - type: mrr_at_5 |
| 1113 | value: 35.148 |
| 1114 | - type: ndcg_at_1 |
| 1115 | value: 29.198 |
| 1116 | - type: ndcg_at_10 |
| 1117 | value: 36.729 |
| 1118 | - type: ndcg_at_100 |
| 1119 | value: 42.114000000000004 |
| 1120 | - type: ndcg_at_1000 |
| 1121 | value: 44.592 |
| 1122 | - type: ndcg_at_3 |
| 1123 | value: 32.644 |
| 1124 | - type: ndcg_at_5 |
| 1125 | value: 34.652 |
| 1126 | - type: precision_at_1 |
| 1127 | value: 29.198 |
| 1128 | - type: precision_at_10 |
| 1129 | value: 5.970000000000001 |
| 1130 | - type: precision_at_100 |
| 1131 | value: 0.967 |
| 1132 | - type: precision_at_1000 |
| 1133 | value: 0.129 |
| 1134 | - type: precision_at_3 |
| 1135 | value: 14.396999999999998 |
| 1136 | - type: precision_at_5 |
| 1137 | value: 10.093 |
| 1138 | - type: recall_at_1 |
| 1139 | value: 25.095 |
| 1140 | - type: recall_at_10 |
| 1141 | value: 46.392 |
| 1142 | - type: recall_at_100 |
| 1143 | value: 69.706 |
| 1144 | - type: recall_at_1000 |
| 1145 | value: 87.738 |
| 1146 | - type: recall_at_3 |
| 1147 | value: 35.303000000000004 |
| 1148 | - type: recall_at_5 |
| 1149 | value: 40.441 |
| 1150 | - task: |
| 1151 | type: Retrieval |
| 1152 | dataset: |
| 1153 | type: BeIR/cqadupstack |
| 1154 | name: MTEB CQADupstackWebmastersRetrieval |
| 1155 | config: default |
| 1156 | split: test |
| 1157 | revision: None |
| 1158 | metrics: |
| 1159 | - type: map_at_1 |
| 1160 | value: 26.857999999999997 |
| 1161 | - type: map_at_10 |
| 1162 | value: 34.066 |
| 1163 | - type: map_at_100 |
| 1164 | value: 35.671 |
| 1165 | - type: map_at_1000 |
| 1166 | value: 35.881 |
| 1167 | - type: map_at_3 |
| 1168 | value: 31.304 |
| 1169 | - type: map_at_5 |
| 1170 | value: 32.885 |
| 1171 | - type: mrr_at_1 |
| 1172 | value: 32.411 |
| 1173 | - type: mrr_at_10 |
| 1174 | value: 38.987 |
| 1175 | - type: mrr_at_100 |
| 1176 | value: 39.894 |
| 1177 | - type: mrr_at_1000 |
| 1178 | value: 39.959 |
| 1179 | - type: mrr_at_3 |
| 1180 | value: 36.626999999999995 |
| 1181 | - type: mrr_at_5 |
| 1182 | value: 38.011 |
| 1183 | - type: ndcg_at_1 |
| 1184 | value: 32.411 |
| 1185 | - type: ndcg_at_10 |
| 1186 | value: 39.208 |
| 1187 | - type: ndcg_at_100 |
| 1188 | value: 44.626 |
| 1189 | - type: ndcg_at_1000 |
| 1190 | value: 47.43 |
| 1191 | - type: ndcg_at_3 |
| 1192 | value: 35.091 |
| 1193 | - type: ndcg_at_5 |
| 1194 | value: 37.119 |
| 1195 | - type: precision_at_1 |
| 1196 | value: 32.411 |
| 1197 | - type: precision_at_10 |
| 1198 | value: 7.51 |
| 1199 | - type: precision_at_100 |
| 1200 | value: 1.486 |
| 1201 | - type: precision_at_1000 |
| 1202 | value: 0.234 |
| 1203 | - type: precision_at_3 |
| 1204 | value: 16.14 |
| 1205 | - type: precision_at_5 |
| 1206 | value: 11.976 |
| 1207 | - type: recall_at_1 |
| 1208 | value: 26.857999999999997 |
| 1209 | - type: recall_at_10 |
| 1210 | value: 47.407 |
| 1211 | - type: recall_at_100 |
| 1212 | value: 72.236 |
| 1213 | - type: recall_at_1000 |
| 1214 | value: 90.77 |
| 1215 | - type: recall_at_3 |
| 1216 | value: 35.125 |
| 1217 | - type: recall_at_5 |
| 1218 | value: 40.522999999999996 |
| 1219 | - task: |
| 1220 | type: Retrieval |
| 1221 | dataset: |
| 1222 | type: BeIR/cqadupstack |
| 1223 | name: MTEB CQADupstackWordpressRetrieval |
| 1224 | config: default |
| 1225 | split: test |
| 1226 | revision: None |
| 1227 | metrics: |
| 1228 | - type: map_at_1 |
| 1229 | value: 21.3 |
| 1230 | - type: map_at_10 |
| 1231 | value: 27.412999999999997 |
| 1232 | - type: map_at_100 |
| 1233 | value: 28.29 |
| 1234 | - type: map_at_1000 |
| 1235 | value: 28.398 |
| 1236 | - type: map_at_3 |
| 1237 | value: 25.169999999999998 |
| 1238 | - type: map_at_5 |
| 1239 | value: 26.496 |
| 1240 | - type: mrr_at_1 |
| 1241 | value: 23.29 |
| 1242 | - type: mrr_at_10 |
| 1243 | value: 29.215000000000003 |
| 1244 | - type: mrr_at_100 |
| 1245 | value: 30.073 |
| 1246 | - type: mrr_at_1000 |
| 1247 | value: 30.156 |
| 1248 | - type: mrr_at_3 |
| 1249 | value: 26.956000000000003 |
| 1250 | - type: mrr_at_5 |
| 1251 | value: 28.38 |
| 1252 | - type: ndcg_at_1 |
| 1253 | value: 23.29 |
| 1254 | - type: ndcg_at_10 |
| 1255 | value: 31.113000000000003 |
| 1256 | - type: ndcg_at_100 |
| 1257 | value: 35.701 |
| 1258 | - type: ndcg_at_1000 |
| 1259 | value: 38.505 |
| 1260 | - type: ndcg_at_3 |
| 1261 | value: 26.727 |
| 1262 | - type: ndcg_at_5 |
| 1263 | value: 29.037000000000003 |
| 1264 | - type: precision_at_1 |
| 1265 | value: 23.29 |
| 1266 | - type: precision_at_10 |
| 1267 | value: 4.787 |
| 1268 | - type: precision_at_100 |
| 1269 | value: 0.763 |
| 1270 | - type: precision_at_1000 |
| 1271 | value: 0.11100000000000002 |
| 1272 | - type: precision_at_3 |
| 1273 | value: 11.091 |
| 1274 | - type: precision_at_5 |
| 1275 | value: 7.985 |
| 1276 | - type: recall_at_1 |
| 1277 | value: 21.3 |
| 1278 | - type: recall_at_10 |
| 1279 | value: 40.782000000000004 |
| 1280 | - type: recall_at_100 |
| 1281 | value: 62.13999999999999 |
| 1282 | - type: recall_at_1000 |
| 1283 | value: 83.012 |
| 1284 | - type: recall_at_3 |
| 1285 | value: 29.131 |
| 1286 | - type: recall_at_5 |
| 1287 | value: 34.624 |
| 1288 | - task: |
| 1289 | type: Retrieval |
| 1290 | dataset: |
| 1291 | type: climate-fever |
| 1292 | name: MTEB ClimateFEVER |
| 1293 | config: default |
| 1294 | split: test |
| 1295 | revision: None |
| 1296 | metrics: |
| 1297 | - type: map_at_1 |
| 1298 | value: 9.631 |
| 1299 | - type: map_at_10 |
| 1300 | value: 16.634999999999998 |
| 1301 | - type: map_at_100 |
| 1302 | value: 18.23 |
| 1303 | - type: map_at_1000 |
| 1304 | value: 18.419 |
| 1305 | - type: map_at_3 |
| 1306 | value: 13.66 |
| 1307 | - type: map_at_5 |
| 1308 | value: 15.173 |
| 1309 | - type: mrr_at_1 |
| 1310 | value: 21.368000000000002 |
| 1311 | - type: mrr_at_10 |
| 1312 | value: 31.56 |
| 1313 | - type: mrr_at_100 |
| 1314 | value: 32.58 |
| 1315 | - type: mrr_at_1000 |
| 1316 | value: 32.633 |
| 1317 | - type: mrr_at_3 |
| 1318 | value: 28.241 |
| 1319 | - type: mrr_at_5 |
| 1320 | value: 30.225 |
| 1321 | - type: ndcg_at_1 |
| 1322 | value: 21.368000000000002 |
| 1323 | - type: ndcg_at_10 |
| 1324 | value: 23.855999999999998 |
| 1325 | - type: ndcg_at_100 |
| 1326 | value: 30.686999999999998 |
| 1327 | - type: ndcg_at_1000 |
| 1328 | value: 34.327000000000005 |
| 1329 | - type: ndcg_at_3 |
| 1330 | value: 18.781 |
| 1331 | - type: ndcg_at_5 |
| 1332 | value: 20.73 |
| 1333 | - type: precision_at_1 |
| 1334 | value: 21.368000000000002 |
| 1335 | - type: precision_at_10 |
| 1336 | value: 7.564 |
| 1337 | - type: precision_at_100 |
| 1338 | value: 1.496 |
| 1339 | - type: precision_at_1000 |
| 1340 | value: 0.217 |
| 1341 | - type: precision_at_3 |
| 1342 | value: 13.876 |
| 1343 | - type: precision_at_5 |
| 1344 | value: 11.062 |
| 1345 | - type: recall_at_1 |
| 1346 | value: 9.631 |
| 1347 | - type: recall_at_10 |
| 1348 | value: 29.517 |
| 1349 | - type: recall_at_100 |
| 1350 | value: 53.452 |
| 1351 | - type: recall_at_1000 |
| 1352 | value: 74.115 |
| 1353 | - type: recall_at_3 |
| 1354 | value: 17.605999999999998 |
| 1355 | - type: recall_at_5 |
| 1356 | value: 22.505 |
| 1357 | - task: |
| 1358 | type: Retrieval |
| 1359 | dataset: |
| 1360 | type: dbpedia-entity |
| 1361 | name: MTEB DBPedia |
| 1362 | config: default |
| 1363 | split: test |
| 1364 | revision: None |
| 1365 | metrics: |
| 1366 | - type: map_at_1 |
| 1367 | value: 8.885 |
| 1368 | - type: map_at_10 |
| 1369 | value: 18.798000000000002 |
| 1370 | - type: map_at_100 |
| 1371 | value: 26.316 |
| 1372 | - type: map_at_1000 |
| 1373 | value: 27.869 |
| 1374 | - type: map_at_3 |
| 1375 | value: 13.719000000000001 |
| 1376 | - type: map_at_5 |
| 1377 | value: 15.716 |
| 1378 | - type: mrr_at_1 |
| 1379 | value: 66 |
| 1380 | - type: mrr_at_10 |
| 1381 | value: 74.263 |
| 1382 | - type: mrr_at_100 |
| 1383 | value: 74.519 |
| 1384 | - type: mrr_at_1000 |
| 1385 | value: 74.531 |
| 1386 | - type: mrr_at_3 |
| 1387 | value: 72.458 |
| 1388 | - type: mrr_at_5 |
| 1389 | value: 73.321 |
| 1390 | - type: ndcg_at_1 |
| 1391 | value: 53.87499999999999 |
| 1392 | - type: ndcg_at_10 |
| 1393 | value: 40.355999999999995 |
| 1394 | - type: ndcg_at_100 |
| 1395 | value: 44.366 |
| 1396 | - type: ndcg_at_1000 |
| 1397 | value: 51.771 |
| 1398 | - type: ndcg_at_3 |
| 1399 | value: 45.195 |
| 1400 | - type: ndcg_at_5 |
| 1401 | value: 42.187000000000005 |
| 1402 | - type: precision_at_1 |
| 1403 | value: 66 |
| 1404 | - type: precision_at_10 |
| 1405 | value: 31.75 |
| 1406 | - type: precision_at_100 |
| 1407 | value: 10.11 |
| 1408 | - type: precision_at_1000 |
| 1409 | value: 1.9800000000000002 |
| 1410 | - type: precision_at_3 |
| 1411 | value: 48.167 |
| 1412 | - type: precision_at_5 |
| 1413 | value: 40.050000000000004 |
| 1414 | - type: recall_at_1 |
| 1415 | value: 8.885 |
| 1416 | - type: recall_at_10 |
| 1417 | value: 24.471999999999998 |
| 1418 | - type: recall_at_100 |
| 1419 | value: 49.669000000000004 |
| 1420 | - type: recall_at_1000 |
| 1421 | value: 73.383 |
| 1422 | - type: recall_at_3 |
| 1423 | value: 14.872 |
| 1424 | - type: recall_at_5 |
| 1425 | value: 18.262999999999998 |
| 1426 | - task: |
| 1427 | type: Classification |
| 1428 | dataset: |
| 1429 | type: mteb/emotion |
| 1430 | name: MTEB EmotionClassification |
| 1431 | config: default |
| 1432 | split: test |
| 1433 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 1434 | metrics: |
| 1435 | - type: accuracy |
| 1436 | value: 45.18 |
| 1437 | - type: f1 |
| 1438 | value: 40.26878691789978 |
| 1439 | - task: |
| 1440 | type: Retrieval |
| 1441 | dataset: |
| 1442 | type: fever |
| 1443 | name: MTEB FEVER |
| 1444 | config: default |
| 1445 | split: test |
| 1446 | revision: None |
| 1447 | metrics: |
| 1448 | - type: map_at_1 |
| 1449 | value: 62.751999999999995 |
| 1450 | - type: map_at_10 |
| 1451 | value: 74.131 |
| 1452 | - type: map_at_100 |
| 1453 | value: 74.407 |
| 1454 | - type: map_at_1000 |
| 1455 | value: 74.423 |
| 1456 | - type: map_at_3 |
| 1457 | value: 72.329 |
| 1458 | - type: map_at_5 |
| 1459 | value: 73.555 |
| 1460 | - type: mrr_at_1 |
| 1461 | value: 67.282 |
| 1462 | - type: mrr_at_10 |
| 1463 | value: 78.292 |
| 1464 | - type: mrr_at_100 |
| 1465 | value: 78.455 |
| 1466 | - type: mrr_at_1000 |
| 1467 | value: 78.458 |
| 1468 | - type: mrr_at_3 |
| 1469 | value: 76.755 |
| 1470 | - type: mrr_at_5 |
| 1471 | value: 77.839 |
| 1472 | - type: ndcg_at_1 |
| 1473 | value: 67.282 |
| 1474 | - type: ndcg_at_10 |
| 1475 | value: 79.443 |
| 1476 | - type: ndcg_at_100 |
| 1477 | value: 80.529 |
| 1478 | - type: ndcg_at_1000 |
| 1479 | value: 80.812 |
| 1480 | - type: ndcg_at_3 |
| 1481 | value: 76.281 |
| 1482 | - type: ndcg_at_5 |
| 1483 | value: 78.235 |
| 1484 | - type: precision_at_1 |
| 1485 | value: 67.282 |
| 1486 | - type: precision_at_10 |
| 1487 | value: 10.078 |
| 1488 | - type: precision_at_100 |
| 1489 | value: 1.082 |
| 1490 | - type: precision_at_1000 |
| 1491 | value: 0.11199999999999999 |
| 1492 | - type: precision_at_3 |
| 1493 | value: 30.178 |
| 1494 | - type: precision_at_5 |
| 1495 | value: 19.232 |
| 1496 | - type: recall_at_1 |
| 1497 | value: 62.751999999999995 |
| 1498 | - type: recall_at_10 |
| 1499 | value: 91.521 |
| 1500 | - type: recall_at_100 |
| 1501 | value: 95.997 |
| 1502 | - type: recall_at_1000 |
| 1503 | value: 97.775 |
| 1504 | - type: recall_at_3 |
| 1505 | value: 83.131 |
| 1506 | - type: recall_at_5 |
| 1507 | value: 87.93299999999999 |
| 1508 | - task: |
| 1509 | type: Retrieval |
| 1510 | dataset: |
| 1511 | type: fiqa |
| 1512 | name: MTEB FiQA2018 |
| 1513 | config: default |
| 1514 | split: test |
| 1515 | revision: None |
| 1516 | metrics: |
| 1517 | - type: map_at_1 |
| 1518 | value: 18.861 |
| 1519 | - type: map_at_10 |
| 1520 | value: 30.252000000000002 |
| 1521 | - type: map_at_100 |
| 1522 | value: 32.082 |
| 1523 | - type: map_at_1000 |
| 1524 | value: 32.261 |
| 1525 | - type: map_at_3 |
| 1526 | value: 25.909 |
| 1527 | - type: map_at_5 |
| 1528 | value: 28.296 |
| 1529 | - type: mrr_at_1 |
| 1530 | value: 37.346000000000004 |
| 1531 | - type: mrr_at_10 |
| 1532 | value: 45.802 |
| 1533 | - type: mrr_at_100 |
| 1534 | value: 46.611999999999995 |
| 1535 | - type: mrr_at_1000 |
| 1536 | value: 46.659 |
| 1537 | - type: mrr_at_3 |
| 1538 | value: 43.056 |
| 1539 | - type: mrr_at_5 |
| 1540 | value: 44.637 |
| 1541 | - type: ndcg_at_1 |
| 1542 | value: 37.346000000000004 |
| 1543 | - type: ndcg_at_10 |
| 1544 | value: 38.169 |
| 1545 | - type: ndcg_at_100 |
| 1546 | value: 44.864 |
| 1547 | - type: ndcg_at_1000 |
| 1548 | value: 47.974 |
| 1549 | - type: ndcg_at_3 |
| 1550 | value: 33.619 |
| 1551 | - type: ndcg_at_5 |
| 1552 | value: 35.317 |
| 1553 | - type: precision_at_1 |
| 1554 | value: 37.346000000000004 |
| 1555 | - type: precision_at_10 |
| 1556 | value: 10.693999999999999 |
| 1557 | - type: precision_at_100 |
| 1558 | value: 1.775 |
| 1559 | - type: precision_at_1000 |
| 1560 | value: 0.231 |
| 1561 | - type: precision_at_3 |
| 1562 | value: 22.325 |
| 1563 | - type: precision_at_5 |
| 1564 | value: 16.852 |
| 1565 | - type: recall_at_1 |
| 1566 | value: 18.861 |
| 1567 | - type: recall_at_10 |
| 1568 | value: 45.672000000000004 |
| 1569 | - type: recall_at_100 |
| 1570 | value: 70.60499999999999 |
| 1571 | - type: recall_at_1000 |
| 1572 | value: 89.216 |
| 1573 | - type: recall_at_3 |
| 1574 | value: 30.361 |
| 1575 | - type: recall_at_5 |
| 1576 | value: 36.998999999999995 |
| 1577 | - task: |
| 1578 | type: Retrieval |
| 1579 | dataset: |
| 1580 | type: hotpotqa |
| 1581 | name: MTEB HotpotQA |
| 1582 | config: default |
| 1583 | split: test |
| 1584 | revision: None |
| 1585 | metrics: |
| 1586 | - type: map_at_1 |
| 1587 | value: 37.852999999999994 |
| 1588 | - type: map_at_10 |
| 1589 | value: 59.961 |
| 1590 | - type: map_at_100 |
| 1591 | value: 60.78 |
| 1592 | - type: map_at_1000 |
| 1593 | value: 60.843 |
| 1594 | - type: map_at_3 |
| 1595 | value: 56.39999999999999 |
| 1596 | - type: map_at_5 |
| 1597 | value: 58.646 |
| 1598 | - type: mrr_at_1 |
| 1599 | value: 75.70599999999999 |
| 1600 | - type: mrr_at_10 |
| 1601 | value: 82.321 |
| 1602 | - type: mrr_at_100 |
| 1603 | value: 82.516 |
| 1604 | - type: mrr_at_1000 |
| 1605 | value: 82.525 |
| 1606 | - type: mrr_at_3 |
| 1607 | value: 81.317 |
| 1608 | - type: mrr_at_5 |
| 1609 | value: 81.922 |
| 1610 | - type: ndcg_at_1 |
| 1611 | value: 75.70599999999999 |
| 1612 | - type: ndcg_at_10 |
| 1613 | value: 68.557 |
| 1614 | - type: ndcg_at_100 |
| 1615 | value: 71.485 |
| 1616 | - type: ndcg_at_1000 |
| 1617 | value: 72.71600000000001 |
| 1618 | - type: ndcg_at_3 |
| 1619 | value: 63.524 |
| 1620 | - type: ndcg_at_5 |
| 1621 | value: 66.338 |
| 1622 | - type: precision_at_1 |
| 1623 | value: 75.70599999999999 |
| 1624 | - type: precision_at_10 |
| 1625 | value: 14.463000000000001 |
| 1626 | - type: precision_at_100 |
| 1627 | value: 1.677 |
| 1628 | - type: precision_at_1000 |
| 1629 | value: 0.184 |
| 1630 | - type: precision_at_3 |
| 1631 | value: 40.806 |
| 1632 | - type: precision_at_5 |
| 1633 | value: 26.709 |
| 1634 | - type: recall_at_1 |
| 1635 | value: 37.852999999999994 |
| 1636 | - type: recall_at_10 |
| 1637 | value: 72.316 |
| 1638 | - type: recall_at_100 |
| 1639 | value: 83.842 |
| 1640 | - type: recall_at_1000 |
| 1641 | value: 91.999 |
| 1642 | - type: recall_at_3 |
| 1643 | value: 61.209 |
| 1644 | - type: recall_at_5 |
| 1645 | value: 66.77199999999999 |
| 1646 | - task: |
| 1647 | type: Classification |
| 1648 | dataset: |
| 1649 | type: mteb/imdb |
| 1650 | name: MTEB ImdbClassification |
| 1651 | config: default |
| 1652 | split: test |
| 1653 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 1654 | metrics: |
| 1655 | - type: accuracy |
| 1656 | value: 85.46039999999999 |
| 1657 | - type: ap |
| 1658 | value: 79.9812521351881 |
| 1659 | - type: f1 |
| 1660 | value: 85.31722909702084 |
| 1661 | - task: |
| 1662 | type: Retrieval |
| 1663 | dataset: |
| 1664 | type: msmarco |
| 1665 | name: MTEB MSMARCO |
| 1666 | config: default |
| 1667 | split: dev |
| 1668 | revision: None |
| 1669 | metrics: |
| 1670 | - type: map_at_1 |
| 1671 | value: 22.704 |
| 1672 | - type: map_at_10 |
| 1673 | value: 35.329 |
| 1674 | - type: map_at_100 |
| 1675 | value: 36.494 |
| 1676 | - type: map_at_1000 |
| 1677 | value: 36.541000000000004 |
| 1678 | - type: map_at_3 |
| 1679 | value: 31.476 |
| 1680 | - type: map_at_5 |
| 1681 | value: 33.731 |
| 1682 | - type: mrr_at_1 |
| 1683 | value: 23.294999999999998 |
| 1684 | - type: mrr_at_10 |
| 1685 | value: 35.859 |
| 1686 | - type: mrr_at_100 |
| 1687 | value: 36.968 |
| 1688 | - type: mrr_at_1000 |
| 1689 | value: 37.008 |
| 1690 | - type: mrr_at_3 |
| 1691 | value: 32.085 |
| 1692 | - type: mrr_at_5 |
| 1693 | value: 34.299 |
| 1694 | - type: ndcg_at_1 |
| 1695 | value: 23.324 |
| 1696 | - type: ndcg_at_10 |
| 1697 | value: 42.274 |
| 1698 | - type: ndcg_at_100 |
| 1699 | value: 47.839999999999996 |
| 1700 | - type: ndcg_at_1000 |
| 1701 | value: 48.971 |
| 1702 | - type: ndcg_at_3 |
| 1703 | value: 34.454 |
| 1704 | - type: ndcg_at_5 |
| 1705 | value: 38.464 |
| 1706 | - type: precision_at_1 |
| 1707 | value: 23.324 |
| 1708 | - type: precision_at_10 |
| 1709 | value: 6.648 |
| 1710 | - type: precision_at_100 |
| 1711 | value: 0.9440000000000001 |
| 1712 | - type: precision_at_1000 |
| 1713 | value: 0.104 |
| 1714 | - type: precision_at_3 |
| 1715 | value: 14.674999999999999 |
| 1716 | - type: precision_at_5 |
| 1717 | value: 10.850999999999999 |
| 1718 | - type: recall_at_1 |
| 1719 | value: 22.704 |
| 1720 | - type: recall_at_10 |
| 1721 | value: 63.660000000000004 |
| 1722 | - type: recall_at_100 |
| 1723 | value: 89.29899999999999 |
| 1724 | - type: recall_at_1000 |
| 1725 | value: 97.88900000000001 |
| 1726 | - type: recall_at_3 |
| 1727 | value: 42.441 |
| 1728 | - type: recall_at_5 |
| 1729 | value: 52.04 |
| 1730 | - task: |
| 1731 | type: Classification |
| 1732 | dataset: |
| 1733 | type: mteb/mtop_domain |
| 1734 | name: MTEB MTOPDomainClassification (en) |
| 1735 | config: en |
| 1736 | split: test |
| 1737 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1738 | metrics: |
| 1739 | - type: accuracy |
| 1740 | value: 93.1326949384405 |
| 1741 | - type: f1 |
| 1742 | value: 92.89743579612082 |
| 1743 | - task: |
| 1744 | type: Classification |
| 1745 | dataset: |
| 1746 | type: mteb/mtop_domain |
| 1747 | name: MTEB MTOPDomainClassification (de) |
| 1748 | config: de |
| 1749 | split: test |
| 1750 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1751 | metrics: |
| 1752 | - type: accuracy |
| 1753 | value: 89.62524654832347 |
| 1754 | - type: f1 |
| 1755 | value: 88.65106082263151 |
| 1756 | - task: |
| 1757 | type: Classification |
| 1758 | dataset: |
| 1759 | type: mteb/mtop_domain |
| 1760 | name: MTEB MTOPDomainClassification (es) |
| 1761 | config: es |
| 1762 | split: test |
| 1763 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1764 | metrics: |
| 1765 | - type: accuracy |
| 1766 | value: 90.59039359573046 |
| 1767 | - type: f1 |
| 1768 | value: 90.31532892105662 |
| 1769 | - task: |
| 1770 | type: Classification |
| 1771 | dataset: |
| 1772 | type: mteb/mtop_domain |
| 1773 | name: MTEB MTOPDomainClassification (fr) |
| 1774 | config: fr |
| 1775 | split: test |
| 1776 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1777 | metrics: |
| 1778 | - type: accuracy |
| 1779 | value: 86.21046038208581 |
| 1780 | - type: f1 |
| 1781 | value: 86.41459529813113 |
| 1782 | - task: |
| 1783 | type: Classification |
| 1784 | dataset: |
| 1785 | type: mteb/mtop_domain |
| 1786 | name: MTEB MTOPDomainClassification (hi) |
| 1787 | config: hi |
| 1788 | split: test |
| 1789 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1790 | metrics: |
| 1791 | - type: accuracy |
| 1792 | value: 87.3180351380423 |
| 1793 | - type: f1 |
| 1794 | value: 86.71383078226444 |
| 1795 | - task: |
| 1796 | type: Classification |
| 1797 | dataset: |
| 1798 | type: mteb/mtop_domain |
| 1799 | name: MTEB MTOPDomainClassification (th) |
| 1800 | config: th |
| 1801 | split: test |
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| 1825 | name: MTEB MTOPIntentClassification (de) |
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| 1838 | name: MTEB MTOPIntentClassification (es) |
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| 1851 | name: MTEB MTOPIntentClassification (fr) |
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| 1864 | name: MTEB MTOPIntentClassification (hi) |
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| 1981 | name: MTEB MassiveIntentClassification (de) |
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| 1994 | name: MTEB MassiveIntentClassification (el) |
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| 2007 | name: MTEB MassiveIntentClassification (en) |
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| 2059 | name: MTEB MassiveIntentClassification (fr) |
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| 2280 | name: MTEB MassiveIntentClassification (ms) |
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| 2941 | dataset: |
| 2942 | type: mteb/amazon_massive_scenario |
| 2943 | name: MTEB MassiveScenarioClassification (ms) |
| 2944 | config: ms |
| 2945 | split: test |
| 2946 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2947 | metrics: |
| 2948 | - type: accuracy |
| 2949 | value: 66.90316072629456 |
| 2950 | - type: f1 |
| 2951 | value: 65.1325924692381 |
| 2952 | - task: |
| 2953 | type: Classification |
| 2954 | dataset: |
| 2955 | type: mteb/amazon_massive_scenario |
| 2956 | name: MTEB MassiveScenarioClassification (my) |
| 2957 | config: my |
| 2958 | split: test |
| 2959 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2960 | metrics: |
| 2961 | - type: accuracy |
| 2962 | value: 61.63752521856086 |
| 2963 | - type: f1 |
| 2964 | value: 59.14284778039585 |
| 2965 | - task: |
| 2966 | type: Classification |
| 2967 | dataset: |
| 2968 | type: mteb/amazon_massive_scenario |
| 2969 | name: MTEB MassiveScenarioClassification (nb) |
| 2970 | config: nb |
| 2971 | split: test |
| 2972 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2973 | metrics: |
| 2974 | - type: accuracy |
| 2975 | value: 71.63080026899797 |
| 2976 | - type: f1 |
| 2977 | value: 70.89771864626877 |
| 2978 | - task: |
| 2979 | type: Classification |
| 2980 | dataset: |
| 2981 | type: mteb/amazon_massive_scenario |
| 2982 | name: MTEB MassiveScenarioClassification (nl) |
| 2983 | config: nl |
| 2984 | split: test |
| 2985 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2986 | metrics: |
| 2987 | - type: accuracy |
| 2988 | value: 72.10827168796234 |
| 2989 | - type: f1 |
| 2990 | value: 71.71954219691159 |
| 2991 | - task: |
| 2992 | type: Classification |
| 2993 | dataset: |
| 2994 | type: mteb/amazon_massive_scenario |
| 2995 | name: MTEB MassiveScenarioClassification (pl) |
| 2996 | config: pl |
| 2997 | split: test |
| 2998 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2999 | metrics: |
| 3000 | - type: accuracy |
| 3001 | value: 70.59515803631471 |
| 3002 | - type: f1 |
| 3003 | value: 70.05040128099003 |
| 3004 | - task: |
| 3005 | type: Classification |
| 3006 | dataset: |
| 3007 | type: mteb/amazon_massive_scenario |
| 3008 | name: MTEB MassiveScenarioClassification (pt) |
| 3009 | config: pt |
| 3010 | split: test |
| 3011 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3012 | metrics: |
| 3013 | - type: accuracy |
| 3014 | value: 70.83389374579691 |
| 3015 | - type: f1 |
| 3016 | value: 70.84877936562735 |
| 3017 | - task: |
| 3018 | type: Classification |
| 3019 | dataset: |
| 3020 | type: mteb/amazon_massive_scenario |
| 3021 | name: MTEB MassiveScenarioClassification (ro) |
| 3022 | config: ro |
| 3023 | split: test |
| 3024 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3025 | metrics: |
| 3026 | - type: accuracy |
| 3027 | value: 69.18628110289173 |
| 3028 | - type: f1 |
| 3029 | value: 68.97232927921841 |
| 3030 | - task: |
| 3031 | type: Classification |
| 3032 | dataset: |
| 3033 | type: mteb/amazon_massive_scenario |
| 3034 | name: MTEB MassiveScenarioClassification (ru) |
| 3035 | config: ru |
| 3036 | split: test |
| 3037 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3038 | metrics: |
| 3039 | - type: accuracy |
| 3040 | value: 72.99260255548083 |
| 3041 | - type: f1 |
| 3042 | value: 72.85139492157732 |
| 3043 | - task: |
| 3044 | type: Classification |
| 3045 | dataset: |
| 3046 | type: mteb/amazon_massive_scenario |
| 3047 | name: MTEB MassiveScenarioClassification (sl) |
| 3048 | config: sl |
| 3049 | split: test |
| 3050 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3051 | metrics: |
| 3052 | - type: accuracy |
| 3053 | value: 65.26227303295225 |
| 3054 | - type: f1 |
| 3055 | value: 65.08833655469431 |
| 3056 | - task: |
| 3057 | type: Classification |
| 3058 | dataset: |
| 3059 | type: mteb/amazon_massive_scenario |
| 3060 | name: MTEB MassiveScenarioClassification (sq) |
| 3061 | config: sq |
| 3062 | split: test |
| 3063 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3064 | metrics: |
| 3065 | - type: accuracy |
| 3066 | value: 66.48621385339611 |
| 3067 | - type: f1 |
| 3068 | value: 64.43483199071298 |
| 3069 | - task: |
| 3070 | type: Classification |
| 3071 | dataset: |
| 3072 | type: mteb/amazon_massive_scenario |
| 3073 | name: MTEB MassiveScenarioClassification (sv) |
| 3074 | config: sv |
| 3075 | split: test |
| 3076 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3077 | metrics: |
| 3078 | - type: accuracy |
| 3079 | value: 73.14391392064559 |
| 3080 | - type: f1 |
| 3081 | value: 72.2580822579741 |
| 3082 | - task: |
| 3083 | type: Classification |
| 3084 | dataset: |
| 3085 | type: mteb/amazon_massive_scenario |
| 3086 | name: MTEB MassiveScenarioClassification (sw) |
| 3087 | config: sw |
| 3088 | split: test |
| 3089 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3090 | metrics: |
| 3091 | - type: accuracy |
| 3092 | value: 59.88567585743107 |
| 3093 | - type: f1 |
| 3094 | value: 58.3073765932569 |
| 3095 | - task: |
| 3096 | type: Classification |
| 3097 | dataset: |
| 3098 | type: mteb/amazon_massive_scenario |
| 3099 | name: MTEB MassiveScenarioClassification (ta) |
| 3100 | config: ta |
| 3101 | split: test |
| 3102 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3103 | metrics: |
| 3104 | - type: accuracy |
| 3105 | value: 62.38399462004034 |
| 3106 | - type: f1 |
| 3107 | value: 60.82139544252606 |
| 3108 | - task: |
| 3109 | type: Classification |
| 3110 | dataset: |
| 3111 | type: mteb/amazon_massive_scenario |
| 3112 | name: MTEB MassiveScenarioClassification (te) |
| 3113 | config: te |
| 3114 | split: test |
| 3115 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3116 | metrics: |
| 3117 | - type: accuracy |
| 3118 | value: 62.58574310692671 |
| 3119 | - type: f1 |
| 3120 | value: 60.71443370385374 |
| 3121 | - task: |
| 3122 | type: Classification |
| 3123 | dataset: |
| 3124 | type: mteb/amazon_massive_scenario |
| 3125 | name: MTEB MassiveScenarioClassification (th) |
| 3126 | config: th |
| 3127 | split: test |
| 3128 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3129 | metrics: |
| 3130 | - type: accuracy |
| 3131 | value: 71.61398789509079 |
| 3132 | - type: f1 |
| 3133 | value: 70.99761812049401 |
| 3134 | - task: |
| 3135 | type: Classification |
| 3136 | dataset: |
| 3137 | type: mteb/amazon_massive_scenario |
| 3138 | name: MTEB MassiveScenarioClassification (tl) |
| 3139 | config: tl |
| 3140 | split: test |
| 3141 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3142 | metrics: |
| 3143 | - type: accuracy |
| 3144 | value: 62.73705447209146 |
| 3145 | - type: f1 |
| 3146 | value: 61.680849331794796 |
| 3147 | - task: |
| 3148 | type: Classification |
| 3149 | dataset: |
| 3150 | type: mteb/amazon_massive_scenario |
| 3151 | name: MTEB MassiveScenarioClassification (tr) |
| 3152 | config: tr |
| 3153 | split: test |
| 3154 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3155 | metrics: |
| 3156 | - type: accuracy |
| 3157 | value: 71.66778749159381 |
| 3158 | - type: f1 |
| 3159 | value: 71.17320646080115 |
| 3160 | - task: |
| 3161 | type: Classification |
| 3162 | dataset: |
| 3163 | type: mteb/amazon_massive_scenario |
| 3164 | name: MTEB MassiveScenarioClassification (ur) |
| 3165 | config: ur |
| 3166 | split: test |
| 3167 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3168 | metrics: |
| 3169 | - type: accuracy |
| 3170 | value: 64.640215198386 |
| 3171 | - type: f1 |
| 3172 | value: 63.301805157015444 |
| 3173 | - task: |
| 3174 | type: Classification |
| 3175 | dataset: |
| 3176 | type: mteb/amazon_massive_scenario |
| 3177 | name: MTEB MassiveScenarioClassification (vi) |
| 3178 | config: vi |
| 3179 | split: test |
| 3180 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3181 | metrics: |
| 3182 | - type: accuracy |
| 3183 | value: 70.00672494956288 |
| 3184 | - type: f1 |
| 3185 | value: 70.26005548582106 |
| 3186 | - task: |
| 3187 | type: Classification |
| 3188 | dataset: |
| 3189 | type: mteb/amazon_massive_scenario |
| 3190 | name: MTEB MassiveScenarioClassification (zh-CN) |
| 3191 | config: zh-CN |
| 3192 | split: test |
| 3193 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3194 | metrics: |
| 3195 | - type: accuracy |
| 3196 | value: 75.42030934767989 |
| 3197 | - type: f1 |
| 3198 | value: 75.2074842882598 |
| 3199 | - task: |
| 3200 | type: Classification |
| 3201 | dataset: |
| 3202 | type: mteb/amazon_massive_scenario |
| 3203 | name: MTEB MassiveScenarioClassification (zh-TW) |
| 3204 | config: zh-TW |
| 3205 | split: test |
| 3206 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 3207 | metrics: |
| 3208 | - type: accuracy |
| 3209 | value: 70.69266980497646 |
| 3210 | - type: f1 |
| 3211 | value: 70.94103167391192 |
| 3212 | - task: |
| 3213 | type: Clustering |
| 3214 | dataset: |
| 3215 | type: mteb/medrxiv-clustering-p2p |
| 3216 | name: MTEB MedrxivClusteringP2P |
| 3217 | config: default |
| 3218 | split: test |
| 3219 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 3220 | metrics: |
| 3221 | - type: v_measure |
| 3222 | value: 28.91697191169135 |
| 3223 | - task: |
| 3224 | type: Clustering |
| 3225 | dataset: |
| 3226 | type: mteb/medrxiv-clustering-s2s |
| 3227 | name: MTEB MedrxivClusteringS2S |
| 3228 | config: default |
| 3229 | split: test |
| 3230 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 3231 | metrics: |
| 3232 | - type: v_measure |
| 3233 | value: 28.434000079573313 |
| 3234 | - task: |
| 3235 | type: Reranking |
| 3236 | dataset: |
| 3237 | type: mteb/mind_small |
| 3238 | name: MTEB MindSmallReranking |
| 3239 | config: default |
| 3240 | split: test |
| 3241 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 3242 | metrics: |
| 3243 | - type: map |
| 3244 | value: 30.96683513343383 |
| 3245 | - type: mrr |
| 3246 | value: 31.967364078714834 |
| 3247 | - task: |
| 3248 | type: Retrieval |
| 3249 | dataset: |
| 3250 | type: nfcorpus |
| 3251 | name: MTEB NFCorpus |
| 3252 | config: default |
| 3253 | split: test |
| 3254 | revision: None |
| 3255 | metrics: |
| 3256 | - type: map_at_1 |
| 3257 | value: 5.5280000000000005 |
| 3258 | - type: map_at_10 |
| 3259 | value: 11.793 |
| 3260 | - type: map_at_100 |
| 3261 | value: 14.496999999999998 |
| 3262 | - type: map_at_1000 |
| 3263 | value: 15.783 |
| 3264 | - type: map_at_3 |
| 3265 | value: 8.838 |
| 3266 | - type: map_at_5 |
| 3267 | value: 10.07 |
| 3268 | - type: mrr_at_1 |
| 3269 | value: 43.653 |
| 3270 | - type: mrr_at_10 |
| 3271 | value: 51.531000000000006 |
| 3272 | - type: mrr_at_100 |
| 3273 | value: 52.205 |
| 3274 | - type: mrr_at_1000 |
| 3275 | value: 52.242999999999995 |
| 3276 | - type: mrr_at_3 |
| 3277 | value: 49.431999999999995 |
| 3278 | - type: mrr_at_5 |
| 3279 | value: 50.470000000000006 |
| 3280 | - type: ndcg_at_1 |
| 3281 | value: 42.415000000000006 |
| 3282 | - type: ndcg_at_10 |
| 3283 | value: 32.464999999999996 |
| 3284 | - type: ndcg_at_100 |
| 3285 | value: 28.927999999999997 |
| 3286 | - type: ndcg_at_1000 |
| 3287 | value: 37.629000000000005 |
| 3288 | - type: ndcg_at_3 |
| 3289 | value: 37.845 |
| 3290 | - type: ndcg_at_5 |
| 3291 | value: 35.147 |
| 3292 | - type: precision_at_1 |
| 3293 | value: 43.653 |
| 3294 | - type: precision_at_10 |
| 3295 | value: 23.932000000000002 |
| 3296 | - type: precision_at_100 |
| 3297 | value: 7.17 |
| 3298 | - type: precision_at_1000 |
| 3299 | value: 1.967 |
| 3300 | - type: precision_at_3 |
| 3301 | value: 35.397 |
| 3302 | - type: precision_at_5 |
| 3303 | value: 29.907 |
| 3304 | - type: recall_at_1 |
| 3305 | value: 5.5280000000000005 |
| 3306 | - type: recall_at_10 |
| 3307 | value: 15.568000000000001 |
| 3308 | - type: recall_at_100 |
| 3309 | value: 28.54 |
| 3310 | - type: recall_at_1000 |
| 3311 | value: 59.864 |
| 3312 | - type: recall_at_3 |
| 3313 | value: 9.822000000000001 |
| 3314 | - type: recall_at_5 |
| 3315 | value: 11.726 |
| 3316 | - task: |
| 3317 | type: Retrieval |
| 3318 | dataset: |
| 3319 | type: nq |
| 3320 | name: MTEB NQ |
| 3321 | config: default |
| 3322 | split: test |
| 3323 | revision: None |
| 3324 | metrics: |
| 3325 | - type: map_at_1 |
| 3326 | value: 37.041000000000004 |
| 3327 | - type: map_at_10 |
| 3328 | value: 52.664 |
| 3329 | - type: map_at_100 |
| 3330 | value: 53.477 |
| 3331 | - type: map_at_1000 |
| 3332 | value: 53.505 |
| 3333 | - type: map_at_3 |
| 3334 | value: 48.510999999999996 |
| 3335 | - type: map_at_5 |
| 3336 | value: 51.036 |
| 3337 | - type: mrr_at_1 |
| 3338 | value: 41.338 |
| 3339 | - type: mrr_at_10 |
| 3340 | value: 55.071000000000005 |
| 3341 | - type: mrr_at_100 |
| 3342 | value: 55.672 |
| 3343 | - type: mrr_at_1000 |
| 3344 | value: 55.689 |
| 3345 | - type: mrr_at_3 |
| 3346 | value: 51.82 |
| 3347 | - type: mrr_at_5 |
| 3348 | value: 53.852 |
| 3349 | - type: ndcg_at_1 |
| 3350 | value: 41.338 |
| 3351 | - type: ndcg_at_10 |
| 3352 | value: 60.01800000000001 |
| 3353 | - type: ndcg_at_100 |
| 3354 | value: 63.409000000000006 |
| 3355 | - type: ndcg_at_1000 |
| 3356 | value: 64.017 |
| 3357 | - type: ndcg_at_3 |
| 3358 | value: 52.44799999999999 |
| 3359 | - type: ndcg_at_5 |
| 3360 | value: 56.571000000000005 |
| 3361 | - type: precision_at_1 |
| 3362 | value: 41.338 |
| 3363 | - type: precision_at_10 |
| 3364 | value: 9.531 |
| 3365 | - type: precision_at_100 |
| 3366 | value: 1.145 |
| 3367 | - type: precision_at_1000 |
| 3368 | value: 0.12 |
| 3369 | - type: precision_at_3 |
| 3370 | value: 23.416 |
| 3371 | - type: precision_at_5 |
| 3372 | value: 16.46 |
| 3373 | - type: recall_at_1 |
| 3374 | value: 37.041000000000004 |
| 3375 | - type: recall_at_10 |
| 3376 | value: 79.76299999999999 |
| 3377 | - type: recall_at_100 |
| 3378 | value: 94.39 |
| 3379 | - type: recall_at_1000 |
| 3380 | value: 98.851 |
| 3381 | - type: recall_at_3 |
| 3382 | value: 60.465 |
| 3383 | - type: recall_at_5 |
| 3384 | value: 69.906 |
| 3385 | - task: |
| 3386 | type: Retrieval |
| 3387 | dataset: |
| 3388 | type: quora |
| 3389 | name: MTEB QuoraRetrieval |
| 3390 | config: default |
| 3391 | split: test |
| 3392 | revision: None |
| 3393 | metrics: |
| 3394 | - type: map_at_1 |
| 3395 | value: 69.952 |
| 3396 | - type: map_at_10 |
| 3397 | value: 83.758 |
| 3398 | - type: map_at_100 |
| 3399 | value: 84.406 |
| 3400 | - type: map_at_1000 |
| 3401 | value: 84.425 |
| 3402 | - type: map_at_3 |
| 3403 | value: 80.839 |
| 3404 | - type: map_at_5 |
| 3405 | value: 82.646 |
| 3406 | - type: mrr_at_1 |
| 3407 | value: 80.62 |
| 3408 | - type: mrr_at_10 |
| 3409 | value: 86.947 |
| 3410 | - type: mrr_at_100 |
| 3411 | value: 87.063 |
| 3412 | - type: mrr_at_1000 |
| 3413 | value: 87.064 |
| 3414 | - type: mrr_at_3 |
| 3415 | value: 85.96000000000001 |
| 3416 | - type: mrr_at_5 |
| 3417 | value: 86.619 |
| 3418 | - type: ndcg_at_1 |
| 3419 | value: 80.63 |
| 3420 | - type: ndcg_at_10 |
| 3421 | value: 87.64800000000001 |
| 3422 | - type: ndcg_at_100 |
| 3423 | value: 88.929 |
| 3424 | - type: ndcg_at_1000 |
| 3425 | value: 89.054 |
| 3426 | - type: ndcg_at_3 |
| 3427 | value: 84.765 |
| 3428 | - type: ndcg_at_5 |
| 3429 | value: 86.291 |
| 3430 | - type: precision_at_1 |
| 3431 | value: 80.63 |
| 3432 | - type: precision_at_10 |
| 3433 | value: 13.314 |
| 3434 | - type: precision_at_100 |
| 3435 | value: 1.525 |
| 3436 | - type: precision_at_1000 |
| 3437 | value: 0.157 |
| 3438 | - type: precision_at_3 |
| 3439 | value: 37.1 |
| 3440 | - type: precision_at_5 |
| 3441 | value: 24.372 |
| 3442 | - type: recall_at_1 |
| 3443 | value: 69.952 |
| 3444 | - type: recall_at_10 |
| 3445 | value: 94.955 |
| 3446 | - type: recall_at_100 |
| 3447 | value: 99.38 |
| 3448 | - type: recall_at_1000 |
| 3449 | value: 99.96000000000001 |
| 3450 | - type: recall_at_3 |
| 3451 | value: 86.60600000000001 |
| 3452 | - type: recall_at_5 |
| 3453 | value: 90.997 |
| 3454 | - task: |
| 3455 | type: Clustering |
| 3456 | dataset: |
| 3457 | type: mteb/reddit-clustering |
| 3458 | name: MTEB RedditClustering |
| 3459 | config: default |
| 3460 | split: test |
| 3461 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 3462 | metrics: |
| 3463 | - type: v_measure |
| 3464 | value: 42.41329517878427 |
| 3465 | - task: |
| 3466 | type: Clustering |
| 3467 | dataset: |
| 3468 | type: mteb/reddit-clustering-p2p |
| 3469 | name: MTEB RedditClusteringP2P |
| 3470 | config: default |
| 3471 | split: test |
| 3472 | revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| 3473 | metrics: |
| 3474 | - type: v_measure |
| 3475 | value: 55.171278362748666 |
| 3476 | - task: |
| 3477 | type: Retrieval |
| 3478 | dataset: |
| 3479 | type: scidocs |
| 3480 | name: MTEB SCIDOCS |
| 3481 | config: default |
| 3482 | split: test |
| 3483 | revision: None |
| 3484 | metrics: |
| 3485 | - type: map_at_1 |
| 3486 | value: 4.213 |
| 3487 | - type: map_at_10 |
| 3488 | value: 9.895 |
| 3489 | - type: map_at_100 |
| 3490 | value: 11.776 |
| 3491 | - type: map_at_1000 |
| 3492 | value: 12.084 |
| 3493 | - type: map_at_3 |
| 3494 | value: 7.2669999999999995 |
| 3495 | - type: map_at_5 |
| 3496 | value: 8.620999999999999 |
| 3497 | - type: mrr_at_1 |
| 3498 | value: 20.8 |
| 3499 | - type: mrr_at_10 |
| 3500 | value: 31.112000000000002 |
| 3501 | - type: mrr_at_100 |
| 3502 | value: 32.274 |
| 3503 | - type: mrr_at_1000 |
| 3504 | value: 32.35 |
| 3505 | - type: mrr_at_3 |
| 3506 | value: 28.133000000000003 |
| 3507 | - type: mrr_at_5 |
| 3508 | value: 29.892999999999997 |
| 3509 | - type: ndcg_at_1 |
| 3510 | value: 20.8 |
| 3511 | - type: ndcg_at_10 |
| 3512 | value: 17.163999999999998 |
| 3513 | - type: ndcg_at_100 |
| 3514 | value: 24.738 |
| 3515 | - type: ndcg_at_1000 |
| 3516 | value: 30.316 |
| 3517 | - type: ndcg_at_3 |
| 3518 | value: 16.665 |
| 3519 | - type: ndcg_at_5 |
| 3520 | value: 14.478 |
| 3521 | - type: precision_at_1 |
| 3522 | value: 20.8 |
| 3523 | - type: precision_at_10 |
| 3524 | value: 8.74 |
| 3525 | - type: precision_at_100 |
| 3526 | value: 1.963 |
| 3527 | - type: precision_at_1000 |
| 3528 | value: 0.33 |
| 3529 | - type: precision_at_3 |
| 3530 | value: 15.467 |
| 3531 | - type: precision_at_5 |
| 3532 | value: 12.6 |
| 3533 | - type: recall_at_1 |
| 3534 | value: 4.213 |
| 3535 | - type: recall_at_10 |
| 3536 | value: 17.698 |
| 3537 | - type: recall_at_100 |
| 3538 | value: 39.838 |
| 3539 | - type: recall_at_1000 |
| 3540 | value: 66.893 |
| 3541 | - type: recall_at_3 |
| 3542 | value: 9.418 |
| 3543 | - type: recall_at_5 |
| 3544 | value: 12.773000000000001 |
| 3545 | - task: |
| 3546 | type: STS |
| 3547 | dataset: |
| 3548 | type: mteb/sickr-sts |
| 3549 | name: MTEB SICK-R |
| 3550 | config: default |
| 3551 | split: test |
| 3552 | revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| 3553 | metrics: |
| 3554 | - type: cos_sim_pearson |
| 3555 | value: 82.90453315738294 |
| 3556 | - type: cos_sim_spearman |
| 3557 | value: 78.51197850080254 |
| 3558 | - type: euclidean_pearson |
| 3559 | value: 80.09647123597748 |
| 3560 | - type: euclidean_spearman |
| 3561 | value: 78.63548011514061 |
| 3562 | - type: manhattan_pearson |
| 3563 | value: 80.10645285675231 |
| 3564 | - type: manhattan_spearman |
| 3565 | value: 78.57861806068901 |
| 3566 | - task: |
| 3567 | type: STS |
| 3568 | dataset: |
| 3569 | type: mteb/sts12-sts |
| 3570 | name: MTEB STS12 |
| 3571 | config: default |
| 3572 | split: test |
| 3573 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 3574 | metrics: |
| 3575 | - type: cos_sim_pearson |
| 3576 | value: 84.2616156846401 |
| 3577 | - type: cos_sim_spearman |
| 3578 | value: 76.69713867850156 |
| 3579 | - type: euclidean_pearson |
| 3580 | value: 77.97948563800394 |
| 3581 | - type: euclidean_spearman |
| 3582 | value: 74.2371211567807 |
| 3583 | - type: manhattan_pearson |
| 3584 | value: 77.69697879669705 |
| 3585 | - type: manhattan_spearman |
| 3586 | value: 73.86529778022278 |
| 3587 | - task: |
| 3588 | type: STS |
| 3589 | dataset: |
| 3590 | type: mteb/sts13-sts |
| 3591 | name: MTEB STS13 |
| 3592 | config: default |
| 3593 | split: test |
| 3594 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 3595 | metrics: |
| 3596 | - type: cos_sim_pearson |
| 3597 | value: 77.0293269315045 |
| 3598 | - type: cos_sim_spearman |
| 3599 | value: 78.02555120584198 |
| 3600 | - type: euclidean_pearson |
| 3601 | value: 78.25398100379078 |
| 3602 | - type: euclidean_spearman |
| 3603 | value: 78.66963870599464 |
| 3604 | - type: manhattan_pearson |
| 3605 | value: 78.14314682167348 |
| 3606 | - type: manhattan_spearman |
| 3607 | value: 78.57692322969135 |
| 3608 | - task: |
| 3609 | type: STS |
| 3610 | dataset: |
| 3611 | type: mteb/sts14-sts |
| 3612 | name: MTEB STS14 |
| 3613 | config: default |
| 3614 | split: test |
| 3615 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 3616 | metrics: |
| 3617 | - type: cos_sim_pearson |
| 3618 | value: 79.16989925136942 |
| 3619 | - type: cos_sim_spearman |
| 3620 | value: 76.5996225327091 |
| 3621 | - type: euclidean_pearson |
| 3622 | value: 77.8319003279786 |
| 3623 | - type: euclidean_spearman |
| 3624 | value: 76.42824009468998 |
| 3625 | - type: manhattan_pearson |
| 3626 | value: 77.69118862737736 |
| 3627 | - type: manhattan_spearman |
| 3628 | value: 76.25568104762812 |
| 3629 | - task: |
| 3630 | type: STS |
| 3631 | dataset: |
| 3632 | type: mteb/sts15-sts |
| 3633 | name: MTEB STS15 |
| 3634 | config: default |
| 3635 | split: test |
| 3636 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 3637 | metrics: |
| 3638 | - type: cos_sim_pearson |
| 3639 | value: 87.42012286935325 |
| 3640 | - type: cos_sim_spearman |
| 3641 | value: 88.15654297884122 |
| 3642 | - type: euclidean_pearson |
| 3643 | value: 87.34082819427852 |
| 3644 | - type: euclidean_spearman |
| 3645 | value: 88.06333589547084 |
| 3646 | - type: manhattan_pearson |
| 3647 | value: 87.25115596784842 |
| 3648 | - type: manhattan_spearman |
| 3649 | value: 87.9559927695203 |
| 3650 | - task: |
| 3651 | type: STS |
| 3652 | dataset: |
| 3653 | type: mteb/sts16-sts |
| 3654 | name: MTEB STS16 |
| 3655 | config: default |
| 3656 | split: test |
| 3657 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 3658 | metrics: |
| 3659 | - type: cos_sim_pearson |
| 3660 | value: 82.88222044996712 |
| 3661 | - type: cos_sim_spearman |
| 3662 | value: 84.28476589061077 |
| 3663 | - type: euclidean_pearson |
| 3664 | value: 83.17399758058309 |
| 3665 | - type: euclidean_spearman |
| 3666 | value: 83.85497357244542 |
| 3667 | - type: manhattan_pearson |
| 3668 | value: 83.0308397703786 |
| 3669 | - type: manhattan_spearman |
| 3670 | value: 83.71554539935046 |
| 3671 | - task: |
| 3672 | type: STS |
| 3673 | dataset: |
| 3674 | type: mteb/sts17-crosslingual-sts |
| 3675 | name: MTEB STS17 (ko-ko) |
| 3676 | config: ko-ko |
| 3677 | split: test |
| 3678 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3679 | metrics: |
| 3680 | - type: cos_sim_pearson |
| 3681 | value: 80.20682986257339 |
| 3682 | - type: cos_sim_spearman |
| 3683 | value: 79.94567120362092 |
| 3684 | - type: euclidean_pearson |
| 3685 | value: 79.43122480368902 |
| 3686 | - type: euclidean_spearman |
| 3687 | value: 79.94802077264987 |
| 3688 | - type: manhattan_pearson |
| 3689 | value: 79.32653021527081 |
| 3690 | - type: manhattan_spearman |
| 3691 | value: 79.80961146709178 |
| 3692 | - task: |
| 3693 | type: STS |
| 3694 | dataset: |
| 3695 | type: mteb/sts17-crosslingual-sts |
| 3696 | name: MTEB STS17 (ar-ar) |
| 3697 | config: ar-ar |
| 3698 | split: test |
| 3699 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3700 | metrics: |
| 3701 | - type: cos_sim_pearson |
| 3702 | value: 74.46578144394383 |
| 3703 | - type: cos_sim_spearman |
| 3704 | value: 74.52496637472179 |
| 3705 | - type: euclidean_pearson |
| 3706 | value: 72.2903807076809 |
| 3707 | - type: euclidean_spearman |
| 3708 | value: 73.55549359771645 |
| 3709 | - type: manhattan_pearson |
| 3710 | value: 72.09324837709393 |
| 3711 | - type: manhattan_spearman |
| 3712 | value: 73.36743103606581 |
| 3713 | - task: |
| 3714 | type: STS |
| 3715 | dataset: |
| 3716 | type: mteb/sts17-crosslingual-sts |
| 3717 | name: MTEB STS17 (en-ar) |
| 3718 | config: en-ar |
| 3719 | split: test |
| 3720 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3721 | metrics: |
| 3722 | - type: cos_sim_pearson |
| 3723 | value: 71.37272335116 |
| 3724 | - type: cos_sim_spearman |
| 3725 | value: 71.26702117766037 |
| 3726 | - type: euclidean_pearson |
| 3727 | value: 67.114829954434 |
| 3728 | - type: euclidean_spearman |
| 3729 | value: 66.37938893947761 |
| 3730 | - type: manhattan_pearson |
| 3731 | value: 66.79688574095246 |
| 3732 | - type: manhattan_spearman |
| 3733 | value: 66.17292828079667 |
| 3734 | - task: |
| 3735 | type: STS |
| 3736 | dataset: |
| 3737 | type: mteb/sts17-crosslingual-sts |
| 3738 | name: MTEB STS17 (en-de) |
| 3739 | config: en-de |
| 3740 | split: test |
| 3741 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3742 | metrics: |
| 3743 | - type: cos_sim_pearson |
| 3744 | value: 80.61016770129092 |
| 3745 | - type: cos_sim_spearman |
| 3746 | value: 82.08515426632214 |
| 3747 | - type: euclidean_pearson |
| 3748 | value: 80.557340361131 |
| 3749 | - type: euclidean_spearman |
| 3750 | value: 80.37585812266175 |
| 3751 | - type: manhattan_pearson |
| 3752 | value: 80.6782873404285 |
| 3753 | - type: manhattan_spearman |
| 3754 | value: 80.6678073032024 |
| 3755 | - task: |
| 3756 | type: STS |
| 3757 | dataset: |
| 3758 | type: mteb/sts17-crosslingual-sts |
| 3759 | name: MTEB STS17 (en-en) |
| 3760 | config: en-en |
| 3761 | split: test |
| 3762 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3763 | metrics: |
| 3764 | - type: cos_sim_pearson |
| 3765 | value: 87.00150745350108 |
| 3766 | - type: cos_sim_spearman |
| 3767 | value: 87.83441972211425 |
| 3768 | - type: euclidean_pearson |
| 3769 | value: 87.94826702308792 |
| 3770 | - type: euclidean_spearman |
| 3771 | value: 87.46143974860725 |
| 3772 | - type: manhattan_pearson |
| 3773 | value: 87.97560344306105 |
| 3774 | - type: manhattan_spearman |
| 3775 | value: 87.5267102829796 |
| 3776 | - task: |
| 3777 | type: STS |
| 3778 | dataset: |
| 3779 | type: mteb/sts17-crosslingual-sts |
| 3780 | name: MTEB STS17 (en-tr) |
| 3781 | config: en-tr |
| 3782 | split: test |
| 3783 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3784 | metrics: |
| 3785 | - type: cos_sim_pearson |
| 3786 | value: 64.76325252267235 |
| 3787 | - type: cos_sim_spearman |
| 3788 | value: 63.32615095463905 |
| 3789 | - type: euclidean_pearson |
| 3790 | value: 64.07920669155716 |
| 3791 | - type: euclidean_spearman |
| 3792 | value: 61.21409893072176 |
| 3793 | - type: manhattan_pearson |
| 3794 | value: 64.26308625680016 |
| 3795 | - type: manhattan_spearman |
| 3796 | value: 61.2438185254079 |
| 3797 | - task: |
| 3798 | type: STS |
| 3799 | dataset: |
| 3800 | type: mteb/sts17-crosslingual-sts |
| 3801 | name: MTEB STS17 (es-en) |
| 3802 | config: es-en |
| 3803 | split: test |
| 3804 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3805 | metrics: |
| 3806 | - type: cos_sim_pearson |
| 3807 | value: 75.82644463022595 |
| 3808 | - type: cos_sim_spearman |
| 3809 | value: 76.50381269945073 |
| 3810 | - type: euclidean_pearson |
| 3811 | value: 75.1328548315934 |
| 3812 | - type: euclidean_spearman |
| 3813 | value: 75.63761139408453 |
| 3814 | - type: manhattan_pearson |
| 3815 | value: 75.18610101241407 |
| 3816 | - type: manhattan_spearman |
| 3817 | value: 75.30669266354164 |
| 3818 | - task: |
| 3819 | type: STS |
| 3820 | dataset: |
| 3821 | type: mteb/sts17-crosslingual-sts |
| 3822 | name: MTEB STS17 (es-es) |
| 3823 | config: es-es |
| 3824 | split: test |
| 3825 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3826 | metrics: |
| 3827 | - type: cos_sim_pearson |
| 3828 | value: 87.49994164686832 |
| 3829 | - type: cos_sim_spearman |
| 3830 | value: 86.73743986245549 |
| 3831 | - type: euclidean_pearson |
| 3832 | value: 86.8272894387145 |
| 3833 | - type: euclidean_spearman |
| 3834 | value: 85.97608491000507 |
| 3835 | - type: manhattan_pearson |
| 3836 | value: 86.74960140396779 |
| 3837 | - type: manhattan_spearman |
| 3838 | value: 85.79285984190273 |
| 3839 | - task: |
| 3840 | type: STS |
| 3841 | dataset: |
| 3842 | type: mteb/sts17-crosslingual-sts |
| 3843 | name: MTEB STS17 (fr-en) |
| 3844 | config: fr-en |
| 3845 | split: test |
| 3846 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3847 | metrics: |
| 3848 | - type: cos_sim_pearson |
| 3849 | value: 79.58172210788469 |
| 3850 | - type: cos_sim_spearman |
| 3851 | value: 80.17516468334607 |
| 3852 | - type: euclidean_pearson |
| 3853 | value: 77.56537843470504 |
| 3854 | - type: euclidean_spearman |
| 3855 | value: 77.57264627395521 |
| 3856 | - type: manhattan_pearson |
| 3857 | value: 78.09703521695943 |
| 3858 | - type: manhattan_spearman |
| 3859 | value: 78.15942760916954 |
| 3860 | - task: |
| 3861 | type: STS |
| 3862 | dataset: |
| 3863 | type: mteb/sts17-crosslingual-sts |
| 3864 | name: MTEB STS17 (it-en) |
| 3865 | config: it-en |
| 3866 | split: test |
| 3867 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3868 | metrics: |
| 3869 | - type: cos_sim_pearson |
| 3870 | value: 79.7589932931751 |
| 3871 | - type: cos_sim_spearman |
| 3872 | value: 80.15210089028162 |
| 3873 | - type: euclidean_pearson |
| 3874 | value: 77.54135223516057 |
| 3875 | - type: euclidean_spearman |
| 3876 | value: 77.52697996368764 |
| 3877 | - type: manhattan_pearson |
| 3878 | value: 77.65734439572518 |
| 3879 | - type: manhattan_spearman |
| 3880 | value: 77.77702992016121 |
| 3881 | - task: |
| 3882 | type: STS |
| 3883 | dataset: |
| 3884 | type: mteb/sts17-crosslingual-sts |
| 3885 | name: MTEB STS17 (nl-en) |
| 3886 | config: nl-en |
| 3887 | split: test |
| 3888 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3889 | metrics: |
| 3890 | - type: cos_sim_pearson |
| 3891 | value: 79.16682365511267 |
| 3892 | - type: cos_sim_spearman |
| 3893 | value: 79.25311267628506 |
| 3894 | - type: euclidean_pearson |
| 3895 | value: 77.54882036762244 |
| 3896 | - type: euclidean_spearman |
| 3897 | value: 77.33212935194827 |
| 3898 | - type: manhattan_pearson |
| 3899 | value: 77.98405516064015 |
| 3900 | - type: manhattan_spearman |
| 3901 | value: 77.85075717865719 |
| 3902 | - task: |
| 3903 | type: STS |
| 3904 | dataset: |
| 3905 | type: mteb/sts22-crosslingual-sts |
| 3906 | name: MTEB STS22 (en) |
| 3907 | config: en |
| 3908 | split: test |
| 3909 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3910 | metrics: |
| 3911 | - type: cos_sim_pearson |
| 3912 | value: 59.10473294775917 |
| 3913 | - type: cos_sim_spearman |
| 3914 | value: 61.82780474476838 |
| 3915 | - type: euclidean_pearson |
| 3916 | value: 45.885111672377256 |
| 3917 | - type: euclidean_spearman |
| 3918 | value: 56.88306351932454 |
| 3919 | - type: manhattan_pearson |
| 3920 | value: 46.101218127323186 |
| 3921 | - type: manhattan_spearman |
| 3922 | value: 56.80953694186333 |
| 3923 | - task: |
| 3924 | type: STS |
| 3925 | dataset: |
| 3926 | type: mteb/sts22-crosslingual-sts |
| 3927 | name: MTEB STS22 (de) |
| 3928 | config: de |
| 3929 | split: test |
| 3930 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3931 | metrics: |
| 3932 | - type: cos_sim_pearson |
| 3933 | value: 45.781923079584146 |
| 3934 | - type: cos_sim_spearman |
| 3935 | value: 55.95098449691107 |
| 3936 | - type: euclidean_pearson |
| 3937 | value: 25.4571031323205 |
| 3938 | - type: euclidean_spearman |
| 3939 | value: 49.859978118078935 |
| 3940 | - type: manhattan_pearson |
| 3941 | value: 25.624938455041384 |
| 3942 | - type: manhattan_spearman |
| 3943 | value: 49.99546185049401 |
| 3944 | - task: |
| 3945 | type: STS |
| 3946 | dataset: |
| 3947 | type: mteb/sts22-crosslingual-sts |
| 3948 | name: MTEB STS22 (es) |
| 3949 | config: es |
| 3950 | split: test |
| 3951 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3952 | metrics: |
| 3953 | - type: cos_sim_pearson |
| 3954 | value: 60.00618133997907 |
| 3955 | - type: cos_sim_spearman |
| 3956 | value: 66.57896677718321 |
| 3957 | - type: euclidean_pearson |
| 3958 | value: 42.60118466388821 |
| 3959 | - type: euclidean_spearman |
| 3960 | value: 62.8210759715209 |
| 3961 | - type: manhattan_pearson |
| 3962 | value: 42.63446860604094 |
| 3963 | - type: manhattan_spearman |
| 3964 | value: 62.73803068925271 |
| 3965 | - task: |
| 3966 | type: STS |
| 3967 | dataset: |
| 3968 | type: mteb/sts22-crosslingual-sts |
| 3969 | name: MTEB STS22 (pl) |
| 3970 | config: pl |
| 3971 | split: test |
| 3972 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3973 | metrics: |
| 3974 | - type: cos_sim_pearson |
| 3975 | value: 28.460759121626943 |
| 3976 | - type: cos_sim_spearman |
| 3977 | value: 34.13459007469131 |
| 3978 | - type: euclidean_pearson |
| 3979 | value: 6.0917739325525195 |
| 3980 | - type: euclidean_spearman |
| 3981 | value: 27.9947262664867 |
| 3982 | - type: manhattan_pearson |
| 3983 | value: 6.16877864169911 |
| 3984 | - type: manhattan_spearman |
| 3985 | value: 28.00664163971514 |
| 3986 | - task: |
| 3987 | type: STS |
| 3988 | dataset: |
| 3989 | type: mteb/sts22-crosslingual-sts |
| 3990 | name: MTEB STS22 (tr) |
| 3991 | config: tr |
| 3992 | split: test |
| 3993 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3994 | metrics: |
| 3995 | - type: cos_sim_pearson |
| 3996 | value: 57.42546621771696 |
| 3997 | - type: cos_sim_spearman |
| 3998 | value: 63.699663168970474 |
| 3999 | - type: euclidean_pearson |
| 4000 | value: 38.12085278789738 |
| 4001 | - type: euclidean_spearman |
| 4002 | value: 58.12329140741536 |
| 4003 | - type: manhattan_pearson |
| 4004 | value: 37.97364549443335 |
| 4005 | - type: manhattan_spearman |
| 4006 | value: 57.81545502318733 |
| 4007 | - task: |
| 4008 | type: STS |
| 4009 | dataset: |
| 4010 | type: mteb/sts22-crosslingual-sts |
| 4011 | name: MTEB STS22 (ar) |
| 4012 | config: ar |
| 4013 | split: test |
| 4014 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4015 | metrics: |
| 4016 | - type: cos_sim_pearson |
| 4017 | value: 46.82241380954213 |
| 4018 | - type: cos_sim_spearman |
| 4019 | value: 57.86569456006391 |
| 4020 | - type: euclidean_pearson |
| 4021 | value: 31.80480070178813 |
| 4022 | - type: euclidean_spearman |
| 4023 | value: 52.484000620130104 |
| 4024 | - type: manhattan_pearson |
| 4025 | value: 31.952708554646097 |
| 4026 | - type: manhattan_spearman |
| 4027 | value: 52.8560972356195 |
| 4028 | - task: |
| 4029 | type: STS |
| 4030 | dataset: |
| 4031 | type: mteb/sts22-crosslingual-sts |
| 4032 | name: MTEB STS22 (ru) |
| 4033 | config: ru |
| 4034 | split: test |
| 4035 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4036 | metrics: |
| 4037 | - type: cos_sim_pearson |
| 4038 | value: 52.00447170498087 |
| 4039 | - type: cos_sim_spearman |
| 4040 | value: 60.664116225735164 |
| 4041 | - type: euclidean_pearson |
| 4042 | value: 33.87382555421702 |
| 4043 | - type: euclidean_spearman |
| 4044 | value: 55.74649067458667 |
| 4045 | - type: manhattan_pearson |
| 4046 | value: 33.99117246759437 |
| 4047 | - type: manhattan_spearman |
| 4048 | value: 55.98749034923899 |
| 4049 | - task: |
| 4050 | type: STS |
| 4051 | dataset: |
| 4052 | type: mteb/sts22-crosslingual-sts |
| 4053 | name: MTEB STS22 (zh) |
| 4054 | config: zh |
| 4055 | split: test |
| 4056 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4057 | metrics: |
| 4058 | - type: cos_sim_pearson |
| 4059 | value: 58.06497233105448 |
| 4060 | - type: cos_sim_spearman |
| 4061 | value: 65.62968801135676 |
| 4062 | - type: euclidean_pearson |
| 4063 | value: 47.482076613243905 |
| 4064 | - type: euclidean_spearman |
| 4065 | value: 62.65137791498299 |
| 4066 | - type: manhattan_pearson |
| 4067 | value: 47.57052626104093 |
| 4068 | - type: manhattan_spearman |
| 4069 | value: 62.436916516613294 |
| 4070 | - task: |
| 4071 | type: STS |
| 4072 | dataset: |
| 4073 | type: mteb/sts22-crosslingual-sts |
| 4074 | name: MTEB STS22 (fr) |
| 4075 | config: fr |
| 4076 | split: test |
| 4077 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4078 | metrics: |
| 4079 | - type: cos_sim_pearson |
| 4080 | value: 70.49397298562575 |
| 4081 | - type: cos_sim_spearman |
| 4082 | value: 74.79604041187868 |
| 4083 | - type: euclidean_pearson |
| 4084 | value: 49.661891561317795 |
| 4085 | - type: euclidean_spearman |
| 4086 | value: 70.31535537621006 |
| 4087 | - type: manhattan_pearson |
| 4088 | value: 49.553715741850006 |
| 4089 | - type: manhattan_spearman |
| 4090 | value: 70.24779344636806 |
| 4091 | - task: |
| 4092 | type: STS |
| 4093 | dataset: |
| 4094 | type: mteb/sts22-crosslingual-sts |
| 4095 | name: MTEB STS22 (de-en) |
| 4096 | config: de-en |
| 4097 | split: test |
| 4098 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4099 | metrics: |
| 4100 | - type: cos_sim_pearson |
| 4101 | value: 55.640574515348696 |
| 4102 | - type: cos_sim_spearman |
| 4103 | value: 54.927959317689 |
| 4104 | - type: euclidean_pearson |
| 4105 | value: 29.00139666967476 |
| 4106 | - type: euclidean_spearman |
| 4107 | value: 41.86386566971605 |
| 4108 | - type: manhattan_pearson |
| 4109 | value: 29.47411067730344 |
| 4110 | - type: manhattan_spearman |
| 4111 | value: 42.337438424952786 |
| 4112 | - task: |
| 4113 | type: STS |
| 4114 | dataset: |
| 4115 | type: mteb/sts22-crosslingual-sts |
| 4116 | name: MTEB STS22 (es-en) |
| 4117 | config: es-en |
| 4118 | split: test |
| 4119 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4120 | metrics: |
| 4121 | - type: cos_sim_pearson |
| 4122 | value: 68.14095292259312 |
| 4123 | - type: cos_sim_spearman |
| 4124 | value: 73.99017581234789 |
| 4125 | - type: euclidean_pearson |
| 4126 | value: 46.46304297872084 |
| 4127 | - type: euclidean_spearman |
| 4128 | value: 60.91834114800041 |
| 4129 | - type: manhattan_pearson |
| 4130 | value: 47.07072666338692 |
| 4131 | - type: manhattan_spearman |
| 4132 | value: 61.70415727977926 |
| 4133 | - task: |
| 4134 | type: STS |
| 4135 | dataset: |
| 4136 | type: mteb/sts22-crosslingual-sts |
| 4137 | name: MTEB STS22 (it) |
| 4138 | config: it |
| 4139 | split: test |
| 4140 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4141 | metrics: |
| 4142 | - type: cos_sim_pearson |
| 4143 | value: 73.27184653359575 |
| 4144 | - type: cos_sim_spearman |
| 4145 | value: 77.76070252418626 |
| 4146 | - type: euclidean_pearson |
| 4147 | value: 62.30586577544778 |
| 4148 | - type: euclidean_spearman |
| 4149 | value: 75.14246629110978 |
| 4150 | - type: manhattan_pearson |
| 4151 | value: 62.328196884927046 |
| 4152 | - type: manhattan_spearman |
| 4153 | value: 75.1282792981433 |
| 4154 | - task: |
| 4155 | type: STS |
| 4156 | dataset: |
| 4157 | type: mteb/sts22-crosslingual-sts |
| 4158 | name: MTEB STS22 (pl-en) |
| 4159 | config: pl-en |
| 4160 | split: test |
| 4161 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4162 | metrics: |
| 4163 | - type: cos_sim_pearson |
| 4164 | value: 71.59448528829957 |
| 4165 | - type: cos_sim_spearman |
| 4166 | value: 70.37277734222123 |
| 4167 | - type: euclidean_pearson |
| 4168 | value: 57.63145565721123 |
| 4169 | - type: euclidean_spearman |
| 4170 | value: 66.10113048304427 |
| 4171 | - type: manhattan_pearson |
| 4172 | value: 57.18897811586808 |
| 4173 | - type: manhattan_spearman |
| 4174 | value: 66.5595511215901 |
| 4175 | - task: |
| 4176 | type: STS |
| 4177 | dataset: |
| 4178 | type: mteb/sts22-crosslingual-sts |
| 4179 | name: MTEB STS22 (zh-en) |
| 4180 | config: zh-en |
| 4181 | split: test |
| 4182 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4183 | metrics: |
| 4184 | - type: cos_sim_pearson |
| 4185 | value: 66.37520607720838 |
| 4186 | - type: cos_sim_spearman |
| 4187 | value: 69.92282148997948 |
| 4188 | - type: euclidean_pearson |
| 4189 | value: 40.55768770125291 |
| 4190 | - type: euclidean_spearman |
| 4191 | value: 55.189128944669605 |
| 4192 | - type: manhattan_pearson |
| 4193 | value: 41.03566433468883 |
| 4194 | - type: manhattan_spearman |
| 4195 | value: 55.61251893174558 |
| 4196 | - task: |
| 4197 | type: STS |
| 4198 | dataset: |
| 4199 | type: mteb/sts22-crosslingual-sts |
| 4200 | name: MTEB STS22 (es-it) |
| 4201 | config: es-it |
| 4202 | split: test |
| 4203 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4204 | metrics: |
| 4205 | - type: cos_sim_pearson |
| 4206 | value: 57.791929533771835 |
| 4207 | - type: cos_sim_spearman |
| 4208 | value: 66.45819707662093 |
| 4209 | - type: euclidean_pearson |
| 4210 | value: 39.03686018511092 |
| 4211 | - type: euclidean_spearman |
| 4212 | value: 56.01282695640428 |
| 4213 | - type: manhattan_pearson |
| 4214 | value: 38.91586623619632 |
| 4215 | - type: manhattan_spearman |
| 4216 | value: 56.69394943612747 |
| 4217 | - task: |
| 4218 | type: STS |
| 4219 | dataset: |
| 4220 | type: mteb/sts22-crosslingual-sts |
| 4221 | name: MTEB STS22 (de-fr) |
| 4222 | config: de-fr |
| 4223 | split: test |
| 4224 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4225 | metrics: |
| 4226 | - type: cos_sim_pearson |
| 4227 | value: 47.82224468473866 |
| 4228 | - type: cos_sim_spearman |
| 4229 | value: 59.467307194781164 |
| 4230 | - type: euclidean_pearson |
| 4231 | value: 27.428459190256145 |
| 4232 | - type: euclidean_spearman |
| 4233 | value: 60.83463107397519 |
| 4234 | - type: manhattan_pearson |
| 4235 | value: 27.487391578496638 |
| 4236 | - type: manhattan_spearman |
| 4237 | value: 61.281380460246496 |
| 4238 | - task: |
| 4239 | type: STS |
| 4240 | dataset: |
| 4241 | type: mteb/sts22-crosslingual-sts |
| 4242 | name: MTEB STS22 (de-pl) |
| 4243 | config: de-pl |
| 4244 | split: test |
| 4245 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4246 | metrics: |
| 4247 | - type: cos_sim_pearson |
| 4248 | value: 16.306666792752644 |
| 4249 | - type: cos_sim_spearman |
| 4250 | value: 39.35486427252405 |
| 4251 | - type: euclidean_pearson |
| 4252 | value: -2.7887154897955435 |
| 4253 | - type: euclidean_spearman |
| 4254 | value: 27.1296051831719 |
| 4255 | - type: manhattan_pearson |
| 4256 | value: -3.202291270581297 |
| 4257 | - type: manhattan_spearman |
| 4258 | value: 26.32895849218158 |
| 4259 | - task: |
| 4260 | type: STS |
| 4261 | dataset: |
| 4262 | type: mteb/sts22-crosslingual-sts |
| 4263 | name: MTEB STS22 (fr-pl) |
| 4264 | config: fr-pl |
| 4265 | split: test |
| 4266 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 4267 | metrics: |
| 4268 | - type: cos_sim_pearson |
| 4269 | value: 59.67006803805076 |
| 4270 | - type: cos_sim_spearman |
| 4271 | value: 73.24670207647144 |
| 4272 | - type: euclidean_pearson |
| 4273 | value: 46.91884681500483 |
| 4274 | - type: euclidean_spearman |
| 4275 | value: 16.903085094570333 |
| 4276 | - type: manhattan_pearson |
| 4277 | value: 46.88391675325812 |
| 4278 | - type: manhattan_spearman |
| 4279 | value: 28.17180849095055 |
| 4280 | - task: |
| 4281 | type: STS |
| 4282 | dataset: |
| 4283 | type: mteb/stsbenchmark-sts |
| 4284 | name: MTEB STSBenchmark |
| 4285 | config: default |
| 4286 | split: test |
| 4287 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 4288 | metrics: |
| 4289 | - type: cos_sim_pearson |
| 4290 | value: 83.79555591223837 |
| 4291 | - type: cos_sim_spearman |
| 4292 | value: 85.63658602085185 |
| 4293 | - type: euclidean_pearson |
| 4294 | value: 85.22080894037671 |
| 4295 | - type: euclidean_spearman |
| 4296 | value: 85.54113580167038 |
| 4297 | - type: manhattan_pearson |
| 4298 | value: 85.1639505960118 |
| 4299 | - type: manhattan_spearman |
| 4300 | value: 85.43502665436196 |
| 4301 | - task: |
| 4302 | type: Reranking |
| 4303 | dataset: |
| 4304 | type: mteb/scidocs-reranking |
| 4305 | name: MTEB SciDocsRR |
| 4306 | config: default |
| 4307 | split: test |
| 4308 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 4309 | metrics: |
| 4310 | - type: map |
| 4311 | value: 80.73900991689766 |
| 4312 | - type: mrr |
| 4313 | value: 94.81624131133934 |
| 4314 | - task: |
| 4315 | type: Retrieval |
| 4316 | dataset: |
| 4317 | type: scifact |
| 4318 | name: MTEB SciFact |
| 4319 | config: default |
| 4320 | split: test |
| 4321 | revision: None |
| 4322 | metrics: |
| 4323 | - type: map_at_1 |
| 4324 | value: 55.678000000000004 |
| 4325 | - type: map_at_10 |
| 4326 | value: 65.135 |
| 4327 | - type: map_at_100 |
| 4328 | value: 65.824 |
| 4329 | - type: map_at_1000 |
| 4330 | value: 65.852 |
| 4331 | - type: map_at_3 |
| 4332 | value: 62.736000000000004 |
| 4333 | - type: map_at_5 |
| 4334 | value: 64.411 |
| 4335 | - type: mrr_at_1 |
| 4336 | value: 58.333 |
| 4337 | - type: mrr_at_10 |
| 4338 | value: 66.5 |
| 4339 | - type: mrr_at_100 |
| 4340 | value: 67.053 |
| 4341 | - type: mrr_at_1000 |
| 4342 | value: 67.08 |
| 4343 | - type: mrr_at_3 |
| 4344 | value: 64.944 |
| 4345 | - type: mrr_at_5 |
| 4346 | value: 65.89399999999999 |
| 4347 | - type: ndcg_at_1 |
| 4348 | value: 58.333 |
| 4349 | - type: ndcg_at_10 |
| 4350 | value: 69.34700000000001 |
| 4351 | - type: ndcg_at_100 |
| 4352 | value: 72.32 |
| 4353 | - type: ndcg_at_1000 |
| 4354 | value: 73.014 |
| 4355 | - type: ndcg_at_3 |
| 4356 | value: 65.578 |
| 4357 | - type: ndcg_at_5 |
| 4358 | value: 67.738 |
| 4359 | - type: precision_at_1 |
| 4360 | value: 58.333 |
| 4361 | - type: precision_at_10 |
| 4362 | value: 9.033 |
| 4363 | - type: precision_at_100 |
| 4364 | value: 1.0670000000000002 |
| 4365 | - type: precision_at_1000 |
| 4366 | value: 0.11199999999999999 |
| 4367 | - type: precision_at_3 |
| 4368 | value: 25.444 |
| 4369 | - type: precision_at_5 |
| 4370 | value: 16.933 |
| 4371 | - type: recall_at_1 |
| 4372 | value: 55.678000000000004 |
| 4373 | - type: recall_at_10 |
| 4374 | value: 80.72200000000001 |
| 4375 | - type: recall_at_100 |
| 4376 | value: 93.93299999999999 |
| 4377 | - type: recall_at_1000 |
| 4378 | value: 99.333 |
| 4379 | - type: recall_at_3 |
| 4380 | value: 70.783 |
| 4381 | - type: recall_at_5 |
| 4382 | value: 75.978 |
| 4383 | - task: |
| 4384 | type: PairClassification |
| 4385 | dataset: |
| 4386 | type: mteb/sprintduplicatequestions-pairclassification |
| 4387 | name: MTEB SprintDuplicateQuestions |
| 4388 | config: default |
| 4389 | split: test |
| 4390 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 4391 | metrics: |
| 4392 | - type: cos_sim_accuracy |
| 4393 | value: 99.74653465346535 |
| 4394 | - type: cos_sim_ap |
| 4395 | value: 93.01476369929063 |
| 4396 | - type: cos_sim_f1 |
| 4397 | value: 86.93009118541033 |
| 4398 | - type: cos_sim_precision |
| 4399 | value: 88.09034907597535 |
| 4400 | - type: cos_sim_recall |
| 4401 | value: 85.8 |
| 4402 | - type: dot_accuracy |
| 4403 | value: 99.22970297029703 |
| 4404 | - type: dot_ap |
| 4405 | value: 51.58725659485144 |
| 4406 | - type: dot_f1 |
| 4407 | value: 53.51351351351352 |
| 4408 | - type: dot_precision |
| 4409 | value: 58.235294117647065 |
| 4410 | - type: dot_recall |
| 4411 | value: 49.5 |
| 4412 | - type: euclidean_accuracy |
| 4413 | value: 99.74356435643564 |
| 4414 | - type: euclidean_ap |
| 4415 | value: 92.40332894384368 |
| 4416 | - type: euclidean_f1 |
| 4417 | value: 86.97838109602817 |
| 4418 | - type: euclidean_precision |
| 4419 | value: 87.46208291203236 |
| 4420 | - type: euclidean_recall |
| 4421 | value: 86.5 |
| 4422 | - type: manhattan_accuracy |
| 4423 | value: 99.73069306930694 |
| 4424 | - type: manhattan_ap |
| 4425 | value: 92.01320815721121 |
| 4426 | - type: manhattan_f1 |
| 4427 | value: 86.4135864135864 |
| 4428 | - type: manhattan_precision |
| 4429 | value: 86.32734530938124 |
| 4430 | - type: manhattan_recall |
| 4431 | value: 86.5 |
| 4432 | - type: max_accuracy |
| 4433 | value: 99.74653465346535 |
| 4434 | - type: max_ap |
| 4435 | value: 93.01476369929063 |
| 4436 | - type: max_f1 |
| 4437 | value: 86.97838109602817 |
| 4438 | - task: |
| 4439 | type: Clustering |
| 4440 | dataset: |
| 4441 | type: mteb/stackexchange-clustering |
| 4442 | name: MTEB StackExchangeClustering |
| 4443 | config: default |
| 4444 | split: test |
| 4445 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 4446 | metrics: |
| 4447 | - type: v_measure |
| 4448 | value: 55.2660514302523 |
| 4449 | - task: |
| 4450 | type: Clustering |
| 4451 | dataset: |
| 4452 | type: mteb/stackexchange-clustering-p2p |
| 4453 | name: MTEB StackExchangeClusteringP2P |
| 4454 | config: default |
| 4455 | split: test |
| 4456 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 4457 | metrics: |
| 4458 | - type: v_measure |
| 4459 | value: 30.4637783572547 |
| 4460 | - task: |
| 4461 | type: Reranking |
| 4462 | dataset: |
| 4463 | type: mteb/stackoverflowdupquestions-reranking |
| 4464 | name: MTEB StackOverflowDupQuestions |
| 4465 | config: default |
| 4466 | split: test |
| 4467 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 4468 | metrics: |
| 4469 | - type: map |
| 4470 | value: 49.41377758357637 |
| 4471 | - type: mrr |
| 4472 | value: 50.138451213818854 |
| 4473 | - task: |
| 4474 | type: Summarization |
| 4475 | dataset: |
| 4476 | type: mteb/summeval |
| 4477 | name: MTEB SummEval |
| 4478 | config: default |
| 4479 | split: test |
| 4480 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 4481 | metrics: |
| 4482 | - type: cos_sim_pearson |
| 4483 | value: 28.887846011166594 |
| 4484 | - type: cos_sim_spearman |
| 4485 | value: 30.10823258355903 |
| 4486 | - type: dot_pearson |
| 4487 | value: 12.888049550236385 |
| 4488 | - type: dot_spearman |
| 4489 | value: 12.827495903098123 |
| 4490 | - task: |
| 4491 | type: Retrieval |
| 4492 | dataset: |
| 4493 | type: trec-covid |
| 4494 | name: MTEB TRECCOVID |
| 4495 | config: default |
| 4496 | split: test |
| 4497 | revision: None |
| 4498 | metrics: |
| 4499 | - type: map_at_1 |
| 4500 | value: 0.21 |
| 4501 | - type: map_at_10 |
| 4502 | value: 1.667 |
| 4503 | - type: map_at_100 |
| 4504 | value: 9.15 |
| 4505 | - type: map_at_1000 |
| 4506 | value: 22.927 |
| 4507 | - type: map_at_3 |
| 4508 | value: 0.573 |
| 4509 | - type: map_at_5 |
| 4510 | value: 0.915 |
| 4511 | - type: mrr_at_1 |
| 4512 | value: 80 |
| 4513 | - type: mrr_at_10 |
| 4514 | value: 87.167 |
| 4515 | - type: mrr_at_100 |
| 4516 | value: 87.167 |
| 4517 | - type: mrr_at_1000 |
| 4518 | value: 87.167 |
| 4519 | - type: mrr_at_3 |
| 4520 | value: 85.667 |
| 4521 | - type: mrr_at_5 |
| 4522 | value: 87.167 |
| 4523 | - type: ndcg_at_1 |
| 4524 | value: 76 |
| 4525 | - type: ndcg_at_10 |
| 4526 | value: 69.757 |
| 4527 | - type: ndcg_at_100 |
| 4528 | value: 52.402 |
| 4529 | - type: ndcg_at_1000 |
| 4530 | value: 47.737 |
| 4531 | - type: ndcg_at_3 |
| 4532 | value: 71.866 |
| 4533 | - type: ndcg_at_5 |
| 4534 | value: 72.225 |
| 4535 | - type: precision_at_1 |
| 4536 | value: 80 |
| 4537 | - type: precision_at_10 |
| 4538 | value: 75 |
| 4539 | - type: precision_at_100 |
| 4540 | value: 53.959999999999994 |
| 4541 | - type: precision_at_1000 |
| 4542 | value: 21.568 |
| 4543 | - type: precision_at_3 |
| 4544 | value: 76.667 |
| 4545 | - type: precision_at_5 |
| 4546 | value: 78 |
| 4547 | - type: recall_at_1 |
| 4548 | value: 0.21 |
| 4549 | - type: recall_at_10 |
| 4550 | value: 1.9189999999999998 |
| 4551 | - type: recall_at_100 |
| 4552 | value: 12.589 |
| 4553 | - type: recall_at_1000 |
| 4554 | value: 45.312000000000005 |
| 4555 | - type: recall_at_3 |
| 4556 | value: 0.61 |
| 4557 | - type: recall_at_5 |
| 4558 | value: 1.019 |
| 4559 | - task: |
| 4560 | type: BitextMining |
| 4561 | dataset: |
| 4562 | type: mteb/tatoeba-bitext-mining |
| 4563 | name: MTEB Tatoeba (sqi-eng) |
| 4564 | config: sqi-eng |
| 4565 | split: test |
| 4566 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4567 | metrics: |
| 4568 | - type: accuracy |
| 4569 | value: 92.10000000000001 |
| 4570 | - type: f1 |
| 4571 | value: 90.06 |
| 4572 | - type: precision |
| 4573 | value: 89.17333333333333 |
| 4574 | - type: recall |
| 4575 | value: 92.10000000000001 |
| 4576 | - task: |
| 4577 | type: BitextMining |
| 4578 | dataset: |
| 4579 | type: mteb/tatoeba-bitext-mining |
| 4580 | name: MTEB Tatoeba (fry-eng) |
| 4581 | config: fry-eng |
| 4582 | split: test |
| 4583 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4584 | metrics: |
| 4585 | - type: accuracy |
| 4586 | value: 56.06936416184971 |
| 4587 | - type: f1 |
| 4588 | value: 50.87508028259473 |
| 4589 | - type: precision |
| 4590 | value: 48.97398843930635 |
| 4591 | - type: recall |
| 4592 | value: 56.06936416184971 |
| 4593 | - task: |
| 4594 | type: BitextMining |
| 4595 | dataset: |
| 4596 | type: mteb/tatoeba-bitext-mining |
| 4597 | name: MTEB Tatoeba (kur-eng) |
| 4598 | config: kur-eng |
| 4599 | split: test |
| 4600 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4601 | metrics: |
| 4602 | - type: accuracy |
| 4603 | value: 57.3170731707317 |
| 4604 | - type: f1 |
| 4605 | value: 52.96080139372822 |
| 4606 | - type: precision |
| 4607 | value: 51.67861124382864 |
| 4608 | - type: recall |
| 4609 | value: 57.3170731707317 |
| 4610 | - task: |
| 4611 | type: BitextMining |
| 4612 | dataset: |
| 4613 | type: mteb/tatoeba-bitext-mining |
| 4614 | name: MTEB Tatoeba (tur-eng) |
| 4615 | config: tur-eng |
| 4616 | split: test |
| 4617 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4618 | metrics: |
| 4619 | - type: accuracy |
| 4620 | value: 94.3 |
| 4621 | - type: f1 |
| 4622 | value: 92.67333333333333 |
| 4623 | - type: precision |
| 4624 | value: 91.90833333333333 |
| 4625 | - type: recall |
| 4626 | value: 94.3 |
| 4627 | - task: |
| 4628 | type: BitextMining |
| 4629 | dataset: |
| 4630 | type: mteb/tatoeba-bitext-mining |
| 4631 | name: MTEB Tatoeba (deu-eng) |
| 4632 | config: deu-eng |
| 4633 | split: test |
| 4634 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4635 | metrics: |
| 4636 | - type: accuracy |
| 4637 | value: 97.7 |
| 4638 | - type: f1 |
| 4639 | value: 97.07333333333332 |
| 4640 | - type: precision |
| 4641 | value: 96.79500000000002 |
| 4642 | - type: recall |
| 4643 | value: 97.7 |
| 4644 | - task: |
| 4645 | type: BitextMining |
| 4646 | dataset: |
| 4647 | type: mteb/tatoeba-bitext-mining |
| 4648 | name: MTEB Tatoeba (nld-eng) |
| 4649 | config: nld-eng |
| 4650 | split: test |
| 4651 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4652 | metrics: |
| 4653 | - type: accuracy |
| 4654 | value: 94.69999999999999 |
| 4655 | - type: f1 |
| 4656 | value: 93.2 |
| 4657 | - type: precision |
| 4658 | value: 92.48333333333333 |
| 4659 | - type: recall |
| 4660 | value: 94.69999999999999 |
| 4661 | - task: |
| 4662 | type: BitextMining |
| 4663 | dataset: |
| 4664 | type: mteb/tatoeba-bitext-mining |
| 4665 | name: MTEB Tatoeba (ron-eng) |
| 4666 | config: ron-eng |
| 4667 | split: test |
| 4668 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4669 | metrics: |
| 4670 | - type: accuracy |
| 4671 | value: 92.9 |
| 4672 | - type: f1 |
| 4673 | value: 91.26666666666667 |
| 4674 | - type: precision |
| 4675 | value: 90.59444444444445 |
| 4676 | - type: recall |
| 4677 | value: 92.9 |
| 4678 | - task: |
| 4679 | type: BitextMining |
| 4680 | dataset: |
| 4681 | type: mteb/tatoeba-bitext-mining |
| 4682 | name: MTEB Tatoeba (ang-eng) |
| 4683 | config: ang-eng |
| 4684 | split: test |
| 4685 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4686 | metrics: |
| 4687 | - type: accuracy |
| 4688 | value: 34.32835820895522 |
| 4689 | - type: f1 |
| 4690 | value: 29.074180380150533 |
| 4691 | - type: precision |
| 4692 | value: 28.068207322920596 |
| 4693 | - type: recall |
| 4694 | value: 34.32835820895522 |
| 4695 | - task: |
| 4696 | type: BitextMining |
| 4697 | dataset: |
| 4698 | type: mteb/tatoeba-bitext-mining |
| 4699 | name: MTEB Tatoeba (ido-eng) |
| 4700 | config: ido-eng |
| 4701 | split: test |
| 4702 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4703 | metrics: |
| 4704 | - type: accuracy |
| 4705 | value: 78.5 |
| 4706 | - type: f1 |
| 4707 | value: 74.3945115995116 |
| 4708 | - type: precision |
| 4709 | value: 72.82967843459222 |
| 4710 | - type: recall |
| 4711 | value: 78.5 |
| 4712 | - task: |
| 4713 | type: BitextMining |
| 4714 | dataset: |
| 4715 | type: mteb/tatoeba-bitext-mining |
| 4716 | name: MTEB Tatoeba (jav-eng) |
| 4717 | config: jav-eng |
| 4718 | split: test |
| 4719 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4720 | metrics: |
| 4721 | - type: accuracy |
| 4722 | value: 66.34146341463415 |
| 4723 | - type: f1 |
| 4724 | value: 61.2469400518181 |
| 4725 | - type: precision |
| 4726 | value: 59.63977756660683 |
| 4727 | - type: recall |
| 4728 | value: 66.34146341463415 |
| 4729 | - task: |
| 4730 | type: BitextMining |
| 4731 | dataset: |
| 4732 | type: mteb/tatoeba-bitext-mining |
| 4733 | name: MTEB Tatoeba (isl-eng) |
| 4734 | config: isl-eng |
| 4735 | split: test |
| 4736 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4737 | metrics: |
| 4738 | - type: accuracy |
| 4739 | value: 80.9 |
| 4740 | - type: f1 |
| 4741 | value: 76.90349206349207 |
| 4742 | - type: precision |
| 4743 | value: 75.32921568627451 |
| 4744 | - type: recall |
| 4745 | value: 80.9 |
| 4746 | - task: |
| 4747 | type: BitextMining |
| 4748 | dataset: |
| 4749 | type: mteb/tatoeba-bitext-mining |
| 4750 | name: MTEB Tatoeba (slv-eng) |
| 4751 | config: slv-eng |
| 4752 | split: test |
| 4753 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4754 | metrics: |
| 4755 | - type: accuracy |
| 4756 | value: 84.93317132442284 |
| 4757 | - type: f1 |
| 4758 | value: 81.92519105034295 |
| 4759 | - type: precision |
| 4760 | value: 80.71283920615635 |
| 4761 | - type: recall |
| 4762 | value: 84.93317132442284 |
| 4763 | - task: |
| 4764 | type: BitextMining |
| 4765 | dataset: |
| 4766 | type: mteb/tatoeba-bitext-mining |
| 4767 | name: MTEB Tatoeba (cym-eng) |
| 4768 | config: cym-eng |
| 4769 | split: test |
| 4770 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4771 | metrics: |
| 4772 | - type: accuracy |
| 4773 | value: 71.1304347826087 |
| 4774 | - type: f1 |
| 4775 | value: 65.22394755003451 |
| 4776 | - type: precision |
| 4777 | value: 62.912422360248435 |
| 4778 | - type: recall |
| 4779 | value: 71.1304347826087 |
| 4780 | - task: |
| 4781 | type: BitextMining |
| 4782 | dataset: |
| 4783 | type: mteb/tatoeba-bitext-mining |
| 4784 | name: MTEB Tatoeba (kaz-eng) |
| 4785 | config: kaz-eng |
| 4786 | split: test |
| 4787 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4788 | metrics: |
| 4789 | - type: accuracy |
| 4790 | value: 79.82608695652173 |
| 4791 | - type: f1 |
| 4792 | value: 75.55693581780538 |
| 4793 | - type: precision |
| 4794 | value: 73.79420289855072 |
| 4795 | - type: recall |
| 4796 | value: 79.82608695652173 |
| 4797 | - task: |
| 4798 | type: BitextMining |
| 4799 | dataset: |
| 4800 | type: mteb/tatoeba-bitext-mining |
| 4801 | name: MTEB Tatoeba (est-eng) |
| 4802 | config: est-eng |
| 4803 | split: test |
| 4804 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4805 | metrics: |
| 4806 | - type: accuracy |
| 4807 | value: 74 |
| 4808 | - type: f1 |
| 4809 | value: 70.51022222222223 |
| 4810 | - type: precision |
| 4811 | value: 69.29673599347512 |
| 4812 | - type: recall |
| 4813 | value: 74 |
| 4814 | - task: |
| 4815 | type: BitextMining |
| 4816 | dataset: |
| 4817 | type: mteb/tatoeba-bitext-mining |
| 4818 | name: MTEB Tatoeba (heb-eng) |
| 4819 | config: heb-eng |
| 4820 | split: test |
| 4821 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4822 | metrics: |
| 4823 | - type: accuracy |
| 4824 | value: 78.7 |
| 4825 | - type: f1 |
| 4826 | value: 74.14238095238095 |
| 4827 | - type: precision |
| 4828 | value: 72.27214285714285 |
| 4829 | - type: recall |
| 4830 | value: 78.7 |
| 4831 | - task: |
| 4832 | type: BitextMining |
| 4833 | dataset: |
| 4834 | type: mteb/tatoeba-bitext-mining |
| 4835 | name: MTEB Tatoeba (gla-eng) |
| 4836 | config: gla-eng |
| 4837 | split: test |
| 4838 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4839 | metrics: |
| 4840 | - type: accuracy |
| 4841 | value: 48.97466827503016 |
| 4842 | - type: f1 |
| 4843 | value: 43.080330405420874 |
| 4844 | - type: precision |
| 4845 | value: 41.36505499593557 |
| 4846 | - type: recall |
| 4847 | value: 48.97466827503016 |
| 4848 | - task: |
| 4849 | type: BitextMining |
| 4850 | dataset: |
| 4851 | type: mteb/tatoeba-bitext-mining |
| 4852 | name: MTEB Tatoeba (mar-eng) |
| 4853 | config: mar-eng |
| 4854 | split: test |
| 4855 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4856 | metrics: |
| 4857 | - type: accuracy |
| 4858 | value: 89.60000000000001 |
| 4859 | - type: f1 |
| 4860 | value: 86.62333333333333 |
| 4861 | - type: precision |
| 4862 | value: 85.225 |
| 4863 | - type: recall |
| 4864 | value: 89.60000000000001 |
| 4865 | - task: |
| 4866 | type: BitextMining |
| 4867 | dataset: |
| 4868 | type: mteb/tatoeba-bitext-mining |
| 4869 | name: MTEB Tatoeba (lat-eng) |
| 4870 | config: lat-eng |
| 4871 | split: test |
| 4872 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4873 | metrics: |
| 4874 | - type: accuracy |
| 4875 | value: 45.2 |
| 4876 | - type: f1 |
| 4877 | value: 39.5761253006253 |
| 4878 | - type: precision |
| 4879 | value: 37.991358436312 |
| 4880 | - type: recall |
| 4881 | value: 45.2 |
| 4882 | - task: |
| 4883 | type: BitextMining |
| 4884 | dataset: |
| 4885 | type: mteb/tatoeba-bitext-mining |
| 4886 | name: MTEB Tatoeba (bel-eng) |
| 4887 | config: bel-eng |
| 4888 | split: test |
| 4889 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4890 | metrics: |
| 4891 | - type: accuracy |
| 4892 | value: 89.5 |
| 4893 | - type: f1 |
| 4894 | value: 86.70333333333333 |
| 4895 | - type: precision |
| 4896 | value: 85.53166666666667 |
| 4897 | - type: recall |
| 4898 | value: 89.5 |
| 4899 | - task: |
| 4900 | type: BitextMining |
| 4901 | dataset: |
| 4902 | type: mteb/tatoeba-bitext-mining |
| 4903 | name: MTEB Tatoeba (pms-eng) |
| 4904 | config: pms-eng |
| 4905 | split: test |
| 4906 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4907 | metrics: |
| 4908 | - type: accuracy |
| 4909 | value: 50.095238095238095 |
| 4910 | - type: f1 |
| 4911 | value: 44.60650460650461 |
| 4912 | - type: precision |
| 4913 | value: 42.774116796477045 |
| 4914 | - type: recall |
| 4915 | value: 50.095238095238095 |
| 4916 | - task: |
| 4917 | type: BitextMining |
| 4918 | dataset: |
| 4919 | type: mteb/tatoeba-bitext-mining |
| 4920 | name: MTEB Tatoeba (gle-eng) |
| 4921 | config: gle-eng |
| 4922 | split: test |
| 4923 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4924 | metrics: |
| 4925 | - type: accuracy |
| 4926 | value: 63.4 |
| 4927 | - type: f1 |
| 4928 | value: 58.35967261904762 |
| 4929 | - type: precision |
| 4930 | value: 56.54857142857143 |
| 4931 | - type: recall |
| 4932 | value: 63.4 |
| 4933 | - task: |
| 4934 | type: BitextMining |
| 4935 | dataset: |
| 4936 | type: mteb/tatoeba-bitext-mining |
| 4937 | name: MTEB Tatoeba (pes-eng) |
| 4938 | config: pes-eng |
| 4939 | split: test |
| 4940 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4941 | metrics: |
| 4942 | - type: accuracy |
| 4943 | value: 89.2 |
| 4944 | - type: f1 |
| 4945 | value: 87.075 |
| 4946 | - type: precision |
| 4947 | value: 86.12095238095239 |
| 4948 | - type: recall |
| 4949 | value: 89.2 |
| 4950 | - task: |
| 4951 | type: BitextMining |
| 4952 | dataset: |
| 4953 | type: mteb/tatoeba-bitext-mining |
| 4954 | name: MTEB Tatoeba (nob-eng) |
| 4955 | config: nob-eng |
| 4956 | split: test |
| 4957 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4958 | metrics: |
| 4959 | - type: accuracy |
| 4960 | value: 96.8 |
| 4961 | - type: f1 |
| 4962 | value: 95.90333333333334 |
| 4963 | - type: precision |
| 4964 | value: 95.50833333333333 |
| 4965 | - type: recall |
| 4966 | value: 96.8 |
| 4967 | - task: |
| 4968 | type: BitextMining |
| 4969 | dataset: |
| 4970 | type: mteb/tatoeba-bitext-mining |
| 4971 | name: MTEB Tatoeba (bul-eng) |
| 4972 | config: bul-eng |
| 4973 | split: test |
| 4974 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4975 | metrics: |
| 4976 | - type: accuracy |
| 4977 | value: 90.9 |
| 4978 | - type: f1 |
| 4979 | value: 88.6288888888889 |
| 4980 | - type: precision |
| 4981 | value: 87.61607142857142 |
| 4982 | - type: recall |
| 4983 | value: 90.9 |
| 4984 | - task: |
| 4985 | type: BitextMining |
| 4986 | dataset: |
| 4987 | type: mteb/tatoeba-bitext-mining |
| 4988 | name: MTEB Tatoeba (cbk-eng) |
| 4989 | config: cbk-eng |
| 4990 | split: test |
| 4991 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4992 | metrics: |
| 4993 | - type: accuracy |
| 4994 | value: 65.2 |
| 4995 | - type: f1 |
| 4996 | value: 60.54377630539395 |
| 4997 | - type: precision |
| 4998 | value: 58.89434482711381 |
| 4999 | - type: recall |
| 5000 | value: 65.2 |
| 5001 | - task: |
| 5002 | type: BitextMining |
| 5003 | dataset: |
| 5004 | type: mteb/tatoeba-bitext-mining |
| 5005 | name: MTEB Tatoeba (hun-eng) |
| 5006 | config: hun-eng |
| 5007 | split: test |
| 5008 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5009 | metrics: |
| 5010 | - type: accuracy |
| 5011 | value: 87 |
| 5012 | - type: f1 |
| 5013 | value: 84.32412698412699 |
| 5014 | - type: precision |
| 5015 | value: 83.25527777777778 |
| 5016 | - type: recall |
| 5017 | value: 87 |
| 5018 | - task: |
| 5019 | type: BitextMining |
| 5020 | dataset: |
| 5021 | type: mteb/tatoeba-bitext-mining |
| 5022 | name: MTEB Tatoeba (uig-eng) |
| 5023 | config: uig-eng |
| 5024 | split: test |
| 5025 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5026 | metrics: |
| 5027 | - type: accuracy |
| 5028 | value: 68.7 |
| 5029 | - type: f1 |
| 5030 | value: 63.07883541295306 |
| 5031 | - type: precision |
| 5032 | value: 61.06117424242426 |
| 5033 | - type: recall |
| 5034 | value: 68.7 |
| 5035 | - task: |
| 5036 | type: BitextMining |
| 5037 | dataset: |
| 5038 | type: mteb/tatoeba-bitext-mining |
| 5039 | name: MTEB Tatoeba (rus-eng) |
| 5040 | config: rus-eng |
| 5041 | split: test |
| 5042 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5043 | metrics: |
| 5044 | - type: accuracy |
| 5045 | value: 93.7 |
| 5046 | - type: f1 |
| 5047 | value: 91.78333333333335 |
| 5048 | - type: precision |
| 5049 | value: 90.86666666666667 |
| 5050 | - type: recall |
| 5051 | value: 93.7 |
| 5052 | - task: |
| 5053 | type: BitextMining |
| 5054 | dataset: |
| 5055 | type: mteb/tatoeba-bitext-mining |
| 5056 | name: MTEB Tatoeba (spa-eng) |
| 5057 | config: spa-eng |
| 5058 | split: test |
| 5059 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5060 | metrics: |
| 5061 | - type: accuracy |
| 5062 | value: 97.7 |
| 5063 | - type: f1 |
| 5064 | value: 96.96666666666667 |
| 5065 | - type: precision |
| 5066 | value: 96.61666666666667 |
| 5067 | - type: recall |
| 5068 | value: 97.7 |
| 5069 | - task: |
| 5070 | type: BitextMining |
| 5071 | dataset: |
| 5072 | type: mteb/tatoeba-bitext-mining |
| 5073 | name: MTEB Tatoeba (hye-eng) |
| 5074 | config: hye-eng |
| 5075 | split: test |
| 5076 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5077 | metrics: |
| 5078 | - type: accuracy |
| 5079 | value: 88.27493261455525 |
| 5080 | - type: f1 |
| 5081 | value: 85.90745732255168 |
| 5082 | - type: precision |
| 5083 | value: 84.91389637616052 |
| 5084 | - type: recall |
| 5085 | value: 88.27493261455525 |
| 5086 | - task: |
| 5087 | type: BitextMining |
| 5088 | dataset: |
| 5089 | type: mteb/tatoeba-bitext-mining |
| 5090 | name: MTEB Tatoeba (tel-eng) |
| 5091 | config: tel-eng |
| 5092 | split: test |
| 5093 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5094 | metrics: |
| 5095 | - type: accuracy |
| 5096 | value: 90.5982905982906 |
| 5097 | - type: f1 |
| 5098 | value: 88.4900284900285 |
| 5099 | - type: precision |
| 5100 | value: 87.57122507122507 |
| 5101 | - type: recall |
| 5102 | value: 90.5982905982906 |
| 5103 | - task: |
| 5104 | type: BitextMining |
| 5105 | dataset: |
| 5106 | type: mteb/tatoeba-bitext-mining |
| 5107 | name: MTEB Tatoeba (afr-eng) |
| 5108 | config: afr-eng |
| 5109 | split: test |
| 5110 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5111 | metrics: |
| 5112 | - type: accuracy |
| 5113 | value: 89.5 |
| 5114 | - type: f1 |
| 5115 | value: 86.90769841269842 |
| 5116 | - type: precision |
| 5117 | value: 85.80178571428571 |
| 5118 | - type: recall |
| 5119 | value: 89.5 |
| 5120 | - task: |
| 5121 | type: BitextMining |
| 5122 | dataset: |
| 5123 | type: mteb/tatoeba-bitext-mining |
| 5124 | name: MTEB Tatoeba (mon-eng) |
| 5125 | config: mon-eng |
| 5126 | split: test |
| 5127 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5128 | metrics: |
| 5129 | - type: accuracy |
| 5130 | value: 82.5 |
| 5131 | - type: f1 |
| 5132 | value: 78.36796536796538 |
| 5133 | - type: precision |
| 5134 | value: 76.82196969696969 |
| 5135 | - type: recall |
| 5136 | value: 82.5 |
| 5137 | - task: |
| 5138 | type: BitextMining |
| 5139 | dataset: |
| 5140 | type: mteb/tatoeba-bitext-mining |
| 5141 | name: MTEB Tatoeba (arz-eng) |
| 5142 | config: arz-eng |
| 5143 | split: test |
| 5144 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5145 | metrics: |
| 5146 | - type: accuracy |
| 5147 | value: 71.48846960167715 |
| 5148 | - type: f1 |
| 5149 | value: 66.78771089148448 |
| 5150 | - type: precision |
| 5151 | value: 64.98302885095339 |
| 5152 | - type: recall |
| 5153 | value: 71.48846960167715 |
| 5154 | - task: |
| 5155 | type: BitextMining |
| 5156 | dataset: |
| 5157 | type: mteb/tatoeba-bitext-mining |
| 5158 | name: MTEB Tatoeba (hrv-eng) |
| 5159 | config: hrv-eng |
| 5160 | split: test |
| 5161 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5162 | metrics: |
| 5163 | - type: accuracy |
| 5164 | value: 94.1 |
| 5165 | - type: f1 |
| 5166 | value: 92.50333333333333 |
| 5167 | - type: precision |
| 5168 | value: 91.77499999999999 |
| 5169 | - type: recall |
| 5170 | value: 94.1 |
| 5171 | - task: |
| 5172 | type: BitextMining |
| 5173 | dataset: |
| 5174 | type: mteb/tatoeba-bitext-mining |
| 5175 | name: MTEB Tatoeba (nov-eng) |
| 5176 | config: nov-eng |
| 5177 | split: test |
| 5178 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5179 | metrics: |
| 5180 | - type: accuracy |
| 5181 | value: 71.20622568093385 |
| 5182 | - type: f1 |
| 5183 | value: 66.83278891450098 |
| 5184 | - type: precision |
| 5185 | value: 65.35065777283677 |
| 5186 | - type: recall |
| 5187 | value: 71.20622568093385 |
| 5188 | - task: |
| 5189 | type: BitextMining |
| 5190 | dataset: |
| 5191 | type: mteb/tatoeba-bitext-mining |
| 5192 | name: MTEB Tatoeba (gsw-eng) |
| 5193 | config: gsw-eng |
| 5194 | split: test |
| 5195 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5196 | metrics: |
| 5197 | - type: accuracy |
| 5198 | value: 48.717948717948715 |
| 5199 | - type: f1 |
| 5200 | value: 43.53146853146853 |
| 5201 | - type: precision |
| 5202 | value: 42.04721204721204 |
| 5203 | - type: recall |
| 5204 | value: 48.717948717948715 |
| 5205 | - task: |
| 5206 | type: BitextMining |
| 5207 | dataset: |
| 5208 | type: mteb/tatoeba-bitext-mining |
| 5209 | name: MTEB Tatoeba (nds-eng) |
| 5210 | config: nds-eng |
| 5211 | split: test |
| 5212 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5213 | metrics: |
| 5214 | - type: accuracy |
| 5215 | value: 58.5 |
| 5216 | - type: f1 |
| 5217 | value: 53.8564991863928 |
| 5218 | - type: precision |
| 5219 | value: 52.40329436122275 |
| 5220 | - type: recall |
| 5221 | value: 58.5 |
| 5222 | - task: |
| 5223 | type: BitextMining |
| 5224 | dataset: |
| 5225 | type: mteb/tatoeba-bitext-mining |
| 5226 | name: MTEB Tatoeba (ukr-eng) |
| 5227 | config: ukr-eng |
| 5228 | split: test |
| 5229 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5230 | metrics: |
| 5231 | - type: accuracy |
| 5232 | value: 90.8 |
| 5233 | - type: f1 |
| 5234 | value: 88.29 |
| 5235 | - type: precision |
| 5236 | value: 87.09166666666667 |
| 5237 | - type: recall |
| 5238 | value: 90.8 |
| 5239 | - task: |
| 5240 | type: BitextMining |
| 5241 | dataset: |
| 5242 | type: mteb/tatoeba-bitext-mining |
| 5243 | name: MTEB Tatoeba (uzb-eng) |
| 5244 | config: uzb-eng |
| 5245 | split: test |
| 5246 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5247 | metrics: |
| 5248 | - type: accuracy |
| 5249 | value: 67.28971962616822 |
| 5250 | - type: f1 |
| 5251 | value: 62.63425307817832 |
| 5252 | - type: precision |
| 5253 | value: 60.98065939771546 |
| 5254 | - type: recall |
| 5255 | value: 67.28971962616822 |
| 5256 | - task: |
| 5257 | type: BitextMining |
| 5258 | dataset: |
| 5259 | type: mteb/tatoeba-bitext-mining |
| 5260 | name: MTEB Tatoeba (lit-eng) |
| 5261 | config: lit-eng |
| 5262 | split: test |
| 5263 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5264 | metrics: |
| 5265 | - type: accuracy |
| 5266 | value: 78.7 |
| 5267 | - type: f1 |
| 5268 | value: 75.5264472455649 |
| 5269 | - type: precision |
| 5270 | value: 74.38205086580086 |
| 5271 | - type: recall |
| 5272 | value: 78.7 |
| 5273 | - task: |
| 5274 | type: BitextMining |
| 5275 | dataset: |
| 5276 | type: mteb/tatoeba-bitext-mining |
| 5277 | name: MTEB Tatoeba (ina-eng) |
| 5278 | config: ina-eng |
| 5279 | split: test |
| 5280 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5281 | metrics: |
| 5282 | - type: accuracy |
| 5283 | value: 88.7 |
| 5284 | - type: f1 |
| 5285 | value: 86.10809523809525 |
| 5286 | - type: precision |
| 5287 | value: 85.07602564102565 |
| 5288 | - type: recall |
| 5289 | value: 88.7 |
| 5290 | - task: |
| 5291 | type: BitextMining |
| 5292 | dataset: |
| 5293 | type: mteb/tatoeba-bitext-mining |
| 5294 | name: MTEB Tatoeba (lfn-eng) |
| 5295 | config: lfn-eng |
| 5296 | split: test |
| 5297 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5298 | metrics: |
| 5299 | - type: accuracy |
| 5300 | value: 56.99999999999999 |
| 5301 | - type: f1 |
| 5302 | value: 52.85487521402737 |
| 5303 | - type: precision |
| 5304 | value: 51.53985162713104 |
| 5305 | - type: recall |
| 5306 | value: 56.99999999999999 |
| 5307 | - task: |
| 5308 | type: BitextMining |
| 5309 | dataset: |
| 5310 | type: mteb/tatoeba-bitext-mining |
| 5311 | name: MTEB Tatoeba (zsm-eng) |
| 5312 | config: zsm-eng |
| 5313 | split: test |
| 5314 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5315 | metrics: |
| 5316 | - type: accuracy |
| 5317 | value: 94 |
| 5318 | - type: f1 |
| 5319 | value: 92.45333333333333 |
| 5320 | - type: precision |
| 5321 | value: 91.79166666666667 |
| 5322 | - type: recall |
| 5323 | value: 94 |
| 5324 | - task: |
| 5325 | type: BitextMining |
| 5326 | dataset: |
| 5327 | type: mteb/tatoeba-bitext-mining |
| 5328 | name: MTEB Tatoeba (ita-eng) |
| 5329 | config: ita-eng |
| 5330 | split: test |
| 5331 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5332 | metrics: |
| 5333 | - type: accuracy |
| 5334 | value: 92.30000000000001 |
| 5335 | - type: f1 |
| 5336 | value: 90.61333333333333 |
| 5337 | - type: precision |
| 5338 | value: 89.83333333333331 |
| 5339 | - type: recall |
| 5340 | value: 92.30000000000001 |
| 5341 | - task: |
| 5342 | type: BitextMining |
| 5343 | dataset: |
| 5344 | type: mteb/tatoeba-bitext-mining |
| 5345 | name: MTEB Tatoeba (cmn-eng) |
| 5346 | config: cmn-eng |
| 5347 | split: test |
| 5348 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5349 | metrics: |
| 5350 | - type: accuracy |
| 5351 | value: 94.69999999999999 |
| 5352 | - type: f1 |
| 5353 | value: 93.34555555555555 |
| 5354 | - type: precision |
| 5355 | value: 92.75416666666668 |
| 5356 | - type: recall |
| 5357 | value: 94.69999999999999 |
| 5358 | - task: |
| 5359 | type: BitextMining |
| 5360 | dataset: |
| 5361 | type: mteb/tatoeba-bitext-mining |
| 5362 | name: MTEB Tatoeba (lvs-eng) |
| 5363 | config: lvs-eng |
| 5364 | split: test |
| 5365 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5366 | metrics: |
| 5367 | - type: accuracy |
| 5368 | value: 80.2 |
| 5369 | - type: f1 |
| 5370 | value: 76.6563035113035 |
| 5371 | - type: precision |
| 5372 | value: 75.3014652014652 |
| 5373 | - type: recall |
| 5374 | value: 80.2 |
| 5375 | - task: |
| 5376 | type: BitextMining |
| 5377 | dataset: |
| 5378 | type: mteb/tatoeba-bitext-mining |
| 5379 | name: MTEB Tatoeba (glg-eng) |
| 5380 | config: glg-eng |
| 5381 | split: test |
| 5382 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5383 | metrics: |
| 5384 | - type: accuracy |
| 5385 | value: 84.7 |
| 5386 | - type: f1 |
| 5387 | value: 82.78689263765207 |
| 5388 | - type: precision |
| 5389 | value: 82.06705086580087 |
| 5390 | - type: recall |
| 5391 | value: 84.7 |
| 5392 | - task: |
| 5393 | type: BitextMining |
| 5394 | dataset: |
| 5395 | type: mteb/tatoeba-bitext-mining |
| 5396 | name: MTEB Tatoeba (ceb-eng) |
| 5397 | config: ceb-eng |
| 5398 | split: test |
| 5399 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5400 | metrics: |
| 5401 | - type: accuracy |
| 5402 | value: 50.33333333333333 |
| 5403 | - type: f1 |
| 5404 | value: 45.461523661523664 |
| 5405 | - type: precision |
| 5406 | value: 43.93545574795575 |
| 5407 | - type: recall |
| 5408 | value: 50.33333333333333 |
| 5409 | - task: |
| 5410 | type: BitextMining |
| 5411 | dataset: |
| 5412 | type: mteb/tatoeba-bitext-mining |
| 5413 | name: MTEB Tatoeba (bre-eng) |
| 5414 | config: bre-eng |
| 5415 | split: test |
| 5416 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5417 | metrics: |
| 5418 | - type: accuracy |
| 5419 | value: 6.6000000000000005 |
| 5420 | - type: f1 |
| 5421 | value: 5.442121400446441 |
| 5422 | - type: precision |
| 5423 | value: 5.146630385487529 |
| 5424 | - type: recall |
| 5425 | value: 6.6000000000000005 |
| 5426 | - task: |
| 5427 | type: BitextMining |
| 5428 | dataset: |
| 5429 | type: mteb/tatoeba-bitext-mining |
| 5430 | name: MTEB Tatoeba (ben-eng) |
| 5431 | config: ben-eng |
| 5432 | split: test |
| 5433 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5434 | metrics: |
| 5435 | - type: accuracy |
| 5436 | value: 85 |
| 5437 | - type: f1 |
| 5438 | value: 81.04666666666667 |
| 5439 | - type: precision |
| 5440 | value: 79.25 |
| 5441 | - type: recall |
| 5442 | value: 85 |
| 5443 | - task: |
| 5444 | type: BitextMining |
| 5445 | dataset: |
| 5446 | type: mteb/tatoeba-bitext-mining |
| 5447 | name: MTEB Tatoeba (swg-eng) |
| 5448 | config: swg-eng |
| 5449 | split: test |
| 5450 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5451 | metrics: |
| 5452 | - type: accuracy |
| 5453 | value: 47.32142857142857 |
| 5454 | - type: f1 |
| 5455 | value: 42.333333333333336 |
| 5456 | - type: precision |
| 5457 | value: 40.69196428571429 |
| 5458 | - type: recall |
| 5459 | value: 47.32142857142857 |
| 5460 | - task: |
| 5461 | type: BitextMining |
| 5462 | dataset: |
| 5463 | type: mteb/tatoeba-bitext-mining |
| 5464 | name: MTEB Tatoeba (arq-eng) |
| 5465 | config: arq-eng |
| 5466 | split: test |
| 5467 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5468 | metrics: |
| 5469 | - type: accuracy |
| 5470 | value: 30.735455543358945 |
| 5471 | - type: f1 |
| 5472 | value: 26.73616790022338 |
| 5473 | - type: precision |
| 5474 | value: 25.397823220451283 |
| 5475 | - type: recall |
| 5476 | value: 30.735455543358945 |
| 5477 | - task: |
| 5478 | type: BitextMining |
| 5479 | dataset: |
| 5480 | type: mteb/tatoeba-bitext-mining |
| 5481 | name: MTEB Tatoeba (kab-eng) |
| 5482 | config: kab-eng |
| 5483 | split: test |
| 5484 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5485 | metrics: |
| 5486 | - type: accuracy |
| 5487 | value: 25.1 |
| 5488 | - type: f1 |
| 5489 | value: 21.975989896371022 |
| 5490 | - type: precision |
| 5491 | value: 21.059885632257203 |
| 5492 | - type: recall |
| 5493 | value: 25.1 |
| 5494 | - task: |
| 5495 | type: BitextMining |
| 5496 | dataset: |
| 5497 | type: mteb/tatoeba-bitext-mining |
| 5498 | name: MTEB Tatoeba (fra-eng) |
| 5499 | config: fra-eng |
| 5500 | split: test |
| 5501 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5502 | metrics: |
| 5503 | - type: accuracy |
| 5504 | value: 94.3 |
| 5505 | - type: f1 |
| 5506 | value: 92.75666666666666 |
| 5507 | - type: precision |
| 5508 | value: 92.06166666666665 |
| 5509 | - type: recall |
| 5510 | value: 94.3 |
| 5511 | - task: |
| 5512 | type: BitextMining |
| 5513 | dataset: |
| 5514 | type: mteb/tatoeba-bitext-mining |
| 5515 | name: MTEB Tatoeba (por-eng) |
| 5516 | config: por-eng |
| 5517 | split: test |
| 5518 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5519 | metrics: |
| 5520 | - type: accuracy |
| 5521 | value: 94.1 |
| 5522 | - type: f1 |
| 5523 | value: 92.74 |
| 5524 | - type: precision |
| 5525 | value: 92.09166666666667 |
| 5526 | - type: recall |
| 5527 | value: 94.1 |
| 5528 | - task: |
| 5529 | type: BitextMining |
| 5530 | dataset: |
| 5531 | type: mteb/tatoeba-bitext-mining |
| 5532 | name: MTEB Tatoeba (tat-eng) |
| 5533 | config: tat-eng |
| 5534 | split: test |
| 5535 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5536 | metrics: |
| 5537 | - type: accuracy |
| 5538 | value: 71.3 |
| 5539 | - type: f1 |
| 5540 | value: 66.922442002442 |
| 5541 | - type: precision |
| 5542 | value: 65.38249567099568 |
| 5543 | - type: recall |
| 5544 | value: 71.3 |
| 5545 | - task: |
| 5546 | type: BitextMining |
| 5547 | dataset: |
| 5548 | type: mteb/tatoeba-bitext-mining |
| 5549 | name: MTEB Tatoeba (oci-eng) |
| 5550 | config: oci-eng |
| 5551 | split: test |
| 5552 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5553 | metrics: |
| 5554 | - type: accuracy |
| 5555 | value: 40.300000000000004 |
| 5556 | - type: f1 |
| 5557 | value: 35.78682789299971 |
| 5558 | - type: precision |
| 5559 | value: 34.66425128716588 |
| 5560 | - type: recall |
| 5561 | value: 40.300000000000004 |
| 5562 | - task: |
| 5563 | type: BitextMining |
| 5564 | dataset: |
| 5565 | type: mteb/tatoeba-bitext-mining |
| 5566 | name: MTEB Tatoeba (pol-eng) |
| 5567 | config: pol-eng |
| 5568 | split: test |
| 5569 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5570 | metrics: |
| 5571 | - type: accuracy |
| 5572 | value: 96 |
| 5573 | - type: f1 |
| 5574 | value: 94.82333333333334 |
| 5575 | - type: precision |
| 5576 | value: 94.27833333333334 |
| 5577 | - type: recall |
| 5578 | value: 96 |
| 5579 | - task: |
| 5580 | type: BitextMining |
| 5581 | dataset: |
| 5582 | type: mteb/tatoeba-bitext-mining |
| 5583 | name: MTEB Tatoeba (war-eng) |
| 5584 | config: war-eng |
| 5585 | split: test |
| 5586 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5587 | metrics: |
| 5588 | - type: accuracy |
| 5589 | value: 51.1 |
| 5590 | - type: f1 |
| 5591 | value: 47.179074753133584 |
| 5592 | - type: precision |
| 5593 | value: 46.06461044702424 |
| 5594 | - type: recall |
| 5595 | value: 51.1 |
| 5596 | - task: |
| 5597 | type: BitextMining |
| 5598 | dataset: |
| 5599 | type: mteb/tatoeba-bitext-mining |
| 5600 | name: MTEB Tatoeba (aze-eng) |
| 5601 | config: aze-eng |
| 5602 | split: test |
| 5603 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5604 | metrics: |
| 5605 | - type: accuracy |
| 5606 | value: 87.7 |
| 5607 | - type: f1 |
| 5608 | value: 84.71 |
| 5609 | - type: precision |
| 5610 | value: 83.46166666666667 |
| 5611 | - type: recall |
| 5612 | value: 87.7 |
| 5613 | - task: |
| 5614 | type: BitextMining |
| 5615 | dataset: |
| 5616 | type: mteb/tatoeba-bitext-mining |
| 5617 | name: MTEB Tatoeba (vie-eng) |
| 5618 | config: vie-eng |
| 5619 | split: test |
| 5620 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5621 | metrics: |
| 5622 | - type: accuracy |
| 5623 | value: 95.8 |
| 5624 | - type: f1 |
| 5625 | value: 94.68333333333334 |
| 5626 | - type: precision |
| 5627 | value: 94.13333333333334 |
| 5628 | - type: recall |
| 5629 | value: 95.8 |
| 5630 | - task: |
| 5631 | type: BitextMining |
| 5632 | dataset: |
| 5633 | type: mteb/tatoeba-bitext-mining |
| 5634 | name: MTEB Tatoeba (nno-eng) |
| 5635 | config: nno-eng |
| 5636 | split: test |
| 5637 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5638 | metrics: |
| 5639 | - type: accuracy |
| 5640 | value: 85.39999999999999 |
| 5641 | - type: f1 |
| 5642 | value: 82.5577380952381 |
| 5643 | - type: precision |
| 5644 | value: 81.36833333333334 |
| 5645 | - type: recall |
| 5646 | value: 85.39999999999999 |
| 5647 | - task: |
| 5648 | type: BitextMining |
| 5649 | dataset: |
| 5650 | type: mteb/tatoeba-bitext-mining |
| 5651 | name: MTEB Tatoeba (cha-eng) |
| 5652 | config: cha-eng |
| 5653 | split: test |
| 5654 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5655 | metrics: |
| 5656 | - type: accuracy |
| 5657 | value: 21.16788321167883 |
| 5658 | - type: f1 |
| 5659 | value: 16.948865627297987 |
| 5660 | - type: precision |
| 5661 | value: 15.971932568647897 |
| 5662 | - type: recall |
| 5663 | value: 21.16788321167883 |
| 5664 | - task: |
| 5665 | type: BitextMining |
| 5666 | dataset: |
| 5667 | type: mteb/tatoeba-bitext-mining |
| 5668 | name: MTEB Tatoeba (mhr-eng) |
| 5669 | config: mhr-eng |
| 5670 | split: test |
| 5671 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5672 | metrics: |
| 5673 | - type: accuracy |
| 5674 | value: 6.9 |
| 5675 | - type: f1 |
| 5676 | value: 5.515526831658907 |
| 5677 | - type: precision |
| 5678 | value: 5.141966366966367 |
| 5679 | - type: recall |
| 5680 | value: 6.9 |
| 5681 | - task: |
| 5682 | type: BitextMining |
| 5683 | dataset: |
| 5684 | type: mteb/tatoeba-bitext-mining |
| 5685 | name: MTEB Tatoeba (dan-eng) |
| 5686 | config: dan-eng |
| 5687 | split: test |
| 5688 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5689 | metrics: |
| 5690 | - type: accuracy |
| 5691 | value: 93.2 |
| 5692 | - type: f1 |
| 5693 | value: 91.39666666666668 |
| 5694 | - type: precision |
| 5695 | value: 90.58666666666667 |
| 5696 | - type: recall |
| 5697 | value: 93.2 |
| 5698 | - task: |
| 5699 | type: BitextMining |
| 5700 | dataset: |
| 5701 | type: mteb/tatoeba-bitext-mining |
| 5702 | name: MTEB Tatoeba (ell-eng) |
| 5703 | config: ell-eng |
| 5704 | split: test |
| 5705 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5706 | metrics: |
| 5707 | - type: accuracy |
| 5708 | value: 92.2 |
| 5709 | - type: f1 |
| 5710 | value: 89.95666666666666 |
| 5711 | - type: precision |
| 5712 | value: 88.92833333333333 |
| 5713 | - type: recall |
| 5714 | value: 92.2 |
| 5715 | - task: |
| 5716 | type: BitextMining |
| 5717 | dataset: |
| 5718 | type: mteb/tatoeba-bitext-mining |
| 5719 | name: MTEB Tatoeba (amh-eng) |
| 5720 | config: amh-eng |
| 5721 | split: test |
| 5722 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5723 | metrics: |
| 5724 | - type: accuracy |
| 5725 | value: 79.76190476190477 |
| 5726 | - type: f1 |
| 5727 | value: 74.93386243386244 |
| 5728 | - type: precision |
| 5729 | value: 73.11011904761904 |
| 5730 | - type: recall |
| 5731 | value: 79.76190476190477 |
| 5732 | - task: |
| 5733 | type: BitextMining |
| 5734 | dataset: |
| 5735 | type: mteb/tatoeba-bitext-mining |
| 5736 | name: MTEB Tatoeba (pam-eng) |
| 5737 | config: pam-eng |
| 5738 | split: test |
| 5739 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5740 | metrics: |
| 5741 | - type: accuracy |
| 5742 | value: 8.799999999999999 |
| 5743 | - type: f1 |
| 5744 | value: 6.921439712248537 |
| 5745 | - type: precision |
| 5746 | value: 6.489885109680683 |
| 5747 | - type: recall |
| 5748 | value: 8.799999999999999 |
| 5749 | - task: |
| 5750 | type: BitextMining |
| 5751 | dataset: |
| 5752 | type: mteb/tatoeba-bitext-mining |
| 5753 | name: MTEB Tatoeba (hsb-eng) |
| 5754 | config: hsb-eng |
| 5755 | split: test |
| 5756 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5757 | metrics: |
| 5758 | - type: accuracy |
| 5759 | value: 45.75569358178054 |
| 5760 | - type: f1 |
| 5761 | value: 40.34699501312631 |
| 5762 | - type: precision |
| 5763 | value: 38.57886764719063 |
| 5764 | - type: recall |
| 5765 | value: 45.75569358178054 |
| 5766 | - task: |
| 5767 | type: BitextMining |
| 5768 | dataset: |
| 5769 | type: mteb/tatoeba-bitext-mining |
| 5770 | name: MTEB Tatoeba (srp-eng) |
| 5771 | config: srp-eng |
| 5772 | split: test |
| 5773 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5774 | metrics: |
| 5775 | - type: accuracy |
| 5776 | value: 91.4 |
| 5777 | - type: f1 |
| 5778 | value: 89.08333333333333 |
| 5779 | - type: precision |
| 5780 | value: 88.01666666666668 |
| 5781 | - type: recall |
| 5782 | value: 91.4 |
| 5783 | - task: |
| 5784 | type: BitextMining |
| 5785 | dataset: |
| 5786 | type: mteb/tatoeba-bitext-mining |
| 5787 | name: MTEB Tatoeba (epo-eng) |
| 5788 | config: epo-eng |
| 5789 | split: test |
| 5790 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5791 | metrics: |
| 5792 | - type: accuracy |
| 5793 | value: 93.60000000000001 |
| 5794 | - type: f1 |
| 5795 | value: 92.06690476190477 |
| 5796 | - type: precision |
| 5797 | value: 91.45095238095239 |
| 5798 | - type: recall |
| 5799 | value: 93.60000000000001 |
| 5800 | - task: |
| 5801 | type: BitextMining |
| 5802 | dataset: |
| 5803 | type: mteb/tatoeba-bitext-mining |
| 5804 | name: MTEB Tatoeba (kzj-eng) |
| 5805 | config: kzj-eng |
| 5806 | split: test |
| 5807 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5808 | metrics: |
| 5809 | - type: accuracy |
| 5810 | value: 7.5 |
| 5811 | - type: f1 |
| 5812 | value: 6.200363129378736 |
| 5813 | - type: precision |
| 5814 | value: 5.89115314822466 |
| 5815 | - type: recall |
| 5816 | value: 7.5 |
| 5817 | - task: |
| 5818 | type: BitextMining |
| 5819 | dataset: |
| 5820 | type: mteb/tatoeba-bitext-mining |
| 5821 | name: MTEB Tatoeba (awa-eng) |
| 5822 | config: awa-eng |
| 5823 | split: test |
| 5824 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5825 | metrics: |
| 5826 | - type: accuracy |
| 5827 | value: 73.59307359307358 |
| 5828 | - type: f1 |
| 5829 | value: 68.38933553219267 |
| 5830 | - type: precision |
| 5831 | value: 66.62698412698413 |
| 5832 | - type: recall |
| 5833 | value: 73.59307359307358 |
| 5834 | - task: |
| 5835 | type: BitextMining |
| 5836 | dataset: |
| 5837 | type: mteb/tatoeba-bitext-mining |
| 5838 | name: MTEB Tatoeba (fao-eng) |
| 5839 | config: fao-eng |
| 5840 | split: test |
| 5841 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5842 | metrics: |
| 5843 | - type: accuracy |
| 5844 | value: 69.8473282442748 |
| 5845 | - type: f1 |
| 5846 | value: 64.72373682297346 |
| 5847 | - type: precision |
| 5848 | value: 62.82834214131924 |
| 5849 | - type: recall |
| 5850 | value: 69.8473282442748 |
| 5851 | - task: |
| 5852 | type: BitextMining |
| 5853 | dataset: |
| 5854 | type: mteb/tatoeba-bitext-mining |
| 5855 | name: MTEB Tatoeba (mal-eng) |
| 5856 | config: mal-eng |
| 5857 | split: test |
| 5858 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5859 | metrics: |
| 5860 | - type: accuracy |
| 5861 | value: 97.5254730713246 |
| 5862 | - type: f1 |
| 5863 | value: 96.72489082969432 |
| 5864 | - type: precision |
| 5865 | value: 96.33672974284326 |
| 5866 | - type: recall |
| 5867 | value: 97.5254730713246 |
| 5868 | - task: |
| 5869 | type: BitextMining |
| 5870 | dataset: |
| 5871 | type: mteb/tatoeba-bitext-mining |
| 5872 | name: MTEB Tatoeba (ile-eng) |
| 5873 | config: ile-eng |
| 5874 | split: test |
| 5875 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5876 | metrics: |
| 5877 | - type: accuracy |
| 5878 | value: 75.6 |
| 5879 | - type: f1 |
| 5880 | value: 72.42746031746033 |
| 5881 | - type: precision |
| 5882 | value: 71.14036630036631 |
| 5883 | - type: recall |
| 5884 | value: 75.6 |
| 5885 | - task: |
| 5886 | type: BitextMining |
| 5887 | dataset: |
| 5888 | type: mteb/tatoeba-bitext-mining |
| 5889 | name: MTEB Tatoeba (bos-eng) |
| 5890 | config: bos-eng |
| 5891 | split: test |
| 5892 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5893 | metrics: |
| 5894 | - type: accuracy |
| 5895 | value: 91.24293785310734 |
| 5896 | - type: f1 |
| 5897 | value: 88.86064030131826 |
| 5898 | - type: precision |
| 5899 | value: 87.73540489642184 |
| 5900 | - type: recall |
| 5901 | value: 91.24293785310734 |
| 5902 | - task: |
| 5903 | type: BitextMining |
| 5904 | dataset: |
| 5905 | type: mteb/tatoeba-bitext-mining |
| 5906 | name: MTEB Tatoeba (cor-eng) |
| 5907 | config: cor-eng |
| 5908 | split: test |
| 5909 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5910 | metrics: |
| 5911 | - type: accuracy |
| 5912 | value: 6.2 |
| 5913 | - type: f1 |
| 5914 | value: 4.383083659794954 |
| 5915 | - type: precision |
| 5916 | value: 4.027861324289673 |
| 5917 | - type: recall |
| 5918 | value: 6.2 |
| 5919 | - task: |
| 5920 | type: BitextMining |
| 5921 | dataset: |
| 5922 | type: mteb/tatoeba-bitext-mining |
| 5923 | name: MTEB Tatoeba (cat-eng) |
| 5924 | config: cat-eng |
| 5925 | split: test |
| 5926 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5927 | metrics: |
| 5928 | - type: accuracy |
| 5929 | value: 86.8 |
| 5930 | - type: f1 |
| 5931 | value: 84.09428571428572 |
| 5932 | - type: precision |
| 5933 | value: 83.00333333333333 |
| 5934 | - type: recall |
| 5935 | value: 86.8 |
| 5936 | - task: |
| 5937 | type: BitextMining |
| 5938 | dataset: |
| 5939 | type: mteb/tatoeba-bitext-mining |
| 5940 | name: MTEB Tatoeba (eus-eng) |
| 5941 | config: eus-eng |
| 5942 | split: test |
| 5943 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5944 | metrics: |
| 5945 | - type: accuracy |
| 5946 | value: 60.699999999999996 |
| 5947 | - type: f1 |
| 5948 | value: 56.1584972394755 |
| 5949 | - type: precision |
| 5950 | value: 54.713456330903135 |
| 5951 | - type: recall |
| 5952 | value: 60.699999999999996 |
| 5953 | - task: |
| 5954 | type: BitextMining |
| 5955 | dataset: |
| 5956 | type: mteb/tatoeba-bitext-mining |
| 5957 | name: MTEB Tatoeba (yue-eng) |
| 5958 | config: yue-eng |
| 5959 | split: test |
| 5960 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5961 | metrics: |
| 5962 | - type: accuracy |
| 5963 | value: 84.2 |
| 5964 | - type: f1 |
| 5965 | value: 80.66190476190475 |
| 5966 | - type: precision |
| 5967 | value: 79.19690476190476 |
| 5968 | - type: recall |
| 5969 | value: 84.2 |
| 5970 | - task: |
| 5971 | type: BitextMining |
| 5972 | dataset: |
| 5973 | type: mteb/tatoeba-bitext-mining |
| 5974 | name: MTEB Tatoeba (swe-eng) |
| 5975 | config: swe-eng |
| 5976 | split: test |
| 5977 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5978 | metrics: |
| 5979 | - type: accuracy |
| 5980 | value: 93.2 |
| 5981 | - type: f1 |
| 5982 | value: 91.33 |
| 5983 | - type: precision |
| 5984 | value: 90.45 |
| 5985 | - type: recall |
| 5986 | value: 93.2 |
| 5987 | - task: |
| 5988 | type: BitextMining |
| 5989 | dataset: |
| 5990 | type: mteb/tatoeba-bitext-mining |
| 5991 | name: MTEB Tatoeba (dtp-eng) |
| 5992 | config: dtp-eng |
| 5993 | split: test |
| 5994 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5995 | metrics: |
| 5996 | - type: accuracy |
| 5997 | value: 6.3 |
| 5998 | - type: f1 |
| 5999 | value: 5.126828976748276 |
| 6000 | - type: precision |
| 6001 | value: 4.853614328966668 |
| 6002 | - type: recall |
| 6003 | value: 6.3 |
| 6004 | - task: |
| 6005 | type: BitextMining |
| 6006 | dataset: |
| 6007 | type: mteb/tatoeba-bitext-mining |
| 6008 | name: MTEB Tatoeba (kat-eng) |
| 6009 | config: kat-eng |
| 6010 | split: test |
| 6011 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6012 | metrics: |
| 6013 | - type: accuracy |
| 6014 | value: 81.76943699731903 |
| 6015 | - type: f1 |
| 6016 | value: 77.82873739308057 |
| 6017 | - type: precision |
| 6018 | value: 76.27622452019234 |
| 6019 | - type: recall |
| 6020 | value: 81.76943699731903 |
| 6021 | - task: |
| 6022 | type: BitextMining |
| 6023 | dataset: |
| 6024 | type: mteb/tatoeba-bitext-mining |
| 6025 | name: MTEB Tatoeba (jpn-eng) |
| 6026 | config: jpn-eng |
| 6027 | split: test |
| 6028 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6029 | metrics: |
| 6030 | - type: accuracy |
| 6031 | value: 92.30000000000001 |
| 6032 | - type: f1 |
| 6033 | value: 90.29666666666665 |
| 6034 | - type: precision |
| 6035 | value: 89.40333333333334 |
| 6036 | - type: recall |
| 6037 | value: 92.30000000000001 |
| 6038 | - task: |
| 6039 | type: BitextMining |
| 6040 | dataset: |
| 6041 | type: mteb/tatoeba-bitext-mining |
| 6042 | name: MTEB Tatoeba (csb-eng) |
| 6043 | config: csb-eng |
| 6044 | split: test |
| 6045 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6046 | metrics: |
| 6047 | - type: accuracy |
| 6048 | value: 29.249011857707508 |
| 6049 | - type: f1 |
| 6050 | value: 24.561866096392947 |
| 6051 | - type: precision |
| 6052 | value: 23.356583740215456 |
| 6053 | - type: recall |
| 6054 | value: 29.249011857707508 |
| 6055 | - task: |
| 6056 | type: BitextMining |
| 6057 | dataset: |
| 6058 | type: mteb/tatoeba-bitext-mining |
| 6059 | name: MTEB Tatoeba (xho-eng) |
| 6060 | config: xho-eng |
| 6061 | split: test |
| 6062 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6063 | metrics: |
| 6064 | - type: accuracy |
| 6065 | value: 77.46478873239437 |
| 6066 | - type: f1 |
| 6067 | value: 73.23943661971832 |
| 6068 | - type: precision |
| 6069 | value: 71.66666666666667 |
| 6070 | - type: recall |
| 6071 | value: 77.46478873239437 |
| 6072 | - task: |
| 6073 | type: BitextMining |
| 6074 | dataset: |
| 6075 | type: mteb/tatoeba-bitext-mining |
| 6076 | name: MTEB Tatoeba (orv-eng) |
| 6077 | config: orv-eng |
| 6078 | split: test |
| 6079 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6080 | metrics: |
| 6081 | - type: accuracy |
| 6082 | value: 20.35928143712575 |
| 6083 | - type: f1 |
| 6084 | value: 15.997867865075824 |
| 6085 | - type: precision |
| 6086 | value: 14.882104658301346 |
| 6087 | - type: recall |
| 6088 | value: 20.35928143712575 |
| 6089 | - task: |
| 6090 | type: BitextMining |
| 6091 | dataset: |
| 6092 | type: mteb/tatoeba-bitext-mining |
| 6093 | name: MTEB Tatoeba (ind-eng) |
| 6094 | config: ind-eng |
| 6095 | split: test |
| 6096 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6097 | metrics: |
| 6098 | - type: accuracy |
| 6099 | value: 92.2 |
| 6100 | - type: f1 |
| 6101 | value: 90.25999999999999 |
| 6102 | - type: precision |
| 6103 | value: 89.45333333333335 |
| 6104 | - type: recall |
| 6105 | value: 92.2 |
| 6106 | - task: |
| 6107 | type: BitextMining |
| 6108 | dataset: |
| 6109 | type: mteb/tatoeba-bitext-mining |
| 6110 | name: MTEB Tatoeba (tuk-eng) |
| 6111 | config: tuk-eng |
| 6112 | split: test |
| 6113 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6114 | metrics: |
| 6115 | - type: accuracy |
| 6116 | value: 23.15270935960591 |
| 6117 | - type: f1 |
| 6118 | value: 19.65673625772148 |
| 6119 | - type: precision |
| 6120 | value: 18.793705293464992 |
| 6121 | - type: recall |
| 6122 | value: 23.15270935960591 |
| 6123 | - task: |
| 6124 | type: BitextMining |
| 6125 | dataset: |
| 6126 | type: mteb/tatoeba-bitext-mining |
| 6127 | name: MTEB Tatoeba (max-eng) |
| 6128 | config: max-eng |
| 6129 | split: test |
| 6130 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6131 | metrics: |
| 6132 | - type: accuracy |
| 6133 | value: 59.154929577464785 |
| 6134 | - type: f1 |
| 6135 | value: 52.3868463305083 |
| 6136 | - type: precision |
| 6137 | value: 50.14938113529662 |
| 6138 | - type: recall |
| 6139 | value: 59.154929577464785 |
| 6140 | - task: |
| 6141 | type: BitextMining |
| 6142 | dataset: |
| 6143 | type: mteb/tatoeba-bitext-mining |
| 6144 | name: MTEB Tatoeba (swh-eng) |
| 6145 | config: swh-eng |
| 6146 | split: test |
| 6147 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6148 | metrics: |
| 6149 | - type: accuracy |
| 6150 | value: 70.51282051282051 |
| 6151 | - type: f1 |
| 6152 | value: 66.8089133089133 |
| 6153 | - type: precision |
| 6154 | value: 65.37645687645687 |
| 6155 | - type: recall |
| 6156 | value: 70.51282051282051 |
| 6157 | - task: |
| 6158 | type: BitextMining |
| 6159 | dataset: |
| 6160 | type: mteb/tatoeba-bitext-mining |
| 6161 | name: MTEB Tatoeba (hin-eng) |
| 6162 | config: hin-eng |
| 6163 | split: test |
| 6164 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6165 | metrics: |
| 6166 | - type: accuracy |
| 6167 | value: 94.6 |
| 6168 | - type: f1 |
| 6169 | value: 93 |
| 6170 | - type: precision |
| 6171 | value: 92.23333333333333 |
| 6172 | - type: recall |
| 6173 | value: 94.6 |
| 6174 | - task: |
| 6175 | type: BitextMining |
| 6176 | dataset: |
| 6177 | type: mteb/tatoeba-bitext-mining |
| 6178 | name: MTEB Tatoeba (dsb-eng) |
| 6179 | config: dsb-eng |
| 6180 | split: test |
| 6181 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6182 | metrics: |
| 6183 | - type: accuracy |
| 6184 | value: 38.62212943632568 |
| 6185 | - type: f1 |
| 6186 | value: 34.3278276962583 |
| 6187 | - type: precision |
| 6188 | value: 33.07646935732408 |
| 6189 | - type: recall |
| 6190 | value: 38.62212943632568 |
| 6191 | - task: |
| 6192 | type: BitextMining |
| 6193 | dataset: |
| 6194 | type: mteb/tatoeba-bitext-mining |
| 6195 | name: MTEB Tatoeba (ber-eng) |
| 6196 | config: ber-eng |
| 6197 | split: test |
| 6198 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6199 | metrics: |
| 6200 | - type: accuracy |
| 6201 | value: 28.1 |
| 6202 | - type: f1 |
| 6203 | value: 23.579609223054604 |
| 6204 | - type: precision |
| 6205 | value: 22.39622774921555 |
| 6206 | - type: recall |
| 6207 | value: 28.1 |
| 6208 | - task: |
| 6209 | type: BitextMining |
| 6210 | dataset: |
| 6211 | type: mteb/tatoeba-bitext-mining |
| 6212 | name: MTEB Tatoeba (tam-eng) |
| 6213 | config: tam-eng |
| 6214 | split: test |
| 6215 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6216 | metrics: |
| 6217 | - type: accuracy |
| 6218 | value: 88.27361563517914 |
| 6219 | - type: f1 |
| 6220 | value: 85.12486427795874 |
| 6221 | - type: precision |
| 6222 | value: 83.71335504885994 |
| 6223 | - type: recall |
| 6224 | value: 88.27361563517914 |
| 6225 | - task: |
| 6226 | type: BitextMining |
| 6227 | dataset: |
| 6228 | type: mteb/tatoeba-bitext-mining |
| 6229 | name: MTEB Tatoeba (slk-eng) |
| 6230 | config: slk-eng |
| 6231 | split: test |
| 6232 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6233 | metrics: |
| 6234 | - type: accuracy |
| 6235 | value: 88.6 |
| 6236 | - type: f1 |
| 6237 | value: 86.39928571428571 |
| 6238 | - type: precision |
| 6239 | value: 85.4947557997558 |
| 6240 | - type: recall |
| 6241 | value: 88.6 |
| 6242 | - task: |
| 6243 | type: BitextMining |
| 6244 | dataset: |
| 6245 | type: mteb/tatoeba-bitext-mining |
| 6246 | name: MTEB Tatoeba (tgl-eng) |
| 6247 | config: tgl-eng |
| 6248 | split: test |
| 6249 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6250 | metrics: |
| 6251 | - type: accuracy |
| 6252 | value: 86.5 |
| 6253 | - type: f1 |
| 6254 | value: 83.77952380952381 |
| 6255 | - type: precision |
| 6256 | value: 82.67602564102565 |
| 6257 | - type: recall |
| 6258 | value: 86.5 |
| 6259 | - task: |
| 6260 | type: BitextMining |
| 6261 | dataset: |
| 6262 | type: mteb/tatoeba-bitext-mining |
| 6263 | name: MTEB Tatoeba (ast-eng) |
| 6264 | config: ast-eng |
| 6265 | split: test |
| 6266 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6267 | metrics: |
| 6268 | - type: accuracy |
| 6269 | value: 79.52755905511812 |
| 6270 | - type: f1 |
| 6271 | value: 75.3055868016498 |
| 6272 | - type: precision |
| 6273 | value: 73.81889763779527 |
| 6274 | - type: recall |
| 6275 | value: 79.52755905511812 |
| 6276 | - task: |
| 6277 | type: BitextMining |
| 6278 | dataset: |
| 6279 | type: mteb/tatoeba-bitext-mining |
| 6280 | name: MTEB Tatoeba (mkd-eng) |
| 6281 | config: mkd-eng |
| 6282 | split: test |
| 6283 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6284 | metrics: |
| 6285 | - type: accuracy |
| 6286 | value: 77.9 |
| 6287 | - type: f1 |
| 6288 | value: 73.76261904761905 |
| 6289 | - type: precision |
| 6290 | value: 72.11670995670995 |
| 6291 | - type: recall |
| 6292 | value: 77.9 |
| 6293 | - task: |
| 6294 | type: BitextMining |
| 6295 | dataset: |
| 6296 | type: mteb/tatoeba-bitext-mining |
| 6297 | name: MTEB Tatoeba (khm-eng) |
| 6298 | config: khm-eng |
| 6299 | split: test |
| 6300 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6301 | metrics: |
| 6302 | - type: accuracy |
| 6303 | value: 53.8781163434903 |
| 6304 | - type: f1 |
| 6305 | value: 47.25804051288816 |
| 6306 | - type: precision |
| 6307 | value: 45.0603482390186 |
| 6308 | - type: recall |
| 6309 | value: 53.8781163434903 |
| 6310 | - task: |
| 6311 | type: BitextMining |
| 6312 | dataset: |
| 6313 | type: mteb/tatoeba-bitext-mining |
| 6314 | name: MTEB Tatoeba (ces-eng) |
| 6315 | config: ces-eng |
| 6316 | split: test |
| 6317 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6318 | metrics: |
| 6319 | - type: accuracy |
| 6320 | value: 91.10000000000001 |
| 6321 | - type: f1 |
| 6322 | value: 88.88 |
| 6323 | - type: precision |
| 6324 | value: 87.96333333333334 |
| 6325 | - type: recall |
| 6326 | value: 91.10000000000001 |
| 6327 | - task: |
| 6328 | type: BitextMining |
| 6329 | dataset: |
| 6330 | type: mteb/tatoeba-bitext-mining |
| 6331 | name: MTEB Tatoeba (tzl-eng) |
| 6332 | config: tzl-eng |
| 6333 | split: test |
| 6334 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6335 | metrics: |
| 6336 | - type: accuracy |
| 6337 | value: 38.46153846153847 |
| 6338 | - type: f1 |
| 6339 | value: 34.43978243978244 |
| 6340 | - type: precision |
| 6341 | value: 33.429487179487175 |
| 6342 | - type: recall |
| 6343 | value: 38.46153846153847 |
| 6344 | - task: |
| 6345 | type: BitextMining |
| 6346 | dataset: |
| 6347 | type: mteb/tatoeba-bitext-mining |
| 6348 | name: MTEB Tatoeba (urd-eng) |
| 6349 | config: urd-eng |
| 6350 | split: test |
| 6351 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6352 | metrics: |
| 6353 | - type: accuracy |
| 6354 | value: 88.9 |
| 6355 | - type: f1 |
| 6356 | value: 86.19888888888887 |
| 6357 | - type: precision |
| 6358 | value: 85.07440476190476 |
| 6359 | - type: recall |
| 6360 | value: 88.9 |
| 6361 | - task: |
| 6362 | type: BitextMining |
| 6363 | dataset: |
| 6364 | type: mteb/tatoeba-bitext-mining |
| 6365 | name: MTEB Tatoeba (ara-eng) |
| 6366 | config: ara-eng |
| 6367 | split: test |
| 6368 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6369 | metrics: |
| 6370 | - type: accuracy |
| 6371 | value: 85.9 |
| 6372 | - type: f1 |
| 6373 | value: 82.58857142857143 |
| 6374 | - type: precision |
| 6375 | value: 81.15666666666667 |
| 6376 | - type: recall |
| 6377 | value: 85.9 |
| 6378 | - task: |
| 6379 | type: BitextMining |
| 6380 | dataset: |
| 6381 | type: mteb/tatoeba-bitext-mining |
| 6382 | name: MTEB Tatoeba (kor-eng) |
| 6383 | config: kor-eng |
| 6384 | split: test |
| 6385 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6386 | metrics: |
| 6387 | - type: accuracy |
| 6388 | value: 86.8 |
| 6389 | - type: f1 |
| 6390 | value: 83.36999999999999 |
| 6391 | - type: precision |
| 6392 | value: 81.86833333333333 |
| 6393 | - type: recall |
| 6394 | value: 86.8 |
| 6395 | - task: |
| 6396 | type: BitextMining |
| 6397 | dataset: |
| 6398 | type: mteb/tatoeba-bitext-mining |
| 6399 | name: MTEB Tatoeba (yid-eng) |
| 6400 | config: yid-eng |
| 6401 | split: test |
| 6402 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6403 | metrics: |
| 6404 | - type: accuracy |
| 6405 | value: 68.51415094339622 |
| 6406 | - type: f1 |
| 6407 | value: 63.195000099481234 |
| 6408 | - type: precision |
| 6409 | value: 61.394033442972116 |
| 6410 | - type: recall |
| 6411 | value: 68.51415094339622 |
| 6412 | - task: |
| 6413 | type: BitextMining |
| 6414 | dataset: |
| 6415 | type: mteb/tatoeba-bitext-mining |
| 6416 | name: MTEB Tatoeba (fin-eng) |
| 6417 | config: fin-eng |
| 6418 | split: test |
| 6419 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6420 | metrics: |
| 6421 | - type: accuracy |
| 6422 | value: 88.5 |
| 6423 | - type: f1 |
| 6424 | value: 86.14603174603175 |
| 6425 | - type: precision |
| 6426 | value: 85.1162037037037 |
| 6427 | - type: recall |
| 6428 | value: 88.5 |
| 6429 | - task: |
| 6430 | type: BitextMining |
| 6431 | dataset: |
| 6432 | type: mteb/tatoeba-bitext-mining |
| 6433 | name: MTEB Tatoeba (tha-eng) |
| 6434 | config: tha-eng |
| 6435 | split: test |
| 6436 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6437 | metrics: |
| 6438 | - type: accuracy |
| 6439 | value: 95.62043795620438 |
| 6440 | - type: f1 |
| 6441 | value: 94.40389294403892 |
| 6442 | - type: precision |
| 6443 | value: 93.7956204379562 |
| 6444 | - type: recall |
| 6445 | value: 95.62043795620438 |
| 6446 | - task: |
| 6447 | type: BitextMining |
| 6448 | dataset: |
| 6449 | type: mteb/tatoeba-bitext-mining |
| 6450 | name: MTEB Tatoeba (wuu-eng) |
| 6451 | config: wuu-eng |
| 6452 | split: test |
| 6453 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 6454 | metrics: |
| 6455 | - type: accuracy |
| 6456 | value: 81.8 |
| 6457 | - type: f1 |
| 6458 | value: 78.6532178932179 |
| 6459 | - type: precision |
| 6460 | value: 77.46348795840176 |
| 6461 | - type: recall |
| 6462 | value: 81.8 |
| 6463 | - task: |
| 6464 | type: Retrieval |
| 6465 | dataset: |
| 6466 | type: webis-touche2020 |
| 6467 | name: MTEB Touche2020 |
| 6468 | config: default |
| 6469 | split: test |
| 6470 | revision: None |
| 6471 | metrics: |
| 6472 | - type: map_at_1 |
| 6473 | value: 2.603 |
| 6474 | - type: map_at_10 |
| 6475 | value: 8.5 |
| 6476 | - type: map_at_100 |
| 6477 | value: 12.985 |
| 6478 | - type: map_at_1000 |
| 6479 | value: 14.466999999999999 |
| 6480 | - type: map_at_3 |
| 6481 | value: 4.859999999999999 |
| 6482 | - type: map_at_5 |
| 6483 | value: 5.817 |
| 6484 | - type: mrr_at_1 |
| 6485 | value: 28.571 |
| 6486 | - type: mrr_at_10 |
| 6487 | value: 42.331 |
| 6488 | - type: mrr_at_100 |
| 6489 | value: 43.592999999999996 |
| 6490 | - type: mrr_at_1000 |
| 6491 | value: 43.592999999999996 |
| 6492 | - type: mrr_at_3 |
| 6493 | value: 38.435 |
| 6494 | - type: mrr_at_5 |
| 6495 | value: 39.966 |
| 6496 | - type: ndcg_at_1 |
| 6497 | value: 26.531 |
| 6498 | - type: ndcg_at_10 |
| 6499 | value: 21.353 |
| 6500 | - type: ndcg_at_100 |
| 6501 | value: 31.087999999999997 |
| 6502 | - type: ndcg_at_1000 |
| 6503 | value: 43.163000000000004 |
| 6504 | - type: ndcg_at_3 |
| 6505 | value: 22.999 |
| 6506 | - type: ndcg_at_5 |
| 6507 | value: 21.451 |
| 6508 | - type: precision_at_1 |
| 6509 | value: 28.571 |
| 6510 | - type: precision_at_10 |
| 6511 | value: 19.387999999999998 |
| 6512 | - type: precision_at_100 |
| 6513 | value: 6.265 |
| 6514 | - type: precision_at_1000 |
| 6515 | value: 1.4160000000000001 |
| 6516 | - type: precision_at_3 |
| 6517 | value: 24.490000000000002 |
| 6518 | - type: precision_at_5 |
| 6519 | value: 21.224 |
| 6520 | - type: recall_at_1 |
| 6521 | value: 2.603 |
| 6522 | - type: recall_at_10 |
| 6523 | value: 14.474 |
| 6524 | - type: recall_at_100 |
| 6525 | value: 40.287 |
| 6526 | - type: recall_at_1000 |
| 6527 | value: 76.606 |
| 6528 | - type: recall_at_3 |
| 6529 | value: 5.978 |
| 6530 | - type: recall_at_5 |
| 6531 | value: 7.819 |
| 6532 | - task: |
| 6533 | type: Classification |
| 6534 | dataset: |
| 6535 | type: mteb/toxic_conversations_50k |
| 6536 | name: MTEB ToxicConversationsClassification |
| 6537 | config: default |
| 6538 | split: test |
| 6539 | revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| 6540 | metrics: |
| 6541 | - type: accuracy |
| 6542 | value: 69.7848 |
| 6543 | - type: ap |
| 6544 | value: 13.661023167088224 |
| 6545 | - type: f1 |
| 6546 | value: 53.61686134460943 |
| 6547 | - task: |
| 6548 | type: Classification |
| 6549 | dataset: |
| 6550 | type: mteb/tweet_sentiment_extraction |
| 6551 | name: MTEB TweetSentimentExtractionClassification |
| 6552 | config: default |
| 6553 | split: test |
| 6554 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 6555 | metrics: |
| 6556 | - type: accuracy |
| 6557 | value: 61.28183361629882 |
| 6558 | - type: f1 |
| 6559 | value: 61.55481034919965 |
| 6560 | - task: |
| 6561 | type: Clustering |
| 6562 | dataset: |
| 6563 | type: mteb/twentynewsgroups-clustering |
| 6564 | name: MTEB TwentyNewsgroupsClustering |
| 6565 | config: default |
| 6566 | split: test |
| 6567 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 6568 | metrics: |
| 6569 | - type: v_measure |
| 6570 | value: 35.972128420092396 |
| 6571 | - task: |
| 6572 | type: PairClassification |
| 6573 | dataset: |
| 6574 | type: mteb/twittersemeval2015-pairclassification |
| 6575 | name: MTEB TwitterSemEval2015 |
| 6576 | config: default |
| 6577 | split: test |
| 6578 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 6579 | metrics: |
| 6580 | - type: cos_sim_accuracy |
| 6581 | value: 85.59933241938367 |
| 6582 | - type: cos_sim_ap |
| 6583 | value: 72.20760361208136 |
| 6584 | - type: cos_sim_f1 |
| 6585 | value: 66.4447731755424 |
| 6586 | - type: cos_sim_precision |
| 6587 | value: 62.35539102267469 |
| 6588 | - type: cos_sim_recall |
| 6589 | value: 71.10817941952506 |
| 6590 | - type: dot_accuracy |
| 6591 | value: 78.98313166835548 |
| 6592 | - type: dot_ap |
| 6593 | value: 44.492521645493795 |
| 6594 | - type: dot_f1 |
| 6595 | value: 45.814889336016094 |
| 6596 | - type: dot_precision |
| 6597 | value: 37.02439024390244 |
| 6598 | - type: dot_recall |
| 6599 | value: 60.07915567282321 |
| 6600 | - type: euclidean_accuracy |
| 6601 | value: 85.3907134767837 |
| 6602 | - type: euclidean_ap |
| 6603 | value: 71.53847289080343 |
| 6604 | - type: euclidean_f1 |
| 6605 | value: 65.95952206778834 |
| 6606 | - type: euclidean_precision |
| 6607 | value: 61.31006346328196 |
| 6608 | - type: euclidean_recall |
| 6609 | value: 71.37203166226914 |
| 6610 | - type: manhattan_accuracy |
| 6611 | value: 85.40859510043511 |
| 6612 | - type: manhattan_ap |
| 6613 | value: 71.49664104395515 |
| 6614 | - type: manhattan_f1 |
| 6615 | value: 65.98569969356485 |
| 6616 | - type: manhattan_precision |
| 6617 | value: 63.928748144482924 |
| 6618 | - type: manhattan_recall |
| 6619 | value: 68.17941952506597 |
| 6620 | - type: max_accuracy |
| 6621 | value: 85.59933241938367 |
| 6622 | - type: max_ap |
| 6623 | value: 72.20760361208136 |
| 6624 | - type: max_f1 |
| 6625 | value: 66.4447731755424 |
| 6626 | - task: |
| 6627 | type: PairClassification |
| 6628 | dataset: |
| 6629 | type: mteb/twitterurlcorpus-pairclassification |
| 6630 | name: MTEB TwitterURLCorpus |
| 6631 | config: default |
| 6632 | split: test |
| 6633 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 6634 | metrics: |
| 6635 | - type: cos_sim_accuracy |
| 6636 | value: 88.83261536073273 |
| 6637 | - type: cos_sim_ap |
| 6638 | value: 85.48178133644264 |
| 6639 | - type: cos_sim_f1 |
| 6640 | value: 77.87816307403935 |
| 6641 | - type: cos_sim_precision |
| 6642 | value: 75.88953021114926 |
| 6643 | - type: cos_sim_recall |
| 6644 | value: 79.97382198952879 |
| 6645 | - type: dot_accuracy |
| 6646 | value: 79.76287499514883 |
| 6647 | - type: dot_ap |
| 6648 | value: 59.17438838475084 |
| 6649 | - type: dot_f1 |
| 6650 | value: 56.34566667855996 |
| 6651 | - type: dot_precision |
| 6652 | value: 52.50349092359864 |
| 6653 | - type: dot_recall |
| 6654 | value: 60.794579611949494 |
| 6655 | - type: euclidean_accuracy |
| 6656 | value: 88.76857996662397 |
| 6657 | - type: euclidean_ap |
| 6658 | value: 85.22764834359887 |
| 6659 | - type: euclidean_f1 |
| 6660 | value: 77.65379751543554 |
| 6661 | - type: euclidean_precision |
| 6662 | value: 75.11152683839401 |
| 6663 | - type: euclidean_recall |
| 6664 | value: 80.37419156144134 |
| 6665 | - type: manhattan_accuracy |
| 6666 | value: 88.6987231730508 |
| 6667 | - type: manhattan_ap |
| 6668 | value: 85.18907981724007 |
| 6669 | - type: manhattan_f1 |
| 6670 | value: 77.51967028849757 |
| 6671 | - type: manhattan_precision |
| 6672 | value: 75.49992701795358 |
| 6673 | - type: manhattan_recall |
| 6674 | value: 79.65044656606098 |
| 6675 | - type: max_accuracy |
| 6676 | value: 88.83261536073273 |
| 6677 | - type: max_ap |
| 6678 | value: 85.48178133644264 |
| 6679 | - type: max_f1 |
| 6680 | value: 77.87816307403935 |
| 6681 | language: |
| 6682 | - multilingual |
| 6683 | - af |
| 6684 | - am |
| 6685 | - ar |
| 6686 | - as |
| 6687 | - az |
| 6688 | - be |
| 6689 | - bg |
| 6690 | - bn |
| 6691 | - br |
| 6692 | - bs |
| 6693 | - ca |
| 6694 | - cs |
| 6695 | - cy |
| 6696 | - da |
| 6697 | - de |
| 6698 | - el |
| 6699 | - en |
| 6700 | - eo |
| 6701 | - es |
| 6702 | - et |
| 6703 | - eu |
| 6704 | - fa |
| 6705 | - fi |
| 6706 | - fr |
| 6707 | - fy |
| 6708 | - ga |
| 6709 | - gd |
| 6710 | - gl |
| 6711 | - gu |
| 6712 | - ha |
| 6713 | - he |
| 6714 | - hi |
| 6715 | - hr |
| 6716 | - hu |
| 6717 | - hy |
| 6718 | - id |
| 6719 | - is |
| 6720 | - it |
| 6721 | - ja |
| 6722 | - jv |
| 6723 | - ka |
| 6724 | - kk |
| 6725 | - km |
| 6726 | - kn |
| 6727 | - ko |
| 6728 | - ku |
| 6729 | - ky |
| 6730 | - la |
| 6731 | - lo |
| 6732 | - lt |
| 6733 | - lv |
| 6734 | - mg |
| 6735 | - mk |
| 6736 | - ml |
| 6737 | - mn |
| 6738 | - mr |
| 6739 | - ms |
| 6740 | - my |
| 6741 | - ne |
| 6742 | - nl |
| 6743 | - 'no' |
| 6744 | - om |
| 6745 | - or |
| 6746 | - pa |
| 6747 | - pl |
| 6748 | - ps |
| 6749 | - pt |
| 6750 | - ro |
| 6751 | - ru |
| 6752 | - sa |
| 6753 | - sd |
| 6754 | - si |
| 6755 | - sk |
| 6756 | - sl |
| 6757 | - so |
| 6758 | - sq |
| 6759 | - sr |
| 6760 | - su |
| 6761 | - sv |
| 6762 | - sw |
| 6763 | - ta |
| 6764 | - te |
| 6765 | - th |
| 6766 | - tl |
| 6767 | - tr |
| 6768 | - ug |
| 6769 | - uk |
| 6770 | - ur |
| 6771 | - uz |
| 6772 | - vi |
| 6773 | - xh |
| 6774 | - yi |
| 6775 | - zh |
| 6776 | license: mit |
| 6777 | --- |
| 6778 | |
| 6779 | ## Multilingual-E5-base |
| 6780 | |
| 6781 | [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). |
| 6782 | Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 |
| 6783 | |
| 6784 | This model has 12 layers and the embedding size is 768. |
| 6785 | |
| 6786 | ## Usage |
| 6787 | |
| 6788 | Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
| 6789 | |
| 6790 | ```python |
| 6791 | import torch.nn.functional as F |
| 6792 | |
| 6793 | from torch import Tensor |
| 6794 | from transformers import AutoTokenizer, AutoModel |
| 6795 | |
| 6796 | |
| 6797 | def average_pool(last_hidden_states: Tensor, |
| 6798 | attention_mask: Tensor) -> Tensor: |
| 6799 | last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
| 6800 | return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
| 6801 | |
| 6802 | |
| 6803 | # Each input text should start with "query: " or "passage: ", even for non-English texts. |
| 6804 | # For tasks other than retrieval, you can simply use the "query: " prefix. |
| 6805 | input_texts = ['query: how much protein should a female eat', |
| 6806 | 'query: 南瓜的家常做法', |
| 6807 | "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| 6808 | "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] |
| 6809 | |
| 6810 | tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base') |
| 6811 | model = AutoModel.from_pretrained('intfloat/multilingual-e5-base') |
| 6812 | |
| 6813 | # Tokenize the input texts |
| 6814 | batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
| 6815 | |
| 6816 | outputs = model(**batch_dict) |
| 6817 | embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
| 6818 | |
| 6819 | # normalize embeddings |
| 6820 | embeddings = F.normalize(embeddings, p=2, dim=1) |
| 6821 | scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| 6822 | print(scores.tolist()) |
| 6823 | ``` |
| 6824 | |
| 6825 | ## Supported Languages |
| 6826 | |
| 6827 | This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) |
| 6828 | and continually trained on a mixture of multilingual datasets. |
| 6829 | It supports 100 languages from xlm-roberta, |
| 6830 | but low-resource languages may see performance degradation. |
| 6831 | |
| 6832 | ## Training Details |
| 6833 | |
| 6834 | **Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) |
| 6835 | |
| 6836 | **First stage**: contrastive pre-training with weak supervision |
| 6837 | |
| 6838 | | Dataset | Weak supervision | # of text pairs | |
| 6839 | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| |
| 6840 | | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | |
| 6841 | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | |
| 6842 | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | |
| 6843 | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | |
| 6844 | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | |
| 6845 | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | |
| 6846 | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | |
| 6847 | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | |
| 6848 | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | |
| 6849 | |
| 6850 | **Second stage**: supervised fine-tuning |
| 6851 | |
| 6852 | | Dataset | Language | # of text pairs | |
| 6853 | |----------------------------------------------------------------------------------------|--------------|-----------------| |
| 6854 | | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | |
| 6855 | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | |
| 6856 | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | |
| 6857 | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | |
| 6858 | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | |
| 6859 | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | |
| 6860 | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 6861 | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 6862 | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | |
| 6863 | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | |
| 6864 | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | |
| 6865 | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | |
| 6866 | |
| 6867 | For all labeled datasets, we only use its training set for fine-tuning. |
| 6868 | |
| 6869 | For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). |
| 6870 | |
| 6871 | ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) |
| 6872 | |
| 6873 | | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |
| 6874 | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | |
| 6875 | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | |
| 6876 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | |
| 6877 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | |
| 6878 | | | | |
| 6879 | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | |
| 6880 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | |
| 6881 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | |
| 6882 | |
| 6883 | ## MTEB Benchmark Evaluation |
| 6884 | |
| 6885 | Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
| 6886 | on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
| 6887 | |
| 6888 | ## Support for Sentence Transformers |
| 6889 | |
| 6890 | Below is an example for usage with sentence_transformers. |
| 6891 | ```python |
| 6892 | from sentence_transformers import SentenceTransformer |
| 6893 | model = SentenceTransformer('intfloat/multilingual-e5-base') |
| 6894 | input_texts = [ |
| 6895 | 'query: how much protein should a female eat', |
| 6896 | 'query: 南瓜的家常做法', |
| 6897 | "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| 6898 | "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" |
| 6899 | ] |
| 6900 | embeddings = model.encode(input_texts, normalize_embeddings=True) |
| 6901 | ``` |
| 6902 | |
| 6903 | Package requirements |
| 6904 | |
| 6905 | `pip install sentence_transformers~=2.2.2` |
| 6906 | |
| 6907 | Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
| 6908 | |
| 6909 | ## FAQ |
| 6910 | |
| 6911 | **1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
| 6912 | |
| 6913 | Yes, this is how the model is trained, otherwise you will see a performance degradation. |
| 6914 | |
| 6915 | Here are some rules of thumb: |
| 6916 | - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
| 6917 | |
| 6918 | - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. |
| 6919 | |
| 6920 | - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
| 6921 | |
| 6922 | **2. Why are my reproduced results slightly different from reported in the model card?** |
| 6923 | |
| 6924 | Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
| 6925 | |
| 6926 | **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
| 6927 | |
| 6928 | This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
| 6929 | |
| 6930 | For text embedding tasks like text retrieval or semantic similarity, |
| 6931 | what matters is the relative order of the scores instead of the absolute values, |
| 6932 | so this should not be an issue. |
| 6933 | |
| 6934 | ## Citation |
| 6935 | |
| 6936 | If you find our paper or models helpful, please consider cite as follows: |
| 6937 | |
| 6938 | ``` |
| 6939 | @article{wang2024multilingual, |
| 6940 | title={Multilingual E5 Text Embeddings: A Technical Report}, |
| 6941 | author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, |
| 6942 | journal={arXiv preprint arXiv:2402.05672}, |
| 6943 | year={2024} |
| 6944 | } |
| 6945 | ``` |
| 6946 | |
| 6947 | ## Limitations |
| 6948 | |
| 6949 | Long texts will be truncated to at most 512 tokens. |
| 6950 | |