README.md
| 1 | --- |
| 2 | tags: |
| 3 | - mteb |
| 4 | - Sentence Transformers |
| 5 | - sentence-similarity |
| 6 | - feature-extraction |
| 7 | - sentence-transformers |
| 8 | model-index: |
| 9 | - name: multilingual-e5-large |
| 10 | results: |
| 11 | - task: |
| 12 | type: Classification |
| 13 | dataset: |
| 14 | type: mteb/amazon_counterfactual |
| 15 | name: MTEB AmazonCounterfactualClassification (en) |
| 16 | config: en |
| 17 | split: test |
| 18 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 19 | metrics: |
| 20 | - type: accuracy |
| 21 | value: 79.05970149253731 |
| 22 | - type: ap |
| 23 | value: 43.486574390835635 |
| 24 | - type: f1 |
| 25 | value: 73.32700092140148 |
| 26 | - task: |
| 27 | type: Classification |
| 28 | dataset: |
| 29 | type: mteb/amazon_counterfactual |
| 30 | name: MTEB AmazonCounterfactualClassification (de) |
| 31 | config: de |
| 32 | split: test |
| 33 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 34 | metrics: |
| 35 | - type: accuracy |
| 36 | value: 71.22055674518201 |
| 37 | - type: ap |
| 38 | value: 81.55756710830498 |
| 39 | - type: f1 |
| 40 | value: 69.28271787752661 |
| 41 | - task: |
| 42 | type: Classification |
| 43 | dataset: |
| 44 | type: mteb/amazon_counterfactual |
| 45 | name: MTEB AmazonCounterfactualClassification (en-ext) |
| 46 | config: en-ext |
| 47 | split: test |
| 48 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 49 | metrics: |
| 50 | - type: accuracy |
| 51 | value: 80.41979010494754 |
| 52 | - type: ap |
| 53 | value: 29.34879922376344 |
| 54 | - type: f1 |
| 55 | value: 67.62475449011278 |
| 56 | - task: |
| 57 | type: Classification |
| 58 | dataset: |
| 59 | type: mteb/amazon_counterfactual |
| 60 | name: MTEB AmazonCounterfactualClassification (ja) |
| 61 | config: ja |
| 62 | split: test |
| 63 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 64 | metrics: |
| 65 | - type: accuracy |
| 66 | value: 77.8372591006424 |
| 67 | - type: ap |
| 68 | value: 26.557560591210738 |
| 69 | - type: f1 |
| 70 | value: 64.96619417368707 |
| 71 | - task: |
| 72 | type: Classification |
| 73 | dataset: |
| 74 | type: mteb/amazon_polarity |
| 75 | name: MTEB AmazonPolarityClassification |
| 76 | config: default |
| 77 | split: test |
| 78 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 79 | metrics: |
| 80 | - type: accuracy |
| 81 | value: 93.489875 |
| 82 | - type: ap |
| 83 | value: 90.98758636917603 |
| 84 | - type: f1 |
| 85 | value: 93.48554819717332 |
| 86 | - task: |
| 87 | type: Classification |
| 88 | dataset: |
| 89 | type: mteb/amazon_reviews_multi |
| 90 | name: MTEB AmazonReviewsClassification (en) |
| 91 | config: en |
| 92 | split: test |
| 93 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 94 | metrics: |
| 95 | - type: accuracy |
| 96 | value: 47.564 |
| 97 | - type: f1 |
| 98 | value: 46.75122173518047 |
| 99 | - task: |
| 100 | type: Classification |
| 101 | dataset: |
| 102 | type: mteb/amazon_reviews_multi |
| 103 | name: MTEB AmazonReviewsClassification (de) |
| 104 | config: de |
| 105 | split: test |
| 106 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 107 | metrics: |
| 108 | - type: accuracy |
| 109 | value: 45.400000000000006 |
| 110 | - type: f1 |
| 111 | value: 44.17195682400632 |
| 112 | - task: |
| 113 | type: Classification |
| 114 | dataset: |
| 115 | type: mteb/amazon_reviews_multi |
| 116 | name: MTEB AmazonReviewsClassification (es) |
| 117 | config: es |
| 118 | split: test |
| 119 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 120 | metrics: |
| 121 | - type: accuracy |
| 122 | value: 43.068 |
| 123 | - type: f1 |
| 124 | value: 42.38155696855596 |
| 125 | - task: |
| 126 | type: Classification |
| 127 | dataset: |
| 128 | type: mteb/amazon_reviews_multi |
| 129 | name: MTEB AmazonReviewsClassification (fr) |
| 130 | config: fr |
| 131 | split: test |
| 132 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 133 | metrics: |
| 134 | - type: accuracy |
| 135 | value: 41.89 |
| 136 | - type: f1 |
| 137 | value: 40.84407321682663 |
| 138 | - task: |
| 139 | type: Classification |
| 140 | dataset: |
| 141 | type: mteb/amazon_reviews_multi |
| 142 | name: MTEB AmazonReviewsClassification (ja) |
| 143 | config: ja |
| 144 | split: test |
| 145 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 146 | metrics: |
| 147 | - type: accuracy |
| 148 | value: 40.120000000000005 |
| 149 | - type: f1 |
| 150 | value: 39.522976223819114 |
| 151 | - task: |
| 152 | type: Classification |
| 153 | dataset: |
| 154 | type: mteb/amazon_reviews_multi |
| 155 | name: MTEB AmazonReviewsClassification (zh) |
| 156 | config: zh |
| 157 | split: test |
| 158 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 159 | metrics: |
| 160 | - type: accuracy |
| 161 | value: 38.832 |
| 162 | - type: f1 |
| 163 | value: 38.0392533394713 |
| 164 | - task: |
| 165 | type: Retrieval |
| 166 | dataset: |
| 167 | type: arguana |
| 168 | name: MTEB ArguAna |
| 169 | config: default |
| 170 | split: test |
| 171 | revision: None |
| 172 | metrics: |
| 173 | - type: map_at_1 |
| 174 | value: 30.725 |
| 175 | - type: map_at_10 |
| 176 | value: 46.055 |
| 177 | - type: map_at_100 |
| 178 | value: 46.900999999999996 |
| 179 | - type: map_at_1000 |
| 180 | value: 46.911 |
| 181 | - type: map_at_3 |
| 182 | value: 41.548 |
| 183 | - type: map_at_5 |
| 184 | value: 44.297 |
| 185 | - type: mrr_at_1 |
| 186 | value: 31.152 |
| 187 | - type: mrr_at_10 |
| 188 | value: 46.231 |
| 189 | - type: mrr_at_100 |
| 190 | value: 47.07 |
| 191 | - type: mrr_at_1000 |
| 192 | value: 47.08 |
| 193 | - type: mrr_at_3 |
| 194 | value: 41.738 |
| 195 | - type: mrr_at_5 |
| 196 | value: 44.468999999999994 |
| 197 | - type: ndcg_at_1 |
| 198 | value: 30.725 |
| 199 | - type: ndcg_at_10 |
| 200 | value: 54.379999999999995 |
| 201 | - type: ndcg_at_100 |
| 202 | value: 58.138 |
| 203 | - type: ndcg_at_1000 |
| 204 | value: 58.389 |
| 205 | - type: ndcg_at_3 |
| 206 | value: 45.156 |
| 207 | - type: ndcg_at_5 |
| 208 | value: 50.123 |
| 209 | - type: precision_at_1 |
| 210 | value: 30.725 |
| 211 | - type: precision_at_10 |
| 212 | value: 8.087 |
| 213 | - type: precision_at_100 |
| 214 | value: 0.9769999999999999 |
| 215 | - type: precision_at_1000 |
| 216 | value: 0.1 |
| 217 | - type: precision_at_3 |
| 218 | value: 18.54 |
| 219 | - type: precision_at_5 |
| 220 | value: 13.542000000000002 |
| 221 | - type: recall_at_1 |
| 222 | value: 30.725 |
| 223 | - type: recall_at_10 |
| 224 | value: 80.868 |
| 225 | - type: recall_at_100 |
| 226 | value: 97.653 |
| 227 | - type: recall_at_1000 |
| 228 | value: 99.57300000000001 |
| 229 | - type: recall_at_3 |
| 230 | value: 55.619 |
| 231 | - type: recall_at_5 |
| 232 | value: 67.71000000000001 |
| 233 | - task: |
| 234 | type: Clustering |
| 235 | dataset: |
| 236 | type: mteb/arxiv-clustering-p2p |
| 237 | name: MTEB ArxivClusteringP2P |
| 238 | config: default |
| 239 | split: test |
| 240 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 241 | metrics: |
| 242 | - type: v_measure |
| 243 | value: 44.30960650674069 |
| 244 | - task: |
| 245 | type: Clustering |
| 246 | dataset: |
| 247 | type: mteb/arxiv-clustering-s2s |
| 248 | name: MTEB ArxivClusteringS2S |
| 249 | config: default |
| 250 | split: test |
| 251 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 252 | metrics: |
| 253 | - type: v_measure |
| 254 | value: 38.427074197498996 |
| 255 | - task: |
| 256 | type: Reranking |
| 257 | dataset: |
| 258 | type: mteb/askubuntudupquestions-reranking |
| 259 | name: MTEB AskUbuntuDupQuestions |
| 260 | config: default |
| 261 | split: test |
| 262 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 263 | metrics: |
| 264 | - type: map |
| 265 | value: 60.28270056031872 |
| 266 | - type: mrr |
| 267 | value: 74.38332673789738 |
| 268 | - task: |
| 269 | type: STS |
| 270 | dataset: |
| 271 | type: mteb/biosses-sts |
| 272 | name: MTEB BIOSSES |
| 273 | config: default |
| 274 | split: test |
| 275 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 276 | metrics: |
| 277 | - type: cos_sim_pearson |
| 278 | value: 84.05942144105269 |
| 279 | - type: cos_sim_spearman |
| 280 | value: 82.51212105850809 |
| 281 | - type: euclidean_pearson |
| 282 | value: 81.95639829909122 |
| 283 | - type: euclidean_spearman |
| 284 | value: 82.3717564144213 |
| 285 | - type: manhattan_pearson |
| 286 | value: 81.79273425468256 |
| 287 | - type: manhattan_spearman |
| 288 | value: 82.20066817871039 |
| 289 | - task: |
| 290 | type: BitextMining |
| 291 | dataset: |
| 292 | type: mteb/bucc-bitext-mining |
| 293 | name: MTEB BUCC (de-en) |
| 294 | config: de-en |
| 295 | split: test |
| 296 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 297 | metrics: |
| 298 | - type: accuracy |
| 299 | value: 99.46764091858039 |
| 300 | - type: f1 |
| 301 | value: 99.37717466945023 |
| 302 | - type: precision |
| 303 | value: 99.33194154488518 |
| 304 | - type: recall |
| 305 | value: 99.46764091858039 |
| 306 | - task: |
| 307 | type: BitextMining |
| 308 | dataset: |
| 309 | type: mteb/bucc-bitext-mining |
| 310 | name: MTEB BUCC (fr-en) |
| 311 | config: fr-en |
| 312 | split: test |
| 313 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 314 | metrics: |
| 315 | - type: accuracy |
| 316 | value: 98.29407880255337 |
| 317 | - type: f1 |
| 318 | value: 98.11248073959938 |
| 319 | - type: precision |
| 320 | value: 98.02443319392472 |
| 321 | - type: recall |
| 322 | value: 98.29407880255337 |
| 323 | - task: |
| 324 | type: BitextMining |
| 325 | dataset: |
| 326 | type: mteb/bucc-bitext-mining |
| 327 | name: MTEB BUCC (ru-en) |
| 328 | config: ru-en |
| 329 | split: test |
| 330 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 331 | metrics: |
| 332 | - type: accuracy |
| 333 | value: 97.79009352268791 |
| 334 | - type: f1 |
| 335 | value: 97.5176076665512 |
| 336 | - type: precision |
| 337 | value: 97.38136473848286 |
| 338 | - type: recall |
| 339 | value: 97.79009352268791 |
| 340 | - task: |
| 341 | type: BitextMining |
| 342 | dataset: |
| 343 | type: mteb/bucc-bitext-mining |
| 344 | name: MTEB BUCC (zh-en) |
| 345 | config: zh-en |
| 346 | split: test |
| 347 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 348 | metrics: |
| 349 | - type: accuracy |
| 350 | value: 99.26276987888363 |
| 351 | - type: f1 |
| 352 | value: 99.20133403545726 |
| 353 | - type: precision |
| 354 | value: 99.17500438827453 |
| 355 | - type: recall |
| 356 | value: 99.26276987888363 |
| 357 | - task: |
| 358 | type: Classification |
| 359 | dataset: |
| 360 | type: mteb/banking77 |
| 361 | name: MTEB Banking77Classification |
| 362 | config: default |
| 363 | split: test |
| 364 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 365 | metrics: |
| 366 | - type: accuracy |
| 367 | value: 84.72727272727273 |
| 368 | - type: f1 |
| 369 | value: 84.67672206031433 |
| 370 | - task: |
| 371 | type: Clustering |
| 372 | dataset: |
| 373 | type: mteb/biorxiv-clustering-p2p |
| 374 | name: MTEB BiorxivClusteringP2P |
| 375 | config: default |
| 376 | split: test |
| 377 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 378 | metrics: |
| 379 | - type: v_measure |
| 380 | value: 35.34220182511161 |
| 381 | - task: |
| 382 | type: Clustering |
| 383 | dataset: |
| 384 | type: mteb/biorxiv-clustering-s2s |
| 385 | name: MTEB BiorxivClusteringS2S |
| 386 | config: default |
| 387 | split: test |
| 388 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 389 | metrics: |
| 390 | - type: v_measure |
| 391 | value: 33.4987096128766 |
| 392 | - task: |
| 393 | type: Retrieval |
| 394 | dataset: |
| 395 | type: BeIR/cqadupstack |
| 396 | name: MTEB CQADupstackRetrieval |
| 397 | config: default |
| 398 | split: test |
| 399 | revision: None |
| 400 | metrics: |
| 401 | - type: map_at_1 |
| 402 | value: 25.558249999999997 |
| 403 | - type: map_at_10 |
| 404 | value: 34.44425000000001 |
| 405 | - type: map_at_100 |
| 406 | value: 35.59833333333333 |
| 407 | - type: map_at_1000 |
| 408 | value: 35.706916666666665 |
| 409 | - type: map_at_3 |
| 410 | value: 31.691749999999995 |
| 411 | - type: map_at_5 |
| 412 | value: 33.252916666666664 |
| 413 | - type: mrr_at_1 |
| 414 | value: 30.252666666666666 |
| 415 | - type: mrr_at_10 |
| 416 | value: 38.60675 |
| 417 | - type: mrr_at_100 |
| 418 | value: 39.42666666666666 |
| 419 | - type: mrr_at_1000 |
| 420 | value: 39.48408333333334 |
| 421 | - type: mrr_at_3 |
| 422 | value: 36.17441666666665 |
| 423 | - type: mrr_at_5 |
| 424 | value: 37.56275 |
| 425 | - type: ndcg_at_1 |
| 426 | value: 30.252666666666666 |
| 427 | - type: ndcg_at_10 |
| 428 | value: 39.683 |
| 429 | - type: ndcg_at_100 |
| 430 | value: 44.68541666666667 |
| 431 | - type: ndcg_at_1000 |
| 432 | value: 46.94316666666668 |
| 433 | - type: ndcg_at_3 |
| 434 | value: 34.961749999999995 |
| 435 | - type: ndcg_at_5 |
| 436 | value: 37.215666666666664 |
| 437 | - type: precision_at_1 |
| 438 | value: 30.252666666666666 |
| 439 | - type: precision_at_10 |
| 440 | value: 6.904166666666667 |
| 441 | - type: precision_at_100 |
| 442 | value: 1.0989999999999995 |
| 443 | - type: precision_at_1000 |
| 444 | value: 0.14733333333333334 |
| 445 | - type: precision_at_3 |
| 446 | value: 16.037666666666667 |
| 447 | - type: precision_at_5 |
| 448 | value: 11.413583333333333 |
| 449 | - type: recall_at_1 |
| 450 | value: 25.558249999999997 |
| 451 | - type: recall_at_10 |
| 452 | value: 51.13341666666666 |
| 453 | - type: recall_at_100 |
| 454 | value: 73.08366666666667 |
| 455 | - type: recall_at_1000 |
| 456 | value: 88.79483333333334 |
| 457 | - type: recall_at_3 |
| 458 | value: 37.989083333333326 |
| 459 | - type: recall_at_5 |
| 460 | value: 43.787833333333325 |
| 461 | - task: |
| 462 | type: Retrieval |
| 463 | dataset: |
| 464 | type: climate-fever |
| 465 | name: MTEB ClimateFEVER |
| 466 | config: default |
| 467 | split: test |
| 468 | revision: None |
| 469 | metrics: |
| 470 | - type: map_at_1 |
| 471 | value: 10.338 |
| 472 | - type: map_at_10 |
| 473 | value: 18.360000000000003 |
| 474 | - type: map_at_100 |
| 475 | value: 19.942 |
| 476 | - type: map_at_1000 |
| 477 | value: 20.134 |
| 478 | - type: map_at_3 |
| 479 | value: 15.174000000000001 |
| 480 | - type: map_at_5 |
| 481 | value: 16.830000000000002 |
| 482 | - type: mrr_at_1 |
| 483 | value: 23.257 |
| 484 | - type: mrr_at_10 |
| 485 | value: 33.768 |
| 486 | - type: mrr_at_100 |
| 487 | value: 34.707 |
| 488 | - type: mrr_at_1000 |
| 489 | value: 34.766000000000005 |
| 490 | - type: mrr_at_3 |
| 491 | value: 30.977 |
| 492 | - type: mrr_at_5 |
| 493 | value: 32.528 |
| 494 | - type: ndcg_at_1 |
| 495 | value: 23.257 |
| 496 | - type: ndcg_at_10 |
| 497 | value: 25.733 |
| 498 | - type: ndcg_at_100 |
| 499 | value: 32.288 |
| 500 | - type: ndcg_at_1000 |
| 501 | value: 35.992000000000004 |
| 502 | - type: ndcg_at_3 |
| 503 | value: 20.866 |
| 504 | - type: ndcg_at_5 |
| 505 | value: 22.612 |
| 506 | - type: precision_at_1 |
| 507 | value: 23.257 |
| 508 | - type: precision_at_10 |
| 509 | value: 8.124 |
| 510 | - type: precision_at_100 |
| 511 | value: 1.518 |
| 512 | - type: precision_at_1000 |
| 513 | value: 0.219 |
| 514 | - type: precision_at_3 |
| 515 | value: 15.679000000000002 |
| 516 | - type: precision_at_5 |
| 517 | value: 12.117 |
| 518 | - type: recall_at_1 |
| 519 | value: 10.338 |
| 520 | - type: recall_at_10 |
| 521 | value: 31.154 |
| 522 | - type: recall_at_100 |
| 523 | value: 54.161 |
| 524 | - type: recall_at_1000 |
| 525 | value: 75.21900000000001 |
| 526 | - type: recall_at_3 |
| 527 | value: 19.427 |
| 528 | - type: recall_at_5 |
| 529 | value: 24.214 |
| 530 | - task: |
| 531 | type: Retrieval |
| 532 | dataset: |
| 533 | type: dbpedia-entity |
| 534 | name: MTEB DBPedia |
| 535 | config: default |
| 536 | split: test |
| 537 | revision: None |
| 538 | metrics: |
| 539 | - type: map_at_1 |
| 540 | value: 8.498 |
| 541 | - type: map_at_10 |
| 542 | value: 19.103 |
| 543 | - type: map_at_100 |
| 544 | value: 27.375 |
| 545 | - type: map_at_1000 |
| 546 | value: 28.981 |
| 547 | - type: map_at_3 |
| 548 | value: 13.764999999999999 |
| 549 | - type: map_at_5 |
| 550 | value: 15.950000000000001 |
| 551 | - type: mrr_at_1 |
| 552 | value: 65.5 |
| 553 | - type: mrr_at_10 |
| 554 | value: 74.53800000000001 |
| 555 | - type: mrr_at_100 |
| 556 | value: 74.71799999999999 |
| 557 | - type: mrr_at_1000 |
| 558 | value: 74.725 |
| 559 | - type: mrr_at_3 |
| 560 | value: 72.792 |
| 561 | - type: mrr_at_5 |
| 562 | value: 73.554 |
| 563 | - type: ndcg_at_1 |
| 564 | value: 53.37499999999999 |
| 565 | - type: ndcg_at_10 |
| 566 | value: 41.286 |
| 567 | - type: ndcg_at_100 |
| 568 | value: 45.972 |
| 569 | - type: ndcg_at_1000 |
| 570 | value: 53.123 |
| 571 | - type: ndcg_at_3 |
| 572 | value: 46.172999999999995 |
| 573 | - type: ndcg_at_5 |
| 574 | value: 43.033 |
| 575 | - type: precision_at_1 |
| 576 | value: 65.5 |
| 577 | - type: precision_at_10 |
| 578 | value: 32.725 |
| 579 | - type: precision_at_100 |
| 580 | value: 10.683 |
| 581 | - type: precision_at_1000 |
| 582 | value: 1.978 |
| 583 | - type: precision_at_3 |
| 584 | value: 50 |
| 585 | - type: precision_at_5 |
| 586 | value: 41.349999999999994 |
| 587 | - type: recall_at_1 |
| 588 | value: 8.498 |
| 589 | - type: recall_at_10 |
| 590 | value: 25.070999999999998 |
| 591 | - type: recall_at_100 |
| 592 | value: 52.383 |
| 593 | - type: recall_at_1000 |
| 594 | value: 74.91499999999999 |
| 595 | - type: recall_at_3 |
| 596 | value: 15.207999999999998 |
| 597 | - type: recall_at_5 |
| 598 | value: 18.563 |
| 599 | - task: |
| 600 | type: Classification |
| 601 | dataset: |
| 602 | type: mteb/emotion |
| 603 | name: MTEB EmotionClassification |
| 604 | config: default |
| 605 | split: test |
| 606 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 607 | metrics: |
| 608 | - type: accuracy |
| 609 | value: 46.5 |
| 610 | - type: f1 |
| 611 | value: 41.93833713984145 |
| 612 | - task: |
| 613 | type: Retrieval |
| 614 | dataset: |
| 615 | type: fever |
| 616 | name: MTEB FEVER |
| 617 | config: default |
| 618 | split: test |
| 619 | revision: None |
| 620 | metrics: |
| 621 | - type: map_at_1 |
| 622 | value: 67.914 |
| 623 | - type: map_at_10 |
| 624 | value: 78.10000000000001 |
| 625 | - type: map_at_100 |
| 626 | value: 78.333 |
| 627 | - type: map_at_1000 |
| 628 | value: 78.346 |
| 629 | - type: map_at_3 |
| 630 | value: 76.626 |
| 631 | - type: map_at_5 |
| 632 | value: 77.627 |
| 633 | - type: mrr_at_1 |
| 634 | value: 72.74199999999999 |
| 635 | - type: mrr_at_10 |
| 636 | value: 82.414 |
| 637 | - type: mrr_at_100 |
| 638 | value: 82.511 |
| 639 | - type: mrr_at_1000 |
| 640 | value: 82.513 |
| 641 | - type: mrr_at_3 |
| 642 | value: 81.231 |
| 643 | - type: mrr_at_5 |
| 644 | value: 82.065 |
| 645 | - type: ndcg_at_1 |
| 646 | value: 72.74199999999999 |
| 647 | - type: ndcg_at_10 |
| 648 | value: 82.806 |
| 649 | - type: ndcg_at_100 |
| 650 | value: 83.677 |
| 651 | - type: ndcg_at_1000 |
| 652 | value: 83.917 |
| 653 | - type: ndcg_at_3 |
| 654 | value: 80.305 |
| 655 | - type: ndcg_at_5 |
| 656 | value: 81.843 |
| 657 | - type: precision_at_1 |
| 658 | value: 72.74199999999999 |
| 659 | - type: precision_at_10 |
| 660 | value: 10.24 |
| 661 | - type: precision_at_100 |
| 662 | value: 1.089 |
| 663 | - type: precision_at_1000 |
| 664 | value: 0.11299999999999999 |
| 665 | - type: precision_at_3 |
| 666 | value: 31.268 |
| 667 | - type: precision_at_5 |
| 668 | value: 19.706000000000003 |
| 669 | - type: recall_at_1 |
| 670 | value: 67.914 |
| 671 | - type: recall_at_10 |
| 672 | value: 92.889 |
| 673 | - type: recall_at_100 |
| 674 | value: 96.42699999999999 |
| 675 | - type: recall_at_1000 |
| 676 | value: 97.92 |
| 677 | - type: recall_at_3 |
| 678 | value: 86.21 |
| 679 | - type: recall_at_5 |
| 680 | value: 90.036 |
| 681 | - task: |
| 682 | type: Retrieval |
| 683 | dataset: |
| 684 | type: fiqa |
| 685 | name: MTEB FiQA2018 |
| 686 | config: default |
| 687 | split: test |
| 688 | revision: None |
| 689 | metrics: |
| 690 | - type: map_at_1 |
| 691 | value: 22.166 |
| 692 | - type: map_at_10 |
| 693 | value: 35.57 |
| 694 | - type: map_at_100 |
| 695 | value: 37.405 |
| 696 | - type: map_at_1000 |
| 697 | value: 37.564 |
| 698 | - type: map_at_3 |
| 699 | value: 30.379 |
| 700 | - type: map_at_5 |
| 701 | value: 33.324 |
| 702 | - type: mrr_at_1 |
| 703 | value: 43.519000000000005 |
| 704 | - type: mrr_at_10 |
| 705 | value: 51.556000000000004 |
| 706 | - type: mrr_at_100 |
| 707 | value: 52.344 |
| 708 | - type: mrr_at_1000 |
| 709 | value: 52.373999999999995 |
| 710 | - type: mrr_at_3 |
| 711 | value: 48.868 |
| 712 | - type: mrr_at_5 |
| 713 | value: 50.319 |
| 714 | - type: ndcg_at_1 |
| 715 | value: 43.519000000000005 |
| 716 | - type: ndcg_at_10 |
| 717 | value: 43.803 |
| 718 | - type: ndcg_at_100 |
| 719 | value: 50.468999999999994 |
| 720 | - type: ndcg_at_1000 |
| 721 | value: 53.111 |
| 722 | - type: ndcg_at_3 |
| 723 | value: 38.893 |
| 724 | - type: ndcg_at_5 |
| 725 | value: 40.653 |
| 726 | - type: precision_at_1 |
| 727 | value: 43.519000000000005 |
| 728 | - type: precision_at_10 |
| 729 | value: 12.253 |
| 730 | - type: precision_at_100 |
| 731 | value: 1.931 |
| 732 | - type: precision_at_1000 |
| 733 | value: 0.242 |
| 734 | - type: precision_at_3 |
| 735 | value: 25.617 |
| 736 | - type: precision_at_5 |
| 737 | value: 19.383 |
| 738 | - type: recall_at_1 |
| 739 | value: 22.166 |
| 740 | - type: recall_at_10 |
| 741 | value: 51.6 |
| 742 | - type: recall_at_100 |
| 743 | value: 76.574 |
| 744 | - type: recall_at_1000 |
| 745 | value: 92.192 |
| 746 | - type: recall_at_3 |
| 747 | value: 34.477999999999994 |
| 748 | - type: recall_at_5 |
| 749 | value: 41.835 |
| 750 | - task: |
| 751 | type: Retrieval |
| 752 | dataset: |
| 753 | type: hotpotqa |
| 754 | name: MTEB HotpotQA |
| 755 | config: default |
| 756 | split: test |
| 757 | revision: None |
| 758 | metrics: |
| 759 | - type: map_at_1 |
| 760 | value: 39.041 |
| 761 | - type: map_at_10 |
| 762 | value: 62.961999999999996 |
| 763 | - type: map_at_100 |
| 764 | value: 63.79899999999999 |
| 765 | - type: map_at_1000 |
| 766 | value: 63.854 |
| 767 | - type: map_at_3 |
| 768 | value: 59.399 |
| 769 | - type: map_at_5 |
| 770 | value: 61.669 |
| 771 | - type: mrr_at_1 |
| 772 | value: 78.082 |
| 773 | - type: mrr_at_10 |
| 774 | value: 84.321 |
| 775 | - type: mrr_at_100 |
| 776 | value: 84.49600000000001 |
| 777 | - type: mrr_at_1000 |
| 778 | value: 84.502 |
| 779 | - type: mrr_at_3 |
| 780 | value: 83.421 |
| 781 | - type: mrr_at_5 |
| 782 | value: 83.977 |
| 783 | - type: ndcg_at_1 |
| 784 | value: 78.082 |
| 785 | - type: ndcg_at_10 |
| 786 | value: 71.229 |
| 787 | - type: ndcg_at_100 |
| 788 | value: 74.10900000000001 |
| 789 | - type: ndcg_at_1000 |
| 790 | value: 75.169 |
| 791 | - type: ndcg_at_3 |
| 792 | value: 66.28699999999999 |
| 793 | - type: ndcg_at_5 |
| 794 | value: 69.084 |
| 795 | - type: precision_at_1 |
| 796 | value: 78.082 |
| 797 | - type: precision_at_10 |
| 798 | value: 14.993 |
| 799 | - type: precision_at_100 |
| 800 | value: 1.7239999999999998 |
| 801 | - type: precision_at_1000 |
| 802 | value: 0.186 |
| 803 | - type: precision_at_3 |
| 804 | value: 42.737 |
| 805 | - type: precision_at_5 |
| 806 | value: 27.843 |
| 807 | - type: recall_at_1 |
| 808 | value: 39.041 |
| 809 | - type: recall_at_10 |
| 810 | value: 74.96300000000001 |
| 811 | - type: recall_at_100 |
| 812 | value: 86.199 |
| 813 | - type: recall_at_1000 |
| 814 | value: 93.228 |
| 815 | - type: recall_at_3 |
| 816 | value: 64.105 |
| 817 | - type: recall_at_5 |
| 818 | value: 69.608 |
| 819 | - task: |
| 820 | type: Classification |
| 821 | dataset: |
| 822 | type: mteb/imdb |
| 823 | name: MTEB ImdbClassification |
| 824 | config: default |
| 825 | split: test |
| 826 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 827 | metrics: |
| 828 | - type: accuracy |
| 829 | value: 90.23160000000001 |
| 830 | - type: ap |
| 831 | value: 85.5674856808308 |
| 832 | - type: f1 |
| 833 | value: 90.18033354786317 |
| 834 | - task: |
| 835 | type: Retrieval |
| 836 | dataset: |
| 837 | type: msmarco |
| 838 | name: MTEB MSMARCO |
| 839 | config: default |
| 840 | split: dev |
| 841 | revision: None |
| 842 | metrics: |
| 843 | - type: map_at_1 |
| 844 | value: 24.091 |
| 845 | - type: map_at_10 |
| 846 | value: 36.753 |
| 847 | - type: map_at_100 |
| 848 | value: 37.913000000000004 |
| 849 | - type: map_at_1000 |
| 850 | value: 37.958999999999996 |
| 851 | - type: map_at_3 |
| 852 | value: 32.818999999999996 |
| 853 | - type: map_at_5 |
| 854 | value: 35.171 |
| 855 | - type: mrr_at_1 |
| 856 | value: 24.742 |
| 857 | - type: mrr_at_10 |
| 858 | value: 37.285000000000004 |
| 859 | - type: mrr_at_100 |
| 860 | value: 38.391999999999996 |
| 861 | - type: mrr_at_1000 |
| 862 | value: 38.431 |
| 863 | - type: mrr_at_3 |
| 864 | value: 33.440999999999995 |
| 865 | - type: mrr_at_5 |
| 866 | value: 35.75 |
| 867 | - type: ndcg_at_1 |
| 868 | value: 24.742 |
| 869 | - type: ndcg_at_10 |
| 870 | value: 43.698 |
| 871 | - type: ndcg_at_100 |
| 872 | value: 49.145 |
| 873 | - type: ndcg_at_1000 |
| 874 | value: 50.23800000000001 |
| 875 | - type: ndcg_at_3 |
| 876 | value: 35.769 |
| 877 | - type: ndcg_at_5 |
| 878 | value: 39.961999999999996 |
| 879 | - type: precision_at_1 |
| 880 | value: 24.742 |
| 881 | - type: precision_at_10 |
| 882 | value: 6.7989999999999995 |
| 883 | - type: precision_at_100 |
| 884 | value: 0.95 |
| 885 | - type: precision_at_1000 |
| 886 | value: 0.104 |
| 887 | - type: precision_at_3 |
| 888 | value: 15.096000000000002 |
| 889 | - type: precision_at_5 |
| 890 | value: 11.183 |
| 891 | - type: recall_at_1 |
| 892 | value: 24.091 |
| 893 | - type: recall_at_10 |
| 894 | value: 65.068 |
| 895 | - type: recall_at_100 |
| 896 | value: 89.899 |
| 897 | - type: recall_at_1000 |
| 898 | value: 98.16 |
| 899 | - type: recall_at_3 |
| 900 | value: 43.68 |
| 901 | - type: recall_at_5 |
| 902 | value: 53.754999999999995 |
| 903 | - task: |
| 904 | type: Classification |
| 905 | dataset: |
| 906 | type: mteb/mtop_domain |
| 907 | name: MTEB MTOPDomainClassification (en) |
| 908 | config: en |
| 909 | split: test |
| 910 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 911 | metrics: |
| 912 | - type: accuracy |
| 913 | value: 93.66621067031465 |
| 914 | - type: f1 |
| 915 | value: 93.49622853272142 |
| 916 | - task: |
| 917 | type: Classification |
| 918 | dataset: |
| 919 | type: mteb/mtop_domain |
| 920 | name: MTEB MTOPDomainClassification (de) |
| 921 | config: de |
| 922 | split: test |
| 923 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 924 | metrics: |
| 925 | - type: accuracy |
| 926 | value: 91.94702733164272 |
| 927 | - type: f1 |
| 928 | value: 91.17043441745282 |
| 929 | - task: |
| 930 | type: Classification |
| 931 | dataset: |
| 932 | type: mteb/mtop_domain |
| 933 | name: MTEB MTOPDomainClassification (es) |
| 934 | config: es |
| 935 | split: test |
| 936 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 937 | metrics: |
| 938 | - type: accuracy |
| 939 | value: 92.20146764509674 |
| 940 | - type: f1 |
| 941 | value: 91.98359080555608 |
| 942 | - task: |
| 943 | type: Classification |
| 944 | dataset: |
| 945 | type: mteb/mtop_domain |
| 946 | name: MTEB MTOPDomainClassification (fr) |
| 947 | config: fr |
| 948 | split: test |
| 949 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 950 | metrics: |
| 951 | - type: accuracy |
| 952 | value: 88.99780770435328 |
| 953 | - type: f1 |
| 954 | value: 89.19746342724068 |
| 955 | - task: |
| 956 | type: Classification |
| 957 | dataset: |
| 958 | type: mteb/mtop_domain |
| 959 | name: MTEB MTOPDomainClassification (hi) |
| 960 | config: hi |
| 961 | split: test |
| 962 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 963 | metrics: |
| 964 | - type: accuracy |
| 965 | value: 89.78486912871998 |
| 966 | - type: f1 |
| 967 | value: 89.24578823628642 |
| 968 | - task: |
| 969 | type: Classification |
| 970 | dataset: |
| 971 | type: mteb/mtop_domain |
| 972 | name: MTEB MTOPDomainClassification (th) |
| 973 | config: th |
| 974 | split: test |
| 975 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 976 | metrics: |
| 977 | - type: accuracy |
| 978 | value: 88.74502712477394 |
| 979 | - type: f1 |
| 980 | value: 89.00297573881542 |
| 981 | - task: |
| 982 | type: Classification |
| 983 | dataset: |
| 984 | type: mteb/mtop_intent |
| 985 | name: MTEB MTOPIntentClassification (en) |
| 986 | config: en |
| 987 | split: test |
| 988 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 989 | metrics: |
| 990 | - type: accuracy |
| 991 | value: 77.9046967624259 |
| 992 | - type: f1 |
| 993 | value: 59.36787125785957 |
| 994 | - task: |
| 995 | type: Classification |
| 996 | dataset: |
| 997 | type: mteb/mtop_intent |
| 998 | name: MTEB MTOPIntentClassification (de) |
| 999 | config: de |
| 1000 | split: test |
| 1001 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1002 | metrics: |
| 1003 | - type: accuracy |
| 1004 | value: 74.5280360664976 |
| 1005 | - type: f1 |
| 1006 | value: 57.17723440888718 |
| 1007 | - task: |
| 1008 | type: Classification |
| 1009 | dataset: |
| 1010 | type: mteb/mtop_intent |
| 1011 | name: MTEB MTOPIntentClassification (es) |
| 1012 | config: es |
| 1013 | split: test |
| 1014 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1015 | metrics: |
| 1016 | - type: accuracy |
| 1017 | value: 75.44029352901934 |
| 1018 | - type: f1 |
| 1019 | value: 54.052855531072964 |
| 1020 | - task: |
| 1021 | type: Classification |
| 1022 | dataset: |
| 1023 | type: mteb/mtop_intent |
| 1024 | name: MTEB MTOPIntentClassification (fr) |
| 1025 | config: fr |
| 1026 | split: test |
| 1027 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1028 | metrics: |
| 1029 | - type: accuracy |
| 1030 | value: 70.5606013153774 |
| 1031 | - type: f1 |
| 1032 | value: 52.62215934386531 |
| 1033 | - task: |
| 1034 | type: Classification |
| 1035 | dataset: |
| 1036 | type: mteb/mtop_intent |
| 1037 | name: MTEB MTOPIntentClassification (hi) |
| 1038 | config: hi |
| 1039 | split: test |
| 1040 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1041 | metrics: |
| 1042 | - type: accuracy |
| 1043 | value: 73.11581211903908 |
| 1044 | - type: f1 |
| 1045 | value: 52.341291845645465 |
| 1046 | - task: |
| 1047 | type: Classification |
| 1048 | dataset: |
| 1049 | type: mteb/mtop_intent |
| 1050 | name: MTEB MTOPIntentClassification (th) |
| 1051 | config: th |
| 1052 | split: test |
| 1053 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1054 | metrics: |
| 1055 | - type: accuracy |
| 1056 | value: 74.28933092224233 |
| 1057 | - type: f1 |
| 1058 | value: 57.07918745504911 |
| 1059 | - task: |
| 1060 | type: Classification |
| 1061 | dataset: |
| 1062 | type: mteb/amazon_massive_intent |
| 1063 | name: MTEB MassiveIntentClassification (af) |
| 1064 | config: af |
| 1065 | split: test |
| 1066 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1067 | metrics: |
| 1068 | - type: accuracy |
| 1069 | value: 62.38063214525892 |
| 1070 | - type: f1 |
| 1071 | value: 59.46463723443009 |
| 1072 | - task: |
| 1073 | type: Classification |
| 1074 | dataset: |
| 1075 | type: mteb/amazon_massive_intent |
| 1076 | name: MTEB MassiveIntentClassification (am) |
| 1077 | config: am |
| 1078 | split: test |
| 1079 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1080 | metrics: |
| 1081 | - type: accuracy |
| 1082 | value: 56.06926698049766 |
| 1083 | - type: f1 |
| 1084 | value: 52.49084283283562 |
| 1085 | - task: |
| 1086 | type: Classification |
| 1087 | dataset: |
| 1088 | type: mteb/amazon_massive_intent |
| 1089 | name: MTEB MassiveIntentClassification (ar) |
| 1090 | config: ar |
| 1091 | split: test |
| 1092 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1093 | metrics: |
| 1094 | - type: accuracy |
| 1095 | value: 60.74983187626093 |
| 1096 | - type: f1 |
| 1097 | value: 56.960640620165904 |
| 1098 | - task: |
| 1099 | type: Classification |
| 1100 | dataset: |
| 1101 | type: mteb/amazon_massive_intent |
| 1102 | name: MTEB MassiveIntentClassification (az) |
| 1103 | config: az |
| 1104 | split: test |
| 1105 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1106 | metrics: |
| 1107 | - type: accuracy |
| 1108 | value: 64.86550100874243 |
| 1109 | - type: f1 |
| 1110 | value: 62.47370548140688 |
| 1111 | - task: |
| 1112 | type: Classification |
| 1113 | dataset: |
| 1114 | type: mteb/amazon_massive_intent |
| 1115 | name: MTEB MassiveIntentClassification (bn) |
| 1116 | config: bn |
| 1117 | split: test |
| 1118 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1119 | metrics: |
| 1120 | - type: accuracy |
| 1121 | value: 63.971082716879636 |
| 1122 | - type: f1 |
| 1123 | value: 61.03812421957381 |
| 1124 | - task: |
| 1125 | type: Classification |
| 1126 | dataset: |
| 1127 | type: mteb/amazon_massive_intent |
| 1128 | name: MTEB MassiveIntentClassification (cy) |
| 1129 | config: cy |
| 1130 | split: test |
| 1131 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1132 | metrics: |
| 1133 | - type: accuracy |
| 1134 | value: 54.98318762609282 |
| 1135 | - type: f1 |
| 1136 | value: 51.51207916008392 |
| 1137 | - task: |
| 1138 | type: Classification |
| 1139 | dataset: |
| 1140 | type: mteb/amazon_massive_intent |
| 1141 | name: MTEB MassiveIntentClassification (da) |
| 1142 | config: da |
| 1143 | split: test |
| 1144 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1145 | metrics: |
| 1146 | - type: accuracy |
| 1147 | value: 69.45527908540686 |
| 1148 | - type: f1 |
| 1149 | value: 66.16631905400318 |
| 1150 | - task: |
| 1151 | type: Classification |
| 1152 | dataset: |
| 1153 | type: mteb/amazon_massive_intent |
| 1154 | name: MTEB MassiveIntentClassification (de) |
| 1155 | config: de |
| 1156 | split: test |
| 1157 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1158 | metrics: |
| 1159 | - type: accuracy |
| 1160 | value: 69.32750504371216 |
| 1161 | - type: f1 |
| 1162 | value: 66.16755288646591 |
| 1163 | - task: |
| 1164 | type: Classification |
| 1165 | dataset: |
| 1166 | type: mteb/amazon_massive_intent |
| 1167 | name: MTEB MassiveIntentClassification (el) |
| 1168 | config: el |
| 1169 | split: test |
| 1170 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1171 | metrics: |
| 1172 | - type: accuracy |
| 1173 | value: 69.09213180901143 |
| 1174 | - type: f1 |
| 1175 | value: 66.95654394661507 |
| 1176 | - task: |
| 1177 | type: Classification |
| 1178 | dataset: |
| 1179 | type: mteb/amazon_massive_intent |
| 1180 | name: MTEB MassiveIntentClassification (en) |
| 1181 | config: en |
| 1182 | split: test |
| 1183 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1184 | metrics: |
| 1185 | - type: accuracy |
| 1186 | value: 73.75588433086752 |
| 1187 | - type: f1 |
| 1188 | value: 71.79973779656923 |
| 1189 | - task: |
| 1190 | type: Classification |
| 1191 | dataset: |
| 1192 | type: mteb/amazon_massive_intent |
| 1193 | name: MTEB MassiveIntentClassification (es) |
| 1194 | config: es |
| 1195 | split: test |
| 1196 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1197 | metrics: |
| 1198 | - type: accuracy |
| 1199 | value: 70.49428379287154 |
| 1200 | - type: f1 |
| 1201 | value: 68.37494379215734 |
| 1202 | - task: |
| 1203 | type: Classification |
| 1204 | dataset: |
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| 1700 | name: MTEB MassiveIntentClassification (zh-CN) |
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| 1765 | name: MTEB MassiveScenarioClassification (az) |
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| 1895 | name: MTEB MassiveScenarioClassification (fr) |
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| 2340 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2341 | metrics: |
| 2342 | - type: accuracy |
| 2343 | value: 68.15400134498992 |
| 2344 | - type: f1 |
| 2345 | value: 67.09433241421094 |
| 2346 | - task: |
| 2347 | type: Classification |
| 2348 | dataset: |
| 2349 | type: mteb/amazon_massive_scenario |
| 2350 | name: MTEB MassiveScenarioClassification (vi) |
| 2351 | config: vi |
| 2352 | split: test |
| 2353 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2354 | metrics: |
| 2355 | - type: accuracy |
| 2356 | value: 73.11365164761264 |
| 2357 | - type: f1 |
| 2358 | value: 73.59502539433753 |
| 2359 | - task: |
| 2360 | type: Classification |
| 2361 | dataset: |
| 2362 | type: mteb/amazon_massive_scenario |
| 2363 | name: MTEB MassiveScenarioClassification (zh-CN) |
| 2364 | config: zh-CN |
| 2365 | split: test |
| 2366 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2367 | metrics: |
| 2368 | - type: accuracy |
| 2369 | value: 76.82582380632145 |
| 2370 | - type: f1 |
| 2371 | value: 76.89992945316313 |
| 2372 | - task: |
| 2373 | type: Classification |
| 2374 | dataset: |
| 2375 | type: mteb/amazon_massive_scenario |
| 2376 | name: MTEB MassiveScenarioClassification (zh-TW) |
| 2377 | config: zh-TW |
| 2378 | split: test |
| 2379 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2380 | metrics: |
| 2381 | - type: accuracy |
| 2382 | value: 71.81237390719569 |
| 2383 | - type: f1 |
| 2384 | value: 72.36499770986265 |
| 2385 | - task: |
| 2386 | type: Clustering |
| 2387 | dataset: |
| 2388 | type: mteb/medrxiv-clustering-p2p |
| 2389 | name: MTEB MedrxivClusteringP2P |
| 2390 | config: default |
| 2391 | split: test |
| 2392 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 2393 | metrics: |
| 2394 | - type: v_measure |
| 2395 | value: 31.480506569594695 |
| 2396 | - task: |
| 2397 | type: Clustering |
| 2398 | dataset: |
| 2399 | type: mteb/medrxiv-clustering-s2s |
| 2400 | name: MTEB MedrxivClusteringS2S |
| 2401 | config: default |
| 2402 | split: test |
| 2403 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 2404 | metrics: |
| 2405 | - type: v_measure |
| 2406 | value: 29.71252128004552 |
| 2407 | - task: |
| 2408 | type: Reranking |
| 2409 | dataset: |
| 2410 | type: mteb/mind_small |
| 2411 | name: MTEB MindSmallReranking |
| 2412 | config: default |
| 2413 | split: test |
| 2414 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 2415 | metrics: |
| 2416 | - type: map |
| 2417 | value: 31.421396787056548 |
| 2418 | - type: mrr |
| 2419 | value: 32.48155274872267 |
| 2420 | - task: |
| 2421 | type: Retrieval |
| 2422 | dataset: |
| 2423 | type: nfcorpus |
| 2424 | name: MTEB NFCorpus |
| 2425 | config: default |
| 2426 | split: test |
| 2427 | revision: None |
| 2428 | metrics: |
| 2429 | - type: map_at_1 |
| 2430 | value: 5.595 |
| 2431 | - type: map_at_10 |
| 2432 | value: 12.642000000000001 |
| 2433 | - type: map_at_100 |
| 2434 | value: 15.726 |
| 2435 | - type: map_at_1000 |
| 2436 | value: 17.061999999999998 |
| 2437 | - type: map_at_3 |
| 2438 | value: 9.125 |
| 2439 | - type: map_at_5 |
| 2440 | value: 10.866000000000001 |
| 2441 | - type: mrr_at_1 |
| 2442 | value: 43.344 |
| 2443 | - type: mrr_at_10 |
| 2444 | value: 52.227999999999994 |
| 2445 | - type: mrr_at_100 |
| 2446 | value: 52.898999999999994 |
| 2447 | - type: mrr_at_1000 |
| 2448 | value: 52.944 |
| 2449 | - type: mrr_at_3 |
| 2450 | value: 49.845 |
| 2451 | - type: mrr_at_5 |
| 2452 | value: 51.115 |
| 2453 | - type: ndcg_at_1 |
| 2454 | value: 41.949999999999996 |
| 2455 | - type: ndcg_at_10 |
| 2456 | value: 33.995 |
| 2457 | - type: ndcg_at_100 |
| 2458 | value: 30.869999999999997 |
| 2459 | - type: ndcg_at_1000 |
| 2460 | value: 39.487 |
| 2461 | - type: ndcg_at_3 |
| 2462 | value: 38.903999999999996 |
| 2463 | - type: ndcg_at_5 |
| 2464 | value: 37.236999999999995 |
| 2465 | - type: precision_at_1 |
| 2466 | value: 43.344 |
| 2467 | - type: precision_at_10 |
| 2468 | value: 25.480000000000004 |
| 2469 | - type: precision_at_100 |
| 2470 | value: 7.672 |
| 2471 | - type: precision_at_1000 |
| 2472 | value: 2.028 |
| 2473 | - type: precision_at_3 |
| 2474 | value: 36.636 |
| 2475 | - type: precision_at_5 |
| 2476 | value: 32.632 |
| 2477 | - type: recall_at_1 |
| 2478 | value: 5.595 |
| 2479 | - type: recall_at_10 |
| 2480 | value: 16.466 |
| 2481 | - type: recall_at_100 |
| 2482 | value: 31.226 |
| 2483 | - type: recall_at_1000 |
| 2484 | value: 62.778999999999996 |
| 2485 | - type: recall_at_3 |
| 2486 | value: 9.931 |
| 2487 | - type: recall_at_5 |
| 2488 | value: 12.884 |
| 2489 | - task: |
| 2490 | type: Retrieval |
| 2491 | dataset: |
| 2492 | type: nq |
| 2493 | name: MTEB NQ |
| 2494 | config: default |
| 2495 | split: test |
| 2496 | revision: None |
| 2497 | metrics: |
| 2498 | - type: map_at_1 |
| 2499 | value: 40.414 |
| 2500 | - type: map_at_10 |
| 2501 | value: 56.754000000000005 |
| 2502 | - type: map_at_100 |
| 2503 | value: 57.457 |
| 2504 | - type: map_at_1000 |
| 2505 | value: 57.477999999999994 |
| 2506 | - type: map_at_3 |
| 2507 | value: 52.873999999999995 |
| 2508 | - type: map_at_5 |
| 2509 | value: 55.175 |
| 2510 | - type: mrr_at_1 |
| 2511 | value: 45.278 |
| 2512 | - type: mrr_at_10 |
| 2513 | value: 59.192 |
| 2514 | - type: mrr_at_100 |
| 2515 | value: 59.650000000000006 |
| 2516 | - type: mrr_at_1000 |
| 2517 | value: 59.665 |
| 2518 | - type: mrr_at_3 |
| 2519 | value: 56.141 |
| 2520 | - type: mrr_at_5 |
| 2521 | value: 57.998000000000005 |
| 2522 | - type: ndcg_at_1 |
| 2523 | value: 45.278 |
| 2524 | - type: ndcg_at_10 |
| 2525 | value: 64.056 |
| 2526 | - type: ndcg_at_100 |
| 2527 | value: 66.89 |
| 2528 | - type: ndcg_at_1000 |
| 2529 | value: 67.364 |
| 2530 | - type: ndcg_at_3 |
| 2531 | value: 56.97 |
| 2532 | - type: ndcg_at_5 |
| 2533 | value: 60.719 |
| 2534 | - type: precision_at_1 |
| 2535 | value: 45.278 |
| 2536 | - type: precision_at_10 |
| 2537 | value: 9.994 |
| 2538 | - type: precision_at_100 |
| 2539 | value: 1.165 |
| 2540 | - type: precision_at_1000 |
| 2541 | value: 0.121 |
| 2542 | - type: precision_at_3 |
| 2543 | value: 25.512 |
| 2544 | - type: precision_at_5 |
| 2545 | value: 17.509 |
| 2546 | - type: recall_at_1 |
| 2547 | value: 40.414 |
| 2548 | - type: recall_at_10 |
| 2549 | value: 83.596 |
| 2550 | - type: recall_at_100 |
| 2551 | value: 95.72 |
| 2552 | - type: recall_at_1000 |
| 2553 | value: 99.24 |
| 2554 | - type: recall_at_3 |
| 2555 | value: 65.472 |
| 2556 | - type: recall_at_5 |
| 2557 | value: 74.039 |
| 2558 | - task: |
| 2559 | type: Retrieval |
| 2560 | dataset: |
| 2561 | type: quora |
| 2562 | name: MTEB QuoraRetrieval |
| 2563 | config: default |
| 2564 | split: test |
| 2565 | revision: None |
| 2566 | metrics: |
| 2567 | - type: map_at_1 |
| 2568 | value: 70.352 |
| 2569 | - type: map_at_10 |
| 2570 | value: 84.369 |
| 2571 | - type: map_at_100 |
| 2572 | value: 85.02499999999999 |
| 2573 | - type: map_at_1000 |
| 2574 | value: 85.04 |
| 2575 | - type: map_at_3 |
| 2576 | value: 81.42399999999999 |
| 2577 | - type: map_at_5 |
| 2578 | value: 83.279 |
| 2579 | - type: mrr_at_1 |
| 2580 | value: 81.05 |
| 2581 | - type: mrr_at_10 |
| 2582 | value: 87.401 |
| 2583 | - type: mrr_at_100 |
| 2584 | value: 87.504 |
| 2585 | - type: mrr_at_1000 |
| 2586 | value: 87.505 |
| 2587 | - type: mrr_at_3 |
| 2588 | value: 86.443 |
| 2589 | - type: mrr_at_5 |
| 2590 | value: 87.10799999999999 |
| 2591 | - type: ndcg_at_1 |
| 2592 | value: 81.04 |
| 2593 | - type: ndcg_at_10 |
| 2594 | value: 88.181 |
| 2595 | - type: ndcg_at_100 |
| 2596 | value: 89.411 |
| 2597 | - type: ndcg_at_1000 |
| 2598 | value: 89.507 |
| 2599 | - type: ndcg_at_3 |
| 2600 | value: 85.28099999999999 |
| 2601 | - type: ndcg_at_5 |
| 2602 | value: 86.888 |
| 2603 | - type: precision_at_1 |
| 2604 | value: 81.04 |
| 2605 | - type: precision_at_10 |
| 2606 | value: 13.406 |
| 2607 | - type: precision_at_100 |
| 2608 | value: 1.5350000000000001 |
| 2609 | - type: precision_at_1000 |
| 2610 | value: 0.157 |
| 2611 | - type: precision_at_3 |
| 2612 | value: 37.31 |
| 2613 | - type: precision_at_5 |
| 2614 | value: 24.54 |
| 2615 | - type: recall_at_1 |
| 2616 | value: 70.352 |
| 2617 | - type: recall_at_10 |
| 2618 | value: 95.358 |
| 2619 | - type: recall_at_100 |
| 2620 | value: 99.541 |
| 2621 | - type: recall_at_1000 |
| 2622 | value: 99.984 |
| 2623 | - type: recall_at_3 |
| 2624 | value: 87.111 |
| 2625 | - type: recall_at_5 |
| 2626 | value: 91.643 |
| 2627 | - task: |
| 2628 | type: Clustering |
| 2629 | dataset: |
| 2630 | type: mteb/reddit-clustering |
| 2631 | name: MTEB RedditClustering |
| 2632 | config: default |
| 2633 | split: test |
| 2634 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 2635 | metrics: |
| 2636 | - type: v_measure |
| 2637 | value: 46.54068723291946 |
| 2638 | - task: |
| 2639 | type: Clustering |
| 2640 | dataset: |
| 2641 | type: mteb/reddit-clustering-p2p |
| 2642 | name: MTEB RedditClusteringP2P |
| 2643 | config: default |
| 2644 | split: test |
| 2645 | revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| 2646 | metrics: |
| 2647 | - type: v_measure |
| 2648 | value: 63.216287629895994 |
| 2649 | - task: |
| 2650 | type: Retrieval |
| 2651 | dataset: |
| 2652 | type: scidocs |
| 2653 | name: MTEB SCIDOCS |
| 2654 | config: default |
| 2655 | split: test |
| 2656 | revision: None |
| 2657 | metrics: |
| 2658 | - type: map_at_1 |
| 2659 | value: 4.023000000000001 |
| 2660 | - type: map_at_10 |
| 2661 | value: 10.071 |
| 2662 | - type: map_at_100 |
| 2663 | value: 11.892 |
| 2664 | - type: map_at_1000 |
| 2665 | value: 12.196 |
| 2666 | - type: map_at_3 |
| 2667 | value: 7.234 |
| 2668 | - type: map_at_5 |
| 2669 | value: 8.613999999999999 |
| 2670 | - type: mrr_at_1 |
| 2671 | value: 19.900000000000002 |
| 2672 | - type: mrr_at_10 |
| 2673 | value: 30.516 |
| 2674 | - type: mrr_at_100 |
| 2675 | value: 31.656000000000002 |
| 2676 | - type: mrr_at_1000 |
| 2677 | value: 31.723000000000003 |
| 2678 | - type: mrr_at_3 |
| 2679 | value: 27.400000000000002 |
| 2680 | - type: mrr_at_5 |
| 2681 | value: 29.270000000000003 |
| 2682 | - type: ndcg_at_1 |
| 2683 | value: 19.900000000000002 |
| 2684 | - type: ndcg_at_10 |
| 2685 | value: 17.474 |
| 2686 | - type: ndcg_at_100 |
| 2687 | value: 25.020999999999997 |
| 2688 | - type: ndcg_at_1000 |
| 2689 | value: 30.728 |
| 2690 | - type: ndcg_at_3 |
| 2691 | value: 16.588 |
| 2692 | - type: ndcg_at_5 |
| 2693 | value: 14.498 |
| 2694 | - type: precision_at_1 |
| 2695 | value: 19.900000000000002 |
| 2696 | - type: precision_at_10 |
| 2697 | value: 9.139999999999999 |
| 2698 | - type: precision_at_100 |
| 2699 | value: 2.011 |
| 2700 | - type: precision_at_1000 |
| 2701 | value: 0.33899999999999997 |
| 2702 | - type: precision_at_3 |
| 2703 | value: 15.667 |
| 2704 | - type: precision_at_5 |
| 2705 | value: 12.839999999999998 |
| 2706 | - type: recall_at_1 |
| 2707 | value: 4.023000000000001 |
| 2708 | - type: recall_at_10 |
| 2709 | value: 18.497 |
| 2710 | - type: recall_at_100 |
| 2711 | value: 40.8 |
| 2712 | - type: recall_at_1000 |
| 2713 | value: 68.812 |
| 2714 | - type: recall_at_3 |
| 2715 | value: 9.508 |
| 2716 | - type: recall_at_5 |
| 2717 | value: 12.983 |
| 2718 | - task: |
| 2719 | type: STS |
| 2720 | dataset: |
| 2721 | type: mteb/sickr-sts |
| 2722 | name: MTEB SICK-R |
| 2723 | config: default |
| 2724 | split: test |
| 2725 | revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| 2726 | metrics: |
| 2727 | - type: cos_sim_pearson |
| 2728 | value: 83.967008785134 |
| 2729 | - type: cos_sim_spearman |
| 2730 | value: 80.23142141101837 |
| 2731 | - type: euclidean_pearson |
| 2732 | value: 81.20166064704539 |
| 2733 | - type: euclidean_spearman |
| 2734 | value: 80.18961335654585 |
| 2735 | - type: manhattan_pearson |
| 2736 | value: 81.13925443187625 |
| 2737 | - type: manhattan_spearman |
| 2738 | value: 80.07948723044424 |
| 2739 | - task: |
| 2740 | type: STS |
| 2741 | dataset: |
| 2742 | type: mteb/sts12-sts |
| 2743 | name: MTEB STS12 |
| 2744 | config: default |
| 2745 | split: test |
| 2746 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 2747 | metrics: |
| 2748 | - type: cos_sim_pearson |
| 2749 | value: 86.94262461316023 |
| 2750 | - type: cos_sim_spearman |
| 2751 | value: 80.01596278563865 |
| 2752 | - type: euclidean_pearson |
| 2753 | value: 83.80799622922581 |
| 2754 | - type: euclidean_spearman |
| 2755 | value: 79.94984954947103 |
| 2756 | - type: manhattan_pearson |
| 2757 | value: 83.68473841756281 |
| 2758 | - type: manhattan_spearman |
| 2759 | value: 79.84990707951822 |
| 2760 | - task: |
| 2761 | type: STS |
| 2762 | dataset: |
| 2763 | type: mteb/sts13-sts |
| 2764 | name: MTEB STS13 |
| 2765 | config: default |
| 2766 | split: test |
| 2767 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 2768 | metrics: |
| 2769 | - type: cos_sim_pearson |
| 2770 | value: 80.57346443146068 |
| 2771 | - type: cos_sim_spearman |
| 2772 | value: 81.54689837570866 |
| 2773 | - type: euclidean_pearson |
| 2774 | value: 81.10909881516007 |
| 2775 | - type: euclidean_spearman |
| 2776 | value: 81.56746243261762 |
| 2777 | - type: manhattan_pearson |
| 2778 | value: 80.87076036186582 |
| 2779 | - type: manhattan_spearman |
| 2780 | value: 81.33074987964402 |
| 2781 | - task: |
| 2782 | type: STS |
| 2783 | dataset: |
| 2784 | type: mteb/sts14-sts |
| 2785 | name: MTEB STS14 |
| 2786 | config: default |
| 2787 | split: test |
| 2788 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 2789 | metrics: |
| 2790 | - type: cos_sim_pearson |
| 2791 | value: 79.54733787179849 |
| 2792 | - type: cos_sim_spearman |
| 2793 | value: 77.72202105610411 |
| 2794 | - type: euclidean_pearson |
| 2795 | value: 78.9043595478849 |
| 2796 | - type: euclidean_spearman |
| 2797 | value: 77.93422804309435 |
| 2798 | - type: manhattan_pearson |
| 2799 | value: 78.58115121621368 |
| 2800 | - type: manhattan_spearman |
| 2801 | value: 77.62508135122033 |
| 2802 | - task: |
| 2803 | type: STS |
| 2804 | dataset: |
| 2805 | type: mteb/sts15-sts |
| 2806 | name: MTEB STS15 |
| 2807 | config: default |
| 2808 | split: test |
| 2809 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 2810 | metrics: |
| 2811 | - type: cos_sim_pearson |
| 2812 | value: 88.59880017237558 |
| 2813 | - type: cos_sim_spearman |
| 2814 | value: 89.31088630824758 |
| 2815 | - type: euclidean_pearson |
| 2816 | value: 88.47069261564656 |
| 2817 | - type: euclidean_spearman |
| 2818 | value: 89.33581971465233 |
| 2819 | - type: manhattan_pearson |
| 2820 | value: 88.40774264100956 |
| 2821 | - type: manhattan_spearman |
| 2822 | value: 89.28657485627835 |
| 2823 | - task: |
| 2824 | type: STS |
| 2825 | dataset: |
| 2826 | type: mteb/sts16-sts |
| 2827 | name: MTEB STS16 |
| 2828 | config: default |
| 2829 | split: test |
| 2830 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 2831 | metrics: |
| 2832 | - type: cos_sim_pearson |
| 2833 | value: 84.08055117917084 |
| 2834 | - type: cos_sim_spearman |
| 2835 | value: 85.78491813080304 |
| 2836 | - type: euclidean_pearson |
| 2837 | value: 84.99329155500392 |
| 2838 | - type: euclidean_spearman |
| 2839 | value: 85.76728064677287 |
| 2840 | - type: manhattan_pearson |
| 2841 | value: 84.87947428989587 |
| 2842 | - type: manhattan_spearman |
| 2843 | value: 85.62429454917464 |
| 2844 | - task: |
| 2845 | type: STS |
| 2846 | dataset: |
| 2847 | type: mteb/sts17-crosslingual-sts |
| 2848 | name: MTEB STS17 (ko-ko) |
| 2849 | config: ko-ko |
| 2850 | split: test |
| 2851 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2852 | metrics: |
| 2853 | - type: cos_sim_pearson |
| 2854 | value: 82.14190939287384 |
| 2855 | - type: cos_sim_spearman |
| 2856 | value: 82.27331573306041 |
| 2857 | - type: euclidean_pearson |
| 2858 | value: 81.891896953716 |
| 2859 | - type: euclidean_spearman |
| 2860 | value: 82.37695542955998 |
| 2861 | - type: manhattan_pearson |
| 2862 | value: 81.73123869460504 |
| 2863 | - type: manhattan_spearman |
| 2864 | value: 82.19989168441421 |
| 2865 | - task: |
| 2866 | type: STS |
| 2867 | dataset: |
| 2868 | type: mteb/sts17-crosslingual-sts |
| 2869 | name: MTEB STS17 (ar-ar) |
| 2870 | config: ar-ar |
| 2871 | split: test |
| 2872 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2873 | metrics: |
| 2874 | - type: cos_sim_pearson |
| 2875 | value: 76.84695301843362 |
| 2876 | - type: cos_sim_spearman |
| 2877 | value: 77.87790986014461 |
| 2878 | - type: euclidean_pearson |
| 2879 | value: 76.91981583106315 |
| 2880 | - type: euclidean_spearman |
| 2881 | value: 77.88154772749589 |
| 2882 | - type: manhattan_pearson |
| 2883 | value: 76.94953277451093 |
| 2884 | - type: manhattan_spearman |
| 2885 | value: 77.80499230728604 |
| 2886 | - task: |
| 2887 | type: STS |
| 2888 | dataset: |
| 2889 | type: mteb/sts17-crosslingual-sts |
| 2890 | name: MTEB STS17 (en-ar) |
| 2891 | config: en-ar |
| 2892 | split: test |
| 2893 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2894 | metrics: |
| 2895 | - type: cos_sim_pearson |
| 2896 | value: 75.44657840482016 |
| 2897 | - type: cos_sim_spearman |
| 2898 | value: 75.05531095119674 |
| 2899 | - type: euclidean_pearson |
| 2900 | value: 75.88161755829299 |
| 2901 | - type: euclidean_spearman |
| 2902 | value: 74.73176238219332 |
| 2903 | - type: manhattan_pearson |
| 2904 | value: 75.63984765635362 |
| 2905 | - type: manhattan_spearman |
| 2906 | value: 74.86476440770737 |
| 2907 | - task: |
| 2908 | type: STS |
| 2909 | dataset: |
| 2910 | type: mteb/sts17-crosslingual-sts |
| 2911 | name: MTEB STS17 (en-de) |
| 2912 | config: en-de |
| 2913 | split: test |
| 2914 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2915 | metrics: |
| 2916 | - type: cos_sim_pearson |
| 2917 | value: 85.64700140524133 |
| 2918 | - type: cos_sim_spearman |
| 2919 | value: 86.16014210425672 |
| 2920 | - type: euclidean_pearson |
| 2921 | value: 86.49086860843221 |
| 2922 | - type: euclidean_spearman |
| 2923 | value: 86.09729326815614 |
| 2924 | - type: manhattan_pearson |
| 2925 | value: 86.43406265125513 |
| 2926 | - type: manhattan_spearman |
| 2927 | value: 86.17740150939994 |
| 2928 | - task: |
| 2929 | type: STS |
| 2930 | dataset: |
| 2931 | type: mteb/sts17-crosslingual-sts |
| 2932 | name: MTEB STS17 (en-en) |
| 2933 | config: en-en |
| 2934 | split: test |
| 2935 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2936 | metrics: |
| 2937 | - type: cos_sim_pearson |
| 2938 | value: 87.91170098764921 |
| 2939 | - type: cos_sim_spearman |
| 2940 | value: 88.12437004058931 |
| 2941 | - type: euclidean_pearson |
| 2942 | value: 88.81828254494437 |
| 2943 | - type: euclidean_spearman |
| 2944 | value: 88.14831794572122 |
| 2945 | - type: manhattan_pearson |
| 2946 | value: 88.93442183448961 |
| 2947 | - type: manhattan_spearman |
| 2948 | value: 88.15254630778304 |
| 2949 | - task: |
| 2950 | type: STS |
| 2951 | dataset: |
| 2952 | type: mteb/sts17-crosslingual-sts |
| 2953 | name: MTEB STS17 (en-tr) |
| 2954 | config: en-tr |
| 2955 | split: test |
| 2956 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2957 | metrics: |
| 2958 | - type: cos_sim_pearson |
| 2959 | value: 72.91390577997292 |
| 2960 | - type: cos_sim_spearman |
| 2961 | value: 71.22979457536074 |
| 2962 | - type: euclidean_pearson |
| 2963 | value: 74.40314008106749 |
| 2964 | - type: euclidean_spearman |
| 2965 | value: 72.54972136083246 |
| 2966 | - type: manhattan_pearson |
| 2967 | value: 73.85687539530218 |
| 2968 | - type: manhattan_spearman |
| 2969 | value: 72.09500771742637 |
| 2970 | - task: |
| 2971 | type: STS |
| 2972 | dataset: |
| 2973 | type: mteb/sts17-crosslingual-sts |
| 2974 | name: MTEB STS17 (es-en) |
| 2975 | config: es-en |
| 2976 | split: test |
| 2977 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2978 | metrics: |
| 2979 | - type: cos_sim_pearson |
| 2980 | value: 80.9301067983089 |
| 2981 | - type: cos_sim_spearman |
| 2982 | value: 80.74989828346473 |
| 2983 | - type: euclidean_pearson |
| 2984 | value: 81.36781301814257 |
| 2985 | - type: euclidean_spearman |
| 2986 | value: 80.9448819964426 |
| 2987 | - type: manhattan_pearson |
| 2988 | value: 81.0351322685609 |
| 2989 | - type: manhattan_spearman |
| 2990 | value: 80.70192121844177 |
| 2991 | - task: |
| 2992 | type: STS |
| 2993 | dataset: |
| 2994 | type: mteb/sts17-crosslingual-sts |
| 2995 | name: MTEB STS17 (es-es) |
| 2996 | config: es-es |
| 2997 | split: test |
| 2998 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2999 | metrics: |
| 3000 | - type: cos_sim_pearson |
| 3001 | value: 87.13820465980005 |
| 3002 | - type: cos_sim_spearman |
| 3003 | value: 86.73532498758757 |
| 3004 | - type: euclidean_pearson |
| 3005 | value: 87.21329451846637 |
| 3006 | - type: euclidean_spearman |
| 3007 | value: 86.57863198601002 |
| 3008 | - type: manhattan_pearson |
| 3009 | value: 87.06973713818554 |
| 3010 | - type: manhattan_spearman |
| 3011 | value: 86.47534918791499 |
| 3012 | - task: |
| 3013 | type: STS |
| 3014 | dataset: |
| 3015 | type: mteb/sts17-crosslingual-sts |
| 3016 | name: MTEB STS17 (fr-en) |
| 3017 | config: fr-en |
| 3018 | split: test |
| 3019 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3020 | metrics: |
| 3021 | - type: cos_sim_pearson |
| 3022 | value: 85.48720108904415 |
| 3023 | - type: cos_sim_spearman |
| 3024 | value: 85.62221757068387 |
| 3025 | - type: euclidean_pearson |
| 3026 | value: 86.1010129512749 |
| 3027 | - type: euclidean_spearman |
| 3028 | value: 85.86580966509942 |
| 3029 | - type: manhattan_pearson |
| 3030 | value: 86.26800938808971 |
| 3031 | - type: manhattan_spearman |
| 3032 | value: 85.88902721678429 |
| 3033 | - task: |
| 3034 | type: STS |
| 3035 | dataset: |
| 3036 | type: mteb/sts17-crosslingual-sts |
| 3037 | name: MTEB STS17 (it-en) |
| 3038 | config: it-en |
| 3039 | split: test |
| 3040 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3041 | metrics: |
| 3042 | - type: cos_sim_pearson |
| 3043 | value: 83.98021347333516 |
| 3044 | - type: cos_sim_spearman |
| 3045 | value: 84.53806553803501 |
| 3046 | - type: euclidean_pearson |
| 3047 | value: 84.61483347248364 |
| 3048 | - type: euclidean_spearman |
| 3049 | value: 85.14191408011702 |
| 3050 | - type: manhattan_pearson |
| 3051 | value: 84.75297588825967 |
| 3052 | - type: manhattan_spearman |
| 3053 | value: 85.33176753669242 |
| 3054 | - task: |
| 3055 | type: STS |
| 3056 | dataset: |
| 3057 | type: mteb/sts17-crosslingual-sts |
| 3058 | name: MTEB STS17 (nl-en) |
| 3059 | config: nl-en |
| 3060 | split: test |
| 3061 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3062 | metrics: |
| 3063 | - type: cos_sim_pearson |
| 3064 | value: 84.51856644893233 |
| 3065 | - type: cos_sim_spearman |
| 3066 | value: 85.27510748506413 |
| 3067 | - type: euclidean_pearson |
| 3068 | value: 85.09886861540977 |
| 3069 | - type: euclidean_spearman |
| 3070 | value: 85.62579245860887 |
| 3071 | - type: manhattan_pearson |
| 3072 | value: 84.93017860464607 |
| 3073 | - type: manhattan_spearman |
| 3074 | value: 85.5063988898453 |
| 3075 | - task: |
| 3076 | type: STS |
| 3077 | dataset: |
| 3078 | type: mteb/sts22-crosslingual-sts |
| 3079 | name: MTEB STS22 (en) |
| 3080 | config: en |
| 3081 | split: test |
| 3082 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3083 | metrics: |
| 3084 | - type: cos_sim_pearson |
| 3085 | value: 62.581573200584195 |
| 3086 | - type: cos_sim_spearman |
| 3087 | value: 63.05503590247928 |
| 3088 | - type: euclidean_pearson |
| 3089 | value: 63.652564812602094 |
| 3090 | - type: euclidean_spearman |
| 3091 | value: 62.64811520876156 |
| 3092 | - type: manhattan_pearson |
| 3093 | value: 63.506842893061076 |
| 3094 | - type: manhattan_spearman |
| 3095 | value: 62.51289573046917 |
| 3096 | - task: |
| 3097 | type: STS |
| 3098 | dataset: |
| 3099 | type: mteb/sts22-crosslingual-sts |
| 3100 | name: MTEB STS22 (de) |
| 3101 | config: de |
| 3102 | split: test |
| 3103 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3104 | metrics: |
| 3105 | - type: cos_sim_pearson |
| 3106 | value: 48.2248801729127 |
| 3107 | - type: cos_sim_spearman |
| 3108 | value: 56.5936604678561 |
| 3109 | - type: euclidean_pearson |
| 3110 | value: 43.98149464089 |
| 3111 | - type: euclidean_spearman |
| 3112 | value: 56.108561882423615 |
| 3113 | - type: manhattan_pearson |
| 3114 | value: 43.86880305903564 |
| 3115 | - type: manhattan_spearman |
| 3116 | value: 56.04671150510166 |
| 3117 | - task: |
| 3118 | type: STS |
| 3119 | dataset: |
| 3120 | type: mteb/sts22-crosslingual-sts |
| 3121 | name: MTEB STS22 (es) |
| 3122 | config: es |
| 3123 | split: test |
| 3124 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3125 | metrics: |
| 3126 | - type: cos_sim_pearson |
| 3127 | value: 55.17564527009831 |
| 3128 | - type: cos_sim_spearman |
| 3129 | value: 64.57978560979488 |
| 3130 | - type: euclidean_pearson |
| 3131 | value: 58.8818330154583 |
| 3132 | - type: euclidean_spearman |
| 3133 | value: 64.99214839071281 |
| 3134 | - type: manhattan_pearson |
| 3135 | value: 58.72671436121381 |
| 3136 | - type: manhattan_spearman |
| 3137 | value: 65.10713416616109 |
| 3138 | - task: |
| 3139 | type: STS |
| 3140 | dataset: |
| 3141 | type: mteb/sts22-crosslingual-sts |
| 3142 | name: MTEB STS22 (pl) |
| 3143 | config: pl |
| 3144 | split: test |
| 3145 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3146 | metrics: |
| 3147 | - type: cos_sim_pearson |
| 3148 | value: 26.772131864023297 |
| 3149 | - type: cos_sim_spearman |
| 3150 | value: 34.68200792408681 |
| 3151 | - type: euclidean_pearson |
| 3152 | value: 16.68082419005441 |
| 3153 | - type: euclidean_spearman |
| 3154 | value: 34.83099932652166 |
| 3155 | - type: manhattan_pearson |
| 3156 | value: 16.52605949659529 |
| 3157 | - type: manhattan_spearman |
| 3158 | value: 34.82075801399475 |
| 3159 | - task: |
| 3160 | type: STS |
| 3161 | dataset: |
| 3162 | type: mteb/sts22-crosslingual-sts |
| 3163 | name: MTEB STS22 (tr) |
| 3164 | config: tr |
| 3165 | split: test |
| 3166 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3167 | metrics: |
| 3168 | - type: cos_sim_pearson |
| 3169 | value: 54.42415189043831 |
| 3170 | - type: cos_sim_spearman |
| 3171 | value: 63.54594264576758 |
| 3172 | - type: euclidean_pearson |
| 3173 | value: 57.36577498297745 |
| 3174 | - type: euclidean_spearman |
| 3175 | value: 63.111466379158074 |
| 3176 | - type: manhattan_pearson |
| 3177 | value: 57.584543715873885 |
| 3178 | - type: manhattan_spearman |
| 3179 | value: 63.22361054139183 |
| 3180 | - task: |
| 3181 | type: STS |
| 3182 | dataset: |
| 3183 | type: mteb/sts22-crosslingual-sts |
| 3184 | name: MTEB STS22 (ar) |
| 3185 | config: ar |
| 3186 | split: test |
| 3187 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3188 | metrics: |
| 3189 | - type: cos_sim_pearson |
| 3190 | value: 47.55216762405518 |
| 3191 | - type: cos_sim_spearman |
| 3192 | value: 56.98670142896412 |
| 3193 | - type: euclidean_pearson |
| 3194 | value: 50.15318757562699 |
| 3195 | - type: euclidean_spearman |
| 3196 | value: 56.524941926541906 |
| 3197 | - type: manhattan_pearson |
| 3198 | value: 49.955618528674904 |
| 3199 | - type: manhattan_spearman |
| 3200 | value: 56.37102209240117 |
| 3201 | - task: |
| 3202 | type: STS |
| 3203 | dataset: |
| 3204 | type: mteb/sts22-crosslingual-sts |
| 3205 | name: MTEB STS22 (ru) |
| 3206 | config: ru |
| 3207 | split: test |
| 3208 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3209 | metrics: |
| 3210 | - type: cos_sim_pearson |
| 3211 | value: 49.20540980338571 |
| 3212 | - type: cos_sim_spearman |
| 3213 | value: 59.9009453504406 |
| 3214 | - type: euclidean_pearson |
| 3215 | value: 49.557749853620535 |
| 3216 | - type: euclidean_spearman |
| 3217 | value: 59.76631621172456 |
| 3218 | - type: manhattan_pearson |
| 3219 | value: 49.62340591181147 |
| 3220 | - type: manhattan_spearman |
| 3221 | value: 59.94224880322436 |
| 3222 | - task: |
| 3223 | type: STS |
| 3224 | dataset: |
| 3225 | type: mteb/sts22-crosslingual-sts |
| 3226 | name: MTEB STS22 (zh) |
| 3227 | config: zh |
| 3228 | split: test |
| 3229 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3230 | metrics: |
| 3231 | - type: cos_sim_pearson |
| 3232 | value: 51.508169956576985 |
| 3233 | - type: cos_sim_spearman |
| 3234 | value: 66.82461565306046 |
| 3235 | - type: euclidean_pearson |
| 3236 | value: 56.2274426480083 |
| 3237 | - type: euclidean_spearman |
| 3238 | value: 66.6775323848333 |
| 3239 | - type: manhattan_pearson |
| 3240 | value: 55.98277796300661 |
| 3241 | - type: manhattan_spearman |
| 3242 | value: 66.63669848497175 |
| 3243 | - task: |
| 3244 | type: STS |
| 3245 | dataset: |
| 3246 | type: mteb/sts22-crosslingual-sts |
| 3247 | name: MTEB STS22 (fr) |
| 3248 | config: fr |
| 3249 | split: test |
| 3250 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3251 | metrics: |
| 3252 | - type: cos_sim_pearson |
| 3253 | value: 72.86478788045507 |
| 3254 | - type: cos_sim_spearman |
| 3255 | value: 76.7946552053193 |
| 3256 | - type: euclidean_pearson |
| 3257 | value: 75.01598530490269 |
| 3258 | - type: euclidean_spearman |
| 3259 | value: 76.83618917858281 |
| 3260 | - type: manhattan_pearson |
| 3261 | value: 74.68337628304332 |
| 3262 | - type: manhattan_spearman |
| 3263 | value: 76.57480204017773 |
| 3264 | - task: |
| 3265 | type: STS |
| 3266 | dataset: |
| 3267 | type: mteb/sts22-crosslingual-sts |
| 3268 | name: MTEB STS22 (de-en) |
| 3269 | config: de-en |
| 3270 | split: test |
| 3271 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3272 | metrics: |
| 3273 | - type: cos_sim_pearson |
| 3274 | value: 55.922619099401984 |
| 3275 | - type: cos_sim_spearman |
| 3276 | value: 56.599362477240774 |
| 3277 | - type: euclidean_pearson |
| 3278 | value: 56.68307052369783 |
| 3279 | - type: euclidean_spearman |
| 3280 | value: 54.28760436777401 |
| 3281 | - type: manhattan_pearson |
| 3282 | value: 56.67763566500681 |
| 3283 | - type: manhattan_spearman |
| 3284 | value: 53.94619541711359 |
| 3285 | - task: |
| 3286 | type: STS |
| 3287 | dataset: |
| 3288 | type: mteb/sts22-crosslingual-sts |
| 3289 | name: MTEB STS22 (es-en) |
| 3290 | config: es-en |
| 3291 | split: test |
| 3292 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3293 | metrics: |
| 3294 | - type: cos_sim_pearson |
| 3295 | value: 66.74357206710913 |
| 3296 | - type: cos_sim_spearman |
| 3297 | value: 72.5208244925311 |
| 3298 | - type: euclidean_pearson |
| 3299 | value: 67.49254562186032 |
| 3300 | - type: euclidean_spearman |
| 3301 | value: 72.02469076238683 |
| 3302 | - type: manhattan_pearson |
| 3303 | value: 67.45251772238085 |
| 3304 | - type: manhattan_spearman |
| 3305 | value: 72.05538819984538 |
| 3306 | - task: |
| 3307 | type: STS |
| 3308 | dataset: |
| 3309 | type: mteb/sts22-crosslingual-sts |
| 3310 | name: MTEB STS22 (it) |
| 3311 | config: it |
| 3312 | split: test |
| 3313 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3314 | metrics: |
| 3315 | - type: cos_sim_pearson |
| 3316 | value: 71.25734330033191 |
| 3317 | - type: cos_sim_spearman |
| 3318 | value: 76.98349083946823 |
| 3319 | - type: euclidean_pearson |
| 3320 | value: 73.71642838667736 |
| 3321 | - type: euclidean_spearman |
| 3322 | value: 77.01715504651384 |
| 3323 | - type: manhattan_pearson |
| 3324 | value: 73.61712711868105 |
| 3325 | - type: manhattan_spearman |
| 3326 | value: 77.01392571153896 |
| 3327 | - task: |
| 3328 | type: STS |
| 3329 | dataset: |
| 3330 | type: mteb/sts22-crosslingual-sts |
| 3331 | name: MTEB STS22 (pl-en) |
| 3332 | config: pl-en |
| 3333 | split: test |
| 3334 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3335 | metrics: |
| 3336 | - type: cos_sim_pearson |
| 3337 | value: 63.18215462781212 |
| 3338 | - type: cos_sim_spearman |
| 3339 | value: 65.54373266117607 |
| 3340 | - type: euclidean_pearson |
| 3341 | value: 64.54126095439005 |
| 3342 | - type: euclidean_spearman |
| 3343 | value: 65.30410369102711 |
| 3344 | - type: manhattan_pearson |
| 3345 | value: 63.50332221148234 |
| 3346 | - type: manhattan_spearman |
| 3347 | value: 64.3455878104313 |
| 3348 | - task: |
| 3349 | type: STS |
| 3350 | dataset: |
| 3351 | type: mteb/sts22-crosslingual-sts |
| 3352 | name: MTEB STS22 (zh-en) |
| 3353 | config: zh-en |
| 3354 | split: test |
| 3355 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3356 | metrics: |
| 3357 | - type: cos_sim_pearson |
| 3358 | value: 62.30509221440029 |
| 3359 | - type: cos_sim_spearman |
| 3360 | value: 65.99582704642478 |
| 3361 | - type: euclidean_pearson |
| 3362 | value: 63.43818859884195 |
| 3363 | - type: euclidean_spearman |
| 3364 | value: 66.83172582815764 |
| 3365 | - type: manhattan_pearson |
| 3366 | value: 63.055779168508764 |
| 3367 | - type: manhattan_spearman |
| 3368 | value: 65.49585020501449 |
| 3369 | - task: |
| 3370 | type: STS |
| 3371 | dataset: |
| 3372 | type: mteb/sts22-crosslingual-sts |
| 3373 | name: MTEB STS22 (es-it) |
| 3374 | config: es-it |
| 3375 | split: test |
| 3376 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3377 | metrics: |
| 3378 | - type: cos_sim_pearson |
| 3379 | value: 59.587830825340404 |
| 3380 | - type: cos_sim_spearman |
| 3381 | value: 68.93467614588089 |
| 3382 | - type: euclidean_pearson |
| 3383 | value: 62.3073527367404 |
| 3384 | - type: euclidean_spearman |
| 3385 | value: 69.69758171553175 |
| 3386 | - type: manhattan_pearson |
| 3387 | value: 61.9074580815789 |
| 3388 | - type: manhattan_spearman |
| 3389 | value: 69.57696375597865 |
| 3390 | - task: |
| 3391 | type: STS |
| 3392 | dataset: |
| 3393 | type: mteb/sts22-crosslingual-sts |
| 3394 | name: MTEB STS22 (de-fr) |
| 3395 | config: de-fr |
| 3396 | split: test |
| 3397 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3398 | metrics: |
| 3399 | - type: cos_sim_pearson |
| 3400 | value: 57.143220125577066 |
| 3401 | - type: cos_sim_spearman |
| 3402 | value: 67.78857859159226 |
| 3403 | - type: euclidean_pearson |
| 3404 | value: 55.58225107923733 |
| 3405 | - type: euclidean_spearman |
| 3406 | value: 67.80662907184563 |
| 3407 | - type: manhattan_pearson |
| 3408 | value: 56.24953502726514 |
| 3409 | - type: manhattan_spearman |
| 3410 | value: 67.98262125431616 |
| 3411 | - task: |
| 3412 | type: STS |
| 3413 | dataset: |
| 3414 | type: mteb/sts22-crosslingual-sts |
| 3415 | name: MTEB STS22 (de-pl) |
| 3416 | config: de-pl |
| 3417 | split: test |
| 3418 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3419 | metrics: |
| 3420 | - type: cos_sim_pearson |
| 3421 | value: 21.826928900322066 |
| 3422 | - type: cos_sim_spearman |
| 3423 | value: 49.578506634400405 |
| 3424 | - type: euclidean_pearson |
| 3425 | value: 27.939890138843214 |
| 3426 | - type: euclidean_spearman |
| 3427 | value: 52.71950519136242 |
| 3428 | - type: manhattan_pearson |
| 3429 | value: 26.39878683847546 |
| 3430 | - type: manhattan_spearman |
| 3431 | value: 47.54609580342499 |
| 3432 | - task: |
| 3433 | type: STS |
| 3434 | dataset: |
| 3435 | type: mteb/sts22-crosslingual-sts |
| 3436 | name: MTEB STS22 (fr-pl) |
| 3437 | config: fr-pl |
| 3438 | split: test |
| 3439 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3440 | metrics: |
| 3441 | - type: cos_sim_pearson |
| 3442 | value: 57.27603854632001 |
| 3443 | - type: cos_sim_spearman |
| 3444 | value: 50.709255283710995 |
| 3445 | - type: euclidean_pearson |
| 3446 | value: 59.5419024445929 |
| 3447 | - type: euclidean_spearman |
| 3448 | value: 50.709255283710995 |
| 3449 | - type: manhattan_pearson |
| 3450 | value: 59.03256832438492 |
| 3451 | - type: manhattan_spearman |
| 3452 | value: 61.97797868009122 |
| 3453 | - task: |
| 3454 | type: STS |
| 3455 | dataset: |
| 3456 | type: mteb/stsbenchmark-sts |
| 3457 | name: MTEB STSBenchmark |
| 3458 | config: default |
| 3459 | split: test |
| 3460 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 3461 | metrics: |
| 3462 | - type: cos_sim_pearson |
| 3463 | value: 85.00757054859712 |
| 3464 | - type: cos_sim_spearman |
| 3465 | value: 87.29283629622222 |
| 3466 | - type: euclidean_pearson |
| 3467 | value: 86.54824171775536 |
| 3468 | - type: euclidean_spearman |
| 3469 | value: 87.24364730491402 |
| 3470 | - type: manhattan_pearson |
| 3471 | value: 86.5062156915074 |
| 3472 | - type: manhattan_spearman |
| 3473 | value: 87.15052170378574 |
| 3474 | - task: |
| 3475 | type: Reranking |
| 3476 | dataset: |
| 3477 | type: mteb/scidocs-reranking |
| 3478 | name: MTEB SciDocsRR |
| 3479 | config: default |
| 3480 | split: test |
| 3481 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 3482 | metrics: |
| 3483 | - type: map |
| 3484 | value: 82.03549357197389 |
| 3485 | - type: mrr |
| 3486 | value: 95.05437645143527 |
| 3487 | - task: |
| 3488 | type: Retrieval |
| 3489 | dataset: |
| 3490 | type: scifact |
| 3491 | name: MTEB SciFact |
| 3492 | config: default |
| 3493 | split: test |
| 3494 | revision: None |
| 3495 | metrics: |
| 3496 | - type: map_at_1 |
| 3497 | value: 57.260999999999996 |
| 3498 | - type: map_at_10 |
| 3499 | value: 66.259 |
| 3500 | - type: map_at_100 |
| 3501 | value: 66.884 |
| 3502 | - type: map_at_1000 |
| 3503 | value: 66.912 |
| 3504 | - type: map_at_3 |
| 3505 | value: 63.685 |
| 3506 | - type: map_at_5 |
| 3507 | value: 65.35499999999999 |
| 3508 | - type: mrr_at_1 |
| 3509 | value: 60.333000000000006 |
| 3510 | - type: mrr_at_10 |
| 3511 | value: 67.5 |
| 3512 | - type: mrr_at_100 |
| 3513 | value: 68.013 |
| 3514 | - type: mrr_at_1000 |
| 3515 | value: 68.038 |
| 3516 | - type: mrr_at_3 |
| 3517 | value: 65.61099999999999 |
| 3518 | - type: mrr_at_5 |
| 3519 | value: 66.861 |
| 3520 | - type: ndcg_at_1 |
| 3521 | value: 60.333000000000006 |
| 3522 | - type: ndcg_at_10 |
| 3523 | value: 70.41 |
| 3524 | - type: ndcg_at_100 |
| 3525 | value: 73.10600000000001 |
| 3526 | - type: ndcg_at_1000 |
| 3527 | value: 73.846 |
| 3528 | - type: ndcg_at_3 |
| 3529 | value: 66.133 |
| 3530 | - type: ndcg_at_5 |
| 3531 | value: 68.499 |
| 3532 | - type: precision_at_1 |
| 3533 | value: 60.333000000000006 |
| 3534 | - type: precision_at_10 |
| 3535 | value: 9.232999999999999 |
| 3536 | - type: precision_at_100 |
| 3537 | value: 1.0630000000000002 |
| 3538 | - type: precision_at_1000 |
| 3539 | value: 0.11299999999999999 |
| 3540 | - type: precision_at_3 |
| 3541 | value: 25.667 |
| 3542 | - type: precision_at_5 |
| 3543 | value: 17.067 |
| 3544 | - type: recall_at_1 |
| 3545 | value: 57.260999999999996 |
| 3546 | - type: recall_at_10 |
| 3547 | value: 81.94399999999999 |
| 3548 | - type: recall_at_100 |
| 3549 | value: 93.867 |
| 3550 | - type: recall_at_1000 |
| 3551 | value: 99.667 |
| 3552 | - type: recall_at_3 |
| 3553 | value: 70.339 |
| 3554 | - type: recall_at_5 |
| 3555 | value: 76.25 |
| 3556 | - task: |
| 3557 | type: PairClassification |
| 3558 | dataset: |
| 3559 | type: mteb/sprintduplicatequestions-pairclassification |
| 3560 | name: MTEB SprintDuplicateQuestions |
| 3561 | config: default |
| 3562 | split: test |
| 3563 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 3564 | metrics: |
| 3565 | - type: cos_sim_accuracy |
| 3566 | value: 99.74356435643564 |
| 3567 | - type: cos_sim_ap |
| 3568 | value: 93.13411948212683 |
| 3569 | - type: cos_sim_f1 |
| 3570 | value: 86.80521991300147 |
| 3571 | - type: cos_sim_precision |
| 3572 | value: 84.00374181478017 |
| 3573 | - type: cos_sim_recall |
| 3574 | value: 89.8 |
| 3575 | - type: dot_accuracy |
| 3576 | value: 99.67920792079208 |
| 3577 | - type: dot_ap |
| 3578 | value: 89.27277565444479 |
| 3579 | - type: dot_f1 |
| 3580 | value: 83.9276990718124 |
| 3581 | - type: dot_precision |
| 3582 | value: 82.04393505253104 |
| 3583 | - type: dot_recall |
| 3584 | value: 85.9 |
| 3585 | - type: euclidean_accuracy |
| 3586 | value: 99.74257425742574 |
| 3587 | - type: euclidean_ap |
| 3588 | value: 93.17993008259062 |
| 3589 | - type: euclidean_f1 |
| 3590 | value: 86.69396110542476 |
| 3591 | - type: euclidean_precision |
| 3592 | value: 88.78406708595388 |
| 3593 | - type: euclidean_recall |
| 3594 | value: 84.7 |
| 3595 | - type: manhattan_accuracy |
| 3596 | value: 99.74257425742574 |
| 3597 | - type: manhattan_ap |
| 3598 | value: 93.14413755550099 |
| 3599 | - type: manhattan_f1 |
| 3600 | value: 86.82483594144371 |
| 3601 | - type: manhattan_precision |
| 3602 | value: 87.66564729867483 |
| 3603 | - type: manhattan_recall |
| 3604 | value: 86 |
| 3605 | - type: max_accuracy |
| 3606 | value: 99.74356435643564 |
| 3607 | - type: max_ap |
| 3608 | value: 93.17993008259062 |
| 3609 | - type: max_f1 |
| 3610 | value: 86.82483594144371 |
| 3611 | - task: |
| 3612 | type: Clustering |
| 3613 | dataset: |
| 3614 | type: mteb/stackexchange-clustering |
| 3615 | name: MTEB StackExchangeClustering |
| 3616 | config: default |
| 3617 | split: test |
| 3618 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 3619 | metrics: |
| 3620 | - type: v_measure |
| 3621 | value: 57.525863806168566 |
| 3622 | - task: |
| 3623 | type: Clustering |
| 3624 | dataset: |
| 3625 | type: mteb/stackexchange-clustering-p2p |
| 3626 | name: MTEB StackExchangeClusteringP2P |
| 3627 | config: default |
| 3628 | split: test |
| 3629 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 3630 | metrics: |
| 3631 | - type: v_measure |
| 3632 | value: 32.68850574423839 |
| 3633 | - task: |
| 3634 | type: Reranking |
| 3635 | dataset: |
| 3636 | type: mteb/stackoverflowdupquestions-reranking |
| 3637 | name: MTEB StackOverflowDupQuestions |
| 3638 | config: default |
| 3639 | split: test |
| 3640 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 3641 | metrics: |
| 3642 | - type: map |
| 3643 | value: 49.71580650644033 |
| 3644 | - type: mrr |
| 3645 | value: 50.50971903913081 |
| 3646 | - task: |
| 3647 | type: Summarization |
| 3648 | dataset: |
| 3649 | type: mteb/summeval |
| 3650 | name: MTEB SummEval |
| 3651 | config: default |
| 3652 | split: test |
| 3653 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 3654 | metrics: |
| 3655 | - type: cos_sim_pearson |
| 3656 | value: 29.152190498799484 |
| 3657 | - type: cos_sim_spearman |
| 3658 | value: 29.686180371952727 |
| 3659 | - type: dot_pearson |
| 3660 | value: 27.248664793816342 |
| 3661 | - type: dot_spearman |
| 3662 | value: 28.37748983721745 |
| 3663 | - task: |
| 3664 | type: Retrieval |
| 3665 | dataset: |
| 3666 | type: trec-covid |
| 3667 | name: MTEB TRECCOVID |
| 3668 | config: default |
| 3669 | split: test |
| 3670 | revision: None |
| 3671 | metrics: |
| 3672 | - type: map_at_1 |
| 3673 | value: 0.20400000000000001 |
| 3674 | - type: map_at_10 |
| 3675 | value: 1.6209999999999998 |
| 3676 | - type: map_at_100 |
| 3677 | value: 9.690999999999999 |
| 3678 | - type: map_at_1000 |
| 3679 | value: 23.733 |
| 3680 | - type: map_at_3 |
| 3681 | value: 0.575 |
| 3682 | - type: map_at_5 |
| 3683 | value: 0.885 |
| 3684 | - type: mrr_at_1 |
| 3685 | value: 78 |
| 3686 | - type: mrr_at_10 |
| 3687 | value: 86.56700000000001 |
| 3688 | - type: mrr_at_100 |
| 3689 | value: 86.56700000000001 |
| 3690 | - type: mrr_at_1000 |
| 3691 | value: 86.56700000000001 |
| 3692 | - type: mrr_at_3 |
| 3693 | value: 85.667 |
| 3694 | - type: mrr_at_5 |
| 3695 | value: 86.56700000000001 |
| 3696 | - type: ndcg_at_1 |
| 3697 | value: 76 |
| 3698 | - type: ndcg_at_10 |
| 3699 | value: 71.326 |
| 3700 | - type: ndcg_at_100 |
| 3701 | value: 54.208999999999996 |
| 3702 | - type: ndcg_at_1000 |
| 3703 | value: 49.252 |
| 3704 | - type: ndcg_at_3 |
| 3705 | value: 74.235 |
| 3706 | - type: ndcg_at_5 |
| 3707 | value: 73.833 |
| 3708 | - type: precision_at_1 |
| 3709 | value: 78 |
| 3710 | - type: precision_at_10 |
| 3711 | value: 74.8 |
| 3712 | - type: precision_at_100 |
| 3713 | value: 55.50000000000001 |
| 3714 | - type: precision_at_1000 |
| 3715 | value: 21.836 |
| 3716 | - type: precision_at_3 |
| 3717 | value: 78 |
| 3718 | - type: precision_at_5 |
| 3719 | value: 78 |
| 3720 | - type: recall_at_1 |
| 3721 | value: 0.20400000000000001 |
| 3722 | - type: recall_at_10 |
| 3723 | value: 1.894 |
| 3724 | - type: recall_at_100 |
| 3725 | value: 13.245999999999999 |
| 3726 | - type: recall_at_1000 |
| 3727 | value: 46.373 |
| 3728 | - type: recall_at_3 |
| 3729 | value: 0.613 |
| 3730 | - type: recall_at_5 |
| 3731 | value: 0.991 |
| 3732 | - task: |
| 3733 | type: BitextMining |
| 3734 | dataset: |
| 3735 | type: mteb/tatoeba-bitext-mining |
| 3736 | name: MTEB Tatoeba (sqi-eng) |
| 3737 | config: sqi-eng |
| 3738 | split: test |
| 3739 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3740 | metrics: |
| 3741 | - type: accuracy |
| 3742 | value: 95.89999999999999 |
| 3743 | - type: f1 |
| 3744 | value: 94.69999999999999 |
| 3745 | - type: precision |
| 3746 | value: 94.11666666666667 |
| 3747 | - type: recall |
| 3748 | value: 95.89999999999999 |
| 3749 | - task: |
| 3750 | type: BitextMining |
| 3751 | dataset: |
| 3752 | type: mteb/tatoeba-bitext-mining |
| 3753 | name: MTEB Tatoeba (fry-eng) |
| 3754 | config: fry-eng |
| 3755 | split: test |
| 3756 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3757 | metrics: |
| 3758 | - type: accuracy |
| 3759 | value: 68.20809248554913 |
| 3760 | - type: f1 |
| 3761 | value: 63.431048720066066 |
| 3762 | - type: precision |
| 3763 | value: 61.69143958161298 |
| 3764 | - type: recall |
| 3765 | value: 68.20809248554913 |
| 3766 | - task: |
| 3767 | type: BitextMining |
| 3768 | dataset: |
| 3769 | type: mteb/tatoeba-bitext-mining |
| 3770 | name: MTEB Tatoeba (kur-eng) |
| 3771 | config: kur-eng |
| 3772 | split: test |
| 3773 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3774 | metrics: |
| 3775 | - type: accuracy |
| 3776 | value: 71.21951219512195 |
| 3777 | - type: f1 |
| 3778 | value: 66.82926829268293 |
| 3779 | - type: precision |
| 3780 | value: 65.1260162601626 |
| 3781 | - type: recall |
| 3782 | value: 71.21951219512195 |
| 3783 | - task: |
| 3784 | type: BitextMining |
| 3785 | dataset: |
| 3786 | type: mteb/tatoeba-bitext-mining |
| 3787 | name: MTEB Tatoeba (tur-eng) |
| 3788 | config: tur-eng |
| 3789 | split: test |
| 3790 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3791 | metrics: |
| 3792 | - type: accuracy |
| 3793 | value: 97.2 |
| 3794 | - type: f1 |
| 3795 | value: 96.26666666666667 |
| 3796 | - type: precision |
| 3797 | value: 95.8 |
| 3798 | - type: recall |
| 3799 | value: 97.2 |
| 3800 | - task: |
| 3801 | type: BitextMining |
| 3802 | dataset: |
| 3803 | type: mteb/tatoeba-bitext-mining |
| 3804 | name: MTEB Tatoeba (deu-eng) |
| 3805 | config: deu-eng |
| 3806 | split: test |
| 3807 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3808 | metrics: |
| 3809 | - type: accuracy |
| 3810 | value: 99.3 |
| 3811 | - type: f1 |
| 3812 | value: 99.06666666666666 |
| 3813 | - type: precision |
| 3814 | value: 98.95 |
| 3815 | - type: recall |
| 3816 | value: 99.3 |
| 3817 | - task: |
| 3818 | type: BitextMining |
| 3819 | dataset: |
| 3820 | type: mteb/tatoeba-bitext-mining |
| 3821 | name: MTEB Tatoeba (nld-eng) |
| 3822 | config: nld-eng |
| 3823 | split: test |
| 3824 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3825 | metrics: |
| 3826 | - type: accuracy |
| 3827 | value: 97.39999999999999 |
| 3828 | - type: f1 |
| 3829 | value: 96.63333333333333 |
| 3830 | - type: precision |
| 3831 | value: 96.26666666666668 |
| 3832 | - type: recall |
| 3833 | value: 97.39999999999999 |
| 3834 | - task: |
| 3835 | type: BitextMining |
| 3836 | dataset: |
| 3837 | type: mteb/tatoeba-bitext-mining |
| 3838 | name: MTEB Tatoeba (ron-eng) |
| 3839 | config: ron-eng |
| 3840 | split: test |
| 3841 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3842 | metrics: |
| 3843 | - type: accuracy |
| 3844 | value: 96 |
| 3845 | - type: f1 |
| 3846 | value: 94.86666666666666 |
| 3847 | - type: precision |
| 3848 | value: 94.31666666666668 |
| 3849 | - type: recall |
| 3850 | value: 96 |
| 3851 | - task: |
| 3852 | type: BitextMining |
| 3853 | dataset: |
| 3854 | type: mteb/tatoeba-bitext-mining |
| 3855 | name: MTEB Tatoeba (ang-eng) |
| 3856 | config: ang-eng |
| 3857 | split: test |
| 3858 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3859 | metrics: |
| 3860 | - type: accuracy |
| 3861 | value: 47.01492537313433 |
| 3862 | - type: f1 |
| 3863 | value: 40.178867566927266 |
| 3864 | - type: precision |
| 3865 | value: 38.179295828549556 |
| 3866 | - type: recall |
| 3867 | value: 47.01492537313433 |
| 3868 | - task: |
| 3869 | type: BitextMining |
| 3870 | dataset: |
| 3871 | type: mteb/tatoeba-bitext-mining |
| 3872 | name: MTEB Tatoeba (ido-eng) |
| 3873 | config: ido-eng |
| 3874 | split: test |
| 3875 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3876 | metrics: |
| 3877 | - type: accuracy |
| 3878 | value: 86.5 |
| 3879 | - type: f1 |
| 3880 | value: 83.62537480063796 |
| 3881 | - type: precision |
| 3882 | value: 82.44555555555554 |
| 3883 | - type: recall |
| 3884 | value: 86.5 |
| 3885 | - task: |
| 3886 | type: BitextMining |
| 3887 | dataset: |
| 3888 | type: mteb/tatoeba-bitext-mining |
| 3889 | name: MTEB Tatoeba (jav-eng) |
| 3890 | config: jav-eng |
| 3891 | split: test |
| 3892 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3893 | metrics: |
| 3894 | - type: accuracy |
| 3895 | value: 80.48780487804879 |
| 3896 | - type: f1 |
| 3897 | value: 75.45644599303138 |
| 3898 | - type: precision |
| 3899 | value: 73.37398373983739 |
| 3900 | - type: recall |
| 3901 | value: 80.48780487804879 |
| 3902 | - task: |
| 3903 | type: BitextMining |
| 3904 | dataset: |
| 3905 | type: mteb/tatoeba-bitext-mining |
| 3906 | name: MTEB Tatoeba (isl-eng) |
| 3907 | config: isl-eng |
| 3908 | split: test |
| 3909 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3910 | metrics: |
| 3911 | - type: accuracy |
| 3912 | value: 93.7 |
| 3913 | - type: f1 |
| 3914 | value: 91.95666666666666 |
| 3915 | - type: precision |
| 3916 | value: 91.125 |
| 3917 | - type: recall |
| 3918 | value: 93.7 |
| 3919 | - task: |
| 3920 | type: BitextMining |
| 3921 | dataset: |
| 3922 | type: mteb/tatoeba-bitext-mining |
| 3923 | name: MTEB Tatoeba (slv-eng) |
| 3924 | config: slv-eng |
| 3925 | split: test |
| 3926 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3927 | metrics: |
| 3928 | - type: accuracy |
| 3929 | value: 91.73754556500607 |
| 3930 | - type: f1 |
| 3931 | value: 89.65168084244632 |
| 3932 | - type: precision |
| 3933 | value: 88.73025516403402 |
| 3934 | - type: recall |
| 3935 | value: 91.73754556500607 |
| 3936 | - task: |
| 3937 | type: BitextMining |
| 3938 | dataset: |
| 3939 | type: mteb/tatoeba-bitext-mining |
| 3940 | name: MTEB Tatoeba (cym-eng) |
| 3941 | config: cym-eng |
| 3942 | split: test |
| 3943 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3944 | metrics: |
| 3945 | - type: accuracy |
| 3946 | value: 81.04347826086956 |
| 3947 | - type: f1 |
| 3948 | value: 76.2128364389234 |
| 3949 | - type: precision |
| 3950 | value: 74.2 |
| 3951 | - type: recall |
| 3952 | value: 81.04347826086956 |
| 3953 | - task: |
| 3954 | type: BitextMining |
| 3955 | dataset: |
| 3956 | type: mteb/tatoeba-bitext-mining |
| 3957 | name: MTEB Tatoeba (kaz-eng) |
| 3958 | config: kaz-eng |
| 3959 | split: test |
| 3960 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3961 | metrics: |
| 3962 | - type: accuracy |
| 3963 | value: 83.65217391304348 |
| 3964 | - type: f1 |
| 3965 | value: 79.4376811594203 |
| 3966 | - type: precision |
| 3967 | value: 77.65797101449274 |
| 3968 | - type: recall |
| 3969 | value: 83.65217391304348 |
| 3970 | - task: |
| 3971 | type: BitextMining |
| 3972 | dataset: |
| 3973 | type: mteb/tatoeba-bitext-mining |
| 3974 | name: MTEB Tatoeba (est-eng) |
| 3975 | config: est-eng |
| 3976 | split: test |
| 3977 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3978 | metrics: |
| 3979 | - type: accuracy |
| 3980 | value: 87.5 |
| 3981 | - type: f1 |
| 3982 | value: 85.02690476190476 |
| 3983 | - type: precision |
| 3984 | value: 83.96261904761904 |
| 3985 | - type: recall |
| 3986 | value: 87.5 |
| 3987 | - task: |
| 3988 | type: BitextMining |
| 3989 | dataset: |
| 3990 | type: mteb/tatoeba-bitext-mining |
| 3991 | name: MTEB Tatoeba (heb-eng) |
| 3992 | config: heb-eng |
| 3993 | split: test |
| 3994 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3995 | metrics: |
| 3996 | - type: accuracy |
| 3997 | value: 89.3 |
| 3998 | - type: f1 |
| 3999 | value: 86.52333333333333 |
| 4000 | - type: precision |
| 4001 | value: 85.22833333333332 |
| 4002 | - type: recall |
| 4003 | value: 89.3 |
| 4004 | - task: |
| 4005 | type: BitextMining |
| 4006 | dataset: |
| 4007 | type: mteb/tatoeba-bitext-mining |
| 4008 | name: MTEB Tatoeba (gla-eng) |
| 4009 | config: gla-eng |
| 4010 | split: test |
| 4011 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4012 | metrics: |
| 4013 | - type: accuracy |
| 4014 | value: 65.01809408926418 |
| 4015 | - type: f1 |
| 4016 | value: 59.00594446432805 |
| 4017 | - type: precision |
| 4018 | value: 56.827215807915444 |
| 4019 | - type: recall |
| 4020 | value: 65.01809408926418 |
| 4021 | - task: |
| 4022 | type: BitextMining |
| 4023 | dataset: |
| 4024 | type: mteb/tatoeba-bitext-mining |
| 4025 | name: MTEB Tatoeba (mar-eng) |
| 4026 | config: mar-eng |
| 4027 | split: test |
| 4028 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4029 | metrics: |
| 4030 | - type: accuracy |
| 4031 | value: 91.2 |
| 4032 | - type: f1 |
| 4033 | value: 88.58 |
| 4034 | - type: precision |
| 4035 | value: 87.33333333333334 |
| 4036 | - type: recall |
| 4037 | value: 91.2 |
| 4038 | - task: |
| 4039 | type: BitextMining |
| 4040 | dataset: |
| 4041 | type: mteb/tatoeba-bitext-mining |
| 4042 | name: MTEB Tatoeba (lat-eng) |
| 4043 | config: lat-eng |
| 4044 | split: test |
| 4045 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4046 | metrics: |
| 4047 | - type: accuracy |
| 4048 | value: 59.199999999999996 |
| 4049 | - type: f1 |
| 4050 | value: 53.299166276284915 |
| 4051 | - type: precision |
| 4052 | value: 51.3383908045977 |
| 4053 | - type: recall |
| 4054 | value: 59.199999999999996 |
| 4055 | - task: |
| 4056 | type: BitextMining |
| 4057 | dataset: |
| 4058 | type: mteb/tatoeba-bitext-mining |
| 4059 | name: MTEB Tatoeba (bel-eng) |
| 4060 | config: bel-eng |
| 4061 | split: test |
| 4062 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4063 | metrics: |
| 4064 | - type: accuracy |
| 4065 | value: 93.2 |
| 4066 | - type: f1 |
| 4067 | value: 91.2 |
| 4068 | - type: precision |
| 4069 | value: 90.25 |
| 4070 | - type: recall |
| 4071 | value: 93.2 |
| 4072 | - task: |
| 4073 | type: BitextMining |
| 4074 | dataset: |
| 4075 | type: mteb/tatoeba-bitext-mining |
| 4076 | name: MTEB Tatoeba (pms-eng) |
| 4077 | config: pms-eng |
| 4078 | split: test |
| 4079 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4080 | metrics: |
| 4081 | - type: accuracy |
| 4082 | value: 64.76190476190476 |
| 4083 | - type: f1 |
| 4084 | value: 59.867110667110666 |
| 4085 | - type: precision |
| 4086 | value: 58.07390192653351 |
| 4087 | - type: recall |
| 4088 | value: 64.76190476190476 |
| 4089 | - task: |
| 4090 | type: BitextMining |
| 4091 | dataset: |
| 4092 | type: mteb/tatoeba-bitext-mining |
| 4093 | name: MTEB Tatoeba (gle-eng) |
| 4094 | config: gle-eng |
| 4095 | split: test |
| 4096 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4097 | metrics: |
| 4098 | - type: accuracy |
| 4099 | value: 76.2 |
| 4100 | - type: f1 |
| 4101 | value: 71.48147546897547 |
| 4102 | - type: precision |
| 4103 | value: 69.65409090909091 |
| 4104 | - type: recall |
| 4105 | value: 76.2 |
| 4106 | - task: |
| 4107 | type: BitextMining |
| 4108 | dataset: |
| 4109 | type: mteb/tatoeba-bitext-mining |
| 4110 | name: MTEB Tatoeba (pes-eng) |
| 4111 | config: pes-eng |
| 4112 | split: test |
| 4113 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4114 | metrics: |
| 4115 | - type: accuracy |
| 4116 | value: 93.8 |
| 4117 | - type: f1 |
| 4118 | value: 92.14 |
| 4119 | - type: precision |
| 4120 | value: 91.35833333333333 |
| 4121 | - type: recall |
| 4122 | value: 93.8 |
| 4123 | - task: |
| 4124 | type: BitextMining |
| 4125 | dataset: |
| 4126 | type: mteb/tatoeba-bitext-mining |
| 4127 | name: MTEB Tatoeba (nob-eng) |
| 4128 | config: nob-eng |
| 4129 | split: test |
| 4130 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4131 | metrics: |
| 4132 | - type: accuracy |
| 4133 | value: 97.89999999999999 |
| 4134 | - type: f1 |
| 4135 | value: 97.2 |
| 4136 | - type: precision |
| 4137 | value: 96.85000000000001 |
| 4138 | - type: recall |
| 4139 | value: 97.89999999999999 |
| 4140 | - task: |
| 4141 | type: BitextMining |
| 4142 | dataset: |
| 4143 | type: mteb/tatoeba-bitext-mining |
| 4144 | name: MTEB Tatoeba (bul-eng) |
| 4145 | config: bul-eng |
| 4146 | split: test |
| 4147 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4148 | metrics: |
| 4149 | - type: accuracy |
| 4150 | value: 94.6 |
| 4151 | - type: f1 |
| 4152 | value: 92.93333333333334 |
| 4153 | - type: precision |
| 4154 | value: 92.13333333333333 |
| 4155 | - type: recall |
| 4156 | value: 94.6 |
| 4157 | - task: |
| 4158 | type: BitextMining |
| 4159 | dataset: |
| 4160 | type: mteb/tatoeba-bitext-mining |
| 4161 | name: MTEB Tatoeba (cbk-eng) |
| 4162 | config: cbk-eng |
| 4163 | split: test |
| 4164 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4165 | metrics: |
| 4166 | - type: accuracy |
| 4167 | value: 74.1 |
| 4168 | - type: f1 |
| 4169 | value: 69.14817460317461 |
| 4170 | - type: precision |
| 4171 | value: 67.2515873015873 |
| 4172 | - type: recall |
| 4173 | value: 74.1 |
| 4174 | - task: |
| 4175 | type: BitextMining |
| 4176 | dataset: |
| 4177 | type: mteb/tatoeba-bitext-mining |
| 4178 | name: MTEB Tatoeba (hun-eng) |
| 4179 | config: hun-eng |
| 4180 | split: test |
| 4181 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4182 | metrics: |
| 4183 | - type: accuracy |
| 4184 | value: 95.19999999999999 |
| 4185 | - type: f1 |
| 4186 | value: 94.01333333333335 |
| 4187 | - type: precision |
| 4188 | value: 93.46666666666667 |
| 4189 | - type: recall |
| 4190 | value: 95.19999999999999 |
| 4191 | - task: |
| 4192 | type: BitextMining |
| 4193 | dataset: |
| 4194 | type: mteb/tatoeba-bitext-mining |
| 4195 | name: MTEB Tatoeba (uig-eng) |
| 4196 | config: uig-eng |
| 4197 | split: test |
| 4198 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4199 | metrics: |
| 4200 | - type: accuracy |
| 4201 | value: 76.9 |
| 4202 | - type: f1 |
| 4203 | value: 72.07523809523809 |
| 4204 | - type: precision |
| 4205 | value: 70.19777777777779 |
| 4206 | - type: recall |
| 4207 | value: 76.9 |
| 4208 | - task: |
| 4209 | type: BitextMining |
| 4210 | dataset: |
| 4211 | type: mteb/tatoeba-bitext-mining |
| 4212 | name: MTEB Tatoeba (rus-eng) |
| 4213 | config: rus-eng |
| 4214 | split: test |
| 4215 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4216 | metrics: |
| 4217 | - type: accuracy |
| 4218 | value: 94.1 |
| 4219 | - type: f1 |
| 4220 | value: 92.31666666666666 |
| 4221 | - type: precision |
| 4222 | value: 91.43333333333332 |
| 4223 | - type: recall |
| 4224 | value: 94.1 |
| 4225 | - task: |
| 4226 | type: BitextMining |
| 4227 | dataset: |
| 4228 | type: mteb/tatoeba-bitext-mining |
| 4229 | name: MTEB Tatoeba (spa-eng) |
| 4230 | config: spa-eng |
| 4231 | split: test |
| 4232 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4233 | metrics: |
| 4234 | - type: accuracy |
| 4235 | value: 97.8 |
| 4236 | - type: f1 |
| 4237 | value: 97.1 |
| 4238 | - type: precision |
| 4239 | value: 96.76666666666668 |
| 4240 | - type: recall |
| 4241 | value: 97.8 |
| 4242 | - task: |
| 4243 | type: BitextMining |
| 4244 | dataset: |
| 4245 | type: mteb/tatoeba-bitext-mining |
| 4246 | name: MTEB Tatoeba (hye-eng) |
| 4247 | config: hye-eng |
| 4248 | split: test |
| 4249 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4250 | metrics: |
| 4251 | - type: accuracy |
| 4252 | value: 92.85714285714286 |
| 4253 | - type: f1 |
| 4254 | value: 90.92093441150045 |
| 4255 | - type: precision |
| 4256 | value: 90.00449236298293 |
| 4257 | - type: recall |
| 4258 | value: 92.85714285714286 |
| 4259 | - task: |
| 4260 | type: BitextMining |
| 4261 | dataset: |
| 4262 | type: mteb/tatoeba-bitext-mining |
| 4263 | name: MTEB Tatoeba (tel-eng) |
| 4264 | config: tel-eng |
| 4265 | split: test |
| 4266 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4267 | metrics: |
| 4268 | - type: accuracy |
| 4269 | value: 93.16239316239316 |
| 4270 | - type: f1 |
| 4271 | value: 91.33903133903132 |
| 4272 | - type: precision |
| 4273 | value: 90.56267806267806 |
| 4274 | - type: recall |
| 4275 | value: 93.16239316239316 |
| 4276 | - task: |
| 4277 | type: BitextMining |
| 4278 | dataset: |
| 4279 | type: mteb/tatoeba-bitext-mining |
| 4280 | name: MTEB Tatoeba (afr-eng) |
| 4281 | config: afr-eng |
| 4282 | split: test |
| 4283 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4284 | metrics: |
| 4285 | - type: accuracy |
| 4286 | value: 92.4 |
| 4287 | - type: f1 |
| 4288 | value: 90.25666666666666 |
| 4289 | - type: precision |
| 4290 | value: 89.25833333333334 |
| 4291 | - type: recall |
| 4292 | value: 92.4 |
| 4293 | - task: |
| 4294 | type: BitextMining |
| 4295 | dataset: |
| 4296 | type: mteb/tatoeba-bitext-mining |
| 4297 | name: MTEB Tatoeba (mon-eng) |
| 4298 | config: mon-eng |
| 4299 | split: test |
| 4300 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4301 | metrics: |
| 4302 | - type: accuracy |
| 4303 | value: 90.22727272727272 |
| 4304 | - type: f1 |
| 4305 | value: 87.53030303030303 |
| 4306 | - type: precision |
| 4307 | value: 86.37121212121211 |
| 4308 | - type: recall |
| 4309 | value: 90.22727272727272 |
| 4310 | - task: |
| 4311 | type: BitextMining |
| 4312 | dataset: |
| 4313 | type: mteb/tatoeba-bitext-mining |
| 4314 | name: MTEB Tatoeba (arz-eng) |
| 4315 | config: arz-eng |
| 4316 | split: test |
| 4317 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4318 | metrics: |
| 4319 | - type: accuracy |
| 4320 | value: 79.03563941299791 |
| 4321 | - type: f1 |
| 4322 | value: 74.7349505840072 |
| 4323 | - type: precision |
| 4324 | value: 72.9035639412998 |
| 4325 | - type: recall |
| 4326 | value: 79.03563941299791 |
| 4327 | - task: |
| 4328 | type: BitextMining |
| 4329 | dataset: |
| 4330 | type: mteb/tatoeba-bitext-mining |
| 4331 | name: MTEB Tatoeba (hrv-eng) |
| 4332 | config: hrv-eng |
| 4333 | split: test |
| 4334 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4335 | metrics: |
| 4336 | - type: accuracy |
| 4337 | value: 97 |
| 4338 | - type: f1 |
| 4339 | value: 96.15 |
| 4340 | - type: precision |
| 4341 | value: 95.76666666666668 |
| 4342 | - type: recall |
| 4343 | value: 97 |
| 4344 | - task: |
| 4345 | type: BitextMining |
| 4346 | dataset: |
| 4347 | type: mteb/tatoeba-bitext-mining |
| 4348 | name: MTEB Tatoeba (nov-eng) |
| 4349 | config: nov-eng |
| 4350 | split: test |
| 4351 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4352 | metrics: |
| 4353 | - type: accuracy |
| 4354 | value: 76.26459143968872 |
| 4355 | - type: f1 |
| 4356 | value: 71.55642023346303 |
| 4357 | - type: precision |
| 4358 | value: 69.7544932369835 |
| 4359 | - type: recall |
| 4360 | value: 76.26459143968872 |
| 4361 | - task: |
| 4362 | type: BitextMining |
| 4363 | dataset: |
| 4364 | type: mteb/tatoeba-bitext-mining |
| 4365 | name: MTEB Tatoeba (gsw-eng) |
| 4366 | config: gsw-eng |
| 4367 | split: test |
| 4368 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4369 | metrics: |
| 4370 | - type: accuracy |
| 4371 | value: 58.119658119658126 |
| 4372 | - type: f1 |
| 4373 | value: 51.65242165242165 |
| 4374 | - type: precision |
| 4375 | value: 49.41768108434775 |
| 4376 | - type: recall |
| 4377 | value: 58.119658119658126 |
| 4378 | - task: |
| 4379 | type: BitextMining |
| 4380 | dataset: |
| 4381 | type: mteb/tatoeba-bitext-mining |
| 4382 | name: MTEB Tatoeba (nds-eng) |
| 4383 | config: nds-eng |
| 4384 | split: test |
| 4385 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4386 | metrics: |
| 4387 | - type: accuracy |
| 4388 | value: 74.3 |
| 4389 | - type: f1 |
| 4390 | value: 69.52055555555555 |
| 4391 | - type: precision |
| 4392 | value: 67.7574938949939 |
| 4393 | - type: recall |
| 4394 | value: 74.3 |
| 4395 | - task: |
| 4396 | type: BitextMining |
| 4397 | dataset: |
| 4398 | type: mteb/tatoeba-bitext-mining |
| 4399 | name: MTEB Tatoeba (ukr-eng) |
| 4400 | config: ukr-eng |
| 4401 | split: test |
| 4402 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4403 | metrics: |
| 4404 | - type: accuracy |
| 4405 | value: 94.8 |
| 4406 | - type: f1 |
| 4407 | value: 93.31666666666666 |
| 4408 | - type: precision |
| 4409 | value: 92.60000000000001 |
| 4410 | - type: recall |
| 4411 | value: 94.8 |
| 4412 | - task: |
| 4413 | type: BitextMining |
| 4414 | dataset: |
| 4415 | type: mteb/tatoeba-bitext-mining |
| 4416 | name: MTEB Tatoeba (uzb-eng) |
| 4417 | config: uzb-eng |
| 4418 | split: test |
| 4419 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4420 | metrics: |
| 4421 | - type: accuracy |
| 4422 | value: 76.63551401869158 |
| 4423 | - type: f1 |
| 4424 | value: 72.35202492211837 |
| 4425 | - type: precision |
| 4426 | value: 70.60358255451713 |
| 4427 | - type: recall |
| 4428 | value: 76.63551401869158 |
| 4429 | - task: |
| 4430 | type: BitextMining |
| 4431 | dataset: |
| 4432 | type: mteb/tatoeba-bitext-mining |
| 4433 | name: MTEB Tatoeba (lit-eng) |
| 4434 | config: lit-eng |
| 4435 | split: test |
| 4436 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4437 | metrics: |
| 4438 | - type: accuracy |
| 4439 | value: 90.4 |
| 4440 | - type: f1 |
| 4441 | value: 88.4811111111111 |
| 4442 | - type: precision |
| 4443 | value: 87.7452380952381 |
| 4444 | - type: recall |
| 4445 | value: 90.4 |
| 4446 | - task: |
| 4447 | type: BitextMining |
| 4448 | dataset: |
| 4449 | type: mteb/tatoeba-bitext-mining |
| 4450 | name: MTEB Tatoeba (ina-eng) |
| 4451 | config: ina-eng |
| 4452 | split: test |
| 4453 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4454 | metrics: |
| 4455 | - type: accuracy |
| 4456 | value: 95 |
| 4457 | - type: f1 |
| 4458 | value: 93.60666666666667 |
| 4459 | - type: precision |
| 4460 | value: 92.975 |
| 4461 | - type: recall |
| 4462 | value: 95 |
| 4463 | - task: |
| 4464 | type: BitextMining |
| 4465 | dataset: |
| 4466 | type: mteb/tatoeba-bitext-mining |
| 4467 | name: MTEB Tatoeba (lfn-eng) |
| 4468 | config: lfn-eng |
| 4469 | split: test |
| 4470 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4471 | metrics: |
| 4472 | - type: accuracy |
| 4473 | value: 67.2 |
| 4474 | - type: f1 |
| 4475 | value: 63.01595782872099 |
| 4476 | - type: precision |
| 4477 | value: 61.596587301587306 |
| 4478 | - type: recall |
| 4479 | value: 67.2 |
| 4480 | - task: |
| 4481 | type: BitextMining |
| 4482 | dataset: |
| 4483 | type: mteb/tatoeba-bitext-mining |
| 4484 | name: MTEB Tatoeba (zsm-eng) |
| 4485 | config: zsm-eng |
| 4486 | split: test |
| 4487 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4488 | metrics: |
| 4489 | - type: accuracy |
| 4490 | value: 95.7 |
| 4491 | - type: f1 |
| 4492 | value: 94.52999999999999 |
| 4493 | - type: precision |
| 4494 | value: 94 |
| 4495 | - type: recall |
| 4496 | value: 95.7 |
| 4497 | - task: |
| 4498 | type: BitextMining |
| 4499 | dataset: |
| 4500 | type: mteb/tatoeba-bitext-mining |
| 4501 | name: MTEB Tatoeba (ita-eng) |
| 4502 | config: ita-eng |
| 4503 | split: test |
| 4504 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4505 | metrics: |
| 4506 | - type: accuracy |
| 4507 | value: 94.6 |
| 4508 | - type: f1 |
| 4509 | value: 93.28999999999999 |
| 4510 | - type: precision |
| 4511 | value: 92.675 |
| 4512 | - type: recall |
| 4513 | value: 94.6 |
| 4514 | - task: |
| 4515 | type: BitextMining |
| 4516 | dataset: |
| 4517 | type: mteb/tatoeba-bitext-mining |
| 4518 | name: MTEB Tatoeba (cmn-eng) |
| 4519 | config: cmn-eng |
| 4520 | split: test |
| 4521 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4522 | metrics: |
| 4523 | - type: accuracy |
| 4524 | value: 96.39999999999999 |
| 4525 | - type: f1 |
| 4526 | value: 95.28333333333333 |
| 4527 | - type: precision |
| 4528 | value: 94.75 |
| 4529 | - type: recall |
| 4530 | value: 96.39999999999999 |
| 4531 | - task: |
| 4532 | type: BitextMining |
| 4533 | dataset: |
| 4534 | type: mteb/tatoeba-bitext-mining |
| 4535 | name: MTEB Tatoeba (lvs-eng) |
| 4536 | config: lvs-eng |
| 4537 | split: test |
| 4538 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4539 | metrics: |
| 4540 | - type: accuracy |
| 4541 | value: 91.9 |
| 4542 | - type: f1 |
| 4543 | value: 89.83 |
| 4544 | - type: precision |
| 4545 | value: 88.92 |
| 4546 | - type: recall |
| 4547 | value: 91.9 |
| 4548 | - task: |
| 4549 | type: BitextMining |
| 4550 | dataset: |
| 4551 | type: mteb/tatoeba-bitext-mining |
| 4552 | name: MTEB Tatoeba (glg-eng) |
| 4553 | config: glg-eng |
| 4554 | split: test |
| 4555 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4556 | metrics: |
| 4557 | - type: accuracy |
| 4558 | value: 94.69999999999999 |
| 4559 | - type: f1 |
| 4560 | value: 93.34222222222223 |
| 4561 | - type: precision |
| 4562 | value: 92.75416666666668 |
| 4563 | - type: recall |
| 4564 | value: 94.69999999999999 |
| 4565 | - task: |
| 4566 | type: BitextMining |
| 4567 | dataset: |
| 4568 | type: mteb/tatoeba-bitext-mining |
| 4569 | name: MTEB Tatoeba (ceb-eng) |
| 4570 | config: ceb-eng |
| 4571 | split: test |
| 4572 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4573 | metrics: |
| 4574 | - type: accuracy |
| 4575 | value: 60.333333333333336 |
| 4576 | - type: f1 |
| 4577 | value: 55.31203703703703 |
| 4578 | - type: precision |
| 4579 | value: 53.39971108326371 |
| 4580 | - type: recall |
| 4581 | value: 60.333333333333336 |
| 4582 | - task: |
| 4583 | type: BitextMining |
| 4584 | dataset: |
| 4585 | type: mteb/tatoeba-bitext-mining |
| 4586 | name: MTEB Tatoeba (bre-eng) |
| 4587 | config: bre-eng |
| 4588 | split: test |
| 4589 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4590 | metrics: |
| 4591 | - type: accuracy |
| 4592 | value: 12.9 |
| 4593 | - type: f1 |
| 4594 | value: 11.099861903031458 |
| 4595 | - type: precision |
| 4596 | value: 10.589187932631877 |
| 4597 | - type: recall |
| 4598 | value: 12.9 |
| 4599 | - task: |
| 4600 | type: BitextMining |
| 4601 | dataset: |
| 4602 | type: mteb/tatoeba-bitext-mining |
| 4603 | name: MTEB Tatoeba (ben-eng) |
| 4604 | config: ben-eng |
| 4605 | split: test |
| 4606 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4607 | metrics: |
| 4608 | - type: accuracy |
| 4609 | value: 86.7 |
| 4610 | - type: f1 |
| 4611 | value: 83.0152380952381 |
| 4612 | - type: precision |
| 4613 | value: 81.37833333333333 |
| 4614 | - type: recall |
| 4615 | value: 86.7 |
| 4616 | - task: |
| 4617 | type: BitextMining |
| 4618 | dataset: |
| 4619 | type: mteb/tatoeba-bitext-mining |
| 4620 | name: MTEB Tatoeba (swg-eng) |
| 4621 | config: swg-eng |
| 4622 | split: test |
| 4623 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4624 | metrics: |
| 4625 | - type: accuracy |
| 4626 | value: 63.39285714285714 |
| 4627 | - type: f1 |
| 4628 | value: 56.832482993197274 |
| 4629 | - type: precision |
| 4630 | value: 54.56845238095237 |
| 4631 | - type: recall |
| 4632 | value: 63.39285714285714 |
| 4633 | - task: |
| 4634 | type: BitextMining |
| 4635 | dataset: |
| 4636 | type: mteb/tatoeba-bitext-mining |
| 4637 | name: MTEB Tatoeba (arq-eng) |
| 4638 | config: arq-eng |
| 4639 | split: test |
| 4640 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4641 | metrics: |
| 4642 | - type: accuracy |
| 4643 | value: 48.73765093304062 |
| 4644 | - type: f1 |
| 4645 | value: 41.555736920720456 |
| 4646 | - type: precision |
| 4647 | value: 39.06874531737319 |
| 4648 | - type: recall |
| 4649 | value: 48.73765093304062 |
| 4650 | - task: |
| 4651 | type: BitextMining |
| 4652 | dataset: |
| 4653 | type: mteb/tatoeba-bitext-mining |
| 4654 | name: MTEB Tatoeba (kab-eng) |
| 4655 | config: kab-eng |
| 4656 | split: test |
| 4657 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4658 | metrics: |
| 4659 | - type: accuracy |
| 4660 | value: 41.099999999999994 |
| 4661 | - type: f1 |
| 4662 | value: 36.540165945165946 |
| 4663 | - type: precision |
| 4664 | value: 35.05175685425686 |
| 4665 | - type: recall |
| 4666 | value: 41.099999999999994 |
| 4667 | - task: |
| 4668 | type: BitextMining |
| 4669 | dataset: |
| 4670 | type: mteb/tatoeba-bitext-mining |
| 4671 | name: MTEB Tatoeba (fra-eng) |
| 4672 | config: fra-eng |
| 4673 | split: test |
| 4674 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4675 | metrics: |
| 4676 | - type: accuracy |
| 4677 | value: 94.89999999999999 |
| 4678 | - type: f1 |
| 4679 | value: 93.42333333333333 |
| 4680 | - type: precision |
| 4681 | value: 92.75833333333333 |
| 4682 | - type: recall |
| 4683 | value: 94.89999999999999 |
| 4684 | - task: |
| 4685 | type: BitextMining |
| 4686 | dataset: |
| 4687 | type: mteb/tatoeba-bitext-mining |
| 4688 | name: MTEB Tatoeba (por-eng) |
| 4689 | config: por-eng |
| 4690 | split: test |
| 4691 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4692 | metrics: |
| 4693 | - type: accuracy |
| 4694 | value: 94.89999999999999 |
| 4695 | - type: f1 |
| 4696 | value: 93.63333333333334 |
| 4697 | - type: precision |
| 4698 | value: 93.01666666666665 |
| 4699 | - type: recall |
| 4700 | value: 94.89999999999999 |
| 4701 | - task: |
| 4702 | type: BitextMining |
| 4703 | dataset: |
| 4704 | type: mteb/tatoeba-bitext-mining |
| 4705 | name: MTEB Tatoeba (tat-eng) |
| 4706 | config: tat-eng |
| 4707 | split: test |
| 4708 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4709 | metrics: |
| 4710 | - type: accuracy |
| 4711 | value: 77.9 |
| 4712 | - type: f1 |
| 4713 | value: 73.64833333333334 |
| 4714 | - type: precision |
| 4715 | value: 71.90282106782105 |
| 4716 | - type: recall |
| 4717 | value: 77.9 |
| 4718 | - task: |
| 4719 | type: BitextMining |
| 4720 | dataset: |
| 4721 | type: mteb/tatoeba-bitext-mining |
| 4722 | name: MTEB Tatoeba (oci-eng) |
| 4723 | config: oci-eng |
| 4724 | split: test |
| 4725 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4726 | metrics: |
| 4727 | - type: accuracy |
| 4728 | value: 59.4 |
| 4729 | - type: f1 |
| 4730 | value: 54.90521367521367 |
| 4731 | - type: precision |
| 4732 | value: 53.432840025471606 |
| 4733 | - type: recall |
| 4734 | value: 59.4 |
| 4735 | - task: |
| 4736 | type: BitextMining |
| 4737 | dataset: |
| 4738 | type: mteb/tatoeba-bitext-mining |
| 4739 | name: MTEB Tatoeba (pol-eng) |
| 4740 | config: pol-eng |
| 4741 | split: test |
| 4742 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4743 | metrics: |
| 4744 | - type: accuracy |
| 4745 | value: 97.39999999999999 |
| 4746 | - type: f1 |
| 4747 | value: 96.6 |
| 4748 | - type: precision |
| 4749 | value: 96.2 |
| 4750 | - type: recall |
| 4751 | value: 97.39999999999999 |
| 4752 | - task: |
| 4753 | type: BitextMining |
| 4754 | dataset: |
| 4755 | type: mteb/tatoeba-bitext-mining |
| 4756 | name: MTEB Tatoeba (war-eng) |
| 4757 | config: war-eng |
| 4758 | split: test |
| 4759 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4760 | metrics: |
| 4761 | - type: accuracy |
| 4762 | value: 67.2 |
| 4763 | - type: f1 |
| 4764 | value: 62.25926129426129 |
| 4765 | - type: precision |
| 4766 | value: 60.408376623376626 |
| 4767 | - type: recall |
| 4768 | value: 67.2 |
| 4769 | - task: |
| 4770 | type: BitextMining |
| 4771 | dataset: |
| 4772 | type: mteb/tatoeba-bitext-mining |
| 4773 | name: MTEB Tatoeba (aze-eng) |
| 4774 | config: aze-eng |
| 4775 | split: test |
| 4776 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4777 | metrics: |
| 4778 | - type: accuracy |
| 4779 | value: 90.2 |
| 4780 | - type: f1 |
| 4781 | value: 87.60666666666667 |
| 4782 | - type: precision |
| 4783 | value: 86.45277777777778 |
| 4784 | - type: recall |
| 4785 | value: 90.2 |
| 4786 | - task: |
| 4787 | type: BitextMining |
| 4788 | dataset: |
| 4789 | type: mteb/tatoeba-bitext-mining |
| 4790 | name: MTEB Tatoeba (vie-eng) |
| 4791 | config: vie-eng |
| 4792 | split: test |
| 4793 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4794 | metrics: |
| 4795 | - type: accuracy |
| 4796 | value: 97.7 |
| 4797 | - type: f1 |
| 4798 | value: 97 |
| 4799 | - type: precision |
| 4800 | value: 96.65 |
| 4801 | - type: recall |
| 4802 | value: 97.7 |
| 4803 | - task: |
| 4804 | type: BitextMining |
| 4805 | dataset: |
| 4806 | type: mteb/tatoeba-bitext-mining |
| 4807 | name: MTEB Tatoeba (nno-eng) |
| 4808 | config: nno-eng |
| 4809 | split: test |
| 4810 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4811 | metrics: |
| 4812 | - type: accuracy |
| 4813 | value: 93.2 |
| 4814 | - type: f1 |
| 4815 | value: 91.39746031746031 |
| 4816 | - type: precision |
| 4817 | value: 90.6125 |
| 4818 | - type: recall |
| 4819 | value: 93.2 |
| 4820 | - task: |
| 4821 | type: BitextMining |
| 4822 | dataset: |
| 4823 | type: mteb/tatoeba-bitext-mining |
| 4824 | name: MTEB Tatoeba (cha-eng) |
| 4825 | config: cha-eng |
| 4826 | split: test |
| 4827 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4828 | metrics: |
| 4829 | - type: accuracy |
| 4830 | value: 32.11678832116788 |
| 4831 | - type: f1 |
| 4832 | value: 27.210415386260234 |
| 4833 | - type: precision |
| 4834 | value: 26.20408990846947 |
| 4835 | - type: recall |
| 4836 | value: 32.11678832116788 |
| 4837 | - task: |
| 4838 | type: BitextMining |
| 4839 | dataset: |
| 4840 | type: mteb/tatoeba-bitext-mining |
| 4841 | name: MTEB Tatoeba (mhr-eng) |
| 4842 | config: mhr-eng |
| 4843 | split: test |
| 4844 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4845 | metrics: |
| 4846 | - type: accuracy |
| 4847 | value: 8.5 |
| 4848 | - type: f1 |
| 4849 | value: 6.787319277832475 |
| 4850 | - type: precision |
| 4851 | value: 6.3452094433344435 |
| 4852 | - type: recall |
| 4853 | value: 8.5 |
| 4854 | - task: |
| 4855 | type: BitextMining |
| 4856 | dataset: |
| 4857 | type: mteb/tatoeba-bitext-mining |
| 4858 | name: MTEB Tatoeba (dan-eng) |
| 4859 | config: dan-eng |
| 4860 | split: test |
| 4861 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4862 | metrics: |
| 4863 | - type: accuracy |
| 4864 | value: 96.1 |
| 4865 | - type: f1 |
| 4866 | value: 95.08 |
| 4867 | - type: precision |
| 4868 | value: 94.61666666666667 |
| 4869 | - type: recall |
| 4870 | value: 96.1 |
| 4871 | - task: |
| 4872 | type: BitextMining |
| 4873 | dataset: |
| 4874 | type: mteb/tatoeba-bitext-mining |
| 4875 | name: MTEB Tatoeba (ell-eng) |
| 4876 | config: ell-eng |
| 4877 | split: test |
| 4878 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4879 | metrics: |
| 4880 | - type: accuracy |
| 4881 | value: 95.3 |
| 4882 | - type: f1 |
| 4883 | value: 93.88333333333333 |
| 4884 | - type: precision |
| 4885 | value: 93.18333333333332 |
| 4886 | - type: recall |
| 4887 | value: 95.3 |
| 4888 | - task: |
| 4889 | type: BitextMining |
| 4890 | dataset: |
| 4891 | type: mteb/tatoeba-bitext-mining |
| 4892 | name: MTEB Tatoeba (amh-eng) |
| 4893 | config: amh-eng |
| 4894 | split: test |
| 4895 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4896 | metrics: |
| 4897 | - type: accuracy |
| 4898 | value: 85.11904761904762 |
| 4899 | - type: f1 |
| 4900 | value: 80.69444444444444 |
| 4901 | - type: precision |
| 4902 | value: 78.72023809523809 |
| 4903 | - type: recall |
| 4904 | value: 85.11904761904762 |
| 4905 | - task: |
| 4906 | type: BitextMining |
| 4907 | dataset: |
| 4908 | type: mteb/tatoeba-bitext-mining |
| 4909 | name: MTEB Tatoeba (pam-eng) |
| 4910 | config: pam-eng |
| 4911 | split: test |
| 4912 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4913 | metrics: |
| 4914 | - type: accuracy |
| 4915 | value: 11.1 |
| 4916 | - type: f1 |
| 4917 | value: 9.276381801735853 |
| 4918 | - type: precision |
| 4919 | value: 8.798174603174601 |
| 4920 | - type: recall |
| 4921 | value: 11.1 |
| 4922 | - task: |
| 4923 | type: BitextMining |
| 4924 | dataset: |
| 4925 | type: mteb/tatoeba-bitext-mining |
| 4926 | name: MTEB Tatoeba (hsb-eng) |
| 4927 | config: hsb-eng |
| 4928 | split: test |
| 4929 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4930 | metrics: |
| 4931 | - type: accuracy |
| 4932 | value: 63.56107660455487 |
| 4933 | - type: f1 |
| 4934 | value: 58.70433569191332 |
| 4935 | - type: precision |
| 4936 | value: 56.896926581464015 |
| 4937 | - type: recall |
| 4938 | value: 63.56107660455487 |
| 4939 | - task: |
| 4940 | type: BitextMining |
| 4941 | dataset: |
| 4942 | type: mteb/tatoeba-bitext-mining |
| 4943 | name: MTEB Tatoeba (srp-eng) |
| 4944 | config: srp-eng |
| 4945 | split: test |
| 4946 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4947 | metrics: |
| 4948 | - type: accuracy |
| 4949 | value: 94.69999999999999 |
| 4950 | - type: f1 |
| 4951 | value: 93.10000000000001 |
| 4952 | - type: precision |
| 4953 | value: 92.35 |
| 4954 | - type: recall |
| 4955 | value: 94.69999999999999 |
| 4956 | - task: |
| 4957 | type: BitextMining |
| 4958 | dataset: |
| 4959 | type: mteb/tatoeba-bitext-mining |
| 4960 | name: MTEB Tatoeba (epo-eng) |
| 4961 | config: epo-eng |
| 4962 | split: test |
| 4963 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4964 | metrics: |
| 4965 | - type: accuracy |
| 4966 | value: 96.8 |
| 4967 | - type: f1 |
| 4968 | value: 96.01222222222222 |
| 4969 | - type: precision |
| 4970 | value: 95.67083333333332 |
| 4971 | - type: recall |
| 4972 | value: 96.8 |
| 4973 | - task: |
| 4974 | type: BitextMining |
| 4975 | dataset: |
| 4976 | type: mteb/tatoeba-bitext-mining |
| 4977 | name: MTEB Tatoeba (kzj-eng) |
| 4978 | config: kzj-eng |
| 4979 | split: test |
| 4980 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4981 | metrics: |
| 4982 | - type: accuracy |
| 4983 | value: 9.2 |
| 4984 | - type: f1 |
| 4985 | value: 7.911555250305249 |
| 4986 | - type: precision |
| 4987 | value: 7.631246556216846 |
| 4988 | - type: recall |
| 4989 | value: 9.2 |
| 4990 | - task: |
| 4991 | type: BitextMining |
| 4992 | dataset: |
| 4993 | type: mteb/tatoeba-bitext-mining |
| 4994 | name: MTEB Tatoeba (awa-eng) |
| 4995 | config: awa-eng |
| 4996 | split: test |
| 4997 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4998 | metrics: |
| 4999 | - type: accuracy |
| 5000 | value: 77.48917748917748 |
| 5001 | - type: f1 |
| 5002 | value: 72.27375798804371 |
| 5003 | - type: precision |
| 5004 | value: 70.14430014430013 |
| 5005 | - type: recall |
| 5006 | value: 77.48917748917748 |
| 5007 | - task: |
| 5008 | type: BitextMining |
| 5009 | dataset: |
| 5010 | type: mteb/tatoeba-bitext-mining |
| 5011 | name: MTEB Tatoeba (fao-eng) |
| 5012 | config: fao-eng |
| 5013 | split: test |
| 5014 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5015 | metrics: |
| 5016 | - type: accuracy |
| 5017 | value: 77.09923664122137 |
| 5018 | - type: f1 |
| 5019 | value: 72.61541257724463 |
| 5020 | - type: precision |
| 5021 | value: 70.8998380754106 |
| 5022 | - type: recall |
| 5023 | value: 77.09923664122137 |
| 5024 | - task: |
| 5025 | type: BitextMining |
| 5026 | dataset: |
| 5027 | type: mteb/tatoeba-bitext-mining |
| 5028 | name: MTEB Tatoeba (mal-eng) |
| 5029 | config: mal-eng |
| 5030 | split: test |
| 5031 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5032 | metrics: |
| 5033 | - type: accuracy |
| 5034 | value: 98.2532751091703 |
| 5035 | - type: f1 |
| 5036 | value: 97.69529354682193 |
| 5037 | - type: precision |
| 5038 | value: 97.42843279961184 |
| 5039 | - type: recall |
| 5040 | value: 98.2532751091703 |
| 5041 | - task: |
| 5042 | type: BitextMining |
| 5043 | dataset: |
| 5044 | type: mteb/tatoeba-bitext-mining |
| 5045 | name: MTEB Tatoeba (ile-eng) |
| 5046 | config: ile-eng |
| 5047 | split: test |
| 5048 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5049 | metrics: |
| 5050 | - type: accuracy |
| 5051 | value: 82.8 |
| 5052 | - type: f1 |
| 5053 | value: 79.14672619047619 |
| 5054 | - type: precision |
| 5055 | value: 77.59489247311828 |
| 5056 | - type: recall |
| 5057 | value: 82.8 |
| 5058 | - task: |
| 5059 | type: BitextMining |
| 5060 | dataset: |
| 5061 | type: mteb/tatoeba-bitext-mining |
| 5062 | name: MTEB Tatoeba (bos-eng) |
| 5063 | config: bos-eng |
| 5064 | split: test |
| 5065 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5066 | metrics: |
| 5067 | - type: accuracy |
| 5068 | value: 94.35028248587571 |
| 5069 | - type: f1 |
| 5070 | value: 92.86252354048965 |
| 5071 | - type: precision |
| 5072 | value: 92.2080979284369 |
| 5073 | - type: recall |
| 5074 | value: 94.35028248587571 |
| 5075 | - task: |
| 5076 | type: BitextMining |
| 5077 | dataset: |
| 5078 | type: mteb/tatoeba-bitext-mining |
| 5079 | name: MTEB Tatoeba (cor-eng) |
| 5080 | config: cor-eng |
| 5081 | split: test |
| 5082 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5083 | metrics: |
| 5084 | - type: accuracy |
| 5085 | value: 8.5 |
| 5086 | - type: f1 |
| 5087 | value: 6.282429263935621 |
| 5088 | - type: precision |
| 5089 | value: 5.783274240739785 |
| 5090 | - type: recall |
| 5091 | value: 8.5 |
| 5092 | - task: |
| 5093 | type: BitextMining |
| 5094 | dataset: |
| 5095 | type: mteb/tatoeba-bitext-mining |
| 5096 | name: MTEB Tatoeba (cat-eng) |
| 5097 | config: cat-eng |
| 5098 | split: test |
| 5099 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5100 | metrics: |
| 5101 | - type: accuracy |
| 5102 | value: 92.7 |
| 5103 | - type: f1 |
| 5104 | value: 91.025 |
| 5105 | - type: precision |
| 5106 | value: 90.30428571428571 |
| 5107 | - type: recall |
| 5108 | value: 92.7 |
| 5109 | - task: |
| 5110 | type: BitextMining |
| 5111 | dataset: |
| 5112 | type: mteb/tatoeba-bitext-mining |
| 5113 | name: MTEB Tatoeba (eus-eng) |
| 5114 | config: eus-eng |
| 5115 | split: test |
| 5116 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5117 | metrics: |
| 5118 | - type: accuracy |
| 5119 | value: 81 |
| 5120 | - type: f1 |
| 5121 | value: 77.8232380952381 |
| 5122 | - type: precision |
| 5123 | value: 76.60194444444444 |
| 5124 | - type: recall |
| 5125 | value: 81 |
| 5126 | - task: |
| 5127 | type: BitextMining |
| 5128 | dataset: |
| 5129 | type: mteb/tatoeba-bitext-mining |
| 5130 | name: MTEB Tatoeba (yue-eng) |
| 5131 | config: yue-eng |
| 5132 | split: test |
| 5133 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5134 | metrics: |
| 5135 | - type: accuracy |
| 5136 | value: 91 |
| 5137 | - type: f1 |
| 5138 | value: 88.70857142857142 |
| 5139 | - type: precision |
| 5140 | value: 87.7 |
| 5141 | - type: recall |
| 5142 | value: 91 |
| 5143 | - task: |
| 5144 | type: BitextMining |
| 5145 | dataset: |
| 5146 | type: mteb/tatoeba-bitext-mining |
| 5147 | name: MTEB Tatoeba (swe-eng) |
| 5148 | config: swe-eng |
| 5149 | split: test |
| 5150 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5151 | metrics: |
| 5152 | - type: accuracy |
| 5153 | value: 96.39999999999999 |
| 5154 | - type: f1 |
| 5155 | value: 95.3 |
| 5156 | - type: precision |
| 5157 | value: 94.76666666666667 |
| 5158 | - type: recall |
| 5159 | value: 96.39999999999999 |
| 5160 | - task: |
| 5161 | type: BitextMining |
| 5162 | dataset: |
| 5163 | type: mteb/tatoeba-bitext-mining |
| 5164 | name: MTEB Tatoeba (dtp-eng) |
| 5165 | config: dtp-eng |
| 5166 | split: test |
| 5167 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5168 | metrics: |
| 5169 | - type: accuracy |
| 5170 | value: 8.1 |
| 5171 | - type: f1 |
| 5172 | value: 7.001008218834307 |
| 5173 | - type: precision |
| 5174 | value: 6.708329562594269 |
| 5175 | - type: recall |
| 5176 | value: 8.1 |
| 5177 | - task: |
| 5178 | type: BitextMining |
| 5179 | dataset: |
| 5180 | type: mteb/tatoeba-bitext-mining |
| 5181 | name: MTEB Tatoeba (kat-eng) |
| 5182 | config: kat-eng |
| 5183 | split: test |
| 5184 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5185 | metrics: |
| 5186 | - type: accuracy |
| 5187 | value: 87.1313672922252 |
| 5188 | - type: f1 |
| 5189 | value: 84.09070598748882 |
| 5190 | - type: precision |
| 5191 | value: 82.79171454104429 |
| 5192 | - type: recall |
| 5193 | value: 87.1313672922252 |
| 5194 | - task: |
| 5195 | type: BitextMining |
| 5196 | dataset: |
| 5197 | type: mteb/tatoeba-bitext-mining |
| 5198 | name: MTEB Tatoeba (jpn-eng) |
| 5199 | config: jpn-eng |
| 5200 | split: test |
| 5201 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5202 | metrics: |
| 5203 | - type: accuracy |
| 5204 | value: 96.39999999999999 |
| 5205 | - type: f1 |
| 5206 | value: 95.28333333333333 |
| 5207 | - type: precision |
| 5208 | value: 94.73333333333332 |
| 5209 | - type: recall |
| 5210 | value: 96.39999999999999 |
| 5211 | - task: |
| 5212 | type: BitextMining |
| 5213 | dataset: |
| 5214 | type: mteb/tatoeba-bitext-mining |
| 5215 | name: MTEB Tatoeba (csb-eng) |
| 5216 | config: csb-eng |
| 5217 | split: test |
| 5218 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5219 | metrics: |
| 5220 | - type: accuracy |
| 5221 | value: 42.29249011857708 |
| 5222 | - type: f1 |
| 5223 | value: 36.981018542283365 |
| 5224 | - type: precision |
| 5225 | value: 35.415877813576024 |
| 5226 | - type: recall |
| 5227 | value: 42.29249011857708 |
| 5228 | - task: |
| 5229 | type: BitextMining |
| 5230 | dataset: |
| 5231 | type: mteb/tatoeba-bitext-mining |
| 5232 | name: MTEB Tatoeba (xho-eng) |
| 5233 | config: xho-eng |
| 5234 | split: test |
| 5235 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5236 | metrics: |
| 5237 | - type: accuracy |
| 5238 | value: 83.80281690140845 |
| 5239 | - type: f1 |
| 5240 | value: 80.86854460093896 |
| 5241 | - type: precision |
| 5242 | value: 79.60093896713614 |
| 5243 | - type: recall |
| 5244 | value: 83.80281690140845 |
| 5245 | - task: |
| 5246 | type: BitextMining |
| 5247 | dataset: |
| 5248 | type: mteb/tatoeba-bitext-mining |
| 5249 | name: MTEB Tatoeba (orv-eng) |
| 5250 | config: orv-eng |
| 5251 | split: test |
| 5252 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5253 | metrics: |
| 5254 | - type: accuracy |
| 5255 | value: 45.26946107784431 |
| 5256 | - type: f1 |
| 5257 | value: 39.80235464678088 |
| 5258 | - type: precision |
| 5259 | value: 38.14342660001342 |
| 5260 | - type: recall |
| 5261 | value: 45.26946107784431 |
| 5262 | - task: |
| 5263 | type: BitextMining |
| 5264 | dataset: |
| 5265 | type: mteb/tatoeba-bitext-mining |
| 5266 | name: MTEB Tatoeba (ind-eng) |
| 5267 | config: ind-eng |
| 5268 | split: test |
| 5269 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5270 | metrics: |
| 5271 | - type: accuracy |
| 5272 | value: 94.3 |
| 5273 | - type: f1 |
| 5274 | value: 92.9 |
| 5275 | - type: precision |
| 5276 | value: 92.26666666666668 |
| 5277 | - type: recall |
| 5278 | value: 94.3 |
| 5279 | - task: |
| 5280 | type: BitextMining |
| 5281 | dataset: |
| 5282 | type: mteb/tatoeba-bitext-mining |
| 5283 | name: MTEB Tatoeba (tuk-eng) |
| 5284 | config: tuk-eng |
| 5285 | split: test |
| 5286 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5287 | metrics: |
| 5288 | - type: accuracy |
| 5289 | value: 37.93103448275862 |
| 5290 | - type: f1 |
| 5291 | value: 33.15192743764172 |
| 5292 | - type: precision |
| 5293 | value: 31.57456528146183 |
| 5294 | - type: recall |
| 5295 | value: 37.93103448275862 |
| 5296 | - task: |
| 5297 | type: BitextMining |
| 5298 | dataset: |
| 5299 | type: mteb/tatoeba-bitext-mining |
| 5300 | name: MTEB Tatoeba (max-eng) |
| 5301 | config: max-eng |
| 5302 | split: test |
| 5303 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5304 | metrics: |
| 5305 | - type: accuracy |
| 5306 | value: 69.01408450704226 |
| 5307 | - type: f1 |
| 5308 | value: 63.41549295774648 |
| 5309 | - type: precision |
| 5310 | value: 61.342778895595806 |
| 5311 | - type: recall |
| 5312 | value: 69.01408450704226 |
| 5313 | - task: |
| 5314 | type: BitextMining |
| 5315 | dataset: |
| 5316 | type: mteb/tatoeba-bitext-mining |
| 5317 | name: MTEB Tatoeba (swh-eng) |
| 5318 | config: swh-eng |
| 5319 | split: test |
| 5320 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5321 | metrics: |
| 5322 | - type: accuracy |
| 5323 | value: 76.66666666666667 |
| 5324 | - type: f1 |
| 5325 | value: 71.60705960705961 |
| 5326 | - type: precision |
| 5327 | value: 69.60683760683762 |
| 5328 | - type: recall |
| 5329 | value: 76.66666666666667 |
| 5330 | - task: |
| 5331 | type: BitextMining |
| 5332 | dataset: |
| 5333 | type: mteb/tatoeba-bitext-mining |
| 5334 | name: MTEB Tatoeba (hin-eng) |
| 5335 | config: hin-eng |
| 5336 | split: test |
| 5337 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5338 | metrics: |
| 5339 | - type: accuracy |
| 5340 | value: 95.8 |
| 5341 | - type: f1 |
| 5342 | value: 94.48333333333333 |
| 5343 | - type: precision |
| 5344 | value: 93.83333333333333 |
| 5345 | - type: recall |
| 5346 | value: 95.8 |
| 5347 | - task: |
| 5348 | type: BitextMining |
| 5349 | dataset: |
| 5350 | type: mteb/tatoeba-bitext-mining |
| 5351 | name: MTEB Tatoeba (dsb-eng) |
| 5352 | config: dsb-eng |
| 5353 | split: test |
| 5354 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5355 | metrics: |
| 5356 | - type: accuracy |
| 5357 | value: 52.81837160751566 |
| 5358 | - type: f1 |
| 5359 | value: 48.435977731384824 |
| 5360 | - type: precision |
| 5361 | value: 47.11291973845539 |
| 5362 | - type: recall |
| 5363 | value: 52.81837160751566 |
| 5364 | - task: |
| 5365 | type: BitextMining |
| 5366 | dataset: |
| 5367 | type: mteb/tatoeba-bitext-mining |
| 5368 | name: MTEB Tatoeba (ber-eng) |
| 5369 | config: ber-eng |
| 5370 | split: test |
| 5371 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5372 | metrics: |
| 5373 | - type: accuracy |
| 5374 | value: 44.9 |
| 5375 | - type: f1 |
| 5376 | value: 38.88962621607783 |
| 5377 | - type: precision |
| 5378 | value: 36.95936507936508 |
| 5379 | - type: recall |
| 5380 | value: 44.9 |
| 5381 | - task: |
| 5382 | type: BitextMining |
| 5383 | dataset: |
| 5384 | type: mteb/tatoeba-bitext-mining |
| 5385 | name: MTEB Tatoeba (tam-eng) |
| 5386 | config: tam-eng |
| 5387 | split: test |
| 5388 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5389 | metrics: |
| 5390 | - type: accuracy |
| 5391 | value: 90.55374592833876 |
| 5392 | - type: f1 |
| 5393 | value: 88.22553125484721 |
| 5394 | - type: precision |
| 5395 | value: 87.26927252985884 |
| 5396 | - type: recall |
| 5397 | value: 90.55374592833876 |
| 5398 | - task: |
| 5399 | type: BitextMining |
| 5400 | dataset: |
| 5401 | type: mteb/tatoeba-bitext-mining |
| 5402 | name: MTEB Tatoeba (slk-eng) |
| 5403 | config: slk-eng |
| 5404 | split: test |
| 5405 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5406 | metrics: |
| 5407 | - type: accuracy |
| 5408 | value: 94.6 |
| 5409 | - type: f1 |
| 5410 | value: 93.13333333333333 |
| 5411 | - type: precision |
| 5412 | value: 92.45333333333333 |
| 5413 | - type: recall |
| 5414 | value: 94.6 |
| 5415 | - task: |
| 5416 | type: BitextMining |
| 5417 | dataset: |
| 5418 | type: mteb/tatoeba-bitext-mining |
| 5419 | name: MTEB Tatoeba (tgl-eng) |
| 5420 | config: tgl-eng |
| 5421 | split: test |
| 5422 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5423 | metrics: |
| 5424 | - type: accuracy |
| 5425 | value: 93.7 |
| 5426 | - type: f1 |
| 5427 | value: 91.99666666666667 |
| 5428 | - type: precision |
| 5429 | value: 91.26666666666668 |
| 5430 | - type: recall |
| 5431 | value: 93.7 |
| 5432 | - task: |
| 5433 | type: BitextMining |
| 5434 | dataset: |
| 5435 | type: mteb/tatoeba-bitext-mining |
| 5436 | name: MTEB Tatoeba (ast-eng) |
| 5437 | config: ast-eng |
| 5438 | split: test |
| 5439 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5440 | metrics: |
| 5441 | - type: accuracy |
| 5442 | value: 85.03937007874016 |
| 5443 | - type: f1 |
| 5444 | value: 81.75853018372703 |
| 5445 | - type: precision |
| 5446 | value: 80.34120734908137 |
| 5447 | - type: recall |
| 5448 | value: 85.03937007874016 |
| 5449 | - task: |
| 5450 | type: BitextMining |
| 5451 | dataset: |
| 5452 | type: mteb/tatoeba-bitext-mining |
| 5453 | name: MTEB Tatoeba (mkd-eng) |
| 5454 | config: mkd-eng |
| 5455 | split: test |
| 5456 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5457 | metrics: |
| 5458 | - type: accuracy |
| 5459 | value: 88.3 |
| 5460 | - type: f1 |
| 5461 | value: 85.5 |
| 5462 | - type: precision |
| 5463 | value: 84.25833333333334 |
| 5464 | - type: recall |
| 5465 | value: 88.3 |
| 5466 | - task: |
| 5467 | type: BitextMining |
| 5468 | dataset: |
| 5469 | type: mteb/tatoeba-bitext-mining |
| 5470 | name: MTEB Tatoeba (khm-eng) |
| 5471 | config: khm-eng |
| 5472 | split: test |
| 5473 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5474 | metrics: |
| 5475 | - type: accuracy |
| 5476 | value: 65.51246537396122 |
| 5477 | - type: f1 |
| 5478 | value: 60.02297410192148 |
| 5479 | - type: precision |
| 5480 | value: 58.133467727289236 |
| 5481 | - type: recall |
| 5482 | value: 65.51246537396122 |
| 5483 | - task: |
| 5484 | type: BitextMining |
| 5485 | dataset: |
| 5486 | type: mteb/tatoeba-bitext-mining |
| 5487 | name: MTEB Tatoeba (ces-eng) |
| 5488 | config: ces-eng |
| 5489 | split: test |
| 5490 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5491 | metrics: |
| 5492 | - type: accuracy |
| 5493 | value: 96 |
| 5494 | - type: f1 |
| 5495 | value: 94.89 |
| 5496 | - type: precision |
| 5497 | value: 94.39166666666667 |
| 5498 | - type: recall |
| 5499 | value: 96 |
| 5500 | - task: |
| 5501 | type: BitextMining |
| 5502 | dataset: |
| 5503 | type: mteb/tatoeba-bitext-mining |
| 5504 | name: MTEB Tatoeba (tzl-eng) |
| 5505 | config: tzl-eng |
| 5506 | split: test |
| 5507 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5508 | metrics: |
| 5509 | - type: accuracy |
| 5510 | value: 57.692307692307686 |
| 5511 | - type: f1 |
| 5512 | value: 53.162393162393165 |
| 5513 | - type: precision |
| 5514 | value: 51.70673076923077 |
| 5515 | - type: recall |
| 5516 | value: 57.692307692307686 |
| 5517 | - task: |
| 5518 | type: BitextMining |
| 5519 | dataset: |
| 5520 | type: mteb/tatoeba-bitext-mining |
| 5521 | name: MTEB Tatoeba (urd-eng) |
| 5522 | config: urd-eng |
| 5523 | split: test |
| 5524 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5525 | metrics: |
| 5526 | - type: accuracy |
| 5527 | value: 91.60000000000001 |
| 5528 | - type: f1 |
| 5529 | value: 89.21190476190475 |
| 5530 | - type: precision |
| 5531 | value: 88.08666666666667 |
| 5532 | - type: recall |
| 5533 | value: 91.60000000000001 |
| 5534 | - task: |
| 5535 | type: BitextMining |
| 5536 | dataset: |
| 5537 | type: mteb/tatoeba-bitext-mining |
| 5538 | name: MTEB Tatoeba (ara-eng) |
| 5539 | config: ara-eng |
| 5540 | split: test |
| 5541 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5542 | metrics: |
| 5543 | - type: accuracy |
| 5544 | value: 88 |
| 5545 | - type: f1 |
| 5546 | value: 85.47 |
| 5547 | - type: precision |
| 5548 | value: 84.43266233766234 |
| 5549 | - type: recall |
| 5550 | value: 88 |
| 5551 | - task: |
| 5552 | type: BitextMining |
| 5553 | dataset: |
| 5554 | type: mteb/tatoeba-bitext-mining |
| 5555 | name: MTEB Tatoeba (kor-eng) |
| 5556 | config: kor-eng |
| 5557 | split: test |
| 5558 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5559 | metrics: |
| 5560 | - type: accuracy |
| 5561 | value: 92.7 |
| 5562 | - type: f1 |
| 5563 | value: 90.64999999999999 |
| 5564 | - type: precision |
| 5565 | value: 89.68333333333332 |
| 5566 | - type: recall |
| 5567 | value: 92.7 |
| 5568 | - task: |
| 5569 | type: BitextMining |
| 5570 | dataset: |
| 5571 | type: mteb/tatoeba-bitext-mining |
| 5572 | name: MTEB Tatoeba (yid-eng) |
| 5573 | config: yid-eng |
| 5574 | split: test |
| 5575 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5576 | metrics: |
| 5577 | - type: accuracy |
| 5578 | value: 80.30660377358491 |
| 5579 | - type: f1 |
| 5580 | value: 76.33044137466307 |
| 5581 | - type: precision |
| 5582 | value: 74.78970125786164 |
| 5583 | - type: recall |
| 5584 | value: 80.30660377358491 |
| 5585 | - task: |
| 5586 | type: BitextMining |
| 5587 | dataset: |
| 5588 | type: mteb/tatoeba-bitext-mining |
| 5589 | name: MTEB Tatoeba (fin-eng) |
| 5590 | config: fin-eng |
| 5591 | split: test |
| 5592 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5593 | metrics: |
| 5594 | - type: accuracy |
| 5595 | value: 96.39999999999999 |
| 5596 | - type: f1 |
| 5597 | value: 95.44 |
| 5598 | - type: precision |
| 5599 | value: 94.99166666666666 |
| 5600 | - type: recall |
| 5601 | value: 96.39999999999999 |
| 5602 | - task: |
| 5603 | type: BitextMining |
| 5604 | dataset: |
| 5605 | type: mteb/tatoeba-bitext-mining |
| 5606 | name: MTEB Tatoeba (tha-eng) |
| 5607 | config: tha-eng |
| 5608 | split: test |
| 5609 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5610 | metrics: |
| 5611 | - type: accuracy |
| 5612 | value: 96.53284671532847 |
| 5613 | - type: f1 |
| 5614 | value: 95.37712895377129 |
| 5615 | - type: precision |
| 5616 | value: 94.7992700729927 |
| 5617 | - type: recall |
| 5618 | value: 96.53284671532847 |
| 5619 | - task: |
| 5620 | type: BitextMining |
| 5621 | dataset: |
| 5622 | type: mteb/tatoeba-bitext-mining |
| 5623 | name: MTEB Tatoeba (wuu-eng) |
| 5624 | config: wuu-eng |
| 5625 | split: test |
| 5626 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5627 | metrics: |
| 5628 | - type: accuracy |
| 5629 | value: 89 |
| 5630 | - type: f1 |
| 5631 | value: 86.23190476190476 |
| 5632 | - type: precision |
| 5633 | value: 85.035 |
| 5634 | - type: recall |
| 5635 | value: 89 |
| 5636 | - task: |
| 5637 | type: Retrieval |
| 5638 | dataset: |
| 5639 | type: webis-touche2020 |
| 5640 | name: MTEB Touche2020 |
| 5641 | config: default |
| 5642 | split: test |
| 5643 | revision: None |
| 5644 | metrics: |
| 5645 | - type: map_at_1 |
| 5646 | value: 2.585 |
| 5647 | - type: map_at_10 |
| 5648 | value: 9.012 |
| 5649 | - type: map_at_100 |
| 5650 | value: 14.027000000000001 |
| 5651 | - type: map_at_1000 |
| 5652 | value: 15.565000000000001 |
| 5653 | - type: map_at_3 |
| 5654 | value: 5.032 |
| 5655 | - type: map_at_5 |
| 5656 | value: 6.657 |
| 5657 | - type: mrr_at_1 |
| 5658 | value: 28.571 |
| 5659 | - type: mrr_at_10 |
| 5660 | value: 45.377 |
| 5661 | - type: mrr_at_100 |
| 5662 | value: 46.119 |
| 5663 | - type: mrr_at_1000 |
| 5664 | value: 46.127 |
| 5665 | - type: mrr_at_3 |
| 5666 | value: 41.156 |
| 5667 | - type: mrr_at_5 |
| 5668 | value: 42.585 |
| 5669 | - type: ndcg_at_1 |
| 5670 | value: 27.551 |
| 5671 | - type: ndcg_at_10 |
| 5672 | value: 23.395 |
| 5673 | - type: ndcg_at_100 |
| 5674 | value: 33.342 |
| 5675 | - type: ndcg_at_1000 |
| 5676 | value: 45.523 |
| 5677 | - type: ndcg_at_3 |
| 5678 | value: 25.158 |
| 5679 | - type: ndcg_at_5 |
| 5680 | value: 23.427 |
| 5681 | - type: precision_at_1 |
| 5682 | value: 28.571 |
| 5683 | - type: precision_at_10 |
| 5684 | value: 21.429000000000002 |
| 5685 | - type: precision_at_100 |
| 5686 | value: 6.714 |
| 5687 | - type: precision_at_1000 |
| 5688 | value: 1.473 |
| 5689 | - type: precision_at_3 |
| 5690 | value: 27.211000000000002 |
| 5691 | - type: precision_at_5 |
| 5692 | value: 24.490000000000002 |
| 5693 | - type: recall_at_1 |
| 5694 | value: 2.585 |
| 5695 | - type: recall_at_10 |
| 5696 | value: 15.418999999999999 |
| 5697 | - type: recall_at_100 |
| 5698 | value: 42.485 |
| 5699 | - type: recall_at_1000 |
| 5700 | value: 79.536 |
| 5701 | - type: recall_at_3 |
| 5702 | value: 6.239999999999999 |
| 5703 | - type: recall_at_5 |
| 5704 | value: 8.996 |
| 5705 | - task: |
| 5706 | type: Classification |
| 5707 | dataset: |
| 5708 | type: mteb/toxic_conversations_50k |
| 5709 | name: MTEB ToxicConversationsClassification |
| 5710 | config: default |
| 5711 | split: test |
| 5712 | revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| 5713 | metrics: |
| 5714 | - type: accuracy |
| 5715 | value: 71.3234 |
| 5716 | - type: ap |
| 5717 | value: 14.361688653847423 |
| 5718 | - type: f1 |
| 5719 | value: 54.819068624319044 |
| 5720 | - task: |
| 5721 | type: Classification |
| 5722 | dataset: |
| 5723 | type: mteb/tweet_sentiment_extraction |
| 5724 | name: MTEB TweetSentimentExtractionClassification |
| 5725 | config: default |
| 5726 | split: test |
| 5727 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 5728 | metrics: |
| 5729 | - type: accuracy |
| 5730 | value: 61.97792869269949 |
| 5731 | - type: f1 |
| 5732 | value: 62.28965628513728 |
| 5733 | - task: |
| 5734 | type: Clustering |
| 5735 | dataset: |
| 5736 | type: mteb/twentynewsgroups-clustering |
| 5737 | name: MTEB TwentyNewsgroupsClustering |
| 5738 | config: default |
| 5739 | split: test |
| 5740 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 5741 | metrics: |
| 5742 | - type: v_measure |
| 5743 | value: 38.90540145385218 |
| 5744 | - task: |
| 5745 | type: PairClassification |
| 5746 | dataset: |
| 5747 | type: mteb/twittersemeval2015-pairclassification |
| 5748 | name: MTEB TwitterSemEval2015 |
| 5749 | config: default |
| 5750 | split: test |
| 5751 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 5752 | metrics: |
| 5753 | - type: cos_sim_accuracy |
| 5754 | value: 86.53513739047506 |
| 5755 | - type: cos_sim_ap |
| 5756 | value: 75.27741586677557 |
| 5757 | - type: cos_sim_f1 |
| 5758 | value: 69.18792902473774 |
| 5759 | - type: cos_sim_precision |
| 5760 | value: 67.94708725515136 |
| 5761 | - type: cos_sim_recall |
| 5762 | value: 70.47493403693932 |
| 5763 | - type: dot_accuracy |
| 5764 | value: 84.7052512368123 |
| 5765 | - type: dot_ap |
| 5766 | value: 69.36075482849378 |
| 5767 | - type: dot_f1 |
| 5768 | value: 64.44688376631296 |
| 5769 | - type: dot_precision |
| 5770 | value: 59.92288500793831 |
| 5771 | - type: dot_recall |
| 5772 | value: 69.70976253298153 |
| 5773 | - type: euclidean_accuracy |
| 5774 | value: 86.60666388508076 |
| 5775 | - type: euclidean_ap |
| 5776 | value: 75.47512772621097 |
| 5777 | - type: euclidean_f1 |
| 5778 | value: 69.413872536473 |
| 5779 | - type: euclidean_precision |
| 5780 | value: 67.39562624254472 |
| 5781 | - type: euclidean_recall |
| 5782 | value: 71.55672823218997 |
| 5783 | - type: manhattan_accuracy |
| 5784 | value: 86.52917684925792 |
| 5785 | - type: manhattan_ap |
| 5786 | value: 75.34000110496703 |
| 5787 | - type: manhattan_f1 |
| 5788 | value: 69.28489190226429 |
| 5789 | - type: manhattan_precision |
| 5790 | value: 67.24608889992551 |
| 5791 | - type: manhattan_recall |
| 5792 | value: 71.45118733509234 |
| 5793 | - type: max_accuracy |
| 5794 | value: 86.60666388508076 |
| 5795 | - type: max_ap |
| 5796 | value: 75.47512772621097 |
| 5797 | - type: max_f1 |
| 5798 | value: 69.413872536473 |
| 5799 | - task: |
| 5800 | type: PairClassification |
| 5801 | dataset: |
| 5802 | type: mteb/twitterurlcorpus-pairclassification |
| 5803 | name: MTEB TwitterURLCorpus |
| 5804 | config: default |
| 5805 | split: test |
| 5806 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 5807 | metrics: |
| 5808 | - type: cos_sim_accuracy |
| 5809 | value: 89.01695967710637 |
| 5810 | - type: cos_sim_ap |
| 5811 | value: 85.8298270742901 |
| 5812 | - type: cos_sim_f1 |
| 5813 | value: 78.46988128389272 |
| 5814 | - type: cos_sim_precision |
| 5815 | value: 74.86017897091722 |
| 5816 | - type: cos_sim_recall |
| 5817 | value: 82.44533415460425 |
| 5818 | - type: dot_accuracy |
| 5819 | value: 88.19420188613343 |
| 5820 | - type: dot_ap |
| 5821 | value: 83.82679165901324 |
| 5822 | - type: dot_f1 |
| 5823 | value: 76.55833777304208 |
| 5824 | - type: dot_precision |
| 5825 | value: 75.6884875846501 |
| 5826 | - type: dot_recall |
| 5827 | value: 77.44841392054204 |
| 5828 | - type: euclidean_accuracy |
| 5829 | value: 89.03054294252338 |
| 5830 | - type: euclidean_ap |
| 5831 | value: 85.89089555185325 |
| 5832 | - type: euclidean_f1 |
| 5833 | value: 78.62997658079624 |
| 5834 | - type: euclidean_precision |
| 5835 | value: 74.92329149232914 |
| 5836 | - type: euclidean_recall |
| 5837 | value: 82.72251308900523 |
| 5838 | - type: manhattan_accuracy |
| 5839 | value: 89.0266620095471 |
| 5840 | - type: manhattan_ap |
| 5841 | value: 85.86458997929147 |
| 5842 | - type: manhattan_f1 |
| 5843 | value: 78.50685331000291 |
| 5844 | - type: manhattan_precision |
| 5845 | value: 74.5499861534201 |
| 5846 | - type: manhattan_recall |
| 5847 | value: 82.90729904527257 |
| 5848 | - type: max_accuracy |
| 5849 | value: 89.03054294252338 |
| 5850 | - type: max_ap |
| 5851 | value: 85.89089555185325 |
| 5852 | - type: max_f1 |
| 5853 | value: 78.62997658079624 |
| 5854 | language: |
| 5855 | - multilingual |
| 5856 | - af |
| 5857 | - am |
| 5858 | - ar |
| 5859 | - as |
| 5860 | - az |
| 5861 | - be |
| 5862 | - bg |
| 5863 | - bn |
| 5864 | - br |
| 5865 | - bs |
| 5866 | - ca |
| 5867 | - cs |
| 5868 | - cy |
| 5869 | - da |
| 5870 | - de |
| 5871 | - el |
| 5872 | - en |
| 5873 | - eo |
| 5874 | - es |
| 5875 | - et |
| 5876 | - eu |
| 5877 | - fa |
| 5878 | - fi |
| 5879 | - fr |
| 5880 | - fy |
| 5881 | - ga |
| 5882 | - gd |
| 5883 | - gl |
| 5884 | - gu |
| 5885 | - ha |
| 5886 | - he |
| 5887 | - hi |
| 5888 | - hr |
| 5889 | - hu |
| 5890 | - hy |
| 5891 | - id |
| 5892 | - is |
| 5893 | - it |
| 5894 | - ja |
| 5895 | - jv |
| 5896 | - ka |
| 5897 | - kk |
| 5898 | - km |
| 5899 | - kn |
| 5900 | - ko |
| 5901 | - ku |
| 5902 | - ky |
| 5903 | - la |
| 5904 | - lo |
| 5905 | - lt |
| 5906 | - lv |
| 5907 | - mg |
| 5908 | - mk |
| 5909 | - ml |
| 5910 | - mn |
| 5911 | - mr |
| 5912 | - ms |
| 5913 | - my |
| 5914 | - ne |
| 5915 | - nl |
| 5916 | - 'no' |
| 5917 | - om |
| 5918 | - or |
| 5919 | - pa |
| 5920 | - pl |
| 5921 | - ps |
| 5922 | - pt |
| 5923 | - ro |
| 5924 | - ru |
| 5925 | - sa |
| 5926 | - sd |
| 5927 | - si |
| 5928 | - sk |
| 5929 | - sl |
| 5930 | - so |
| 5931 | - sq |
| 5932 | - sr |
| 5933 | - su |
| 5934 | - sv |
| 5935 | - sw |
| 5936 | - ta |
| 5937 | - te |
| 5938 | - th |
| 5939 | - tl |
| 5940 | - tr |
| 5941 | - ug |
| 5942 | - uk |
| 5943 | - ur |
| 5944 | - uz |
| 5945 | - vi |
| 5946 | - xh |
| 5947 | - yi |
| 5948 | - zh |
| 5949 | license: mit |
| 5950 | --- |
| 5951 | |
| 5952 | ## Multilingual-E5-large |
| 5953 | |
| 5954 | [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). |
| 5955 | Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 |
| 5956 | |
| 5957 | This model has 24 layers and the embedding size is 1024. |
| 5958 | |
| 5959 | ## Usage |
| 5960 | |
| 5961 | Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
| 5962 | |
| 5963 | ```python |
| 5964 | import torch.nn.functional as F |
| 5965 | |
| 5966 | from torch import Tensor |
| 5967 | from transformers import AutoTokenizer, AutoModel |
| 5968 | |
| 5969 | |
| 5970 | def average_pool(last_hidden_states: Tensor, |
| 5971 | attention_mask: Tensor) -> Tensor: |
| 5972 | last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
| 5973 | return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
| 5974 | |
| 5975 | |
| 5976 | # Each input text should start with "query: " or "passage: ", even for non-English texts. |
| 5977 | # For tasks other than retrieval, you can simply use the "query: " prefix. |
| 5978 | input_texts = ['query: how much protein should a female eat', |
| 5979 | 'query: 南瓜的家常做法', |
| 5980 | "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.", |
| 5981 | "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"] |
| 5982 | |
| 5983 | tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') |
| 5984 | model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') |
| 5985 | |
| 5986 | # Tokenize the input texts |
| 5987 | batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
| 5988 | |
| 5989 | outputs = model(**batch_dict) |
| 5990 | embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
| 5991 | |
| 5992 | # normalize embeddings |
| 5993 | embeddings = F.normalize(embeddings, p=2, dim=1) |
| 5994 | scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| 5995 | print(scores.tolist()) |
| 5996 | ``` |
| 5997 | |
| 5998 | ## Supported Languages |
| 5999 | |
| 6000 | This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) |
| 6001 | and continually trained on a mixture of multilingual datasets. |
| 6002 | It supports 100 languages from xlm-roberta, |
| 6003 | but low-resource languages may see performance degradation. |
| 6004 | |
| 6005 | ## Training Details |
| 6006 | |
| 6007 | **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) |
| 6008 | |
| 6009 | **First stage**: contrastive pre-training with weak supervision |
| 6010 | |
| 6011 | | Dataset | Weak supervision | # of text pairs | |
| 6012 | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| |
| 6013 | | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | |
| 6014 | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | |
| 6015 | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | |
| 6016 | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | |
| 6017 | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | |
| 6018 | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | |
| 6019 | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | |
| 6020 | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | |
| 6021 | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | |
| 6022 | |
| 6023 | **Second stage**: supervised fine-tuning |
| 6024 | |
| 6025 | | Dataset | Language | # of text pairs | |
| 6026 | |----------------------------------------------------------------------------------------|--------------|-----------------| |
| 6027 | | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | |
| 6028 | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | |
| 6029 | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | |
| 6030 | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | |
| 6031 | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | |
| 6032 | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | |
| 6033 | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 6034 | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 6035 | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | |
| 6036 | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | |
| 6037 | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | |
| 6038 | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | |
| 6039 | |
| 6040 | For all labeled datasets, we only use its training set for fine-tuning. |
| 6041 | |
| 6042 | For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). |
| 6043 | |
| 6044 | ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) |
| 6045 | |
| 6046 | | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |
| 6047 | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | |
| 6048 | | 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 | |
| 6049 | | 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 | |
| 6050 | | 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 | |
| 6051 | | | | |
| 6052 | | 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 | |
| 6053 | | 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 | |
| 6054 | | 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 | |
| 6055 | |
| 6056 | ## MTEB Benchmark Evaluation |
| 6057 | |
| 6058 | Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
| 6059 | on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
| 6060 | |
| 6061 | ## Support for Sentence Transformers |
| 6062 | |
| 6063 | Below is an example for usage with sentence_transformers. |
| 6064 | ```python |
| 6065 | from sentence_transformers import SentenceTransformer |
| 6066 | model = SentenceTransformer('intfloat/multilingual-e5-large') |
| 6067 | input_texts = [ |
| 6068 | 'query: how much protein should a female eat', |
| 6069 | 'query: 南瓜的家常做法', |
| 6070 | "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.", |
| 6071 | "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" |
| 6072 | ] |
| 6073 | embeddings = model.encode(input_texts, normalize_embeddings=True) |
| 6074 | ``` |
| 6075 | |
| 6076 | Package requirements |
| 6077 | |
| 6078 | `pip install sentence_transformers~=2.2.2` |
| 6079 | |
| 6080 | Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
| 6081 | |
| 6082 | ## FAQ |
| 6083 | |
| 6084 | **1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
| 6085 | |
| 6086 | Yes, this is how the model is trained, otherwise you will see a performance degradation. |
| 6087 | |
| 6088 | Here are some rules of thumb: |
| 6089 | - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
| 6090 | |
| 6091 | - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. |
| 6092 | |
| 6093 | - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
| 6094 | |
| 6095 | **2. Why are my reproduced results slightly different from reported in the model card?** |
| 6096 | |
| 6097 | Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
| 6098 | |
| 6099 | **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
| 6100 | |
| 6101 | This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
| 6102 | |
| 6103 | For text embedding tasks like text retrieval or semantic similarity, |
| 6104 | what matters is the relative order of the scores instead of the absolute values, |
| 6105 | so this should not be an issue. |
| 6106 | |
| 6107 | ## Citation |
| 6108 | |
| 6109 | If you find our paper or models helpful, please consider cite as follows: |
| 6110 | |
| 6111 | ``` |
| 6112 | @article{wang2024multilingual, |
| 6113 | title={Multilingual E5 Text Embeddings: A Technical Report}, |
| 6114 | author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, |
| 6115 | journal={arXiv preprint arXiv:2402.05672}, |
| 6116 | year={2024} |
| 6117 | } |
| 6118 | ``` |
| 6119 | |
| 6120 | ## Limitations |
| 6121 | |
| 6122 | Long texts will be truncated to at most 512 tokens. |
| 6123 | |