README.md
| 1 | --- |
| 2 | language: |
| 3 | - multilingual |
| 4 | - af |
| 5 | - am |
| 6 | - ar |
| 7 | - as |
| 8 | - az |
| 9 | - be |
| 10 | - bg |
| 11 | - bn |
| 12 | - br |
| 13 | - bs |
| 14 | - ca |
| 15 | - cs |
| 16 | - cy |
| 17 | - da |
| 18 | - de |
| 19 | - el |
| 20 | - en |
| 21 | - eo |
| 22 | - es |
| 23 | - et |
| 24 | - eu |
| 25 | - fa |
| 26 | - fi |
| 27 | - fr |
| 28 | - fy |
| 29 | - ga |
| 30 | - gd |
| 31 | - gl |
| 32 | - gu |
| 33 | - ha |
| 34 | - he |
| 35 | - hi |
| 36 | - hr |
| 37 | - hu |
| 38 | - hy |
| 39 | - id |
| 40 | - is |
| 41 | - it |
| 42 | - ja |
| 43 | - jv |
| 44 | - ka |
| 45 | - kk |
| 46 | - km |
| 47 | - kn |
| 48 | - ko |
| 49 | - ku |
| 50 | - ky |
| 51 | - la |
| 52 | - lo |
| 53 | - lt |
| 54 | - lv |
| 55 | - mg |
| 56 | - mk |
| 57 | - ml |
| 58 | - mn |
| 59 | - mr |
| 60 | - ms |
| 61 | - my |
| 62 | - ne |
| 63 | - nl |
| 64 | - 'no' |
| 65 | - om |
| 66 | - or |
| 67 | - pa |
| 68 | - pl |
| 69 | - ps |
| 70 | - pt |
| 71 | - ro |
| 72 | - ru |
| 73 | - sa |
| 74 | - sd |
| 75 | - si |
| 76 | - sk |
| 77 | - sl |
| 78 | - so |
| 79 | - sq |
| 80 | - sr |
| 81 | - su |
| 82 | - sv |
| 83 | - sw |
| 84 | - ta |
| 85 | - te |
| 86 | - th |
| 87 | - tl |
| 88 | - tr |
| 89 | - ug |
| 90 | - uk |
| 91 | - ur |
| 92 | - uz |
| 93 | - vi |
| 94 | - xh |
| 95 | - yi |
| 96 | - zh |
| 97 | license: mit |
| 98 | model-index: |
| 99 | - name: intfloat/multilingual-e5-small |
| 100 | results: |
| 101 | - dataset: |
| 102 | config: en |
| 103 | name: MTEB AmazonCounterfactualClassification (en) |
| 104 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 105 | split: test |
| 106 | type: mteb/amazon_counterfactual |
| 107 | metrics: |
| 108 | - type: accuracy |
| 109 | value: 73.79104477611939 |
| 110 | - type: ap |
| 111 | value: 36.9996434842022 |
| 112 | - type: f1 |
| 113 | value: 67.95453679103099 |
| 114 | task: |
| 115 | type: Classification |
| 116 | - dataset: |
| 117 | config: de |
| 118 | name: MTEB AmazonCounterfactualClassification (de) |
| 119 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 120 | split: test |
| 121 | type: mteb/amazon_counterfactual |
| 122 | metrics: |
| 123 | - type: accuracy |
| 124 | value: 71.64882226980728 |
| 125 | - type: ap |
| 126 | value: 82.11942130026586 |
| 127 | - type: f1 |
| 128 | value: 69.87963421606715 |
| 129 | task: |
| 130 | type: Classification |
| 131 | - dataset: |
| 132 | config: en-ext |
| 133 | name: MTEB AmazonCounterfactualClassification (en-ext) |
| 134 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 135 | split: test |
| 136 | type: mteb/amazon_counterfactual |
| 137 | metrics: |
| 138 | - type: accuracy |
| 139 | value: 75.8095952023988 |
| 140 | - type: ap |
| 141 | value: 24.46869495579561 |
| 142 | - type: f1 |
| 143 | value: 63.00108480037597 |
| 144 | task: |
| 145 | type: Classification |
| 146 | - dataset: |
| 147 | config: ja |
| 148 | name: MTEB AmazonCounterfactualClassification (ja) |
| 149 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 150 | split: test |
| 151 | type: mteb/amazon_counterfactual |
| 152 | metrics: |
| 153 | - type: accuracy |
| 154 | value: 64.186295503212 |
| 155 | - type: ap |
| 156 | value: 15.496804690197042 |
| 157 | - type: f1 |
| 158 | value: 52.07153895475031 |
| 159 | task: |
| 160 | type: Classification |
| 161 | - dataset: |
| 162 | config: default |
| 163 | name: MTEB AmazonPolarityClassification |
| 164 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 165 | split: test |
| 166 | type: mteb/amazon_polarity |
| 167 | metrics: |
| 168 | - type: accuracy |
| 169 | value: 88.699325 |
| 170 | - type: ap |
| 171 | value: 85.27039559917269 |
| 172 | - type: f1 |
| 173 | value: 88.65556295032513 |
| 174 | task: |
| 175 | type: Classification |
| 176 | - dataset: |
| 177 | config: en |
| 178 | name: MTEB AmazonReviewsClassification (en) |
| 179 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 180 | split: test |
| 181 | type: mteb/amazon_reviews_multi |
| 182 | metrics: |
| 183 | - type: accuracy |
| 184 | value: 44.69799999999999 |
| 185 | - type: f1 |
| 186 | value: 43.73187348654165 |
| 187 | task: |
| 188 | type: Classification |
| 189 | - dataset: |
| 190 | config: de |
| 191 | name: MTEB AmazonReviewsClassification (de) |
| 192 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 193 | split: test |
| 194 | type: mteb/amazon_reviews_multi |
| 195 | metrics: |
| 196 | - type: accuracy |
| 197 | value: 40.245999999999995 |
| 198 | - type: f1 |
| 199 | value: 39.3863530637684 |
| 200 | task: |
| 201 | type: Classification |
| 202 | - dataset: |
| 203 | config: es |
| 204 | name: MTEB AmazonReviewsClassification (es) |
| 205 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 206 | split: test |
| 207 | type: mteb/amazon_reviews_multi |
| 208 | metrics: |
| 209 | - type: accuracy |
| 210 | value: 40.394 |
| 211 | - type: f1 |
| 212 | value: 39.301223469483446 |
| 213 | task: |
| 214 | type: Classification |
| 215 | - dataset: |
| 216 | config: fr |
| 217 | name: MTEB AmazonReviewsClassification (fr) |
| 218 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 219 | split: test |
| 220 | type: mteb/amazon_reviews_multi |
| 221 | metrics: |
| 222 | - type: accuracy |
| 223 | value: 38.864 |
| 224 | - type: f1 |
| 225 | value: 37.97974261868003 |
| 226 | task: |
| 227 | type: Classification |
| 228 | - dataset: |
| 229 | config: ja |
| 230 | name: MTEB AmazonReviewsClassification (ja) |
| 231 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 232 | split: test |
| 233 | type: mteb/amazon_reviews_multi |
| 234 | metrics: |
| 235 | - type: accuracy |
| 236 | value: 37.682 |
| 237 | - type: f1 |
| 238 | value: 37.07399369768313 |
| 239 | task: |
| 240 | type: Classification |
| 241 | - dataset: |
| 242 | config: zh |
| 243 | name: MTEB AmazonReviewsClassification (zh) |
| 244 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 245 | split: test |
| 246 | type: mteb/amazon_reviews_multi |
| 247 | metrics: |
| 248 | - type: accuracy |
| 249 | value: 37.504 |
| 250 | - type: f1 |
| 251 | value: 36.62317273874278 |
| 252 | task: |
| 253 | type: Classification |
| 254 | - dataset: |
| 255 | config: default |
| 256 | name: MTEB ArguAna |
| 257 | revision: None |
| 258 | split: test |
| 259 | type: arguana |
| 260 | metrics: |
| 261 | - type: map_at_1 |
| 262 | value: 19.061 |
| 263 | - type: map_at_10 |
| 264 | value: 31.703 |
| 265 | - type: map_at_100 |
| 266 | value: 32.967 |
| 267 | - type: map_at_1000 |
| 268 | value: 33.001000000000005 |
| 269 | - type: map_at_3 |
| 270 | value: 27.466 |
| 271 | - type: map_at_5 |
| 272 | value: 29.564 |
| 273 | - type: mrr_at_1 |
| 274 | value: 19.559 |
| 275 | - type: mrr_at_10 |
| 276 | value: 31.874999999999996 |
| 277 | - type: mrr_at_100 |
| 278 | value: 33.146 |
| 279 | - type: mrr_at_1000 |
| 280 | value: 33.18 |
| 281 | - type: mrr_at_3 |
| 282 | value: 27.667 |
| 283 | - type: mrr_at_5 |
| 284 | value: 29.74 |
| 285 | - type: ndcg_at_1 |
| 286 | value: 19.061 |
| 287 | - type: ndcg_at_10 |
| 288 | value: 39.062999999999995 |
| 289 | - type: ndcg_at_100 |
| 290 | value: 45.184000000000005 |
| 291 | - type: ndcg_at_1000 |
| 292 | value: 46.115 |
| 293 | - type: ndcg_at_3 |
| 294 | value: 30.203000000000003 |
| 295 | - type: ndcg_at_5 |
| 296 | value: 33.953 |
| 297 | - type: precision_at_1 |
| 298 | value: 19.061 |
| 299 | - type: precision_at_10 |
| 300 | value: 6.279999999999999 |
| 301 | - type: precision_at_100 |
| 302 | value: 0.9129999999999999 |
| 303 | - type: precision_at_1000 |
| 304 | value: 0.099 |
| 305 | - type: precision_at_3 |
| 306 | value: 12.706999999999999 |
| 307 | - type: precision_at_5 |
| 308 | value: 9.431000000000001 |
| 309 | - type: recall_at_1 |
| 310 | value: 19.061 |
| 311 | - type: recall_at_10 |
| 312 | value: 62.802 |
| 313 | - type: recall_at_100 |
| 314 | value: 91.323 |
| 315 | - type: recall_at_1000 |
| 316 | value: 98.72 |
| 317 | - type: recall_at_3 |
| 318 | value: 38.122 |
| 319 | - type: recall_at_5 |
| 320 | value: 47.155 |
| 321 | task: |
| 322 | type: Retrieval |
| 323 | - dataset: |
| 324 | config: default |
| 325 | name: MTEB ArxivClusteringP2P |
| 326 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 327 | split: test |
| 328 | type: mteb/arxiv-clustering-p2p |
| 329 | metrics: |
| 330 | - type: v_measure |
| 331 | value: 39.22266660528253 |
| 332 | task: |
| 333 | type: Clustering |
| 334 | - dataset: |
| 335 | config: default |
| 336 | name: MTEB ArxivClusteringS2S |
| 337 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 338 | split: test |
| 339 | type: mteb/arxiv-clustering-s2s |
| 340 | metrics: |
| 341 | - type: v_measure |
| 342 | value: 30.79980849482483 |
| 343 | task: |
| 344 | type: Clustering |
| 345 | - dataset: |
| 346 | config: default |
| 347 | name: MTEB AskUbuntuDupQuestions |
| 348 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 349 | split: test |
| 350 | type: mteb/askubuntudupquestions-reranking |
| 351 | metrics: |
| 352 | - type: map |
| 353 | value: 57.8790068352054 |
| 354 | - type: mrr |
| 355 | value: 71.78791276436706 |
| 356 | task: |
| 357 | type: Reranking |
| 358 | - dataset: |
| 359 | config: default |
| 360 | name: MTEB BIOSSES |
| 361 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 362 | split: test |
| 363 | type: mteb/biosses-sts |
| 364 | metrics: |
| 365 | - type: cos_sim_pearson |
| 366 | value: 82.36328364043163 |
| 367 | - type: cos_sim_spearman |
| 368 | value: 82.26211536195868 |
| 369 | - type: euclidean_pearson |
| 370 | value: 80.3183865039173 |
| 371 | - type: euclidean_spearman |
| 372 | value: 79.88495276296132 |
| 373 | - type: manhattan_pearson |
| 374 | value: 80.14484480692127 |
| 375 | - type: manhattan_spearman |
| 376 | value: 80.39279565980743 |
| 377 | task: |
| 378 | type: STS |
| 379 | - dataset: |
| 380 | config: de-en |
| 381 | name: MTEB BUCC (de-en) |
| 382 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 383 | split: test |
| 384 | type: mteb/bucc-bitext-mining |
| 385 | metrics: |
| 386 | - type: accuracy |
| 387 | value: 98.0375782881002 |
| 388 | - type: f1 |
| 389 | value: 97.86012526096033 |
| 390 | - type: precision |
| 391 | value: 97.77139874739039 |
| 392 | - type: recall |
| 393 | value: 98.0375782881002 |
| 394 | task: |
| 395 | type: BitextMining |
| 396 | - dataset: |
| 397 | config: fr-en |
| 398 | name: MTEB BUCC (fr-en) |
| 399 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 400 | split: test |
| 401 | type: mteb/bucc-bitext-mining |
| 402 | metrics: |
| 403 | - type: accuracy |
| 404 | value: 93.35241030156286 |
| 405 | - type: f1 |
| 406 | value: 92.66050333846944 |
| 407 | - type: precision |
| 408 | value: 92.3306919069631 |
| 409 | - type: recall |
| 410 | value: 93.35241030156286 |
| 411 | task: |
| 412 | type: BitextMining |
| 413 | - dataset: |
| 414 | config: ru-en |
| 415 | name: MTEB BUCC (ru-en) |
| 416 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 417 | split: test |
| 418 | type: mteb/bucc-bitext-mining |
| 419 | metrics: |
| 420 | - type: accuracy |
| 421 | value: 94.0699688257707 |
| 422 | - type: f1 |
| 423 | value: 93.50236693222492 |
| 424 | - type: precision |
| 425 | value: 93.22791825424315 |
| 426 | - type: recall |
| 427 | value: 94.0699688257707 |
| 428 | task: |
| 429 | type: BitextMining |
| 430 | - dataset: |
| 431 | config: zh-en |
| 432 | name: MTEB BUCC (zh-en) |
| 433 | revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| 434 | split: test |
| 435 | type: mteb/bucc-bitext-mining |
| 436 | metrics: |
| 437 | - type: accuracy |
| 438 | value: 89.25750394944708 |
| 439 | - type: f1 |
| 440 | value: 88.79234684921889 |
| 441 | - type: precision |
| 442 | value: 88.57293312269616 |
| 443 | - type: recall |
| 444 | value: 89.25750394944708 |
| 445 | task: |
| 446 | type: BitextMining |
| 447 | - dataset: |
| 448 | config: default |
| 449 | name: MTEB Banking77Classification |
| 450 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 451 | split: test |
| 452 | type: mteb/banking77 |
| 453 | metrics: |
| 454 | - type: accuracy |
| 455 | value: 79.41558441558442 |
| 456 | - type: f1 |
| 457 | value: 79.25886487487219 |
| 458 | task: |
| 459 | type: Classification |
| 460 | - dataset: |
| 461 | config: default |
| 462 | name: MTEB BiorxivClusteringP2P |
| 463 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 464 | split: test |
| 465 | type: mteb/biorxiv-clustering-p2p |
| 466 | metrics: |
| 467 | - type: v_measure |
| 468 | value: 35.747820820329736 |
| 469 | task: |
| 470 | type: Clustering |
| 471 | - dataset: |
| 472 | config: default |
| 473 | name: MTEB BiorxivClusteringS2S |
| 474 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 475 | split: test |
| 476 | type: mteb/biorxiv-clustering-s2s |
| 477 | metrics: |
| 478 | - type: v_measure |
| 479 | value: 27.045143830596146 |
| 480 | task: |
| 481 | type: Clustering |
| 482 | - dataset: |
| 483 | config: default |
| 484 | name: MTEB CQADupstackRetrieval |
| 485 | revision: None |
| 486 | split: test |
| 487 | type: BeIR/cqadupstack |
| 488 | metrics: |
| 489 | - type: map_at_1 |
| 490 | value: 24.252999999999997 |
| 491 | - type: map_at_10 |
| 492 | value: 31.655916666666666 |
| 493 | - type: map_at_100 |
| 494 | value: 32.680749999999996 |
| 495 | - type: map_at_1000 |
| 496 | value: 32.79483333333334 |
| 497 | - type: map_at_3 |
| 498 | value: 29.43691666666666 |
| 499 | - type: map_at_5 |
| 500 | value: 30.717416666666665 |
| 501 | - type: mrr_at_1 |
| 502 | value: 28.602750000000004 |
| 503 | - type: mrr_at_10 |
| 504 | value: 35.56875 |
| 505 | - type: mrr_at_100 |
| 506 | value: 36.3595 |
| 507 | - type: mrr_at_1000 |
| 508 | value: 36.427749999999996 |
| 509 | - type: mrr_at_3 |
| 510 | value: 33.586166666666664 |
| 511 | - type: mrr_at_5 |
| 512 | value: 34.73641666666666 |
| 513 | - type: ndcg_at_1 |
| 514 | value: 28.602750000000004 |
| 515 | - type: ndcg_at_10 |
| 516 | value: 36.06933333333334 |
| 517 | - type: ndcg_at_100 |
| 518 | value: 40.70141666666667 |
| 519 | - type: ndcg_at_1000 |
| 520 | value: 43.24341666666667 |
| 521 | - type: ndcg_at_3 |
| 522 | value: 32.307916666666664 |
| 523 | - type: ndcg_at_5 |
| 524 | value: 34.129999999999995 |
| 525 | - type: precision_at_1 |
| 526 | value: 28.602750000000004 |
| 527 | - type: precision_at_10 |
| 528 | value: 6.097666666666667 |
| 529 | - type: precision_at_100 |
| 530 | value: 0.9809166666666668 |
| 531 | - type: precision_at_1000 |
| 532 | value: 0.13766666666666663 |
| 533 | - type: precision_at_3 |
| 534 | value: 14.628166666666667 |
| 535 | - type: precision_at_5 |
| 536 | value: 10.266916666666667 |
| 537 | - type: recall_at_1 |
| 538 | value: 24.252999999999997 |
| 539 | - type: recall_at_10 |
| 540 | value: 45.31916666666667 |
| 541 | - type: recall_at_100 |
| 542 | value: 66.03575000000001 |
| 543 | - type: recall_at_1000 |
| 544 | value: 83.94708333333334 |
| 545 | - type: recall_at_3 |
| 546 | value: 34.71941666666666 |
| 547 | - type: recall_at_5 |
| 548 | value: 39.46358333333333 |
| 549 | task: |
| 550 | type: Retrieval |
| 551 | - dataset: |
| 552 | config: default |
| 553 | name: MTEB ClimateFEVER |
| 554 | revision: None |
| 555 | split: test |
| 556 | type: climate-fever |
| 557 | metrics: |
| 558 | - type: map_at_1 |
| 559 | value: 9.024000000000001 |
| 560 | - type: map_at_10 |
| 561 | value: 15.644 |
| 562 | - type: map_at_100 |
| 563 | value: 17.154 |
| 564 | - type: map_at_1000 |
| 565 | value: 17.345 |
| 566 | - type: map_at_3 |
| 567 | value: 13.028 |
| 568 | - type: map_at_5 |
| 569 | value: 14.251 |
| 570 | - type: mrr_at_1 |
| 571 | value: 19.674 |
| 572 | - type: mrr_at_10 |
| 573 | value: 29.826999999999998 |
| 574 | - type: mrr_at_100 |
| 575 | value: 30.935000000000002 |
| 576 | - type: mrr_at_1000 |
| 577 | value: 30.987 |
| 578 | - type: mrr_at_3 |
| 579 | value: 26.645000000000003 |
| 580 | - type: mrr_at_5 |
| 581 | value: 28.29 |
| 582 | - type: ndcg_at_1 |
| 583 | value: 19.674 |
| 584 | - type: ndcg_at_10 |
| 585 | value: 22.545 |
| 586 | - type: ndcg_at_100 |
| 587 | value: 29.207 |
| 588 | - type: ndcg_at_1000 |
| 589 | value: 32.912 |
| 590 | - type: ndcg_at_3 |
| 591 | value: 17.952 |
| 592 | - type: ndcg_at_5 |
| 593 | value: 19.363 |
| 594 | - type: precision_at_1 |
| 595 | value: 19.674 |
| 596 | - type: precision_at_10 |
| 597 | value: 7.212000000000001 |
| 598 | - type: precision_at_100 |
| 599 | value: 1.435 |
| 600 | - type: precision_at_1000 |
| 601 | value: 0.212 |
| 602 | - type: precision_at_3 |
| 603 | value: 13.507 |
| 604 | - type: precision_at_5 |
| 605 | value: 10.397 |
| 606 | - type: recall_at_1 |
| 607 | value: 9.024000000000001 |
| 608 | - type: recall_at_10 |
| 609 | value: 28.077999999999996 |
| 610 | - type: recall_at_100 |
| 611 | value: 51.403 |
| 612 | - type: recall_at_1000 |
| 613 | value: 72.406 |
| 614 | - type: recall_at_3 |
| 615 | value: 16.768 |
| 616 | - type: recall_at_5 |
| 617 | value: 20.737 |
| 618 | task: |
| 619 | type: Retrieval |
| 620 | - dataset: |
| 621 | config: default |
| 622 | name: MTEB DBPedia |
| 623 | revision: None |
| 624 | split: test |
| 625 | type: dbpedia-entity |
| 626 | metrics: |
| 627 | - type: map_at_1 |
| 628 | value: 8.012 |
| 629 | - type: map_at_10 |
| 630 | value: 17.138 |
| 631 | - type: map_at_100 |
| 632 | value: 24.146 |
| 633 | - type: map_at_1000 |
| 634 | value: 25.622 |
| 635 | - type: map_at_3 |
| 636 | value: 12.552 |
| 637 | - type: map_at_5 |
| 638 | value: 14.435 |
| 639 | - type: mrr_at_1 |
| 640 | value: 62.25000000000001 |
| 641 | - type: mrr_at_10 |
| 642 | value: 71.186 |
| 643 | - type: mrr_at_100 |
| 644 | value: 71.504 |
| 645 | - type: mrr_at_1000 |
| 646 | value: 71.514 |
| 647 | - type: mrr_at_3 |
| 648 | value: 69.333 |
| 649 | - type: mrr_at_5 |
| 650 | value: 70.408 |
| 651 | - type: ndcg_at_1 |
| 652 | value: 49.75 |
| 653 | - type: ndcg_at_10 |
| 654 | value: 37.76 |
| 655 | - type: ndcg_at_100 |
| 656 | value: 42.071 |
| 657 | - type: ndcg_at_1000 |
| 658 | value: 49.309 |
| 659 | - type: ndcg_at_3 |
| 660 | value: 41.644 |
| 661 | - type: ndcg_at_5 |
| 662 | value: 39.812999999999995 |
| 663 | - type: precision_at_1 |
| 664 | value: 62.25000000000001 |
| 665 | - type: precision_at_10 |
| 666 | value: 30.15 |
| 667 | - type: precision_at_100 |
| 668 | value: 9.753 |
| 669 | - type: precision_at_1000 |
| 670 | value: 1.9189999999999998 |
| 671 | - type: precision_at_3 |
| 672 | value: 45.667 |
| 673 | - type: precision_at_5 |
| 674 | value: 39.15 |
| 675 | - type: recall_at_1 |
| 676 | value: 8.012 |
| 677 | - type: recall_at_10 |
| 678 | value: 22.599 |
| 679 | - type: recall_at_100 |
| 680 | value: 48.068 |
| 681 | - type: recall_at_1000 |
| 682 | value: 71.328 |
| 683 | - type: recall_at_3 |
| 684 | value: 14.043 |
| 685 | - type: recall_at_5 |
| 686 | value: 17.124 |
| 687 | task: |
| 688 | type: Retrieval |
| 689 | - dataset: |
| 690 | config: default |
| 691 | name: MTEB EmotionClassification |
| 692 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 693 | split: test |
| 694 | type: mteb/emotion |
| 695 | metrics: |
| 696 | - type: accuracy |
| 697 | value: 42.455 |
| 698 | - type: f1 |
| 699 | value: 37.59462649781862 |
| 700 | task: |
| 701 | type: Classification |
| 702 | - dataset: |
| 703 | config: default |
| 704 | name: MTEB FEVER |
| 705 | revision: None |
| 706 | split: test |
| 707 | type: fever |
| 708 | metrics: |
| 709 | - type: map_at_1 |
| 710 | value: 58.092 |
| 711 | - type: map_at_10 |
| 712 | value: 69.586 |
| 713 | - type: map_at_100 |
| 714 | value: 69.968 |
| 715 | - type: map_at_1000 |
| 716 | value: 69.982 |
| 717 | - type: map_at_3 |
| 718 | value: 67.48100000000001 |
| 719 | - type: map_at_5 |
| 720 | value: 68.915 |
| 721 | - type: mrr_at_1 |
| 722 | value: 62.166 |
| 723 | - type: mrr_at_10 |
| 724 | value: 73.588 |
| 725 | - type: mrr_at_100 |
| 726 | value: 73.86399999999999 |
| 727 | - type: mrr_at_1000 |
| 728 | value: 73.868 |
| 729 | - type: mrr_at_3 |
| 730 | value: 71.6 |
| 731 | - type: mrr_at_5 |
| 732 | value: 72.99 |
| 733 | - type: ndcg_at_1 |
| 734 | value: 62.166 |
| 735 | - type: ndcg_at_10 |
| 736 | value: 75.27199999999999 |
| 737 | - type: ndcg_at_100 |
| 738 | value: 76.816 |
| 739 | - type: ndcg_at_1000 |
| 740 | value: 77.09700000000001 |
| 741 | - type: ndcg_at_3 |
| 742 | value: 71.36 |
| 743 | - type: ndcg_at_5 |
| 744 | value: 73.785 |
| 745 | - type: precision_at_1 |
| 746 | value: 62.166 |
| 747 | - type: precision_at_10 |
| 748 | value: 9.716 |
| 749 | - type: precision_at_100 |
| 750 | value: 1.065 |
| 751 | - type: precision_at_1000 |
| 752 | value: 0.11 |
| 753 | - type: precision_at_3 |
| 754 | value: 28.278 |
| 755 | - type: precision_at_5 |
| 756 | value: 18.343999999999998 |
| 757 | - type: recall_at_1 |
| 758 | value: 58.092 |
| 759 | - type: recall_at_10 |
| 760 | value: 88.73400000000001 |
| 761 | - type: recall_at_100 |
| 762 | value: 95.195 |
| 763 | - type: recall_at_1000 |
| 764 | value: 97.04599999999999 |
| 765 | - type: recall_at_3 |
| 766 | value: 78.45 |
| 767 | - type: recall_at_5 |
| 768 | value: 84.316 |
| 769 | task: |
| 770 | type: Retrieval |
| 771 | - dataset: |
| 772 | config: default |
| 773 | name: MTEB FiQA2018 |
| 774 | revision: None |
| 775 | split: test |
| 776 | type: fiqa |
| 777 | metrics: |
| 778 | - type: map_at_1 |
| 779 | value: 16.649 |
| 780 | - type: map_at_10 |
| 781 | value: 26.457000000000004 |
| 782 | - type: map_at_100 |
| 783 | value: 28.169 |
| 784 | - type: map_at_1000 |
| 785 | value: 28.352 |
| 786 | - type: map_at_3 |
| 787 | value: 23.305 |
| 788 | - type: map_at_5 |
| 789 | value: 25.169000000000004 |
| 790 | - type: mrr_at_1 |
| 791 | value: 32.407000000000004 |
| 792 | - type: mrr_at_10 |
| 793 | value: 40.922 |
| 794 | - type: mrr_at_100 |
| 795 | value: 41.931000000000004 |
| 796 | - type: mrr_at_1000 |
| 797 | value: 41.983 |
| 798 | - type: mrr_at_3 |
| 799 | value: 38.786 |
| 800 | - type: mrr_at_5 |
| 801 | value: 40.205999999999996 |
| 802 | - type: ndcg_at_1 |
| 803 | value: 32.407000000000004 |
| 804 | - type: ndcg_at_10 |
| 805 | value: 33.314 |
| 806 | - type: ndcg_at_100 |
| 807 | value: 40.312 |
| 808 | - type: ndcg_at_1000 |
| 809 | value: 43.685 |
| 810 | - type: ndcg_at_3 |
| 811 | value: 30.391000000000002 |
| 812 | - type: ndcg_at_5 |
| 813 | value: 31.525 |
| 814 | - type: precision_at_1 |
| 815 | value: 32.407000000000004 |
| 816 | - type: precision_at_10 |
| 817 | value: 8.966000000000001 |
| 818 | - type: precision_at_100 |
| 819 | value: 1.6019999999999999 |
| 820 | - type: precision_at_1000 |
| 821 | value: 0.22200000000000003 |
| 822 | - type: precision_at_3 |
| 823 | value: 20.165 |
| 824 | - type: precision_at_5 |
| 825 | value: 14.722 |
| 826 | - type: recall_at_1 |
| 827 | value: 16.649 |
| 828 | - type: recall_at_10 |
| 829 | value: 39.117000000000004 |
| 830 | - type: recall_at_100 |
| 831 | value: 65.726 |
| 832 | - type: recall_at_1000 |
| 833 | value: 85.784 |
| 834 | - type: recall_at_3 |
| 835 | value: 27.914 |
| 836 | - type: recall_at_5 |
| 837 | value: 33.289 |
| 838 | task: |
| 839 | type: Retrieval |
| 840 | - dataset: |
| 841 | config: default |
| 842 | name: MTEB HotpotQA |
| 843 | revision: None |
| 844 | split: test |
| 845 | type: hotpotqa |
| 846 | metrics: |
| 847 | - type: map_at_1 |
| 848 | value: 36.253 |
| 849 | - type: map_at_10 |
| 850 | value: 56.16799999999999 |
| 851 | - type: map_at_100 |
| 852 | value: 57.06099999999999 |
| 853 | - type: map_at_1000 |
| 854 | value: 57.126 |
| 855 | - type: map_at_3 |
| 856 | value: 52.644999999999996 |
| 857 | - type: map_at_5 |
| 858 | value: 54.909 |
| 859 | - type: mrr_at_1 |
| 860 | value: 72.505 |
| 861 | - type: mrr_at_10 |
| 862 | value: 79.66 |
| 863 | - type: mrr_at_100 |
| 864 | value: 79.869 |
| 865 | - type: mrr_at_1000 |
| 866 | value: 79.88 |
| 867 | - type: mrr_at_3 |
| 868 | value: 78.411 |
| 869 | - type: mrr_at_5 |
| 870 | value: 79.19800000000001 |
| 871 | - type: ndcg_at_1 |
| 872 | value: 72.505 |
| 873 | - type: ndcg_at_10 |
| 874 | value: 65.094 |
| 875 | - type: ndcg_at_100 |
| 876 | value: 68.219 |
| 877 | - type: ndcg_at_1000 |
| 878 | value: 69.515 |
| 879 | - type: ndcg_at_3 |
| 880 | value: 59.99 |
| 881 | - type: ndcg_at_5 |
| 882 | value: 62.909000000000006 |
| 883 | - type: precision_at_1 |
| 884 | value: 72.505 |
| 885 | - type: precision_at_10 |
| 886 | value: 13.749 |
| 887 | - type: precision_at_100 |
| 888 | value: 1.619 |
| 889 | - type: precision_at_1000 |
| 890 | value: 0.179 |
| 891 | - type: precision_at_3 |
| 892 | value: 38.357 |
| 893 | - type: precision_at_5 |
| 894 | value: 25.313000000000002 |
| 895 | - type: recall_at_1 |
| 896 | value: 36.253 |
| 897 | - type: recall_at_10 |
| 898 | value: 68.744 |
| 899 | - type: recall_at_100 |
| 900 | value: 80.925 |
| 901 | - type: recall_at_1000 |
| 902 | value: 89.534 |
| 903 | - type: recall_at_3 |
| 904 | value: 57.535000000000004 |
| 905 | - type: recall_at_5 |
| 906 | value: 63.282000000000004 |
| 907 | task: |
| 908 | type: Retrieval |
| 909 | - dataset: |
| 910 | config: default |
| 911 | name: MTEB ImdbClassification |
| 912 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 913 | split: test |
| 914 | type: mteb/imdb |
| 915 | metrics: |
| 916 | - type: accuracy |
| 917 | value: 80.82239999999999 |
| 918 | - type: ap |
| 919 | value: 75.65895781725314 |
| 920 | - type: f1 |
| 921 | value: 80.75880969095746 |
| 922 | task: |
| 923 | type: Classification |
| 924 | - dataset: |
| 925 | config: default |
| 926 | name: MTEB MSMARCO |
| 927 | revision: None |
| 928 | split: dev |
| 929 | type: msmarco |
| 930 | metrics: |
| 931 | - type: map_at_1 |
| 932 | value: 21.624 |
| 933 | - type: map_at_10 |
| 934 | value: 34.075 |
| 935 | - type: map_at_100 |
| 936 | value: 35.229 |
| 937 | - type: map_at_1000 |
| 938 | value: 35.276999999999994 |
| 939 | - type: map_at_3 |
| 940 | value: 30.245 |
| 941 | - type: map_at_5 |
| 942 | value: 32.42 |
| 943 | - type: mrr_at_1 |
| 944 | value: 22.264 |
| 945 | - type: mrr_at_10 |
| 946 | value: 34.638000000000005 |
| 947 | - type: mrr_at_100 |
| 948 | value: 35.744 |
| 949 | - type: mrr_at_1000 |
| 950 | value: 35.787 |
| 951 | - type: mrr_at_3 |
| 952 | value: 30.891000000000002 |
| 953 | - type: mrr_at_5 |
| 954 | value: 33.042 |
| 955 | - type: ndcg_at_1 |
| 956 | value: 22.264 |
| 957 | - type: ndcg_at_10 |
| 958 | value: 40.991 |
| 959 | - type: ndcg_at_100 |
| 960 | value: 46.563 |
| 961 | - type: ndcg_at_1000 |
| 962 | value: 47.743 |
| 963 | - type: ndcg_at_3 |
| 964 | value: 33.198 |
| 965 | - type: ndcg_at_5 |
| 966 | value: 37.069 |
| 967 | - type: precision_at_1 |
| 968 | value: 22.264 |
| 969 | - type: precision_at_10 |
| 970 | value: 6.5089999999999995 |
| 971 | - type: precision_at_100 |
| 972 | value: 0.9299999999999999 |
| 973 | - type: precision_at_1000 |
| 974 | value: 0.10300000000000001 |
| 975 | - type: precision_at_3 |
| 976 | value: 14.216999999999999 |
| 977 | - type: precision_at_5 |
| 978 | value: 10.487 |
| 979 | - type: recall_at_1 |
| 980 | value: 21.624 |
| 981 | - type: recall_at_10 |
| 982 | value: 62.303 |
| 983 | - type: recall_at_100 |
| 984 | value: 88.124 |
| 985 | - type: recall_at_1000 |
| 986 | value: 97.08 |
| 987 | - type: recall_at_3 |
| 988 | value: 41.099999999999994 |
| 989 | - type: recall_at_5 |
| 990 | value: 50.381 |
| 991 | task: |
| 992 | type: Retrieval |
| 993 | - dataset: |
| 994 | config: en |
| 995 | name: MTEB MTOPDomainClassification (en) |
| 996 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 997 | split: test |
| 998 | type: mteb/mtop_domain |
| 999 | metrics: |
| 1000 | - type: accuracy |
| 1001 | value: 91.06703146374831 |
| 1002 | - type: f1 |
| 1003 | value: 90.86867815863172 |
| 1004 | task: |
| 1005 | type: Classification |
| 1006 | - dataset: |
| 1007 | config: de |
| 1008 | name: MTEB MTOPDomainClassification (de) |
| 1009 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1010 | split: test |
| 1011 | type: mteb/mtop_domain |
| 1012 | metrics: |
| 1013 | - type: accuracy |
| 1014 | value: 87.46970977740209 |
| 1015 | - type: f1 |
| 1016 | value: 86.36832872036588 |
| 1017 | task: |
| 1018 | type: Classification |
| 1019 | - dataset: |
| 1020 | config: es |
| 1021 | name: MTEB MTOPDomainClassification (es) |
| 1022 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1023 | split: test |
| 1024 | type: mteb/mtop_domain |
| 1025 | metrics: |
| 1026 | - type: accuracy |
| 1027 | value: 89.26951300867245 |
| 1028 | - type: f1 |
| 1029 | value: 88.93561193959502 |
| 1030 | task: |
| 1031 | type: Classification |
| 1032 | - dataset: |
| 1033 | config: fr |
| 1034 | name: MTEB MTOPDomainClassification (fr) |
| 1035 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1036 | split: test |
| 1037 | type: mteb/mtop_domain |
| 1038 | metrics: |
| 1039 | - type: accuracy |
| 1040 | value: 84.22799874725963 |
| 1041 | - type: f1 |
| 1042 | value: 84.30490069236556 |
| 1043 | task: |
| 1044 | type: Classification |
| 1045 | - dataset: |
| 1046 | config: hi |
| 1047 | name: MTEB MTOPDomainClassification (hi) |
| 1048 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1049 | split: test |
| 1050 | type: mteb/mtop_domain |
| 1051 | metrics: |
| 1052 | - type: accuracy |
| 1053 | value: 86.02007888131948 |
| 1054 | - type: f1 |
| 1055 | value: 85.39376041027991 |
| 1056 | task: |
| 1057 | type: Classification |
| 1058 | - dataset: |
| 1059 | config: th |
| 1060 | name: MTEB MTOPDomainClassification (th) |
| 1061 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1062 | split: test |
| 1063 | type: mteb/mtop_domain |
| 1064 | metrics: |
| 1065 | - type: accuracy |
| 1066 | value: 85.34900542495481 |
| 1067 | - type: f1 |
| 1068 | value: 85.39859673336713 |
| 1069 | task: |
| 1070 | type: Classification |
| 1071 | - dataset: |
| 1072 | config: en |
| 1073 | name: MTEB MTOPIntentClassification (en) |
| 1074 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1075 | split: test |
| 1076 | type: mteb/mtop_intent |
| 1077 | metrics: |
| 1078 | - type: accuracy |
| 1079 | value: 71.078431372549 |
| 1080 | - type: f1 |
| 1081 | value: 53.45071102002276 |
| 1082 | task: |
| 1083 | type: Classification |
| 1084 | - dataset: |
| 1085 | config: de |
| 1086 | name: MTEB MTOPIntentClassification (de) |
| 1087 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1088 | split: test |
| 1089 | type: mteb/mtop_intent |
| 1090 | metrics: |
| 1091 | - type: accuracy |
| 1092 | value: 65.85798816568047 |
| 1093 | - type: f1 |
| 1094 | value: 46.53112748993529 |
| 1095 | task: |
| 1096 | type: Classification |
| 1097 | - dataset: |
| 1098 | config: es |
| 1099 | name: MTEB MTOPIntentClassification (es) |
| 1100 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1101 | split: test |
| 1102 | type: mteb/mtop_intent |
| 1103 | metrics: |
| 1104 | - type: accuracy |
| 1105 | value: 67.96864576384256 |
| 1106 | - type: f1 |
| 1107 | value: 45.966703022829506 |
| 1108 | task: |
| 1109 | type: Classification |
| 1110 | - dataset: |
| 1111 | config: fr |
| 1112 | name: MTEB MTOPIntentClassification (fr) |
| 1113 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1114 | split: test |
| 1115 | type: mteb/mtop_intent |
| 1116 | metrics: |
| 1117 | - type: accuracy |
| 1118 | value: 61.31537738803633 |
| 1119 | - type: f1 |
| 1120 | value: 45.52601712835461 |
| 1121 | task: |
| 1122 | type: Classification |
| 1123 | - dataset: |
| 1124 | config: hi |
| 1125 | name: MTEB MTOPIntentClassification (hi) |
| 1126 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1127 | split: test |
| 1128 | type: mteb/mtop_intent |
| 1129 | metrics: |
| 1130 | - type: accuracy |
| 1131 | value: 66.29616349946218 |
| 1132 | - type: f1 |
| 1133 | value: 47.24166485726613 |
| 1134 | task: |
| 1135 | type: Classification |
| 1136 | - dataset: |
| 1137 | config: th |
| 1138 | name: MTEB MTOPIntentClassification (th) |
| 1139 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1140 | split: test |
| 1141 | type: mteb/mtop_intent |
| 1142 | metrics: |
| 1143 | - type: accuracy |
| 1144 | value: 67.51537070524412 |
| 1145 | - type: f1 |
| 1146 | value: 49.463476319014276 |
| 1147 | task: |
| 1148 | type: Classification |
| 1149 | - dataset: |
| 1150 | config: af |
| 1151 | name: MTEB MassiveIntentClassification (af) |
| 1152 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1153 | split: test |
| 1154 | type: mteb/amazon_massive_intent |
| 1155 | metrics: |
| 1156 | - type: accuracy |
| 1157 | value: 57.06792199058508 |
| 1158 | - type: f1 |
| 1159 | value: 54.094921857502285 |
| 1160 | task: |
| 1161 | type: Classification |
| 1162 | - dataset: |
| 1163 | config: am |
| 1164 | name: MTEB MassiveIntentClassification (am) |
| 1165 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1166 | split: test |
| 1167 | type: mteb/amazon_massive_intent |
| 1168 | metrics: |
| 1169 | - type: accuracy |
| 1170 | value: 51.960322797579025 |
| 1171 | - type: f1 |
| 1172 | value: 48.547371223370945 |
| 1173 | task: |
| 1174 | type: Classification |
| 1175 | - dataset: |
| 1176 | config: ar |
| 1177 | name: MTEB MassiveIntentClassification (ar) |
| 1178 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1179 | split: test |
| 1180 | type: mteb/amazon_massive_intent |
| 1181 | metrics: |
| 1182 | - type: accuracy |
| 1183 | value: 54.425016812373904 |
| 1184 | - type: f1 |
| 1185 | value: 50.47069202054312 |
| 1186 | task: |
| 1187 | type: Classification |
| 1188 | - dataset: |
| 1189 | config: az |
| 1190 | name: MTEB MassiveIntentClassification (az) |
| 1191 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1192 | split: test |
| 1193 | type: mteb/amazon_massive_intent |
| 1194 | metrics: |
| 1195 | - type: accuracy |
| 1196 | value: 59.798251513113655 |
| 1197 | - type: f1 |
| 1198 | value: 57.05013069086648 |
| 1199 | task: |
| 1200 | type: Classification |
| 1201 | - dataset: |
| 1202 | config: bn |
| 1203 | name: MTEB MassiveIntentClassification (bn) |
| 1204 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1205 | split: test |
| 1206 | type: mteb/amazon_massive_intent |
| 1207 | metrics: |
| 1208 | - type: accuracy |
| 1209 | value: 59.37794216543376 |
| 1210 | - type: f1 |
| 1211 | value: 56.3607992649805 |
| 1212 | task: |
| 1213 | type: Classification |
| 1214 | - dataset: |
| 1215 | config: cy |
| 1216 | name: MTEB MassiveIntentClassification (cy) |
| 1217 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1218 | split: test |
| 1219 | type: mteb/amazon_massive_intent |
| 1220 | metrics: |
| 1221 | - type: accuracy |
| 1222 | value: 46.56018829858777 |
| 1223 | - type: f1 |
| 1224 | value: 43.87319715715134 |
| 1225 | task: |
| 1226 | type: Classification |
| 1227 | - dataset: |
| 1228 | config: da |
| 1229 | name: MTEB MassiveIntentClassification (da) |
| 1230 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1231 | split: test |
| 1232 | type: mteb/amazon_massive_intent |
| 1233 | metrics: |
| 1234 | - type: accuracy |
| 1235 | value: 62.9724277067922 |
| 1236 | - type: f1 |
| 1237 | value: 59.36480066245562 |
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| 1242 | name: MTEB MassiveIntentClassification (de) |
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| 1766 | name: MTEB MassiveIntentClassification (ur) |
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| 1779 | name: MTEB MassiveIntentClassification (vi) |
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| 1792 | name: MTEB MassiveIntentClassification (zh-CN) |
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| 1800 | value: 65.24917026029749 |
| 1801 | task: |
| 1802 | type: Classification |
| 1803 | - dataset: |
| 1804 | config: zh-TW |
| 1805 | name: MTEB MassiveIntentClassification (zh-TW) |
| 1806 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1807 | split: test |
| 1808 | type: mteb/amazon_massive_intent |
| 1809 | metrics: |
| 1810 | - type: accuracy |
| 1811 | value: 62.53530598520511 |
| 1812 | - type: f1 |
| 1813 | value: 61.71131132295768 |
| 1814 | task: |
| 1815 | type: Classification |
| 1816 | - dataset: |
| 1817 | config: af |
| 1818 | name: MTEB MassiveScenarioClassification (af) |
| 1819 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1820 | split: test |
| 1821 | type: mteb/amazon_massive_scenario |
| 1822 | metrics: |
| 1823 | - type: accuracy |
| 1824 | value: 63.04303967720243 |
| 1825 | - type: f1 |
| 1826 | value: 60.3950085685985 |
| 1827 | task: |
| 1828 | type: Classification |
| 1829 | - dataset: |
| 1830 | config: am |
| 1831 | name: MTEB MassiveScenarioClassification (am) |
| 1832 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1833 | split: test |
| 1834 | type: mteb/amazon_massive_scenario |
| 1835 | metrics: |
| 1836 | - type: accuracy |
| 1837 | value: 56.83591123066578 |
| 1838 | - type: f1 |
| 1839 | value: 54.95059828830849 |
| 1840 | task: |
| 1841 | type: Classification |
| 1842 | - dataset: |
| 1843 | config: ar |
| 1844 | name: MTEB MassiveScenarioClassification (ar) |
| 1845 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1846 | split: test |
| 1847 | type: mteb/amazon_massive_scenario |
| 1848 | metrics: |
| 1849 | - type: accuracy |
| 1850 | value: 59.62340282447881 |
| 1851 | - type: f1 |
| 1852 | value: 59.525159996498225 |
| 1853 | task: |
| 1854 | type: Classification |
| 1855 | - dataset: |
| 1856 | config: az |
| 1857 | name: MTEB MassiveScenarioClassification (az) |
| 1858 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1859 | split: test |
| 1860 | type: mteb/amazon_massive_scenario |
| 1861 | metrics: |
| 1862 | - type: accuracy |
| 1863 | value: 60.85406859448555 |
| 1864 | - type: f1 |
| 1865 | value: 59.129299095681276 |
| 1866 | task: |
| 1867 | type: Classification |
| 1868 | - dataset: |
| 1869 | config: bn |
| 1870 | name: MTEB MassiveScenarioClassification (bn) |
| 1871 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1872 | split: test |
| 1873 | type: mteb/amazon_massive_scenario |
| 1874 | metrics: |
| 1875 | - type: accuracy |
| 1876 | value: 62.76731674512441 |
| 1877 | - type: f1 |
| 1878 | value: 61.159560612627715 |
| 1879 | task: |
| 1880 | type: Classification |
| 1881 | - dataset: |
| 1882 | config: cy |
| 1883 | name: MTEB MassiveScenarioClassification (cy) |
| 1884 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1885 | split: test |
| 1886 | type: mteb/amazon_massive_scenario |
| 1887 | metrics: |
| 1888 | - type: accuracy |
| 1889 | value: 50.181573638197705 |
| 1890 | - type: f1 |
| 1891 | value: 46.98422176289957 |
| 1892 | task: |
| 1893 | type: Classification |
| 1894 | - dataset: |
| 1895 | config: da |
| 1896 | name: MTEB MassiveScenarioClassification (da) |
| 1897 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1898 | split: test |
| 1899 | type: mteb/amazon_massive_scenario |
| 1900 | metrics: |
| 1901 | - type: accuracy |
| 1902 | value: 68.92737054472092 |
| 1903 | - type: f1 |
| 1904 | value: 67.69135611952979 |
| 1905 | task: |
| 1906 | type: Classification |
| 1907 | - dataset: |
| 1908 | config: de |
| 1909 | name: MTEB MassiveScenarioClassification (de) |
| 1910 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1911 | split: test |
| 1912 | type: mteb/amazon_massive_scenario |
| 1913 | metrics: |
| 1914 | - type: accuracy |
| 1915 | value: 69.18964357767318 |
| 1916 | - type: f1 |
| 1917 | value: 68.46106138186214 |
| 1918 | task: |
| 1919 | type: Classification |
| 1920 | - dataset: |
| 1921 | config: el |
| 1922 | name: MTEB MassiveScenarioClassification (el) |
| 1923 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1924 | split: test |
| 1925 | type: mteb/amazon_massive_scenario |
| 1926 | metrics: |
| 1927 | - type: accuracy |
| 1928 | value: 67.0712844653665 |
| 1929 | - type: f1 |
| 1930 | value: 66.75545422473901 |
| 1931 | task: |
| 1932 | type: Classification |
| 1933 | - dataset: |
| 1934 | config: en |
| 1935 | name: MTEB MassiveScenarioClassification (en) |
| 1936 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1937 | split: test |
| 1938 | type: mteb/amazon_massive_scenario |
| 1939 | metrics: |
| 1940 | - type: accuracy |
| 1941 | value: 74.4754539340955 |
| 1942 | - type: f1 |
| 1943 | value: 74.38427146553252 |
| 1944 | task: |
| 1945 | type: Classification |
| 1946 | - dataset: |
| 1947 | config: es |
| 1948 | name: MTEB MassiveScenarioClassification (es) |
| 1949 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1950 | split: test |
| 1951 | type: mteb/amazon_massive_scenario |
| 1952 | metrics: |
| 1953 | - type: accuracy |
| 1954 | value: 69.82515131136518 |
| 1955 | - type: f1 |
| 1956 | value: 69.63516462173847 |
| 1957 | task: |
| 1958 | type: Classification |
| 1959 | - dataset: |
| 1960 | config: fa |
| 1961 | name: MTEB MassiveScenarioClassification (fa) |
| 1962 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1963 | split: test |
| 1964 | type: mteb/amazon_massive_scenario |
| 1965 | metrics: |
| 1966 | - type: accuracy |
| 1967 | value: 68.70880968392737 |
| 1968 | - type: f1 |
| 1969 | value: 67.45420662567926 |
| 1970 | task: |
| 1971 | type: Classification |
| 1972 | - dataset: |
| 1973 | config: fi |
| 1974 | name: MTEB MassiveScenarioClassification (fi) |
| 1975 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1976 | split: test |
| 1977 | type: mteb/amazon_massive_scenario |
| 1978 | metrics: |
| 1979 | - type: accuracy |
| 1980 | value: 65.95494283792871 |
| 1981 | - type: f1 |
| 1982 | value: 65.06191009049222 |
| 1983 | task: |
| 1984 | type: Classification |
| 1985 | - dataset: |
| 1986 | config: fr |
| 1987 | name: MTEB MassiveScenarioClassification (fr) |
| 1988 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1989 | split: test |
| 1990 | type: mteb/amazon_massive_scenario |
| 1991 | metrics: |
| 1992 | - type: accuracy |
| 1993 | value: 68.75924680564896 |
| 1994 | - type: f1 |
| 1995 | value: 68.30833379585945 |
| 1996 | task: |
| 1997 | type: Classification |
| 1998 | - dataset: |
| 1999 | config: he |
| 2000 | name: MTEB MassiveScenarioClassification (he) |
| 2001 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2002 | split: test |
| 2003 | type: mteb/amazon_massive_scenario |
| 2004 | metrics: |
| 2005 | - type: accuracy |
| 2006 | value: 63.806321452589096 |
| 2007 | - type: f1 |
| 2008 | value: 63.273048243765054 |
| 2009 | task: |
| 2010 | type: Classification |
| 2011 | - dataset: |
| 2012 | config: hi |
| 2013 | name: MTEB MassiveScenarioClassification (hi) |
| 2014 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2015 | split: test |
| 2016 | type: mteb/amazon_massive_scenario |
| 2017 | metrics: |
| 2018 | - type: accuracy |
| 2019 | value: 67.68997982515133 |
| 2020 | - type: f1 |
| 2021 | value: 66.54703855381324 |
| 2022 | task: |
| 2023 | type: Classification |
| 2024 | - dataset: |
| 2025 | config: hu |
| 2026 | name: MTEB MassiveScenarioClassification (hu) |
| 2027 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2028 | split: test |
| 2029 | type: mteb/amazon_massive_scenario |
| 2030 | metrics: |
| 2031 | - type: accuracy |
| 2032 | value: 66.46940147948891 |
| 2033 | - type: f1 |
| 2034 | value: 65.91017343463396 |
| 2035 | task: |
| 2036 | type: Classification |
| 2037 | - dataset: |
| 2038 | config: hy |
| 2039 | name: MTEB MassiveScenarioClassification (hy) |
| 2040 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2041 | split: test |
| 2042 | type: mteb/amazon_massive_scenario |
| 2043 | metrics: |
| 2044 | - type: accuracy |
| 2045 | value: 59.49899125756556 |
| 2046 | - type: f1 |
| 2047 | value: 57.90333469917769 |
| 2048 | task: |
| 2049 | type: Classification |
| 2050 | - dataset: |
| 2051 | config: id |
| 2052 | name: MTEB MassiveScenarioClassification (id) |
| 2053 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2054 | split: test |
| 2055 | type: mteb/amazon_massive_scenario |
| 2056 | metrics: |
| 2057 | - type: accuracy |
| 2058 | value: 67.9219905850706 |
| 2059 | - type: f1 |
| 2060 | value: 67.23169403762938 |
| 2061 | task: |
| 2062 | type: Classification |
| 2063 | - dataset: |
| 2064 | config: is |
| 2065 | name: MTEB MassiveScenarioClassification (is) |
| 2066 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2067 | split: test |
| 2068 | type: mteb/amazon_massive_scenario |
| 2069 | metrics: |
| 2070 | - type: accuracy |
| 2071 | value: 56.486213853396094 |
| 2072 | - type: f1 |
| 2073 | value: 54.85282355583758 |
| 2074 | task: |
| 2075 | type: Classification |
| 2076 | - dataset: |
| 2077 | config: it |
| 2078 | name: MTEB MassiveScenarioClassification (it) |
| 2079 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2080 | split: test |
| 2081 | type: mteb/amazon_massive_scenario |
| 2082 | metrics: |
| 2083 | - type: accuracy |
| 2084 | value: 69.04169468728985 |
| 2085 | - type: f1 |
| 2086 | value: 68.83833333320462 |
| 2087 | task: |
| 2088 | type: Classification |
| 2089 | - dataset: |
| 2090 | config: ja |
| 2091 | name: MTEB MassiveScenarioClassification (ja) |
| 2092 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2093 | split: test |
| 2094 | type: mteb/amazon_massive_scenario |
| 2095 | metrics: |
| 2096 | - type: accuracy |
| 2097 | value: 73.88702084734365 |
| 2098 | - type: f1 |
| 2099 | value: 74.04474735232299 |
| 2100 | task: |
| 2101 | type: Classification |
| 2102 | - dataset: |
| 2103 | config: jv |
| 2104 | name: MTEB MassiveScenarioClassification (jv) |
| 2105 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2106 | split: test |
| 2107 | type: mteb/amazon_massive_scenario |
| 2108 | metrics: |
| 2109 | - type: accuracy |
| 2110 | value: 56.63416274377943 |
| 2111 | - type: f1 |
| 2112 | value: 55.11332211687954 |
| 2113 | task: |
| 2114 | type: Classification |
| 2115 | - dataset: |
| 2116 | config: ka |
| 2117 | name: MTEB MassiveScenarioClassification (ka) |
| 2118 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2119 | split: test |
| 2120 | type: mteb/amazon_massive_scenario |
| 2121 | metrics: |
| 2122 | - type: accuracy |
| 2123 | value: 52.23604572965702 |
| 2124 | - type: f1 |
| 2125 | value: 50.86529813991055 |
| 2126 | task: |
| 2127 | type: Classification |
| 2128 | - dataset: |
| 2129 | config: km |
| 2130 | name: MTEB MassiveScenarioClassification (km) |
| 2131 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2132 | split: test |
| 2133 | type: mteb/amazon_massive_scenario |
| 2134 | metrics: |
| 2135 | - type: accuracy |
| 2136 | value: 46.62407531943511 |
| 2137 | - type: f1 |
| 2138 | value: 43.63485467164535 |
| 2139 | task: |
| 2140 | type: Classification |
| 2141 | - dataset: |
| 2142 | config: kn |
| 2143 | name: MTEB MassiveScenarioClassification (kn) |
| 2144 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2145 | split: test |
| 2146 | type: mteb/amazon_massive_scenario |
| 2147 | metrics: |
| 2148 | - type: accuracy |
| 2149 | value: 59.15601882985878 |
| 2150 | - type: f1 |
| 2151 | value: 57.522837510959924 |
| 2152 | task: |
| 2153 | type: Classification |
| 2154 | - dataset: |
| 2155 | config: ko |
| 2156 | name: MTEB MassiveScenarioClassification (ko) |
| 2157 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2158 | split: test |
| 2159 | type: mteb/amazon_massive_scenario |
| 2160 | metrics: |
| 2161 | - type: accuracy |
| 2162 | value: 69.84532616005382 |
| 2163 | - type: f1 |
| 2164 | value: 69.60021127179697 |
| 2165 | task: |
| 2166 | type: Classification |
| 2167 | - dataset: |
| 2168 | config: lv |
| 2169 | name: MTEB MassiveScenarioClassification (lv) |
| 2170 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2171 | split: test |
| 2172 | type: mteb/amazon_massive_scenario |
| 2173 | metrics: |
| 2174 | - type: accuracy |
| 2175 | value: 56.65770006724949 |
| 2176 | - type: f1 |
| 2177 | value: 55.84219135523227 |
| 2178 | task: |
| 2179 | type: Classification |
| 2180 | - dataset: |
| 2181 | config: ml |
| 2182 | name: MTEB MassiveScenarioClassification (ml) |
| 2183 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2184 | split: test |
| 2185 | type: mteb/amazon_massive_scenario |
| 2186 | metrics: |
| 2187 | - type: accuracy |
| 2188 | value: 66.53665097511768 |
| 2189 | - type: f1 |
| 2190 | value: 65.09087787792639 |
| 2191 | task: |
| 2192 | type: Classification |
| 2193 | - dataset: |
| 2194 | config: mn |
| 2195 | name: MTEB MassiveScenarioClassification (mn) |
| 2196 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2197 | split: test |
| 2198 | type: mteb/amazon_massive_scenario |
| 2199 | metrics: |
| 2200 | - type: accuracy |
| 2201 | value: 59.31405514458642 |
| 2202 | - type: f1 |
| 2203 | value: 58.06135303831491 |
| 2204 | task: |
| 2205 | type: Classification |
| 2206 | - dataset: |
| 2207 | config: ms |
| 2208 | name: MTEB MassiveScenarioClassification (ms) |
| 2209 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2210 | split: test |
| 2211 | type: mteb/amazon_massive_scenario |
| 2212 | metrics: |
| 2213 | - type: accuracy |
| 2214 | value: 64.88231338264964 |
| 2215 | - type: f1 |
| 2216 | value: 62.751099407787926 |
| 2217 | task: |
| 2218 | type: Classification |
| 2219 | - dataset: |
| 2220 | config: my |
| 2221 | name: MTEB MassiveScenarioClassification (my) |
| 2222 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2223 | split: test |
| 2224 | type: mteb/amazon_massive_scenario |
| 2225 | metrics: |
| 2226 | - type: accuracy |
| 2227 | value: 58.86012104909213 |
| 2228 | - type: f1 |
| 2229 | value: 56.29118323058282 |
| 2230 | task: |
| 2231 | type: Classification |
| 2232 | - dataset: |
| 2233 | config: nb |
| 2234 | name: MTEB MassiveScenarioClassification (nb) |
| 2235 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2236 | split: test |
| 2237 | type: mteb/amazon_massive_scenario |
| 2238 | metrics: |
| 2239 | - type: accuracy |
| 2240 | value: 67.37390719569602 |
| 2241 | - type: f1 |
| 2242 | value: 66.27922244885102 |
| 2243 | task: |
| 2244 | type: Classification |
| 2245 | - dataset: |
| 2246 | config: nl |
| 2247 | name: MTEB MassiveScenarioClassification (nl) |
| 2248 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2249 | split: test |
| 2250 | type: mteb/amazon_massive_scenario |
| 2251 | metrics: |
| 2252 | - type: accuracy |
| 2253 | value: 70.8675184936113 |
| 2254 | - type: f1 |
| 2255 | value: 70.22146529932019 |
| 2256 | task: |
| 2257 | type: Classification |
| 2258 | - dataset: |
| 2259 | config: pl |
| 2260 | name: MTEB MassiveScenarioClassification (pl) |
| 2261 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2262 | split: test |
| 2263 | type: mteb/amazon_massive_scenario |
| 2264 | metrics: |
| 2265 | - type: accuracy |
| 2266 | value: 68.2212508406187 |
| 2267 | - type: f1 |
| 2268 | value: 67.77454802056282 |
| 2269 | task: |
| 2270 | type: Classification |
| 2271 | - dataset: |
| 2272 | config: pt |
| 2273 | name: MTEB MassiveScenarioClassification (pt) |
| 2274 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2275 | split: test |
| 2276 | type: mteb/amazon_massive_scenario |
| 2277 | metrics: |
| 2278 | - type: accuracy |
| 2279 | value: 68.18090114324143 |
| 2280 | - type: f1 |
| 2281 | value: 68.03737625431621 |
| 2282 | task: |
| 2283 | type: Classification |
| 2284 | - dataset: |
| 2285 | config: ro |
| 2286 | name: MTEB MassiveScenarioClassification (ro) |
| 2287 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2288 | split: test |
| 2289 | type: mteb/amazon_massive_scenario |
| 2290 | metrics: |
| 2291 | - type: accuracy |
| 2292 | value: 64.65030262273034 |
| 2293 | - type: f1 |
| 2294 | value: 63.792945486912856 |
| 2295 | task: |
| 2296 | type: Classification |
| 2297 | - dataset: |
| 2298 | config: ru |
| 2299 | name: MTEB MassiveScenarioClassification (ru) |
| 2300 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2301 | split: test |
| 2302 | type: mteb/amazon_massive_scenario |
| 2303 | metrics: |
| 2304 | - type: accuracy |
| 2305 | value: 63.772749631087066 |
| 2306 | - type: f1 |
| 2307 | value: 63.4539101720024 |
| 2308 | - type: f1_weighted |
| 2309 | value: 62.778603897469566 |
| 2310 | - type: main_score |
| 2311 | value: 63.772749631087066 |
| 2312 | task: |
| 2313 | type: Classification |
| 2314 | - dataset: |
| 2315 | config: sl |
| 2316 | name: MTEB MassiveScenarioClassification (sl) |
| 2317 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2318 | split: test |
| 2319 | type: mteb/amazon_massive_scenario |
| 2320 | metrics: |
| 2321 | - type: accuracy |
| 2322 | value: 60.17821116341627 |
| 2323 | - type: f1 |
| 2324 | value: 59.3935969827171 |
| 2325 | task: |
| 2326 | type: Classification |
| 2327 | - dataset: |
| 2328 | config: sq |
| 2329 | name: MTEB MassiveScenarioClassification (sq) |
| 2330 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2331 | split: test |
| 2332 | type: mteb/amazon_massive_scenario |
| 2333 | metrics: |
| 2334 | - type: accuracy |
| 2335 | value: 62.86146603900471 |
| 2336 | - type: f1 |
| 2337 | value: 60.133692735032376 |
| 2338 | task: |
| 2339 | type: Classification |
| 2340 | - dataset: |
| 2341 | config: sv |
| 2342 | name: MTEB MassiveScenarioClassification (sv) |
| 2343 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2344 | split: test |
| 2345 | type: mteb/amazon_massive_scenario |
| 2346 | metrics: |
| 2347 | - type: accuracy |
| 2348 | value: 70.89441829186282 |
| 2349 | - type: f1 |
| 2350 | value: 70.03064076194089 |
| 2351 | task: |
| 2352 | type: Classification |
| 2353 | - dataset: |
| 2354 | config: sw |
| 2355 | name: MTEB MassiveScenarioClassification (sw) |
| 2356 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2357 | split: test |
| 2358 | type: mteb/amazon_massive_scenario |
| 2359 | metrics: |
| 2360 | - type: accuracy |
| 2361 | value: 58.15063887020847 |
| 2362 | - type: f1 |
| 2363 | value: 56.23326278499678 |
| 2364 | task: |
| 2365 | type: Classification |
| 2366 | - dataset: |
| 2367 | config: ta |
| 2368 | name: MTEB MassiveScenarioClassification (ta) |
| 2369 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2370 | split: test |
| 2371 | type: mteb/amazon_massive_scenario |
| 2372 | metrics: |
| 2373 | - type: accuracy |
| 2374 | value: 59.43846671149966 |
| 2375 | - type: f1 |
| 2376 | value: 57.70440450281974 |
| 2377 | task: |
| 2378 | type: Classification |
| 2379 | - dataset: |
| 2380 | config: te |
| 2381 | name: MTEB MassiveScenarioClassification (te) |
| 2382 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2383 | split: test |
| 2384 | type: mteb/amazon_massive_scenario |
| 2385 | metrics: |
| 2386 | - type: accuracy |
| 2387 | value: 60.8507061197041 |
| 2388 | - type: f1 |
| 2389 | value: 59.22916396061171 |
| 2390 | task: |
| 2391 | type: Classification |
| 2392 | - dataset: |
| 2393 | config: th |
| 2394 | name: MTEB MassiveScenarioClassification (th) |
| 2395 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2396 | split: test |
| 2397 | type: mteb/amazon_massive_scenario |
| 2398 | metrics: |
| 2399 | - type: accuracy |
| 2400 | value: 70.65568258238063 |
| 2401 | - type: f1 |
| 2402 | value: 69.90736239440633 |
| 2403 | task: |
| 2404 | type: Classification |
| 2405 | - dataset: |
| 2406 | config: tl |
| 2407 | name: MTEB MassiveScenarioClassification (tl) |
| 2408 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2409 | split: test |
| 2410 | type: mteb/amazon_massive_scenario |
| 2411 | metrics: |
| 2412 | - type: accuracy |
| 2413 | value: 60.8843308675185 |
| 2414 | - type: f1 |
| 2415 | value: 59.30332663713599 |
| 2416 | task: |
| 2417 | type: Classification |
| 2418 | - dataset: |
| 2419 | config: tr |
| 2420 | name: MTEB MassiveScenarioClassification (tr) |
| 2421 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2422 | split: test |
| 2423 | type: mteb/amazon_massive_scenario |
| 2424 | metrics: |
| 2425 | - type: accuracy |
| 2426 | value: 68.05312710154674 |
| 2427 | - type: f1 |
| 2428 | value: 67.44024062594775 |
| 2429 | task: |
| 2430 | type: Classification |
| 2431 | - dataset: |
| 2432 | config: ur |
| 2433 | name: MTEB MassiveScenarioClassification (ur) |
| 2434 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2435 | split: test |
| 2436 | type: mteb/amazon_massive_scenario |
| 2437 | metrics: |
| 2438 | - type: accuracy |
| 2439 | value: 62.111634162743776 |
| 2440 | - type: f1 |
| 2441 | value: 60.89083013084519 |
| 2442 | task: |
| 2443 | type: Classification |
| 2444 | - dataset: |
| 2445 | config: vi |
| 2446 | name: MTEB MassiveScenarioClassification (vi) |
| 2447 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2448 | split: test |
| 2449 | type: mteb/amazon_massive_scenario |
| 2450 | metrics: |
| 2451 | - type: accuracy |
| 2452 | value: 67.44115669132482 |
| 2453 | - type: f1 |
| 2454 | value: 67.92227541674552 |
| 2455 | task: |
| 2456 | type: Classification |
| 2457 | - dataset: |
| 2458 | config: zh-CN |
| 2459 | name: MTEB MassiveScenarioClassification (zh-CN) |
| 2460 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2461 | split: test |
| 2462 | type: mteb/amazon_massive_scenario |
| 2463 | metrics: |
| 2464 | - type: accuracy |
| 2465 | value: 74.4687289845326 |
| 2466 | - type: f1 |
| 2467 | value: 74.16376793486025 |
| 2468 | task: |
| 2469 | type: Classification |
| 2470 | - dataset: |
| 2471 | config: zh-TW |
| 2472 | name: MTEB MassiveScenarioClassification (zh-TW) |
| 2473 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 2474 | split: test |
| 2475 | type: mteb/amazon_massive_scenario |
| 2476 | metrics: |
| 2477 | - type: accuracy |
| 2478 | value: 68.31876260928043 |
| 2479 | - type: f1 |
| 2480 | value: 68.5246745215607 |
| 2481 | task: |
| 2482 | type: Classification |
| 2483 | - dataset: |
| 2484 | config: default |
| 2485 | name: MTEB MedrxivClusteringP2P |
| 2486 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 2487 | split: test |
| 2488 | type: mteb/medrxiv-clustering-p2p |
| 2489 | metrics: |
| 2490 | - type: v_measure |
| 2491 | value: 30.90431696479766 |
| 2492 | task: |
| 2493 | type: Clustering |
| 2494 | - dataset: |
| 2495 | config: default |
| 2496 | name: MTEB MedrxivClusteringS2S |
| 2497 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 2498 | split: test |
| 2499 | type: mteb/medrxiv-clustering-s2s |
| 2500 | metrics: |
| 2501 | - type: v_measure |
| 2502 | value: 27.259158476693774 |
| 2503 | task: |
| 2504 | type: Clustering |
| 2505 | - dataset: |
| 2506 | config: default |
| 2507 | name: MTEB MindSmallReranking |
| 2508 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 2509 | split: test |
| 2510 | type: mteb/mind_small |
| 2511 | metrics: |
| 2512 | - type: map |
| 2513 | value: 30.28445330838555 |
| 2514 | - type: mrr |
| 2515 | value: 31.15758529581164 |
| 2516 | task: |
| 2517 | type: Reranking |
| 2518 | - dataset: |
| 2519 | config: default |
| 2520 | name: MTEB NFCorpus |
| 2521 | revision: None |
| 2522 | split: test |
| 2523 | type: nfcorpus |
| 2524 | metrics: |
| 2525 | - type: map_at_1 |
| 2526 | value: 5.353 |
| 2527 | - type: map_at_10 |
| 2528 | value: 11.565 |
| 2529 | - type: map_at_100 |
| 2530 | value: 14.097000000000001 |
| 2531 | - type: map_at_1000 |
| 2532 | value: 15.354999999999999 |
| 2533 | - type: map_at_3 |
| 2534 | value: 8.749 |
| 2535 | - type: map_at_5 |
| 2536 | value: 9.974 |
| 2537 | - type: mrr_at_1 |
| 2538 | value: 42.105 |
| 2539 | - type: mrr_at_10 |
| 2540 | value: 50.589 |
| 2541 | - type: mrr_at_100 |
| 2542 | value: 51.187000000000005 |
| 2543 | - type: mrr_at_1000 |
| 2544 | value: 51.233 |
| 2545 | - type: mrr_at_3 |
| 2546 | value: 48.246 |
| 2547 | - type: mrr_at_5 |
| 2548 | value: 49.546 |
| 2549 | - type: ndcg_at_1 |
| 2550 | value: 40.402 |
| 2551 | - type: ndcg_at_10 |
| 2552 | value: 31.009999999999998 |
| 2553 | - type: ndcg_at_100 |
| 2554 | value: 28.026 |
| 2555 | - type: ndcg_at_1000 |
| 2556 | value: 36.905 |
| 2557 | - type: ndcg_at_3 |
| 2558 | value: 35.983 |
| 2559 | - type: ndcg_at_5 |
| 2560 | value: 33.764 |
| 2561 | - type: precision_at_1 |
| 2562 | value: 42.105 |
| 2563 | - type: precision_at_10 |
| 2564 | value: 22.786 |
| 2565 | - type: precision_at_100 |
| 2566 | value: 6.916 |
| 2567 | - type: precision_at_1000 |
| 2568 | value: 1.981 |
| 2569 | - type: precision_at_3 |
| 2570 | value: 33.333 |
| 2571 | - type: precision_at_5 |
| 2572 | value: 28.731 |
| 2573 | - type: recall_at_1 |
| 2574 | value: 5.353 |
| 2575 | - type: recall_at_10 |
| 2576 | value: 15.039 |
| 2577 | - type: recall_at_100 |
| 2578 | value: 27.348 |
| 2579 | - type: recall_at_1000 |
| 2580 | value: 59.453 |
| 2581 | - type: recall_at_3 |
| 2582 | value: 9.792 |
| 2583 | - type: recall_at_5 |
| 2584 | value: 11.882 |
| 2585 | task: |
| 2586 | type: Retrieval |
| 2587 | - dataset: |
| 2588 | config: default |
| 2589 | name: MTEB NQ |
| 2590 | revision: None |
| 2591 | split: test |
| 2592 | type: nq |
| 2593 | metrics: |
| 2594 | - type: map_at_1 |
| 2595 | value: 33.852 |
| 2596 | - type: map_at_10 |
| 2597 | value: 48.924 |
| 2598 | - type: map_at_100 |
| 2599 | value: 49.854 |
| 2600 | - type: map_at_1000 |
| 2601 | value: 49.886 |
| 2602 | - type: map_at_3 |
| 2603 | value: 44.9 |
| 2604 | - type: map_at_5 |
| 2605 | value: 47.387 |
| 2606 | - type: mrr_at_1 |
| 2607 | value: 38.035999999999994 |
| 2608 | - type: mrr_at_10 |
| 2609 | value: 51.644 |
| 2610 | - type: mrr_at_100 |
| 2611 | value: 52.339 |
| 2612 | - type: mrr_at_1000 |
| 2613 | value: 52.35999999999999 |
| 2614 | - type: mrr_at_3 |
| 2615 | value: 48.421 |
| 2616 | - type: mrr_at_5 |
| 2617 | value: 50.468999999999994 |
| 2618 | - type: ndcg_at_1 |
| 2619 | value: 38.007000000000005 |
| 2620 | - type: ndcg_at_10 |
| 2621 | value: 56.293000000000006 |
| 2622 | - type: ndcg_at_100 |
| 2623 | value: 60.167 |
| 2624 | - type: ndcg_at_1000 |
| 2625 | value: 60.916000000000004 |
| 2626 | - type: ndcg_at_3 |
| 2627 | value: 48.903999999999996 |
| 2628 | - type: ndcg_at_5 |
| 2629 | value: 52.978 |
| 2630 | - type: precision_at_1 |
| 2631 | value: 38.007000000000005 |
| 2632 | - type: precision_at_10 |
| 2633 | value: 9.041 |
| 2634 | - type: precision_at_100 |
| 2635 | value: 1.1199999999999999 |
| 2636 | - type: precision_at_1000 |
| 2637 | value: 0.11900000000000001 |
| 2638 | - type: precision_at_3 |
| 2639 | value: 22.084 |
| 2640 | - type: precision_at_5 |
| 2641 | value: 15.608 |
| 2642 | - type: recall_at_1 |
| 2643 | value: 33.852 |
| 2644 | - type: recall_at_10 |
| 2645 | value: 75.893 |
| 2646 | - type: recall_at_100 |
| 2647 | value: 92.589 |
| 2648 | - type: recall_at_1000 |
| 2649 | value: 98.153 |
| 2650 | - type: recall_at_3 |
| 2651 | value: 56.969 |
| 2652 | - type: recall_at_5 |
| 2653 | value: 66.283 |
| 2654 | task: |
| 2655 | type: Retrieval |
| 2656 | - dataset: |
| 2657 | config: default |
| 2658 | name: MTEB QuoraRetrieval |
| 2659 | revision: None |
| 2660 | split: test |
| 2661 | type: quora |
| 2662 | metrics: |
| 2663 | - type: map_at_1 |
| 2664 | value: 69.174 |
| 2665 | - type: map_at_10 |
| 2666 | value: 82.891 |
| 2667 | - type: map_at_100 |
| 2668 | value: 83.545 |
| 2669 | - type: map_at_1000 |
| 2670 | value: 83.56700000000001 |
| 2671 | - type: map_at_3 |
| 2672 | value: 79.944 |
| 2673 | - type: map_at_5 |
| 2674 | value: 81.812 |
| 2675 | - type: mrr_at_1 |
| 2676 | value: 79.67999999999999 |
| 2677 | - type: mrr_at_10 |
| 2678 | value: 86.279 |
| 2679 | - type: mrr_at_100 |
| 2680 | value: 86.39 |
| 2681 | - type: mrr_at_1000 |
| 2682 | value: 86.392 |
| 2683 | - type: mrr_at_3 |
| 2684 | value: 85.21 |
| 2685 | - type: mrr_at_5 |
| 2686 | value: 85.92999999999999 |
| 2687 | - type: ndcg_at_1 |
| 2688 | value: 79.69000000000001 |
| 2689 | - type: ndcg_at_10 |
| 2690 | value: 86.929 |
| 2691 | - type: ndcg_at_100 |
| 2692 | value: 88.266 |
| 2693 | - type: ndcg_at_1000 |
| 2694 | value: 88.428 |
| 2695 | - type: ndcg_at_3 |
| 2696 | value: 83.899 |
| 2697 | - type: ndcg_at_5 |
| 2698 | value: 85.56700000000001 |
| 2699 | - type: precision_at_1 |
| 2700 | value: 79.69000000000001 |
| 2701 | - type: precision_at_10 |
| 2702 | value: 13.161000000000001 |
| 2703 | - type: precision_at_100 |
| 2704 | value: 1.513 |
| 2705 | - type: precision_at_1000 |
| 2706 | value: 0.156 |
| 2707 | - type: precision_at_3 |
| 2708 | value: 36.603 |
| 2709 | - type: precision_at_5 |
| 2710 | value: 24.138 |
| 2711 | - type: recall_at_1 |
| 2712 | value: 69.174 |
| 2713 | - type: recall_at_10 |
| 2714 | value: 94.529 |
| 2715 | - type: recall_at_100 |
| 2716 | value: 99.15 |
| 2717 | - type: recall_at_1000 |
| 2718 | value: 99.925 |
| 2719 | - type: recall_at_3 |
| 2720 | value: 85.86200000000001 |
| 2721 | - type: recall_at_5 |
| 2722 | value: 90.501 |
| 2723 | task: |
| 2724 | type: Retrieval |
| 2725 | - dataset: |
| 2726 | config: default |
| 2727 | name: MTEB RedditClustering |
| 2728 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 2729 | split: test |
| 2730 | type: mteb/reddit-clustering |
| 2731 | metrics: |
| 2732 | - type: v_measure |
| 2733 | value: 39.13064340585255 |
| 2734 | task: |
| 2735 | type: Clustering |
| 2736 | - dataset: |
| 2737 | config: default |
| 2738 | name: MTEB RedditClusteringP2P |
| 2739 | revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| 2740 | split: test |
| 2741 | type: mteb/reddit-clustering-p2p |
| 2742 | metrics: |
| 2743 | - type: v_measure |
| 2744 | value: 58.97884249325877 |
| 2745 | task: |
| 2746 | type: Clustering |
| 2747 | - dataset: |
| 2748 | config: default |
| 2749 | name: MTEB SCIDOCS |
| 2750 | revision: None |
| 2751 | split: test |
| 2752 | type: scidocs |
| 2753 | metrics: |
| 2754 | - type: map_at_1 |
| 2755 | value: 3.4680000000000004 |
| 2756 | - type: map_at_10 |
| 2757 | value: 7.865 |
| 2758 | - type: map_at_100 |
| 2759 | value: 9.332 |
| 2760 | - type: map_at_1000 |
| 2761 | value: 9.587 |
| 2762 | - type: map_at_3 |
| 2763 | value: 5.800000000000001 |
| 2764 | - type: map_at_5 |
| 2765 | value: 6.8790000000000004 |
| 2766 | - type: mrr_at_1 |
| 2767 | value: 17.0 |
| 2768 | - type: mrr_at_10 |
| 2769 | value: 25.629 |
| 2770 | - type: mrr_at_100 |
| 2771 | value: 26.806 |
| 2772 | - type: mrr_at_1000 |
| 2773 | value: 26.889000000000003 |
| 2774 | - type: mrr_at_3 |
| 2775 | value: 22.8 |
| 2776 | - type: mrr_at_5 |
| 2777 | value: 24.26 |
| 2778 | - type: ndcg_at_1 |
| 2779 | value: 17.0 |
| 2780 | - type: ndcg_at_10 |
| 2781 | value: 13.895 |
| 2782 | - type: ndcg_at_100 |
| 2783 | value: 20.491999999999997 |
| 2784 | - type: ndcg_at_1000 |
| 2785 | value: 25.759999999999998 |
| 2786 | - type: ndcg_at_3 |
| 2787 | value: 13.347999999999999 |
| 2788 | - type: ndcg_at_5 |
| 2789 | value: 11.61 |
| 2790 | - type: precision_at_1 |
| 2791 | value: 17.0 |
| 2792 | - type: precision_at_10 |
| 2793 | value: 7.090000000000001 |
| 2794 | - type: precision_at_100 |
| 2795 | value: 1.669 |
| 2796 | - type: precision_at_1000 |
| 2797 | value: 0.294 |
| 2798 | - type: precision_at_3 |
| 2799 | value: 12.3 |
| 2800 | - type: precision_at_5 |
| 2801 | value: 10.02 |
| 2802 | - type: recall_at_1 |
| 2803 | value: 3.4680000000000004 |
| 2804 | - type: recall_at_10 |
| 2805 | value: 14.363000000000001 |
| 2806 | - type: recall_at_100 |
| 2807 | value: 33.875 |
| 2808 | - type: recall_at_1000 |
| 2809 | value: 59.711999999999996 |
| 2810 | - type: recall_at_3 |
| 2811 | value: 7.483 |
| 2812 | - type: recall_at_5 |
| 2813 | value: 10.173 |
| 2814 | task: |
| 2815 | type: Retrieval |
| 2816 | - dataset: |
| 2817 | config: default |
| 2818 | name: MTEB SICK-R |
| 2819 | revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| 2820 | split: test |
| 2821 | type: mteb/sickr-sts |
| 2822 | metrics: |
| 2823 | - type: cos_sim_pearson |
| 2824 | value: 83.04084311714061 |
| 2825 | - type: cos_sim_spearman |
| 2826 | value: 77.51342467443078 |
| 2827 | - type: euclidean_pearson |
| 2828 | value: 80.0321166028479 |
| 2829 | - type: euclidean_spearman |
| 2830 | value: 77.29249114733226 |
| 2831 | - type: manhattan_pearson |
| 2832 | value: 80.03105964262431 |
| 2833 | - type: manhattan_spearman |
| 2834 | value: 77.22373689514794 |
| 2835 | task: |
| 2836 | type: STS |
| 2837 | - dataset: |
| 2838 | config: default |
| 2839 | name: MTEB STS12 |
| 2840 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 2841 | split: test |
| 2842 | type: mteb/sts12-sts |
| 2843 | metrics: |
| 2844 | - type: cos_sim_pearson |
| 2845 | value: 84.1680158034387 |
| 2846 | - type: cos_sim_spearman |
| 2847 | value: 76.55983344071117 |
| 2848 | - type: euclidean_pearson |
| 2849 | value: 79.75266678300143 |
| 2850 | - type: euclidean_spearman |
| 2851 | value: 75.34516823467025 |
| 2852 | - type: manhattan_pearson |
| 2853 | value: 79.75959151517357 |
| 2854 | - type: manhattan_spearman |
| 2855 | value: 75.42330344141912 |
| 2856 | task: |
| 2857 | type: STS |
| 2858 | - dataset: |
| 2859 | config: default |
| 2860 | name: MTEB STS13 |
| 2861 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 2862 | split: test |
| 2863 | type: mteb/sts13-sts |
| 2864 | metrics: |
| 2865 | - type: cos_sim_pearson |
| 2866 | value: 76.48898993209346 |
| 2867 | - type: cos_sim_spearman |
| 2868 | value: 76.96954120323366 |
| 2869 | - type: euclidean_pearson |
| 2870 | value: 76.94139109279668 |
| 2871 | - type: euclidean_spearman |
| 2872 | value: 76.85860283201711 |
| 2873 | - type: manhattan_pearson |
| 2874 | value: 76.6944095091912 |
| 2875 | - type: manhattan_spearman |
| 2876 | value: 76.61096912972553 |
| 2877 | task: |
| 2878 | type: STS |
| 2879 | - dataset: |
| 2880 | config: default |
| 2881 | name: MTEB STS14 |
| 2882 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 2883 | split: test |
| 2884 | type: mteb/sts14-sts |
| 2885 | metrics: |
| 2886 | - type: cos_sim_pearson |
| 2887 | value: 77.85082366246944 |
| 2888 | - type: cos_sim_spearman |
| 2889 | value: 75.52053350101731 |
| 2890 | - type: euclidean_pearson |
| 2891 | value: 77.1165845070926 |
| 2892 | - type: euclidean_spearman |
| 2893 | value: 75.31216065884388 |
| 2894 | - type: manhattan_pearson |
| 2895 | value: 77.06193941833494 |
| 2896 | - type: manhattan_spearman |
| 2897 | value: 75.31003701700112 |
| 2898 | task: |
| 2899 | type: STS |
| 2900 | - dataset: |
| 2901 | config: default |
| 2902 | name: MTEB STS15 |
| 2903 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 2904 | split: test |
| 2905 | type: mteb/sts15-sts |
| 2906 | metrics: |
| 2907 | - type: cos_sim_pearson |
| 2908 | value: 86.36305246526497 |
| 2909 | - type: cos_sim_spearman |
| 2910 | value: 87.11704613927415 |
| 2911 | - type: euclidean_pearson |
| 2912 | value: 86.04199125810939 |
| 2913 | - type: euclidean_spearman |
| 2914 | value: 86.51117572414263 |
| 2915 | - type: manhattan_pearson |
| 2916 | value: 86.0805106816633 |
| 2917 | - type: manhattan_spearman |
| 2918 | value: 86.52798366512229 |
| 2919 | task: |
| 2920 | type: STS |
| 2921 | - dataset: |
| 2922 | config: default |
| 2923 | name: MTEB STS16 |
| 2924 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 2925 | split: test |
| 2926 | type: mteb/sts16-sts |
| 2927 | metrics: |
| 2928 | - type: cos_sim_pearson |
| 2929 | value: 82.18536255599724 |
| 2930 | - type: cos_sim_spearman |
| 2931 | value: 83.63377151025418 |
| 2932 | - type: euclidean_pearson |
| 2933 | value: 83.24657467993141 |
| 2934 | - type: euclidean_spearman |
| 2935 | value: 84.02751481993825 |
| 2936 | - type: manhattan_pearson |
| 2937 | value: 83.11941806582371 |
| 2938 | - type: manhattan_spearman |
| 2939 | value: 83.84251281019304 |
| 2940 | task: |
| 2941 | type: STS |
| 2942 | - dataset: |
| 2943 | config: ko-ko |
| 2944 | name: MTEB STS17 (ko-ko) |
| 2945 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2946 | split: test |
| 2947 | type: mteb/sts17-crosslingual-sts |
| 2948 | metrics: |
| 2949 | - type: cos_sim_pearson |
| 2950 | value: 78.95816528475514 |
| 2951 | - type: cos_sim_spearman |
| 2952 | value: 78.86607380120462 |
| 2953 | - type: euclidean_pearson |
| 2954 | value: 78.51268699230545 |
| 2955 | - type: euclidean_spearman |
| 2956 | value: 79.11649316502229 |
| 2957 | - type: manhattan_pearson |
| 2958 | value: 78.32367302808157 |
| 2959 | - type: manhattan_spearman |
| 2960 | value: 78.90277699624637 |
| 2961 | task: |
| 2962 | type: STS |
| 2963 | - dataset: |
| 2964 | config: ar-ar |
| 2965 | name: MTEB STS17 (ar-ar) |
| 2966 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2967 | split: test |
| 2968 | type: mteb/sts17-crosslingual-sts |
| 2969 | metrics: |
| 2970 | - type: cos_sim_pearson |
| 2971 | value: 72.89126914997624 |
| 2972 | - type: cos_sim_spearman |
| 2973 | value: 73.0296921832678 |
| 2974 | - type: euclidean_pearson |
| 2975 | value: 71.50385903677738 |
| 2976 | - type: euclidean_spearman |
| 2977 | value: 73.13368899716289 |
| 2978 | - type: manhattan_pearson |
| 2979 | value: 71.47421463379519 |
| 2980 | - type: manhattan_spearman |
| 2981 | value: 73.03383242946575 |
| 2982 | task: |
| 2983 | type: STS |
| 2984 | - dataset: |
| 2985 | config: en-ar |
| 2986 | name: MTEB STS17 (en-ar) |
| 2987 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2988 | split: test |
| 2989 | type: mteb/sts17-crosslingual-sts |
| 2990 | metrics: |
| 2991 | - type: cos_sim_pearson |
| 2992 | value: 59.22923684492637 |
| 2993 | - type: cos_sim_spearman |
| 2994 | value: 57.41013211368396 |
| 2995 | - type: euclidean_pearson |
| 2996 | value: 61.21107388080905 |
| 2997 | - type: euclidean_spearman |
| 2998 | value: 60.07620768697254 |
| 2999 | - type: manhattan_pearson |
| 3000 | value: 59.60157142786555 |
| 3001 | - type: manhattan_spearman |
| 3002 | value: 59.14069604103739 |
| 3003 | task: |
| 3004 | type: STS |
| 3005 | - dataset: |
| 3006 | config: en-de |
| 3007 | name: MTEB STS17 (en-de) |
| 3008 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3009 | split: test |
| 3010 | type: mteb/sts17-crosslingual-sts |
| 3011 | metrics: |
| 3012 | - type: cos_sim_pearson |
| 3013 | value: 76.24345978774299 |
| 3014 | - type: cos_sim_spearman |
| 3015 | value: 77.24225743830719 |
| 3016 | - type: euclidean_pearson |
| 3017 | value: 76.66226095469165 |
| 3018 | - type: euclidean_spearman |
| 3019 | value: 77.60708820493146 |
| 3020 | - type: manhattan_pearson |
| 3021 | value: 76.05303324760429 |
| 3022 | - type: manhattan_spearman |
| 3023 | value: 76.96353149912348 |
| 3024 | task: |
| 3025 | type: STS |
| 3026 | - dataset: |
| 3027 | config: en-en |
| 3028 | name: MTEB STS17 (en-en) |
| 3029 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3030 | split: test |
| 3031 | type: mteb/sts17-crosslingual-sts |
| 3032 | metrics: |
| 3033 | - type: cos_sim_pearson |
| 3034 | value: 85.50879160160852 |
| 3035 | - type: cos_sim_spearman |
| 3036 | value: 86.43594662965224 |
| 3037 | - type: euclidean_pearson |
| 3038 | value: 86.06846012826577 |
| 3039 | - type: euclidean_spearman |
| 3040 | value: 86.02041395794136 |
| 3041 | - type: manhattan_pearson |
| 3042 | value: 86.10916255616904 |
| 3043 | - type: manhattan_spearman |
| 3044 | value: 86.07346068198953 |
| 3045 | task: |
| 3046 | type: STS |
| 3047 | - dataset: |
| 3048 | config: en-tr |
| 3049 | name: MTEB STS17 (en-tr) |
| 3050 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3051 | split: test |
| 3052 | type: mteb/sts17-crosslingual-sts |
| 3053 | metrics: |
| 3054 | - type: cos_sim_pearson |
| 3055 | value: 58.39803698977196 |
| 3056 | - type: cos_sim_spearman |
| 3057 | value: 55.96910950423142 |
| 3058 | - type: euclidean_pearson |
| 3059 | value: 58.17941175613059 |
| 3060 | - type: euclidean_spearman |
| 3061 | value: 55.03019330522745 |
| 3062 | - type: manhattan_pearson |
| 3063 | value: 57.333358138183286 |
| 3064 | - type: manhattan_spearman |
| 3065 | value: 54.04614023149965 |
| 3066 | task: |
| 3067 | type: STS |
| 3068 | - dataset: |
| 3069 | config: es-en |
| 3070 | name: MTEB STS17 (es-en) |
| 3071 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3072 | split: test |
| 3073 | type: mteb/sts17-crosslingual-sts |
| 3074 | metrics: |
| 3075 | - type: cos_sim_pearson |
| 3076 | value: 70.98304089637197 |
| 3077 | - type: cos_sim_spearman |
| 3078 | value: 72.44071656215888 |
| 3079 | - type: euclidean_pearson |
| 3080 | value: 72.19224359033983 |
| 3081 | - type: euclidean_spearman |
| 3082 | value: 73.89871188913025 |
| 3083 | - type: manhattan_pearson |
| 3084 | value: 71.21098311547406 |
| 3085 | - type: manhattan_spearman |
| 3086 | value: 72.93405764824821 |
| 3087 | task: |
| 3088 | type: STS |
| 3089 | - dataset: |
| 3090 | config: es-es |
| 3091 | name: MTEB STS17 (es-es) |
| 3092 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3093 | split: test |
| 3094 | type: mteb/sts17-crosslingual-sts |
| 3095 | metrics: |
| 3096 | - type: cos_sim_pearson |
| 3097 | value: 85.99792397466308 |
| 3098 | - type: cos_sim_spearman |
| 3099 | value: 84.83824377879495 |
| 3100 | - type: euclidean_pearson |
| 3101 | value: 85.70043288694438 |
| 3102 | - type: euclidean_spearman |
| 3103 | value: 84.70627558703686 |
| 3104 | - type: manhattan_pearson |
| 3105 | value: 85.89570850150801 |
| 3106 | - type: manhattan_spearman |
| 3107 | value: 84.95806105313007 |
| 3108 | task: |
| 3109 | type: STS |
| 3110 | - dataset: |
| 3111 | config: fr-en |
| 3112 | name: MTEB STS17 (fr-en) |
| 3113 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3114 | split: test |
| 3115 | type: mteb/sts17-crosslingual-sts |
| 3116 | metrics: |
| 3117 | - type: cos_sim_pearson |
| 3118 | value: 72.21850322994712 |
| 3119 | - type: cos_sim_spearman |
| 3120 | value: 72.28669398117248 |
| 3121 | - type: euclidean_pearson |
| 3122 | value: 73.40082510412948 |
| 3123 | - type: euclidean_spearman |
| 3124 | value: 73.0326539281865 |
| 3125 | - type: manhattan_pearson |
| 3126 | value: 71.8659633964841 |
| 3127 | - type: manhattan_spearman |
| 3128 | value: 71.57817425823303 |
| 3129 | task: |
| 3130 | type: STS |
| 3131 | - dataset: |
| 3132 | config: it-en |
| 3133 | name: MTEB STS17 (it-en) |
| 3134 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3135 | split: test |
| 3136 | type: mteb/sts17-crosslingual-sts |
| 3137 | metrics: |
| 3138 | - type: cos_sim_pearson |
| 3139 | value: 75.80921368595645 |
| 3140 | - type: cos_sim_spearman |
| 3141 | value: 77.33209091229315 |
| 3142 | - type: euclidean_pearson |
| 3143 | value: 76.53159540154829 |
| 3144 | - type: euclidean_spearman |
| 3145 | value: 78.17960842810093 |
| 3146 | - type: manhattan_pearson |
| 3147 | value: 76.13530186637601 |
| 3148 | - type: manhattan_spearman |
| 3149 | value: 78.00701437666875 |
| 3150 | task: |
| 3151 | type: STS |
| 3152 | - dataset: |
| 3153 | config: nl-en |
| 3154 | name: MTEB STS17 (nl-en) |
| 3155 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 3156 | split: test |
| 3157 | type: mteb/sts17-crosslingual-sts |
| 3158 | metrics: |
| 3159 | - type: cos_sim_pearson |
| 3160 | value: 74.74980608267349 |
| 3161 | - type: cos_sim_spearman |
| 3162 | value: 75.37597374318821 |
| 3163 | - type: euclidean_pearson |
| 3164 | value: 74.90506081911661 |
| 3165 | - type: euclidean_spearman |
| 3166 | value: 75.30151613124521 |
| 3167 | - type: manhattan_pearson |
| 3168 | value: 74.62642745918002 |
| 3169 | - type: manhattan_spearman |
| 3170 | value: 75.18619716592303 |
| 3171 | task: |
| 3172 | type: STS |
| 3173 | - dataset: |
| 3174 | config: en |
| 3175 | name: MTEB STS22 (en) |
| 3176 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3177 | split: test |
| 3178 | type: mteb/sts22-crosslingual-sts |
| 3179 | metrics: |
| 3180 | - type: cos_sim_pearson |
| 3181 | value: 59.632662289205584 |
| 3182 | - type: cos_sim_spearman |
| 3183 | value: 60.938543391610914 |
| 3184 | - type: euclidean_pearson |
| 3185 | value: 62.113200529767056 |
| 3186 | - type: euclidean_spearman |
| 3187 | value: 61.410312633261164 |
| 3188 | - type: manhattan_pearson |
| 3189 | value: 61.75494698945686 |
| 3190 | - type: manhattan_spearman |
| 3191 | value: 60.92726195322362 |
| 3192 | task: |
| 3193 | type: STS |
| 3194 | - dataset: |
| 3195 | config: de |
| 3196 | name: MTEB STS22 (de) |
| 3197 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3198 | split: test |
| 3199 | type: mteb/sts22-crosslingual-sts |
| 3200 | metrics: |
| 3201 | - type: cos_sim_pearson |
| 3202 | value: 45.283470551557244 |
| 3203 | - type: cos_sim_spearman |
| 3204 | value: 53.44833015864201 |
| 3205 | - type: euclidean_pearson |
| 3206 | value: 41.17892011120893 |
| 3207 | - type: euclidean_spearman |
| 3208 | value: 53.81441383126767 |
| 3209 | - type: manhattan_pearson |
| 3210 | value: 41.17482200420659 |
| 3211 | - type: manhattan_spearman |
| 3212 | value: 53.82180269276363 |
| 3213 | task: |
| 3214 | type: STS |
| 3215 | - dataset: |
| 3216 | config: es |
| 3217 | name: MTEB STS22 (es) |
| 3218 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3219 | split: test |
| 3220 | type: mteb/sts22-crosslingual-sts |
| 3221 | metrics: |
| 3222 | - type: cos_sim_pearson |
| 3223 | value: 60.5069165306236 |
| 3224 | - type: cos_sim_spearman |
| 3225 | value: 66.87803259033826 |
| 3226 | - type: euclidean_pearson |
| 3227 | value: 63.5428979418236 |
| 3228 | - type: euclidean_spearman |
| 3229 | value: 66.9293576586897 |
| 3230 | - type: manhattan_pearson |
| 3231 | value: 63.59789526178922 |
| 3232 | - type: manhattan_spearman |
| 3233 | value: 66.86555009875066 |
| 3234 | task: |
| 3235 | type: STS |
| 3236 | - dataset: |
| 3237 | config: pl |
| 3238 | name: MTEB STS22 (pl) |
| 3239 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3240 | split: test |
| 3241 | type: mteb/sts22-crosslingual-sts |
| 3242 | metrics: |
| 3243 | - type: cos_sim_pearson |
| 3244 | value: 28.23026196280264 |
| 3245 | - type: cos_sim_spearman |
| 3246 | value: 35.79397812652861 |
| 3247 | - type: euclidean_pearson |
| 3248 | value: 17.828102102767353 |
| 3249 | - type: euclidean_spearman |
| 3250 | value: 35.721501145568894 |
| 3251 | - type: manhattan_pearson |
| 3252 | value: 17.77134274219677 |
| 3253 | - type: manhattan_spearman |
| 3254 | value: 35.98107902846267 |
| 3255 | task: |
| 3256 | type: STS |
| 3257 | - dataset: |
| 3258 | config: tr |
| 3259 | name: MTEB STS22 (tr) |
| 3260 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3261 | split: test |
| 3262 | type: mteb/sts22-crosslingual-sts |
| 3263 | metrics: |
| 3264 | - type: cos_sim_pearson |
| 3265 | value: 56.51946541393812 |
| 3266 | - type: cos_sim_spearman |
| 3267 | value: 63.714686006214485 |
| 3268 | - type: euclidean_pearson |
| 3269 | value: 58.32104651305898 |
| 3270 | - type: euclidean_spearman |
| 3271 | value: 62.237110895702216 |
| 3272 | - type: manhattan_pearson |
| 3273 | value: 58.579416468759185 |
| 3274 | - type: manhattan_spearman |
| 3275 | value: 62.459738981727 |
| 3276 | task: |
| 3277 | type: STS |
| 3278 | - dataset: |
| 3279 | config: ar |
| 3280 | name: MTEB STS22 (ar) |
| 3281 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3282 | split: test |
| 3283 | type: mteb/sts22-crosslingual-sts |
| 3284 | metrics: |
| 3285 | - type: cos_sim_pearson |
| 3286 | value: 48.76009839569795 |
| 3287 | - type: cos_sim_spearman |
| 3288 | value: 56.65188431953149 |
| 3289 | - type: euclidean_pearson |
| 3290 | value: 50.997682160915595 |
| 3291 | - type: euclidean_spearman |
| 3292 | value: 55.99910008818135 |
| 3293 | - type: manhattan_pearson |
| 3294 | value: 50.76220659606342 |
| 3295 | - type: manhattan_spearman |
| 3296 | value: 55.517347595391456 |
| 3297 | task: |
| 3298 | type: STS |
| 3299 | - dataset: |
| 3300 | config: ru |
| 3301 | name: MTEB STS22 (ru) |
| 3302 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3303 | split: test |
| 3304 | type: mteb/sts22-crosslingual-sts |
| 3305 | metrics: |
| 3306 | - type: cosine_pearson |
| 3307 | value: 50.724322379215934 |
| 3308 | - type: cosine_spearman |
| 3309 | value: 59.90449732164651 |
| 3310 | - type: euclidean_pearson |
| 3311 | value: 50.227545226784024 |
| 3312 | - type: euclidean_spearman |
| 3313 | value: 59.898906527601085 |
| 3314 | - type: main_score |
| 3315 | value: 59.90449732164651 |
| 3316 | - type: manhattan_pearson |
| 3317 | value: 50.21762139819405 |
| 3318 | - type: manhattan_spearman |
| 3319 | value: 59.761039813759 |
| 3320 | - type: pearson |
| 3321 | value: 50.724322379215934 |
| 3322 | - type: spearman |
| 3323 | value: 59.90449732164651 |
| 3324 | task: |
| 3325 | type: STS |
| 3326 | - dataset: |
| 3327 | config: zh |
| 3328 | name: MTEB STS22 (zh) |
| 3329 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3330 | split: test |
| 3331 | type: mteb/sts22-crosslingual-sts |
| 3332 | metrics: |
| 3333 | - type: cos_sim_pearson |
| 3334 | value: 54.717524559088005 |
| 3335 | - type: cos_sim_spearman |
| 3336 | value: 66.83570886252286 |
| 3337 | - type: euclidean_pearson |
| 3338 | value: 58.41338625505467 |
| 3339 | - type: euclidean_spearman |
| 3340 | value: 66.68991427704938 |
| 3341 | - type: manhattan_pearson |
| 3342 | value: 58.78638572916807 |
| 3343 | - type: manhattan_spearman |
| 3344 | value: 66.58684161046335 |
| 3345 | task: |
| 3346 | type: STS |
| 3347 | - dataset: |
| 3348 | config: fr |
| 3349 | name: MTEB STS22 (fr) |
| 3350 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3351 | split: test |
| 3352 | type: mteb/sts22-crosslingual-sts |
| 3353 | metrics: |
| 3354 | - type: cos_sim_pearson |
| 3355 | value: 73.2962042954962 |
| 3356 | - type: cos_sim_spearman |
| 3357 | value: 76.58255504852025 |
| 3358 | - type: euclidean_pearson |
| 3359 | value: 75.70983192778257 |
| 3360 | - type: euclidean_spearman |
| 3361 | value: 77.4547684870542 |
| 3362 | - type: manhattan_pearson |
| 3363 | value: 75.75565853870485 |
| 3364 | - type: manhattan_spearman |
| 3365 | value: 76.90208974949428 |
| 3366 | task: |
| 3367 | type: STS |
| 3368 | - dataset: |
| 3369 | config: de-en |
| 3370 | name: MTEB STS22 (de-en) |
| 3371 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3372 | split: test |
| 3373 | type: mteb/sts22-crosslingual-sts |
| 3374 | metrics: |
| 3375 | - type: cos_sim_pearson |
| 3376 | value: 54.47396266924846 |
| 3377 | - type: cos_sim_spearman |
| 3378 | value: 56.492267162048606 |
| 3379 | - type: euclidean_pearson |
| 3380 | value: 55.998505203070195 |
| 3381 | - type: euclidean_spearman |
| 3382 | value: 56.46447012960222 |
| 3383 | - type: manhattan_pearson |
| 3384 | value: 54.873172394430995 |
| 3385 | - type: manhattan_spearman |
| 3386 | value: 56.58111534551218 |
| 3387 | task: |
| 3388 | type: STS |
| 3389 | - dataset: |
| 3390 | config: es-en |
| 3391 | name: MTEB STS22 (es-en) |
| 3392 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3393 | split: test |
| 3394 | type: mteb/sts22-crosslingual-sts |
| 3395 | metrics: |
| 3396 | - type: cos_sim_pearson |
| 3397 | value: 69.87177267688686 |
| 3398 | - type: cos_sim_spearman |
| 3399 | value: 74.57160943395763 |
| 3400 | - type: euclidean_pearson |
| 3401 | value: 70.88330406826788 |
| 3402 | - type: euclidean_spearman |
| 3403 | value: 74.29767636038422 |
| 3404 | - type: manhattan_pearson |
| 3405 | value: 71.38245248369536 |
| 3406 | - type: manhattan_spearman |
| 3407 | value: 74.53102232732175 |
| 3408 | task: |
| 3409 | type: STS |
| 3410 | - dataset: |
| 3411 | config: it |
| 3412 | name: MTEB STS22 (it) |
| 3413 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3414 | split: test |
| 3415 | type: mteb/sts22-crosslingual-sts |
| 3416 | metrics: |
| 3417 | - type: cos_sim_pearson |
| 3418 | value: 72.80225656959544 |
| 3419 | - type: cos_sim_spearman |
| 3420 | value: 76.52646173725735 |
| 3421 | - type: euclidean_pearson |
| 3422 | value: 73.95710720200799 |
| 3423 | - type: euclidean_spearman |
| 3424 | value: 76.54040031984111 |
| 3425 | - type: manhattan_pearson |
| 3426 | value: 73.89679971946774 |
| 3427 | - type: manhattan_spearman |
| 3428 | value: 76.60886958161574 |
| 3429 | task: |
| 3430 | type: STS |
| 3431 | - dataset: |
| 3432 | config: pl-en |
| 3433 | name: MTEB STS22 (pl-en) |
| 3434 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3435 | split: test |
| 3436 | type: mteb/sts22-crosslingual-sts |
| 3437 | metrics: |
| 3438 | - type: cos_sim_pearson |
| 3439 | value: 70.70844249898789 |
| 3440 | - type: cos_sim_spearman |
| 3441 | value: 72.68571783670241 |
| 3442 | - type: euclidean_pearson |
| 3443 | value: 72.38800772441031 |
| 3444 | - type: euclidean_spearman |
| 3445 | value: 72.86804422703312 |
| 3446 | - type: manhattan_pearson |
| 3447 | value: 71.29840508203515 |
| 3448 | - type: manhattan_spearman |
| 3449 | value: 71.86264441749513 |
| 3450 | task: |
| 3451 | type: STS |
| 3452 | - dataset: |
| 3453 | config: zh-en |
| 3454 | name: MTEB STS22 (zh-en) |
| 3455 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3456 | split: test |
| 3457 | type: mteb/sts22-crosslingual-sts |
| 3458 | metrics: |
| 3459 | - type: cos_sim_pearson |
| 3460 | value: 58.647478923935694 |
| 3461 | - type: cos_sim_spearman |
| 3462 | value: 63.74453623540931 |
| 3463 | - type: euclidean_pearson |
| 3464 | value: 59.60138032437505 |
| 3465 | - type: euclidean_spearman |
| 3466 | value: 63.947930832166065 |
| 3467 | - type: manhattan_pearson |
| 3468 | value: 58.59735509491861 |
| 3469 | - type: manhattan_spearman |
| 3470 | value: 62.082503844627404 |
| 3471 | task: |
| 3472 | type: STS |
| 3473 | - dataset: |
| 3474 | config: es-it |
| 3475 | name: MTEB STS22 (es-it) |
| 3476 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3477 | split: test |
| 3478 | type: mteb/sts22-crosslingual-sts |
| 3479 | metrics: |
| 3480 | - type: cos_sim_pearson |
| 3481 | value: 65.8722516867162 |
| 3482 | - type: cos_sim_spearman |
| 3483 | value: 71.81208592523012 |
| 3484 | - type: euclidean_pearson |
| 3485 | value: 67.95315252165956 |
| 3486 | - type: euclidean_spearman |
| 3487 | value: 73.00749822046009 |
| 3488 | - type: manhattan_pearson |
| 3489 | value: 68.07884688638924 |
| 3490 | - type: manhattan_spearman |
| 3491 | value: 72.34210325803069 |
| 3492 | task: |
| 3493 | type: STS |
| 3494 | - dataset: |
| 3495 | config: de-fr |
| 3496 | name: MTEB STS22 (de-fr) |
| 3497 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3498 | split: test |
| 3499 | type: mteb/sts22-crosslingual-sts |
| 3500 | metrics: |
| 3501 | - type: cos_sim_pearson |
| 3502 | value: 54.5405814240949 |
| 3503 | - type: cos_sim_spearman |
| 3504 | value: 60.56838649023775 |
| 3505 | - type: euclidean_pearson |
| 3506 | value: 53.011731611314104 |
| 3507 | - type: euclidean_spearman |
| 3508 | value: 58.533194841668426 |
| 3509 | - type: manhattan_pearson |
| 3510 | value: 53.623067729338494 |
| 3511 | - type: manhattan_spearman |
| 3512 | value: 58.018756154446926 |
| 3513 | task: |
| 3514 | type: STS |
| 3515 | - dataset: |
| 3516 | config: de-pl |
| 3517 | name: MTEB STS22 (de-pl) |
| 3518 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3519 | split: test |
| 3520 | type: mteb/sts22-crosslingual-sts |
| 3521 | metrics: |
| 3522 | - type: cos_sim_pearson |
| 3523 | value: 13.611046866216112 |
| 3524 | - type: cos_sim_spearman |
| 3525 | value: 28.238192909158492 |
| 3526 | - type: euclidean_pearson |
| 3527 | value: 22.16189199885129 |
| 3528 | - type: euclidean_spearman |
| 3529 | value: 35.012895679076564 |
| 3530 | - type: manhattan_pearson |
| 3531 | value: 21.969771178698387 |
| 3532 | - type: manhattan_spearman |
| 3533 | value: 32.456985088607475 |
| 3534 | task: |
| 3535 | type: STS |
| 3536 | - dataset: |
| 3537 | config: fr-pl |
| 3538 | name: MTEB STS22 (fr-pl) |
| 3539 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 3540 | split: test |
| 3541 | type: mteb/sts22-crosslingual-sts |
| 3542 | metrics: |
| 3543 | - type: cos_sim_pearson |
| 3544 | value: 74.58077407011655 |
| 3545 | - type: cos_sim_spearman |
| 3546 | value: 84.51542547285167 |
| 3547 | - type: euclidean_pearson |
| 3548 | value: 74.64613843596234 |
| 3549 | - type: euclidean_spearman |
| 3550 | value: 84.51542547285167 |
| 3551 | - type: manhattan_pearson |
| 3552 | value: 75.15335973101396 |
| 3553 | - type: manhattan_spearman |
| 3554 | value: 84.51542547285167 |
| 3555 | task: |
| 3556 | type: STS |
| 3557 | - dataset: |
| 3558 | config: default |
| 3559 | name: MTEB STSBenchmark |
| 3560 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 3561 | split: test |
| 3562 | type: mteb/stsbenchmark-sts |
| 3563 | metrics: |
| 3564 | - type: cos_sim_pearson |
| 3565 | value: 82.0739825531578 |
| 3566 | - type: cos_sim_spearman |
| 3567 | value: 84.01057479311115 |
| 3568 | - type: euclidean_pearson |
| 3569 | value: 83.85453227433344 |
| 3570 | - type: euclidean_spearman |
| 3571 | value: 84.01630226898655 |
| 3572 | - type: manhattan_pearson |
| 3573 | value: 83.75323603028978 |
| 3574 | - type: manhattan_spearman |
| 3575 | value: 83.89677983727685 |
| 3576 | task: |
| 3577 | type: STS |
| 3578 | - dataset: |
| 3579 | config: default |
| 3580 | name: MTEB SciDocsRR |
| 3581 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 3582 | split: test |
| 3583 | type: mteb/scidocs-reranking |
| 3584 | metrics: |
| 3585 | - type: map |
| 3586 | value: 78.12945623123957 |
| 3587 | - type: mrr |
| 3588 | value: 93.87738713719106 |
| 3589 | task: |
| 3590 | type: Reranking |
| 3591 | - dataset: |
| 3592 | config: default |
| 3593 | name: MTEB SciFact |
| 3594 | revision: None |
| 3595 | split: test |
| 3596 | type: scifact |
| 3597 | metrics: |
| 3598 | - type: map_at_1 |
| 3599 | value: 52.983000000000004 |
| 3600 | - type: map_at_10 |
| 3601 | value: 62.946000000000005 |
| 3602 | - type: map_at_100 |
| 3603 | value: 63.514 |
| 3604 | - type: map_at_1000 |
| 3605 | value: 63.554 |
| 3606 | - type: map_at_3 |
| 3607 | value: 60.183 |
| 3608 | - type: map_at_5 |
| 3609 | value: 61.672000000000004 |
| 3610 | - type: mrr_at_1 |
| 3611 | value: 55.667 |
| 3612 | - type: mrr_at_10 |
| 3613 | value: 64.522 |
| 3614 | - type: mrr_at_100 |
| 3615 | value: 64.957 |
| 3616 | - type: mrr_at_1000 |
| 3617 | value: 64.995 |
| 3618 | - type: mrr_at_3 |
| 3619 | value: 62.388999999999996 |
| 3620 | - type: mrr_at_5 |
| 3621 | value: 63.639 |
| 3622 | - type: ndcg_at_1 |
| 3623 | value: 55.667 |
| 3624 | - type: ndcg_at_10 |
| 3625 | value: 67.704 |
| 3626 | - type: ndcg_at_100 |
| 3627 | value: 70.299 |
| 3628 | - type: ndcg_at_1000 |
| 3629 | value: 71.241 |
| 3630 | - type: ndcg_at_3 |
| 3631 | value: 62.866 |
| 3632 | - type: ndcg_at_5 |
| 3633 | value: 65.16999999999999 |
| 3634 | - type: precision_at_1 |
| 3635 | value: 55.667 |
| 3636 | - type: precision_at_10 |
| 3637 | value: 9.033 |
| 3638 | - type: precision_at_100 |
| 3639 | value: 1.053 |
| 3640 | - type: precision_at_1000 |
| 3641 | value: 0.11299999999999999 |
| 3642 | - type: precision_at_3 |
| 3643 | value: 24.444 |
| 3644 | - type: precision_at_5 |
| 3645 | value: 16.133 |
| 3646 | - type: recall_at_1 |
| 3647 | value: 52.983000000000004 |
| 3648 | - type: recall_at_10 |
| 3649 | value: 80.656 |
| 3650 | - type: recall_at_100 |
| 3651 | value: 92.5 |
| 3652 | - type: recall_at_1000 |
| 3653 | value: 99.667 |
| 3654 | - type: recall_at_3 |
| 3655 | value: 67.744 |
| 3656 | - type: recall_at_5 |
| 3657 | value: 73.433 |
| 3658 | task: |
| 3659 | type: Retrieval |
| 3660 | - dataset: |
| 3661 | config: default |
| 3662 | name: MTEB SprintDuplicateQuestions |
| 3663 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 3664 | split: test |
| 3665 | type: mteb/sprintduplicatequestions-pairclassification |
| 3666 | metrics: |
| 3667 | - type: cos_sim_accuracy |
| 3668 | value: 99.72772277227723 |
| 3669 | - type: cos_sim_ap |
| 3670 | value: 92.17845897992215 |
| 3671 | - type: cos_sim_f1 |
| 3672 | value: 85.9746835443038 |
| 3673 | - type: cos_sim_precision |
| 3674 | value: 87.07692307692308 |
| 3675 | - type: cos_sim_recall |
| 3676 | value: 84.89999999999999 |
| 3677 | - type: dot_accuracy |
| 3678 | value: 99.3039603960396 |
| 3679 | - type: dot_ap |
| 3680 | value: 60.70244020124878 |
| 3681 | - type: dot_f1 |
| 3682 | value: 59.92742353551063 |
| 3683 | - type: dot_precision |
| 3684 | value: 62.21743810548978 |
| 3685 | - type: dot_recall |
| 3686 | value: 57.8 |
| 3687 | - type: euclidean_accuracy |
| 3688 | value: 99.71683168316832 |
| 3689 | - type: euclidean_ap |
| 3690 | value: 91.53997039964659 |
| 3691 | - type: euclidean_f1 |
| 3692 | value: 84.88372093023257 |
| 3693 | - type: euclidean_precision |
| 3694 | value: 90.02242152466367 |
| 3695 | - type: euclidean_recall |
| 3696 | value: 80.30000000000001 |
| 3697 | - type: manhattan_accuracy |
| 3698 | value: 99.72376237623763 |
| 3699 | - type: manhattan_ap |
| 3700 | value: 91.80756777790289 |
| 3701 | - type: manhattan_f1 |
| 3702 | value: 85.48468106479157 |
| 3703 | - type: manhattan_precision |
| 3704 | value: 85.8728557013118 |
| 3705 | - type: manhattan_recall |
| 3706 | value: 85.1 |
| 3707 | - type: max_accuracy |
| 3708 | value: 99.72772277227723 |
| 3709 | - type: max_ap |
| 3710 | value: 92.17845897992215 |
| 3711 | - type: max_f1 |
| 3712 | value: 85.9746835443038 |
| 3713 | task: |
| 3714 | type: PairClassification |
| 3715 | - dataset: |
| 3716 | config: default |
| 3717 | name: MTEB StackExchangeClustering |
| 3718 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 3719 | split: test |
| 3720 | type: mteb/stackexchange-clustering |
| 3721 | metrics: |
| 3722 | - type: v_measure |
| 3723 | value: 53.52464042600003 |
| 3724 | task: |
| 3725 | type: Clustering |
| 3726 | - dataset: |
| 3727 | config: default |
| 3728 | name: MTEB StackExchangeClusteringP2P |
| 3729 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 3730 | split: test |
| 3731 | type: mteb/stackexchange-clustering-p2p |
| 3732 | metrics: |
| 3733 | - type: v_measure |
| 3734 | value: 32.071631948736 |
| 3735 | task: |
| 3736 | type: Clustering |
| 3737 | - dataset: |
| 3738 | config: default |
| 3739 | name: MTEB StackOverflowDupQuestions |
| 3740 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 3741 | split: test |
| 3742 | type: mteb/stackoverflowdupquestions-reranking |
| 3743 | metrics: |
| 3744 | - type: map |
| 3745 | value: 49.19552407604654 |
| 3746 | - type: mrr |
| 3747 | value: 49.95269130379425 |
| 3748 | task: |
| 3749 | type: Reranking |
| 3750 | - dataset: |
| 3751 | config: default |
| 3752 | name: MTEB SummEval |
| 3753 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 3754 | split: test |
| 3755 | type: mteb/summeval |
| 3756 | metrics: |
| 3757 | - type: cos_sim_pearson |
| 3758 | value: 29.345293033095427 |
| 3759 | - type: cos_sim_spearman |
| 3760 | value: 29.976931423258403 |
| 3761 | - type: dot_pearson |
| 3762 | value: 27.047078008958408 |
| 3763 | - type: dot_spearman |
| 3764 | value: 27.75894368380218 |
| 3765 | task: |
| 3766 | type: Summarization |
| 3767 | - dataset: |
| 3768 | config: default |
| 3769 | name: MTEB TRECCOVID |
| 3770 | revision: None |
| 3771 | split: test |
| 3772 | type: trec-covid |
| 3773 | metrics: |
| 3774 | - type: map_at_1 |
| 3775 | value: 0.22 |
| 3776 | - type: map_at_10 |
| 3777 | value: 1.706 |
| 3778 | - type: map_at_100 |
| 3779 | value: 9.634 |
| 3780 | - type: map_at_1000 |
| 3781 | value: 23.665 |
| 3782 | - type: map_at_3 |
| 3783 | value: 0.5950000000000001 |
| 3784 | - type: map_at_5 |
| 3785 | value: 0.95 |
| 3786 | - type: mrr_at_1 |
| 3787 | value: 86.0 |
| 3788 | - type: mrr_at_10 |
| 3789 | value: 91.8 |
| 3790 | - type: mrr_at_100 |
| 3791 | value: 91.8 |
| 3792 | - type: mrr_at_1000 |
| 3793 | value: 91.8 |
| 3794 | - type: mrr_at_3 |
| 3795 | value: 91.0 |
| 3796 | - type: mrr_at_5 |
| 3797 | value: 91.8 |
| 3798 | - type: ndcg_at_1 |
| 3799 | value: 80.0 |
| 3800 | - type: ndcg_at_10 |
| 3801 | value: 72.573 |
| 3802 | - type: ndcg_at_100 |
| 3803 | value: 53.954 |
| 3804 | - type: ndcg_at_1000 |
| 3805 | value: 47.760999999999996 |
| 3806 | - type: ndcg_at_3 |
| 3807 | value: 76.173 |
| 3808 | - type: ndcg_at_5 |
| 3809 | value: 75.264 |
| 3810 | - type: precision_at_1 |
| 3811 | value: 86.0 |
| 3812 | - type: precision_at_10 |
| 3813 | value: 76.4 |
| 3814 | - type: precision_at_100 |
| 3815 | value: 55.50000000000001 |
| 3816 | - type: precision_at_1000 |
| 3817 | value: 21.802 |
| 3818 | - type: precision_at_3 |
| 3819 | value: 81.333 |
| 3820 | - type: precision_at_5 |
| 3821 | value: 80.4 |
| 3822 | - type: recall_at_1 |
| 3823 | value: 0.22 |
| 3824 | - type: recall_at_10 |
| 3825 | value: 1.925 |
| 3826 | - type: recall_at_100 |
| 3827 | value: 12.762 |
| 3828 | - type: recall_at_1000 |
| 3829 | value: 44.946000000000005 |
| 3830 | - type: recall_at_3 |
| 3831 | value: 0.634 |
| 3832 | - type: recall_at_5 |
| 3833 | value: 1.051 |
| 3834 | task: |
| 3835 | type: Retrieval |
| 3836 | - dataset: |
| 3837 | config: sqi-eng |
| 3838 | name: MTEB Tatoeba (sqi-eng) |
| 3839 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3840 | split: test |
| 3841 | type: mteb/tatoeba-bitext-mining |
| 3842 | metrics: |
| 3843 | - type: accuracy |
| 3844 | value: 91.0 |
| 3845 | - type: f1 |
| 3846 | value: 88.55666666666666 |
| 3847 | - type: precision |
| 3848 | value: 87.46166666666667 |
| 3849 | - type: recall |
| 3850 | value: 91.0 |
| 3851 | task: |
| 3852 | type: BitextMining |
| 3853 | - dataset: |
| 3854 | config: fry-eng |
| 3855 | name: MTEB Tatoeba (fry-eng) |
| 3856 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3857 | split: test |
| 3858 | type: mteb/tatoeba-bitext-mining |
| 3859 | metrics: |
| 3860 | - type: accuracy |
| 3861 | value: 57.22543352601156 |
| 3862 | - type: f1 |
| 3863 | value: 51.03220478943021 |
| 3864 | - type: precision |
| 3865 | value: 48.8150289017341 |
| 3866 | - type: recall |
| 3867 | value: 57.22543352601156 |
| 3868 | task: |
| 3869 | type: BitextMining |
| 3870 | - dataset: |
| 3871 | config: kur-eng |
| 3872 | name: MTEB Tatoeba (kur-eng) |
| 3873 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3874 | split: test |
| 3875 | type: mteb/tatoeba-bitext-mining |
| 3876 | metrics: |
| 3877 | - type: accuracy |
| 3878 | value: 46.58536585365854 |
| 3879 | - type: f1 |
| 3880 | value: 39.66870798578116 |
| 3881 | - type: precision |
| 3882 | value: 37.416085946573745 |
| 3883 | - type: recall |
| 3884 | value: 46.58536585365854 |
| 3885 | task: |
| 3886 | type: BitextMining |
| 3887 | - dataset: |
| 3888 | config: tur-eng |
| 3889 | name: MTEB Tatoeba (tur-eng) |
| 3890 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3891 | split: test |
| 3892 | type: mteb/tatoeba-bitext-mining |
| 3893 | metrics: |
| 3894 | - type: accuracy |
| 3895 | value: 89.7 |
| 3896 | - type: f1 |
| 3897 | value: 86.77999999999999 |
| 3898 | - type: precision |
| 3899 | value: 85.45333333333332 |
| 3900 | - type: recall |
| 3901 | value: 89.7 |
| 3902 | task: |
| 3903 | type: BitextMining |
| 3904 | - dataset: |
| 3905 | config: deu-eng |
| 3906 | name: MTEB Tatoeba (deu-eng) |
| 3907 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3908 | split: test |
| 3909 | type: mteb/tatoeba-bitext-mining |
| 3910 | metrics: |
| 3911 | - type: accuracy |
| 3912 | value: 97.39999999999999 |
| 3913 | - type: f1 |
| 3914 | value: 96.58333333333331 |
| 3915 | - type: precision |
| 3916 | value: 96.2 |
| 3917 | - type: recall |
| 3918 | value: 97.39999999999999 |
| 3919 | task: |
| 3920 | type: BitextMining |
| 3921 | - dataset: |
| 3922 | config: nld-eng |
| 3923 | name: MTEB Tatoeba (nld-eng) |
| 3924 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3925 | split: test |
| 3926 | type: mteb/tatoeba-bitext-mining |
| 3927 | metrics: |
| 3928 | - type: accuracy |
| 3929 | value: 92.4 |
| 3930 | - type: f1 |
| 3931 | value: 90.3 |
| 3932 | - type: precision |
| 3933 | value: 89.31666666666668 |
| 3934 | - type: recall |
| 3935 | value: 92.4 |
| 3936 | task: |
| 3937 | type: BitextMining |
| 3938 | - dataset: |
| 3939 | config: ron-eng |
| 3940 | name: MTEB Tatoeba (ron-eng) |
| 3941 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3942 | split: test |
| 3943 | type: mteb/tatoeba-bitext-mining |
| 3944 | metrics: |
| 3945 | - type: accuracy |
| 3946 | value: 86.9 |
| 3947 | - type: f1 |
| 3948 | value: 83.67190476190476 |
| 3949 | - type: precision |
| 3950 | value: 82.23333333333332 |
| 3951 | - type: recall |
| 3952 | value: 86.9 |
| 3953 | task: |
| 3954 | type: BitextMining |
| 3955 | - dataset: |
| 3956 | config: ang-eng |
| 3957 | name: MTEB Tatoeba (ang-eng) |
| 3958 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3959 | split: test |
| 3960 | type: mteb/tatoeba-bitext-mining |
| 3961 | metrics: |
| 3962 | - type: accuracy |
| 3963 | value: 50.0 |
| 3964 | - type: f1 |
| 3965 | value: 42.23229092632078 |
| 3966 | - type: precision |
| 3967 | value: 39.851634683724235 |
| 3968 | - type: recall |
| 3969 | value: 50.0 |
| 3970 | task: |
| 3971 | type: BitextMining |
| 3972 | - dataset: |
| 3973 | config: ido-eng |
| 3974 | name: MTEB Tatoeba (ido-eng) |
| 3975 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3976 | split: test |
| 3977 | type: mteb/tatoeba-bitext-mining |
| 3978 | metrics: |
| 3979 | - type: accuracy |
| 3980 | value: 76.3 |
| 3981 | - type: f1 |
| 3982 | value: 70.86190476190477 |
| 3983 | - type: precision |
| 3984 | value: 68.68777777777777 |
| 3985 | - type: recall |
| 3986 | value: 76.3 |
| 3987 | task: |
| 3988 | type: BitextMining |
| 3989 | - dataset: |
| 3990 | config: jav-eng |
| 3991 | name: MTEB Tatoeba (jav-eng) |
| 3992 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 3993 | split: test |
| 3994 | type: mteb/tatoeba-bitext-mining |
| 3995 | metrics: |
| 3996 | - type: accuracy |
| 3997 | value: 57.073170731707314 |
| 3998 | - type: f1 |
| 3999 | value: 50.658958927251604 |
| 4000 | - type: precision |
| 4001 | value: 48.26480836236933 |
| 4002 | - type: recall |
| 4003 | value: 57.073170731707314 |
| 4004 | task: |
| 4005 | type: BitextMining |
| 4006 | - dataset: |
| 4007 | config: isl-eng |
| 4008 | name: MTEB Tatoeba (isl-eng) |
| 4009 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4010 | split: test |
| 4011 | type: mteb/tatoeba-bitext-mining |
| 4012 | metrics: |
| 4013 | - type: accuracy |
| 4014 | value: 68.2 |
| 4015 | - type: f1 |
| 4016 | value: 62.156507936507936 |
| 4017 | - type: precision |
| 4018 | value: 59.84964285714286 |
| 4019 | - type: recall |
| 4020 | value: 68.2 |
| 4021 | task: |
| 4022 | type: BitextMining |
| 4023 | - dataset: |
| 4024 | config: slv-eng |
| 4025 | name: MTEB Tatoeba (slv-eng) |
| 4026 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4027 | split: test |
| 4028 | type: mteb/tatoeba-bitext-mining |
| 4029 | metrics: |
| 4030 | - type: accuracy |
| 4031 | value: 77.52126366950182 |
| 4032 | - type: f1 |
| 4033 | value: 72.8496210148701 |
| 4034 | - type: precision |
| 4035 | value: 70.92171498003819 |
| 4036 | - type: recall |
| 4037 | value: 77.52126366950182 |
| 4038 | task: |
| 4039 | type: BitextMining |
| 4040 | - dataset: |
| 4041 | config: cym-eng |
| 4042 | name: MTEB Tatoeba (cym-eng) |
| 4043 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4044 | split: test |
| 4045 | type: mteb/tatoeba-bitext-mining |
| 4046 | metrics: |
| 4047 | - type: accuracy |
| 4048 | value: 70.78260869565217 |
| 4049 | - type: f1 |
| 4050 | value: 65.32422360248447 |
| 4051 | - type: precision |
| 4052 | value: 63.063067367415194 |
| 4053 | - type: recall |
| 4054 | value: 70.78260869565217 |
| 4055 | task: |
| 4056 | type: BitextMining |
| 4057 | - dataset: |
| 4058 | config: kaz-eng |
| 4059 | name: MTEB Tatoeba (kaz-eng) |
| 4060 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4061 | split: test |
| 4062 | type: mteb/tatoeba-bitext-mining |
| 4063 | metrics: |
| 4064 | - type: accuracy |
| 4065 | value: 78.43478260869566 |
| 4066 | - type: f1 |
| 4067 | value: 73.02608695652172 |
| 4068 | - type: precision |
| 4069 | value: 70.63768115942028 |
| 4070 | - type: recall |
| 4071 | value: 78.43478260869566 |
| 4072 | task: |
| 4073 | type: BitextMining |
| 4074 | - dataset: |
| 4075 | config: est-eng |
| 4076 | name: MTEB Tatoeba (est-eng) |
| 4077 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4078 | split: test |
| 4079 | type: mteb/tatoeba-bitext-mining |
| 4080 | metrics: |
| 4081 | - type: accuracy |
| 4082 | value: 60.9 |
| 4083 | - type: f1 |
| 4084 | value: 55.309753694581275 |
| 4085 | - type: precision |
| 4086 | value: 53.130476190476195 |
| 4087 | - type: recall |
| 4088 | value: 60.9 |
| 4089 | task: |
| 4090 | type: BitextMining |
| 4091 | - dataset: |
| 4092 | config: heb-eng |
| 4093 | name: MTEB Tatoeba (heb-eng) |
| 4094 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4095 | split: test |
| 4096 | type: mteb/tatoeba-bitext-mining |
| 4097 | metrics: |
| 4098 | - type: accuracy |
| 4099 | value: 72.89999999999999 |
| 4100 | - type: f1 |
| 4101 | value: 67.92023809523809 |
| 4102 | - type: precision |
| 4103 | value: 65.82595238095237 |
| 4104 | - type: recall |
| 4105 | value: 72.89999999999999 |
| 4106 | task: |
| 4107 | type: BitextMining |
| 4108 | - dataset: |
| 4109 | config: gla-eng |
| 4110 | name: MTEB Tatoeba (gla-eng) |
| 4111 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4112 | split: test |
| 4113 | type: mteb/tatoeba-bitext-mining |
| 4114 | metrics: |
| 4115 | - type: accuracy |
| 4116 | value: 46.80337756332931 |
| 4117 | - type: f1 |
| 4118 | value: 39.42174900558496 |
| 4119 | - type: precision |
| 4120 | value: 36.97101116280851 |
| 4121 | - type: recall |
| 4122 | value: 46.80337756332931 |
| 4123 | task: |
| 4124 | type: BitextMining |
| 4125 | - dataset: |
| 4126 | config: mar-eng |
| 4127 | name: MTEB Tatoeba (mar-eng) |
| 4128 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4129 | split: test |
| 4130 | type: mteb/tatoeba-bitext-mining |
| 4131 | metrics: |
| 4132 | - type: accuracy |
| 4133 | value: 89.8 |
| 4134 | - type: f1 |
| 4135 | value: 86.79 |
| 4136 | - type: precision |
| 4137 | value: 85.375 |
| 4138 | - type: recall |
| 4139 | value: 89.8 |
| 4140 | task: |
| 4141 | type: BitextMining |
| 4142 | - dataset: |
| 4143 | config: lat-eng |
| 4144 | name: MTEB Tatoeba (lat-eng) |
| 4145 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4146 | split: test |
| 4147 | type: mteb/tatoeba-bitext-mining |
| 4148 | metrics: |
| 4149 | - type: accuracy |
| 4150 | value: 47.199999999999996 |
| 4151 | - type: f1 |
| 4152 | value: 39.95484348984349 |
| 4153 | - type: precision |
| 4154 | value: 37.561071428571424 |
| 4155 | - type: recall |
| 4156 | value: 47.199999999999996 |
| 4157 | task: |
| 4158 | type: BitextMining |
| 4159 | - dataset: |
| 4160 | config: bel-eng |
| 4161 | name: MTEB Tatoeba (bel-eng) |
| 4162 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4163 | split: test |
| 4164 | type: mteb/tatoeba-bitext-mining |
| 4165 | metrics: |
| 4166 | - type: accuracy |
| 4167 | value: 87.8 |
| 4168 | - type: f1 |
| 4169 | value: 84.68190476190475 |
| 4170 | - type: precision |
| 4171 | value: 83.275 |
| 4172 | - type: recall |
| 4173 | value: 87.8 |
| 4174 | task: |
| 4175 | type: BitextMining |
| 4176 | - dataset: |
| 4177 | config: pms-eng |
| 4178 | name: MTEB Tatoeba (pms-eng) |
| 4179 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4180 | split: test |
| 4181 | type: mteb/tatoeba-bitext-mining |
| 4182 | metrics: |
| 4183 | - type: accuracy |
| 4184 | value: 48.76190476190476 |
| 4185 | - type: f1 |
| 4186 | value: 42.14965986394558 |
| 4187 | - type: precision |
| 4188 | value: 39.96743626743626 |
| 4189 | - type: recall |
| 4190 | value: 48.76190476190476 |
| 4191 | task: |
| 4192 | type: BitextMining |
| 4193 | - dataset: |
| 4194 | config: gle-eng |
| 4195 | name: MTEB Tatoeba (gle-eng) |
| 4196 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4197 | split: test |
| 4198 | type: mteb/tatoeba-bitext-mining |
| 4199 | metrics: |
| 4200 | - type: accuracy |
| 4201 | value: 66.10000000000001 |
| 4202 | - type: f1 |
| 4203 | value: 59.58580086580086 |
| 4204 | - type: precision |
| 4205 | value: 57.150238095238095 |
| 4206 | - type: recall |
| 4207 | value: 66.10000000000001 |
| 4208 | task: |
| 4209 | type: BitextMining |
| 4210 | - dataset: |
| 4211 | config: pes-eng |
| 4212 | name: MTEB Tatoeba (pes-eng) |
| 4213 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4214 | split: test |
| 4215 | type: mteb/tatoeba-bitext-mining |
| 4216 | metrics: |
| 4217 | - type: accuracy |
| 4218 | value: 87.3 |
| 4219 | - type: f1 |
| 4220 | value: 84.0 |
| 4221 | - type: precision |
| 4222 | value: 82.48666666666666 |
| 4223 | - type: recall |
| 4224 | value: 87.3 |
| 4225 | task: |
| 4226 | type: BitextMining |
| 4227 | - dataset: |
| 4228 | config: nob-eng |
| 4229 | name: MTEB Tatoeba (nob-eng) |
| 4230 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4231 | split: test |
| 4232 | type: mteb/tatoeba-bitext-mining |
| 4233 | metrics: |
| 4234 | - type: accuracy |
| 4235 | value: 90.4 |
| 4236 | - type: f1 |
| 4237 | value: 87.79523809523809 |
| 4238 | - type: precision |
| 4239 | value: 86.6 |
| 4240 | - type: recall |
| 4241 | value: 90.4 |
| 4242 | task: |
| 4243 | type: BitextMining |
| 4244 | - dataset: |
| 4245 | config: bul-eng |
| 4246 | name: MTEB Tatoeba (bul-eng) |
| 4247 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4248 | split: test |
| 4249 | type: mteb/tatoeba-bitext-mining |
| 4250 | metrics: |
| 4251 | - type: accuracy |
| 4252 | value: 87.0 |
| 4253 | - type: f1 |
| 4254 | value: 83.81 |
| 4255 | - type: precision |
| 4256 | value: 82.36666666666666 |
| 4257 | - type: recall |
| 4258 | value: 87.0 |
| 4259 | task: |
| 4260 | type: BitextMining |
| 4261 | - dataset: |
| 4262 | config: cbk-eng |
| 4263 | name: MTEB Tatoeba (cbk-eng) |
| 4264 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4265 | split: test |
| 4266 | type: mteb/tatoeba-bitext-mining |
| 4267 | metrics: |
| 4268 | - type: accuracy |
| 4269 | value: 63.9 |
| 4270 | - type: f1 |
| 4271 | value: 57.76533189033189 |
| 4272 | - type: precision |
| 4273 | value: 55.50595238095239 |
| 4274 | - type: recall |
| 4275 | value: 63.9 |
| 4276 | task: |
| 4277 | type: BitextMining |
| 4278 | - dataset: |
| 4279 | config: hun-eng |
| 4280 | name: MTEB Tatoeba (hun-eng) |
| 4281 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4282 | split: test |
| 4283 | type: mteb/tatoeba-bitext-mining |
| 4284 | metrics: |
| 4285 | - type: accuracy |
| 4286 | value: 76.1 |
| 4287 | - type: f1 |
| 4288 | value: 71.83690476190478 |
| 4289 | - type: precision |
| 4290 | value: 70.04928571428573 |
| 4291 | - type: recall |
| 4292 | value: 76.1 |
| 4293 | task: |
| 4294 | type: BitextMining |
| 4295 | - dataset: |
| 4296 | config: uig-eng |
| 4297 | name: MTEB Tatoeba (uig-eng) |
| 4298 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4299 | split: test |
| 4300 | type: mteb/tatoeba-bitext-mining |
| 4301 | metrics: |
| 4302 | - type: accuracy |
| 4303 | value: 66.3 |
| 4304 | - type: f1 |
| 4305 | value: 59.32626984126984 |
| 4306 | - type: precision |
| 4307 | value: 56.62535714285713 |
| 4308 | - type: recall |
| 4309 | value: 66.3 |
| 4310 | task: |
| 4311 | type: BitextMining |
| 4312 | - dataset: |
| 4313 | config: rus-eng |
| 4314 | name: MTEB Tatoeba (rus-eng) |
| 4315 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4316 | split: test |
| 4317 | type: mteb/tatoeba-bitext-mining |
| 4318 | metrics: |
| 4319 | - type: accuracy |
| 4320 | value: 92.10000000000001 |
| 4321 | - type: f1 |
| 4322 | value: 89.76666666666667 |
| 4323 | - type: main_score |
| 4324 | value: 89.76666666666667 |
| 4325 | - type: precision |
| 4326 | value: 88.64999999999999 |
| 4327 | - type: recall |
| 4328 | value: 92.10000000000001 |
| 4329 | task: |
| 4330 | type: BitextMining |
| 4331 | - dataset: |
| 4332 | config: spa-eng |
| 4333 | name: MTEB Tatoeba (spa-eng) |
| 4334 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4335 | split: test |
| 4336 | type: mteb/tatoeba-bitext-mining |
| 4337 | metrics: |
| 4338 | - type: accuracy |
| 4339 | value: 93.10000000000001 |
| 4340 | - type: f1 |
| 4341 | value: 91.10000000000001 |
| 4342 | - type: precision |
| 4343 | value: 90.16666666666666 |
| 4344 | - type: recall |
| 4345 | value: 93.10000000000001 |
| 4346 | task: |
| 4347 | type: BitextMining |
| 4348 | - dataset: |
| 4349 | config: hye-eng |
| 4350 | name: MTEB Tatoeba (hye-eng) |
| 4351 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4352 | split: test |
| 4353 | type: mteb/tatoeba-bitext-mining |
| 4354 | metrics: |
| 4355 | - type: accuracy |
| 4356 | value: 85.71428571428571 |
| 4357 | - type: f1 |
| 4358 | value: 82.29142600436403 |
| 4359 | - type: precision |
| 4360 | value: 80.8076626877166 |
| 4361 | - type: recall |
| 4362 | value: 85.71428571428571 |
| 4363 | task: |
| 4364 | type: BitextMining |
| 4365 | - dataset: |
| 4366 | config: tel-eng |
| 4367 | name: MTEB Tatoeba (tel-eng) |
| 4368 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4369 | split: test |
| 4370 | type: mteb/tatoeba-bitext-mining |
| 4371 | metrics: |
| 4372 | - type: accuracy |
| 4373 | value: 88.88888888888889 |
| 4374 | - type: f1 |
| 4375 | value: 85.7834757834758 |
| 4376 | - type: precision |
| 4377 | value: 84.43732193732193 |
| 4378 | - type: recall |
| 4379 | value: 88.88888888888889 |
| 4380 | task: |
| 4381 | type: BitextMining |
| 4382 | - dataset: |
| 4383 | config: afr-eng |
| 4384 | name: MTEB Tatoeba (afr-eng) |
| 4385 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4386 | split: test |
| 4387 | type: mteb/tatoeba-bitext-mining |
| 4388 | metrics: |
| 4389 | - type: accuracy |
| 4390 | value: 88.5 |
| 4391 | - type: f1 |
| 4392 | value: 85.67190476190476 |
| 4393 | - type: precision |
| 4394 | value: 84.43333333333332 |
| 4395 | - type: recall |
| 4396 | value: 88.5 |
| 4397 | task: |
| 4398 | type: BitextMining |
| 4399 | - dataset: |
| 4400 | config: mon-eng |
| 4401 | name: MTEB Tatoeba (mon-eng) |
| 4402 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4403 | split: test |
| 4404 | type: mteb/tatoeba-bitext-mining |
| 4405 | metrics: |
| 4406 | - type: accuracy |
| 4407 | value: 82.72727272727273 |
| 4408 | - type: f1 |
| 4409 | value: 78.21969696969695 |
| 4410 | - type: precision |
| 4411 | value: 76.18181818181819 |
| 4412 | - type: recall |
| 4413 | value: 82.72727272727273 |
| 4414 | task: |
| 4415 | type: BitextMining |
| 4416 | - dataset: |
| 4417 | config: arz-eng |
| 4418 | name: MTEB Tatoeba (arz-eng) |
| 4419 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4420 | split: test |
| 4421 | type: mteb/tatoeba-bitext-mining |
| 4422 | metrics: |
| 4423 | - type: accuracy |
| 4424 | value: 61.0062893081761 |
| 4425 | - type: f1 |
| 4426 | value: 55.13976240391334 |
| 4427 | - type: precision |
| 4428 | value: 52.92112499659669 |
| 4429 | - type: recall |
| 4430 | value: 61.0062893081761 |
| 4431 | task: |
| 4432 | type: BitextMining |
| 4433 | - dataset: |
| 4434 | config: hrv-eng |
| 4435 | name: MTEB Tatoeba (hrv-eng) |
| 4436 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4437 | split: test |
| 4438 | type: mteb/tatoeba-bitext-mining |
| 4439 | metrics: |
| 4440 | - type: accuracy |
| 4441 | value: 89.5 |
| 4442 | - type: f1 |
| 4443 | value: 86.86666666666666 |
| 4444 | - type: precision |
| 4445 | value: 85.69166666666668 |
| 4446 | - type: recall |
| 4447 | value: 89.5 |
| 4448 | task: |
| 4449 | type: BitextMining |
| 4450 | - dataset: |
| 4451 | config: nov-eng |
| 4452 | name: MTEB Tatoeba (nov-eng) |
| 4453 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4454 | split: test |
| 4455 | type: mteb/tatoeba-bitext-mining |
| 4456 | metrics: |
| 4457 | - type: accuracy |
| 4458 | value: 73.54085603112841 |
| 4459 | - type: f1 |
| 4460 | value: 68.56031128404669 |
| 4461 | - type: precision |
| 4462 | value: 66.53047989623866 |
| 4463 | - type: recall |
| 4464 | value: 73.54085603112841 |
| 4465 | task: |
| 4466 | type: BitextMining |
| 4467 | - dataset: |
| 4468 | config: gsw-eng |
| 4469 | name: MTEB Tatoeba (gsw-eng) |
| 4470 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4471 | split: test |
| 4472 | type: mteb/tatoeba-bitext-mining |
| 4473 | metrics: |
| 4474 | - type: accuracy |
| 4475 | value: 43.58974358974359 |
| 4476 | - type: f1 |
| 4477 | value: 36.45299145299145 |
| 4478 | - type: precision |
| 4479 | value: 33.81155881155882 |
| 4480 | - type: recall |
| 4481 | value: 43.58974358974359 |
| 4482 | task: |
| 4483 | type: BitextMining |
| 4484 | - dataset: |
| 4485 | config: nds-eng |
| 4486 | name: MTEB Tatoeba (nds-eng) |
| 4487 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4488 | split: test |
| 4489 | type: mteb/tatoeba-bitext-mining |
| 4490 | metrics: |
| 4491 | - type: accuracy |
| 4492 | value: 59.599999999999994 |
| 4493 | - type: f1 |
| 4494 | value: 53.264689754689755 |
| 4495 | - type: precision |
| 4496 | value: 50.869166666666665 |
| 4497 | - type: recall |
| 4498 | value: 59.599999999999994 |
| 4499 | task: |
| 4500 | type: BitextMining |
| 4501 | - dataset: |
| 4502 | config: ukr-eng |
| 4503 | name: MTEB Tatoeba (ukr-eng) |
| 4504 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4505 | split: test |
| 4506 | type: mteb/tatoeba-bitext-mining |
| 4507 | metrics: |
| 4508 | - type: accuracy |
| 4509 | value: 85.2 |
| 4510 | - type: f1 |
| 4511 | value: 81.61666666666665 |
| 4512 | - type: precision |
| 4513 | value: 80.02833333333335 |
| 4514 | - type: recall |
| 4515 | value: 85.2 |
| 4516 | task: |
| 4517 | type: BitextMining |
| 4518 | - dataset: |
| 4519 | config: uzb-eng |
| 4520 | name: MTEB Tatoeba (uzb-eng) |
| 4521 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4522 | split: test |
| 4523 | type: mteb/tatoeba-bitext-mining |
| 4524 | metrics: |
| 4525 | - type: accuracy |
| 4526 | value: 63.78504672897196 |
| 4527 | - type: f1 |
| 4528 | value: 58.00029669188548 |
| 4529 | - type: precision |
| 4530 | value: 55.815809968847354 |
| 4531 | - type: recall |
| 4532 | value: 63.78504672897196 |
| 4533 | task: |
| 4534 | type: BitextMining |
| 4535 | - dataset: |
| 4536 | config: lit-eng |
| 4537 | name: MTEB Tatoeba (lit-eng) |
| 4538 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4539 | split: test |
| 4540 | type: mteb/tatoeba-bitext-mining |
| 4541 | metrics: |
| 4542 | - type: accuracy |
| 4543 | value: 66.5 |
| 4544 | - type: f1 |
| 4545 | value: 61.518333333333345 |
| 4546 | - type: precision |
| 4547 | value: 59.622363699102834 |
| 4548 | - type: recall |
| 4549 | value: 66.5 |
| 4550 | task: |
| 4551 | type: BitextMining |
| 4552 | - dataset: |
| 4553 | config: ina-eng |
| 4554 | name: MTEB Tatoeba (ina-eng) |
| 4555 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4556 | split: test |
| 4557 | type: mteb/tatoeba-bitext-mining |
| 4558 | metrics: |
| 4559 | - type: accuracy |
| 4560 | value: 88.6 |
| 4561 | - type: f1 |
| 4562 | value: 85.60222222222221 |
| 4563 | - type: precision |
| 4564 | value: 84.27916666666665 |
| 4565 | - type: recall |
| 4566 | value: 88.6 |
| 4567 | task: |
| 4568 | type: BitextMining |
| 4569 | - dataset: |
| 4570 | config: lfn-eng |
| 4571 | name: MTEB Tatoeba (lfn-eng) |
| 4572 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4573 | split: test |
| 4574 | type: mteb/tatoeba-bitext-mining |
| 4575 | metrics: |
| 4576 | - type: accuracy |
| 4577 | value: 58.699999999999996 |
| 4578 | - type: f1 |
| 4579 | value: 52.732375957375965 |
| 4580 | - type: precision |
| 4581 | value: 50.63214035964035 |
| 4582 | - type: recall |
| 4583 | value: 58.699999999999996 |
| 4584 | task: |
| 4585 | type: BitextMining |
| 4586 | - dataset: |
| 4587 | config: zsm-eng |
| 4588 | name: MTEB Tatoeba (zsm-eng) |
| 4589 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4590 | split: test |
| 4591 | type: mteb/tatoeba-bitext-mining |
| 4592 | metrics: |
| 4593 | - type: accuracy |
| 4594 | value: 92.10000000000001 |
| 4595 | - type: f1 |
| 4596 | value: 89.99666666666667 |
| 4597 | - type: precision |
| 4598 | value: 89.03333333333333 |
| 4599 | - type: recall |
| 4600 | value: 92.10000000000001 |
| 4601 | task: |
| 4602 | type: BitextMining |
| 4603 | - dataset: |
| 4604 | config: ita-eng |
| 4605 | name: MTEB Tatoeba (ita-eng) |
| 4606 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4607 | split: test |
| 4608 | type: mteb/tatoeba-bitext-mining |
| 4609 | metrics: |
| 4610 | - type: accuracy |
| 4611 | value: 90.10000000000001 |
| 4612 | - type: f1 |
| 4613 | value: 87.55666666666667 |
| 4614 | - type: precision |
| 4615 | value: 86.36166666666668 |
| 4616 | - type: recall |
| 4617 | value: 90.10000000000001 |
| 4618 | task: |
| 4619 | type: BitextMining |
| 4620 | - dataset: |
| 4621 | config: cmn-eng |
| 4622 | name: MTEB Tatoeba (cmn-eng) |
| 4623 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4624 | split: test |
| 4625 | type: mteb/tatoeba-bitext-mining |
| 4626 | metrics: |
| 4627 | - type: accuracy |
| 4628 | value: 91.4 |
| 4629 | - type: f1 |
| 4630 | value: 88.89000000000001 |
| 4631 | - type: precision |
| 4632 | value: 87.71166666666666 |
| 4633 | - type: recall |
| 4634 | value: 91.4 |
| 4635 | task: |
| 4636 | type: BitextMining |
| 4637 | - dataset: |
| 4638 | config: lvs-eng |
| 4639 | name: MTEB Tatoeba (lvs-eng) |
| 4640 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4641 | split: test |
| 4642 | type: mteb/tatoeba-bitext-mining |
| 4643 | metrics: |
| 4644 | - type: accuracy |
| 4645 | value: 65.7 |
| 4646 | - type: f1 |
| 4647 | value: 60.67427750410509 |
| 4648 | - type: precision |
| 4649 | value: 58.71785714285714 |
| 4650 | - type: recall |
| 4651 | value: 65.7 |
| 4652 | task: |
| 4653 | type: BitextMining |
| 4654 | - dataset: |
| 4655 | config: glg-eng |
| 4656 | name: MTEB Tatoeba (glg-eng) |
| 4657 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4658 | split: test |
| 4659 | type: mteb/tatoeba-bitext-mining |
| 4660 | metrics: |
| 4661 | - type: accuracy |
| 4662 | value: 85.39999999999999 |
| 4663 | - type: f1 |
| 4664 | value: 81.93190476190475 |
| 4665 | - type: precision |
| 4666 | value: 80.37833333333333 |
| 4667 | - type: recall |
| 4668 | value: 85.39999999999999 |
| 4669 | task: |
| 4670 | type: BitextMining |
| 4671 | - dataset: |
| 4672 | config: ceb-eng |
| 4673 | name: MTEB Tatoeba (ceb-eng) |
| 4674 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4675 | split: test |
| 4676 | type: mteb/tatoeba-bitext-mining |
| 4677 | metrics: |
| 4678 | - type: accuracy |
| 4679 | value: 47.833333333333336 |
| 4680 | - type: f1 |
| 4681 | value: 42.006625781625786 |
| 4682 | - type: precision |
| 4683 | value: 40.077380952380956 |
| 4684 | - type: recall |
| 4685 | value: 47.833333333333336 |
| 4686 | task: |
| 4687 | type: BitextMining |
| 4688 | - dataset: |
| 4689 | config: bre-eng |
| 4690 | name: MTEB Tatoeba (bre-eng) |
| 4691 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4692 | split: test |
| 4693 | type: mteb/tatoeba-bitext-mining |
| 4694 | metrics: |
| 4695 | - type: accuracy |
| 4696 | value: 10.4 |
| 4697 | - type: f1 |
| 4698 | value: 8.24465007215007 |
| 4699 | - type: precision |
| 4700 | value: 7.664597069597071 |
| 4701 | - type: recall |
| 4702 | value: 10.4 |
| 4703 | task: |
| 4704 | type: BitextMining |
| 4705 | - dataset: |
| 4706 | config: ben-eng |
| 4707 | name: MTEB Tatoeba (ben-eng) |
| 4708 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4709 | split: test |
| 4710 | type: mteb/tatoeba-bitext-mining |
| 4711 | metrics: |
| 4712 | - type: accuracy |
| 4713 | value: 82.6 |
| 4714 | - type: f1 |
| 4715 | value: 77.76333333333334 |
| 4716 | - type: precision |
| 4717 | value: 75.57833333333332 |
| 4718 | - type: recall |
| 4719 | value: 82.6 |
| 4720 | task: |
| 4721 | type: BitextMining |
| 4722 | - dataset: |
| 4723 | config: swg-eng |
| 4724 | name: MTEB Tatoeba (swg-eng) |
| 4725 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4726 | split: test |
| 4727 | type: mteb/tatoeba-bitext-mining |
| 4728 | metrics: |
| 4729 | - type: accuracy |
| 4730 | value: 52.67857142857143 |
| 4731 | - type: f1 |
| 4732 | value: 44.302721088435376 |
| 4733 | - type: precision |
| 4734 | value: 41.49801587301587 |
| 4735 | - type: recall |
| 4736 | value: 52.67857142857143 |
| 4737 | task: |
| 4738 | type: BitextMining |
| 4739 | - dataset: |
| 4740 | config: arq-eng |
| 4741 | name: MTEB Tatoeba (arq-eng) |
| 4742 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4743 | split: test |
| 4744 | type: mteb/tatoeba-bitext-mining |
| 4745 | metrics: |
| 4746 | - type: accuracy |
| 4747 | value: 28.3205268935236 |
| 4748 | - type: f1 |
| 4749 | value: 22.426666605171157 |
| 4750 | - type: precision |
| 4751 | value: 20.685900116470915 |
| 4752 | - type: recall |
| 4753 | value: 28.3205268935236 |
| 4754 | task: |
| 4755 | type: BitextMining |
| 4756 | - dataset: |
| 4757 | config: kab-eng |
| 4758 | name: MTEB Tatoeba (kab-eng) |
| 4759 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4760 | split: test |
| 4761 | type: mteb/tatoeba-bitext-mining |
| 4762 | metrics: |
| 4763 | - type: accuracy |
| 4764 | value: 22.7 |
| 4765 | - type: f1 |
| 4766 | value: 17.833970473970474 |
| 4767 | - type: precision |
| 4768 | value: 16.407335164835164 |
| 4769 | - type: recall |
| 4770 | value: 22.7 |
| 4771 | task: |
| 4772 | type: BitextMining |
| 4773 | - dataset: |
| 4774 | config: fra-eng |
| 4775 | name: MTEB Tatoeba (fra-eng) |
| 4776 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4777 | split: test |
| 4778 | type: mteb/tatoeba-bitext-mining |
| 4779 | metrics: |
| 4780 | - type: accuracy |
| 4781 | value: 92.2 |
| 4782 | - type: f1 |
| 4783 | value: 89.92999999999999 |
| 4784 | - type: precision |
| 4785 | value: 88.87 |
| 4786 | - type: recall |
| 4787 | value: 92.2 |
| 4788 | task: |
| 4789 | type: BitextMining |
| 4790 | - dataset: |
| 4791 | config: por-eng |
| 4792 | name: MTEB Tatoeba (por-eng) |
| 4793 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4794 | split: test |
| 4795 | type: mteb/tatoeba-bitext-mining |
| 4796 | metrics: |
| 4797 | - type: accuracy |
| 4798 | value: 91.4 |
| 4799 | - type: f1 |
| 4800 | value: 89.25 |
| 4801 | - type: precision |
| 4802 | value: 88.21666666666667 |
| 4803 | - type: recall |
| 4804 | value: 91.4 |
| 4805 | task: |
| 4806 | type: BitextMining |
| 4807 | - dataset: |
| 4808 | config: tat-eng |
| 4809 | name: MTEB Tatoeba (tat-eng) |
| 4810 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4811 | split: test |
| 4812 | type: mteb/tatoeba-bitext-mining |
| 4813 | metrics: |
| 4814 | - type: accuracy |
| 4815 | value: 69.19999999999999 |
| 4816 | - type: f1 |
| 4817 | value: 63.38269841269841 |
| 4818 | - type: precision |
| 4819 | value: 61.14773809523809 |
| 4820 | - type: recall |
| 4821 | value: 69.19999999999999 |
| 4822 | task: |
| 4823 | type: BitextMining |
| 4824 | - dataset: |
| 4825 | config: oci-eng |
| 4826 | name: MTEB Tatoeba (oci-eng) |
| 4827 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4828 | split: test |
| 4829 | type: mteb/tatoeba-bitext-mining |
| 4830 | metrics: |
| 4831 | - type: accuracy |
| 4832 | value: 48.8 |
| 4833 | - type: f1 |
| 4834 | value: 42.839915639915645 |
| 4835 | - type: precision |
| 4836 | value: 40.770287114845935 |
| 4837 | - type: recall |
| 4838 | value: 48.8 |
| 4839 | task: |
| 4840 | type: BitextMining |
| 4841 | - dataset: |
| 4842 | config: pol-eng |
| 4843 | name: MTEB Tatoeba (pol-eng) |
| 4844 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4845 | split: test |
| 4846 | type: mteb/tatoeba-bitext-mining |
| 4847 | metrics: |
| 4848 | - type: accuracy |
| 4849 | value: 88.8 |
| 4850 | - type: f1 |
| 4851 | value: 85.90666666666668 |
| 4852 | - type: precision |
| 4853 | value: 84.54166666666666 |
| 4854 | - type: recall |
| 4855 | value: 88.8 |
| 4856 | task: |
| 4857 | type: BitextMining |
| 4858 | - dataset: |
| 4859 | config: war-eng |
| 4860 | name: MTEB Tatoeba (war-eng) |
| 4861 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4862 | split: test |
| 4863 | type: mteb/tatoeba-bitext-mining |
| 4864 | metrics: |
| 4865 | - type: accuracy |
| 4866 | value: 46.6 |
| 4867 | - type: f1 |
| 4868 | value: 40.85892920804686 |
| 4869 | - type: precision |
| 4870 | value: 38.838223114604695 |
| 4871 | - type: recall |
| 4872 | value: 46.6 |
| 4873 | task: |
| 4874 | type: BitextMining |
| 4875 | - dataset: |
| 4876 | config: aze-eng |
| 4877 | name: MTEB Tatoeba (aze-eng) |
| 4878 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4879 | split: test |
| 4880 | type: mteb/tatoeba-bitext-mining |
| 4881 | metrics: |
| 4882 | - type: accuracy |
| 4883 | value: 84.0 |
| 4884 | - type: f1 |
| 4885 | value: 80.14190476190475 |
| 4886 | - type: precision |
| 4887 | value: 78.45333333333333 |
| 4888 | - type: recall |
| 4889 | value: 84.0 |
| 4890 | task: |
| 4891 | type: BitextMining |
| 4892 | - dataset: |
| 4893 | config: vie-eng |
| 4894 | name: MTEB Tatoeba (vie-eng) |
| 4895 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4896 | split: test |
| 4897 | type: mteb/tatoeba-bitext-mining |
| 4898 | metrics: |
| 4899 | - type: accuracy |
| 4900 | value: 90.5 |
| 4901 | - type: f1 |
| 4902 | value: 87.78333333333333 |
| 4903 | - type: precision |
| 4904 | value: 86.5 |
| 4905 | - type: recall |
| 4906 | value: 90.5 |
| 4907 | task: |
| 4908 | type: BitextMining |
| 4909 | - dataset: |
| 4910 | config: nno-eng |
| 4911 | name: MTEB Tatoeba (nno-eng) |
| 4912 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4913 | split: test |
| 4914 | type: mteb/tatoeba-bitext-mining |
| 4915 | metrics: |
| 4916 | - type: accuracy |
| 4917 | value: 74.5 |
| 4918 | - type: f1 |
| 4919 | value: 69.48397546897547 |
| 4920 | - type: precision |
| 4921 | value: 67.51869047619049 |
| 4922 | - type: recall |
| 4923 | value: 74.5 |
| 4924 | task: |
| 4925 | type: BitextMining |
| 4926 | - dataset: |
| 4927 | config: cha-eng |
| 4928 | name: MTEB Tatoeba (cha-eng) |
| 4929 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4930 | split: test |
| 4931 | type: mteb/tatoeba-bitext-mining |
| 4932 | metrics: |
| 4933 | - type: accuracy |
| 4934 | value: 32.846715328467155 |
| 4935 | - type: f1 |
| 4936 | value: 27.828177499710343 |
| 4937 | - type: precision |
| 4938 | value: 26.63451511991658 |
| 4939 | - type: recall |
| 4940 | value: 32.846715328467155 |
| 4941 | task: |
| 4942 | type: BitextMining |
| 4943 | - dataset: |
| 4944 | config: mhr-eng |
| 4945 | name: MTEB Tatoeba (mhr-eng) |
| 4946 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4947 | split: test |
| 4948 | type: mteb/tatoeba-bitext-mining |
| 4949 | metrics: |
| 4950 | - type: accuracy |
| 4951 | value: 8.0 |
| 4952 | - type: f1 |
| 4953 | value: 6.07664116764988 |
| 4954 | - type: precision |
| 4955 | value: 5.544177607179943 |
| 4956 | - type: recall |
| 4957 | value: 8.0 |
| 4958 | task: |
| 4959 | type: BitextMining |
| 4960 | - dataset: |
| 4961 | config: dan-eng |
| 4962 | name: MTEB Tatoeba (dan-eng) |
| 4963 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4964 | split: test |
| 4965 | type: mteb/tatoeba-bitext-mining |
| 4966 | metrics: |
| 4967 | - type: accuracy |
| 4968 | value: 87.6 |
| 4969 | - type: f1 |
| 4970 | value: 84.38555555555554 |
| 4971 | - type: precision |
| 4972 | value: 82.91583333333334 |
| 4973 | - type: recall |
| 4974 | value: 87.6 |
| 4975 | task: |
| 4976 | type: BitextMining |
| 4977 | - dataset: |
| 4978 | config: ell-eng |
| 4979 | name: MTEB Tatoeba (ell-eng) |
| 4980 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4981 | split: test |
| 4982 | type: mteb/tatoeba-bitext-mining |
| 4983 | metrics: |
| 4984 | - type: accuracy |
| 4985 | value: 87.5 |
| 4986 | - type: f1 |
| 4987 | value: 84.08333333333331 |
| 4988 | - type: precision |
| 4989 | value: 82.47333333333333 |
| 4990 | - type: recall |
| 4991 | value: 87.5 |
| 4992 | task: |
| 4993 | type: BitextMining |
| 4994 | - dataset: |
| 4995 | config: amh-eng |
| 4996 | name: MTEB Tatoeba (amh-eng) |
| 4997 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 4998 | split: test |
| 4999 | type: mteb/tatoeba-bitext-mining |
| 5000 | metrics: |
| 5001 | - type: accuracy |
| 5002 | value: 80.95238095238095 |
| 5003 | - type: f1 |
| 5004 | value: 76.13095238095238 |
| 5005 | - type: precision |
| 5006 | value: 74.05753968253967 |
| 5007 | - type: recall |
| 5008 | value: 80.95238095238095 |
| 5009 | task: |
| 5010 | type: BitextMining |
| 5011 | - dataset: |
| 5012 | config: pam-eng |
| 5013 | name: MTEB Tatoeba (pam-eng) |
| 5014 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5015 | split: test |
| 5016 | type: mteb/tatoeba-bitext-mining |
| 5017 | metrics: |
| 5018 | - type: accuracy |
| 5019 | value: 8.799999999999999 |
| 5020 | - type: f1 |
| 5021 | value: 6.971422975172975 |
| 5022 | - type: precision |
| 5023 | value: 6.557814916172301 |
| 5024 | - type: recall |
| 5025 | value: 8.799999999999999 |
| 5026 | task: |
| 5027 | type: BitextMining |
| 5028 | - dataset: |
| 5029 | config: hsb-eng |
| 5030 | name: MTEB Tatoeba (hsb-eng) |
| 5031 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5032 | split: test |
| 5033 | type: mteb/tatoeba-bitext-mining |
| 5034 | metrics: |
| 5035 | - type: accuracy |
| 5036 | value: 44.099378881987576 |
| 5037 | - type: f1 |
| 5038 | value: 37.01649742022413 |
| 5039 | - type: precision |
| 5040 | value: 34.69420618488942 |
| 5041 | - type: recall |
| 5042 | value: 44.099378881987576 |
| 5043 | task: |
| 5044 | type: BitextMining |
| 5045 | - dataset: |
| 5046 | config: srp-eng |
| 5047 | name: MTEB Tatoeba (srp-eng) |
| 5048 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5049 | split: test |
| 5050 | type: mteb/tatoeba-bitext-mining |
| 5051 | metrics: |
| 5052 | - type: accuracy |
| 5053 | value: 84.3 |
| 5054 | - type: f1 |
| 5055 | value: 80.32666666666667 |
| 5056 | - type: precision |
| 5057 | value: 78.60666666666665 |
| 5058 | - type: recall |
| 5059 | value: 84.3 |
| 5060 | task: |
| 5061 | type: BitextMining |
| 5062 | - dataset: |
| 5063 | config: epo-eng |
| 5064 | name: MTEB Tatoeba (epo-eng) |
| 5065 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5066 | split: test |
| 5067 | type: mteb/tatoeba-bitext-mining |
| 5068 | metrics: |
| 5069 | - type: accuracy |
| 5070 | value: 92.5 |
| 5071 | - type: f1 |
| 5072 | value: 90.49666666666666 |
| 5073 | - type: precision |
| 5074 | value: 89.56666666666668 |
| 5075 | - type: recall |
| 5076 | value: 92.5 |
| 5077 | task: |
| 5078 | type: BitextMining |
| 5079 | - dataset: |
| 5080 | config: kzj-eng |
| 5081 | name: MTEB Tatoeba (kzj-eng) |
| 5082 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5083 | split: test |
| 5084 | type: mteb/tatoeba-bitext-mining |
| 5085 | metrics: |
| 5086 | - type: accuracy |
| 5087 | value: 10.0 |
| 5088 | - type: f1 |
| 5089 | value: 8.268423529875141 |
| 5090 | - type: precision |
| 5091 | value: 7.878118605532398 |
| 5092 | - type: recall |
| 5093 | value: 10.0 |
| 5094 | task: |
| 5095 | type: BitextMining |
| 5096 | - dataset: |
| 5097 | config: awa-eng |
| 5098 | name: MTEB Tatoeba (awa-eng) |
| 5099 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5100 | split: test |
| 5101 | type: mteb/tatoeba-bitext-mining |
| 5102 | metrics: |
| 5103 | - type: accuracy |
| 5104 | value: 79.22077922077922 |
| 5105 | - type: f1 |
| 5106 | value: 74.27128427128426 |
| 5107 | - type: precision |
| 5108 | value: 72.28715728715729 |
| 5109 | - type: recall |
| 5110 | value: 79.22077922077922 |
| 5111 | task: |
| 5112 | type: BitextMining |
| 5113 | - dataset: |
| 5114 | config: fao-eng |
| 5115 | name: MTEB Tatoeba (fao-eng) |
| 5116 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5117 | split: test |
| 5118 | type: mteb/tatoeba-bitext-mining |
| 5119 | metrics: |
| 5120 | - type: accuracy |
| 5121 | value: 65.64885496183206 |
| 5122 | - type: f1 |
| 5123 | value: 58.87495456197747 |
| 5124 | - type: precision |
| 5125 | value: 55.992366412213734 |
| 5126 | - type: recall |
| 5127 | value: 65.64885496183206 |
| 5128 | task: |
| 5129 | type: BitextMining |
| 5130 | - dataset: |
| 5131 | config: mal-eng |
| 5132 | name: MTEB Tatoeba (mal-eng) |
| 5133 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5134 | split: test |
| 5135 | type: mteb/tatoeba-bitext-mining |
| 5136 | metrics: |
| 5137 | - type: accuracy |
| 5138 | value: 96.06986899563319 |
| 5139 | - type: f1 |
| 5140 | value: 94.78408539543909 |
| 5141 | - type: precision |
| 5142 | value: 94.15332362930616 |
| 5143 | - type: recall |
| 5144 | value: 96.06986899563319 |
| 5145 | task: |
| 5146 | type: BitextMining |
| 5147 | - dataset: |
| 5148 | config: ile-eng |
| 5149 | name: MTEB Tatoeba (ile-eng) |
| 5150 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5151 | split: test |
| 5152 | type: mteb/tatoeba-bitext-mining |
| 5153 | metrics: |
| 5154 | - type: accuracy |
| 5155 | value: 77.2 |
| 5156 | - type: f1 |
| 5157 | value: 71.72571428571428 |
| 5158 | - type: precision |
| 5159 | value: 69.41000000000001 |
| 5160 | - type: recall |
| 5161 | value: 77.2 |
| 5162 | task: |
| 5163 | type: BitextMining |
| 5164 | - dataset: |
| 5165 | config: bos-eng |
| 5166 | name: MTEB Tatoeba (bos-eng) |
| 5167 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5168 | split: test |
| 5169 | type: mteb/tatoeba-bitext-mining |
| 5170 | metrics: |
| 5171 | - type: accuracy |
| 5172 | value: 86.4406779661017 |
| 5173 | - type: f1 |
| 5174 | value: 83.2391713747646 |
| 5175 | - type: precision |
| 5176 | value: 81.74199623352166 |
| 5177 | - type: recall |
| 5178 | value: 86.4406779661017 |
| 5179 | task: |
| 5180 | type: BitextMining |
| 5181 | - dataset: |
| 5182 | config: cor-eng |
| 5183 | name: MTEB Tatoeba (cor-eng) |
| 5184 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5185 | split: test |
| 5186 | type: mteb/tatoeba-bitext-mining |
| 5187 | metrics: |
| 5188 | - type: accuracy |
| 5189 | value: 8.4 |
| 5190 | - type: f1 |
| 5191 | value: 6.017828743398003 |
| 5192 | - type: precision |
| 5193 | value: 5.4829865484756795 |
| 5194 | - type: recall |
| 5195 | value: 8.4 |
| 5196 | task: |
| 5197 | type: BitextMining |
| 5198 | - dataset: |
| 5199 | config: cat-eng |
| 5200 | name: MTEB Tatoeba (cat-eng) |
| 5201 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5202 | split: test |
| 5203 | type: mteb/tatoeba-bitext-mining |
| 5204 | metrics: |
| 5205 | - type: accuracy |
| 5206 | value: 83.5 |
| 5207 | - type: f1 |
| 5208 | value: 79.74833333333333 |
| 5209 | - type: precision |
| 5210 | value: 78.04837662337664 |
| 5211 | - type: recall |
| 5212 | value: 83.5 |
| 5213 | task: |
| 5214 | type: BitextMining |
| 5215 | - dataset: |
| 5216 | config: eus-eng |
| 5217 | name: MTEB Tatoeba (eus-eng) |
| 5218 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5219 | split: test |
| 5220 | type: mteb/tatoeba-bitext-mining |
| 5221 | metrics: |
| 5222 | - type: accuracy |
| 5223 | value: 60.4 |
| 5224 | - type: f1 |
| 5225 | value: 54.467301587301584 |
| 5226 | - type: precision |
| 5227 | value: 52.23242424242424 |
| 5228 | - type: recall |
| 5229 | value: 60.4 |
| 5230 | task: |
| 5231 | type: BitextMining |
| 5232 | - dataset: |
| 5233 | config: yue-eng |
| 5234 | name: MTEB Tatoeba (yue-eng) |
| 5235 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5236 | split: test |
| 5237 | type: mteb/tatoeba-bitext-mining |
| 5238 | metrics: |
| 5239 | - type: accuracy |
| 5240 | value: 74.9 |
| 5241 | - type: f1 |
| 5242 | value: 69.68699134199134 |
| 5243 | - type: precision |
| 5244 | value: 67.59873015873016 |
| 5245 | - type: recall |
| 5246 | value: 74.9 |
| 5247 | task: |
| 5248 | type: BitextMining |
| 5249 | - dataset: |
| 5250 | config: swe-eng |
| 5251 | name: MTEB Tatoeba (swe-eng) |
| 5252 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5253 | split: test |
| 5254 | type: mteb/tatoeba-bitext-mining |
| 5255 | metrics: |
| 5256 | - type: accuracy |
| 5257 | value: 88.0 |
| 5258 | - type: f1 |
| 5259 | value: 84.9652380952381 |
| 5260 | - type: precision |
| 5261 | value: 83.66166666666666 |
| 5262 | - type: recall |
| 5263 | value: 88.0 |
| 5264 | task: |
| 5265 | type: BitextMining |
| 5266 | - dataset: |
| 5267 | config: dtp-eng |
| 5268 | name: MTEB Tatoeba (dtp-eng) |
| 5269 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5270 | split: test |
| 5271 | type: mteb/tatoeba-bitext-mining |
| 5272 | metrics: |
| 5273 | - type: accuracy |
| 5274 | value: 9.1 |
| 5275 | - type: f1 |
| 5276 | value: 7.681244588744588 |
| 5277 | - type: precision |
| 5278 | value: 7.370043290043291 |
| 5279 | - type: recall |
| 5280 | value: 9.1 |
| 5281 | task: |
| 5282 | type: BitextMining |
| 5283 | - dataset: |
| 5284 | config: kat-eng |
| 5285 | name: MTEB Tatoeba (kat-eng) |
| 5286 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5287 | split: test |
| 5288 | type: mteb/tatoeba-bitext-mining |
| 5289 | metrics: |
| 5290 | - type: accuracy |
| 5291 | value: 80.9651474530831 |
| 5292 | - type: f1 |
| 5293 | value: 76.84220605132133 |
| 5294 | - type: precision |
| 5295 | value: 75.19606398962966 |
| 5296 | - type: recall |
| 5297 | value: 80.9651474530831 |
| 5298 | task: |
| 5299 | type: BitextMining |
| 5300 | - dataset: |
| 5301 | config: jpn-eng |
| 5302 | name: MTEB Tatoeba (jpn-eng) |
| 5303 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5304 | split: test |
| 5305 | type: mteb/tatoeba-bitext-mining |
| 5306 | metrics: |
| 5307 | - type: accuracy |
| 5308 | value: 86.9 |
| 5309 | - type: f1 |
| 5310 | value: 83.705 |
| 5311 | - type: precision |
| 5312 | value: 82.3120634920635 |
| 5313 | - type: recall |
| 5314 | value: 86.9 |
| 5315 | task: |
| 5316 | type: BitextMining |
| 5317 | - dataset: |
| 5318 | config: csb-eng |
| 5319 | name: MTEB Tatoeba (csb-eng) |
| 5320 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5321 | split: test |
| 5322 | type: mteb/tatoeba-bitext-mining |
| 5323 | metrics: |
| 5324 | - type: accuracy |
| 5325 | value: 29.64426877470356 |
| 5326 | - type: f1 |
| 5327 | value: 23.98763072676116 |
| 5328 | - type: precision |
| 5329 | value: 22.506399397703746 |
| 5330 | - type: recall |
| 5331 | value: 29.64426877470356 |
| 5332 | task: |
| 5333 | type: BitextMining |
| 5334 | - dataset: |
| 5335 | config: xho-eng |
| 5336 | name: MTEB Tatoeba (xho-eng) |
| 5337 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5338 | split: test |
| 5339 | type: mteb/tatoeba-bitext-mining |
| 5340 | metrics: |
| 5341 | - type: accuracy |
| 5342 | value: 70.4225352112676 |
| 5343 | - type: f1 |
| 5344 | value: 62.84037558685445 |
| 5345 | - type: precision |
| 5346 | value: 59.56572769953053 |
| 5347 | - type: recall |
| 5348 | value: 70.4225352112676 |
| 5349 | task: |
| 5350 | type: BitextMining |
| 5351 | - dataset: |
| 5352 | config: orv-eng |
| 5353 | name: MTEB Tatoeba (orv-eng) |
| 5354 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5355 | split: test |
| 5356 | type: mteb/tatoeba-bitext-mining |
| 5357 | metrics: |
| 5358 | - type: accuracy |
| 5359 | value: 19.64071856287425 |
| 5360 | - type: f1 |
| 5361 | value: 15.125271011207756 |
| 5362 | - type: precision |
| 5363 | value: 13.865019261197494 |
| 5364 | - type: recall |
| 5365 | value: 19.64071856287425 |
| 5366 | task: |
| 5367 | type: BitextMining |
| 5368 | - dataset: |
| 5369 | config: ind-eng |
| 5370 | name: MTEB Tatoeba (ind-eng) |
| 5371 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5372 | split: test |
| 5373 | type: mteb/tatoeba-bitext-mining |
| 5374 | metrics: |
| 5375 | - type: accuracy |
| 5376 | value: 90.2 |
| 5377 | - type: f1 |
| 5378 | value: 87.80666666666666 |
| 5379 | - type: precision |
| 5380 | value: 86.70833333333331 |
| 5381 | - type: recall |
| 5382 | value: 90.2 |
| 5383 | task: |
| 5384 | type: BitextMining |
| 5385 | - dataset: |
| 5386 | config: tuk-eng |
| 5387 | name: MTEB Tatoeba (tuk-eng) |
| 5388 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5389 | split: test |
| 5390 | type: mteb/tatoeba-bitext-mining |
| 5391 | metrics: |
| 5392 | - type: accuracy |
| 5393 | value: 23.15270935960591 |
| 5394 | - type: f1 |
| 5395 | value: 18.407224958949097 |
| 5396 | - type: precision |
| 5397 | value: 16.982385430661292 |
| 5398 | - type: recall |
| 5399 | value: 23.15270935960591 |
| 5400 | task: |
| 5401 | type: BitextMining |
| 5402 | - dataset: |
| 5403 | config: max-eng |
| 5404 | name: MTEB Tatoeba (max-eng) |
| 5405 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5406 | split: test |
| 5407 | type: mteb/tatoeba-bitext-mining |
| 5408 | metrics: |
| 5409 | - type: accuracy |
| 5410 | value: 55.98591549295775 |
| 5411 | - type: f1 |
| 5412 | value: 49.94718309859154 |
| 5413 | - type: precision |
| 5414 | value: 47.77864154624717 |
| 5415 | - type: recall |
| 5416 | value: 55.98591549295775 |
| 5417 | task: |
| 5418 | type: BitextMining |
| 5419 | - dataset: |
| 5420 | config: swh-eng |
| 5421 | name: MTEB Tatoeba (swh-eng) |
| 5422 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5423 | split: test |
| 5424 | type: mteb/tatoeba-bitext-mining |
| 5425 | metrics: |
| 5426 | - type: accuracy |
| 5427 | value: 73.07692307692307 |
| 5428 | - type: f1 |
| 5429 | value: 66.74358974358974 |
| 5430 | - type: precision |
| 5431 | value: 64.06837606837607 |
| 5432 | - type: recall |
| 5433 | value: 73.07692307692307 |
| 5434 | task: |
| 5435 | type: BitextMining |
| 5436 | - dataset: |
| 5437 | config: hin-eng |
| 5438 | name: MTEB Tatoeba (hin-eng) |
| 5439 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5440 | split: test |
| 5441 | type: mteb/tatoeba-bitext-mining |
| 5442 | metrics: |
| 5443 | - type: accuracy |
| 5444 | value: 94.89999999999999 |
| 5445 | - type: f1 |
| 5446 | value: 93.25 |
| 5447 | - type: precision |
| 5448 | value: 92.43333333333332 |
| 5449 | - type: recall |
| 5450 | value: 94.89999999999999 |
| 5451 | task: |
| 5452 | type: BitextMining |
| 5453 | - dataset: |
| 5454 | config: dsb-eng |
| 5455 | name: MTEB Tatoeba (dsb-eng) |
| 5456 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5457 | split: test |
| 5458 | type: mteb/tatoeba-bitext-mining |
| 5459 | metrics: |
| 5460 | - type: accuracy |
| 5461 | value: 37.78705636743215 |
| 5462 | - type: f1 |
| 5463 | value: 31.63899658680452 |
| 5464 | - type: precision |
| 5465 | value: 29.72264397629742 |
| 5466 | - type: recall |
| 5467 | value: 37.78705636743215 |
| 5468 | task: |
| 5469 | type: BitextMining |
| 5470 | - dataset: |
| 5471 | config: ber-eng |
| 5472 | name: MTEB Tatoeba (ber-eng) |
| 5473 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5474 | split: test |
| 5475 | type: mteb/tatoeba-bitext-mining |
| 5476 | metrics: |
| 5477 | - type: accuracy |
| 5478 | value: 21.6 |
| 5479 | - type: f1 |
| 5480 | value: 16.91697302697303 |
| 5481 | - type: precision |
| 5482 | value: 15.71225147075147 |
| 5483 | - type: recall |
| 5484 | value: 21.6 |
| 5485 | task: |
| 5486 | type: BitextMining |
| 5487 | - dataset: |
| 5488 | config: tam-eng |
| 5489 | name: MTEB Tatoeba (tam-eng) |
| 5490 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5491 | split: test |
| 5492 | type: mteb/tatoeba-bitext-mining |
| 5493 | metrics: |
| 5494 | - type: accuracy |
| 5495 | value: 85.01628664495115 |
| 5496 | - type: f1 |
| 5497 | value: 81.38514037536838 |
| 5498 | - type: precision |
| 5499 | value: 79.83170466883823 |
| 5500 | - type: recall |
| 5501 | value: 85.01628664495115 |
| 5502 | task: |
| 5503 | type: BitextMining |
| 5504 | - dataset: |
| 5505 | config: slk-eng |
| 5506 | name: MTEB Tatoeba (slk-eng) |
| 5507 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5508 | split: test |
| 5509 | type: mteb/tatoeba-bitext-mining |
| 5510 | metrics: |
| 5511 | - type: accuracy |
| 5512 | value: 83.39999999999999 |
| 5513 | - type: f1 |
| 5514 | value: 79.96380952380952 |
| 5515 | - type: precision |
| 5516 | value: 78.48333333333333 |
| 5517 | - type: recall |
| 5518 | value: 83.39999999999999 |
| 5519 | task: |
| 5520 | type: BitextMining |
| 5521 | - dataset: |
| 5522 | config: tgl-eng |
| 5523 | name: MTEB Tatoeba (tgl-eng) |
| 5524 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5525 | split: test |
| 5526 | type: mteb/tatoeba-bitext-mining |
| 5527 | metrics: |
| 5528 | - type: accuracy |
| 5529 | value: 83.2 |
| 5530 | - type: f1 |
| 5531 | value: 79.26190476190476 |
| 5532 | - type: precision |
| 5533 | value: 77.58833333333334 |
| 5534 | - type: recall |
| 5535 | value: 83.2 |
| 5536 | task: |
| 5537 | type: BitextMining |
| 5538 | - dataset: |
| 5539 | config: ast-eng |
| 5540 | name: MTEB Tatoeba (ast-eng) |
| 5541 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5542 | split: test |
| 5543 | type: mteb/tatoeba-bitext-mining |
| 5544 | metrics: |
| 5545 | - type: accuracy |
| 5546 | value: 75.59055118110236 |
| 5547 | - type: f1 |
| 5548 | value: 71.66854143232096 |
| 5549 | - type: precision |
| 5550 | value: 70.30183727034121 |
| 5551 | - type: recall |
| 5552 | value: 75.59055118110236 |
| 5553 | task: |
| 5554 | type: BitextMining |
| 5555 | - dataset: |
| 5556 | config: mkd-eng |
| 5557 | name: MTEB Tatoeba (mkd-eng) |
| 5558 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5559 | split: test |
| 5560 | type: mteb/tatoeba-bitext-mining |
| 5561 | metrics: |
| 5562 | - type: accuracy |
| 5563 | value: 65.5 |
| 5564 | - type: f1 |
| 5565 | value: 59.26095238095238 |
| 5566 | - type: precision |
| 5567 | value: 56.81909090909092 |
| 5568 | - type: recall |
| 5569 | value: 65.5 |
| 5570 | task: |
| 5571 | type: BitextMining |
| 5572 | - dataset: |
| 5573 | config: khm-eng |
| 5574 | name: MTEB Tatoeba (khm-eng) |
| 5575 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5576 | split: test |
| 5577 | type: mteb/tatoeba-bitext-mining |
| 5578 | metrics: |
| 5579 | - type: accuracy |
| 5580 | value: 55.26315789473685 |
| 5581 | - type: f1 |
| 5582 | value: 47.986523325858506 |
| 5583 | - type: precision |
| 5584 | value: 45.33950006595436 |
| 5585 | - type: recall |
| 5586 | value: 55.26315789473685 |
| 5587 | task: |
| 5588 | type: BitextMining |
| 5589 | - dataset: |
| 5590 | config: ces-eng |
| 5591 | name: MTEB Tatoeba (ces-eng) |
| 5592 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5593 | split: test |
| 5594 | type: mteb/tatoeba-bitext-mining |
| 5595 | metrics: |
| 5596 | - type: accuracy |
| 5597 | value: 82.89999999999999 |
| 5598 | - type: f1 |
| 5599 | value: 78.835 |
| 5600 | - type: precision |
| 5601 | value: 77.04761904761905 |
| 5602 | - type: recall |
| 5603 | value: 82.89999999999999 |
| 5604 | task: |
| 5605 | type: BitextMining |
| 5606 | - dataset: |
| 5607 | config: tzl-eng |
| 5608 | name: MTEB Tatoeba (tzl-eng) |
| 5609 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5610 | split: test |
| 5611 | type: mteb/tatoeba-bitext-mining |
| 5612 | metrics: |
| 5613 | - type: accuracy |
| 5614 | value: 43.269230769230774 |
| 5615 | - type: f1 |
| 5616 | value: 36.20421245421245 |
| 5617 | - type: precision |
| 5618 | value: 33.57371794871795 |
| 5619 | - type: recall |
| 5620 | value: 43.269230769230774 |
| 5621 | task: |
| 5622 | type: BitextMining |
| 5623 | - dataset: |
| 5624 | config: urd-eng |
| 5625 | name: MTEB Tatoeba (urd-eng) |
| 5626 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5627 | split: test |
| 5628 | type: mteb/tatoeba-bitext-mining |
| 5629 | metrics: |
| 5630 | - type: accuracy |
| 5631 | value: 88.0 |
| 5632 | - type: f1 |
| 5633 | value: 84.70666666666666 |
| 5634 | - type: precision |
| 5635 | value: 83.23166666666665 |
| 5636 | - type: recall |
| 5637 | value: 88.0 |
| 5638 | task: |
| 5639 | type: BitextMining |
| 5640 | - dataset: |
| 5641 | config: ara-eng |
| 5642 | name: MTEB Tatoeba (ara-eng) |
| 5643 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5644 | split: test |
| 5645 | type: mteb/tatoeba-bitext-mining |
| 5646 | metrics: |
| 5647 | - type: accuracy |
| 5648 | value: 77.4 |
| 5649 | - type: f1 |
| 5650 | value: 72.54666666666667 |
| 5651 | - type: precision |
| 5652 | value: 70.54318181818181 |
| 5653 | - type: recall |
| 5654 | value: 77.4 |
| 5655 | task: |
| 5656 | type: BitextMining |
| 5657 | - dataset: |
| 5658 | config: kor-eng |
| 5659 | name: MTEB Tatoeba (kor-eng) |
| 5660 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5661 | split: test |
| 5662 | type: mteb/tatoeba-bitext-mining |
| 5663 | metrics: |
| 5664 | - type: accuracy |
| 5665 | value: 78.60000000000001 |
| 5666 | - type: f1 |
| 5667 | value: 74.1588888888889 |
| 5668 | - type: precision |
| 5669 | value: 72.30250000000001 |
| 5670 | - type: recall |
| 5671 | value: 78.60000000000001 |
| 5672 | task: |
| 5673 | type: BitextMining |
| 5674 | - dataset: |
| 5675 | config: yid-eng |
| 5676 | name: MTEB Tatoeba (yid-eng) |
| 5677 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5678 | split: test |
| 5679 | type: mteb/tatoeba-bitext-mining |
| 5680 | metrics: |
| 5681 | - type: accuracy |
| 5682 | value: 72.40566037735849 |
| 5683 | - type: f1 |
| 5684 | value: 66.82587328813744 |
| 5685 | - type: precision |
| 5686 | value: 64.75039308176099 |
| 5687 | - type: recall |
| 5688 | value: 72.40566037735849 |
| 5689 | task: |
| 5690 | type: BitextMining |
| 5691 | - dataset: |
| 5692 | config: fin-eng |
| 5693 | name: MTEB Tatoeba (fin-eng) |
| 5694 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5695 | split: test |
| 5696 | type: mteb/tatoeba-bitext-mining |
| 5697 | metrics: |
| 5698 | - type: accuracy |
| 5699 | value: 73.8 |
| 5700 | - type: f1 |
| 5701 | value: 68.56357142857144 |
| 5702 | - type: precision |
| 5703 | value: 66.3178822055138 |
| 5704 | - type: recall |
| 5705 | value: 73.8 |
| 5706 | task: |
| 5707 | type: BitextMining |
| 5708 | - dataset: |
| 5709 | config: tha-eng |
| 5710 | name: MTEB Tatoeba (tha-eng) |
| 5711 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5712 | split: test |
| 5713 | type: mteb/tatoeba-bitext-mining |
| 5714 | metrics: |
| 5715 | - type: accuracy |
| 5716 | value: 91.78832116788321 |
| 5717 | - type: f1 |
| 5718 | value: 89.3552311435523 |
| 5719 | - type: precision |
| 5720 | value: 88.20559610705597 |
| 5721 | - type: recall |
| 5722 | value: 91.78832116788321 |
| 5723 | task: |
| 5724 | type: BitextMining |
| 5725 | - dataset: |
| 5726 | config: wuu-eng |
| 5727 | name: MTEB Tatoeba (wuu-eng) |
| 5728 | revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| 5729 | split: test |
| 5730 | type: mteb/tatoeba-bitext-mining |
| 5731 | metrics: |
| 5732 | - type: accuracy |
| 5733 | value: 74.3 |
| 5734 | - type: f1 |
| 5735 | value: 69.05085581085581 |
| 5736 | - type: precision |
| 5737 | value: 66.955 |
| 5738 | - type: recall |
| 5739 | value: 74.3 |
| 5740 | task: |
| 5741 | type: BitextMining |
| 5742 | - dataset: |
| 5743 | config: default |
| 5744 | name: MTEB Touche2020 |
| 5745 | revision: None |
| 5746 | split: test |
| 5747 | type: webis-touche2020 |
| 5748 | metrics: |
| 5749 | - type: map_at_1 |
| 5750 | value: 2.896 |
| 5751 | - type: map_at_10 |
| 5752 | value: 8.993 |
| 5753 | - type: map_at_100 |
| 5754 | value: 14.133999999999999 |
| 5755 | - type: map_at_1000 |
| 5756 | value: 15.668000000000001 |
| 5757 | - type: map_at_3 |
| 5758 | value: 5.862 |
| 5759 | - type: map_at_5 |
| 5760 | value: 7.17 |
| 5761 | - type: mrr_at_1 |
| 5762 | value: 34.694 |
| 5763 | - type: mrr_at_10 |
| 5764 | value: 42.931000000000004 |
| 5765 | - type: mrr_at_100 |
| 5766 | value: 44.81 |
| 5767 | - type: mrr_at_1000 |
| 5768 | value: 44.81 |
| 5769 | - type: mrr_at_3 |
| 5770 | value: 38.435 |
| 5771 | - type: mrr_at_5 |
| 5772 | value: 41.701 |
| 5773 | - type: ndcg_at_1 |
| 5774 | value: 31.633 |
| 5775 | - type: ndcg_at_10 |
| 5776 | value: 21.163 |
| 5777 | - type: ndcg_at_100 |
| 5778 | value: 33.306000000000004 |
| 5779 | - type: ndcg_at_1000 |
| 5780 | value: 45.275999999999996 |
| 5781 | - type: ndcg_at_3 |
| 5782 | value: 25.685999999999996 |
| 5783 | - type: ndcg_at_5 |
| 5784 | value: 23.732 |
| 5785 | - type: precision_at_1 |
| 5786 | value: 34.694 |
| 5787 | - type: precision_at_10 |
| 5788 | value: 17.755000000000003 |
| 5789 | - type: precision_at_100 |
| 5790 | value: 6.938999999999999 |
| 5791 | - type: precision_at_1000 |
| 5792 | value: 1.48 |
| 5793 | - type: precision_at_3 |
| 5794 | value: 25.85 |
| 5795 | - type: precision_at_5 |
| 5796 | value: 23.265 |
| 5797 | - type: recall_at_1 |
| 5798 | value: 2.896 |
| 5799 | - type: recall_at_10 |
| 5800 | value: 13.333999999999998 |
| 5801 | - type: recall_at_100 |
| 5802 | value: 43.517 |
| 5803 | - type: recall_at_1000 |
| 5804 | value: 79.836 |
| 5805 | - type: recall_at_3 |
| 5806 | value: 6.306000000000001 |
| 5807 | - type: recall_at_5 |
| 5808 | value: 8.825 |
| 5809 | task: |
| 5810 | type: Retrieval |
| 5811 | - dataset: |
| 5812 | config: default |
| 5813 | name: MTEB ToxicConversationsClassification |
| 5814 | revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| 5815 | split: test |
| 5816 | type: mteb/toxic_conversations_50k |
| 5817 | metrics: |
| 5818 | - type: accuracy |
| 5819 | value: 69.3874 |
| 5820 | - type: ap |
| 5821 | value: 13.829909072469423 |
| 5822 | - type: f1 |
| 5823 | value: 53.54534203543492 |
| 5824 | task: |
| 5825 | type: Classification |
| 5826 | - dataset: |
| 5827 | config: default |
| 5828 | name: MTEB TweetSentimentExtractionClassification |
| 5829 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 5830 | split: test |
| 5831 | type: mteb/tweet_sentiment_extraction |
| 5832 | metrics: |
| 5833 | - type: accuracy |
| 5834 | value: 62.62026032823995 |
| 5835 | - type: f1 |
| 5836 | value: 62.85251350485221 |
| 5837 | task: |
| 5838 | type: Classification |
| 5839 | - dataset: |
| 5840 | config: default |
| 5841 | name: MTEB TwentyNewsgroupsClustering |
| 5842 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 5843 | split: test |
| 5844 | type: mteb/twentynewsgroups-clustering |
| 5845 | metrics: |
| 5846 | - type: v_measure |
| 5847 | value: 33.21527881409797 |
| 5848 | task: |
| 5849 | type: Clustering |
| 5850 | - dataset: |
| 5851 | config: default |
| 5852 | name: MTEB TwitterSemEval2015 |
| 5853 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 5854 | split: test |
| 5855 | type: mteb/twittersemeval2015-pairclassification |
| 5856 | metrics: |
| 5857 | - type: cos_sim_accuracy |
| 5858 | value: 84.97943613280086 |
| 5859 | - type: cos_sim_ap |
| 5860 | value: 70.75454316885921 |
| 5861 | - type: cos_sim_f1 |
| 5862 | value: 65.38274012676743 |
| 5863 | - type: cos_sim_precision |
| 5864 | value: 60.761214318078835 |
| 5865 | - type: cos_sim_recall |
| 5866 | value: 70.76517150395777 |
| 5867 | - type: dot_accuracy |
| 5868 | value: 79.0546581629612 |
| 5869 | - type: dot_ap |
| 5870 | value: 47.3197121792147 |
| 5871 | - type: dot_f1 |
| 5872 | value: 49.20106524633821 |
| 5873 | - type: dot_precision |
| 5874 | value: 42.45499808502489 |
| 5875 | - type: dot_recall |
| 5876 | value: 58.49604221635884 |
| 5877 | - type: euclidean_accuracy |
| 5878 | value: 85.08076533349228 |
| 5879 | - type: euclidean_ap |
| 5880 | value: 70.95016106374474 |
| 5881 | - type: euclidean_f1 |
| 5882 | value: 65.43987900176455 |
| 5883 | - type: euclidean_precision |
| 5884 | value: 62.64478764478765 |
| 5885 | - type: euclidean_recall |
| 5886 | value: 68.49604221635884 |
| 5887 | - type: manhattan_accuracy |
| 5888 | value: 84.93771234428085 |
| 5889 | - type: manhattan_ap |
| 5890 | value: 70.63668388755362 |
| 5891 | - type: manhattan_f1 |
| 5892 | value: 65.23895401262398 |
| 5893 | - type: manhattan_precision |
| 5894 | value: 56.946084218811485 |
| 5895 | - type: manhattan_recall |
| 5896 | value: 76.35883905013192 |
| 5897 | - type: max_accuracy |
| 5898 | value: 85.08076533349228 |
| 5899 | - type: max_ap |
| 5900 | value: 70.95016106374474 |
| 5901 | - type: max_f1 |
| 5902 | value: 65.43987900176455 |
| 5903 | task: |
| 5904 | type: PairClassification |
| 5905 | - dataset: |
| 5906 | config: default |
| 5907 | name: MTEB TwitterURLCorpus |
| 5908 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 5909 | split: test |
| 5910 | type: mteb/twitterurlcorpus-pairclassification |
| 5911 | metrics: |
| 5912 | - type: cos_sim_accuracy |
| 5913 | value: 88.69096130709822 |
| 5914 | - type: cos_sim_ap |
| 5915 | value: 84.82526278228542 |
| 5916 | - type: cos_sim_f1 |
| 5917 | value: 77.65485060585536 |
| 5918 | - type: cos_sim_precision |
| 5919 | value: 75.94582658619167 |
| 5920 | - type: cos_sim_recall |
| 5921 | value: 79.44256236526024 |
| 5922 | - type: dot_accuracy |
| 5923 | value: 80.97954748321496 |
| 5924 | - type: dot_ap |
| 5925 | value: 64.81642914145866 |
| 5926 | - type: dot_f1 |
| 5927 | value: 60.631996987229975 |
| 5928 | - type: dot_precision |
| 5929 | value: 54.5897293631712 |
| 5930 | - type: dot_recall |
| 5931 | value: 68.17831844779796 |
| 5932 | - type: euclidean_accuracy |
| 5933 | value: 88.6987231730508 |
| 5934 | - type: euclidean_ap |
| 5935 | value: 84.80003825477253 |
| 5936 | - type: euclidean_f1 |
| 5937 | value: 77.67194179854496 |
| 5938 | - type: euclidean_precision |
| 5939 | value: 75.7128235122094 |
| 5940 | - type: euclidean_recall |
| 5941 | value: 79.73514012935017 |
| 5942 | - type: manhattan_accuracy |
| 5943 | value: 88.62692591298949 |
| 5944 | - type: manhattan_ap |
| 5945 | value: 84.80451408255276 |
| 5946 | - type: manhattan_f1 |
| 5947 | value: 77.69888949572183 |
| 5948 | - type: manhattan_precision |
| 5949 | value: 73.70311528631622 |
| 5950 | - type: manhattan_recall |
| 5951 | value: 82.15275639051433 |
| 5952 | - type: max_accuracy |
| 5953 | value: 88.6987231730508 |
| 5954 | - type: max_ap |
| 5955 | value: 84.82526278228542 |
| 5956 | - type: max_f1 |
| 5957 | value: 77.69888949572183 |
| 5958 | task: |
| 5959 | type: PairClassification |
| 5960 | - dataset: |
| 5961 | config: ru-en |
| 5962 | name: MTEB BUCC.v2 (ru-en) |
| 5963 | revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677 |
| 5964 | split: test |
| 5965 | type: mteb/bucc-bitext-mining |
| 5966 | metrics: |
| 5967 | - type: accuracy |
| 5968 | value: 95.72566678212678 |
| 5969 | - type: f1 |
| 5970 | value: 94.42443135896548 |
| 5971 | - type: main_score |
| 5972 | value: 94.42443135896548 |
| 5973 | - type: precision |
| 5974 | value: 93.80868260016165 |
| 5975 | - type: recall |
| 5976 | value: 95.72566678212678 |
| 5977 | task: |
| 5978 | type: BitextMining |
| 5979 | - dataset: |
| 5980 | config: rus_Cyrl-rus_Cyrl |
| 5981 | name: MTEB BelebeleRetrieval (rus_Cyrl-rus_Cyrl) |
| 5982 | revision: 75b399394a9803252cfec289d103de462763db7c |
| 5983 | split: test |
| 5984 | type: facebook/belebele |
| 5985 | metrics: |
| 5986 | - type: main_score |
| 5987 | value: 92.23599999999999 |
| 5988 | - type: map_at_1 |
| 5989 | value: 87.111 |
| 5990 | - type: map_at_10 |
| 5991 | value: 90.717 |
| 5992 | - type: map_at_100 |
| 5993 | value: 90.879 |
| 5994 | - type: map_at_1000 |
| 5995 | value: 90.881 |
| 5996 | - type: map_at_20 |
| 5997 | value: 90.849 |
| 5998 | - type: map_at_3 |
| 5999 | value: 90.074 |
| 6000 | - type: map_at_5 |
| 6001 | value: 90.535 |
| 6002 | - type: mrr_at_1 |
| 6003 | value: 87.1111111111111 |
| 6004 | - type: mrr_at_10 |
| 6005 | value: 90.7173721340388 |
| 6006 | - type: mrr_at_100 |
| 6007 | value: 90.87859682638407 |
| 6008 | - type: mrr_at_1000 |
| 6009 | value: 90.88093553612326 |
| 6010 | - type: mrr_at_20 |
| 6011 | value: 90.84863516113515 |
| 6012 | - type: mrr_at_3 |
| 6013 | value: 90.07407407407409 |
| 6014 | - type: mrr_at_5 |
| 6015 | value: 90.53518518518521 |
| 6016 | - type: nauc_map_at_1000_diff1 |
| 6017 | value: 92.37373187280554 |
| 6018 | - type: nauc_map_at_1000_max |
| 6019 | value: 79.90465445423249 |
| 6020 | - type: nauc_map_at_1000_std |
| 6021 | value: -0.6220290556185463 |
| 6022 | - type: nauc_map_at_100_diff1 |
| 6023 | value: 92.37386697345335 |
| 6024 | - type: nauc_map_at_100_max |
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| 6850 | task: |
| 6851 | type: Retrieval |
| 6852 | - dataset: |
| 6853 | config: eng_Latn-rus_Cyrl |
| 6854 | name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl) |
| 6855 | revision: 264a18480c529d9e922483839b4b9758e690b762 |
| 6856 | split: train |
| 6857 | type: davidstap/biblenlp-corpus-mmteb |
| 6858 | metrics: |
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| 6860 | value: 96.875 |
| 6861 | - type: f1 |
| 6862 | value: 95.83333333333333 |
| 6863 | - type: main_score |
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| 6865 | - type: precision |
| 6866 | value: 95.3125 |
| 6867 | - type: recall |
| 6868 | value: 96.875 |
| 6869 | task: |
| 6870 | type: BitextMining |
| 6871 | - dataset: |
| 6872 | config: rus_Cyrl-eng_Latn |
| 6873 | name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn) |
| 6874 | revision: 264a18480c529d9e922483839b4b9758e690b762 |
| 6875 | split: train |
| 6876 | type: davidstap/biblenlp-corpus-mmteb |
| 6877 | metrics: |
| 6878 | - type: accuracy |
| 6879 | value: 88.671875 |
| 6880 | - type: f1 |
| 6881 | value: 85.3515625 |
| 6882 | - type: main_score |
| 6883 | value: 85.3515625 |
| 6884 | - type: precision |
| 6885 | value: 83.85416666666667 |
| 6886 | - type: recall |
| 6887 | value: 88.671875 |
| 6888 | task: |
| 6889 | type: BitextMining |
| 6890 | - dataset: |
| 6891 | config: default |
| 6892 | name: MTEB CEDRClassification (default) |
| 6893 | revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4 |
| 6894 | split: test |
| 6895 | type: ai-forever/cedr-classification |
| 6896 | metrics: |
| 6897 | - type: accuracy |
| 6898 | value: 40.06907545164719 |
| 6899 | - type: f1 |
| 6900 | value: 26.285000550712407 |
| 6901 | - type: lrap |
| 6902 | value: 64.4280021253997 |
| 6903 | - type: main_score |
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| 6905 | task: |
| 6906 | type: MultilabelClassification |
| 6907 | - dataset: |
| 6908 | config: default |
| 6909 | name: MTEB CyrillicTurkicLangClassification (default) |
| 6910 | revision: e42d330f33d65b7b72dfd408883daf1661f06f18 |
| 6911 | split: test |
| 6912 | type: tatiana-merz/cyrillic_turkic_langs |
| 6913 | metrics: |
| 6914 | - type: accuracy |
| 6915 | value: 43.3447265625 |
| 6916 | - type: f1 |
| 6917 | value: 40.08400146827895 |
| 6918 | - type: f1_weighted |
| 6919 | value: 40.08499428040896 |
| 6920 | - type: main_score |
| 6921 | value: 43.3447265625 |
| 6922 | task: |
| 6923 | type: Classification |
| 6924 | - dataset: |
| 6925 | config: ace_Arab-rus_Cyrl |
| 6926 | name: MTEB FloresBitextMining (ace_Arab-rus_Cyrl) |
| 6927 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 6928 | split: devtest |
| 6929 | type: mteb/flores |
| 6930 | metrics: |
| 6931 | - type: accuracy |
| 6932 | value: 6.225296442687747 |
| 6933 | - type: f1 |
| 6934 | value: 5.5190958860075 |
| 6935 | - type: main_score |
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| 6937 | - type: precision |
| 6938 | value: 5.3752643758000005 |
| 6939 | - type: recall |
| 6940 | value: 6.225296442687747 |
| 6941 | task: |
| 6942 | type: BitextMining |
| 6943 | - dataset: |
| 6944 | config: bam_Latn-rus_Cyrl |
| 6945 | name: MTEB FloresBitextMining (bam_Latn-rus_Cyrl) |
| 6946 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 6947 | split: devtest |
| 6948 | type: mteb/flores |
| 6949 | metrics: |
| 6950 | - type: accuracy |
| 6951 | value: 68.37944664031622 |
| 6952 | - type: f1 |
| 6953 | value: 64.54819836666252 |
| 6954 | - type: main_score |
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| 6956 | - type: precision |
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| 6958 | - type: recall |
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| 6960 | task: |
| 6961 | type: BitextMining |
| 6962 | - dataset: |
| 6963 | config: dzo_Tibt-rus_Cyrl |
| 6964 | name: MTEB FloresBitextMining (dzo_Tibt-rus_Cyrl) |
| 6965 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 6966 | split: devtest |
| 6967 | type: mteb/flores |
| 6968 | metrics: |
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| 6970 | value: 0.09881422924901186 |
| 6971 | - type: f1 |
| 6972 | value: 0.00019509225912934226 |
| 6973 | - type: main_score |
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| 6975 | - type: precision |
| 6976 | value: 9.76425190207627e-05 |
| 6977 | - type: recall |
| 6978 | value: 0.09881422924901186 |
| 6979 | task: |
| 6980 | type: BitextMining |
| 6981 | - dataset: |
| 6982 | config: hin_Deva-rus_Cyrl |
| 6983 | name: MTEB FloresBitextMining (hin_Deva-rus_Cyrl) |
| 6984 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 6985 | split: devtest |
| 6986 | type: mteb/flores |
| 6987 | metrics: |
| 6988 | - type: accuracy |
| 6989 | value: 99.60474308300395 |
| 6990 | - type: f1 |
| 6991 | value: 99.47299077733861 |
| 6992 | - type: main_score |
| 6993 | value: 99.47299077733861 |
| 6994 | - type: precision |
| 6995 | value: 99.40711462450594 |
| 6996 | - type: recall |
| 6997 | value: 99.60474308300395 |
| 6998 | task: |
| 6999 | type: BitextMining |
| 7000 | - dataset: |
| 7001 | config: khm_Khmr-rus_Cyrl |
| 7002 | name: MTEB FloresBitextMining (khm_Khmr-rus_Cyrl) |
| 7003 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7004 | split: devtest |
| 7005 | type: mteb/flores |
| 7006 | metrics: |
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| 7008 | value: 88.83399209486166 |
| 7009 | - type: f1 |
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| 7011 | - type: main_score |
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| 7013 | - type: precision |
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| 7015 | - type: recall |
| 7016 | value: 88.83399209486166 |
| 7017 | task: |
| 7018 | type: BitextMining |
| 7019 | - dataset: |
| 7020 | config: mag_Deva-rus_Cyrl |
| 7021 | name: MTEB FloresBitextMining (mag_Deva-rus_Cyrl) |
| 7022 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7023 | split: devtest |
| 7024 | type: mteb/flores |
| 7025 | metrics: |
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| 7027 | value: 98.02371541501977 |
| 7028 | - type: f1 |
| 7029 | value: 97.7239789196311 |
| 7030 | - type: main_score |
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| 7032 | - type: precision |
| 7033 | value: 97.61904761904762 |
| 7034 | - type: recall |
| 7035 | value: 98.02371541501977 |
| 7036 | task: |
| 7037 | type: BitextMining |
| 7038 | - dataset: |
| 7039 | config: pap_Latn-rus_Cyrl |
| 7040 | name: MTEB FloresBitextMining (pap_Latn-rus_Cyrl) |
| 7041 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7042 | split: devtest |
| 7043 | type: mteb/flores |
| 7044 | metrics: |
| 7045 | - type: accuracy |
| 7046 | value: 94.0711462450593 |
| 7047 | - type: f1 |
| 7048 | value: 93.68187806922984 |
| 7049 | - type: main_score |
| 7050 | value: 93.68187806922984 |
| 7051 | - type: precision |
| 7052 | value: 93.58925452707051 |
| 7053 | - type: recall |
| 7054 | value: 94.0711462450593 |
| 7055 | task: |
| 7056 | type: BitextMining |
| 7057 | - dataset: |
| 7058 | config: sot_Latn-rus_Cyrl |
| 7059 | name: MTEB FloresBitextMining (sot_Latn-rus_Cyrl) |
| 7060 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7061 | split: devtest |
| 7062 | type: mteb/flores |
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| 7065 | value: 90.9090909090909 |
| 7066 | - type: f1 |
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| 7068 | - type: main_score |
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| 7070 | - type: precision |
| 7071 | value: 88.51790014083866 |
| 7072 | - type: recall |
| 7073 | value: 90.9090909090909 |
| 7074 | task: |
| 7075 | type: BitextMining |
| 7076 | - dataset: |
| 7077 | config: tur_Latn-rus_Cyrl |
| 7078 | name: MTEB FloresBitextMining (tur_Latn-rus_Cyrl) |
| 7079 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7080 | split: devtest |
| 7081 | type: mteb/flores |
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| 7084 | value: 99.2094861660079 |
| 7085 | - type: f1 |
| 7086 | value: 98.9459815546772 |
| 7087 | - type: main_score |
| 7088 | value: 98.9459815546772 |
| 7089 | - type: precision |
| 7090 | value: 98.81422924901186 |
| 7091 | - type: recall |
| 7092 | value: 99.2094861660079 |
| 7093 | task: |
| 7094 | type: BitextMining |
| 7095 | - dataset: |
| 7096 | config: ace_Latn-rus_Cyrl |
| 7097 | name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl) |
| 7098 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7099 | split: devtest |
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| 7104 | - type: f1 |
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| 7106 | - type: main_score |
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| 7108 | - type: precision |
| 7109 | value: 63.01614067933451 |
| 7110 | - type: recall |
| 7111 | value: 66.10671936758892 |
| 7112 | task: |
| 7113 | type: BitextMining |
| 7114 | - dataset: |
| 7115 | config: ban_Latn-rus_Cyrl |
| 7116 | name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl) |
| 7117 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7118 | split: devtest |
| 7119 | type: mteb/flores |
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| 7122 | value: 79.44664031620553 |
| 7123 | - type: f1 |
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| 7125 | - type: main_score |
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| 7127 | - type: precision |
| 7128 | value: 76.93977931929739 |
| 7129 | - type: recall |
| 7130 | value: 79.44664031620553 |
| 7131 | task: |
| 7132 | type: BitextMining |
| 7133 | - dataset: |
| 7134 | config: ell_Grek-rus_Cyrl |
| 7135 | name: MTEB FloresBitextMining (ell_Grek-rus_Cyrl) |
| 7136 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7137 | split: devtest |
| 7138 | type: mteb/flores |
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| 7141 | value: 99.40711462450594 |
| 7142 | - type: f1 |
| 7143 | value: 99.2094861660079 |
| 7144 | - type: main_score |
| 7145 | value: 99.2094861660079 |
| 7146 | - type: precision |
| 7147 | value: 99.1106719367589 |
| 7148 | - type: recall |
| 7149 | value: 99.40711462450594 |
| 7150 | task: |
| 7151 | type: BitextMining |
| 7152 | - dataset: |
| 7153 | config: hne_Deva-rus_Cyrl |
| 7154 | name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl) |
| 7155 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7156 | split: devtest |
| 7157 | type: mteb/flores |
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| 7161 | - type: f1 |
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| 7163 | - type: main_score |
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| 7167 | - type: recall |
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| 7169 | task: |
| 7170 | type: BitextMining |
| 7171 | - dataset: |
| 7172 | config: kik_Latn-rus_Cyrl |
| 7173 | name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl) |
| 7174 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7175 | split: devtest |
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| 7182 | - type: main_score |
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| 7186 | - type: recall |
| 7187 | value: 76.28458498023716 |
| 7188 | task: |
| 7189 | type: BitextMining |
| 7190 | - dataset: |
| 7191 | config: mai_Deva-rus_Cyrl |
| 7192 | name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl) |
| 7193 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7194 | split: devtest |
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| 7198 | value: 97.72727272727273 |
| 7199 | - type: f1 |
| 7200 | value: 97.37812911725956 |
| 7201 | - type: main_score |
| 7202 | value: 97.37812911725956 |
| 7203 | - type: precision |
| 7204 | value: 97.26002258610953 |
| 7205 | - type: recall |
| 7206 | value: 97.72727272727273 |
| 7207 | task: |
| 7208 | type: BitextMining |
| 7209 | - dataset: |
| 7210 | config: pbt_Arab-rus_Cyrl |
| 7211 | name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl) |
| 7212 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7213 | split: devtest |
| 7214 | type: mteb/flores |
| 7215 | metrics: |
| 7216 | - type: accuracy |
| 7217 | value: 94.0711462450593 |
| 7218 | - type: f1 |
| 7219 | value: 93.34700387331966 |
| 7220 | - type: main_score |
| 7221 | value: 93.34700387331966 |
| 7222 | - type: precision |
| 7223 | value: 93.06920556920556 |
| 7224 | - type: recall |
| 7225 | value: 94.0711462450593 |
| 7226 | task: |
| 7227 | type: BitextMining |
| 7228 | - dataset: |
| 7229 | config: spa_Latn-rus_Cyrl |
| 7230 | name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl) |
| 7231 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7232 | split: devtest |
| 7233 | type: mteb/flores |
| 7234 | metrics: |
| 7235 | - type: accuracy |
| 7236 | value: 99.2094861660079 |
| 7237 | - type: f1 |
| 7238 | value: 98.9459815546772 |
| 7239 | - type: main_score |
| 7240 | value: 98.9459815546772 |
| 7241 | - type: precision |
| 7242 | value: 98.81422924901186 |
| 7243 | - type: recall |
| 7244 | value: 99.2094861660079 |
| 7245 | task: |
| 7246 | type: BitextMining |
| 7247 | - dataset: |
| 7248 | config: twi_Latn-rus_Cyrl |
| 7249 | name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl) |
| 7250 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7251 | split: devtest |
| 7252 | type: mteb/flores |
| 7253 | metrics: |
| 7254 | - type: accuracy |
| 7255 | value: 80.73122529644269 |
| 7256 | - type: f1 |
| 7257 | value: 77.77434363246721 |
| 7258 | - type: main_score |
| 7259 | value: 77.77434363246721 |
| 7260 | - type: precision |
| 7261 | value: 76.54444287596462 |
| 7262 | - type: recall |
| 7263 | value: 80.73122529644269 |
| 7264 | task: |
| 7265 | type: BitextMining |
| 7266 | - dataset: |
| 7267 | config: acm_Arab-rus_Cyrl |
| 7268 | name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl) |
| 7269 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7270 | split: devtest |
| 7271 | type: mteb/flores |
| 7272 | metrics: |
| 7273 | - type: accuracy |
| 7274 | value: 94.56521739130434 |
| 7275 | - type: f1 |
| 7276 | value: 92.92490118577075 |
| 7277 | - type: main_score |
| 7278 | value: 92.92490118577075 |
| 7279 | - type: precision |
| 7280 | value: 92.16897233201581 |
| 7281 | - type: recall |
| 7282 | value: 94.56521739130434 |
| 7283 | task: |
| 7284 | type: BitextMining |
| 7285 | - dataset: |
| 7286 | config: bel_Cyrl-rus_Cyrl |
| 7287 | name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl) |
| 7288 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7289 | split: devtest |
| 7290 | type: mteb/flores |
| 7291 | metrics: |
| 7292 | - type: accuracy |
| 7293 | value: 99.2094861660079 |
| 7294 | - type: f1 |
| 7295 | value: 98.98550724637681 |
| 7296 | - type: main_score |
| 7297 | value: 98.98550724637681 |
| 7298 | - type: precision |
| 7299 | value: 98.88833992094862 |
| 7300 | - type: recall |
| 7301 | value: 99.2094861660079 |
| 7302 | task: |
| 7303 | type: BitextMining |
| 7304 | - dataset: |
| 7305 | config: eng_Latn-rus_Cyrl |
| 7306 | name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl) |
| 7307 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7308 | split: devtest |
| 7309 | type: mteb/flores |
| 7310 | metrics: |
| 7311 | - type: accuracy |
| 7312 | value: 99.60474308300395 |
| 7313 | - type: f1 |
| 7314 | value: 99.4729907773386 |
| 7315 | - type: main_score |
| 7316 | value: 99.4729907773386 |
| 7317 | - type: precision |
| 7318 | value: 99.40711462450594 |
| 7319 | - type: recall |
| 7320 | value: 99.60474308300395 |
| 7321 | task: |
| 7322 | type: BitextMining |
| 7323 | - dataset: |
| 7324 | config: hrv_Latn-rus_Cyrl |
| 7325 | name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl) |
| 7326 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7327 | split: devtest |
| 7328 | type: mteb/flores |
| 7329 | metrics: |
| 7330 | - type: accuracy |
| 7331 | value: 99.2094861660079 |
| 7332 | - type: f1 |
| 7333 | value: 99.05138339920948 |
| 7334 | - type: main_score |
| 7335 | value: 99.05138339920948 |
| 7336 | - type: precision |
| 7337 | value: 99.00691699604744 |
| 7338 | - type: recall |
| 7339 | value: 99.2094861660079 |
| 7340 | task: |
| 7341 | type: BitextMining |
| 7342 | - dataset: |
| 7343 | config: kin_Latn-rus_Cyrl |
| 7344 | name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl) |
| 7345 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7346 | split: devtest |
| 7347 | type: mteb/flores |
| 7348 | metrics: |
| 7349 | - type: accuracy |
| 7350 | value: 88.2411067193676 |
| 7351 | - type: f1 |
| 7352 | value: 86.5485246227658 |
| 7353 | - type: main_score |
| 7354 | value: 86.5485246227658 |
| 7355 | - type: precision |
| 7356 | value: 85.90652101521667 |
| 7357 | - type: recall |
| 7358 | value: 88.2411067193676 |
| 7359 | task: |
| 7360 | type: BitextMining |
| 7361 | - dataset: |
| 7362 | config: mal_Mlym-rus_Cyrl |
| 7363 | name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl) |
| 7364 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7365 | split: devtest |
| 7366 | type: mteb/flores |
| 7367 | metrics: |
| 7368 | - type: accuracy |
| 7369 | value: 98.51778656126481 |
| 7370 | - type: f1 |
| 7371 | value: 98.07971014492753 |
| 7372 | - type: main_score |
| 7373 | value: 98.07971014492753 |
| 7374 | - type: precision |
| 7375 | value: 97.88372859025033 |
| 7376 | - type: recall |
| 7377 | value: 98.51778656126481 |
| 7378 | task: |
| 7379 | type: BitextMining |
| 7380 | - dataset: |
| 7381 | config: pes_Arab-rus_Cyrl |
| 7382 | name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl) |
| 7383 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7384 | split: devtest |
| 7385 | type: mteb/flores |
| 7386 | metrics: |
| 7387 | - type: accuracy |
| 7388 | value: 98.51778656126481 |
| 7389 | - type: f1 |
| 7390 | value: 98.0566534914361 |
| 7391 | - type: main_score |
| 7392 | value: 98.0566534914361 |
| 7393 | - type: precision |
| 7394 | value: 97.82608695652173 |
| 7395 | - type: recall |
| 7396 | value: 98.51778656126481 |
| 7397 | task: |
| 7398 | type: BitextMining |
| 7399 | - dataset: |
| 7400 | config: srd_Latn-rus_Cyrl |
| 7401 | name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl) |
| 7402 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7403 | split: devtest |
| 7404 | type: mteb/flores |
| 7405 | metrics: |
| 7406 | - type: accuracy |
| 7407 | value: 82.6086956521739 |
| 7408 | - type: f1 |
| 7409 | value: 80.9173470979821 |
| 7410 | - type: main_score |
| 7411 | value: 80.9173470979821 |
| 7412 | - type: precision |
| 7413 | value: 80.24468672882627 |
| 7414 | - type: recall |
| 7415 | value: 82.6086956521739 |
| 7416 | task: |
| 7417 | type: BitextMining |
| 7418 | - dataset: |
| 7419 | config: tzm_Tfng-rus_Cyrl |
| 7420 | name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl) |
| 7421 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7422 | split: devtest |
| 7423 | type: mteb/flores |
| 7424 | metrics: |
| 7425 | - type: accuracy |
| 7426 | value: 7.41106719367589 |
| 7427 | - type: f1 |
| 7428 | value: 6.363562740945329 |
| 7429 | - type: main_score |
| 7430 | value: 6.363562740945329 |
| 7431 | - type: precision |
| 7432 | value: 6.090373175353411 |
| 7433 | - type: recall |
| 7434 | value: 7.41106719367589 |
| 7435 | task: |
| 7436 | type: BitextMining |
| 7437 | - dataset: |
| 7438 | config: acq_Arab-rus_Cyrl |
| 7439 | name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl) |
| 7440 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7441 | split: devtest |
| 7442 | type: mteb/flores |
| 7443 | metrics: |
| 7444 | - type: accuracy |
| 7445 | value: 95.25691699604744 |
| 7446 | - type: f1 |
| 7447 | value: 93.81422924901187 |
| 7448 | - type: main_score |
| 7449 | value: 93.81422924901187 |
| 7450 | - type: precision |
| 7451 | value: 93.14064558629775 |
| 7452 | - type: recall |
| 7453 | value: 95.25691699604744 |
| 7454 | task: |
| 7455 | type: BitextMining |
| 7456 | - dataset: |
| 7457 | config: bem_Latn-rus_Cyrl |
| 7458 | name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl) |
| 7459 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7460 | split: devtest |
| 7461 | type: mteb/flores |
| 7462 | metrics: |
| 7463 | - type: accuracy |
| 7464 | value: 68.08300395256917 |
| 7465 | - type: f1 |
| 7466 | value: 65.01368772860867 |
| 7467 | - type: main_score |
| 7468 | value: 65.01368772860867 |
| 7469 | - type: precision |
| 7470 | value: 63.91052337510628 |
| 7471 | - type: recall |
| 7472 | value: 68.08300395256917 |
| 7473 | task: |
| 7474 | type: BitextMining |
| 7475 | - dataset: |
| 7476 | config: epo_Latn-rus_Cyrl |
| 7477 | name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl) |
| 7478 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7479 | split: devtest |
| 7480 | type: mteb/flores |
| 7481 | metrics: |
| 7482 | - type: accuracy |
| 7483 | value: 98.41897233201581 |
| 7484 | - type: f1 |
| 7485 | value: 98.17193675889328 |
| 7486 | - type: main_score |
| 7487 | value: 98.17193675889328 |
| 7488 | - type: precision |
| 7489 | value: 98.08210564139418 |
| 7490 | - type: recall |
| 7491 | value: 98.41897233201581 |
| 7492 | task: |
| 7493 | type: BitextMining |
| 7494 | - dataset: |
| 7495 | config: hun_Latn-rus_Cyrl |
| 7496 | name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl) |
| 7497 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7498 | split: devtest |
| 7499 | type: mteb/flores |
| 7500 | metrics: |
| 7501 | - type: accuracy |
| 7502 | value: 99.30830039525692 |
| 7503 | - type: f1 |
| 7504 | value: 99.1106719367589 |
| 7505 | - type: main_score |
| 7506 | value: 99.1106719367589 |
| 7507 | - type: precision |
| 7508 | value: 99.01185770750988 |
| 7509 | - type: recall |
| 7510 | value: 99.30830039525692 |
| 7511 | task: |
| 7512 | type: BitextMining |
| 7513 | - dataset: |
| 7514 | config: kir_Cyrl-rus_Cyrl |
| 7515 | name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl) |
| 7516 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7517 | split: devtest |
| 7518 | type: mteb/flores |
| 7519 | metrics: |
| 7520 | - type: accuracy |
| 7521 | value: 97.5296442687747 |
| 7522 | - type: f1 |
| 7523 | value: 97.07549806364035 |
| 7524 | - type: main_score |
| 7525 | value: 97.07549806364035 |
| 7526 | - type: precision |
| 7527 | value: 96.90958498023716 |
| 7528 | - type: recall |
| 7529 | value: 97.5296442687747 |
| 7530 | task: |
| 7531 | type: BitextMining |
| 7532 | - dataset: |
| 7533 | config: mar_Deva-rus_Cyrl |
| 7534 | name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl) |
| 7535 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7536 | split: devtest |
| 7537 | type: mteb/flores |
| 7538 | metrics: |
| 7539 | - type: accuracy |
| 7540 | value: 97.82608695652173 |
| 7541 | - type: f1 |
| 7542 | value: 97.44400527009222 |
| 7543 | - type: main_score |
| 7544 | value: 97.44400527009222 |
| 7545 | - type: precision |
| 7546 | value: 97.28966685488425 |
| 7547 | - type: recall |
| 7548 | value: 97.82608695652173 |
| 7549 | task: |
| 7550 | type: BitextMining |
| 7551 | - dataset: |
| 7552 | config: plt_Latn-rus_Cyrl |
| 7553 | name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl) |
| 7554 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7555 | split: devtest |
| 7556 | type: mteb/flores |
| 7557 | metrics: |
| 7558 | - type: accuracy |
| 7559 | value: 79.9407114624506 |
| 7560 | - type: f1 |
| 7561 | value: 78.3154177760691 |
| 7562 | - type: main_score |
| 7563 | value: 78.3154177760691 |
| 7564 | - type: precision |
| 7565 | value: 77.69877344877344 |
| 7566 | - type: recall |
| 7567 | value: 79.9407114624506 |
| 7568 | task: |
| 7569 | type: BitextMining |
| 7570 | - dataset: |
| 7571 | config: srp_Cyrl-rus_Cyrl |
| 7572 | name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl) |
| 7573 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7574 | split: devtest |
| 7575 | type: mteb/flores |
| 7576 | metrics: |
| 7577 | - type: accuracy |
| 7578 | value: 99.70355731225297 |
| 7579 | - type: f1 |
| 7580 | value: 99.60474308300395 |
| 7581 | - type: main_score |
| 7582 | value: 99.60474308300395 |
| 7583 | - type: precision |
| 7584 | value: 99.55533596837944 |
| 7585 | - type: recall |
| 7586 | value: 99.70355731225297 |
| 7587 | task: |
| 7588 | type: BitextMining |
| 7589 | - dataset: |
| 7590 | config: uig_Arab-rus_Cyrl |
| 7591 | name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl) |
| 7592 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7593 | split: devtest |
| 7594 | type: mteb/flores |
| 7595 | metrics: |
| 7596 | - type: accuracy |
| 7597 | value: 83.20158102766798 |
| 7598 | - type: f1 |
| 7599 | value: 81.44381923034585 |
| 7600 | - type: main_score |
| 7601 | value: 81.44381923034585 |
| 7602 | - type: precision |
| 7603 | value: 80.78813411582477 |
| 7604 | - type: recall |
| 7605 | value: 83.20158102766798 |
| 7606 | task: |
| 7607 | type: BitextMining |
| 7608 | - dataset: |
| 7609 | config: aeb_Arab-rus_Cyrl |
| 7610 | name: MTEB FloresBitextMining (aeb_Arab-rus_Cyrl) |
| 7611 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7612 | split: devtest |
| 7613 | type: mteb/flores |
| 7614 | metrics: |
| 7615 | - type: accuracy |
| 7616 | value: 91.20553359683794 |
| 7617 | - type: f1 |
| 7618 | value: 88.75352907961603 |
| 7619 | - type: main_score |
| 7620 | value: 88.75352907961603 |
| 7621 | - type: precision |
| 7622 | value: 87.64328063241106 |
| 7623 | - type: recall |
| 7624 | value: 91.20553359683794 |
| 7625 | task: |
| 7626 | type: BitextMining |
| 7627 | - dataset: |
| 7628 | config: ben_Beng-rus_Cyrl |
| 7629 | name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl) |
| 7630 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7631 | split: devtest |
| 7632 | type: mteb/flores |
| 7633 | metrics: |
| 7634 | - type: accuracy |
| 7635 | value: 98.91304347826086 |
| 7636 | - type: f1 |
| 7637 | value: 98.60671936758894 |
| 7638 | - type: main_score |
| 7639 | value: 98.60671936758894 |
| 7640 | - type: precision |
| 7641 | value: 98.4766139657444 |
| 7642 | - type: recall |
| 7643 | value: 98.91304347826086 |
| 7644 | task: |
| 7645 | type: BitextMining |
| 7646 | - dataset: |
| 7647 | config: est_Latn-rus_Cyrl |
| 7648 | name: MTEB FloresBitextMining (est_Latn-rus_Cyrl) |
| 7649 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7650 | split: devtest |
| 7651 | type: mteb/flores |
| 7652 | metrics: |
| 7653 | - type: accuracy |
| 7654 | value: 96.24505928853755 |
| 7655 | - type: f1 |
| 7656 | value: 95.27417027417027 |
| 7657 | - type: main_score |
| 7658 | value: 95.27417027417027 |
| 7659 | - type: precision |
| 7660 | value: 94.84107378129117 |
| 7661 | - type: recall |
| 7662 | value: 96.24505928853755 |
| 7663 | task: |
| 7664 | type: BitextMining |
| 7665 | - dataset: |
| 7666 | config: hye_Armn-rus_Cyrl |
| 7667 | name: MTEB FloresBitextMining (hye_Armn-rus_Cyrl) |
| 7668 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7669 | split: devtest |
| 7670 | type: mteb/flores |
| 7671 | metrics: |
| 7672 | - type: accuracy |
| 7673 | value: 98.02371541501977 |
| 7674 | - type: f1 |
| 7675 | value: 97.67786561264822 |
| 7676 | - type: main_score |
| 7677 | value: 97.67786561264822 |
| 7678 | - type: precision |
| 7679 | value: 97.55839022637441 |
| 7680 | - type: recall |
| 7681 | value: 98.02371541501977 |
| 7682 | task: |
| 7683 | type: BitextMining |
| 7684 | - dataset: |
| 7685 | config: kmb_Latn-rus_Cyrl |
| 7686 | name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl) |
| 7687 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7688 | split: devtest |
| 7689 | type: mteb/flores |
| 7690 | metrics: |
| 7691 | - type: accuracy |
| 7692 | value: 46.047430830039524 |
| 7693 | - type: f1 |
| 7694 | value: 42.94464804804471 |
| 7695 | - type: main_score |
| 7696 | value: 42.94464804804471 |
| 7697 | - type: precision |
| 7698 | value: 41.9851895607238 |
| 7699 | - type: recall |
| 7700 | value: 46.047430830039524 |
| 7701 | task: |
| 7702 | type: BitextMining |
| 7703 | - dataset: |
| 7704 | config: min_Arab-rus_Cyrl |
| 7705 | name: MTEB FloresBitextMining (min_Arab-rus_Cyrl) |
| 7706 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7707 | split: devtest |
| 7708 | type: mteb/flores |
| 7709 | metrics: |
| 7710 | - type: accuracy |
| 7711 | value: 3.9525691699604746 |
| 7712 | - type: f1 |
| 7713 | value: 3.402665192725756 |
| 7714 | - type: main_score |
| 7715 | value: 3.402665192725756 |
| 7716 | - type: precision |
| 7717 | value: 3.303787557740127 |
| 7718 | - type: recall |
| 7719 | value: 3.9525691699604746 |
| 7720 | task: |
| 7721 | type: BitextMining |
| 7722 | - dataset: |
| 7723 | config: pol_Latn-rus_Cyrl |
| 7724 | name: MTEB FloresBitextMining (pol_Latn-rus_Cyrl) |
| 7725 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7726 | split: devtest |
| 7727 | type: mteb/flores |
| 7728 | metrics: |
| 7729 | - type: accuracy |
| 7730 | value: 99.60474308300395 |
| 7731 | - type: f1 |
| 7732 | value: 99.4729907773386 |
| 7733 | - type: main_score |
| 7734 | value: 99.4729907773386 |
| 7735 | - type: precision |
| 7736 | value: 99.40711462450594 |
| 7737 | - type: recall |
| 7738 | value: 99.60474308300395 |
| 7739 | task: |
| 7740 | type: BitextMining |
| 7741 | - dataset: |
| 7742 | config: ssw_Latn-rus_Cyrl |
| 7743 | name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl) |
| 7744 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7745 | split: devtest |
| 7746 | type: mteb/flores |
| 7747 | metrics: |
| 7748 | - type: accuracy |
| 7749 | value: 73.22134387351778 |
| 7750 | - type: f1 |
| 7751 | value: 70.43086049508975 |
| 7752 | - type: main_score |
| 7753 | value: 70.43086049508975 |
| 7754 | - type: precision |
| 7755 | value: 69.35312022355656 |
| 7756 | - type: recall |
| 7757 | value: 73.22134387351778 |
| 7758 | task: |
| 7759 | type: BitextMining |
| 7760 | - dataset: |
| 7761 | config: ukr_Cyrl-rus_Cyrl |
| 7762 | name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl) |
| 7763 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7764 | split: devtest |
| 7765 | type: mteb/flores |
| 7766 | metrics: |
| 7767 | - type: accuracy |
| 7768 | value: 99.90118577075098 |
| 7769 | - type: f1 |
| 7770 | value: 99.86824769433464 |
| 7771 | - type: main_score |
| 7772 | value: 99.86824769433464 |
| 7773 | - type: precision |
| 7774 | value: 99.85177865612648 |
| 7775 | - type: recall |
| 7776 | value: 99.90118577075098 |
| 7777 | task: |
| 7778 | type: BitextMining |
| 7779 | - dataset: |
| 7780 | config: afr_Latn-rus_Cyrl |
| 7781 | name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl) |
| 7782 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7783 | split: devtest |
| 7784 | type: mteb/flores |
| 7785 | metrics: |
| 7786 | - type: accuracy |
| 7787 | value: 99.2094861660079 |
| 7788 | - type: f1 |
| 7789 | value: 98.9459815546772 |
| 7790 | - type: main_score |
| 7791 | value: 98.9459815546772 |
| 7792 | - type: precision |
| 7793 | value: 98.81422924901186 |
| 7794 | - type: recall |
| 7795 | value: 99.2094861660079 |
| 7796 | task: |
| 7797 | type: BitextMining |
| 7798 | - dataset: |
| 7799 | config: bho_Deva-rus_Cyrl |
| 7800 | name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl) |
| 7801 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7802 | split: devtest |
| 7803 | type: mteb/flores |
| 7804 | metrics: |
| 7805 | - type: accuracy |
| 7806 | value: 94.0711462450593 |
| 7807 | - type: f1 |
| 7808 | value: 93.12182382834557 |
| 7809 | - type: main_score |
| 7810 | value: 93.12182382834557 |
| 7811 | - type: precision |
| 7812 | value: 92.7523453232338 |
| 7813 | - type: recall |
| 7814 | value: 94.0711462450593 |
| 7815 | task: |
| 7816 | type: BitextMining |
| 7817 | - dataset: |
| 7818 | config: eus_Latn-rus_Cyrl |
| 7819 | name: MTEB FloresBitextMining (eus_Latn-rus_Cyrl) |
| 7820 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7821 | split: devtest |
| 7822 | type: mteb/flores |
| 7823 | metrics: |
| 7824 | - type: accuracy |
| 7825 | value: 92.19367588932806 |
| 7826 | - type: f1 |
| 7827 | value: 91.23604975587072 |
| 7828 | - type: main_score |
| 7829 | value: 91.23604975587072 |
| 7830 | - type: precision |
| 7831 | value: 90.86697443588663 |
| 7832 | - type: recall |
| 7833 | value: 92.19367588932806 |
| 7834 | task: |
| 7835 | type: BitextMining |
| 7836 | - dataset: |
| 7837 | config: ibo_Latn-rus_Cyrl |
| 7838 | name: MTEB FloresBitextMining (ibo_Latn-rus_Cyrl) |
| 7839 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7840 | split: devtest |
| 7841 | type: mteb/flores |
| 7842 | metrics: |
| 7843 | - type: accuracy |
| 7844 | value: 82.21343873517787 |
| 7845 | - type: f1 |
| 7846 | value: 80.17901604858126 |
| 7847 | - type: main_score |
| 7848 | value: 80.17901604858126 |
| 7849 | - type: precision |
| 7850 | value: 79.3792284780028 |
| 7851 | - type: recall |
| 7852 | value: 82.21343873517787 |
| 7853 | task: |
| 7854 | type: BitextMining |
| 7855 | - dataset: |
| 7856 | config: kmr_Latn-rus_Cyrl |
| 7857 | name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl) |
| 7858 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7859 | split: devtest |
| 7860 | type: mteb/flores |
| 7861 | metrics: |
| 7862 | - type: accuracy |
| 7863 | value: 68.67588932806325 |
| 7864 | - type: f1 |
| 7865 | value: 66.72311714750278 |
| 7866 | - type: main_score |
| 7867 | value: 66.72311714750278 |
| 7868 | - type: precision |
| 7869 | value: 66.00178401554004 |
| 7870 | - type: recall |
| 7871 | value: 68.67588932806325 |
| 7872 | task: |
| 7873 | type: BitextMining |
| 7874 | - dataset: |
| 7875 | config: min_Latn-rus_Cyrl |
| 7876 | name: MTEB FloresBitextMining (min_Latn-rus_Cyrl) |
| 7877 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7878 | split: devtest |
| 7879 | type: mteb/flores |
| 7880 | metrics: |
| 7881 | - type: accuracy |
| 7882 | value: 78.65612648221344 |
| 7883 | - type: f1 |
| 7884 | value: 76.26592719972166 |
| 7885 | - type: main_score |
| 7886 | value: 76.26592719972166 |
| 7887 | - type: precision |
| 7888 | value: 75.39980459997484 |
| 7889 | - type: recall |
| 7890 | value: 78.65612648221344 |
| 7891 | task: |
| 7892 | type: BitextMining |
| 7893 | - dataset: |
| 7894 | config: por_Latn-rus_Cyrl |
| 7895 | name: MTEB FloresBitextMining (por_Latn-rus_Cyrl) |
| 7896 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7897 | split: devtest |
| 7898 | type: mteb/flores |
| 7899 | metrics: |
| 7900 | - type: accuracy |
| 7901 | value: 96.83794466403161 |
| 7902 | - type: f1 |
| 7903 | value: 95.9669678147939 |
| 7904 | - type: main_score |
| 7905 | value: 95.9669678147939 |
| 7906 | - type: precision |
| 7907 | value: 95.59453227931488 |
| 7908 | - type: recall |
| 7909 | value: 96.83794466403161 |
| 7910 | task: |
| 7911 | type: BitextMining |
| 7912 | - dataset: |
| 7913 | config: sun_Latn-rus_Cyrl |
| 7914 | name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl) |
| 7915 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7916 | split: devtest |
| 7917 | type: mteb/flores |
| 7918 | metrics: |
| 7919 | - type: accuracy |
| 7920 | value: 92.4901185770751 |
| 7921 | - type: f1 |
| 7922 | value: 91.66553983773662 |
| 7923 | - type: main_score |
| 7924 | value: 91.66553983773662 |
| 7925 | - type: precision |
| 7926 | value: 91.34530928009188 |
| 7927 | - type: recall |
| 7928 | value: 92.4901185770751 |
| 7929 | task: |
| 7930 | type: BitextMining |
| 7931 | - dataset: |
| 7932 | config: umb_Latn-rus_Cyrl |
| 7933 | name: MTEB FloresBitextMining (umb_Latn-rus_Cyrl) |
| 7934 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7935 | split: devtest |
| 7936 | type: mteb/flores |
| 7937 | metrics: |
| 7938 | - type: accuracy |
| 7939 | value: 41.00790513833992 |
| 7940 | - type: f1 |
| 7941 | value: 38.21319326004483 |
| 7942 | - type: main_score |
| 7943 | value: 38.21319326004483 |
| 7944 | - type: precision |
| 7945 | value: 37.200655467675546 |
| 7946 | - type: recall |
| 7947 | value: 41.00790513833992 |
| 7948 | task: |
| 7949 | type: BitextMining |
| 7950 | - dataset: |
| 7951 | config: ajp_Arab-rus_Cyrl |
| 7952 | name: MTEB FloresBitextMining (ajp_Arab-rus_Cyrl) |
| 7953 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7954 | split: devtest |
| 7955 | type: mteb/flores |
| 7956 | metrics: |
| 7957 | - type: accuracy |
| 7958 | value: 95.35573122529645 |
| 7959 | - type: f1 |
| 7960 | value: 93.97233201581028 |
| 7961 | - type: main_score |
| 7962 | value: 93.97233201581028 |
| 7963 | - type: precision |
| 7964 | value: 93.33333333333333 |
| 7965 | - type: recall |
| 7966 | value: 95.35573122529645 |
| 7967 | task: |
| 7968 | type: BitextMining |
| 7969 | - dataset: |
| 7970 | config: bjn_Arab-rus_Cyrl |
| 7971 | name: MTEB FloresBitextMining (bjn_Arab-rus_Cyrl) |
| 7972 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7973 | split: devtest |
| 7974 | type: mteb/flores |
| 7975 | metrics: |
| 7976 | - type: accuracy |
| 7977 | value: 3.6561264822134385 |
| 7978 | - type: f1 |
| 7979 | value: 3.1071978056336484 |
| 7980 | - type: main_score |
| 7981 | value: 3.1071978056336484 |
| 7982 | - type: precision |
| 7983 | value: 3.0039741229718215 |
| 7984 | - type: recall |
| 7985 | value: 3.6561264822134385 |
| 7986 | task: |
| 7987 | type: BitextMining |
| 7988 | - dataset: |
| 7989 | config: ewe_Latn-rus_Cyrl |
| 7990 | name: MTEB FloresBitextMining (ewe_Latn-rus_Cyrl) |
| 7991 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 7992 | split: devtest |
| 7993 | type: mteb/flores |
| 7994 | metrics: |
| 7995 | - type: accuracy |
| 7996 | value: 62.845849802371546 |
| 7997 | - type: f1 |
| 7998 | value: 59.82201175670472 |
| 7999 | - type: main_score |
| 8000 | value: 59.82201175670472 |
| 8001 | - type: precision |
| 8002 | value: 58.72629236362003 |
| 8003 | - type: recall |
| 8004 | value: 62.845849802371546 |
| 8005 | task: |
| 8006 | type: BitextMining |
| 8007 | - dataset: |
| 8008 | config: ilo_Latn-rus_Cyrl |
| 8009 | name: MTEB FloresBitextMining (ilo_Latn-rus_Cyrl) |
| 8010 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8011 | split: devtest |
| 8012 | type: mteb/flores |
| 8013 | metrics: |
| 8014 | - type: accuracy |
| 8015 | value: 83.10276679841897 |
| 8016 | - type: f1 |
| 8017 | value: 80.75065288987582 |
| 8018 | - type: main_score |
| 8019 | value: 80.75065288987582 |
| 8020 | - type: precision |
| 8021 | value: 79.80726451662179 |
| 8022 | - type: recall |
| 8023 | value: 83.10276679841897 |
| 8024 | task: |
| 8025 | type: BitextMining |
| 8026 | - dataset: |
| 8027 | config: knc_Arab-rus_Cyrl |
| 8028 | name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl) |
| 8029 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8030 | split: devtest |
| 8031 | type: mteb/flores |
| 8032 | metrics: |
| 8033 | - type: accuracy |
| 8034 | value: 10.079051383399209 |
| 8035 | - type: f1 |
| 8036 | value: 8.759282456080921 |
| 8037 | - type: main_score |
| 8038 | value: 8.759282456080921 |
| 8039 | - type: precision |
| 8040 | value: 8.474735138956142 |
| 8041 | - type: recall |
| 8042 | value: 10.079051383399209 |
| 8043 | task: |
| 8044 | type: BitextMining |
| 8045 | - dataset: |
| 8046 | config: mkd_Cyrl-rus_Cyrl |
| 8047 | name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl) |
| 8048 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8049 | split: devtest |
| 8050 | type: mteb/flores |
| 8051 | metrics: |
| 8052 | - type: accuracy |
| 8053 | value: 98.91304347826086 |
| 8054 | - type: f1 |
| 8055 | value: 98.55072463768116 |
| 8056 | - type: main_score |
| 8057 | value: 98.55072463768116 |
| 8058 | - type: precision |
| 8059 | value: 98.36956521739131 |
| 8060 | - type: recall |
| 8061 | value: 98.91304347826086 |
| 8062 | task: |
| 8063 | type: BitextMining |
| 8064 | - dataset: |
| 8065 | config: prs_Arab-rus_Cyrl |
| 8066 | name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl) |
| 8067 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8068 | split: devtest |
| 8069 | type: mteb/flores |
| 8070 | metrics: |
| 8071 | - type: accuracy |
| 8072 | value: 99.01185770750988 |
| 8073 | - type: f1 |
| 8074 | value: 98.68247694334651 |
| 8075 | - type: main_score |
| 8076 | value: 98.68247694334651 |
| 8077 | - type: precision |
| 8078 | value: 98.51778656126481 |
| 8079 | - type: recall |
| 8080 | value: 99.01185770750988 |
| 8081 | task: |
| 8082 | type: BitextMining |
| 8083 | - dataset: |
| 8084 | config: swe_Latn-rus_Cyrl |
| 8085 | name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl) |
| 8086 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8087 | split: devtest |
| 8088 | type: mteb/flores |
| 8089 | metrics: |
| 8090 | - type: accuracy |
| 8091 | value: 99.40711462450594 |
| 8092 | - type: f1 |
| 8093 | value: 99.22595520421606 |
| 8094 | - type: main_score |
| 8095 | value: 99.22595520421606 |
| 8096 | - type: precision |
| 8097 | value: 99.14361001317523 |
| 8098 | - type: recall |
| 8099 | value: 99.40711462450594 |
| 8100 | task: |
| 8101 | type: BitextMining |
| 8102 | - dataset: |
| 8103 | config: urd_Arab-rus_Cyrl |
| 8104 | name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl) |
| 8105 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8106 | split: devtest |
| 8107 | type: mteb/flores |
| 8108 | metrics: |
| 8109 | - type: accuracy |
| 8110 | value: 97.82608695652173 |
| 8111 | - type: f1 |
| 8112 | value: 97.25625823451911 |
| 8113 | - type: main_score |
| 8114 | value: 97.25625823451911 |
| 8115 | - type: precision |
| 8116 | value: 97.03063241106719 |
| 8117 | - type: recall |
| 8118 | value: 97.82608695652173 |
| 8119 | task: |
| 8120 | type: BitextMining |
| 8121 | - dataset: |
| 8122 | config: aka_Latn-rus_Cyrl |
| 8123 | name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl) |
| 8124 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8125 | split: devtest |
| 8126 | type: mteb/flores |
| 8127 | metrics: |
| 8128 | - type: accuracy |
| 8129 | value: 81.22529644268775 |
| 8130 | - type: f1 |
| 8131 | value: 77.94307687941227 |
| 8132 | - type: main_score |
| 8133 | value: 77.94307687941227 |
| 8134 | - type: precision |
| 8135 | value: 76.58782793293665 |
| 8136 | - type: recall |
| 8137 | value: 81.22529644268775 |
| 8138 | task: |
| 8139 | type: BitextMining |
| 8140 | - dataset: |
| 8141 | config: bjn_Latn-rus_Cyrl |
| 8142 | name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl) |
| 8143 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8144 | split: devtest |
| 8145 | type: mteb/flores |
| 8146 | metrics: |
| 8147 | - type: accuracy |
| 8148 | value: 85.27667984189723 |
| 8149 | - type: f1 |
| 8150 | value: 83.6869192829922 |
| 8151 | - type: main_score |
| 8152 | value: 83.6869192829922 |
| 8153 | - type: precision |
| 8154 | value: 83.08670670691656 |
| 8155 | - type: recall |
| 8156 | value: 85.27667984189723 |
| 8157 | task: |
| 8158 | type: BitextMining |
| 8159 | - dataset: |
| 8160 | config: fao_Latn-rus_Cyrl |
| 8161 | name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl) |
| 8162 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8163 | split: devtest |
| 8164 | type: mteb/flores |
| 8165 | metrics: |
| 8166 | - type: accuracy |
| 8167 | value: 80.9288537549407 |
| 8168 | - type: f1 |
| 8169 | value: 79.29806087454745 |
| 8170 | - type: main_score |
| 8171 | value: 79.29806087454745 |
| 8172 | - type: precision |
| 8173 | value: 78.71445871526987 |
| 8174 | - type: recall |
| 8175 | value: 80.9288537549407 |
| 8176 | task: |
| 8177 | type: BitextMining |
| 8178 | - dataset: |
| 8179 | config: ind_Latn-rus_Cyrl |
| 8180 | name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl) |
| 8181 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8182 | split: devtest |
| 8183 | type: mteb/flores |
| 8184 | metrics: |
| 8185 | - type: accuracy |
| 8186 | value: 98.12252964426878 |
| 8187 | - type: f1 |
| 8188 | value: 97.5296442687747 |
| 8189 | - type: main_score |
| 8190 | value: 97.5296442687747 |
| 8191 | - type: precision |
| 8192 | value: 97.23320158102767 |
| 8193 | - type: recall |
| 8194 | value: 98.12252964426878 |
| 8195 | task: |
| 8196 | type: BitextMining |
| 8197 | - dataset: |
| 8198 | config: knc_Latn-rus_Cyrl |
| 8199 | name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl) |
| 8200 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8201 | split: devtest |
| 8202 | type: mteb/flores |
| 8203 | metrics: |
| 8204 | - type: accuracy |
| 8205 | value: 33.49802371541502 |
| 8206 | - type: f1 |
| 8207 | value: 32.02378215033989 |
| 8208 | - type: main_score |
| 8209 | value: 32.02378215033989 |
| 8210 | - type: precision |
| 8211 | value: 31.511356103747406 |
| 8212 | - type: recall |
| 8213 | value: 33.49802371541502 |
| 8214 | task: |
| 8215 | type: BitextMining |
| 8216 | - dataset: |
| 8217 | config: mlt_Latn-rus_Cyrl |
| 8218 | name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl) |
| 8219 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8220 | split: devtest |
| 8221 | type: mteb/flores |
| 8222 | metrics: |
| 8223 | - type: accuracy |
| 8224 | value: 91.40316205533597 |
| 8225 | - type: f1 |
| 8226 | value: 90.35317684386006 |
| 8227 | - type: main_score |
| 8228 | value: 90.35317684386006 |
| 8229 | - type: precision |
| 8230 | value: 89.94845939633488 |
| 8231 | - type: recall |
| 8232 | value: 91.40316205533597 |
| 8233 | task: |
| 8234 | type: BitextMining |
| 8235 | - dataset: |
| 8236 | config: quy_Latn-rus_Cyrl |
| 8237 | name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl) |
| 8238 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8239 | split: devtest |
| 8240 | type: mteb/flores |
| 8241 | metrics: |
| 8242 | - type: accuracy |
| 8243 | value: 40.612648221343875 |
| 8244 | - type: f1 |
| 8245 | value: 38.74337544712602 |
| 8246 | - type: main_score |
| 8247 | value: 38.74337544712602 |
| 8248 | - type: precision |
| 8249 | value: 38.133716022178575 |
| 8250 | - type: recall |
| 8251 | value: 40.612648221343875 |
| 8252 | task: |
| 8253 | type: BitextMining |
| 8254 | - dataset: |
| 8255 | config: swh_Latn-rus_Cyrl |
| 8256 | name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl) |
| 8257 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8258 | split: devtest |
| 8259 | type: mteb/flores |
| 8260 | metrics: |
| 8261 | - type: accuracy |
| 8262 | value: 97.13438735177866 |
| 8263 | - type: f1 |
| 8264 | value: 96.47435897435898 |
| 8265 | - type: main_score |
| 8266 | value: 96.47435897435898 |
| 8267 | - type: precision |
| 8268 | value: 96.18741765480895 |
| 8269 | - type: recall |
| 8270 | value: 97.13438735177866 |
| 8271 | task: |
| 8272 | type: BitextMining |
| 8273 | - dataset: |
| 8274 | config: uzn_Latn-rus_Cyrl |
| 8275 | name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl) |
| 8276 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8277 | split: devtest |
| 8278 | type: mteb/flores |
| 8279 | metrics: |
| 8280 | - type: accuracy |
| 8281 | value: 96.83794466403161 |
| 8282 | - type: f1 |
| 8283 | value: 96.26355528529442 |
| 8284 | - type: main_score |
| 8285 | value: 96.26355528529442 |
| 8286 | - type: precision |
| 8287 | value: 96.0501756697409 |
| 8288 | - type: recall |
| 8289 | value: 96.83794466403161 |
| 8290 | task: |
| 8291 | type: BitextMining |
| 8292 | - dataset: |
| 8293 | config: als_Latn-rus_Cyrl |
| 8294 | name: MTEB FloresBitextMining (als_Latn-rus_Cyrl) |
| 8295 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8296 | split: devtest |
| 8297 | type: mteb/flores |
| 8298 | metrics: |
| 8299 | - type: accuracy |
| 8300 | value: 98.91304347826086 |
| 8301 | - type: f1 |
| 8302 | value: 98.6907114624506 |
| 8303 | - type: main_score |
| 8304 | value: 98.6907114624506 |
| 8305 | - type: precision |
| 8306 | value: 98.6142480707698 |
| 8307 | - type: recall |
| 8308 | value: 98.91304347826086 |
| 8309 | task: |
| 8310 | type: BitextMining |
| 8311 | - dataset: |
| 8312 | config: bod_Tibt-rus_Cyrl |
| 8313 | name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl) |
| 8314 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8315 | split: devtest |
| 8316 | type: mteb/flores |
| 8317 | metrics: |
| 8318 | - type: accuracy |
| 8319 | value: 1.0869565217391304 |
| 8320 | - type: f1 |
| 8321 | value: 0.9224649610442628 |
| 8322 | - type: main_score |
| 8323 | value: 0.9224649610442628 |
| 8324 | - type: precision |
| 8325 | value: 0.8894275740459898 |
| 8326 | - type: recall |
| 8327 | value: 1.0869565217391304 |
| 8328 | task: |
| 8329 | type: BitextMining |
| 8330 | - dataset: |
| 8331 | config: fij_Latn-rus_Cyrl |
| 8332 | name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl) |
| 8333 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8334 | split: devtest |
| 8335 | type: mteb/flores |
| 8336 | metrics: |
| 8337 | - type: accuracy |
| 8338 | value: 63.24110671936759 |
| 8339 | - type: f1 |
| 8340 | value: 60.373189068189525 |
| 8341 | - type: main_score |
| 8342 | value: 60.373189068189525 |
| 8343 | - type: precision |
| 8344 | value: 59.32326368115546 |
| 8345 | - type: recall |
| 8346 | value: 63.24110671936759 |
| 8347 | task: |
| 8348 | type: BitextMining |
| 8349 | - dataset: |
| 8350 | config: isl_Latn-rus_Cyrl |
| 8351 | name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl) |
| 8352 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8353 | split: devtest |
| 8354 | type: mteb/flores |
| 8355 | metrics: |
| 8356 | - type: accuracy |
| 8357 | value: 89.03162055335969 |
| 8358 | - type: f1 |
| 8359 | value: 87.3102634715907 |
| 8360 | - type: main_score |
| 8361 | value: 87.3102634715907 |
| 8362 | - type: precision |
| 8363 | value: 86.65991814698712 |
| 8364 | - type: recall |
| 8365 | value: 89.03162055335969 |
| 8366 | task: |
| 8367 | type: BitextMining |
| 8368 | - dataset: |
| 8369 | config: kon_Latn-rus_Cyrl |
| 8370 | name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl) |
| 8371 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8372 | split: devtest |
| 8373 | type: mteb/flores |
| 8374 | metrics: |
| 8375 | - type: accuracy |
| 8376 | value: 73.91304347826086 |
| 8377 | - type: f1 |
| 8378 | value: 71.518235523573 |
| 8379 | - type: main_score |
| 8380 | value: 71.518235523573 |
| 8381 | - type: precision |
| 8382 | value: 70.58714102449801 |
| 8383 | - type: recall |
| 8384 | value: 73.91304347826086 |
| 8385 | task: |
| 8386 | type: BitextMining |
| 8387 | - dataset: |
| 8388 | config: mni_Beng-rus_Cyrl |
| 8389 | name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl) |
| 8390 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8391 | split: devtest |
| 8392 | type: mteb/flores |
| 8393 | metrics: |
| 8394 | - type: accuracy |
| 8395 | value: 29.545454545454547 |
| 8396 | - type: f1 |
| 8397 | value: 27.59513619889114 |
| 8398 | - type: main_score |
| 8399 | value: 27.59513619889114 |
| 8400 | - type: precision |
| 8401 | value: 26.983849851025344 |
| 8402 | - type: recall |
| 8403 | value: 29.545454545454547 |
| 8404 | task: |
| 8405 | type: BitextMining |
| 8406 | - dataset: |
| 8407 | config: ron_Latn-rus_Cyrl |
| 8408 | name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl) |
| 8409 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8410 | split: devtest |
| 8411 | type: mteb/flores |
| 8412 | metrics: |
| 8413 | - type: accuracy |
| 8414 | value: 99.40711462450594 |
| 8415 | - type: f1 |
| 8416 | value: 99.2094861660079 |
| 8417 | - type: main_score |
| 8418 | value: 99.2094861660079 |
| 8419 | - type: precision |
| 8420 | value: 99.1106719367589 |
| 8421 | - type: recall |
| 8422 | value: 99.40711462450594 |
| 8423 | task: |
| 8424 | type: BitextMining |
| 8425 | - dataset: |
| 8426 | config: szl_Latn-rus_Cyrl |
| 8427 | name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl) |
| 8428 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8429 | split: devtest |
| 8430 | type: mteb/flores |
| 8431 | metrics: |
| 8432 | - type: accuracy |
| 8433 | value: 86.26482213438736 |
| 8434 | - type: f1 |
| 8435 | value: 85.18912031587512 |
| 8436 | - type: main_score |
| 8437 | value: 85.18912031587512 |
| 8438 | - type: precision |
| 8439 | value: 84.77199409959775 |
| 8440 | - type: recall |
| 8441 | value: 86.26482213438736 |
| 8442 | task: |
| 8443 | type: BitextMining |
| 8444 | - dataset: |
| 8445 | config: vec_Latn-rus_Cyrl |
| 8446 | name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl) |
| 8447 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8448 | split: devtest |
| 8449 | type: mteb/flores |
| 8450 | metrics: |
| 8451 | - type: accuracy |
| 8452 | value: 85.67193675889328 |
| 8453 | - type: f1 |
| 8454 | value: 84.62529734716581 |
| 8455 | - type: main_score |
| 8456 | value: 84.62529734716581 |
| 8457 | - type: precision |
| 8458 | value: 84.2611422440705 |
| 8459 | - type: recall |
| 8460 | value: 85.67193675889328 |
| 8461 | task: |
| 8462 | type: BitextMining |
| 8463 | - dataset: |
| 8464 | config: amh_Ethi-rus_Cyrl |
| 8465 | name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl) |
| 8466 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8467 | split: devtest |
| 8468 | type: mteb/flores |
| 8469 | metrics: |
| 8470 | - type: accuracy |
| 8471 | value: 94.76284584980237 |
| 8472 | - type: f1 |
| 8473 | value: 93.91735076517685 |
| 8474 | - type: main_score |
| 8475 | value: 93.91735076517685 |
| 8476 | - type: precision |
| 8477 | value: 93.57553798858147 |
| 8478 | - type: recall |
| 8479 | value: 94.76284584980237 |
| 8480 | task: |
| 8481 | type: BitextMining |
| 8482 | - dataset: |
| 8483 | config: bos_Latn-rus_Cyrl |
| 8484 | name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl) |
| 8485 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8486 | split: devtest |
| 8487 | type: mteb/flores |
| 8488 | metrics: |
| 8489 | - type: accuracy |
| 8490 | value: 99.2094861660079 |
| 8491 | - type: f1 |
| 8492 | value: 99.05655938264634 |
| 8493 | - type: main_score |
| 8494 | value: 99.05655938264634 |
| 8495 | - type: precision |
| 8496 | value: 99.01185770750988 |
| 8497 | - type: recall |
| 8498 | value: 99.2094861660079 |
| 8499 | task: |
| 8500 | type: BitextMining |
| 8501 | - dataset: |
| 8502 | config: fin_Latn-rus_Cyrl |
| 8503 | name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl) |
| 8504 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8505 | split: devtest |
| 8506 | type: mteb/flores |
| 8507 | metrics: |
| 8508 | - type: accuracy |
| 8509 | value: 98.02371541501977 |
| 8510 | - type: f1 |
| 8511 | value: 97.43741765480895 |
| 8512 | - type: main_score |
| 8513 | value: 97.43741765480895 |
| 8514 | - type: precision |
| 8515 | value: 97.1590909090909 |
| 8516 | - type: recall |
| 8517 | value: 98.02371541501977 |
| 8518 | task: |
| 8519 | type: BitextMining |
| 8520 | - dataset: |
| 8521 | config: ita_Latn-rus_Cyrl |
| 8522 | name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl) |
| 8523 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8524 | split: devtest |
| 8525 | type: mteb/flores |
| 8526 | metrics: |
| 8527 | - type: accuracy |
| 8528 | value: 99.70355731225297 |
| 8529 | - type: f1 |
| 8530 | value: 99.60474308300395 |
| 8531 | - type: main_score |
| 8532 | value: 99.60474308300395 |
| 8533 | - type: precision |
| 8534 | value: 99.55533596837944 |
| 8535 | - type: recall |
| 8536 | value: 99.70355731225297 |
| 8537 | task: |
| 8538 | type: BitextMining |
| 8539 | - dataset: |
| 8540 | config: kor_Hang-rus_Cyrl |
| 8541 | name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl) |
| 8542 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8543 | split: devtest |
| 8544 | type: mteb/flores |
| 8545 | metrics: |
| 8546 | - type: accuracy |
| 8547 | value: 97.33201581027669 |
| 8548 | - type: f1 |
| 8549 | value: 96.49868247694334 |
| 8550 | - type: main_score |
| 8551 | value: 96.49868247694334 |
| 8552 | - type: precision |
| 8553 | value: 96.10507246376811 |
| 8554 | - type: recall |
| 8555 | value: 97.33201581027669 |
| 8556 | task: |
| 8557 | type: BitextMining |
| 8558 | - dataset: |
| 8559 | config: mos_Latn-rus_Cyrl |
| 8560 | name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl) |
| 8561 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8562 | split: devtest |
| 8563 | type: mteb/flores |
| 8564 | metrics: |
| 8565 | - type: accuracy |
| 8566 | value: 34.683794466403164 |
| 8567 | - type: f1 |
| 8568 | value: 32.766819308009076 |
| 8569 | - type: main_score |
| 8570 | value: 32.766819308009076 |
| 8571 | - type: precision |
| 8572 | value: 32.1637493670237 |
| 8573 | - type: recall |
| 8574 | value: 34.683794466403164 |
| 8575 | task: |
| 8576 | type: BitextMining |
| 8577 | - dataset: |
| 8578 | config: run_Latn-rus_Cyrl |
| 8579 | name: MTEB FloresBitextMining (run_Latn-rus_Cyrl) |
| 8580 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8581 | split: devtest |
| 8582 | type: mteb/flores |
| 8583 | metrics: |
| 8584 | - type: accuracy |
| 8585 | value: 83.399209486166 |
| 8586 | - type: f1 |
| 8587 | value: 81.10578750604326 |
| 8588 | - type: main_score |
| 8589 | value: 81.10578750604326 |
| 8590 | - type: precision |
| 8591 | value: 80.16763162673529 |
| 8592 | - type: recall |
| 8593 | value: 83.399209486166 |
| 8594 | task: |
| 8595 | type: BitextMining |
| 8596 | - dataset: |
| 8597 | config: tam_Taml-rus_Cyrl |
| 8598 | name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl) |
| 8599 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8600 | split: devtest |
| 8601 | type: mteb/flores |
| 8602 | metrics: |
| 8603 | - type: accuracy |
| 8604 | value: 98.41897233201581 |
| 8605 | - type: f1 |
| 8606 | value: 98.01548089591567 |
| 8607 | - type: main_score |
| 8608 | value: 98.01548089591567 |
| 8609 | - type: precision |
| 8610 | value: 97.84020327498588 |
| 8611 | - type: recall |
| 8612 | value: 98.41897233201581 |
| 8613 | task: |
| 8614 | type: BitextMining |
| 8615 | - dataset: |
| 8616 | config: vie_Latn-rus_Cyrl |
| 8617 | name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl) |
| 8618 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8619 | split: devtest |
| 8620 | type: mteb/flores |
| 8621 | metrics: |
| 8622 | - type: accuracy |
| 8623 | value: 99.1106719367589 |
| 8624 | - type: f1 |
| 8625 | value: 98.81422924901186 |
| 8626 | - type: main_score |
| 8627 | value: 98.81422924901186 |
| 8628 | - type: precision |
| 8629 | value: 98.66600790513834 |
| 8630 | - type: recall |
| 8631 | value: 99.1106719367589 |
| 8632 | task: |
| 8633 | type: BitextMining |
| 8634 | - dataset: |
| 8635 | config: apc_Arab-rus_Cyrl |
| 8636 | name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl) |
| 8637 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8638 | split: devtest |
| 8639 | type: mteb/flores |
| 8640 | metrics: |
| 8641 | - type: accuracy |
| 8642 | value: 93.87351778656127 |
| 8643 | - type: f1 |
| 8644 | value: 92.10803689064558 |
| 8645 | - type: main_score |
| 8646 | value: 92.10803689064558 |
| 8647 | - type: precision |
| 8648 | value: 91.30434782608695 |
| 8649 | - type: recall |
| 8650 | value: 93.87351778656127 |
| 8651 | task: |
| 8652 | type: BitextMining |
| 8653 | - dataset: |
| 8654 | config: bug_Latn-rus_Cyrl |
| 8655 | name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl) |
| 8656 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8657 | split: devtest |
| 8658 | type: mteb/flores |
| 8659 | metrics: |
| 8660 | - type: accuracy |
| 8661 | value: 57.608695652173914 |
| 8662 | - type: f1 |
| 8663 | value: 54.95878654927162 |
| 8664 | - type: main_score |
| 8665 | value: 54.95878654927162 |
| 8666 | - type: precision |
| 8667 | value: 54.067987427805654 |
| 8668 | - type: recall |
| 8669 | value: 57.608695652173914 |
| 8670 | task: |
| 8671 | type: BitextMining |
| 8672 | - dataset: |
| 8673 | config: fon_Latn-rus_Cyrl |
| 8674 | name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl) |
| 8675 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8676 | split: devtest |
| 8677 | type: mteb/flores |
| 8678 | metrics: |
| 8679 | - type: accuracy |
| 8680 | value: 61.95652173913043 |
| 8681 | - type: f1 |
| 8682 | value: 58.06537275812945 |
| 8683 | - type: main_score |
| 8684 | value: 58.06537275812945 |
| 8685 | - type: precision |
| 8686 | value: 56.554057596959204 |
| 8687 | - type: recall |
| 8688 | value: 61.95652173913043 |
| 8689 | task: |
| 8690 | type: BitextMining |
| 8691 | - dataset: |
| 8692 | config: jav_Latn-rus_Cyrl |
| 8693 | name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl) |
| 8694 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8695 | split: devtest |
| 8696 | type: mteb/flores |
| 8697 | metrics: |
| 8698 | - type: accuracy |
| 8699 | value: 93.47826086956522 |
| 8700 | - type: f1 |
| 8701 | value: 92.4784405318002 |
| 8702 | - type: main_score |
| 8703 | value: 92.4784405318002 |
| 8704 | - type: precision |
| 8705 | value: 92.09168143201127 |
| 8706 | - type: recall |
| 8707 | value: 93.47826086956522 |
| 8708 | task: |
| 8709 | type: BitextMining |
| 8710 | - dataset: |
| 8711 | config: lao_Laoo-rus_Cyrl |
| 8712 | name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl) |
| 8713 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8714 | split: devtest |
| 8715 | type: mteb/flores |
| 8716 | metrics: |
| 8717 | - type: accuracy |
| 8718 | value: 91.10671936758892 |
| 8719 | - type: f1 |
| 8720 | value: 89.76104922745239 |
| 8721 | - type: main_score |
| 8722 | value: 89.76104922745239 |
| 8723 | - type: precision |
| 8724 | value: 89.24754593232855 |
| 8725 | - type: recall |
| 8726 | value: 91.10671936758892 |
| 8727 | task: |
| 8728 | type: BitextMining |
| 8729 | - dataset: |
| 8730 | config: mri_Latn-rus_Cyrl |
| 8731 | name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl) |
| 8732 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8733 | split: devtest |
| 8734 | type: mteb/flores |
| 8735 | metrics: |
| 8736 | - type: accuracy |
| 8737 | value: 71.14624505928853 |
| 8738 | - type: f1 |
| 8739 | value: 68.26947125119062 |
| 8740 | - type: main_score |
| 8741 | value: 68.26947125119062 |
| 8742 | - type: precision |
| 8743 | value: 67.15942311051006 |
| 8744 | - type: recall |
| 8745 | value: 71.14624505928853 |
| 8746 | task: |
| 8747 | type: BitextMining |
| 8748 | - dataset: |
| 8749 | config: rus_Cyrl-ace_Arab |
| 8750 | name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab) |
| 8751 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8752 | split: devtest |
| 8753 | type: mteb/flores |
| 8754 | metrics: |
| 8755 | - type: accuracy |
| 8756 | value: 19.565217391304348 |
| 8757 | - type: f1 |
| 8758 | value: 16.321465000323805 |
| 8759 | - type: main_score |
| 8760 | value: 16.321465000323805 |
| 8761 | - type: precision |
| 8762 | value: 15.478527409347508 |
| 8763 | - type: recall |
| 8764 | value: 19.565217391304348 |
| 8765 | task: |
| 8766 | type: BitextMining |
| 8767 | - dataset: |
| 8768 | config: rus_Cyrl-bam_Latn |
| 8769 | name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn) |
| 8770 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8771 | split: devtest |
| 8772 | type: mteb/flores |
| 8773 | metrics: |
| 8774 | - type: accuracy |
| 8775 | value: 73.41897233201581 |
| 8776 | - type: f1 |
| 8777 | value: 68.77366228182746 |
| 8778 | - type: main_score |
| 8779 | value: 68.77366228182746 |
| 8780 | - type: precision |
| 8781 | value: 66.96012924273795 |
| 8782 | - type: recall |
| 8783 | value: 73.41897233201581 |
| 8784 | task: |
| 8785 | type: BitextMining |
| 8786 | - dataset: |
| 8787 | config: rus_Cyrl-dzo_Tibt |
| 8788 | name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt) |
| 8789 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8790 | split: devtest |
| 8791 | type: mteb/flores |
| 8792 | metrics: |
| 8793 | - type: accuracy |
| 8794 | value: 0.592885375494071 |
| 8795 | - type: f1 |
| 8796 | value: 0.02458062426370458 |
| 8797 | - type: main_score |
| 8798 | value: 0.02458062426370458 |
| 8799 | - type: precision |
| 8800 | value: 0.012824114724683876 |
| 8801 | - type: recall |
| 8802 | value: 0.592885375494071 |
| 8803 | task: |
| 8804 | type: BitextMining |
| 8805 | - dataset: |
| 8806 | config: rus_Cyrl-hin_Deva |
| 8807 | name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva) |
| 8808 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8809 | split: devtest |
| 8810 | type: mteb/flores |
| 8811 | metrics: |
| 8812 | - type: accuracy |
| 8813 | value: 99.90118577075098 |
| 8814 | - type: f1 |
| 8815 | value: 99.86824769433464 |
| 8816 | - type: main_score |
| 8817 | value: 99.86824769433464 |
| 8818 | - type: precision |
| 8819 | value: 99.85177865612648 |
| 8820 | - type: recall |
| 8821 | value: 99.90118577075098 |
| 8822 | task: |
| 8823 | type: BitextMining |
| 8824 | - dataset: |
| 8825 | config: rus_Cyrl-khm_Khmr |
| 8826 | name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr) |
| 8827 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8828 | split: devtest |
| 8829 | type: mteb/flores |
| 8830 | metrics: |
| 8831 | - type: accuracy |
| 8832 | value: 97.13438735177866 |
| 8833 | - type: f1 |
| 8834 | value: 96.24505928853755 |
| 8835 | - type: main_score |
| 8836 | value: 96.24505928853755 |
| 8837 | - type: precision |
| 8838 | value: 95.81686429512516 |
| 8839 | - type: recall |
| 8840 | value: 97.13438735177866 |
| 8841 | task: |
| 8842 | type: BitextMining |
| 8843 | - dataset: |
| 8844 | config: rus_Cyrl-mag_Deva |
| 8845 | name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva) |
| 8846 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8847 | split: devtest |
| 8848 | type: mteb/flores |
| 8849 | metrics: |
| 8850 | - type: accuracy |
| 8851 | value: 99.50592885375494 |
| 8852 | - type: f1 |
| 8853 | value: 99.35770750988142 |
| 8854 | - type: main_score |
| 8855 | value: 99.35770750988142 |
| 8856 | - type: precision |
| 8857 | value: 99.29183135704875 |
| 8858 | - type: recall |
| 8859 | value: 99.50592885375494 |
| 8860 | task: |
| 8861 | type: BitextMining |
| 8862 | - dataset: |
| 8863 | config: rus_Cyrl-pap_Latn |
| 8864 | name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn) |
| 8865 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8866 | split: devtest |
| 8867 | type: mteb/flores |
| 8868 | metrics: |
| 8869 | - type: accuracy |
| 8870 | value: 96.93675889328063 |
| 8871 | - type: f1 |
| 8872 | value: 96.05072463768116 |
| 8873 | - type: main_score |
| 8874 | value: 96.05072463768116 |
| 8875 | - type: precision |
| 8876 | value: 95.66040843214758 |
| 8877 | - type: recall |
| 8878 | value: 96.93675889328063 |
| 8879 | task: |
| 8880 | type: BitextMining |
| 8881 | - dataset: |
| 8882 | config: rus_Cyrl-sot_Latn |
| 8883 | name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn) |
| 8884 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8885 | split: devtest |
| 8886 | type: mteb/flores |
| 8887 | metrics: |
| 8888 | - type: accuracy |
| 8889 | value: 93.67588932806325 |
| 8890 | - type: f1 |
| 8891 | value: 91.7786561264822 |
| 8892 | - type: main_score |
| 8893 | value: 91.7786561264822 |
| 8894 | - type: precision |
| 8895 | value: 90.91238471673255 |
| 8896 | - type: recall |
| 8897 | value: 93.67588932806325 |
| 8898 | task: |
| 8899 | type: BitextMining |
| 8900 | - dataset: |
| 8901 | config: rus_Cyrl-tur_Latn |
| 8902 | name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn) |
| 8903 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8904 | split: devtest |
| 8905 | type: mteb/flores |
| 8906 | metrics: |
| 8907 | - type: accuracy |
| 8908 | value: 99.01185770750988 |
| 8909 | - type: f1 |
| 8910 | value: 98.68247694334651 |
| 8911 | - type: main_score |
| 8912 | value: 98.68247694334651 |
| 8913 | - type: precision |
| 8914 | value: 98.51778656126481 |
| 8915 | - type: recall |
| 8916 | value: 99.01185770750988 |
| 8917 | task: |
| 8918 | type: BitextMining |
| 8919 | - dataset: |
| 8920 | config: rus_Cyrl-ace_Latn |
| 8921 | name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn) |
| 8922 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8923 | split: devtest |
| 8924 | type: mteb/flores |
| 8925 | metrics: |
| 8926 | - type: accuracy |
| 8927 | value: 74.1106719367589 |
| 8928 | - type: f1 |
| 8929 | value: 70.21737923911836 |
| 8930 | - type: main_score |
| 8931 | value: 70.21737923911836 |
| 8932 | - type: precision |
| 8933 | value: 68.7068791410511 |
| 8934 | - type: recall |
| 8935 | value: 74.1106719367589 |
| 8936 | task: |
| 8937 | type: BitextMining |
| 8938 | - dataset: |
| 8939 | config: rus_Cyrl-ban_Latn |
| 8940 | name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn) |
| 8941 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8942 | split: devtest |
| 8943 | type: mteb/flores |
| 8944 | metrics: |
| 8945 | - type: accuracy |
| 8946 | value: 81.7193675889328 |
| 8947 | - type: f1 |
| 8948 | value: 78.76470334510617 |
| 8949 | - type: main_score |
| 8950 | value: 78.76470334510617 |
| 8951 | - type: precision |
| 8952 | value: 77.76208475761422 |
| 8953 | - type: recall |
| 8954 | value: 81.7193675889328 |
| 8955 | task: |
| 8956 | type: BitextMining |
| 8957 | - dataset: |
| 8958 | config: rus_Cyrl-ell_Grek |
| 8959 | name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek) |
| 8960 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8961 | split: devtest |
| 8962 | type: mteb/flores |
| 8963 | metrics: |
| 8964 | - type: accuracy |
| 8965 | value: 98.3201581027668 |
| 8966 | - type: f1 |
| 8967 | value: 97.76021080368908 |
| 8968 | - type: main_score |
| 8969 | value: 97.76021080368908 |
| 8970 | - type: precision |
| 8971 | value: 97.48023715415019 |
| 8972 | - type: recall |
| 8973 | value: 98.3201581027668 |
| 8974 | task: |
| 8975 | type: BitextMining |
| 8976 | - dataset: |
| 8977 | config: rus_Cyrl-hne_Deva |
| 8978 | name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva) |
| 8979 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8980 | split: devtest |
| 8981 | type: mteb/flores |
| 8982 | metrics: |
| 8983 | - type: accuracy |
| 8984 | value: 98.51778656126481 |
| 8985 | - type: f1 |
| 8986 | value: 98.0566534914361 |
| 8987 | - type: main_score |
| 8988 | value: 98.0566534914361 |
| 8989 | - type: precision |
| 8990 | value: 97.82608695652173 |
| 8991 | - type: recall |
| 8992 | value: 98.51778656126481 |
| 8993 | task: |
| 8994 | type: BitextMining |
| 8995 | - dataset: |
| 8996 | config: rus_Cyrl-kik_Latn |
| 8997 | name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn) |
| 8998 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 8999 | split: devtest |
| 9000 | type: mteb/flores |
| 9001 | metrics: |
| 9002 | - type: accuracy |
| 9003 | value: 80.73122529644269 |
| 9004 | - type: f1 |
| 9005 | value: 76.42689244220864 |
| 9006 | - type: main_score |
| 9007 | value: 76.42689244220864 |
| 9008 | - type: precision |
| 9009 | value: 74.63877909530083 |
| 9010 | - type: recall |
| 9011 | value: 80.73122529644269 |
| 9012 | task: |
| 9013 | type: BitextMining |
| 9014 | - dataset: |
| 9015 | config: rus_Cyrl-mai_Deva |
| 9016 | name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva) |
| 9017 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9018 | split: devtest |
| 9019 | type: mteb/flores |
| 9020 | metrics: |
| 9021 | - type: accuracy |
| 9022 | value: 98.91304347826086 |
| 9023 | - type: f1 |
| 9024 | value: 98.56719367588933 |
| 9025 | - type: main_score |
| 9026 | value: 98.56719367588933 |
| 9027 | - type: precision |
| 9028 | value: 98.40250329380763 |
| 9029 | - type: recall |
| 9030 | value: 98.91304347826086 |
| 9031 | task: |
| 9032 | type: BitextMining |
| 9033 | - dataset: |
| 9034 | config: rus_Cyrl-pbt_Arab |
| 9035 | name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab) |
| 9036 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9037 | split: devtest |
| 9038 | type: mteb/flores |
| 9039 | metrics: |
| 9040 | - type: accuracy |
| 9041 | value: 97.5296442687747 |
| 9042 | - type: f1 |
| 9043 | value: 96.73913043478261 |
| 9044 | - type: main_score |
| 9045 | value: 96.73913043478261 |
| 9046 | - type: precision |
| 9047 | value: 96.36034255599473 |
| 9048 | - type: recall |
| 9049 | value: 97.5296442687747 |
| 9050 | task: |
| 9051 | type: BitextMining |
| 9052 | - dataset: |
| 9053 | config: rus_Cyrl-spa_Latn |
| 9054 | name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn) |
| 9055 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9056 | split: devtest |
| 9057 | type: mteb/flores |
| 9058 | metrics: |
| 9059 | - type: accuracy |
| 9060 | value: 99.40711462450594 |
| 9061 | - type: f1 |
| 9062 | value: 99.20948616600789 |
| 9063 | - type: main_score |
| 9064 | value: 99.20948616600789 |
| 9065 | - type: precision |
| 9066 | value: 99.1106719367589 |
| 9067 | - type: recall |
| 9068 | value: 99.40711462450594 |
| 9069 | task: |
| 9070 | type: BitextMining |
| 9071 | - dataset: |
| 9072 | config: rus_Cyrl-twi_Latn |
| 9073 | name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn) |
| 9074 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9075 | split: devtest |
| 9076 | type: mteb/flores |
| 9077 | metrics: |
| 9078 | - type: accuracy |
| 9079 | value: 82.01581027667984 |
| 9080 | - type: f1 |
| 9081 | value: 78.064787822953 |
| 9082 | - type: main_score |
| 9083 | value: 78.064787822953 |
| 9084 | - type: precision |
| 9085 | value: 76.43272186750448 |
| 9086 | - type: recall |
| 9087 | value: 82.01581027667984 |
| 9088 | task: |
| 9089 | type: BitextMining |
| 9090 | - dataset: |
| 9091 | config: rus_Cyrl-acm_Arab |
| 9092 | name: MTEB FloresBitextMining (rus_Cyrl-acm_Arab) |
| 9093 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9094 | split: devtest |
| 9095 | type: mteb/flores |
| 9096 | metrics: |
| 9097 | - type: accuracy |
| 9098 | value: 98.3201581027668 |
| 9099 | - type: f1 |
| 9100 | value: 97.76021080368908 |
| 9101 | - type: main_score |
| 9102 | value: 97.76021080368908 |
| 9103 | - type: precision |
| 9104 | value: 97.48023715415019 |
| 9105 | - type: recall |
| 9106 | value: 98.3201581027668 |
| 9107 | task: |
| 9108 | type: BitextMining |
| 9109 | - dataset: |
| 9110 | config: rus_Cyrl-bel_Cyrl |
| 9111 | name: MTEB FloresBitextMining (rus_Cyrl-bel_Cyrl) |
| 9112 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9113 | split: devtest |
| 9114 | type: mteb/flores |
| 9115 | metrics: |
| 9116 | - type: accuracy |
| 9117 | value: 98.22134387351778 |
| 9118 | - type: f1 |
| 9119 | value: 97.67786561264822 |
| 9120 | - type: main_score |
| 9121 | value: 97.67786561264822 |
| 9122 | - type: precision |
| 9123 | value: 97.4308300395257 |
| 9124 | - type: recall |
| 9125 | value: 98.22134387351778 |
| 9126 | task: |
| 9127 | type: BitextMining |
| 9128 | - dataset: |
| 9129 | config: rus_Cyrl-eng_Latn |
| 9130 | name: MTEB FloresBitextMining (rus_Cyrl-eng_Latn) |
| 9131 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9132 | split: devtest |
| 9133 | type: mteb/flores |
| 9134 | metrics: |
| 9135 | - type: accuracy |
| 9136 | value: 99.70355731225297 |
| 9137 | - type: f1 |
| 9138 | value: 99.60474308300395 |
| 9139 | - type: main_score |
| 9140 | value: 99.60474308300395 |
| 9141 | - type: precision |
| 9142 | value: 99.55533596837944 |
| 9143 | - type: recall |
| 9144 | value: 99.70355731225297 |
| 9145 | task: |
| 9146 | type: BitextMining |
| 9147 | - dataset: |
| 9148 | config: rus_Cyrl-hrv_Latn |
| 9149 | name: MTEB FloresBitextMining (rus_Cyrl-hrv_Latn) |
| 9150 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9151 | split: devtest |
| 9152 | type: mteb/flores |
| 9153 | metrics: |
| 9154 | - type: accuracy |
| 9155 | value: 99.1106719367589 |
| 9156 | - type: f1 |
| 9157 | value: 98.83069828722002 |
| 9158 | - type: main_score |
| 9159 | value: 98.83069828722002 |
| 9160 | - type: precision |
| 9161 | value: 98.69894598155466 |
| 9162 | - type: recall |
| 9163 | value: 99.1106719367589 |
| 9164 | task: |
| 9165 | type: BitextMining |
| 9166 | - dataset: |
| 9167 | config: rus_Cyrl-kin_Latn |
| 9168 | name: MTEB FloresBitextMining (rus_Cyrl-kin_Latn) |
| 9169 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9170 | split: devtest |
| 9171 | type: mteb/flores |
| 9172 | metrics: |
| 9173 | - type: accuracy |
| 9174 | value: 93.37944664031622 |
| 9175 | - type: f1 |
| 9176 | value: 91.53162055335969 |
| 9177 | - type: main_score |
| 9178 | value: 91.53162055335969 |
| 9179 | - type: precision |
| 9180 | value: 90.71475625823452 |
| 9181 | - type: recall |
| 9182 | value: 93.37944664031622 |
| 9183 | task: |
| 9184 | type: BitextMining |
| 9185 | - dataset: |
| 9186 | config: rus_Cyrl-mal_Mlym |
| 9187 | name: MTEB FloresBitextMining (rus_Cyrl-mal_Mlym) |
| 9188 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9189 | split: devtest |
| 9190 | type: mteb/flores |
| 9191 | metrics: |
| 9192 | - type: accuracy |
| 9193 | value: 99.30830039525692 |
| 9194 | - type: f1 |
| 9195 | value: 99.07773386034255 |
| 9196 | - type: main_score |
| 9197 | value: 99.07773386034255 |
| 9198 | - type: precision |
| 9199 | value: 98.96245059288538 |
| 9200 | - type: recall |
| 9201 | value: 99.30830039525692 |
| 9202 | task: |
| 9203 | type: BitextMining |
| 9204 | - dataset: |
| 9205 | config: rus_Cyrl-pes_Arab |
| 9206 | name: MTEB FloresBitextMining (rus_Cyrl-pes_Arab) |
| 9207 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9208 | split: devtest |
| 9209 | type: mteb/flores |
| 9210 | metrics: |
| 9211 | - type: accuracy |
| 9212 | value: 98.71541501976284 |
| 9213 | - type: f1 |
| 9214 | value: 98.30368906455863 |
| 9215 | - type: main_score |
| 9216 | value: 98.30368906455863 |
| 9217 | - type: precision |
| 9218 | value: 98.10606060606061 |
| 9219 | - type: recall |
| 9220 | value: 98.71541501976284 |
| 9221 | task: |
| 9222 | type: BitextMining |
| 9223 | - dataset: |
| 9224 | config: rus_Cyrl-srd_Latn |
| 9225 | name: MTEB FloresBitextMining (rus_Cyrl-srd_Latn) |
| 9226 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9227 | split: devtest |
| 9228 | type: mteb/flores |
| 9229 | metrics: |
| 9230 | - type: accuracy |
| 9231 | value: 89.03162055335969 |
| 9232 | - type: f1 |
| 9233 | value: 86.11048371917937 |
| 9234 | - type: main_score |
| 9235 | value: 86.11048371917937 |
| 9236 | - type: precision |
| 9237 | value: 84.86001317523056 |
| 9238 | - type: recall |
| 9239 | value: 89.03162055335969 |
| 9240 | task: |
| 9241 | type: BitextMining |
| 9242 | - dataset: |
| 9243 | config: rus_Cyrl-tzm_Tfng |
| 9244 | name: MTEB FloresBitextMining (rus_Cyrl-tzm_Tfng) |
| 9245 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9246 | split: devtest |
| 9247 | type: mteb/flores |
| 9248 | metrics: |
| 9249 | - type: accuracy |
| 9250 | value: 12.351778656126482 |
| 9251 | - type: f1 |
| 9252 | value: 10.112177999067715 |
| 9253 | - type: main_score |
| 9254 | value: 10.112177999067715 |
| 9255 | - type: precision |
| 9256 | value: 9.53495885438645 |
| 9257 | - type: recall |
| 9258 | value: 12.351778656126482 |
| 9259 | task: |
| 9260 | type: BitextMining |
| 9261 | - dataset: |
| 9262 | config: rus_Cyrl-acq_Arab |
| 9263 | name: MTEB FloresBitextMining (rus_Cyrl-acq_Arab) |
| 9264 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9265 | split: devtest |
| 9266 | type: mteb/flores |
| 9267 | metrics: |
| 9268 | - type: accuracy |
| 9269 | value: 98.91304347826086 |
| 9270 | - type: f1 |
| 9271 | value: 98.55072463768116 |
| 9272 | - type: main_score |
| 9273 | value: 98.55072463768116 |
| 9274 | - type: precision |
| 9275 | value: 98.36956521739131 |
| 9276 | - type: recall |
| 9277 | value: 98.91304347826086 |
| 9278 | task: |
| 9279 | type: BitextMining |
| 9280 | - dataset: |
| 9281 | config: rus_Cyrl-bem_Latn |
| 9282 | name: MTEB FloresBitextMining (rus_Cyrl-bem_Latn) |
| 9283 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9284 | split: devtest |
| 9285 | type: mteb/flores |
| 9286 | metrics: |
| 9287 | - type: accuracy |
| 9288 | value: 73.22134387351778 |
| 9289 | - type: f1 |
| 9290 | value: 68.30479412989295 |
| 9291 | - type: main_score |
| 9292 | value: 68.30479412989295 |
| 9293 | - type: precision |
| 9294 | value: 66.40073447632736 |
| 9295 | - type: recall |
| 9296 | value: 73.22134387351778 |
| 9297 | task: |
| 9298 | type: BitextMining |
| 9299 | - dataset: |
| 9300 | config: rus_Cyrl-epo_Latn |
| 9301 | name: MTEB FloresBitextMining (rus_Cyrl-epo_Latn) |
| 9302 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9303 | split: devtest |
| 9304 | type: mteb/flores |
| 9305 | metrics: |
| 9306 | - type: accuracy |
| 9307 | value: 99.1106719367589 |
| 9308 | - type: f1 |
| 9309 | value: 98.81422924901186 |
| 9310 | - type: main_score |
| 9311 | value: 98.81422924901186 |
| 9312 | - type: precision |
| 9313 | value: 98.66600790513834 |
| 9314 | - type: recall |
| 9315 | value: 99.1106719367589 |
| 9316 | task: |
| 9317 | type: BitextMining |
| 9318 | - dataset: |
| 9319 | config: rus_Cyrl-hun_Latn |
| 9320 | name: MTEB FloresBitextMining (rus_Cyrl-hun_Latn) |
| 9321 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9322 | split: devtest |
| 9323 | type: mteb/flores |
| 9324 | metrics: |
| 9325 | - type: accuracy |
| 9326 | value: 96.83794466403161 |
| 9327 | - type: f1 |
| 9328 | value: 95.88274044795784 |
| 9329 | - type: main_score |
| 9330 | value: 95.88274044795784 |
| 9331 | - type: precision |
| 9332 | value: 95.45454545454545 |
| 9333 | - type: recall |
| 9334 | value: 96.83794466403161 |
| 9335 | task: |
| 9336 | type: BitextMining |
| 9337 | - dataset: |
| 9338 | config: rus_Cyrl-kir_Cyrl |
| 9339 | name: MTEB FloresBitextMining (rus_Cyrl-kir_Cyrl) |
| 9340 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9341 | split: devtest |
| 9342 | type: mteb/flores |
| 9343 | metrics: |
| 9344 | - type: accuracy |
| 9345 | value: 96.34387351778656 |
| 9346 | - type: f1 |
| 9347 | value: 95.49280429715212 |
| 9348 | - type: main_score |
| 9349 | value: 95.49280429715212 |
| 9350 | - type: precision |
| 9351 | value: 95.14163372859026 |
| 9352 | - type: recall |
| 9353 | value: 96.34387351778656 |
| 9354 | task: |
| 9355 | type: BitextMining |
| 9356 | - dataset: |
| 9357 | config: rus_Cyrl-mar_Deva |
| 9358 | name: MTEB FloresBitextMining (rus_Cyrl-mar_Deva) |
| 9359 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9360 | split: devtest |
| 9361 | type: mteb/flores |
| 9362 | metrics: |
| 9363 | - type: accuracy |
| 9364 | value: 98.71541501976284 |
| 9365 | - type: f1 |
| 9366 | value: 98.28722002635047 |
| 9367 | - type: main_score |
| 9368 | value: 98.28722002635047 |
| 9369 | - type: precision |
| 9370 | value: 98.07312252964427 |
| 9371 | - type: recall |
| 9372 | value: 98.71541501976284 |
| 9373 | task: |
| 9374 | type: BitextMining |
| 9375 | - dataset: |
| 9376 | config: rus_Cyrl-plt_Latn |
| 9377 | name: MTEB FloresBitextMining (rus_Cyrl-plt_Latn) |
| 9378 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9379 | split: devtest |
| 9380 | type: mteb/flores |
| 9381 | metrics: |
| 9382 | - type: accuracy |
| 9383 | value: 88.04347826086956 |
| 9384 | - type: f1 |
| 9385 | value: 85.14328063241106 |
| 9386 | - type: main_score |
| 9387 | value: 85.14328063241106 |
| 9388 | - type: precision |
| 9389 | value: 83.96339168078298 |
| 9390 | - type: recall |
| 9391 | value: 88.04347826086956 |
| 9392 | task: |
| 9393 | type: BitextMining |
| 9394 | - dataset: |
| 9395 | config: rus_Cyrl-srp_Cyrl |
| 9396 | name: MTEB FloresBitextMining (rus_Cyrl-srp_Cyrl) |
| 9397 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9398 | split: devtest |
| 9399 | type: mteb/flores |
| 9400 | metrics: |
| 9401 | - type: accuracy |
| 9402 | value: 99.40711462450594 |
| 9403 | - type: f1 |
| 9404 | value: 99.2094861660079 |
| 9405 | - type: main_score |
| 9406 | value: 99.2094861660079 |
| 9407 | - type: precision |
| 9408 | value: 99.1106719367589 |
| 9409 | - type: recall |
| 9410 | value: 99.40711462450594 |
| 9411 | task: |
| 9412 | type: BitextMining |
| 9413 | - dataset: |
| 9414 | config: rus_Cyrl-uig_Arab |
| 9415 | name: MTEB FloresBitextMining (rus_Cyrl-uig_Arab) |
| 9416 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9417 | split: devtest |
| 9418 | type: mteb/flores |
| 9419 | metrics: |
| 9420 | - type: accuracy |
| 9421 | value: 92.19367588932806 |
| 9422 | - type: f1 |
| 9423 | value: 89.98541313758706 |
| 9424 | - type: main_score |
| 9425 | value: 89.98541313758706 |
| 9426 | - type: precision |
| 9427 | value: 89.01021080368906 |
| 9428 | - type: recall |
| 9429 | value: 92.19367588932806 |
| 9430 | task: |
| 9431 | type: BitextMining |
| 9432 | - dataset: |
| 9433 | config: rus_Cyrl-aeb_Arab |
| 9434 | name: MTEB FloresBitextMining (rus_Cyrl-aeb_Arab) |
| 9435 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9436 | split: devtest |
| 9437 | type: mteb/flores |
| 9438 | metrics: |
| 9439 | - type: accuracy |
| 9440 | value: 95.8498023715415 |
| 9441 | - type: f1 |
| 9442 | value: 94.63109354413703 |
| 9443 | - type: main_score |
| 9444 | value: 94.63109354413703 |
| 9445 | - type: precision |
| 9446 | value: 94.05467720685111 |
| 9447 | - type: recall |
| 9448 | value: 95.8498023715415 |
| 9449 | task: |
| 9450 | type: BitextMining |
| 9451 | - dataset: |
| 9452 | config: rus_Cyrl-ben_Beng |
| 9453 | name: MTEB FloresBitextMining (rus_Cyrl-ben_Beng) |
| 9454 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9455 | split: devtest |
| 9456 | type: mteb/flores |
| 9457 | metrics: |
| 9458 | - type: accuracy |
| 9459 | value: 99.40711462450594 |
| 9460 | - type: f1 |
| 9461 | value: 99.2094861660079 |
| 9462 | - type: main_score |
| 9463 | value: 99.2094861660079 |
| 9464 | - type: precision |
| 9465 | value: 99.1106719367589 |
| 9466 | - type: recall |
| 9467 | value: 99.40711462450594 |
| 9468 | task: |
| 9469 | type: BitextMining |
| 9470 | - dataset: |
| 9471 | config: rus_Cyrl-est_Latn |
| 9472 | name: MTEB FloresBitextMining (rus_Cyrl-est_Latn) |
| 9473 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9474 | split: devtest |
| 9475 | type: mteb/flores |
| 9476 | metrics: |
| 9477 | - type: accuracy |
| 9478 | value: 95.55335968379447 |
| 9479 | - type: f1 |
| 9480 | value: 94.2588932806324 |
| 9481 | - type: main_score |
| 9482 | value: 94.2588932806324 |
| 9483 | - type: precision |
| 9484 | value: 93.65118577075098 |
| 9485 | - type: recall |
| 9486 | value: 95.55335968379447 |
| 9487 | task: |
| 9488 | type: BitextMining |
| 9489 | - dataset: |
| 9490 | config: rus_Cyrl-hye_Armn |
| 9491 | name: MTEB FloresBitextMining (rus_Cyrl-hye_Armn) |
| 9492 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9493 | split: devtest |
| 9494 | type: mteb/flores |
| 9495 | metrics: |
| 9496 | - type: accuracy |
| 9497 | value: 98.71541501976284 |
| 9498 | - type: f1 |
| 9499 | value: 98.28722002635045 |
| 9500 | - type: main_score |
| 9501 | value: 98.28722002635045 |
| 9502 | - type: precision |
| 9503 | value: 98.07312252964427 |
| 9504 | - type: recall |
| 9505 | value: 98.71541501976284 |
| 9506 | task: |
| 9507 | type: BitextMining |
| 9508 | - dataset: |
| 9509 | config: rus_Cyrl-kmb_Latn |
| 9510 | name: MTEB FloresBitextMining (rus_Cyrl-kmb_Latn) |
| 9511 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9512 | split: devtest |
| 9513 | type: mteb/flores |
| 9514 | metrics: |
| 9515 | - type: accuracy |
| 9516 | value: 54.24901185770751 |
| 9517 | - type: f1 |
| 9518 | value: 49.46146674116913 |
| 9519 | - type: main_score |
| 9520 | value: 49.46146674116913 |
| 9521 | - type: precision |
| 9522 | value: 47.81033799314432 |
| 9523 | - type: recall |
| 9524 | value: 54.24901185770751 |
| 9525 | task: |
| 9526 | type: BitextMining |
| 9527 | - dataset: |
| 9528 | config: rus_Cyrl-min_Arab |
| 9529 | name: MTEB FloresBitextMining (rus_Cyrl-min_Arab) |
| 9530 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9531 | split: devtest |
| 9532 | type: mteb/flores |
| 9533 | metrics: |
| 9534 | - type: accuracy |
| 9535 | value: 15.810276679841898 |
| 9536 | - type: f1 |
| 9537 | value: 13.271207641419332 |
| 9538 | - type: main_score |
| 9539 | value: 13.271207641419332 |
| 9540 | - type: precision |
| 9541 | value: 12.510673148766033 |
| 9542 | - type: recall |
| 9543 | value: 15.810276679841898 |
| 9544 | task: |
| 9545 | type: BitextMining |
| 9546 | - dataset: |
| 9547 | config: rus_Cyrl-pol_Latn |
| 9548 | name: MTEB FloresBitextMining (rus_Cyrl-pol_Latn) |
| 9549 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9550 | split: devtest |
| 9551 | type: mteb/flores |
| 9552 | metrics: |
| 9553 | - type: accuracy |
| 9554 | value: 98.71541501976284 |
| 9555 | - type: f1 |
| 9556 | value: 98.32674571805006 |
| 9557 | - type: main_score |
| 9558 | value: 98.32674571805006 |
| 9559 | - type: precision |
| 9560 | value: 98.14723320158103 |
| 9561 | - type: recall |
| 9562 | value: 98.71541501976284 |
| 9563 | task: |
| 9564 | type: BitextMining |
| 9565 | - dataset: |
| 9566 | config: rus_Cyrl-ssw_Latn |
| 9567 | name: MTEB FloresBitextMining (rus_Cyrl-ssw_Latn) |
| 9568 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9569 | split: devtest |
| 9570 | type: mteb/flores |
| 9571 | metrics: |
| 9572 | - type: accuracy |
| 9573 | value: 80.8300395256917 |
| 9574 | - type: f1 |
| 9575 | value: 76.51717847370023 |
| 9576 | - type: main_score |
| 9577 | value: 76.51717847370023 |
| 9578 | - type: precision |
| 9579 | value: 74.74143610013175 |
| 9580 | - type: recall |
| 9581 | value: 80.8300395256917 |
| 9582 | task: |
| 9583 | type: BitextMining |
| 9584 | - dataset: |
| 9585 | config: rus_Cyrl-ukr_Cyrl |
| 9586 | name: MTEB FloresBitextMining (rus_Cyrl-ukr_Cyrl) |
| 9587 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9588 | split: devtest |
| 9589 | type: mteb/flores |
| 9590 | metrics: |
| 9591 | - type: accuracy |
| 9592 | value: 99.60474308300395 |
| 9593 | - type: f1 |
| 9594 | value: 99.4729907773386 |
| 9595 | - type: main_score |
| 9596 | value: 99.4729907773386 |
| 9597 | - type: precision |
| 9598 | value: 99.40711462450594 |
| 9599 | - type: recall |
| 9600 | value: 99.60474308300395 |
| 9601 | task: |
| 9602 | type: BitextMining |
| 9603 | - dataset: |
| 9604 | config: rus_Cyrl-afr_Latn |
| 9605 | name: MTEB FloresBitextMining (rus_Cyrl-afr_Latn) |
| 9606 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9607 | split: devtest |
| 9608 | type: mteb/flores |
| 9609 | metrics: |
| 9610 | - type: accuracy |
| 9611 | value: 99.1106719367589 |
| 9612 | - type: f1 |
| 9613 | value: 98.81422924901186 |
| 9614 | - type: main_score |
| 9615 | value: 98.81422924901186 |
| 9616 | - type: precision |
| 9617 | value: 98.66600790513834 |
| 9618 | - type: recall |
| 9619 | value: 99.1106719367589 |
| 9620 | task: |
| 9621 | type: BitextMining |
| 9622 | - dataset: |
| 9623 | config: rus_Cyrl-bho_Deva |
| 9624 | name: MTEB FloresBitextMining (rus_Cyrl-bho_Deva) |
| 9625 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9626 | split: devtest |
| 9627 | type: mteb/flores |
| 9628 | metrics: |
| 9629 | - type: accuracy |
| 9630 | value: 96.6403162055336 |
| 9631 | - type: f1 |
| 9632 | value: 95.56982872200265 |
| 9633 | - type: main_score |
| 9634 | value: 95.56982872200265 |
| 9635 | - type: precision |
| 9636 | value: 95.0592885375494 |
| 9637 | - type: recall |
| 9638 | value: 96.6403162055336 |
| 9639 | task: |
| 9640 | type: BitextMining |
| 9641 | - dataset: |
| 9642 | config: rus_Cyrl-eus_Latn |
| 9643 | name: MTEB FloresBitextMining (rus_Cyrl-eus_Latn) |
| 9644 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9645 | split: devtest |
| 9646 | type: mteb/flores |
| 9647 | metrics: |
| 9648 | - type: accuracy |
| 9649 | value: 97.62845849802372 |
| 9650 | - type: f1 |
| 9651 | value: 96.9038208168643 |
| 9652 | - type: main_score |
| 9653 | value: 96.9038208168643 |
| 9654 | - type: precision |
| 9655 | value: 96.55797101449275 |
| 9656 | - type: recall |
| 9657 | value: 97.62845849802372 |
| 9658 | task: |
| 9659 | type: BitextMining |
| 9660 | - dataset: |
| 9661 | config: rus_Cyrl-ibo_Latn |
| 9662 | name: MTEB FloresBitextMining (rus_Cyrl-ibo_Latn) |
| 9663 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9664 | split: devtest |
| 9665 | type: mteb/flores |
| 9666 | metrics: |
| 9667 | - type: accuracy |
| 9668 | value: 89.2292490118577 |
| 9669 | - type: f1 |
| 9670 | value: 86.35234330886506 |
| 9671 | - type: main_score |
| 9672 | value: 86.35234330886506 |
| 9673 | - type: precision |
| 9674 | value: 85.09881422924902 |
| 9675 | - type: recall |
| 9676 | value: 89.2292490118577 |
| 9677 | task: |
| 9678 | type: BitextMining |
| 9679 | - dataset: |
| 9680 | config: rus_Cyrl-kmr_Latn |
| 9681 | name: MTEB FloresBitextMining (rus_Cyrl-kmr_Latn) |
| 9682 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9683 | split: devtest |
| 9684 | type: mteb/flores |
| 9685 | metrics: |
| 9686 | - type: accuracy |
| 9687 | value: 83.49802371541502 |
| 9688 | - type: f1 |
| 9689 | value: 79.23630717108978 |
| 9690 | - type: main_score |
| 9691 | value: 79.23630717108978 |
| 9692 | - type: precision |
| 9693 | value: 77.48188405797102 |
| 9694 | - type: recall |
| 9695 | value: 83.49802371541502 |
| 9696 | task: |
| 9697 | type: BitextMining |
| 9698 | - dataset: |
| 9699 | config: rus_Cyrl-min_Latn |
| 9700 | name: MTEB FloresBitextMining (rus_Cyrl-min_Latn) |
| 9701 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9702 | split: devtest |
| 9703 | type: mteb/flores |
| 9704 | metrics: |
| 9705 | - type: accuracy |
| 9706 | value: 79.34782608695652 |
| 9707 | - type: f1 |
| 9708 | value: 75.31689928429059 |
| 9709 | - type: main_score |
| 9710 | value: 75.31689928429059 |
| 9711 | - type: precision |
| 9712 | value: 73.91519410541149 |
| 9713 | - type: recall |
| 9714 | value: 79.34782608695652 |
| 9715 | task: |
| 9716 | type: BitextMining |
| 9717 | - dataset: |
| 9718 | config: rus_Cyrl-por_Latn |
| 9719 | name: MTEB FloresBitextMining (rus_Cyrl-por_Latn) |
| 9720 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9721 | split: devtest |
| 9722 | type: mteb/flores |
| 9723 | metrics: |
| 9724 | - type: accuracy |
| 9725 | value: 96.54150197628458 |
| 9726 | - type: f1 |
| 9727 | value: 95.53218520609825 |
| 9728 | - type: main_score |
| 9729 | value: 95.53218520609825 |
| 9730 | - type: precision |
| 9731 | value: 95.07575757575756 |
| 9732 | - type: recall |
| 9733 | value: 96.54150197628458 |
| 9734 | task: |
| 9735 | type: BitextMining |
| 9736 | - dataset: |
| 9737 | config: rus_Cyrl-sun_Latn |
| 9738 | name: MTEB FloresBitextMining (rus_Cyrl-sun_Latn) |
| 9739 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9740 | split: devtest |
| 9741 | type: mteb/flores |
| 9742 | metrics: |
| 9743 | - type: accuracy |
| 9744 | value: 93.2806324110672 |
| 9745 | - type: f1 |
| 9746 | value: 91.56973461321287 |
| 9747 | - type: main_score |
| 9748 | value: 91.56973461321287 |
| 9749 | - type: precision |
| 9750 | value: 90.84396334890405 |
| 9751 | - type: recall |
| 9752 | value: 93.2806324110672 |
| 9753 | task: |
| 9754 | type: BitextMining |
| 9755 | - dataset: |
| 9756 | config: rus_Cyrl-umb_Latn |
| 9757 | name: MTEB FloresBitextMining (rus_Cyrl-umb_Latn) |
| 9758 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9759 | split: devtest |
| 9760 | type: mteb/flores |
| 9761 | metrics: |
| 9762 | - type: accuracy |
| 9763 | value: 51.87747035573123 |
| 9764 | - type: f1 |
| 9765 | value: 46.36591778884269 |
| 9766 | - type: main_score |
| 9767 | value: 46.36591778884269 |
| 9768 | - type: precision |
| 9769 | value: 44.57730391234227 |
| 9770 | - type: recall |
| 9771 | value: 51.87747035573123 |
| 9772 | task: |
| 9773 | type: BitextMining |
| 9774 | - dataset: |
| 9775 | config: rus_Cyrl-ajp_Arab |
| 9776 | name: MTEB FloresBitextMining (rus_Cyrl-ajp_Arab) |
| 9777 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9778 | split: devtest |
| 9779 | type: mteb/flores |
| 9780 | metrics: |
| 9781 | - type: accuracy |
| 9782 | value: 98.71541501976284 |
| 9783 | - type: f1 |
| 9784 | value: 98.30368906455863 |
| 9785 | - type: main_score |
| 9786 | value: 98.30368906455863 |
| 9787 | - type: precision |
| 9788 | value: 98.10606060606061 |
| 9789 | - type: recall |
| 9790 | value: 98.71541501976284 |
| 9791 | task: |
| 9792 | type: BitextMining |
| 9793 | - dataset: |
| 9794 | config: rus_Cyrl-bjn_Arab |
| 9795 | name: MTEB FloresBitextMining (rus_Cyrl-bjn_Arab) |
| 9796 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9797 | split: devtest |
| 9798 | type: mteb/flores |
| 9799 | metrics: |
| 9800 | - type: accuracy |
| 9801 | value: 14.82213438735178 |
| 9802 | - type: f1 |
| 9803 | value: 12.365434276616856 |
| 9804 | - type: main_score |
| 9805 | value: 12.365434276616856 |
| 9806 | - type: precision |
| 9807 | value: 11.802079517180589 |
| 9808 | - type: recall |
| 9809 | value: 14.82213438735178 |
| 9810 | task: |
| 9811 | type: BitextMining |
| 9812 | - dataset: |
| 9813 | config: rus_Cyrl-ewe_Latn |
| 9814 | name: MTEB FloresBitextMining (rus_Cyrl-ewe_Latn) |
| 9815 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9816 | split: devtest |
| 9817 | type: mteb/flores |
| 9818 | metrics: |
| 9819 | - type: accuracy |
| 9820 | value: 71.44268774703558 |
| 9821 | - type: f1 |
| 9822 | value: 66.74603174603175 |
| 9823 | - type: main_score |
| 9824 | value: 66.74603174603175 |
| 9825 | - type: precision |
| 9826 | value: 64.99933339607253 |
| 9827 | - type: recall |
| 9828 | value: 71.44268774703558 |
| 9829 | task: |
| 9830 | type: BitextMining |
| 9831 | - dataset: |
| 9832 | config: rus_Cyrl-ilo_Latn |
| 9833 | name: MTEB FloresBitextMining (rus_Cyrl-ilo_Latn) |
| 9834 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9835 | split: devtest |
| 9836 | type: mteb/flores |
| 9837 | metrics: |
| 9838 | - type: accuracy |
| 9839 | value: 85.86956521739131 |
| 9840 | - type: f1 |
| 9841 | value: 83.00139015960917 |
| 9842 | - type: main_score |
| 9843 | value: 83.00139015960917 |
| 9844 | - type: precision |
| 9845 | value: 81.91411396574439 |
| 9846 | - type: recall |
| 9847 | value: 85.86956521739131 |
| 9848 | task: |
| 9849 | type: BitextMining |
| 9850 | - dataset: |
| 9851 | config: rus_Cyrl-knc_Arab |
| 9852 | name: MTEB FloresBitextMining (rus_Cyrl-knc_Arab) |
| 9853 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9854 | split: devtest |
| 9855 | type: mteb/flores |
| 9856 | metrics: |
| 9857 | - type: accuracy |
| 9858 | value: 14.525691699604742 |
| 9859 | - type: f1 |
| 9860 | value: 12.618283715726806 |
| 9861 | - type: main_score |
| 9862 | value: 12.618283715726806 |
| 9863 | - type: precision |
| 9864 | value: 12.048458493742352 |
| 9865 | - type: recall |
| 9866 | value: 14.525691699604742 |
| 9867 | task: |
| 9868 | type: BitextMining |
| 9869 | - dataset: |
| 9870 | config: rus_Cyrl-mkd_Cyrl |
| 9871 | name: MTEB FloresBitextMining (rus_Cyrl-mkd_Cyrl) |
| 9872 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9873 | split: devtest |
| 9874 | type: mteb/flores |
| 9875 | metrics: |
| 9876 | - type: accuracy |
| 9877 | value: 99.40711462450594 |
| 9878 | - type: f1 |
| 9879 | value: 99.22595520421606 |
| 9880 | - type: main_score |
| 9881 | value: 99.22595520421606 |
| 9882 | - type: precision |
| 9883 | value: 99.14361001317523 |
| 9884 | - type: recall |
| 9885 | value: 99.40711462450594 |
| 9886 | task: |
| 9887 | type: BitextMining |
| 9888 | - dataset: |
| 9889 | config: rus_Cyrl-prs_Arab |
| 9890 | name: MTEB FloresBitextMining (rus_Cyrl-prs_Arab) |
| 9891 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9892 | split: devtest |
| 9893 | type: mteb/flores |
| 9894 | metrics: |
| 9895 | - type: accuracy |
| 9896 | value: 99.30830039525692 |
| 9897 | - type: f1 |
| 9898 | value: 99.07773386034255 |
| 9899 | - type: main_score |
| 9900 | value: 99.07773386034255 |
| 9901 | - type: precision |
| 9902 | value: 98.96245059288538 |
| 9903 | - type: recall |
| 9904 | value: 99.30830039525692 |
| 9905 | task: |
| 9906 | type: BitextMining |
| 9907 | - dataset: |
| 9908 | config: rus_Cyrl-swe_Latn |
| 9909 | name: MTEB FloresBitextMining (rus_Cyrl-swe_Latn) |
| 9910 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9911 | split: devtest |
| 9912 | type: mteb/flores |
| 9913 | metrics: |
| 9914 | - type: accuracy |
| 9915 | value: 99.30830039525692 |
| 9916 | - type: f1 |
| 9917 | value: 99.07773386034256 |
| 9918 | - type: main_score |
| 9919 | value: 99.07773386034256 |
| 9920 | - type: precision |
| 9921 | value: 98.96245059288538 |
| 9922 | - type: recall |
| 9923 | value: 99.30830039525692 |
| 9924 | task: |
| 9925 | type: BitextMining |
| 9926 | - dataset: |
| 9927 | config: rus_Cyrl-urd_Arab |
| 9928 | name: MTEB FloresBitextMining (rus_Cyrl-urd_Arab) |
| 9929 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9930 | split: devtest |
| 9931 | type: mteb/flores |
| 9932 | metrics: |
| 9933 | - type: accuracy |
| 9934 | value: 98.61660079051383 |
| 9935 | - type: f1 |
| 9936 | value: 98.15546772068511 |
| 9937 | - type: main_score |
| 9938 | value: 98.15546772068511 |
| 9939 | - type: precision |
| 9940 | value: 97.92490118577075 |
| 9941 | - type: recall |
| 9942 | value: 98.61660079051383 |
| 9943 | task: |
| 9944 | type: BitextMining |
| 9945 | - dataset: |
| 9946 | config: rus_Cyrl-aka_Latn |
| 9947 | name: MTEB FloresBitextMining (rus_Cyrl-aka_Latn) |
| 9948 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9949 | split: devtest |
| 9950 | type: mteb/flores |
| 9951 | metrics: |
| 9952 | - type: accuracy |
| 9953 | value: 81.02766798418972 |
| 9954 | - type: f1 |
| 9955 | value: 76.73277809147375 |
| 9956 | - type: main_score |
| 9957 | value: 76.73277809147375 |
| 9958 | - type: precision |
| 9959 | value: 74.97404165882426 |
| 9960 | - type: recall |
| 9961 | value: 81.02766798418972 |
| 9962 | task: |
| 9963 | type: BitextMining |
| 9964 | - dataset: |
| 9965 | config: rus_Cyrl-bjn_Latn |
| 9966 | name: MTEB FloresBitextMining (rus_Cyrl-bjn_Latn) |
| 9967 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9968 | split: devtest |
| 9969 | type: mteb/flores |
| 9970 | metrics: |
| 9971 | - type: accuracy |
| 9972 | value: 86.7588932806324 |
| 9973 | - type: f1 |
| 9974 | value: 83.92064566965753 |
| 9975 | - type: main_score |
| 9976 | value: 83.92064566965753 |
| 9977 | - type: precision |
| 9978 | value: 82.83734079929732 |
| 9979 | - type: recall |
| 9980 | value: 86.7588932806324 |
| 9981 | task: |
| 9982 | type: BitextMining |
| 9983 | - dataset: |
| 9984 | config: rus_Cyrl-fao_Latn |
| 9985 | name: MTEB FloresBitextMining (rus_Cyrl-fao_Latn) |
| 9986 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 9987 | split: devtest |
| 9988 | type: mteb/flores |
| 9989 | metrics: |
| 9990 | - type: accuracy |
| 9991 | value: 88.43873517786561 |
| 9992 | - type: f1 |
| 9993 | value: 85.48136645962732 |
| 9994 | - type: main_score |
| 9995 | value: 85.48136645962732 |
| 9996 | - type: precision |
| 9997 | value: 84.23418972332016 |
| 9998 | - type: recall |
| 9999 | value: 88.43873517786561 |
| 10000 | task: |
| 10001 | type: BitextMining |
| 10002 | - dataset: |
| 10003 | config: rus_Cyrl-ind_Latn |
| 10004 | name: MTEB FloresBitextMining (rus_Cyrl-ind_Latn) |
| 10005 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10006 | split: devtest |
| 10007 | type: mteb/flores |
| 10008 | metrics: |
| 10009 | - type: accuracy |
| 10010 | value: 99.01185770750988 |
| 10011 | - type: f1 |
| 10012 | value: 98.68247694334651 |
| 10013 | - type: main_score |
| 10014 | value: 98.68247694334651 |
| 10015 | - type: precision |
| 10016 | value: 98.51778656126481 |
| 10017 | - type: recall |
| 10018 | value: 99.01185770750988 |
| 10019 | task: |
| 10020 | type: BitextMining |
| 10021 | - dataset: |
| 10022 | config: rus_Cyrl-knc_Latn |
| 10023 | name: MTEB FloresBitextMining (rus_Cyrl-knc_Latn) |
| 10024 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10025 | split: devtest |
| 10026 | type: mteb/flores |
| 10027 | metrics: |
| 10028 | - type: accuracy |
| 10029 | value: 45.8498023715415 |
| 10030 | - type: f1 |
| 10031 | value: 40.112030865489366 |
| 10032 | - type: main_score |
| 10033 | value: 40.112030865489366 |
| 10034 | - type: precision |
| 10035 | value: 38.28262440050776 |
| 10036 | - type: recall |
| 10037 | value: 45.8498023715415 |
| 10038 | task: |
| 10039 | type: BitextMining |
| 10040 | - dataset: |
| 10041 | config: rus_Cyrl-mlt_Latn |
| 10042 | name: MTEB FloresBitextMining (rus_Cyrl-mlt_Latn) |
| 10043 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10044 | split: devtest |
| 10045 | type: mteb/flores |
| 10046 | metrics: |
| 10047 | - type: accuracy |
| 10048 | value: 93.18181818181817 |
| 10049 | - type: f1 |
| 10050 | value: 91.30787690570298 |
| 10051 | - type: main_score |
| 10052 | value: 91.30787690570298 |
| 10053 | - type: precision |
| 10054 | value: 90.4983060417843 |
| 10055 | - type: recall |
| 10056 | value: 93.18181818181817 |
| 10057 | task: |
| 10058 | type: BitextMining |
| 10059 | - dataset: |
| 10060 | config: rus_Cyrl-quy_Latn |
| 10061 | name: MTEB FloresBitextMining (rus_Cyrl-quy_Latn) |
| 10062 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10063 | split: devtest |
| 10064 | type: mteb/flores |
| 10065 | metrics: |
| 10066 | - type: accuracy |
| 10067 | value: 62.450592885375485 |
| 10068 | - type: f1 |
| 10069 | value: 57.28742975628178 |
| 10070 | - type: main_score |
| 10071 | value: 57.28742975628178 |
| 10072 | - type: precision |
| 10073 | value: 55.56854987623269 |
| 10074 | - type: recall |
| 10075 | value: 62.450592885375485 |
| 10076 | task: |
| 10077 | type: BitextMining |
| 10078 | - dataset: |
| 10079 | config: rus_Cyrl-swh_Latn |
| 10080 | name: MTEB FloresBitextMining (rus_Cyrl-swh_Latn) |
| 10081 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10082 | split: devtest |
| 10083 | type: mteb/flores |
| 10084 | metrics: |
| 10085 | - type: accuracy |
| 10086 | value: 98.3201581027668 |
| 10087 | - type: f1 |
| 10088 | value: 97.77667984189723 |
| 10089 | - type: main_score |
| 10090 | value: 97.77667984189723 |
| 10091 | - type: precision |
| 10092 | value: 97.51317523056655 |
| 10093 | - type: recall |
| 10094 | value: 98.3201581027668 |
| 10095 | task: |
| 10096 | type: BitextMining |
| 10097 | - dataset: |
| 10098 | config: rus_Cyrl-uzn_Latn |
| 10099 | name: MTEB FloresBitextMining (rus_Cyrl-uzn_Latn) |
| 10100 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10101 | split: devtest |
| 10102 | type: mteb/flores |
| 10103 | metrics: |
| 10104 | - type: accuracy |
| 10105 | value: 98.12252964426878 |
| 10106 | - type: f1 |
| 10107 | value: 97.59081498211933 |
| 10108 | - type: main_score |
| 10109 | value: 97.59081498211933 |
| 10110 | - type: precision |
| 10111 | value: 97.34848484848484 |
| 10112 | - type: recall |
| 10113 | value: 98.12252964426878 |
| 10114 | task: |
| 10115 | type: BitextMining |
| 10116 | - dataset: |
| 10117 | config: rus_Cyrl-als_Latn |
| 10118 | name: MTEB FloresBitextMining (rus_Cyrl-als_Latn) |
| 10119 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10120 | split: devtest |
| 10121 | type: mteb/flores |
| 10122 | metrics: |
| 10123 | - type: accuracy |
| 10124 | value: 99.30830039525692 |
| 10125 | - type: f1 |
| 10126 | value: 99.09420289855073 |
| 10127 | - type: main_score |
| 10128 | value: 99.09420289855073 |
| 10129 | - type: precision |
| 10130 | value: 98.99538866930172 |
| 10131 | - type: recall |
| 10132 | value: 99.30830039525692 |
| 10133 | task: |
| 10134 | type: BitextMining |
| 10135 | - dataset: |
| 10136 | config: rus_Cyrl-bod_Tibt |
| 10137 | name: MTEB FloresBitextMining (rus_Cyrl-bod_Tibt) |
| 10138 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10139 | split: devtest |
| 10140 | type: mteb/flores |
| 10141 | metrics: |
| 10142 | - type: accuracy |
| 10143 | value: 11.561264822134387 |
| 10144 | - type: f1 |
| 10145 | value: 8.121312045385636 |
| 10146 | - type: main_score |
| 10147 | value: 8.121312045385636 |
| 10148 | - type: precision |
| 10149 | value: 7.350577020893972 |
| 10150 | - type: recall |
| 10151 | value: 11.561264822134387 |
| 10152 | task: |
| 10153 | type: BitextMining |
| 10154 | - dataset: |
| 10155 | config: rus_Cyrl-fij_Latn |
| 10156 | name: MTEB FloresBitextMining (rus_Cyrl-fij_Latn) |
| 10157 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10158 | split: devtest |
| 10159 | type: mteb/flores |
| 10160 | metrics: |
| 10161 | - type: accuracy |
| 10162 | value: 72.23320158102767 |
| 10163 | - type: f1 |
| 10164 | value: 67.21000233846082 |
| 10165 | - type: main_score |
| 10166 | value: 67.21000233846082 |
| 10167 | - type: precision |
| 10168 | value: 65.3869439739005 |
| 10169 | - type: recall |
| 10170 | value: 72.23320158102767 |
| 10171 | task: |
| 10172 | type: BitextMining |
| 10173 | - dataset: |
| 10174 | config: rus_Cyrl-isl_Latn |
| 10175 | name: MTEB FloresBitextMining (rus_Cyrl-isl_Latn) |
| 10176 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10177 | split: devtest |
| 10178 | type: mteb/flores |
| 10179 | metrics: |
| 10180 | - type: accuracy |
| 10181 | value: 91.99604743083005 |
| 10182 | - type: f1 |
| 10183 | value: 89.75955204216073 |
| 10184 | - type: main_score |
| 10185 | value: 89.75955204216073 |
| 10186 | - type: precision |
| 10187 | value: 88.7598814229249 |
| 10188 | - type: recall |
| 10189 | value: 91.99604743083005 |
| 10190 | task: |
| 10191 | type: BitextMining |
| 10192 | - dataset: |
| 10193 | config: rus_Cyrl-kon_Latn |
| 10194 | name: MTEB FloresBitextMining (rus_Cyrl-kon_Latn) |
| 10195 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10196 | split: devtest |
| 10197 | type: mteb/flores |
| 10198 | metrics: |
| 10199 | - type: accuracy |
| 10200 | value: 81.81818181818183 |
| 10201 | - type: f1 |
| 10202 | value: 77.77800098452272 |
| 10203 | - type: main_score |
| 10204 | value: 77.77800098452272 |
| 10205 | - type: precision |
| 10206 | value: 76.1521268586486 |
| 10207 | - type: recall |
| 10208 | value: 81.81818181818183 |
| 10209 | task: |
| 10210 | type: BitextMining |
| 10211 | - dataset: |
| 10212 | config: rus_Cyrl-mni_Beng |
| 10213 | name: MTEB FloresBitextMining (rus_Cyrl-mni_Beng) |
| 10214 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10215 | split: devtest |
| 10216 | type: mteb/flores |
| 10217 | metrics: |
| 10218 | - type: accuracy |
| 10219 | value: 54.74308300395256 |
| 10220 | - type: f1 |
| 10221 | value: 48.97285299254615 |
| 10222 | - type: main_score |
| 10223 | value: 48.97285299254615 |
| 10224 | - type: precision |
| 10225 | value: 46.95125742968299 |
| 10226 | - type: recall |
| 10227 | value: 54.74308300395256 |
| 10228 | task: |
| 10229 | type: BitextMining |
| 10230 | - dataset: |
| 10231 | config: rus_Cyrl-ron_Latn |
| 10232 | name: MTEB FloresBitextMining (rus_Cyrl-ron_Latn) |
| 10233 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10234 | split: devtest |
| 10235 | type: mteb/flores |
| 10236 | metrics: |
| 10237 | - type: accuracy |
| 10238 | value: 98.22134387351778 |
| 10239 | - type: f1 |
| 10240 | value: 97.64492753623189 |
| 10241 | - type: main_score |
| 10242 | value: 97.64492753623189 |
| 10243 | - type: precision |
| 10244 | value: 97.36495388669302 |
| 10245 | - type: recall |
| 10246 | value: 98.22134387351778 |
| 10247 | task: |
| 10248 | type: BitextMining |
| 10249 | - dataset: |
| 10250 | config: rus_Cyrl-szl_Latn |
| 10251 | name: MTEB FloresBitextMining (rus_Cyrl-szl_Latn) |
| 10252 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10253 | split: devtest |
| 10254 | type: mteb/flores |
| 10255 | metrics: |
| 10256 | - type: accuracy |
| 10257 | value: 92.09486166007905 |
| 10258 | - type: f1 |
| 10259 | value: 90.10375494071147 |
| 10260 | - type: main_score |
| 10261 | value: 90.10375494071147 |
| 10262 | - type: precision |
| 10263 | value: 89.29606625258798 |
| 10264 | - type: recall |
| 10265 | value: 92.09486166007905 |
| 10266 | task: |
| 10267 | type: BitextMining |
| 10268 | - dataset: |
| 10269 | config: rus_Cyrl-vec_Latn |
| 10270 | name: MTEB FloresBitextMining (rus_Cyrl-vec_Latn) |
| 10271 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10272 | split: devtest |
| 10273 | type: mteb/flores |
| 10274 | metrics: |
| 10275 | - type: accuracy |
| 10276 | value: 92.4901185770751 |
| 10277 | - type: f1 |
| 10278 | value: 90.51430453604365 |
| 10279 | - type: main_score |
| 10280 | value: 90.51430453604365 |
| 10281 | - type: precision |
| 10282 | value: 89.69367588932808 |
| 10283 | - type: recall |
| 10284 | value: 92.4901185770751 |
| 10285 | task: |
| 10286 | type: BitextMining |
| 10287 | - dataset: |
| 10288 | config: rus_Cyrl-amh_Ethi |
| 10289 | name: MTEB FloresBitextMining (rus_Cyrl-amh_Ethi) |
| 10290 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10291 | split: devtest |
| 10292 | type: mteb/flores |
| 10293 | metrics: |
| 10294 | - type: accuracy |
| 10295 | value: 97.82608695652173 |
| 10296 | - type: f1 |
| 10297 | value: 97.11791831357048 |
| 10298 | - type: main_score |
| 10299 | value: 97.11791831357048 |
| 10300 | - type: precision |
| 10301 | value: 96.77206851119894 |
| 10302 | - type: recall |
| 10303 | value: 97.82608695652173 |
| 10304 | task: |
| 10305 | type: BitextMining |
| 10306 | - dataset: |
| 10307 | config: rus_Cyrl-bos_Latn |
| 10308 | name: MTEB FloresBitextMining (rus_Cyrl-bos_Latn) |
| 10309 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10310 | split: devtest |
| 10311 | type: mteb/flores |
| 10312 | metrics: |
| 10313 | - type: accuracy |
| 10314 | value: 98.91304347826086 |
| 10315 | - type: f1 |
| 10316 | value: 98.55072463768116 |
| 10317 | - type: main_score |
| 10318 | value: 98.55072463768116 |
| 10319 | - type: precision |
| 10320 | value: 98.36956521739131 |
| 10321 | - type: recall |
| 10322 | value: 98.91304347826086 |
| 10323 | task: |
| 10324 | type: BitextMining |
| 10325 | - dataset: |
| 10326 | config: rus_Cyrl-fin_Latn |
| 10327 | name: MTEB FloresBitextMining (rus_Cyrl-fin_Latn) |
| 10328 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10329 | split: devtest |
| 10330 | type: mteb/flores |
| 10331 | metrics: |
| 10332 | - type: accuracy |
| 10333 | value: 95.65217391304348 |
| 10334 | - type: f1 |
| 10335 | value: 94.4235836627141 |
| 10336 | - type: main_score |
| 10337 | value: 94.4235836627141 |
| 10338 | - type: precision |
| 10339 | value: 93.84881422924902 |
| 10340 | - type: recall |
| 10341 | value: 95.65217391304348 |
| 10342 | task: |
| 10343 | type: BitextMining |
| 10344 | - dataset: |
| 10345 | config: rus_Cyrl-ita_Latn |
| 10346 | name: MTEB FloresBitextMining (rus_Cyrl-ita_Latn) |
| 10347 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10348 | split: devtest |
| 10349 | type: mteb/flores |
| 10350 | metrics: |
| 10351 | - type: accuracy |
| 10352 | value: 98.91304347826086 |
| 10353 | - type: f1 |
| 10354 | value: 98.55072463768117 |
| 10355 | - type: main_score |
| 10356 | value: 98.55072463768117 |
| 10357 | - type: precision |
| 10358 | value: 98.36956521739131 |
| 10359 | - type: recall |
| 10360 | value: 98.91304347826086 |
| 10361 | task: |
| 10362 | type: BitextMining |
| 10363 | - dataset: |
| 10364 | config: rus_Cyrl-kor_Hang |
| 10365 | name: MTEB FloresBitextMining (rus_Cyrl-kor_Hang) |
| 10366 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10367 | split: devtest |
| 10368 | type: mteb/flores |
| 10369 | metrics: |
| 10370 | - type: accuracy |
| 10371 | value: 95.55335968379447 |
| 10372 | - type: f1 |
| 10373 | value: 94.15349143610013 |
| 10374 | - type: main_score |
| 10375 | value: 94.15349143610013 |
| 10376 | - type: precision |
| 10377 | value: 93.49472990777339 |
| 10378 | - type: recall |
| 10379 | value: 95.55335968379447 |
| 10380 | task: |
| 10381 | type: BitextMining |
| 10382 | - dataset: |
| 10383 | config: rus_Cyrl-mos_Latn |
| 10384 | name: MTEB FloresBitextMining (rus_Cyrl-mos_Latn) |
| 10385 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10386 | split: devtest |
| 10387 | type: mteb/flores |
| 10388 | metrics: |
| 10389 | - type: accuracy |
| 10390 | value: 43.67588932806324 |
| 10391 | - type: f1 |
| 10392 | value: 38.84849721190082 |
| 10393 | - type: main_score |
| 10394 | value: 38.84849721190082 |
| 10395 | - type: precision |
| 10396 | value: 37.43294462099682 |
| 10397 | - type: recall |
| 10398 | value: 43.67588932806324 |
| 10399 | task: |
| 10400 | type: BitextMining |
| 10401 | - dataset: |
| 10402 | config: rus_Cyrl-run_Latn |
| 10403 | name: MTEB FloresBitextMining (rus_Cyrl-run_Latn) |
| 10404 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10405 | split: devtest |
| 10406 | type: mteb/flores |
| 10407 | metrics: |
| 10408 | - type: accuracy |
| 10409 | value: 90.21739130434783 |
| 10410 | - type: f1 |
| 10411 | value: 87.37483530961792 |
| 10412 | - type: main_score |
| 10413 | value: 87.37483530961792 |
| 10414 | - type: precision |
| 10415 | value: 86.07872200263506 |
| 10416 | - type: recall |
| 10417 | value: 90.21739130434783 |
| 10418 | task: |
| 10419 | type: BitextMining |
| 10420 | - dataset: |
| 10421 | config: rus_Cyrl-tam_Taml |
| 10422 | name: MTEB FloresBitextMining (rus_Cyrl-tam_Taml) |
| 10423 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10424 | split: devtest |
| 10425 | type: mteb/flores |
| 10426 | metrics: |
| 10427 | - type: accuracy |
| 10428 | value: 99.40711462450594 |
| 10429 | - type: f1 |
| 10430 | value: 99.2094861660079 |
| 10431 | - type: main_score |
| 10432 | value: 99.2094861660079 |
| 10433 | - type: precision |
| 10434 | value: 99.1106719367589 |
| 10435 | - type: recall |
| 10436 | value: 99.40711462450594 |
| 10437 | task: |
| 10438 | type: BitextMining |
| 10439 | - dataset: |
| 10440 | config: rus_Cyrl-vie_Latn |
| 10441 | name: MTEB FloresBitextMining (rus_Cyrl-vie_Latn) |
| 10442 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10443 | split: devtest |
| 10444 | type: mteb/flores |
| 10445 | metrics: |
| 10446 | - type: accuracy |
| 10447 | value: 97.03557312252964 |
| 10448 | - type: f1 |
| 10449 | value: 96.13636363636364 |
| 10450 | - type: main_score |
| 10451 | value: 96.13636363636364 |
| 10452 | - type: precision |
| 10453 | value: 95.70981554677206 |
| 10454 | - type: recall |
| 10455 | value: 97.03557312252964 |
| 10456 | task: |
| 10457 | type: BitextMining |
| 10458 | - dataset: |
| 10459 | config: rus_Cyrl-apc_Arab |
| 10460 | name: MTEB FloresBitextMining (rus_Cyrl-apc_Arab) |
| 10461 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10462 | split: devtest |
| 10463 | type: mteb/flores |
| 10464 | metrics: |
| 10465 | - type: accuracy |
| 10466 | value: 98.12252964426878 |
| 10467 | - type: f1 |
| 10468 | value: 97.49670619235836 |
| 10469 | - type: main_score |
| 10470 | value: 97.49670619235836 |
| 10471 | - type: precision |
| 10472 | value: 97.18379446640316 |
| 10473 | - type: recall |
| 10474 | value: 98.12252964426878 |
| 10475 | task: |
| 10476 | type: BitextMining |
| 10477 | - dataset: |
| 10478 | config: rus_Cyrl-bug_Latn |
| 10479 | name: MTEB FloresBitextMining (rus_Cyrl-bug_Latn) |
| 10480 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10481 | split: devtest |
| 10482 | type: mteb/flores |
| 10483 | metrics: |
| 10484 | - type: accuracy |
| 10485 | value: 67.29249011857708 |
| 10486 | - type: f1 |
| 10487 | value: 62.09268717667927 |
| 10488 | - type: main_score |
| 10489 | value: 62.09268717667927 |
| 10490 | - type: precision |
| 10491 | value: 60.28554009748714 |
| 10492 | - type: recall |
| 10493 | value: 67.29249011857708 |
| 10494 | task: |
| 10495 | type: BitextMining |
| 10496 | - dataset: |
| 10497 | config: rus_Cyrl-fon_Latn |
| 10498 | name: MTEB FloresBitextMining (rus_Cyrl-fon_Latn) |
| 10499 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10500 | split: devtest |
| 10501 | type: mteb/flores |
| 10502 | metrics: |
| 10503 | - type: accuracy |
| 10504 | value: 63.43873517786561 |
| 10505 | - type: f1 |
| 10506 | value: 57.66660107569199 |
| 10507 | - type: main_score |
| 10508 | value: 57.66660107569199 |
| 10509 | - type: precision |
| 10510 | value: 55.66676396919363 |
| 10511 | - type: recall |
| 10512 | value: 63.43873517786561 |
| 10513 | task: |
| 10514 | type: BitextMining |
| 10515 | - dataset: |
| 10516 | config: rus_Cyrl-jav_Latn |
| 10517 | name: MTEB FloresBitextMining (rus_Cyrl-jav_Latn) |
| 10518 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10519 | split: devtest |
| 10520 | type: mteb/flores |
| 10521 | metrics: |
| 10522 | - type: accuracy |
| 10523 | value: 94.46640316205533 |
| 10524 | - type: f1 |
| 10525 | value: 92.89384528514964 |
| 10526 | - type: main_score |
| 10527 | value: 92.89384528514964 |
| 10528 | - type: precision |
| 10529 | value: 92.19367588932806 |
| 10530 | - type: recall |
| 10531 | value: 94.46640316205533 |
| 10532 | task: |
| 10533 | type: BitextMining |
| 10534 | - dataset: |
| 10535 | config: rus_Cyrl-lao_Laoo |
| 10536 | name: MTEB FloresBitextMining (rus_Cyrl-lao_Laoo) |
| 10537 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10538 | split: devtest |
| 10539 | type: mteb/flores |
| 10540 | metrics: |
| 10541 | - type: accuracy |
| 10542 | value: 97.23320158102767 |
| 10543 | - type: f1 |
| 10544 | value: 96.40974967061922 |
| 10545 | - type: main_score |
| 10546 | value: 96.40974967061922 |
| 10547 | - type: precision |
| 10548 | value: 96.034255599473 |
| 10549 | - type: recall |
| 10550 | value: 97.23320158102767 |
| 10551 | task: |
| 10552 | type: BitextMining |
| 10553 | - dataset: |
| 10554 | config: rus_Cyrl-mri_Latn |
| 10555 | name: MTEB FloresBitextMining (rus_Cyrl-mri_Latn) |
| 10556 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10557 | split: devtest |
| 10558 | type: mteb/flores |
| 10559 | metrics: |
| 10560 | - type: accuracy |
| 10561 | value: 76.77865612648222 |
| 10562 | - type: f1 |
| 10563 | value: 73.11286539547409 |
| 10564 | - type: main_score |
| 10565 | value: 73.11286539547409 |
| 10566 | - type: precision |
| 10567 | value: 71.78177214337046 |
| 10568 | - type: recall |
| 10569 | value: 76.77865612648222 |
| 10570 | task: |
| 10571 | type: BitextMining |
| 10572 | - dataset: |
| 10573 | config: rus_Cyrl-taq_Latn |
| 10574 | name: MTEB FloresBitextMining (rus_Cyrl-taq_Latn) |
| 10575 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10576 | split: devtest |
| 10577 | type: mteb/flores |
| 10578 | metrics: |
| 10579 | - type: accuracy |
| 10580 | value: 41.99604743083004 |
| 10581 | - type: f1 |
| 10582 | value: 37.25127063318763 |
| 10583 | - type: main_score |
| 10584 | value: 37.25127063318763 |
| 10585 | - type: precision |
| 10586 | value: 35.718929186985726 |
| 10587 | - type: recall |
| 10588 | value: 41.99604743083004 |
| 10589 | task: |
| 10590 | type: BitextMining |
| 10591 | - dataset: |
| 10592 | config: rus_Cyrl-war_Latn |
| 10593 | name: MTEB FloresBitextMining (rus_Cyrl-war_Latn) |
| 10594 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10595 | split: devtest |
| 10596 | type: mteb/flores |
| 10597 | metrics: |
| 10598 | - type: accuracy |
| 10599 | value: 95.55335968379447 |
| 10600 | - type: f1 |
| 10601 | value: 94.1699604743083 |
| 10602 | - type: main_score |
| 10603 | value: 94.1699604743083 |
| 10604 | - type: precision |
| 10605 | value: 93.52766798418972 |
| 10606 | - type: recall |
| 10607 | value: 95.55335968379447 |
| 10608 | task: |
| 10609 | type: BitextMining |
| 10610 | - dataset: |
| 10611 | config: rus_Cyrl-arb_Arab |
| 10612 | name: MTEB FloresBitextMining (rus_Cyrl-arb_Arab) |
| 10613 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10614 | split: devtest |
| 10615 | type: mteb/flores |
| 10616 | metrics: |
| 10617 | - type: accuracy |
| 10618 | value: 99.60474308300395 |
| 10619 | - type: f1 |
| 10620 | value: 99.4729907773386 |
| 10621 | - type: main_score |
| 10622 | value: 99.4729907773386 |
| 10623 | - type: precision |
| 10624 | value: 99.40711462450594 |
| 10625 | - type: recall |
| 10626 | value: 99.60474308300395 |
| 10627 | task: |
| 10628 | type: BitextMining |
| 10629 | - dataset: |
| 10630 | config: rus_Cyrl-bul_Cyrl |
| 10631 | name: MTEB FloresBitextMining (rus_Cyrl-bul_Cyrl) |
| 10632 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10633 | split: devtest |
| 10634 | type: mteb/flores |
| 10635 | metrics: |
| 10636 | - type: accuracy |
| 10637 | value: 99.70355731225297 |
| 10638 | - type: f1 |
| 10639 | value: 99.60474308300395 |
| 10640 | - type: main_score |
| 10641 | value: 99.60474308300395 |
| 10642 | - type: precision |
| 10643 | value: 99.55533596837944 |
| 10644 | - type: recall |
| 10645 | value: 99.70355731225297 |
| 10646 | task: |
| 10647 | type: BitextMining |
| 10648 | - dataset: |
| 10649 | config: rus_Cyrl-fra_Latn |
| 10650 | name: MTEB FloresBitextMining (rus_Cyrl-fra_Latn) |
| 10651 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10652 | split: devtest |
| 10653 | type: mteb/flores |
| 10654 | metrics: |
| 10655 | - type: accuracy |
| 10656 | value: 99.60474308300395 |
| 10657 | - type: f1 |
| 10658 | value: 99.47299077733861 |
| 10659 | - type: main_score |
| 10660 | value: 99.47299077733861 |
| 10661 | - type: precision |
| 10662 | value: 99.40711462450594 |
| 10663 | - type: recall |
| 10664 | value: 99.60474308300395 |
| 10665 | task: |
| 10666 | type: BitextMining |
| 10667 | - dataset: |
| 10668 | config: rus_Cyrl-jpn_Jpan |
| 10669 | name: MTEB FloresBitextMining (rus_Cyrl-jpn_Jpan) |
| 10670 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10671 | split: devtest |
| 10672 | type: mteb/flores |
| 10673 | metrics: |
| 10674 | - type: accuracy |
| 10675 | value: 96.44268774703558 |
| 10676 | - type: f1 |
| 10677 | value: 95.30632411067194 |
| 10678 | - type: main_score |
| 10679 | value: 95.30632411067194 |
| 10680 | - type: precision |
| 10681 | value: 94.76284584980237 |
| 10682 | - type: recall |
| 10683 | value: 96.44268774703558 |
| 10684 | task: |
| 10685 | type: BitextMining |
| 10686 | - dataset: |
| 10687 | config: rus_Cyrl-lij_Latn |
| 10688 | name: MTEB FloresBitextMining (rus_Cyrl-lij_Latn) |
| 10689 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10690 | split: devtest |
| 10691 | type: mteb/flores |
| 10692 | metrics: |
| 10693 | - type: accuracy |
| 10694 | value: 90.21739130434783 |
| 10695 | - type: f1 |
| 10696 | value: 87.4703557312253 |
| 10697 | - type: main_score |
| 10698 | value: 87.4703557312253 |
| 10699 | - type: precision |
| 10700 | value: 86.29611330698287 |
| 10701 | - type: recall |
| 10702 | value: 90.21739130434783 |
| 10703 | task: |
| 10704 | type: BitextMining |
| 10705 | - dataset: |
| 10706 | config: rus_Cyrl-mya_Mymr |
| 10707 | name: MTEB FloresBitextMining (rus_Cyrl-mya_Mymr) |
| 10708 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10709 | split: devtest |
| 10710 | type: mteb/flores |
| 10711 | metrics: |
| 10712 | - type: accuracy |
| 10713 | value: 98.02371541501977 |
| 10714 | - type: f1 |
| 10715 | value: 97.364953886693 |
| 10716 | - type: main_score |
| 10717 | value: 97.364953886693 |
| 10718 | - type: precision |
| 10719 | value: 97.03557312252964 |
| 10720 | - type: recall |
| 10721 | value: 98.02371541501977 |
| 10722 | task: |
| 10723 | type: BitextMining |
| 10724 | - dataset: |
| 10725 | config: rus_Cyrl-sag_Latn |
| 10726 | name: MTEB FloresBitextMining (rus_Cyrl-sag_Latn) |
| 10727 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10728 | split: devtest |
| 10729 | type: mteb/flores |
| 10730 | metrics: |
| 10731 | - type: accuracy |
| 10732 | value: 54.841897233201585 |
| 10733 | - type: f1 |
| 10734 | value: 49.61882037503349 |
| 10735 | - type: main_score |
| 10736 | value: 49.61882037503349 |
| 10737 | - type: precision |
| 10738 | value: 47.831968755881796 |
| 10739 | - type: recall |
| 10740 | value: 54.841897233201585 |
| 10741 | task: |
| 10742 | type: BitextMining |
| 10743 | - dataset: |
| 10744 | config: rus_Cyrl-taq_Tfng |
| 10745 | name: MTEB FloresBitextMining (rus_Cyrl-taq_Tfng) |
| 10746 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10747 | split: devtest |
| 10748 | type: mteb/flores |
| 10749 | metrics: |
| 10750 | - type: accuracy |
| 10751 | value: 15.316205533596838 |
| 10752 | - type: f1 |
| 10753 | value: 11.614836360389717 |
| 10754 | - type: main_score |
| 10755 | value: 11.614836360389717 |
| 10756 | - type: precision |
| 10757 | value: 10.741446193235223 |
| 10758 | - type: recall |
| 10759 | value: 15.316205533596838 |
| 10760 | task: |
| 10761 | type: BitextMining |
| 10762 | - dataset: |
| 10763 | config: rus_Cyrl-wol_Latn |
| 10764 | name: MTEB FloresBitextMining (rus_Cyrl-wol_Latn) |
| 10765 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10766 | split: devtest |
| 10767 | type: mteb/flores |
| 10768 | metrics: |
| 10769 | - type: accuracy |
| 10770 | value: 67.88537549407114 |
| 10771 | - type: f1 |
| 10772 | value: 62.2536417249856 |
| 10773 | - type: main_score |
| 10774 | value: 62.2536417249856 |
| 10775 | - type: precision |
| 10776 | value: 60.27629128666678 |
| 10777 | - type: recall |
| 10778 | value: 67.88537549407114 |
| 10779 | task: |
| 10780 | type: BitextMining |
| 10781 | - dataset: |
| 10782 | config: rus_Cyrl-arb_Latn |
| 10783 | name: MTEB FloresBitextMining (rus_Cyrl-arb_Latn) |
| 10784 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10785 | split: devtest |
| 10786 | type: mteb/flores |
| 10787 | metrics: |
| 10788 | - type: accuracy |
| 10789 | value: 27.766798418972332 |
| 10790 | - type: f1 |
| 10791 | value: 23.39674889624077 |
| 10792 | - type: main_score |
| 10793 | value: 23.39674889624077 |
| 10794 | - type: precision |
| 10795 | value: 22.28521155585345 |
| 10796 | - type: recall |
| 10797 | value: 27.766798418972332 |
| 10798 | task: |
| 10799 | type: BitextMining |
| 10800 | - dataset: |
| 10801 | config: rus_Cyrl-cat_Latn |
| 10802 | name: MTEB FloresBitextMining (rus_Cyrl-cat_Latn) |
| 10803 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10804 | split: devtest |
| 10805 | type: mteb/flores |
| 10806 | metrics: |
| 10807 | - type: accuracy |
| 10808 | value: 97.23320158102767 |
| 10809 | - type: f1 |
| 10810 | value: 96.42151326933936 |
| 10811 | - type: main_score |
| 10812 | value: 96.42151326933936 |
| 10813 | - type: precision |
| 10814 | value: 96.04743083003953 |
| 10815 | - type: recall |
| 10816 | value: 97.23320158102767 |
| 10817 | task: |
| 10818 | type: BitextMining |
| 10819 | - dataset: |
| 10820 | config: rus_Cyrl-fur_Latn |
| 10821 | name: MTEB FloresBitextMining (rus_Cyrl-fur_Latn) |
| 10822 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10823 | split: devtest |
| 10824 | type: mteb/flores |
| 10825 | metrics: |
| 10826 | - type: accuracy |
| 10827 | value: 88.63636363636364 |
| 10828 | - type: f1 |
| 10829 | value: 85.80792396009788 |
| 10830 | - type: main_score |
| 10831 | value: 85.80792396009788 |
| 10832 | - type: precision |
| 10833 | value: 84.61508901726293 |
| 10834 | - type: recall |
| 10835 | value: 88.63636363636364 |
| 10836 | task: |
| 10837 | type: BitextMining |
| 10838 | - dataset: |
| 10839 | config: rus_Cyrl-kab_Latn |
| 10840 | name: MTEB FloresBitextMining (rus_Cyrl-kab_Latn) |
| 10841 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10842 | split: devtest |
| 10843 | type: mteb/flores |
| 10844 | metrics: |
| 10845 | - type: accuracy |
| 10846 | value: 48.12252964426877 |
| 10847 | - type: f1 |
| 10848 | value: 43.05387582971066 |
| 10849 | - type: main_score |
| 10850 | value: 43.05387582971066 |
| 10851 | - type: precision |
| 10852 | value: 41.44165117538212 |
| 10853 | - type: recall |
| 10854 | value: 48.12252964426877 |
| 10855 | task: |
| 10856 | type: BitextMining |
| 10857 | - dataset: |
| 10858 | config: rus_Cyrl-lim_Latn |
| 10859 | name: MTEB FloresBitextMining (rus_Cyrl-lim_Latn) |
| 10860 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10861 | split: devtest |
| 10862 | type: mteb/flores |
| 10863 | metrics: |
| 10864 | - type: accuracy |
| 10865 | value: 81.81818181818183 |
| 10866 | - type: f1 |
| 10867 | value: 77.81676163099087 |
| 10868 | - type: main_score |
| 10869 | value: 77.81676163099087 |
| 10870 | - type: precision |
| 10871 | value: 76.19565217391305 |
| 10872 | - type: recall |
| 10873 | value: 81.81818181818183 |
| 10874 | task: |
| 10875 | type: BitextMining |
| 10876 | - dataset: |
| 10877 | config: rus_Cyrl-nld_Latn |
| 10878 | name: MTEB FloresBitextMining (rus_Cyrl-nld_Latn) |
| 10879 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10880 | split: devtest |
| 10881 | type: mteb/flores |
| 10882 | metrics: |
| 10883 | - type: accuracy |
| 10884 | value: 97.33201581027669 |
| 10885 | - type: f1 |
| 10886 | value: 96.4756258234519 |
| 10887 | - type: main_score |
| 10888 | value: 96.4756258234519 |
| 10889 | - type: precision |
| 10890 | value: 96.06389986824769 |
| 10891 | - type: recall |
| 10892 | value: 97.33201581027669 |
| 10893 | task: |
| 10894 | type: BitextMining |
| 10895 | - dataset: |
| 10896 | config: rus_Cyrl-san_Deva |
| 10897 | name: MTEB FloresBitextMining (rus_Cyrl-san_Deva) |
| 10898 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10899 | split: devtest |
| 10900 | type: mteb/flores |
| 10901 | metrics: |
| 10902 | - type: accuracy |
| 10903 | value: 93.47826086956522 |
| 10904 | - type: f1 |
| 10905 | value: 91.70289855072463 |
| 10906 | - type: main_score |
| 10907 | value: 91.70289855072463 |
| 10908 | - type: precision |
| 10909 | value: 90.9370882740448 |
| 10910 | - type: recall |
| 10911 | value: 93.47826086956522 |
| 10912 | task: |
| 10913 | type: BitextMining |
| 10914 | - dataset: |
| 10915 | config: rus_Cyrl-tat_Cyrl |
| 10916 | name: MTEB FloresBitextMining (rus_Cyrl-tat_Cyrl) |
| 10917 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10918 | split: devtest |
| 10919 | type: mteb/flores |
| 10920 | metrics: |
| 10921 | - type: accuracy |
| 10922 | value: 97.72727272727273 |
| 10923 | - type: f1 |
| 10924 | value: 97.00263504611331 |
| 10925 | - type: main_score |
| 10926 | value: 97.00263504611331 |
| 10927 | - type: precision |
| 10928 | value: 96.65678524374177 |
| 10929 | - type: recall |
| 10930 | value: 97.72727272727273 |
| 10931 | task: |
| 10932 | type: BitextMining |
| 10933 | - dataset: |
| 10934 | config: rus_Cyrl-xho_Latn |
| 10935 | name: MTEB FloresBitextMining (rus_Cyrl-xho_Latn) |
| 10936 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10937 | split: devtest |
| 10938 | type: mteb/flores |
| 10939 | metrics: |
| 10940 | - type: accuracy |
| 10941 | value: 93.08300395256917 |
| 10942 | - type: f1 |
| 10943 | value: 91.12977602108036 |
| 10944 | - type: main_score |
| 10945 | value: 91.12977602108036 |
| 10946 | - type: precision |
| 10947 | value: 90.22562582345192 |
| 10948 | - type: recall |
| 10949 | value: 93.08300395256917 |
| 10950 | task: |
| 10951 | type: BitextMining |
| 10952 | - dataset: |
| 10953 | config: rus_Cyrl-ars_Arab |
| 10954 | name: MTEB FloresBitextMining (rus_Cyrl-ars_Arab) |
| 10955 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10956 | split: devtest |
| 10957 | type: mteb/flores |
| 10958 | metrics: |
| 10959 | - type: accuracy |
| 10960 | value: 99.40711462450594 |
| 10961 | - type: f1 |
| 10962 | value: 99.2094861660079 |
| 10963 | - type: main_score |
| 10964 | value: 99.2094861660079 |
| 10965 | - type: precision |
| 10966 | value: 99.1106719367589 |
| 10967 | - type: recall |
| 10968 | value: 99.40711462450594 |
| 10969 | task: |
| 10970 | type: BitextMining |
| 10971 | - dataset: |
| 10972 | config: rus_Cyrl-ceb_Latn |
| 10973 | name: MTEB FloresBitextMining (rus_Cyrl-ceb_Latn) |
| 10974 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10975 | split: devtest |
| 10976 | type: mteb/flores |
| 10977 | metrics: |
| 10978 | - type: accuracy |
| 10979 | value: 95.65217391304348 |
| 10980 | - type: f1 |
| 10981 | value: 94.3544137022398 |
| 10982 | - type: main_score |
| 10983 | value: 94.3544137022398 |
| 10984 | - type: precision |
| 10985 | value: 93.76646903820817 |
| 10986 | - type: recall |
| 10987 | value: 95.65217391304348 |
| 10988 | task: |
| 10989 | type: BitextMining |
| 10990 | - dataset: |
| 10991 | config: rus_Cyrl-fuv_Latn |
| 10992 | name: MTEB FloresBitextMining (rus_Cyrl-fuv_Latn) |
| 10993 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 10994 | split: devtest |
| 10995 | type: mteb/flores |
| 10996 | metrics: |
| 10997 | - type: accuracy |
| 10998 | value: 51.18577075098815 |
| 10999 | - type: f1 |
| 11000 | value: 44.5990252610806 |
| 11001 | - type: main_score |
| 11002 | value: 44.5990252610806 |
| 11003 | - type: precision |
| 11004 | value: 42.34331599450177 |
| 11005 | - type: recall |
| 11006 | value: 51.18577075098815 |
| 11007 | task: |
| 11008 | type: BitextMining |
| 11009 | - dataset: |
| 11010 | config: rus_Cyrl-kac_Latn |
| 11011 | name: MTEB FloresBitextMining (rus_Cyrl-kac_Latn) |
| 11012 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11013 | split: devtest |
| 11014 | type: mteb/flores |
| 11015 | metrics: |
| 11016 | - type: accuracy |
| 11017 | value: 46.93675889328063 |
| 11018 | - type: f1 |
| 11019 | value: 41.79004018701787 |
| 11020 | - type: main_score |
| 11021 | value: 41.79004018701787 |
| 11022 | - type: precision |
| 11023 | value: 40.243355662392624 |
| 11024 | - type: recall |
| 11025 | value: 46.93675889328063 |
| 11026 | task: |
| 11027 | type: BitextMining |
| 11028 | - dataset: |
| 11029 | config: rus_Cyrl-lin_Latn |
| 11030 | name: MTEB FloresBitextMining (rus_Cyrl-lin_Latn) |
| 11031 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11032 | split: devtest |
| 11033 | type: mteb/flores |
| 11034 | metrics: |
| 11035 | - type: accuracy |
| 11036 | value: 91.50197628458498 |
| 11037 | - type: f1 |
| 11038 | value: 89.1205533596838 |
| 11039 | - type: main_score |
| 11040 | value: 89.1205533596838 |
| 11041 | - type: precision |
| 11042 | value: 88.07147562582345 |
| 11043 | - type: recall |
| 11044 | value: 91.50197628458498 |
| 11045 | task: |
| 11046 | type: BitextMining |
| 11047 | - dataset: |
| 11048 | config: rus_Cyrl-nno_Latn |
| 11049 | name: MTEB FloresBitextMining (rus_Cyrl-nno_Latn) |
| 11050 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11051 | split: devtest |
| 11052 | type: mteb/flores |
| 11053 | metrics: |
| 11054 | - type: accuracy |
| 11055 | value: 98.81422924901186 |
| 11056 | - type: f1 |
| 11057 | value: 98.41897233201581 |
| 11058 | - type: main_score |
| 11059 | value: 98.41897233201581 |
| 11060 | - type: precision |
| 11061 | value: 98.22134387351778 |
| 11062 | - type: recall |
| 11063 | value: 98.81422924901186 |
| 11064 | task: |
| 11065 | type: BitextMining |
| 11066 | - dataset: |
| 11067 | config: rus_Cyrl-sat_Olck |
| 11068 | name: MTEB FloresBitextMining (rus_Cyrl-sat_Olck) |
| 11069 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11070 | split: devtest |
| 11071 | type: mteb/flores |
| 11072 | metrics: |
| 11073 | - type: accuracy |
| 11074 | value: 2.371541501976284 |
| 11075 | - type: f1 |
| 11076 | value: 1.0726274943087382 |
| 11077 | - type: main_score |
| 11078 | value: 1.0726274943087382 |
| 11079 | - type: precision |
| 11080 | value: 0.875279634748803 |
| 11081 | - type: recall |
| 11082 | value: 2.371541501976284 |
| 11083 | task: |
| 11084 | type: BitextMining |
| 11085 | - dataset: |
| 11086 | config: rus_Cyrl-tel_Telu |
| 11087 | name: MTEB FloresBitextMining (rus_Cyrl-tel_Telu) |
| 11088 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11089 | split: devtest |
| 11090 | type: mteb/flores |
| 11091 | metrics: |
| 11092 | - type: accuracy |
| 11093 | value: 99.01185770750988 |
| 11094 | - type: f1 |
| 11095 | value: 98.68247694334651 |
| 11096 | - type: main_score |
| 11097 | value: 98.68247694334651 |
| 11098 | - type: precision |
| 11099 | value: 98.51778656126481 |
| 11100 | - type: recall |
| 11101 | value: 99.01185770750988 |
| 11102 | task: |
| 11103 | type: BitextMining |
| 11104 | - dataset: |
| 11105 | config: rus_Cyrl-ydd_Hebr |
| 11106 | name: MTEB FloresBitextMining (rus_Cyrl-ydd_Hebr) |
| 11107 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11108 | split: devtest |
| 11109 | type: mteb/flores |
| 11110 | metrics: |
| 11111 | - type: accuracy |
| 11112 | value: 89.42687747035573 |
| 11113 | - type: f1 |
| 11114 | value: 86.47609636740073 |
| 11115 | - type: main_score |
| 11116 | value: 86.47609636740073 |
| 11117 | - type: precision |
| 11118 | value: 85.13669301712781 |
| 11119 | - type: recall |
| 11120 | value: 89.42687747035573 |
| 11121 | task: |
| 11122 | type: BitextMining |
| 11123 | - dataset: |
| 11124 | config: rus_Cyrl-ary_Arab |
| 11125 | name: MTEB FloresBitextMining (rus_Cyrl-ary_Arab) |
| 11126 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11127 | split: devtest |
| 11128 | type: mteb/flores |
| 11129 | metrics: |
| 11130 | - type: accuracy |
| 11131 | value: 89.82213438735178 |
| 11132 | - type: f1 |
| 11133 | value: 87.04545454545456 |
| 11134 | - type: main_score |
| 11135 | value: 87.04545454545456 |
| 11136 | - type: precision |
| 11137 | value: 85.76910408432148 |
| 11138 | - type: recall |
| 11139 | value: 89.82213438735178 |
| 11140 | task: |
| 11141 | type: BitextMining |
| 11142 | - dataset: |
| 11143 | config: rus_Cyrl-ces_Latn |
| 11144 | name: MTEB FloresBitextMining (rus_Cyrl-ces_Latn) |
| 11145 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11146 | split: devtest |
| 11147 | type: mteb/flores |
| 11148 | metrics: |
| 11149 | - type: accuracy |
| 11150 | value: 99.2094861660079 |
| 11151 | - type: f1 |
| 11152 | value: 98.9459815546772 |
| 11153 | - type: main_score |
| 11154 | value: 98.9459815546772 |
| 11155 | - type: precision |
| 11156 | value: 98.81422924901186 |
| 11157 | - type: recall |
| 11158 | value: 99.2094861660079 |
| 11159 | task: |
| 11160 | type: BitextMining |
| 11161 | - dataset: |
| 11162 | config: rus_Cyrl-gaz_Latn |
| 11163 | name: MTEB FloresBitextMining (rus_Cyrl-gaz_Latn) |
| 11164 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11165 | split: devtest |
| 11166 | type: mteb/flores |
| 11167 | metrics: |
| 11168 | - type: accuracy |
| 11169 | value: 64.9209486166008 |
| 11170 | - type: f1 |
| 11171 | value: 58.697458119394874 |
| 11172 | - type: main_score |
| 11173 | value: 58.697458119394874 |
| 11174 | - type: precision |
| 11175 | value: 56.43402189597842 |
| 11176 | - type: recall |
| 11177 | value: 64.9209486166008 |
| 11178 | task: |
| 11179 | type: BitextMining |
| 11180 | - dataset: |
| 11181 | config: rus_Cyrl-kam_Latn |
| 11182 | name: MTEB FloresBitextMining (rus_Cyrl-kam_Latn) |
| 11183 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11184 | split: devtest |
| 11185 | type: mteb/flores |
| 11186 | metrics: |
| 11187 | - type: accuracy |
| 11188 | value: 59.18972332015811 |
| 11189 | - type: f1 |
| 11190 | value: 53.19031511966295 |
| 11191 | - type: main_score |
| 11192 | value: 53.19031511966295 |
| 11193 | - type: precision |
| 11194 | value: 51.08128357343655 |
| 11195 | - type: recall |
| 11196 | value: 59.18972332015811 |
| 11197 | task: |
| 11198 | type: BitextMining |
| 11199 | - dataset: |
| 11200 | config: rus_Cyrl-lit_Latn |
| 11201 | name: MTEB FloresBitextMining (rus_Cyrl-lit_Latn) |
| 11202 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11203 | split: devtest |
| 11204 | type: mteb/flores |
| 11205 | metrics: |
| 11206 | - type: accuracy |
| 11207 | value: 96.54150197628458 |
| 11208 | - type: f1 |
| 11209 | value: 95.5368906455863 |
| 11210 | - type: main_score |
| 11211 | value: 95.5368906455863 |
| 11212 | - type: precision |
| 11213 | value: 95.0592885375494 |
| 11214 | - type: recall |
| 11215 | value: 96.54150197628458 |
| 11216 | task: |
| 11217 | type: BitextMining |
| 11218 | - dataset: |
| 11219 | config: rus_Cyrl-nob_Latn |
| 11220 | name: MTEB FloresBitextMining (rus_Cyrl-nob_Latn) |
| 11221 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11222 | split: devtest |
| 11223 | type: mteb/flores |
| 11224 | metrics: |
| 11225 | - type: accuracy |
| 11226 | value: 98.12252964426878 |
| 11227 | - type: f1 |
| 11228 | value: 97.51317523056655 |
| 11229 | - type: main_score |
| 11230 | value: 97.51317523056655 |
| 11231 | - type: precision |
| 11232 | value: 97.2167325428195 |
| 11233 | - type: recall |
| 11234 | value: 98.12252964426878 |
| 11235 | task: |
| 11236 | type: BitextMining |
| 11237 | - dataset: |
| 11238 | config: rus_Cyrl-scn_Latn |
| 11239 | name: MTEB FloresBitextMining (rus_Cyrl-scn_Latn) |
| 11240 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11241 | split: devtest |
| 11242 | type: mteb/flores |
| 11243 | metrics: |
| 11244 | - type: accuracy |
| 11245 | value: 84.0909090909091 |
| 11246 | - type: f1 |
| 11247 | value: 80.37000439174352 |
| 11248 | - type: main_score |
| 11249 | value: 80.37000439174352 |
| 11250 | - type: precision |
| 11251 | value: 78.83994628559846 |
| 11252 | - type: recall |
| 11253 | value: 84.0909090909091 |
| 11254 | task: |
| 11255 | type: BitextMining |
| 11256 | - dataset: |
| 11257 | config: rus_Cyrl-tgk_Cyrl |
| 11258 | name: MTEB FloresBitextMining (rus_Cyrl-tgk_Cyrl) |
| 11259 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11260 | split: devtest |
| 11261 | type: mteb/flores |
| 11262 | metrics: |
| 11263 | - type: accuracy |
| 11264 | value: 92.68774703557312 |
| 11265 | - type: f1 |
| 11266 | value: 90.86344814605684 |
| 11267 | - type: main_score |
| 11268 | value: 90.86344814605684 |
| 11269 | - type: precision |
| 11270 | value: 90.12516469038208 |
| 11271 | - type: recall |
| 11272 | value: 92.68774703557312 |
| 11273 | task: |
| 11274 | type: BitextMining |
| 11275 | - dataset: |
| 11276 | config: rus_Cyrl-yor_Latn |
| 11277 | name: MTEB FloresBitextMining (rus_Cyrl-yor_Latn) |
| 11278 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11279 | split: devtest |
| 11280 | type: mteb/flores |
| 11281 | metrics: |
| 11282 | - type: accuracy |
| 11283 | value: 72.13438735177866 |
| 11284 | - type: f1 |
| 11285 | value: 66.78759646150951 |
| 11286 | - type: main_score |
| 11287 | value: 66.78759646150951 |
| 11288 | - type: precision |
| 11289 | value: 64.85080192096002 |
| 11290 | - type: recall |
| 11291 | value: 72.13438735177866 |
| 11292 | task: |
| 11293 | type: BitextMining |
| 11294 | - dataset: |
| 11295 | config: rus_Cyrl-arz_Arab |
| 11296 | name: MTEB FloresBitextMining (rus_Cyrl-arz_Arab) |
| 11297 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11298 | split: devtest |
| 11299 | type: mteb/flores |
| 11300 | metrics: |
| 11301 | - type: accuracy |
| 11302 | value: 98.02371541501977 |
| 11303 | - type: f1 |
| 11304 | value: 97.364953886693 |
| 11305 | - type: main_score |
| 11306 | value: 97.364953886693 |
| 11307 | - type: precision |
| 11308 | value: 97.03557312252964 |
| 11309 | - type: recall |
| 11310 | value: 98.02371541501977 |
| 11311 | task: |
| 11312 | type: BitextMining |
| 11313 | - dataset: |
| 11314 | config: rus_Cyrl-cjk_Latn |
| 11315 | name: MTEB FloresBitextMining (rus_Cyrl-cjk_Latn) |
| 11316 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11317 | split: devtest |
| 11318 | type: mteb/flores |
| 11319 | metrics: |
| 11320 | - type: accuracy |
| 11321 | value: 51.976284584980235 |
| 11322 | - type: f1 |
| 11323 | value: 46.468762353149714 |
| 11324 | - type: main_score |
| 11325 | value: 46.468762353149714 |
| 11326 | - type: precision |
| 11327 | value: 44.64073366247278 |
| 11328 | - type: recall |
| 11329 | value: 51.976284584980235 |
| 11330 | task: |
| 11331 | type: BitextMining |
| 11332 | - dataset: |
| 11333 | config: rus_Cyrl-gla_Latn |
| 11334 | name: MTEB FloresBitextMining (rus_Cyrl-gla_Latn) |
| 11335 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11336 | split: devtest |
| 11337 | type: mteb/flores |
| 11338 | metrics: |
| 11339 | - type: accuracy |
| 11340 | value: 79.74308300395256 |
| 11341 | - type: f1 |
| 11342 | value: 75.55611165294958 |
| 11343 | - type: main_score |
| 11344 | value: 75.55611165294958 |
| 11345 | - type: precision |
| 11346 | value: 73.95033408620365 |
| 11347 | - type: recall |
| 11348 | value: 79.74308300395256 |
| 11349 | task: |
| 11350 | type: BitextMining |
| 11351 | - dataset: |
| 11352 | config: rus_Cyrl-kan_Knda |
| 11353 | name: MTEB FloresBitextMining (rus_Cyrl-kan_Knda) |
| 11354 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11355 | split: devtest |
| 11356 | type: mteb/flores |
| 11357 | metrics: |
| 11358 | - type: accuracy |
| 11359 | value: 99.2094861660079 |
| 11360 | - type: f1 |
| 11361 | value: 98.96245059288538 |
| 11362 | - type: main_score |
| 11363 | value: 98.96245059288538 |
| 11364 | - type: precision |
| 11365 | value: 98.84716732542819 |
| 11366 | - type: recall |
| 11367 | value: 99.2094861660079 |
| 11368 | task: |
| 11369 | type: BitextMining |
| 11370 | - dataset: |
| 11371 | config: rus_Cyrl-lmo_Latn |
| 11372 | name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn) |
| 11373 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11374 | split: devtest |
| 11375 | type: mteb/flores |
| 11376 | metrics: |
| 11377 | - type: accuracy |
| 11378 | value: 82.41106719367589 |
| 11379 | - type: f1 |
| 11380 | value: 78.56413514022209 |
| 11381 | - type: main_score |
| 11382 | value: 78.56413514022209 |
| 11383 | - type: precision |
| 11384 | value: 77.15313068573938 |
| 11385 | - type: recall |
| 11386 | value: 82.41106719367589 |
| 11387 | task: |
| 11388 | type: BitextMining |
| 11389 | - dataset: |
| 11390 | config: rus_Cyrl-npi_Deva |
| 11391 | name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva) |
| 11392 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11393 | split: devtest |
| 11394 | type: mteb/flores |
| 11395 | metrics: |
| 11396 | - type: accuracy |
| 11397 | value: 98.71541501976284 |
| 11398 | - type: f1 |
| 11399 | value: 98.3201581027668 |
| 11400 | - type: main_score |
| 11401 | value: 98.3201581027668 |
| 11402 | - type: precision |
| 11403 | value: 98.12252964426878 |
| 11404 | - type: recall |
| 11405 | value: 98.71541501976284 |
| 11406 | task: |
| 11407 | type: BitextMining |
| 11408 | - dataset: |
| 11409 | config: rus_Cyrl-shn_Mymr |
| 11410 | name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr) |
| 11411 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11412 | split: devtest |
| 11413 | type: mteb/flores |
| 11414 | metrics: |
| 11415 | - type: accuracy |
| 11416 | value: 57.11462450592886 |
| 11417 | - type: f1 |
| 11418 | value: 51.51361369197337 |
| 11419 | - type: main_score |
| 11420 | value: 51.51361369197337 |
| 11421 | - type: precision |
| 11422 | value: 49.71860043649573 |
| 11423 | - type: recall |
| 11424 | value: 57.11462450592886 |
| 11425 | task: |
| 11426 | type: BitextMining |
| 11427 | - dataset: |
| 11428 | config: rus_Cyrl-tgl_Latn |
| 11429 | name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn) |
| 11430 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11431 | split: devtest |
| 11432 | type: mteb/flores |
| 11433 | metrics: |
| 11434 | - type: accuracy |
| 11435 | value: 97.82608695652173 |
| 11436 | - type: f1 |
| 11437 | value: 97.18379446640316 |
| 11438 | - type: main_score |
| 11439 | value: 97.18379446640316 |
| 11440 | - type: precision |
| 11441 | value: 96.88735177865613 |
| 11442 | - type: recall |
| 11443 | value: 97.82608695652173 |
| 11444 | task: |
| 11445 | type: BitextMining |
| 11446 | - dataset: |
| 11447 | config: rus_Cyrl-yue_Hant |
| 11448 | name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant) |
| 11449 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11450 | split: devtest |
| 11451 | type: mteb/flores |
| 11452 | metrics: |
| 11453 | - type: accuracy |
| 11454 | value: 99.30830039525692 |
| 11455 | - type: f1 |
| 11456 | value: 99.09420289855072 |
| 11457 | - type: main_score |
| 11458 | value: 99.09420289855072 |
| 11459 | - type: precision |
| 11460 | value: 98.9953886693017 |
| 11461 | - type: recall |
| 11462 | value: 99.30830039525692 |
| 11463 | task: |
| 11464 | type: BitextMining |
| 11465 | - dataset: |
| 11466 | config: rus_Cyrl-asm_Beng |
| 11467 | name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng) |
| 11468 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11469 | split: devtest |
| 11470 | type: mteb/flores |
| 11471 | metrics: |
| 11472 | - type: accuracy |
| 11473 | value: 95.55335968379447 |
| 11474 | - type: f1 |
| 11475 | value: 94.16007905138339 |
| 11476 | - type: main_score |
| 11477 | value: 94.16007905138339 |
| 11478 | - type: precision |
| 11479 | value: 93.50296442687747 |
| 11480 | - type: recall |
| 11481 | value: 95.55335968379447 |
| 11482 | task: |
| 11483 | type: BitextMining |
| 11484 | - dataset: |
| 11485 | config: rus_Cyrl-ckb_Arab |
| 11486 | name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab) |
| 11487 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11488 | split: devtest |
| 11489 | type: mteb/flores |
| 11490 | metrics: |
| 11491 | - type: accuracy |
| 11492 | value: 92.88537549407114 |
| 11493 | - type: f1 |
| 11494 | value: 90.76745718050066 |
| 11495 | - type: main_score |
| 11496 | value: 90.76745718050066 |
| 11497 | - type: precision |
| 11498 | value: 89.80072463768116 |
| 11499 | - type: recall |
| 11500 | value: 92.88537549407114 |
| 11501 | task: |
| 11502 | type: BitextMining |
| 11503 | - dataset: |
| 11504 | config: rus_Cyrl-gle_Latn |
| 11505 | name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn) |
| 11506 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11507 | split: devtest |
| 11508 | type: mteb/flores |
| 11509 | metrics: |
| 11510 | - type: accuracy |
| 11511 | value: 91.699604743083 |
| 11512 | - type: f1 |
| 11513 | value: 89.40899680030115 |
| 11514 | - type: main_score |
| 11515 | value: 89.40899680030115 |
| 11516 | - type: precision |
| 11517 | value: 88.40085638998683 |
| 11518 | - type: recall |
| 11519 | value: 91.699604743083 |
| 11520 | task: |
| 11521 | type: BitextMining |
| 11522 | - dataset: |
| 11523 | config: rus_Cyrl-kas_Arab |
| 11524 | name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab) |
| 11525 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11526 | split: devtest |
| 11527 | type: mteb/flores |
| 11528 | metrics: |
| 11529 | - type: accuracy |
| 11530 | value: 88.3399209486166 |
| 11531 | - type: f1 |
| 11532 | value: 85.14351590438548 |
| 11533 | - type: main_score |
| 11534 | value: 85.14351590438548 |
| 11535 | - type: precision |
| 11536 | value: 83.72364953886692 |
| 11537 | - type: recall |
| 11538 | value: 88.3399209486166 |
| 11539 | task: |
| 11540 | type: BitextMining |
| 11541 | - dataset: |
| 11542 | config: rus_Cyrl-ltg_Latn |
| 11543 | name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn) |
| 11544 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11545 | split: devtest |
| 11546 | type: mteb/flores |
| 11547 | metrics: |
| 11548 | - type: accuracy |
| 11549 | value: 83.399209486166 |
| 11550 | - type: f1 |
| 11551 | value: 79.88408934061107 |
| 11552 | - type: main_score |
| 11553 | value: 79.88408934061107 |
| 11554 | - type: precision |
| 11555 | value: 78.53794509179885 |
| 11556 | - type: recall |
| 11557 | value: 83.399209486166 |
| 11558 | task: |
| 11559 | type: BitextMining |
| 11560 | - dataset: |
| 11561 | config: rus_Cyrl-nso_Latn |
| 11562 | name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn) |
| 11563 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11564 | split: devtest |
| 11565 | type: mteb/flores |
| 11566 | metrics: |
| 11567 | - type: accuracy |
| 11568 | value: 91.20553359683794 |
| 11569 | - type: f1 |
| 11570 | value: 88.95406635525212 |
| 11571 | - type: main_score |
| 11572 | value: 88.95406635525212 |
| 11573 | - type: precision |
| 11574 | value: 88.01548089591567 |
| 11575 | - type: recall |
| 11576 | value: 91.20553359683794 |
| 11577 | task: |
| 11578 | type: BitextMining |
| 11579 | - dataset: |
| 11580 | config: rus_Cyrl-sin_Sinh |
| 11581 | name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh) |
| 11582 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11583 | split: devtest |
| 11584 | type: mteb/flores |
| 11585 | metrics: |
| 11586 | - type: accuracy |
| 11587 | value: 98.91304347826086 |
| 11588 | - type: f1 |
| 11589 | value: 98.56719367588933 |
| 11590 | - type: main_score |
| 11591 | value: 98.56719367588933 |
| 11592 | - type: precision |
| 11593 | value: 98.40250329380763 |
| 11594 | - type: recall |
| 11595 | value: 98.91304347826086 |
| 11596 | task: |
| 11597 | type: BitextMining |
| 11598 | - dataset: |
| 11599 | config: rus_Cyrl-tha_Thai |
| 11600 | name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai) |
| 11601 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11602 | split: devtest |
| 11603 | type: mteb/flores |
| 11604 | metrics: |
| 11605 | - type: accuracy |
| 11606 | value: 95.94861660079052 |
| 11607 | - type: f1 |
| 11608 | value: 94.66403162055336 |
| 11609 | - type: main_score |
| 11610 | value: 94.66403162055336 |
| 11611 | - type: precision |
| 11612 | value: 94.03820816864295 |
| 11613 | - type: recall |
| 11614 | value: 95.94861660079052 |
| 11615 | task: |
| 11616 | type: BitextMining |
| 11617 | - dataset: |
| 11618 | config: rus_Cyrl-zho_Hans |
| 11619 | name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans) |
| 11620 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11621 | split: devtest |
| 11622 | type: mteb/flores |
| 11623 | metrics: |
| 11624 | - type: accuracy |
| 11625 | value: 97.4308300395257 |
| 11626 | - type: f1 |
| 11627 | value: 96.5909090909091 |
| 11628 | - type: main_score |
| 11629 | value: 96.5909090909091 |
| 11630 | - type: precision |
| 11631 | value: 96.17918313570487 |
| 11632 | - type: recall |
| 11633 | value: 97.4308300395257 |
| 11634 | task: |
| 11635 | type: BitextMining |
| 11636 | - dataset: |
| 11637 | config: rus_Cyrl-ast_Latn |
| 11638 | name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn) |
| 11639 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11640 | split: devtest |
| 11641 | type: mteb/flores |
| 11642 | metrics: |
| 11643 | - type: accuracy |
| 11644 | value: 94.46640316205533 |
| 11645 | - type: f1 |
| 11646 | value: 92.86890645586297 |
| 11647 | - type: main_score |
| 11648 | value: 92.86890645586297 |
| 11649 | - type: precision |
| 11650 | value: 92.14756258234519 |
| 11651 | - type: recall |
| 11652 | value: 94.46640316205533 |
| 11653 | task: |
| 11654 | type: BitextMining |
| 11655 | - dataset: |
| 11656 | config: rus_Cyrl-crh_Latn |
| 11657 | name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn) |
| 11658 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11659 | split: devtest |
| 11660 | type: mteb/flores |
| 11661 | metrics: |
| 11662 | - type: accuracy |
| 11663 | value: 94.66403162055336 |
| 11664 | - type: f1 |
| 11665 | value: 93.2663592446201 |
| 11666 | - type: main_score |
| 11667 | value: 93.2663592446201 |
| 11668 | - type: precision |
| 11669 | value: 92.66716073781292 |
| 11670 | - type: recall |
| 11671 | value: 94.66403162055336 |
| 11672 | task: |
| 11673 | type: BitextMining |
| 11674 | - dataset: |
| 11675 | config: rus_Cyrl-glg_Latn |
| 11676 | name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn) |
| 11677 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11678 | split: devtest |
| 11679 | type: mteb/flores |
| 11680 | metrics: |
| 11681 | - type: accuracy |
| 11682 | value: 98.81422924901186 |
| 11683 | - type: f1 |
| 11684 | value: 98.46837944664031 |
| 11685 | - type: main_score |
| 11686 | value: 98.46837944664031 |
| 11687 | - type: precision |
| 11688 | value: 98.3201581027668 |
| 11689 | - type: recall |
| 11690 | value: 98.81422924901186 |
| 11691 | task: |
| 11692 | type: BitextMining |
| 11693 | - dataset: |
| 11694 | config: rus_Cyrl-kas_Deva |
| 11695 | name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva) |
| 11696 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11697 | split: devtest |
| 11698 | type: mteb/flores |
| 11699 | metrics: |
| 11700 | - type: accuracy |
| 11701 | value: 69.1699604743083 |
| 11702 | - type: f1 |
| 11703 | value: 63.05505292906477 |
| 11704 | - type: main_score |
| 11705 | value: 63.05505292906477 |
| 11706 | - type: precision |
| 11707 | value: 60.62594108789761 |
| 11708 | - type: recall |
| 11709 | value: 69.1699604743083 |
| 11710 | task: |
| 11711 | type: BitextMining |
| 11712 | - dataset: |
| 11713 | config: rus_Cyrl-ltz_Latn |
| 11714 | name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn) |
| 11715 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11716 | split: devtest |
| 11717 | type: mteb/flores |
| 11718 | metrics: |
| 11719 | - type: accuracy |
| 11720 | value: 91.40316205533597 |
| 11721 | - type: f1 |
| 11722 | value: 89.26571616789009 |
| 11723 | - type: main_score |
| 11724 | value: 89.26571616789009 |
| 11725 | - type: precision |
| 11726 | value: 88.40179747788443 |
| 11727 | - type: recall |
| 11728 | value: 91.40316205533597 |
| 11729 | task: |
| 11730 | type: BitextMining |
| 11731 | - dataset: |
| 11732 | config: rus_Cyrl-nus_Latn |
| 11733 | name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn) |
| 11734 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11735 | split: devtest |
| 11736 | type: mteb/flores |
| 11737 | metrics: |
| 11738 | - type: accuracy |
| 11739 | value: 38.93280632411067 |
| 11740 | - type: f1 |
| 11741 | value: 33.98513032905371 |
| 11742 | - type: main_score |
| 11743 | value: 33.98513032905371 |
| 11744 | - type: precision |
| 11745 | value: 32.56257884802308 |
| 11746 | - type: recall |
| 11747 | value: 38.93280632411067 |
| 11748 | task: |
| 11749 | type: BitextMining |
| 11750 | - dataset: |
| 11751 | config: rus_Cyrl-slk_Latn |
| 11752 | name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn) |
| 11753 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11754 | split: devtest |
| 11755 | type: mteb/flores |
| 11756 | metrics: |
| 11757 | - type: accuracy |
| 11758 | value: 98.02371541501977 |
| 11759 | - type: f1 |
| 11760 | value: 97.42094861660078 |
| 11761 | - type: main_score |
| 11762 | value: 97.42094861660078 |
| 11763 | - type: precision |
| 11764 | value: 97.14262187088273 |
| 11765 | - type: recall |
| 11766 | value: 98.02371541501977 |
| 11767 | task: |
| 11768 | type: BitextMining |
| 11769 | - dataset: |
| 11770 | config: rus_Cyrl-tir_Ethi |
| 11771 | name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi) |
| 11772 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11773 | split: devtest |
| 11774 | type: mteb/flores |
| 11775 | metrics: |
| 11776 | - type: accuracy |
| 11777 | value: 91.30434782608695 |
| 11778 | - type: f1 |
| 11779 | value: 88.78129117259552 |
| 11780 | - type: main_score |
| 11781 | value: 88.78129117259552 |
| 11782 | - type: precision |
| 11783 | value: 87.61528326745717 |
| 11784 | - type: recall |
| 11785 | value: 91.30434782608695 |
| 11786 | task: |
| 11787 | type: BitextMining |
| 11788 | - dataset: |
| 11789 | config: rus_Cyrl-zho_Hant |
| 11790 | name: MTEB FloresBitextMining (rus_Cyrl-zho_Hant) |
| 11791 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11792 | split: devtest |
| 11793 | type: mteb/flores |
| 11794 | metrics: |
| 11795 | - type: accuracy |
| 11796 | value: 99.1106719367589 |
| 11797 | - type: f1 |
| 11798 | value: 98.81422924901186 |
| 11799 | - type: main_score |
| 11800 | value: 98.81422924901186 |
| 11801 | - type: precision |
| 11802 | value: 98.66600790513834 |
| 11803 | - type: recall |
| 11804 | value: 99.1106719367589 |
| 11805 | task: |
| 11806 | type: BitextMining |
| 11807 | - dataset: |
| 11808 | config: rus_Cyrl-awa_Deva |
| 11809 | name: MTEB FloresBitextMining (rus_Cyrl-awa_Deva) |
| 11810 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11811 | split: devtest |
| 11812 | type: mteb/flores |
| 11813 | metrics: |
| 11814 | - type: accuracy |
| 11815 | value: 98.12252964426878 |
| 11816 | - type: f1 |
| 11817 | value: 97.70092226613966 |
| 11818 | - type: main_score |
| 11819 | value: 97.70092226613966 |
| 11820 | - type: precision |
| 11821 | value: 97.50494071146245 |
| 11822 | - type: recall |
| 11823 | value: 98.12252964426878 |
| 11824 | task: |
| 11825 | type: BitextMining |
| 11826 | - dataset: |
| 11827 | config: rus_Cyrl-cym_Latn |
| 11828 | name: MTEB FloresBitextMining (rus_Cyrl-cym_Latn) |
| 11829 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11830 | split: devtest |
| 11831 | type: mteb/flores |
| 11832 | metrics: |
| 11833 | - type: accuracy |
| 11834 | value: 95.94861660079052 |
| 11835 | - type: f1 |
| 11836 | value: 94.74308300395256 |
| 11837 | - type: main_score |
| 11838 | value: 94.74308300395256 |
| 11839 | - type: precision |
| 11840 | value: 94.20289855072464 |
| 11841 | - type: recall |
| 11842 | value: 95.94861660079052 |
| 11843 | task: |
| 11844 | type: BitextMining |
| 11845 | - dataset: |
| 11846 | config: rus_Cyrl-grn_Latn |
| 11847 | name: MTEB FloresBitextMining (rus_Cyrl-grn_Latn) |
| 11848 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11849 | split: devtest |
| 11850 | type: mteb/flores |
| 11851 | metrics: |
| 11852 | - type: accuracy |
| 11853 | value: 77.96442687747036 |
| 11854 | - type: f1 |
| 11855 | value: 73.64286789187975 |
| 11856 | - type: main_score |
| 11857 | value: 73.64286789187975 |
| 11858 | - type: precision |
| 11859 | value: 71.99324893260821 |
| 11860 | - type: recall |
| 11861 | value: 77.96442687747036 |
| 11862 | task: |
| 11863 | type: BitextMining |
| 11864 | - dataset: |
| 11865 | config: rus_Cyrl-kat_Geor |
| 11866 | name: MTEB FloresBitextMining (rus_Cyrl-kat_Geor) |
| 11867 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11868 | split: devtest |
| 11869 | type: mteb/flores |
| 11870 | metrics: |
| 11871 | - type: accuracy |
| 11872 | value: 98.91304347826086 |
| 11873 | - type: f1 |
| 11874 | value: 98.56719367588933 |
| 11875 | - type: main_score |
| 11876 | value: 98.56719367588933 |
| 11877 | - type: precision |
| 11878 | value: 98.40250329380764 |
| 11879 | - type: recall |
| 11880 | value: 98.91304347826086 |
| 11881 | task: |
| 11882 | type: BitextMining |
| 11883 | - dataset: |
| 11884 | config: rus_Cyrl-lua_Latn |
| 11885 | name: MTEB FloresBitextMining (rus_Cyrl-lua_Latn) |
| 11886 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11887 | split: devtest |
| 11888 | type: mteb/flores |
| 11889 | metrics: |
| 11890 | - type: accuracy |
| 11891 | value: 72.03557312252964 |
| 11892 | - type: f1 |
| 11893 | value: 67.23928163404449 |
| 11894 | - type: main_score |
| 11895 | value: 67.23928163404449 |
| 11896 | - type: precision |
| 11897 | value: 65.30797101449275 |
| 11898 | - type: recall |
| 11899 | value: 72.03557312252964 |
| 11900 | task: |
| 11901 | type: BitextMining |
| 11902 | - dataset: |
| 11903 | config: rus_Cyrl-nya_Latn |
| 11904 | name: MTEB FloresBitextMining (rus_Cyrl-nya_Latn) |
| 11905 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11906 | split: devtest |
| 11907 | type: mteb/flores |
| 11908 | metrics: |
| 11909 | - type: accuracy |
| 11910 | value: 92.29249011857708 |
| 11911 | - type: f1 |
| 11912 | value: 90.0494071146245 |
| 11913 | - type: main_score |
| 11914 | value: 90.0494071146245 |
| 11915 | - type: precision |
| 11916 | value: 89.04808959156786 |
| 11917 | - type: recall |
| 11918 | value: 92.29249011857708 |
| 11919 | task: |
| 11920 | type: BitextMining |
| 11921 | - dataset: |
| 11922 | config: rus_Cyrl-slv_Latn |
| 11923 | name: MTEB FloresBitextMining (rus_Cyrl-slv_Latn) |
| 11924 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11925 | split: devtest |
| 11926 | type: mteb/flores |
| 11927 | metrics: |
| 11928 | - type: accuracy |
| 11929 | value: 98.71541501976284 |
| 11930 | - type: f1 |
| 11931 | value: 98.30368906455863 |
| 11932 | - type: main_score |
| 11933 | value: 98.30368906455863 |
| 11934 | - type: precision |
| 11935 | value: 98.10606060606061 |
| 11936 | - type: recall |
| 11937 | value: 98.71541501976284 |
| 11938 | task: |
| 11939 | type: BitextMining |
| 11940 | - dataset: |
| 11941 | config: rus_Cyrl-tpi_Latn |
| 11942 | name: MTEB FloresBitextMining (rus_Cyrl-tpi_Latn) |
| 11943 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11944 | split: devtest |
| 11945 | type: mteb/flores |
| 11946 | metrics: |
| 11947 | - type: accuracy |
| 11948 | value: 80.53359683794467 |
| 11949 | - type: f1 |
| 11950 | value: 76.59481822525301 |
| 11951 | - type: main_score |
| 11952 | value: 76.59481822525301 |
| 11953 | - type: precision |
| 11954 | value: 75.12913223140497 |
| 11955 | - type: recall |
| 11956 | value: 80.53359683794467 |
| 11957 | task: |
| 11958 | type: BitextMining |
| 11959 | - dataset: |
| 11960 | config: rus_Cyrl-zsm_Latn |
| 11961 | name: MTEB FloresBitextMining (rus_Cyrl-zsm_Latn) |
| 11962 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11963 | split: devtest |
| 11964 | type: mteb/flores |
| 11965 | metrics: |
| 11966 | - type: accuracy |
| 11967 | value: 97.33201581027669 |
| 11968 | - type: f1 |
| 11969 | value: 96.58620365142104 |
| 11970 | - type: main_score |
| 11971 | value: 96.58620365142104 |
| 11972 | - type: precision |
| 11973 | value: 96.26152832674572 |
| 11974 | - type: recall |
| 11975 | value: 97.33201581027669 |
| 11976 | task: |
| 11977 | type: BitextMining |
| 11978 | - dataset: |
| 11979 | config: rus_Cyrl-ayr_Latn |
| 11980 | name: MTEB FloresBitextMining (rus_Cyrl-ayr_Latn) |
| 11981 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 11982 | split: devtest |
| 11983 | type: mteb/flores |
| 11984 | metrics: |
| 11985 | - type: accuracy |
| 11986 | value: 45.55335968379446 |
| 11987 | - type: f1 |
| 11988 | value: 40.13076578531388 |
| 11989 | - type: main_score |
| 11990 | value: 40.13076578531388 |
| 11991 | - type: precision |
| 11992 | value: 38.398064362362355 |
| 11993 | - type: recall |
| 11994 | value: 45.55335968379446 |
| 11995 | task: |
| 11996 | type: BitextMining |
| 11997 | - dataset: |
| 11998 | config: rus_Cyrl-dan_Latn |
| 11999 | name: MTEB FloresBitextMining (rus_Cyrl-dan_Latn) |
| 12000 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12001 | split: devtest |
| 12002 | type: mteb/flores |
| 12003 | metrics: |
| 12004 | - type: accuracy |
| 12005 | value: 99.01185770750988 |
| 12006 | - type: f1 |
| 12007 | value: 98.68247694334651 |
| 12008 | - type: main_score |
| 12009 | value: 98.68247694334651 |
| 12010 | - type: precision |
| 12011 | value: 98.51778656126481 |
| 12012 | - type: recall |
| 12013 | value: 99.01185770750988 |
| 12014 | task: |
| 12015 | type: BitextMining |
| 12016 | - dataset: |
| 12017 | config: rus_Cyrl-guj_Gujr |
| 12018 | name: MTEB FloresBitextMining (rus_Cyrl-guj_Gujr) |
| 12019 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12020 | split: devtest |
| 12021 | type: mteb/flores |
| 12022 | metrics: |
| 12023 | - type: accuracy |
| 12024 | value: 99.01185770750988 |
| 12025 | - type: f1 |
| 12026 | value: 98.68247694334651 |
| 12027 | - type: main_score |
| 12028 | value: 98.68247694334651 |
| 12029 | - type: precision |
| 12030 | value: 98.51778656126481 |
| 12031 | - type: recall |
| 12032 | value: 99.01185770750988 |
| 12033 | task: |
| 12034 | type: BitextMining |
| 12035 | - dataset: |
| 12036 | config: rus_Cyrl-kaz_Cyrl |
| 12037 | name: MTEB FloresBitextMining (rus_Cyrl-kaz_Cyrl) |
| 12038 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12039 | split: devtest |
| 12040 | type: mteb/flores |
| 12041 | metrics: |
| 12042 | - type: accuracy |
| 12043 | value: 98.81422924901186 |
| 12044 | - type: f1 |
| 12045 | value: 98.43544137022398 |
| 12046 | - type: main_score |
| 12047 | value: 98.43544137022398 |
| 12048 | - type: precision |
| 12049 | value: 98.25428194993412 |
| 12050 | - type: recall |
| 12051 | value: 98.81422924901186 |
| 12052 | task: |
| 12053 | type: BitextMining |
| 12054 | - dataset: |
| 12055 | config: rus_Cyrl-lug_Latn |
| 12056 | name: MTEB FloresBitextMining (rus_Cyrl-lug_Latn) |
| 12057 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12058 | split: devtest |
| 12059 | type: mteb/flores |
| 12060 | metrics: |
| 12061 | - type: accuracy |
| 12062 | value: 82.21343873517787 |
| 12063 | - type: f1 |
| 12064 | value: 77.97485726833554 |
| 12065 | - type: main_score |
| 12066 | value: 77.97485726833554 |
| 12067 | - type: precision |
| 12068 | value: 76.22376717485415 |
| 12069 | - type: recall |
| 12070 | value: 82.21343873517787 |
| 12071 | task: |
| 12072 | type: BitextMining |
| 12073 | - dataset: |
| 12074 | config: rus_Cyrl-oci_Latn |
| 12075 | name: MTEB FloresBitextMining (rus_Cyrl-oci_Latn) |
| 12076 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12077 | split: devtest |
| 12078 | type: mteb/flores |
| 12079 | metrics: |
| 12080 | - type: accuracy |
| 12081 | value: 93.87351778656127 |
| 12082 | - type: f1 |
| 12083 | value: 92.25319969885187 |
| 12084 | - type: main_score |
| 12085 | value: 92.25319969885187 |
| 12086 | - type: precision |
| 12087 | value: 91.5638528138528 |
| 12088 | - type: recall |
| 12089 | value: 93.87351778656127 |
| 12090 | task: |
| 12091 | type: BitextMining |
| 12092 | - dataset: |
| 12093 | config: rus_Cyrl-smo_Latn |
| 12094 | name: MTEB FloresBitextMining (rus_Cyrl-smo_Latn) |
| 12095 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12096 | split: devtest |
| 12097 | type: mteb/flores |
| 12098 | metrics: |
| 12099 | - type: accuracy |
| 12100 | value: 84.88142292490119 |
| 12101 | - type: f1 |
| 12102 | value: 81.24364765669114 |
| 12103 | - type: main_score |
| 12104 | value: 81.24364765669114 |
| 12105 | - type: precision |
| 12106 | value: 79.69991416137661 |
| 12107 | - type: recall |
| 12108 | value: 84.88142292490119 |
| 12109 | task: |
| 12110 | type: BitextMining |
| 12111 | - dataset: |
| 12112 | config: rus_Cyrl-tsn_Latn |
| 12113 | name: MTEB FloresBitextMining (rus_Cyrl-tsn_Latn) |
| 12114 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12115 | split: devtest |
| 12116 | type: mteb/flores |
| 12117 | metrics: |
| 12118 | - type: accuracy |
| 12119 | value: 87.05533596837944 |
| 12120 | - type: f1 |
| 12121 | value: 83.90645586297761 |
| 12122 | - type: main_score |
| 12123 | value: 83.90645586297761 |
| 12124 | - type: precision |
| 12125 | value: 82.56752305665349 |
| 12126 | - type: recall |
| 12127 | value: 87.05533596837944 |
| 12128 | task: |
| 12129 | type: BitextMining |
| 12130 | - dataset: |
| 12131 | config: rus_Cyrl-zul_Latn |
| 12132 | name: MTEB FloresBitextMining (rus_Cyrl-zul_Latn) |
| 12133 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12134 | split: devtest |
| 12135 | type: mteb/flores |
| 12136 | metrics: |
| 12137 | - type: accuracy |
| 12138 | value: 95.15810276679841 |
| 12139 | - type: f1 |
| 12140 | value: 93.77140974967062 |
| 12141 | - type: main_score |
| 12142 | value: 93.77140974967062 |
| 12143 | - type: precision |
| 12144 | value: 93.16534914361002 |
| 12145 | - type: recall |
| 12146 | value: 95.15810276679841 |
| 12147 | task: |
| 12148 | type: BitextMining |
| 12149 | - dataset: |
| 12150 | config: rus_Cyrl-azb_Arab |
| 12151 | name: MTEB FloresBitextMining (rus_Cyrl-azb_Arab) |
| 12152 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12153 | split: devtest |
| 12154 | type: mteb/flores |
| 12155 | metrics: |
| 12156 | - type: accuracy |
| 12157 | value: 81.91699604743083 |
| 12158 | - type: f1 |
| 12159 | value: 77.18050065876152 |
| 12160 | - type: main_score |
| 12161 | value: 77.18050065876152 |
| 12162 | - type: precision |
| 12163 | value: 75.21519543258673 |
| 12164 | - type: recall |
| 12165 | value: 81.91699604743083 |
| 12166 | task: |
| 12167 | type: BitextMining |
| 12168 | - dataset: |
| 12169 | config: rus_Cyrl-deu_Latn |
| 12170 | name: MTEB FloresBitextMining (rus_Cyrl-deu_Latn) |
| 12171 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12172 | split: devtest |
| 12173 | type: mteb/flores |
| 12174 | metrics: |
| 12175 | - type: accuracy |
| 12176 | value: 99.50592885375494 |
| 12177 | - type: f1 |
| 12178 | value: 99.34123847167325 |
| 12179 | - type: main_score |
| 12180 | value: 99.34123847167325 |
| 12181 | - type: precision |
| 12182 | value: 99.2588932806324 |
| 12183 | - type: recall |
| 12184 | value: 99.50592885375494 |
| 12185 | task: |
| 12186 | type: BitextMining |
| 12187 | - dataset: |
| 12188 | config: rus_Cyrl-hat_Latn |
| 12189 | name: MTEB FloresBitextMining (rus_Cyrl-hat_Latn) |
| 12190 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12191 | split: devtest |
| 12192 | type: mteb/flores |
| 12193 | metrics: |
| 12194 | - type: accuracy |
| 12195 | value: 91.00790513833992 |
| 12196 | - type: f1 |
| 12197 | value: 88.69126043039086 |
| 12198 | - type: main_score |
| 12199 | value: 88.69126043039086 |
| 12200 | - type: precision |
| 12201 | value: 87.75774044795784 |
| 12202 | - type: recall |
| 12203 | value: 91.00790513833992 |
| 12204 | task: |
| 12205 | type: BitextMining |
| 12206 | - dataset: |
| 12207 | config: rus_Cyrl-kbp_Latn |
| 12208 | name: MTEB FloresBitextMining (rus_Cyrl-kbp_Latn) |
| 12209 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12210 | split: devtest |
| 12211 | type: mteb/flores |
| 12212 | metrics: |
| 12213 | - type: accuracy |
| 12214 | value: 47.233201581027664 |
| 12215 | - type: f1 |
| 12216 | value: 43.01118618096943 |
| 12217 | - type: main_score |
| 12218 | value: 43.01118618096943 |
| 12219 | - type: precision |
| 12220 | value: 41.739069205043556 |
| 12221 | - type: recall |
| 12222 | value: 47.233201581027664 |
| 12223 | task: |
| 12224 | type: BitextMining |
| 12225 | - dataset: |
| 12226 | config: rus_Cyrl-luo_Latn |
| 12227 | name: MTEB FloresBitextMining (rus_Cyrl-luo_Latn) |
| 12228 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12229 | split: devtest |
| 12230 | type: mteb/flores |
| 12231 | metrics: |
| 12232 | - type: accuracy |
| 12233 | value: 60.47430830039525 |
| 12234 | - type: f1 |
| 12235 | value: 54.83210565429816 |
| 12236 | - type: main_score |
| 12237 | value: 54.83210565429816 |
| 12238 | - type: precision |
| 12239 | value: 52.81630744284779 |
| 12240 | - type: recall |
| 12241 | value: 60.47430830039525 |
| 12242 | task: |
| 12243 | type: BitextMining |
| 12244 | - dataset: |
| 12245 | config: rus_Cyrl-ory_Orya |
| 12246 | name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya) |
| 12247 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12248 | split: devtest |
| 12249 | type: mteb/flores |
| 12250 | metrics: |
| 12251 | - type: accuracy |
| 12252 | value: 99.1106719367589 |
| 12253 | - type: f1 |
| 12254 | value: 98.83069828722003 |
| 12255 | - type: main_score |
| 12256 | value: 98.83069828722003 |
| 12257 | - type: precision |
| 12258 | value: 98.69894598155467 |
| 12259 | - type: recall |
| 12260 | value: 99.1106719367589 |
| 12261 | task: |
| 12262 | type: BitextMining |
| 12263 | - dataset: |
| 12264 | config: rus_Cyrl-sna_Latn |
| 12265 | name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn) |
| 12266 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12267 | split: devtest |
| 12268 | type: mteb/flores |
| 12269 | metrics: |
| 12270 | - type: accuracy |
| 12271 | value: 89.72332015810277 |
| 12272 | - type: f1 |
| 12273 | value: 87.30013645774514 |
| 12274 | - type: main_score |
| 12275 | value: 87.30013645774514 |
| 12276 | - type: precision |
| 12277 | value: 86.25329380764163 |
| 12278 | - type: recall |
| 12279 | value: 89.72332015810277 |
| 12280 | task: |
| 12281 | type: BitextMining |
| 12282 | - dataset: |
| 12283 | config: rus_Cyrl-tso_Latn |
| 12284 | name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn) |
| 12285 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12286 | split: devtest |
| 12287 | type: mteb/flores |
| 12288 | metrics: |
| 12289 | - type: accuracy |
| 12290 | value: 84.38735177865613 |
| 12291 | - type: f1 |
| 12292 | value: 80.70424744337788 |
| 12293 | - type: main_score |
| 12294 | value: 80.70424744337788 |
| 12295 | - type: precision |
| 12296 | value: 79.18560606060606 |
| 12297 | - type: recall |
| 12298 | value: 84.38735177865613 |
| 12299 | task: |
| 12300 | type: BitextMining |
| 12301 | - dataset: |
| 12302 | config: rus_Cyrl-azj_Latn |
| 12303 | name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn) |
| 12304 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12305 | split: devtest |
| 12306 | type: mteb/flores |
| 12307 | metrics: |
| 12308 | - type: accuracy |
| 12309 | value: 97.33201581027669 |
| 12310 | - type: f1 |
| 12311 | value: 96.56455862977602 |
| 12312 | - type: main_score |
| 12313 | value: 96.56455862977602 |
| 12314 | - type: precision |
| 12315 | value: 96.23682476943345 |
| 12316 | - type: recall |
| 12317 | value: 97.33201581027669 |
| 12318 | task: |
| 12319 | type: BitextMining |
| 12320 | - dataset: |
| 12321 | config: rus_Cyrl-dik_Latn |
| 12322 | name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn) |
| 12323 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12324 | split: devtest |
| 12325 | type: mteb/flores |
| 12326 | metrics: |
| 12327 | - type: accuracy |
| 12328 | value: 46.047430830039524 |
| 12329 | - type: f1 |
| 12330 | value: 40.05513069495283 |
| 12331 | - type: main_score |
| 12332 | value: 40.05513069495283 |
| 12333 | - type: precision |
| 12334 | value: 38.072590197096126 |
| 12335 | - type: recall |
| 12336 | value: 46.047430830039524 |
| 12337 | task: |
| 12338 | type: BitextMining |
| 12339 | - dataset: |
| 12340 | config: rus_Cyrl-hau_Latn |
| 12341 | name: MTEB FloresBitextMining (rus_Cyrl-hau_Latn) |
| 12342 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12343 | split: devtest |
| 12344 | type: mteb/flores |
| 12345 | metrics: |
| 12346 | - type: accuracy |
| 12347 | value: 87.94466403162056 |
| 12348 | - type: f1 |
| 12349 | value: 84.76943346508563 |
| 12350 | - type: main_score |
| 12351 | value: 84.76943346508563 |
| 12352 | - type: precision |
| 12353 | value: 83.34486166007905 |
| 12354 | - type: recall |
| 12355 | value: 87.94466403162056 |
| 12356 | task: |
| 12357 | type: BitextMining |
| 12358 | - dataset: |
| 12359 | config: rus_Cyrl-kea_Latn |
| 12360 | name: MTEB FloresBitextMining (rus_Cyrl-kea_Latn) |
| 12361 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12362 | split: devtest |
| 12363 | type: mteb/flores |
| 12364 | metrics: |
| 12365 | - type: accuracy |
| 12366 | value: 89.42687747035573 |
| 12367 | - type: f1 |
| 12368 | value: 86.83803021747684 |
| 12369 | - type: main_score |
| 12370 | value: 86.83803021747684 |
| 12371 | - type: precision |
| 12372 | value: 85.78416149068323 |
| 12373 | - type: recall |
| 12374 | value: 89.42687747035573 |
| 12375 | task: |
| 12376 | type: BitextMining |
| 12377 | - dataset: |
| 12378 | config: rus_Cyrl-lus_Latn |
| 12379 | name: MTEB FloresBitextMining (rus_Cyrl-lus_Latn) |
| 12380 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12381 | split: devtest |
| 12382 | type: mteb/flores |
| 12383 | metrics: |
| 12384 | - type: accuracy |
| 12385 | value: 68.97233201581028 |
| 12386 | - type: f1 |
| 12387 | value: 64.05480726292745 |
| 12388 | - type: main_score |
| 12389 | value: 64.05480726292745 |
| 12390 | - type: precision |
| 12391 | value: 62.42670749487858 |
| 12392 | - type: recall |
| 12393 | value: 68.97233201581028 |
| 12394 | task: |
| 12395 | type: BitextMining |
| 12396 | - dataset: |
| 12397 | config: rus_Cyrl-pag_Latn |
| 12398 | name: MTEB FloresBitextMining (rus_Cyrl-pag_Latn) |
| 12399 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12400 | split: devtest |
| 12401 | type: mteb/flores |
| 12402 | metrics: |
| 12403 | - type: accuracy |
| 12404 | value: 78.75494071146245 |
| 12405 | - type: f1 |
| 12406 | value: 74.58573558401933 |
| 12407 | - type: main_score |
| 12408 | value: 74.58573558401933 |
| 12409 | - type: precision |
| 12410 | value: 73.05532028358115 |
| 12411 | - type: recall |
| 12412 | value: 78.75494071146245 |
| 12413 | task: |
| 12414 | type: BitextMining |
| 12415 | - dataset: |
| 12416 | config: rus_Cyrl-snd_Arab |
| 12417 | name: MTEB FloresBitextMining (rus_Cyrl-snd_Arab) |
| 12418 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12419 | split: devtest |
| 12420 | type: mteb/flores |
| 12421 | metrics: |
| 12422 | - type: accuracy |
| 12423 | value: 95.8498023715415 |
| 12424 | - type: f1 |
| 12425 | value: 94.56521739130434 |
| 12426 | - type: main_score |
| 12427 | value: 94.56521739130434 |
| 12428 | - type: precision |
| 12429 | value: 93.97233201581028 |
| 12430 | - type: recall |
| 12431 | value: 95.8498023715415 |
| 12432 | task: |
| 12433 | type: BitextMining |
| 12434 | - dataset: |
| 12435 | config: rus_Cyrl-tuk_Latn |
| 12436 | name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn) |
| 12437 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12438 | split: devtest |
| 12439 | type: mteb/flores |
| 12440 | metrics: |
| 12441 | - type: accuracy |
| 12442 | value: 68.08300395256917 |
| 12443 | - type: f1 |
| 12444 | value: 62.93565240205557 |
| 12445 | - type: main_score |
| 12446 | value: 62.93565240205557 |
| 12447 | - type: precision |
| 12448 | value: 61.191590257043934 |
| 12449 | - type: recall |
| 12450 | value: 68.08300395256917 |
| 12451 | task: |
| 12452 | type: BitextMining |
| 12453 | - dataset: |
| 12454 | config: rus_Cyrl-bak_Cyrl |
| 12455 | name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl) |
| 12456 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12457 | split: devtest |
| 12458 | type: mteb/flores |
| 12459 | metrics: |
| 12460 | - type: accuracy |
| 12461 | value: 96.04743083003953 |
| 12462 | - type: f1 |
| 12463 | value: 94.86824769433464 |
| 12464 | - type: main_score |
| 12465 | value: 94.86824769433464 |
| 12466 | - type: precision |
| 12467 | value: 94.34288537549406 |
| 12468 | - type: recall |
| 12469 | value: 96.04743083003953 |
| 12470 | task: |
| 12471 | type: BitextMining |
| 12472 | - dataset: |
| 12473 | config: rus_Cyrl-dyu_Latn |
| 12474 | name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn) |
| 12475 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12476 | split: devtest |
| 12477 | type: mteb/flores |
| 12478 | metrics: |
| 12479 | - type: accuracy |
| 12480 | value: 37.45059288537549 |
| 12481 | - type: f1 |
| 12482 | value: 31.670482312800807 |
| 12483 | - type: main_score |
| 12484 | value: 31.670482312800807 |
| 12485 | - type: precision |
| 12486 | value: 29.99928568357422 |
| 12487 | - type: recall |
| 12488 | value: 37.45059288537549 |
| 12489 | task: |
| 12490 | type: BitextMining |
| 12491 | - dataset: |
| 12492 | config: rus_Cyrl-heb_Hebr |
| 12493 | name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr) |
| 12494 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12495 | split: devtest |
| 12496 | type: mteb/flores |
| 12497 | metrics: |
| 12498 | - type: accuracy |
| 12499 | value: 97.23320158102767 |
| 12500 | - type: f1 |
| 12501 | value: 96.38998682476942 |
| 12502 | - type: main_score |
| 12503 | value: 96.38998682476942 |
| 12504 | - type: precision |
| 12505 | value: 95.99802371541502 |
| 12506 | - type: recall |
| 12507 | value: 97.23320158102767 |
| 12508 | task: |
| 12509 | type: BitextMining |
| 12510 | - dataset: |
| 12511 | config: rus_Cyrl-khk_Cyrl |
| 12512 | name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl) |
| 12513 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12514 | split: devtest |
| 12515 | type: mteb/flores |
| 12516 | metrics: |
| 12517 | - type: accuracy |
| 12518 | value: 98.41897233201581 |
| 12519 | - type: f1 |
| 12520 | value: 98.00724637681158 |
| 12521 | - type: main_score |
| 12522 | value: 98.00724637681158 |
| 12523 | - type: precision |
| 12524 | value: 97.82938076416336 |
| 12525 | - type: recall |
| 12526 | value: 98.41897233201581 |
| 12527 | task: |
| 12528 | type: BitextMining |
| 12529 | - dataset: |
| 12530 | config: rus_Cyrl-lvs_Latn |
| 12531 | name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn) |
| 12532 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12533 | split: devtest |
| 12534 | type: mteb/flores |
| 12535 | metrics: |
| 12536 | - type: accuracy |
| 12537 | value: 97.4308300395257 |
| 12538 | - type: f1 |
| 12539 | value: 96.61396574440053 |
| 12540 | - type: main_score |
| 12541 | value: 96.61396574440053 |
| 12542 | - type: precision |
| 12543 | value: 96.2203557312253 |
| 12544 | - type: recall |
| 12545 | value: 97.4308300395257 |
| 12546 | task: |
| 12547 | type: BitextMining |
| 12548 | - dataset: |
| 12549 | config: rus_Cyrl-pan_Guru |
| 12550 | name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru) |
| 12551 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12552 | split: devtest |
| 12553 | type: mteb/flores |
| 12554 | metrics: |
| 12555 | - type: accuracy |
| 12556 | value: 99.30830039525692 |
| 12557 | - type: f1 |
| 12558 | value: 99.07773386034256 |
| 12559 | - type: main_score |
| 12560 | value: 99.07773386034256 |
| 12561 | - type: precision |
| 12562 | value: 98.96245059288538 |
| 12563 | - type: recall |
| 12564 | value: 99.30830039525692 |
| 12565 | task: |
| 12566 | type: BitextMining |
| 12567 | - dataset: |
| 12568 | config: rus_Cyrl-som_Latn |
| 12569 | name: MTEB FloresBitextMining (rus_Cyrl-som_Latn) |
| 12570 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12571 | split: devtest |
| 12572 | type: mteb/flores |
| 12573 | metrics: |
| 12574 | - type: accuracy |
| 12575 | value: 87.74703557312253 |
| 12576 | - type: f1 |
| 12577 | value: 84.52898550724638 |
| 12578 | - type: main_score |
| 12579 | value: 84.52898550724638 |
| 12580 | - type: precision |
| 12581 | value: 83.09288537549409 |
| 12582 | - type: recall |
| 12583 | value: 87.74703557312253 |
| 12584 | task: |
| 12585 | type: BitextMining |
| 12586 | - dataset: |
| 12587 | config: rus_Cyrl-tum_Latn |
| 12588 | name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn) |
| 12589 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12590 | split: devtest |
| 12591 | type: mteb/flores |
| 12592 | metrics: |
| 12593 | - type: accuracy |
| 12594 | value: 87.15415019762845 |
| 12595 | - type: f1 |
| 12596 | value: 83.85069640504425 |
| 12597 | - type: main_score |
| 12598 | value: 83.85069640504425 |
| 12599 | - type: precision |
| 12600 | value: 82.43671183888576 |
| 12601 | - type: recall |
| 12602 | value: 87.15415019762845 |
| 12603 | task: |
| 12604 | type: BitextMining |
| 12605 | - dataset: |
| 12606 | config: taq_Latn-rus_Cyrl |
| 12607 | name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl) |
| 12608 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12609 | split: devtest |
| 12610 | type: mteb/flores |
| 12611 | metrics: |
| 12612 | - type: accuracy |
| 12613 | value: 28.55731225296443 |
| 12614 | - type: f1 |
| 12615 | value: 26.810726360049568 |
| 12616 | - type: main_score |
| 12617 | value: 26.810726360049568 |
| 12618 | - type: precision |
| 12619 | value: 26.260342858265577 |
| 12620 | - type: recall |
| 12621 | value: 28.55731225296443 |
| 12622 | task: |
| 12623 | type: BitextMining |
| 12624 | - dataset: |
| 12625 | config: war_Latn-rus_Cyrl |
| 12626 | name: MTEB FloresBitextMining (war_Latn-rus_Cyrl) |
| 12627 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12628 | split: devtest |
| 12629 | type: mteb/flores |
| 12630 | metrics: |
| 12631 | - type: accuracy |
| 12632 | value: 94.86166007905138 |
| 12633 | - type: f1 |
| 12634 | value: 94.03147083483051 |
| 12635 | - type: main_score |
| 12636 | value: 94.03147083483051 |
| 12637 | - type: precision |
| 12638 | value: 93.70653606003322 |
| 12639 | - type: recall |
| 12640 | value: 94.86166007905138 |
| 12641 | task: |
| 12642 | type: BitextMining |
| 12643 | - dataset: |
| 12644 | config: arb_Arab-rus_Cyrl |
| 12645 | name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl) |
| 12646 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12647 | split: devtest |
| 12648 | type: mteb/flores |
| 12649 | metrics: |
| 12650 | - type: accuracy |
| 12651 | value: 96.34387351778656 |
| 12652 | - type: f1 |
| 12653 | value: 95.23056653491436 |
| 12654 | - type: main_score |
| 12655 | value: 95.23056653491436 |
| 12656 | - type: precision |
| 12657 | value: 94.70520421607378 |
| 12658 | - type: recall |
| 12659 | value: 96.34387351778656 |
| 12660 | task: |
| 12661 | type: BitextMining |
| 12662 | - dataset: |
| 12663 | config: bul_Cyrl-rus_Cyrl |
| 12664 | name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl) |
| 12665 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12666 | split: devtest |
| 12667 | type: mteb/flores |
| 12668 | metrics: |
| 12669 | - type: accuracy |
| 12670 | value: 99.90118577075098 |
| 12671 | - type: f1 |
| 12672 | value: 99.86824769433464 |
| 12673 | - type: main_score |
| 12674 | value: 99.86824769433464 |
| 12675 | - type: precision |
| 12676 | value: 99.85177865612648 |
| 12677 | - type: recall |
| 12678 | value: 99.90118577075098 |
| 12679 | task: |
| 12680 | type: BitextMining |
| 12681 | - dataset: |
| 12682 | config: fra_Latn-rus_Cyrl |
| 12683 | name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl) |
| 12684 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12685 | split: devtest |
| 12686 | type: mteb/flores |
| 12687 | metrics: |
| 12688 | - type: accuracy |
| 12689 | value: 99.2094861660079 |
| 12690 | - type: f1 |
| 12691 | value: 98.9459815546772 |
| 12692 | - type: main_score |
| 12693 | value: 98.9459815546772 |
| 12694 | - type: precision |
| 12695 | value: 98.81422924901186 |
| 12696 | - type: recall |
| 12697 | value: 99.2094861660079 |
| 12698 | task: |
| 12699 | type: BitextMining |
| 12700 | - dataset: |
| 12701 | config: jpn_Jpan-rus_Cyrl |
| 12702 | name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl) |
| 12703 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12704 | split: devtest |
| 12705 | type: mteb/flores |
| 12706 | metrics: |
| 12707 | - type: accuracy |
| 12708 | value: 98.3201581027668 |
| 12709 | - type: f1 |
| 12710 | value: 97.76021080368905 |
| 12711 | - type: main_score |
| 12712 | value: 97.76021080368905 |
| 12713 | - type: precision |
| 12714 | value: 97.48023715415019 |
| 12715 | - type: recall |
| 12716 | value: 98.3201581027668 |
| 12717 | task: |
| 12718 | type: BitextMining |
| 12719 | - dataset: |
| 12720 | config: lij_Latn-rus_Cyrl |
| 12721 | name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl) |
| 12722 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12723 | split: devtest |
| 12724 | type: mteb/flores |
| 12725 | metrics: |
| 12726 | - type: accuracy |
| 12727 | value: 83.49802371541502 |
| 12728 | - type: f1 |
| 12729 | value: 81.64800059239636 |
| 12730 | - type: main_score |
| 12731 | value: 81.64800059239636 |
| 12732 | - type: precision |
| 12733 | value: 80.9443055878478 |
| 12734 | - type: recall |
| 12735 | value: 83.49802371541502 |
| 12736 | task: |
| 12737 | type: BitextMining |
| 12738 | - dataset: |
| 12739 | config: mya_Mymr-rus_Cyrl |
| 12740 | name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl) |
| 12741 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12742 | split: devtest |
| 12743 | type: mteb/flores |
| 12744 | metrics: |
| 12745 | - type: accuracy |
| 12746 | value: 90.21739130434783 |
| 12747 | - type: f1 |
| 12748 | value: 88.76776366313682 |
| 12749 | - type: main_score |
| 12750 | value: 88.76776366313682 |
| 12751 | - type: precision |
| 12752 | value: 88.18370446119435 |
| 12753 | - type: recall |
| 12754 | value: 90.21739130434783 |
| 12755 | task: |
| 12756 | type: BitextMining |
| 12757 | - dataset: |
| 12758 | config: sag_Latn-rus_Cyrl |
| 12759 | name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl) |
| 12760 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12761 | split: devtest |
| 12762 | type: mteb/flores |
| 12763 | metrics: |
| 12764 | - type: accuracy |
| 12765 | value: 41.699604743083 |
| 12766 | - type: f1 |
| 12767 | value: 39.53066322643847 |
| 12768 | - type: main_score |
| 12769 | value: 39.53066322643847 |
| 12770 | - type: precision |
| 12771 | value: 38.822876239229274 |
| 12772 | - type: recall |
| 12773 | value: 41.699604743083 |
| 12774 | task: |
| 12775 | type: BitextMining |
| 12776 | - dataset: |
| 12777 | config: taq_Tfng-rus_Cyrl |
| 12778 | name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl) |
| 12779 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12780 | split: devtest |
| 12781 | type: mteb/flores |
| 12782 | metrics: |
| 12783 | - type: accuracy |
| 12784 | value: 10.67193675889328 |
| 12785 | - type: f1 |
| 12786 | value: 9.205744965817951 |
| 12787 | - type: main_score |
| 12788 | value: 9.205744965817951 |
| 12789 | - type: precision |
| 12790 | value: 8.85195219073817 |
| 12791 | - type: recall |
| 12792 | value: 10.67193675889328 |
| 12793 | task: |
| 12794 | type: BitextMining |
| 12795 | - dataset: |
| 12796 | config: wol_Latn-rus_Cyrl |
| 12797 | name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl) |
| 12798 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12799 | split: devtest |
| 12800 | type: mteb/flores |
| 12801 | metrics: |
| 12802 | - type: accuracy |
| 12803 | value: 63.537549407114625 |
| 12804 | - type: f1 |
| 12805 | value: 60.65190727391827 |
| 12806 | - type: main_score |
| 12807 | value: 60.65190727391827 |
| 12808 | - type: precision |
| 12809 | value: 59.61144833427442 |
| 12810 | - type: recall |
| 12811 | value: 63.537549407114625 |
| 12812 | task: |
| 12813 | type: BitextMining |
| 12814 | - dataset: |
| 12815 | config: arb_Latn-rus_Cyrl |
| 12816 | name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl) |
| 12817 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12818 | split: devtest |
| 12819 | type: mteb/flores |
| 12820 | metrics: |
| 12821 | - type: accuracy |
| 12822 | value: 13.142292490118576 |
| 12823 | - type: f1 |
| 12824 | value: 12.372910318176764 |
| 12825 | - type: main_score |
| 12826 | value: 12.372910318176764 |
| 12827 | - type: precision |
| 12828 | value: 12.197580895919188 |
| 12829 | - type: recall |
| 12830 | value: 13.142292490118576 |
| 12831 | task: |
| 12832 | type: BitextMining |
| 12833 | - dataset: |
| 12834 | config: cat_Latn-rus_Cyrl |
| 12835 | name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl) |
| 12836 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12837 | split: devtest |
| 12838 | type: mteb/flores |
| 12839 | metrics: |
| 12840 | - type: accuracy |
| 12841 | value: 99.01185770750988 |
| 12842 | - type: f1 |
| 12843 | value: 98.80599472990777 |
| 12844 | - type: main_score |
| 12845 | value: 98.80599472990777 |
| 12846 | - type: precision |
| 12847 | value: 98.72953133822698 |
| 12848 | - type: recall |
| 12849 | value: 99.01185770750988 |
| 12850 | task: |
| 12851 | type: BitextMining |
| 12852 | - dataset: |
| 12853 | config: fur_Latn-rus_Cyrl |
| 12854 | name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl) |
| 12855 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12856 | split: devtest |
| 12857 | type: mteb/flores |
| 12858 | metrics: |
| 12859 | - type: accuracy |
| 12860 | value: 81.02766798418972 |
| 12861 | - type: f1 |
| 12862 | value: 79.36184294084613 |
| 12863 | - type: main_score |
| 12864 | value: 79.36184294084613 |
| 12865 | - type: precision |
| 12866 | value: 78.69187826527705 |
| 12867 | - type: recall |
| 12868 | value: 81.02766798418972 |
| 12869 | task: |
| 12870 | type: BitextMining |
| 12871 | - dataset: |
| 12872 | config: kab_Latn-rus_Cyrl |
| 12873 | name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl) |
| 12874 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12875 | split: devtest |
| 12876 | type: mteb/flores |
| 12877 | metrics: |
| 12878 | - type: accuracy |
| 12879 | value: 34.387351778656125 |
| 12880 | - type: f1 |
| 12881 | value: 32.02306921576947 |
| 12882 | - type: main_score |
| 12883 | value: 32.02306921576947 |
| 12884 | - type: precision |
| 12885 | value: 31.246670347137467 |
| 12886 | - type: recall |
| 12887 | value: 34.387351778656125 |
| 12888 | task: |
| 12889 | type: BitextMining |
| 12890 | - dataset: |
| 12891 | config: lim_Latn-rus_Cyrl |
| 12892 | name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl) |
| 12893 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12894 | split: devtest |
| 12895 | type: mteb/flores |
| 12896 | metrics: |
| 12897 | - type: accuracy |
| 12898 | value: 78.26086956521739 |
| 12899 | - type: f1 |
| 12900 | value: 75.90239449214359 |
| 12901 | - type: main_score |
| 12902 | value: 75.90239449214359 |
| 12903 | - type: precision |
| 12904 | value: 75.02211430745493 |
| 12905 | - type: recall |
| 12906 | value: 78.26086956521739 |
| 12907 | task: |
| 12908 | type: BitextMining |
| 12909 | - dataset: |
| 12910 | config: nld_Latn-rus_Cyrl |
| 12911 | name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl) |
| 12912 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12913 | split: devtest |
| 12914 | type: mteb/flores |
| 12915 | metrics: |
| 12916 | - type: accuracy |
| 12917 | value: 99.2094861660079 |
| 12918 | - type: f1 |
| 12919 | value: 98.9459815546772 |
| 12920 | - type: main_score |
| 12921 | value: 98.9459815546772 |
| 12922 | - type: precision |
| 12923 | value: 98.81422924901186 |
| 12924 | - type: recall |
| 12925 | value: 99.2094861660079 |
| 12926 | task: |
| 12927 | type: BitextMining |
| 12928 | - dataset: |
| 12929 | config: san_Deva-rus_Cyrl |
| 12930 | name: MTEB FloresBitextMining (san_Deva-rus_Cyrl) |
| 12931 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12932 | split: devtest |
| 12933 | type: mteb/flores |
| 12934 | metrics: |
| 12935 | - type: accuracy |
| 12936 | value: 87.94466403162056 |
| 12937 | - type: f1 |
| 12938 | value: 86.68928897189767 |
| 12939 | - type: main_score |
| 12940 | value: 86.68928897189767 |
| 12941 | - type: precision |
| 12942 | value: 86.23822997079216 |
| 12943 | - type: recall |
| 12944 | value: 87.94466403162056 |
| 12945 | task: |
| 12946 | type: BitextMining |
| 12947 | - dataset: |
| 12948 | config: tat_Cyrl-rus_Cyrl |
| 12949 | name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl) |
| 12950 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12951 | split: devtest |
| 12952 | type: mteb/flores |
| 12953 | metrics: |
| 12954 | - type: accuracy |
| 12955 | value: 97.03557312252964 |
| 12956 | - type: f1 |
| 12957 | value: 96.4167365353136 |
| 12958 | - type: main_score |
| 12959 | value: 96.4167365353136 |
| 12960 | - type: precision |
| 12961 | value: 96.16847826086958 |
| 12962 | - type: recall |
| 12963 | value: 97.03557312252964 |
| 12964 | task: |
| 12965 | type: BitextMining |
| 12966 | - dataset: |
| 12967 | config: xho_Latn-rus_Cyrl |
| 12968 | name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl) |
| 12969 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12970 | split: devtest |
| 12971 | type: mteb/flores |
| 12972 | metrics: |
| 12973 | - type: accuracy |
| 12974 | value: 86.95652173913044 |
| 12975 | - type: f1 |
| 12976 | value: 85.5506497283435 |
| 12977 | - type: main_score |
| 12978 | value: 85.5506497283435 |
| 12979 | - type: precision |
| 12980 | value: 84.95270479733395 |
| 12981 | - type: recall |
| 12982 | value: 86.95652173913044 |
| 12983 | task: |
| 12984 | type: BitextMining |
| 12985 | - dataset: |
| 12986 | config: ars_Arab-rus_Cyrl |
| 12987 | name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl) |
| 12988 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 12989 | split: devtest |
| 12990 | type: mteb/flores |
| 12991 | metrics: |
| 12992 | - type: accuracy |
| 12993 | value: 96.6403162055336 |
| 12994 | - type: f1 |
| 12995 | value: 95.60935441370223 |
| 12996 | - type: main_score |
| 12997 | value: 95.60935441370223 |
| 12998 | - type: precision |
| 12999 | value: 95.13339920948617 |
| 13000 | - type: recall |
| 13001 | value: 96.6403162055336 |
| 13002 | task: |
| 13003 | type: BitextMining |
| 13004 | - dataset: |
| 13005 | config: ceb_Latn-rus_Cyrl |
| 13006 | name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl) |
| 13007 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13008 | split: devtest |
| 13009 | type: mteb/flores |
| 13010 | metrics: |
| 13011 | - type: accuracy |
| 13012 | value: 95.7509881422925 |
| 13013 | - type: f1 |
| 13014 | value: 95.05209198303827 |
| 13015 | - type: main_score |
| 13016 | value: 95.05209198303827 |
| 13017 | - type: precision |
| 13018 | value: 94.77662283368805 |
| 13019 | - type: recall |
| 13020 | value: 95.7509881422925 |
| 13021 | task: |
| 13022 | type: BitextMining |
| 13023 | - dataset: |
| 13024 | config: fuv_Latn-rus_Cyrl |
| 13025 | name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl) |
| 13026 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13027 | split: devtest |
| 13028 | type: mteb/flores |
| 13029 | metrics: |
| 13030 | - type: accuracy |
| 13031 | value: 45.25691699604743 |
| 13032 | - type: f1 |
| 13033 | value: 42.285666666742365 |
| 13034 | - type: main_score |
| 13035 | value: 42.285666666742365 |
| 13036 | - type: precision |
| 13037 | value: 41.21979853402283 |
| 13038 | - type: recall |
| 13039 | value: 45.25691699604743 |
| 13040 | task: |
| 13041 | type: BitextMining |
| 13042 | - dataset: |
| 13043 | config: kac_Latn-rus_Cyrl |
| 13044 | name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl) |
| 13045 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13046 | split: devtest |
| 13047 | type: mteb/flores |
| 13048 | metrics: |
| 13049 | - type: accuracy |
| 13050 | value: 34.683794466403164 |
| 13051 | - type: f1 |
| 13052 | value: 33.3235346229031 |
| 13053 | - type: main_score |
| 13054 | value: 33.3235346229031 |
| 13055 | - type: precision |
| 13056 | value: 32.94673924616852 |
| 13057 | - type: recall |
| 13058 | value: 34.683794466403164 |
| 13059 | task: |
| 13060 | type: BitextMining |
| 13061 | - dataset: |
| 13062 | config: lin_Latn-rus_Cyrl |
| 13063 | name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl) |
| 13064 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13065 | split: devtest |
| 13066 | type: mteb/flores |
| 13067 | metrics: |
| 13068 | - type: accuracy |
| 13069 | value: 86.85770750988142 |
| 13070 | - type: f1 |
| 13071 | value: 85.1867110799439 |
| 13072 | - type: main_score |
| 13073 | value: 85.1867110799439 |
| 13074 | - type: precision |
| 13075 | value: 84.53038212173273 |
| 13076 | - type: recall |
| 13077 | value: 86.85770750988142 |
| 13078 | task: |
| 13079 | type: BitextMining |
| 13080 | - dataset: |
| 13081 | config: nno_Latn-rus_Cyrl |
| 13082 | name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl) |
| 13083 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13084 | split: devtest |
| 13085 | type: mteb/flores |
| 13086 | metrics: |
| 13087 | - type: accuracy |
| 13088 | value: 97.4308300395257 |
| 13089 | - type: f1 |
| 13090 | value: 96.78383210991906 |
| 13091 | - type: main_score |
| 13092 | value: 96.78383210991906 |
| 13093 | - type: precision |
| 13094 | value: 96.51185770750989 |
| 13095 | - type: recall |
| 13096 | value: 97.4308300395257 |
| 13097 | task: |
| 13098 | type: BitextMining |
| 13099 | - dataset: |
| 13100 | config: sat_Olck-rus_Cyrl |
| 13101 | name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl) |
| 13102 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13103 | split: devtest |
| 13104 | type: mteb/flores |
| 13105 | metrics: |
| 13106 | - type: accuracy |
| 13107 | value: 1.185770750988142 |
| 13108 | - type: f1 |
| 13109 | value: 1.0279253129117258 |
| 13110 | - type: main_score |
| 13111 | value: 1.0279253129117258 |
| 13112 | - type: precision |
| 13113 | value: 1.0129746819135175 |
| 13114 | - type: recall |
| 13115 | value: 1.185770750988142 |
| 13116 | task: |
| 13117 | type: BitextMining |
| 13118 | - dataset: |
| 13119 | config: tel_Telu-rus_Cyrl |
| 13120 | name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl) |
| 13121 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13122 | split: devtest |
| 13123 | type: mteb/flores |
| 13124 | metrics: |
| 13125 | - type: accuracy |
| 13126 | value: 98.12252964426878 |
| 13127 | - type: f1 |
| 13128 | value: 97.61198945981555 |
| 13129 | - type: main_score |
| 13130 | value: 97.61198945981555 |
| 13131 | - type: precision |
| 13132 | value: 97.401185770751 |
| 13133 | - type: recall |
| 13134 | value: 98.12252964426878 |
| 13135 | task: |
| 13136 | type: BitextMining |
| 13137 | - dataset: |
| 13138 | config: ydd_Hebr-rus_Cyrl |
| 13139 | name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl) |
| 13140 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13141 | split: devtest |
| 13142 | type: mteb/flores |
| 13143 | metrics: |
| 13144 | - type: accuracy |
| 13145 | value: 75.8893280632411 |
| 13146 | - type: f1 |
| 13147 | value: 74.00244008018511 |
| 13148 | - type: main_score |
| 13149 | value: 74.00244008018511 |
| 13150 | - type: precision |
| 13151 | value: 73.25683020960382 |
| 13152 | - type: recall |
| 13153 | value: 75.8893280632411 |
| 13154 | task: |
| 13155 | type: BitextMining |
| 13156 | - dataset: |
| 13157 | config: ary_Arab-rus_Cyrl |
| 13158 | name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl) |
| 13159 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13160 | split: devtest |
| 13161 | type: mteb/flores |
| 13162 | metrics: |
| 13163 | - type: accuracy |
| 13164 | value: 86.56126482213439 |
| 13165 | - type: f1 |
| 13166 | value: 83.72796285839765 |
| 13167 | - type: main_score |
| 13168 | value: 83.72796285839765 |
| 13169 | - type: precision |
| 13170 | value: 82.65014273166447 |
| 13171 | - type: recall |
| 13172 | value: 86.56126482213439 |
| 13173 | task: |
| 13174 | type: BitextMining |
| 13175 | - dataset: |
| 13176 | config: ces_Latn-rus_Cyrl |
| 13177 | name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl) |
| 13178 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13179 | split: devtest |
| 13180 | type: mteb/flores |
| 13181 | metrics: |
| 13182 | - type: accuracy |
| 13183 | value: 99.60474308300395 |
| 13184 | - type: f1 |
| 13185 | value: 99.4729907773386 |
| 13186 | - type: main_score |
| 13187 | value: 99.4729907773386 |
| 13188 | - type: precision |
| 13189 | value: 99.40711462450594 |
| 13190 | - type: recall |
| 13191 | value: 99.60474308300395 |
| 13192 | task: |
| 13193 | type: BitextMining |
| 13194 | - dataset: |
| 13195 | config: gaz_Latn-rus_Cyrl |
| 13196 | name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl) |
| 13197 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13198 | split: devtest |
| 13199 | type: mteb/flores |
| 13200 | metrics: |
| 13201 | - type: accuracy |
| 13202 | value: 42.58893280632411 |
| 13203 | - type: f1 |
| 13204 | value: 40.75832866805978 |
| 13205 | - type: main_score |
| 13206 | value: 40.75832866805978 |
| 13207 | - type: precision |
| 13208 | value: 40.14285046917723 |
| 13209 | - type: recall |
| 13210 | value: 42.58893280632411 |
| 13211 | task: |
| 13212 | type: BitextMining |
| 13213 | - dataset: |
| 13214 | config: kam_Latn-rus_Cyrl |
| 13215 | name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl) |
| 13216 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13217 | split: devtest |
| 13218 | type: mteb/flores |
| 13219 | metrics: |
| 13220 | - type: accuracy |
| 13221 | value: 45.25691699604743 |
| 13222 | - type: f1 |
| 13223 | value: 42.6975518029456 |
| 13224 | - type: main_score |
| 13225 | value: 42.6975518029456 |
| 13226 | - type: precision |
| 13227 | value: 41.87472710984596 |
| 13228 | - type: recall |
| 13229 | value: 45.25691699604743 |
| 13230 | task: |
| 13231 | type: BitextMining |
| 13232 | - dataset: |
| 13233 | config: lit_Latn-rus_Cyrl |
| 13234 | name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl) |
| 13235 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13236 | split: devtest |
| 13237 | type: mteb/flores |
| 13238 | metrics: |
| 13239 | - type: accuracy |
| 13240 | value: 97.33201581027669 |
| 13241 | - type: f1 |
| 13242 | value: 96.62384716732542 |
| 13243 | - type: main_score |
| 13244 | value: 96.62384716732542 |
| 13245 | - type: precision |
| 13246 | value: 96.3175230566535 |
| 13247 | - type: recall |
| 13248 | value: 97.33201581027669 |
| 13249 | task: |
| 13250 | type: BitextMining |
| 13251 | - dataset: |
| 13252 | config: nob_Latn-rus_Cyrl |
| 13253 | name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl) |
| 13254 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13255 | split: devtest |
| 13256 | type: mteb/flores |
| 13257 | metrics: |
| 13258 | - type: accuracy |
| 13259 | value: 98.71541501976284 |
| 13260 | - type: f1 |
| 13261 | value: 98.30368906455863 |
| 13262 | - type: main_score |
| 13263 | value: 98.30368906455863 |
| 13264 | - type: precision |
| 13265 | value: 98.10606060606061 |
| 13266 | - type: recall |
| 13267 | value: 98.71541501976284 |
| 13268 | task: |
| 13269 | type: BitextMining |
| 13270 | - dataset: |
| 13271 | config: scn_Latn-rus_Cyrl |
| 13272 | name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl) |
| 13273 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13274 | split: devtest |
| 13275 | type: mteb/flores |
| 13276 | metrics: |
| 13277 | - type: accuracy |
| 13278 | value: 70.45454545454545 |
| 13279 | - type: f1 |
| 13280 | value: 68.62561022640075 |
| 13281 | - type: main_score |
| 13282 | value: 68.62561022640075 |
| 13283 | - type: precision |
| 13284 | value: 67.95229103411222 |
| 13285 | - type: recall |
| 13286 | value: 70.45454545454545 |
| 13287 | task: |
| 13288 | type: BitextMining |
| 13289 | - dataset: |
| 13290 | config: tgk_Cyrl-rus_Cyrl |
| 13291 | name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl) |
| 13292 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13293 | split: devtest |
| 13294 | type: mteb/flores |
| 13295 | metrics: |
| 13296 | - type: accuracy |
| 13297 | value: 92.4901185770751 |
| 13298 | - type: f1 |
| 13299 | value: 91.58514492753623 |
| 13300 | - type: main_score |
| 13301 | value: 91.58514492753623 |
| 13302 | - type: precision |
| 13303 | value: 91.24759298672342 |
| 13304 | - type: recall |
| 13305 | value: 92.4901185770751 |
| 13306 | task: |
| 13307 | type: BitextMining |
| 13308 | - dataset: |
| 13309 | config: yor_Latn-rus_Cyrl |
| 13310 | name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl) |
| 13311 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13312 | split: devtest |
| 13313 | type: mteb/flores |
| 13314 | metrics: |
| 13315 | - type: accuracy |
| 13316 | value: 67.98418972332016 |
| 13317 | - type: f1 |
| 13318 | value: 64.72874247330768 |
| 13319 | - type: main_score |
| 13320 | value: 64.72874247330768 |
| 13321 | - type: precision |
| 13322 | value: 63.450823399938685 |
| 13323 | - type: recall |
| 13324 | value: 67.98418972332016 |
| 13325 | task: |
| 13326 | type: BitextMining |
| 13327 | - dataset: |
| 13328 | config: arz_Arab-rus_Cyrl |
| 13329 | name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl) |
| 13330 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13331 | split: devtest |
| 13332 | type: mteb/flores |
| 13333 | metrics: |
| 13334 | - type: accuracy |
| 13335 | value: 94.56521739130434 |
| 13336 | - type: f1 |
| 13337 | value: 93.07971014492755 |
| 13338 | - type: main_score |
| 13339 | value: 93.07971014492755 |
| 13340 | - type: precision |
| 13341 | value: 92.42753623188406 |
| 13342 | - type: recall |
| 13343 | value: 94.56521739130434 |
| 13344 | task: |
| 13345 | type: BitextMining |
| 13346 | - dataset: |
| 13347 | config: cjk_Latn-rus_Cyrl |
| 13348 | name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl) |
| 13349 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13350 | split: devtest |
| 13351 | type: mteb/flores |
| 13352 | metrics: |
| 13353 | - type: accuracy |
| 13354 | value: 38.63636363636363 |
| 13355 | - type: f1 |
| 13356 | value: 36.25747140862938 |
| 13357 | - type: main_score |
| 13358 | value: 36.25747140862938 |
| 13359 | - type: precision |
| 13360 | value: 35.49101355074723 |
| 13361 | - type: recall |
| 13362 | value: 38.63636363636363 |
| 13363 | task: |
| 13364 | type: BitextMining |
| 13365 | - dataset: |
| 13366 | config: gla_Latn-rus_Cyrl |
| 13367 | name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl) |
| 13368 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13369 | split: devtest |
| 13370 | type: mteb/flores |
| 13371 | metrics: |
| 13372 | - type: accuracy |
| 13373 | value: 69.26877470355731 |
| 13374 | - type: f1 |
| 13375 | value: 66.11797423328613 |
| 13376 | - type: main_score |
| 13377 | value: 66.11797423328613 |
| 13378 | - type: precision |
| 13379 | value: 64.89369649409694 |
| 13380 | - type: recall |
| 13381 | value: 69.26877470355731 |
| 13382 | task: |
| 13383 | type: BitextMining |
| 13384 | - dataset: |
| 13385 | config: kan_Knda-rus_Cyrl |
| 13386 | name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl) |
| 13387 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13388 | split: devtest |
| 13389 | type: mteb/flores |
| 13390 | metrics: |
| 13391 | - type: accuracy |
| 13392 | value: 98.02371541501977 |
| 13393 | - type: f1 |
| 13394 | value: 97.51505740636176 |
| 13395 | - type: main_score |
| 13396 | value: 97.51505740636176 |
| 13397 | - type: precision |
| 13398 | value: 97.30731225296442 |
| 13399 | - type: recall |
| 13400 | value: 98.02371541501977 |
| 13401 | task: |
| 13402 | type: BitextMining |
| 13403 | - dataset: |
| 13404 | config: lmo_Latn-rus_Cyrl |
| 13405 | name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl) |
| 13406 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13407 | split: devtest |
| 13408 | type: mteb/flores |
| 13409 | metrics: |
| 13410 | - type: accuracy |
| 13411 | value: 73.3201581027668 |
| 13412 | - type: f1 |
| 13413 | value: 71.06371608677273 |
| 13414 | - type: main_score |
| 13415 | value: 71.06371608677273 |
| 13416 | - type: precision |
| 13417 | value: 70.26320288266223 |
| 13418 | - type: recall |
| 13419 | value: 73.3201581027668 |
| 13420 | task: |
| 13421 | type: BitextMining |
| 13422 | - dataset: |
| 13423 | config: npi_Deva-rus_Cyrl |
| 13424 | name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl) |
| 13425 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13426 | split: devtest |
| 13427 | type: mteb/flores |
| 13428 | metrics: |
| 13429 | - type: accuracy |
| 13430 | value: 97.82608695652173 |
| 13431 | - type: f1 |
| 13432 | value: 97.36645107198466 |
| 13433 | - type: main_score |
| 13434 | value: 97.36645107198466 |
| 13435 | - type: precision |
| 13436 | value: 97.1772068511199 |
| 13437 | - type: recall |
| 13438 | value: 97.82608695652173 |
| 13439 | task: |
| 13440 | type: BitextMining |
| 13441 | - dataset: |
| 13442 | config: shn_Mymr-rus_Cyrl |
| 13443 | name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl) |
| 13444 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13445 | split: devtest |
| 13446 | type: mteb/flores |
| 13447 | metrics: |
| 13448 | - type: accuracy |
| 13449 | value: 39.426877470355734 |
| 13450 | - type: f1 |
| 13451 | value: 37.16728785513024 |
| 13452 | - type: main_score |
| 13453 | value: 37.16728785513024 |
| 13454 | - type: precision |
| 13455 | value: 36.56918548278505 |
| 13456 | - type: recall |
| 13457 | value: 39.426877470355734 |
| 13458 | task: |
| 13459 | type: BitextMining |
| 13460 | - dataset: |
| 13461 | config: tgl_Latn-rus_Cyrl |
| 13462 | name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl) |
| 13463 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13464 | split: devtest |
| 13465 | type: mteb/flores |
| 13466 | metrics: |
| 13467 | - type: accuracy |
| 13468 | value: 97.92490118577075 |
| 13469 | - type: f1 |
| 13470 | value: 97.6378693769998 |
| 13471 | - type: main_score |
| 13472 | value: 97.6378693769998 |
| 13473 | - type: precision |
| 13474 | value: 97.55371440154047 |
| 13475 | - type: recall |
| 13476 | value: 97.92490118577075 |
| 13477 | task: |
| 13478 | type: BitextMining |
| 13479 | - dataset: |
| 13480 | config: yue_Hant-rus_Cyrl |
| 13481 | name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl) |
| 13482 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13483 | split: devtest |
| 13484 | type: mteb/flores |
| 13485 | metrics: |
| 13486 | - type: accuracy |
| 13487 | value: 97.92490118577075 |
| 13488 | - type: f1 |
| 13489 | value: 97.3833051006964 |
| 13490 | - type: main_score |
| 13491 | value: 97.3833051006964 |
| 13492 | - type: precision |
| 13493 | value: 97.1590909090909 |
| 13494 | - type: recall |
| 13495 | value: 97.92490118577075 |
| 13496 | task: |
| 13497 | type: BitextMining |
| 13498 | - dataset: |
| 13499 | config: asm_Beng-rus_Cyrl |
| 13500 | name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl) |
| 13501 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13502 | split: devtest |
| 13503 | type: mteb/flores |
| 13504 | metrics: |
| 13505 | - type: accuracy |
| 13506 | value: 92.78656126482213 |
| 13507 | - type: f1 |
| 13508 | value: 91.76917395296842 |
| 13509 | - type: main_score |
| 13510 | value: 91.76917395296842 |
| 13511 | - type: precision |
| 13512 | value: 91.38292866553736 |
| 13513 | - type: recall |
| 13514 | value: 92.78656126482213 |
| 13515 | task: |
| 13516 | type: BitextMining |
| 13517 | - dataset: |
| 13518 | config: ckb_Arab-rus_Cyrl |
| 13519 | name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl) |
| 13520 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13521 | split: devtest |
| 13522 | type: mteb/flores |
| 13523 | metrics: |
| 13524 | - type: accuracy |
| 13525 | value: 80.8300395256917 |
| 13526 | - type: f1 |
| 13527 | value: 79.17664345468799 |
| 13528 | - type: main_score |
| 13529 | value: 79.17664345468799 |
| 13530 | - type: precision |
| 13531 | value: 78.5622171683459 |
| 13532 | - type: recall |
| 13533 | value: 80.8300395256917 |
| 13534 | task: |
| 13535 | type: BitextMining |
| 13536 | - dataset: |
| 13537 | config: gle_Latn-rus_Cyrl |
| 13538 | name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl) |
| 13539 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13540 | split: devtest |
| 13541 | type: mteb/flores |
| 13542 | metrics: |
| 13543 | - type: accuracy |
| 13544 | value: 85.86956521739131 |
| 13545 | - type: f1 |
| 13546 | value: 84.45408265372492 |
| 13547 | - type: main_score |
| 13548 | value: 84.45408265372492 |
| 13549 | - type: precision |
| 13550 | value: 83.8774340026703 |
| 13551 | - type: recall |
| 13552 | value: 85.86956521739131 |
| 13553 | task: |
| 13554 | type: BitextMining |
| 13555 | - dataset: |
| 13556 | config: kas_Arab-rus_Cyrl |
| 13557 | name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl) |
| 13558 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13559 | split: devtest |
| 13560 | type: mteb/flores |
| 13561 | metrics: |
| 13562 | - type: accuracy |
| 13563 | value: 76.28458498023716 |
| 13564 | - type: f1 |
| 13565 | value: 74.11216313578267 |
| 13566 | - type: main_score |
| 13567 | value: 74.11216313578267 |
| 13568 | - type: precision |
| 13569 | value: 73.2491277759584 |
| 13570 | - type: recall |
| 13571 | value: 76.28458498023716 |
| 13572 | task: |
| 13573 | type: BitextMining |
| 13574 | - dataset: |
| 13575 | config: ltg_Latn-rus_Cyrl |
| 13576 | name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl) |
| 13577 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13578 | split: devtest |
| 13579 | type: mteb/flores |
| 13580 | metrics: |
| 13581 | - type: accuracy |
| 13582 | value: 71.14624505928853 |
| 13583 | - type: f1 |
| 13584 | value: 68.69245357723618 |
| 13585 | - type: main_score |
| 13586 | value: 68.69245357723618 |
| 13587 | - type: precision |
| 13588 | value: 67.8135329666459 |
| 13589 | - type: recall |
| 13590 | value: 71.14624505928853 |
| 13591 | task: |
| 13592 | type: BitextMining |
| 13593 | - dataset: |
| 13594 | config: nso_Latn-rus_Cyrl |
| 13595 | name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl) |
| 13596 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13597 | split: devtest |
| 13598 | type: mteb/flores |
| 13599 | metrics: |
| 13600 | - type: accuracy |
| 13601 | value: 87.64822134387352 |
| 13602 | - type: f1 |
| 13603 | value: 85.98419219986725 |
| 13604 | - type: main_score |
| 13605 | value: 85.98419219986725 |
| 13606 | - type: precision |
| 13607 | value: 85.32513873917036 |
| 13608 | - type: recall |
| 13609 | value: 87.64822134387352 |
| 13610 | task: |
| 13611 | type: BitextMining |
| 13612 | - dataset: |
| 13613 | config: sin_Sinh-rus_Cyrl |
| 13614 | name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl) |
| 13615 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13616 | split: devtest |
| 13617 | type: mteb/flores |
| 13618 | metrics: |
| 13619 | - type: accuracy |
| 13620 | value: 97.62845849802372 |
| 13621 | - type: f1 |
| 13622 | value: 97.10144927536231 |
| 13623 | - type: main_score |
| 13624 | value: 97.10144927536231 |
| 13625 | - type: precision |
| 13626 | value: 96.87986585219788 |
| 13627 | - type: recall |
| 13628 | value: 97.62845849802372 |
| 13629 | task: |
| 13630 | type: BitextMining |
| 13631 | - dataset: |
| 13632 | config: tha_Thai-rus_Cyrl |
| 13633 | name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl) |
| 13634 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13635 | split: devtest |
| 13636 | type: mteb/flores |
| 13637 | metrics: |
| 13638 | - type: accuracy |
| 13639 | value: 98.71541501976284 |
| 13640 | - type: f1 |
| 13641 | value: 98.28722002635045 |
| 13642 | - type: main_score |
| 13643 | value: 98.28722002635045 |
| 13644 | - type: precision |
| 13645 | value: 98.07312252964427 |
| 13646 | - type: recall |
| 13647 | value: 98.71541501976284 |
| 13648 | task: |
| 13649 | type: BitextMining |
| 13650 | - dataset: |
| 13651 | config: zho_Hans-rus_Cyrl |
| 13652 | name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl) |
| 13653 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13654 | split: devtest |
| 13655 | type: mteb/flores |
| 13656 | metrics: |
| 13657 | - type: accuracy |
| 13658 | value: 99.01185770750988 |
| 13659 | - type: f1 |
| 13660 | value: 98.68247694334651 |
| 13661 | - type: main_score |
| 13662 | value: 98.68247694334651 |
| 13663 | - type: precision |
| 13664 | value: 98.51778656126481 |
| 13665 | - type: recall |
| 13666 | value: 99.01185770750988 |
| 13667 | task: |
| 13668 | type: BitextMining |
| 13669 | - dataset: |
| 13670 | config: ast_Latn-rus_Cyrl |
| 13671 | name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl) |
| 13672 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13673 | split: devtest |
| 13674 | type: mteb/flores |
| 13675 | metrics: |
| 13676 | - type: accuracy |
| 13677 | value: 95.65217391304348 |
| 13678 | - type: f1 |
| 13679 | value: 94.90649683857505 |
| 13680 | - type: main_score |
| 13681 | value: 94.90649683857505 |
| 13682 | - type: precision |
| 13683 | value: 94.61352657004831 |
| 13684 | - type: recall |
| 13685 | value: 95.65217391304348 |
| 13686 | task: |
| 13687 | type: BitextMining |
| 13688 | - dataset: |
| 13689 | config: crh_Latn-rus_Cyrl |
| 13690 | name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl) |
| 13691 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13692 | split: devtest |
| 13693 | type: mteb/flores |
| 13694 | metrics: |
| 13695 | - type: accuracy |
| 13696 | value: 93.08300395256917 |
| 13697 | - type: f1 |
| 13698 | value: 92.20988998886428 |
| 13699 | - type: main_score |
| 13700 | value: 92.20988998886428 |
| 13701 | - type: precision |
| 13702 | value: 91.85631013694254 |
| 13703 | - type: recall |
| 13704 | value: 93.08300395256917 |
| 13705 | task: |
| 13706 | type: BitextMining |
| 13707 | - dataset: |
| 13708 | config: glg_Latn-rus_Cyrl |
| 13709 | name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl) |
| 13710 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13711 | split: devtest |
| 13712 | type: mteb/flores |
| 13713 | metrics: |
| 13714 | - type: accuracy |
| 13715 | value: 95.55335968379447 |
| 13716 | - type: f1 |
| 13717 | value: 95.18006148440931 |
| 13718 | - type: main_score |
| 13719 | value: 95.18006148440931 |
| 13720 | - type: precision |
| 13721 | value: 95.06540560888386 |
| 13722 | - type: recall |
| 13723 | value: 95.55335968379447 |
| 13724 | task: |
| 13725 | type: BitextMining |
| 13726 | - dataset: |
| 13727 | config: kas_Deva-rus_Cyrl |
| 13728 | name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl) |
| 13729 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13730 | split: devtest |
| 13731 | type: mteb/flores |
| 13732 | metrics: |
| 13733 | - type: accuracy |
| 13734 | value: 55.03952569169961 |
| 13735 | - type: f1 |
| 13736 | value: 52.19871938895554 |
| 13737 | - type: main_score |
| 13738 | value: 52.19871938895554 |
| 13739 | - type: precision |
| 13740 | value: 51.17660971469557 |
| 13741 | - type: recall |
| 13742 | value: 55.03952569169961 |
| 13743 | task: |
| 13744 | type: BitextMining |
| 13745 | - dataset: |
| 13746 | config: ltz_Latn-rus_Cyrl |
| 13747 | name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl) |
| 13748 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13749 | split: devtest |
| 13750 | type: mteb/flores |
| 13751 | metrics: |
| 13752 | - type: accuracy |
| 13753 | value: 87.64822134387352 |
| 13754 | - type: f1 |
| 13755 | value: 86.64179841897234 |
| 13756 | - type: main_score |
| 13757 | value: 86.64179841897234 |
| 13758 | - type: precision |
| 13759 | value: 86.30023235431587 |
| 13760 | - type: recall |
| 13761 | value: 87.64822134387352 |
| 13762 | task: |
| 13763 | type: BitextMining |
| 13764 | - dataset: |
| 13765 | config: nus_Latn-rus_Cyrl |
| 13766 | name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl) |
| 13767 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13768 | split: devtest |
| 13769 | type: mteb/flores |
| 13770 | metrics: |
| 13771 | - type: accuracy |
| 13772 | value: 27.4703557312253 |
| 13773 | - type: f1 |
| 13774 | value: 25.703014277858088 |
| 13775 | - type: main_score |
| 13776 | value: 25.703014277858088 |
| 13777 | - type: precision |
| 13778 | value: 25.194105476917315 |
| 13779 | - type: recall |
| 13780 | value: 27.4703557312253 |
| 13781 | task: |
| 13782 | type: BitextMining |
| 13783 | - dataset: |
| 13784 | config: slk_Latn-rus_Cyrl |
| 13785 | name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl) |
| 13786 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13787 | split: devtest |
| 13788 | type: mteb/flores |
| 13789 | metrics: |
| 13790 | - type: accuracy |
| 13791 | value: 99.30830039525692 |
| 13792 | - type: f1 |
| 13793 | value: 99.1106719367589 |
| 13794 | - type: main_score |
| 13795 | value: 99.1106719367589 |
| 13796 | - type: precision |
| 13797 | value: 99.02832674571805 |
| 13798 | - type: recall |
| 13799 | value: 99.30830039525692 |
| 13800 | task: |
| 13801 | type: BitextMining |
| 13802 | - dataset: |
| 13803 | config: tir_Ethi-rus_Cyrl |
| 13804 | name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl) |
| 13805 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13806 | split: devtest |
| 13807 | type: mteb/flores |
| 13808 | metrics: |
| 13809 | - type: accuracy |
| 13810 | value: 80.73122529644269 |
| 13811 | - type: f1 |
| 13812 | value: 78.66903754775608 |
| 13813 | - type: main_score |
| 13814 | value: 78.66903754775608 |
| 13815 | - type: precision |
| 13816 | value: 77.86431694163612 |
| 13817 | - type: recall |
| 13818 | value: 80.73122529644269 |
| 13819 | task: |
| 13820 | type: BitextMining |
| 13821 | - dataset: |
| 13822 | config: zho_Hant-rus_Cyrl |
| 13823 | name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl) |
| 13824 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13825 | split: devtest |
| 13826 | type: mteb/flores |
| 13827 | metrics: |
| 13828 | - type: accuracy |
| 13829 | value: 98.22134387351778 |
| 13830 | - type: f1 |
| 13831 | value: 97.66798418972333 |
| 13832 | - type: main_score |
| 13833 | value: 97.66798418972333 |
| 13834 | - type: precision |
| 13835 | value: 97.40612648221344 |
| 13836 | - type: recall |
| 13837 | value: 98.22134387351778 |
| 13838 | task: |
| 13839 | type: BitextMining |
| 13840 | - dataset: |
| 13841 | config: awa_Deva-rus_Cyrl |
| 13842 | name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl) |
| 13843 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13844 | split: devtest |
| 13845 | type: mteb/flores |
| 13846 | metrics: |
| 13847 | - type: accuracy |
| 13848 | value: 97.5296442687747 |
| 13849 | - type: f1 |
| 13850 | value: 96.94224857268335 |
| 13851 | - type: main_score |
| 13852 | value: 96.94224857268335 |
| 13853 | - type: precision |
| 13854 | value: 96.68560606060606 |
| 13855 | - type: recall |
| 13856 | value: 97.5296442687747 |
| 13857 | task: |
| 13858 | type: BitextMining |
| 13859 | - dataset: |
| 13860 | config: cym_Latn-rus_Cyrl |
| 13861 | name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl) |
| 13862 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13863 | split: devtest |
| 13864 | type: mteb/flores |
| 13865 | metrics: |
| 13866 | - type: accuracy |
| 13867 | value: 92.68774703557312 |
| 13868 | - type: f1 |
| 13869 | value: 91.69854302097961 |
| 13870 | - type: main_score |
| 13871 | value: 91.69854302097961 |
| 13872 | - type: precision |
| 13873 | value: 91.31236846157795 |
| 13874 | - type: recall |
| 13875 | value: 92.68774703557312 |
| 13876 | task: |
| 13877 | type: BitextMining |
| 13878 | - dataset: |
| 13879 | config: grn_Latn-rus_Cyrl |
| 13880 | name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl) |
| 13881 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13882 | split: devtest |
| 13883 | type: mteb/flores |
| 13884 | metrics: |
| 13885 | - type: accuracy |
| 13886 | value: 64.13043478260869 |
| 13887 | - type: f1 |
| 13888 | value: 61.850586118740004 |
| 13889 | - type: main_score |
| 13890 | value: 61.850586118740004 |
| 13891 | - type: precision |
| 13892 | value: 61.0049495186209 |
| 13893 | - type: recall |
| 13894 | value: 64.13043478260869 |
| 13895 | task: |
| 13896 | type: BitextMining |
| 13897 | - dataset: |
| 13898 | config: kat_Geor-rus_Cyrl |
| 13899 | name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl) |
| 13900 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13901 | split: devtest |
| 13902 | type: mteb/flores |
| 13903 | metrics: |
| 13904 | - type: accuracy |
| 13905 | value: 98.02371541501977 |
| 13906 | - type: f1 |
| 13907 | value: 97.59881422924902 |
| 13908 | - type: main_score |
| 13909 | value: 97.59881422924902 |
| 13910 | - type: precision |
| 13911 | value: 97.42534036012296 |
| 13912 | - type: recall |
| 13913 | value: 98.02371541501977 |
| 13914 | task: |
| 13915 | type: BitextMining |
| 13916 | - dataset: |
| 13917 | config: lua_Latn-rus_Cyrl |
| 13918 | name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl) |
| 13919 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13920 | split: devtest |
| 13921 | type: mteb/flores |
| 13922 | metrics: |
| 13923 | - type: accuracy |
| 13924 | value: 63.63636363636363 |
| 13925 | - type: f1 |
| 13926 | value: 60.9709122526128 |
| 13927 | - type: main_score |
| 13928 | value: 60.9709122526128 |
| 13929 | - type: precision |
| 13930 | value: 60.03915902282226 |
| 13931 | - type: recall |
| 13932 | value: 63.63636363636363 |
| 13933 | task: |
| 13934 | type: BitextMining |
| 13935 | - dataset: |
| 13936 | config: nya_Latn-rus_Cyrl |
| 13937 | name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl) |
| 13938 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13939 | split: devtest |
| 13940 | type: mteb/flores |
| 13941 | metrics: |
| 13942 | - type: accuracy |
| 13943 | value: 89.2292490118577 |
| 13944 | - type: f1 |
| 13945 | value: 87.59723824473149 |
| 13946 | - type: main_score |
| 13947 | value: 87.59723824473149 |
| 13948 | - type: precision |
| 13949 | value: 86.90172707867349 |
| 13950 | - type: recall |
| 13951 | value: 89.2292490118577 |
| 13952 | task: |
| 13953 | type: BitextMining |
| 13954 | - dataset: |
| 13955 | config: slv_Latn-rus_Cyrl |
| 13956 | name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl) |
| 13957 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13958 | split: devtest |
| 13959 | type: mteb/flores |
| 13960 | metrics: |
| 13961 | - type: accuracy |
| 13962 | value: 99.01185770750988 |
| 13963 | - type: f1 |
| 13964 | value: 98.74835309617917 |
| 13965 | - type: main_score |
| 13966 | value: 98.74835309617917 |
| 13967 | - type: precision |
| 13968 | value: 98.63636363636364 |
| 13969 | - type: recall |
| 13970 | value: 99.01185770750988 |
| 13971 | task: |
| 13972 | type: BitextMining |
| 13973 | - dataset: |
| 13974 | config: tpi_Latn-rus_Cyrl |
| 13975 | name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl) |
| 13976 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13977 | split: devtest |
| 13978 | type: mteb/flores |
| 13979 | metrics: |
| 13980 | - type: accuracy |
| 13981 | value: 77.37154150197628 |
| 13982 | - type: f1 |
| 13983 | value: 75.44251611276084 |
| 13984 | - type: main_score |
| 13985 | value: 75.44251611276084 |
| 13986 | - type: precision |
| 13987 | value: 74.78103665109595 |
| 13988 | - type: recall |
| 13989 | value: 77.37154150197628 |
| 13990 | task: |
| 13991 | type: BitextMining |
| 13992 | - dataset: |
| 13993 | config: zsm_Latn-rus_Cyrl |
| 13994 | name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl) |
| 13995 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 13996 | split: devtest |
| 13997 | type: mteb/flores |
| 13998 | metrics: |
| 13999 | - type: accuracy |
| 14000 | value: 99.2094861660079 |
| 14001 | - type: f1 |
| 14002 | value: 98.96245059288538 |
| 14003 | - type: main_score |
| 14004 | value: 98.96245059288538 |
| 14005 | - type: precision |
| 14006 | value: 98.8471673254282 |
| 14007 | - type: recall |
| 14008 | value: 99.2094861660079 |
| 14009 | task: |
| 14010 | type: BitextMining |
| 14011 | - dataset: |
| 14012 | config: ayr_Latn-rus_Cyrl |
| 14013 | name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl) |
| 14014 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14015 | split: devtest |
| 14016 | type: mteb/flores |
| 14017 | metrics: |
| 14018 | - type: accuracy |
| 14019 | value: 27.766798418972332 |
| 14020 | - type: f1 |
| 14021 | value: 26.439103195281312 |
| 14022 | - type: main_score |
| 14023 | value: 26.439103195281312 |
| 14024 | - type: precision |
| 14025 | value: 26.052655604573964 |
| 14026 | - type: recall |
| 14027 | value: 27.766798418972332 |
| 14028 | task: |
| 14029 | type: BitextMining |
| 14030 | - dataset: |
| 14031 | config: dan_Latn-rus_Cyrl |
| 14032 | name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl) |
| 14033 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14034 | split: devtest |
| 14035 | type: mteb/flores |
| 14036 | metrics: |
| 14037 | - type: accuracy |
| 14038 | value: 99.30830039525692 |
| 14039 | - type: f1 |
| 14040 | value: 99.07773386034255 |
| 14041 | - type: main_score |
| 14042 | value: 99.07773386034255 |
| 14043 | - type: precision |
| 14044 | value: 98.96245059288538 |
| 14045 | - type: recall |
| 14046 | value: 99.30830039525692 |
| 14047 | task: |
| 14048 | type: BitextMining |
| 14049 | - dataset: |
| 14050 | config: guj_Gujr-rus_Cyrl |
| 14051 | name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl) |
| 14052 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14053 | split: devtest |
| 14054 | type: mteb/flores |
| 14055 | metrics: |
| 14056 | - type: accuracy |
| 14057 | value: 97.82608695652173 |
| 14058 | - type: f1 |
| 14059 | value: 97.26449275362317 |
| 14060 | - type: main_score |
| 14061 | value: 97.26449275362317 |
| 14062 | - type: precision |
| 14063 | value: 97.02498588368154 |
| 14064 | - type: recall |
| 14065 | value: 97.82608695652173 |
| 14066 | task: |
| 14067 | type: BitextMining |
| 14068 | - dataset: |
| 14069 | config: kaz_Cyrl-rus_Cyrl |
| 14070 | name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl) |
| 14071 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14072 | split: devtest |
| 14073 | type: mteb/flores |
| 14074 | metrics: |
| 14075 | - type: accuracy |
| 14076 | value: 97.5296442687747 |
| 14077 | - type: f1 |
| 14078 | value: 97.03557312252964 |
| 14079 | - type: main_score |
| 14080 | value: 97.03557312252964 |
| 14081 | - type: precision |
| 14082 | value: 96.85022158342316 |
| 14083 | - type: recall |
| 14084 | value: 97.5296442687747 |
| 14085 | task: |
| 14086 | type: BitextMining |
| 14087 | - dataset: |
| 14088 | config: lug_Latn-rus_Cyrl |
| 14089 | name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl) |
| 14090 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14091 | split: devtest |
| 14092 | type: mteb/flores |
| 14093 | metrics: |
| 14094 | - type: accuracy |
| 14095 | value: 68.57707509881423 |
| 14096 | - type: f1 |
| 14097 | value: 65.93361605820395 |
| 14098 | - type: main_score |
| 14099 | value: 65.93361605820395 |
| 14100 | - type: precision |
| 14101 | value: 64.90348248593789 |
| 14102 | - type: recall |
| 14103 | value: 68.57707509881423 |
| 14104 | task: |
| 14105 | type: BitextMining |
| 14106 | - dataset: |
| 14107 | config: oci_Latn-rus_Cyrl |
| 14108 | name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl) |
| 14109 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14110 | split: devtest |
| 14111 | type: mteb/flores |
| 14112 | metrics: |
| 14113 | - type: accuracy |
| 14114 | value: 86.26482213438736 |
| 14115 | - type: f1 |
| 14116 | value: 85.33176417155623 |
| 14117 | - type: main_score |
| 14118 | value: 85.33176417155623 |
| 14119 | - type: precision |
| 14120 | value: 85.00208833384637 |
| 14121 | - type: recall |
| 14122 | value: 86.26482213438736 |
| 14123 | task: |
| 14124 | type: BitextMining |
| 14125 | - dataset: |
| 14126 | config: smo_Latn-rus_Cyrl |
| 14127 | name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl) |
| 14128 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14129 | split: devtest |
| 14130 | type: mteb/flores |
| 14131 | metrics: |
| 14132 | - type: accuracy |
| 14133 | value: 77.96442687747036 |
| 14134 | - type: f1 |
| 14135 | value: 75.70960450188885 |
| 14136 | - type: main_score |
| 14137 | value: 75.70960450188885 |
| 14138 | - type: precision |
| 14139 | value: 74.8312632736777 |
| 14140 | - type: recall |
| 14141 | value: 77.96442687747036 |
| 14142 | task: |
| 14143 | type: BitextMining |
| 14144 | - dataset: |
| 14145 | config: tsn_Latn-rus_Cyrl |
| 14146 | name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl) |
| 14147 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14148 | split: devtest |
| 14149 | type: mteb/flores |
| 14150 | metrics: |
| 14151 | - type: accuracy |
| 14152 | value: 84.38735177865613 |
| 14153 | - type: f1 |
| 14154 | value: 82.13656376349225 |
| 14155 | - type: main_score |
| 14156 | value: 82.13656376349225 |
| 14157 | - type: precision |
| 14158 | value: 81.16794543904518 |
| 14159 | - type: recall |
| 14160 | value: 84.38735177865613 |
| 14161 | task: |
| 14162 | type: BitextMining |
| 14163 | - dataset: |
| 14164 | config: zul_Latn-rus_Cyrl |
| 14165 | name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl) |
| 14166 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14167 | split: devtest |
| 14168 | type: mteb/flores |
| 14169 | metrics: |
| 14170 | - type: accuracy |
| 14171 | value: 90.21739130434783 |
| 14172 | - type: f1 |
| 14173 | value: 88.77570602050753 |
| 14174 | - type: main_score |
| 14175 | value: 88.77570602050753 |
| 14176 | - type: precision |
| 14177 | value: 88.15978104021582 |
| 14178 | - type: recall |
| 14179 | value: 90.21739130434783 |
| 14180 | task: |
| 14181 | type: BitextMining |
| 14182 | - dataset: |
| 14183 | config: azb_Arab-rus_Cyrl |
| 14184 | name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl) |
| 14185 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14186 | split: devtest |
| 14187 | type: mteb/flores |
| 14188 | metrics: |
| 14189 | - type: accuracy |
| 14190 | value: 65.71146245059289 |
| 14191 | - type: f1 |
| 14192 | value: 64.18825390221271 |
| 14193 | - type: main_score |
| 14194 | value: 64.18825390221271 |
| 14195 | - type: precision |
| 14196 | value: 63.66811154793568 |
| 14197 | - type: recall |
| 14198 | value: 65.71146245059289 |
| 14199 | task: |
| 14200 | type: BitextMining |
| 14201 | - dataset: |
| 14202 | config: deu_Latn-rus_Cyrl |
| 14203 | name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl) |
| 14204 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14205 | split: devtest |
| 14206 | type: mteb/flores |
| 14207 | metrics: |
| 14208 | - type: accuracy |
| 14209 | value: 99.70355731225297 |
| 14210 | - type: f1 |
| 14211 | value: 99.60474308300395 |
| 14212 | - type: main_score |
| 14213 | value: 99.60474308300395 |
| 14214 | - type: precision |
| 14215 | value: 99.55533596837944 |
| 14216 | - type: recall |
| 14217 | value: 99.70355731225297 |
| 14218 | task: |
| 14219 | type: BitextMining |
| 14220 | - dataset: |
| 14221 | config: hat_Latn-rus_Cyrl |
| 14222 | name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl) |
| 14223 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14224 | split: devtest |
| 14225 | type: mteb/flores |
| 14226 | metrics: |
| 14227 | - type: accuracy |
| 14228 | value: 86.7588932806324 |
| 14229 | - type: f1 |
| 14230 | value: 85.86738623695146 |
| 14231 | - type: main_score |
| 14232 | value: 85.86738623695146 |
| 14233 | - type: precision |
| 14234 | value: 85.55235467420822 |
| 14235 | - type: recall |
| 14236 | value: 86.7588932806324 |
| 14237 | task: |
| 14238 | type: BitextMining |
| 14239 | - dataset: |
| 14240 | config: kbp_Latn-rus_Cyrl |
| 14241 | name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl) |
| 14242 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14243 | split: devtest |
| 14244 | type: mteb/flores |
| 14245 | metrics: |
| 14246 | - type: accuracy |
| 14247 | value: 34.88142292490119 |
| 14248 | - type: f1 |
| 14249 | value: 32.16511669463015 |
| 14250 | - type: main_score |
| 14251 | value: 32.16511669463015 |
| 14252 | - type: precision |
| 14253 | value: 31.432098549546318 |
| 14254 | - type: recall |
| 14255 | value: 34.88142292490119 |
| 14256 | task: |
| 14257 | type: BitextMining |
| 14258 | - dataset: |
| 14259 | config: luo_Latn-rus_Cyrl |
| 14260 | name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl) |
| 14261 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14262 | split: devtest |
| 14263 | type: mteb/flores |
| 14264 | metrics: |
| 14265 | - type: accuracy |
| 14266 | value: 52.27272727272727 |
| 14267 | - type: f1 |
| 14268 | value: 49.60489626836975 |
| 14269 | - type: main_score |
| 14270 | value: 49.60489626836975 |
| 14271 | - type: precision |
| 14272 | value: 48.69639631803339 |
| 14273 | - type: recall |
| 14274 | value: 52.27272727272727 |
| 14275 | task: |
| 14276 | type: BitextMining |
| 14277 | - dataset: |
| 14278 | config: ory_Orya-rus_Cyrl |
| 14279 | name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl) |
| 14280 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14281 | split: devtest |
| 14282 | type: mteb/flores |
| 14283 | metrics: |
| 14284 | - type: accuracy |
| 14285 | value: 97.82608695652173 |
| 14286 | - type: f1 |
| 14287 | value: 97.27437417654808 |
| 14288 | - type: main_score |
| 14289 | value: 97.27437417654808 |
| 14290 | - type: precision |
| 14291 | value: 97.04968944099377 |
| 14292 | - type: recall |
| 14293 | value: 97.82608695652173 |
| 14294 | task: |
| 14295 | type: BitextMining |
| 14296 | - dataset: |
| 14297 | config: sna_Latn-rus_Cyrl |
| 14298 | name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl) |
| 14299 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14300 | split: devtest |
| 14301 | type: mteb/flores |
| 14302 | metrics: |
| 14303 | - type: accuracy |
| 14304 | value: 85.37549407114624 |
| 14305 | - type: f1 |
| 14306 | value: 83.09911316305177 |
| 14307 | - type: main_score |
| 14308 | value: 83.09911316305177 |
| 14309 | - type: precision |
| 14310 | value: 82.1284950958864 |
| 14311 | - type: recall |
| 14312 | value: 85.37549407114624 |
| 14313 | task: |
| 14314 | type: BitextMining |
| 14315 | - dataset: |
| 14316 | config: tso_Latn-rus_Cyrl |
| 14317 | name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl) |
| 14318 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14319 | split: devtest |
| 14320 | type: mteb/flores |
| 14321 | metrics: |
| 14322 | - type: accuracy |
| 14323 | value: 82.90513833992095 |
| 14324 | - type: f1 |
| 14325 | value: 80.28290385503824 |
| 14326 | - type: main_score |
| 14327 | value: 80.28290385503824 |
| 14328 | - type: precision |
| 14329 | value: 79.23672543237761 |
| 14330 | - type: recall |
| 14331 | value: 82.90513833992095 |
| 14332 | task: |
| 14333 | type: BitextMining |
| 14334 | - dataset: |
| 14335 | config: azj_Latn-rus_Cyrl |
| 14336 | name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl) |
| 14337 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14338 | split: devtest |
| 14339 | type: mteb/flores |
| 14340 | metrics: |
| 14341 | - type: accuracy |
| 14342 | value: 98.02371541501977 |
| 14343 | - type: f1 |
| 14344 | value: 97.49200075287031 |
| 14345 | - type: main_score |
| 14346 | value: 97.49200075287031 |
| 14347 | - type: precision |
| 14348 | value: 97.266139657444 |
| 14349 | - type: recall |
| 14350 | value: 98.02371541501977 |
| 14351 | task: |
| 14352 | type: BitextMining |
| 14353 | - dataset: |
| 14354 | config: dik_Latn-rus_Cyrl |
| 14355 | name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl) |
| 14356 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14357 | split: devtest |
| 14358 | type: mteb/flores |
| 14359 | metrics: |
| 14360 | - type: accuracy |
| 14361 | value: 38.43873517786561 |
| 14362 | - type: f1 |
| 14363 | value: 35.78152442955223 |
| 14364 | - type: main_score |
| 14365 | value: 35.78152442955223 |
| 14366 | - type: precision |
| 14367 | value: 34.82424325078237 |
| 14368 | - type: recall |
| 14369 | value: 38.43873517786561 |
| 14370 | task: |
| 14371 | type: BitextMining |
| 14372 | - dataset: |
| 14373 | config: hau_Latn-rus_Cyrl |
| 14374 | name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl) |
| 14375 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14376 | split: devtest |
| 14377 | type: mteb/flores |
| 14378 | metrics: |
| 14379 | - type: accuracy |
| 14380 | value: 81.42292490118577 |
| 14381 | - type: f1 |
| 14382 | value: 79.24612283124593 |
| 14383 | - type: main_score |
| 14384 | value: 79.24612283124593 |
| 14385 | - type: precision |
| 14386 | value: 78.34736070751448 |
| 14387 | - type: recall |
| 14388 | value: 81.42292490118577 |
| 14389 | task: |
| 14390 | type: BitextMining |
| 14391 | - dataset: |
| 14392 | config: kea_Latn-rus_Cyrl |
| 14393 | name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl) |
| 14394 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14395 | split: devtest |
| 14396 | type: mteb/flores |
| 14397 | metrics: |
| 14398 | - type: accuracy |
| 14399 | value: 81.62055335968378 |
| 14400 | - type: f1 |
| 14401 | value: 80.47015182884748 |
| 14402 | - type: main_score |
| 14403 | value: 80.47015182884748 |
| 14404 | - type: precision |
| 14405 | value: 80.02671028885862 |
| 14406 | - type: recall |
| 14407 | value: 81.62055335968378 |
| 14408 | task: |
| 14409 | type: BitextMining |
| 14410 | - dataset: |
| 14411 | config: lus_Latn-rus_Cyrl |
| 14412 | name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl) |
| 14413 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14414 | split: devtest |
| 14415 | type: mteb/flores |
| 14416 | metrics: |
| 14417 | - type: accuracy |
| 14418 | value: 62.74703557312253 |
| 14419 | - type: f1 |
| 14420 | value: 60.53900079111122 |
| 14421 | - type: main_score |
| 14422 | value: 60.53900079111122 |
| 14423 | - type: precision |
| 14424 | value: 59.80024202850289 |
| 14425 | - type: recall |
| 14426 | value: 62.74703557312253 |
| 14427 | task: |
| 14428 | type: BitextMining |
| 14429 | - dataset: |
| 14430 | config: pag_Latn-rus_Cyrl |
| 14431 | name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl) |
| 14432 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14433 | split: devtest |
| 14434 | type: mteb/flores |
| 14435 | metrics: |
| 14436 | - type: accuracy |
| 14437 | value: 74.01185770750988 |
| 14438 | - type: f1 |
| 14439 | value: 72.57280648279529 |
| 14440 | - type: main_score |
| 14441 | value: 72.57280648279529 |
| 14442 | - type: precision |
| 14443 | value: 71.99952968456789 |
| 14444 | - type: recall |
| 14445 | value: 74.01185770750988 |
| 14446 | task: |
| 14447 | type: BitextMining |
| 14448 | - dataset: |
| 14449 | config: snd_Arab-rus_Cyrl |
| 14450 | name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl) |
| 14451 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14452 | split: devtest |
| 14453 | type: mteb/flores |
| 14454 | metrics: |
| 14455 | - type: accuracy |
| 14456 | value: 91.30434782608695 |
| 14457 | - type: f1 |
| 14458 | value: 90.24653499445358 |
| 14459 | - type: main_score |
| 14460 | value: 90.24653499445358 |
| 14461 | - type: precision |
| 14462 | value: 89.83134068200232 |
| 14463 | - type: recall |
| 14464 | value: 91.30434782608695 |
| 14465 | task: |
| 14466 | type: BitextMining |
| 14467 | - dataset: |
| 14468 | config: tuk_Latn-rus_Cyrl |
| 14469 | name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl) |
| 14470 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14471 | split: devtest |
| 14472 | type: mteb/flores |
| 14473 | metrics: |
| 14474 | - type: accuracy |
| 14475 | value: 47.62845849802372 |
| 14476 | - type: f1 |
| 14477 | value: 45.812928836644254 |
| 14478 | - type: main_score |
| 14479 | value: 45.812928836644254 |
| 14480 | - type: precision |
| 14481 | value: 45.23713833170355 |
| 14482 | - type: recall |
| 14483 | value: 47.62845849802372 |
| 14484 | task: |
| 14485 | type: BitextMining |
| 14486 | - dataset: |
| 14487 | config: bak_Cyrl-rus_Cyrl |
| 14488 | name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl) |
| 14489 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14490 | split: devtest |
| 14491 | type: mteb/flores |
| 14492 | metrics: |
| 14493 | - type: accuracy |
| 14494 | value: 95.8498023715415 |
| 14495 | - type: f1 |
| 14496 | value: 95.18904459615922 |
| 14497 | - type: main_score |
| 14498 | value: 95.18904459615922 |
| 14499 | - type: precision |
| 14500 | value: 94.92812441182006 |
| 14501 | - type: recall |
| 14502 | value: 95.8498023715415 |
| 14503 | task: |
| 14504 | type: BitextMining |
| 14505 | - dataset: |
| 14506 | config: dyu_Latn-rus_Cyrl |
| 14507 | name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl) |
| 14508 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14509 | split: devtest |
| 14510 | type: mteb/flores |
| 14511 | metrics: |
| 14512 | - type: accuracy |
| 14513 | value: 29.64426877470356 |
| 14514 | - type: f1 |
| 14515 | value: 27.287335193938166 |
| 14516 | - type: main_score |
| 14517 | value: 27.287335193938166 |
| 14518 | - type: precision |
| 14519 | value: 26.583996026587492 |
| 14520 | - type: recall |
| 14521 | value: 29.64426877470356 |
| 14522 | task: |
| 14523 | type: BitextMining |
| 14524 | - dataset: |
| 14525 | config: heb_Hebr-rus_Cyrl |
| 14526 | name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl) |
| 14527 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14528 | split: devtest |
| 14529 | type: mteb/flores |
| 14530 | metrics: |
| 14531 | - type: accuracy |
| 14532 | value: 98.91304347826086 |
| 14533 | - type: f1 |
| 14534 | value: 98.55072463768116 |
| 14535 | - type: main_score |
| 14536 | value: 98.55072463768116 |
| 14537 | - type: precision |
| 14538 | value: 98.36956521739131 |
| 14539 | - type: recall |
| 14540 | value: 98.91304347826086 |
| 14541 | task: |
| 14542 | type: BitextMining |
| 14543 | - dataset: |
| 14544 | config: khk_Cyrl-rus_Cyrl |
| 14545 | name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl) |
| 14546 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14547 | split: devtest |
| 14548 | type: mteb/flores |
| 14549 | metrics: |
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| 14551 | value: 95.15810276679841 |
| 14552 | - type: f1 |
| 14553 | value: 94.44009547764487 |
| 14554 | - type: main_score |
| 14555 | value: 94.44009547764487 |
| 14556 | - type: precision |
| 14557 | value: 94.16579797014579 |
| 14558 | - type: recall |
| 14559 | value: 95.15810276679841 |
| 14560 | task: |
| 14561 | type: BitextMining |
| 14562 | - dataset: |
| 14563 | config: lvs_Latn-rus_Cyrl |
| 14564 | name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl) |
| 14565 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14566 | split: devtest |
| 14567 | type: mteb/flores |
| 14568 | metrics: |
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| 14570 | value: 97.92490118577075 |
| 14571 | - type: f1 |
| 14572 | value: 97.51467241585817 |
| 14573 | - type: main_score |
| 14574 | value: 97.51467241585817 |
| 14575 | - type: precision |
| 14576 | value: 97.36166007905138 |
| 14577 | - type: recall |
| 14578 | value: 97.92490118577075 |
| 14579 | task: |
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| 14581 | - dataset: |
| 14582 | config: pan_Guru-rus_Cyrl |
| 14583 | name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl) |
| 14584 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14585 | split: devtest |
| 14586 | type: mteb/flores |
| 14587 | metrics: |
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| 14589 | value: 97.92490118577075 |
| 14590 | - type: f1 |
| 14591 | value: 97.42918313570486 |
| 14592 | - type: main_score |
| 14593 | value: 97.42918313570486 |
| 14594 | - type: precision |
| 14595 | value: 97.22261434217955 |
| 14596 | - type: recall |
| 14597 | value: 97.92490118577075 |
| 14598 | task: |
| 14599 | type: BitextMining |
| 14600 | - dataset: |
| 14601 | config: som_Latn-rus_Cyrl |
| 14602 | name: MTEB FloresBitextMining (som_Latn-rus_Cyrl) |
| 14603 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14604 | split: devtest |
| 14605 | type: mteb/flores |
| 14606 | metrics: |
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| 14608 | value: 75.69169960474308 |
| 14609 | - type: f1 |
| 14610 | value: 73.7211667065916 |
| 14611 | - type: main_score |
| 14612 | value: 73.7211667065916 |
| 14613 | - type: precision |
| 14614 | value: 72.95842401892384 |
| 14615 | - type: recall |
| 14616 | value: 75.69169960474308 |
| 14617 | task: |
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| 14619 | - dataset: |
| 14620 | config: tum_Latn-rus_Cyrl |
| 14621 | name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl) |
| 14622 | revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e |
| 14623 | split: devtest |
| 14624 | type: mteb/flores |
| 14625 | metrics: |
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| 14628 | - type: f1 |
| 14629 | value: 82.9296066252588 |
| 14630 | - type: main_score |
| 14631 | value: 82.9296066252588 |
| 14632 | - type: precision |
| 14633 | value: 81.77330225447936 |
| 14634 | - type: recall |
| 14635 | value: 85.67193675889328 |
| 14636 | task: |
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| 14638 | - dataset: |
| 14639 | config: default |
| 14640 | name: MTEB GeoreviewClassification (default) |
| 14641 | revision: 3765c0d1de6b7d264bc459433c45e5a75513839c |
| 14642 | split: test |
| 14643 | type: ai-forever/georeview-classification |
| 14644 | metrics: |
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| 14646 | value: 44.6630859375 |
| 14647 | - type: f1 |
| 14648 | value: 42.607425073610536 |
| 14649 | - type: f1_weighted |
| 14650 | value: 42.60639474586065 |
| 14651 | - type: main_score |
| 14652 | value: 44.6630859375 |
| 14653 | task: |
| 14654 | type: Classification |
| 14655 | - dataset: |
| 14656 | config: default |
| 14657 | name: MTEB GeoreviewClusteringP2P (default) |
| 14658 | revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec |
| 14659 | split: test |
| 14660 | type: ai-forever/georeview-clustering-p2p |
| 14661 | metrics: |
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| 14663 | value: 58.15951247070825 |
| 14664 | - type: v_measure |
| 14665 | value: 58.15951247070825 |
| 14666 | - type: v_measure_std |
| 14667 | value: 0.6739615788288809 |
| 14668 | task: |
| 14669 | type: Clustering |
| 14670 | - dataset: |
| 14671 | config: default |
| 14672 | name: MTEB HeadlineClassification (default) |
| 14673 | revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb |
| 14674 | split: test |
| 14675 | type: ai-forever/headline-classification |
| 14676 | metrics: |
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| 14678 | value: 73.935546875 |
| 14679 | - type: f1 |
| 14680 | value: 73.8654872186846 |
| 14681 | - type: f1_weighted |
| 14682 | value: 73.86733122685095 |
| 14683 | - type: main_score |
| 14684 | value: 73.935546875 |
| 14685 | task: |
| 14686 | type: Classification |
| 14687 | - dataset: |
| 14688 | config: default |
| 14689 | name: MTEB InappropriatenessClassification (default) |
| 14690 | revision: 601651fdc45ef243751676e62dd7a19f491c0285 |
| 14691 | split: test |
| 14692 | type: ai-forever/inappropriateness-classification |
| 14693 | metrics: |
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| 14695 | value: 59.16015624999999 |
| 14696 | - type: ap |
| 14697 | value: 55.52276605836938 |
| 14698 | - type: ap_weighted |
| 14699 | value: 55.52276605836938 |
| 14700 | - type: f1 |
| 14701 | value: 58.614248199637956 |
| 14702 | - type: f1_weighted |
| 14703 | value: 58.614248199637956 |
| 14704 | - type: main_score |
| 14705 | value: 59.16015624999999 |
| 14706 | task: |
| 14707 | type: Classification |
| 14708 | - dataset: |
| 14709 | config: default |
| 14710 | name: MTEB KinopoiskClassification (default) |
| 14711 | revision: 5911f26666ac11af46cb9c6849d0dc80a378af24 |
| 14712 | split: test |
| 14713 | type: ai-forever/kinopoisk-sentiment-classification |
| 14714 | metrics: |
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| 14716 | value: 49.959999999999994 |
| 14717 | - type: f1 |
| 14718 | value: 48.4900332316098 |
| 14719 | - type: f1_weighted |
| 14720 | value: 48.4900332316098 |
| 14721 | - type: main_score |
| 14722 | value: 49.959999999999994 |
| 14723 | task: |
| 14724 | type: Classification |
| 14725 | - dataset: |
| 14726 | config: default |
| 14727 | name: MTEB LanguageClassification (default) |
| 14728 | revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2 |
| 14729 | split: test |
| 14730 | type: papluca/language-identification |
| 14731 | metrics: |
| 14732 | - type: accuracy |
| 14733 | value: 71.005859375 |
| 14734 | - type: f1 |
| 14735 | value: 69.63481100303348 |
| 14736 | - type: f1_weighted |
| 14737 | value: 69.64640413409529 |
| 14738 | - type: main_score |
| 14739 | value: 71.005859375 |
| 14740 | task: |
| 14741 | type: Classification |
| 14742 | - dataset: |
| 14743 | config: ru |
| 14744 | name: MTEB MLSUMClusteringP2P (ru) |
| 14745 | revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
| 14746 | split: test |
| 14747 | type: reciTAL/mlsum |
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| 14751 | - type: v_measure |
| 14752 | value: 42.11280087032343 |
| 14753 | - type: v_measure_std |
| 14754 | value: 6.7619971723605135 |
| 14755 | task: |
| 14756 | type: Clustering |
| 14757 | - dataset: |
| 14758 | config: ru |
| 14759 | name: MTEB MLSUMClusteringP2P.v2 (ru) |
| 14760 | revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
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| 14766 | - type: v_measure |
| 14767 | value: 43.00112546945811 |
| 14768 | - type: v_measure_std |
| 14769 | value: 1.4740560414835675 |
| 14770 | task: |
| 14771 | type: Clustering |
| 14772 | - dataset: |
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| 14774 | name: MTEB MLSUMClusteringS2S (ru) |
| 14775 | revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
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| 14781 | - type: v_measure |
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| 14783 | - type: v_measure_std |
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| 14789 | name: MTEB MLSUMClusteringS2S.v2 (ru) |
| 14790 | revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
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| 14804 | name: MTEB MultiLongDocRetrieval (ru) |
| 14805 | revision: d67138e705d963e346253a80e59676ddb418810a |
| 14806 | split: dev |
| 14807 | type: Shitao/MLDR |
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| 14811 | - type: map_at_1 |
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| 14813 | - type: map_at_10 |
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| 14823 | - type: map_at_5 |
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| 15012 | value: 40.60788709618142 |
| 15013 | - type: nauc_recall_at_100_diff1 |
| 15014 | value: 46.49620002554603 |
| 15015 | - type: nauc_recall_at_100_max |
| 15016 | value: 53.02960148167071 |
| 15017 | - type: nauc_recall_at_100_std |
| 15018 | value: 28.206028867032835 |
| 15019 | - type: nauc_recall_at_10_diff1 |
| 15020 | value: 56.562744749606765 |
| 15021 | - type: nauc_recall_at_10_max |
| 15022 | value: 56.00594967783549 |
| 15023 | - type: nauc_recall_at_10_std |
| 15024 | value: 8.368379831645147 |
| 15025 | - type: nauc_recall_at_1_diff1 |
| 15026 | value: 52.57059856776112 |
| 15027 | - type: nauc_recall_at_1_max |
| 15028 | value: 50.55668152952304 |
| 15029 | - type: nauc_recall_at_1_std |
| 15030 | value: 1.6572084853398048 |
| 15031 | - type: nauc_recall_at_20_diff1 |
| 15032 | value: 53.259157546141154 |
| 15033 | - type: nauc_recall_at_20_max |
| 15034 | value: 54.03255118937038 |
| 15035 | - type: nauc_recall_at_20_std |
| 15036 | value: 15.16161167427274 |
| 15037 | - type: nauc_recall_at_3_diff1 |
| 15038 | value: 60.72678574894387 |
| 15039 | - type: nauc_recall_at_3_max |
| 15040 | value: 56.13989687586933 |
| 15041 | - type: nauc_recall_at_3_std |
| 15042 | value: 2.2306901035770066 |
| 15043 | - type: nauc_recall_at_5_diff1 |
| 15044 | value: 57.12011275251864 |
| 15045 | - type: nauc_recall_at_5_max |
| 15046 | value: 53.28665761862502 |
| 15047 | - type: nauc_recall_at_5_std |
| 15048 | value: 4.3587200501122245 |
| 15049 | - type: ndcg_at_1 |
| 15050 | value: 30.0 |
| 15051 | - type: ndcg_at_10 |
| 15052 | value: 38.671 |
| 15053 | - type: ndcg_at_100 |
| 15054 | value: 42.173 |
| 15055 | - type: ndcg_at_1000 |
| 15056 | value: 44.016 |
| 15057 | - type: ndcg_at_20 |
| 15058 | value: 39.845000000000006 |
| 15059 | - type: ndcg_at_3 |
| 15060 | value: 36.863 |
| 15061 | - type: ndcg_at_5 |
| 15062 | value: 37.874 |
| 15063 | - type: precision_at_1 |
| 15064 | value: 30.0 |
| 15065 | - type: precision_at_10 |
| 15066 | value: 4.65 |
| 15067 | - type: precision_at_100 |
| 15068 | value: 0.64 |
| 15069 | - type: precision_at_1000 |
| 15070 | value: 0.08 |
| 15071 | - type: precision_at_20 |
| 15072 | value: 2.55 |
| 15073 | - type: precision_at_3 |
| 15074 | value: 13.833 |
| 15075 | - type: precision_at_5 |
| 15076 | value: 8.799999999999999 |
| 15077 | - type: recall_at_1 |
| 15078 | value: 30.0 |
| 15079 | - type: recall_at_10 |
| 15080 | value: 46.5 |
| 15081 | - type: recall_at_100 |
| 15082 | value: 64.0 |
| 15083 | - type: recall_at_1000 |
| 15084 | value: 79.5 |
| 15085 | - type: recall_at_20 |
| 15086 | value: 51.0 |
| 15087 | - type: recall_at_3 |
| 15088 | value: 41.5 |
| 15089 | - type: recall_at_5 |
| 15090 | value: 44.0 |
| 15091 | task: |
| 15092 | type: Retrieval |
| 15093 | - dataset: |
| 15094 | config: rus |
| 15095 | name: MTEB MultilingualSentimentClassification (rus) |
| 15096 | revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33 |
| 15097 | split: test |
| 15098 | type: mteb/multilingual-sentiment-classification |
| 15099 | metrics: |
| 15100 | - type: accuracy |
| 15101 | value: 79.52710495963092 |
| 15102 | - type: ap |
| 15103 | value: 84.5713457178972 |
| 15104 | - type: ap_weighted |
| 15105 | value: 84.5713457178972 |
| 15106 | - type: f1 |
| 15107 | value: 77.88661181524105 |
| 15108 | - type: f1_weighted |
| 15109 | value: 79.87563079922718 |
| 15110 | - type: main_score |
| 15111 | value: 79.52710495963092 |
| 15112 | task: |
| 15113 | type: Classification |
| 15114 | - dataset: |
| 15115 | config: arb_Arab-rus_Cyrl |
| 15116 | name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl) |
| 15117 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15118 | split: test |
| 15119 | type: mteb/NTREX |
| 15120 | metrics: |
| 15121 | - type: accuracy |
| 15122 | value: 86.47971957936905 |
| 15123 | - type: f1 |
| 15124 | value: 82.79864240805654 |
| 15125 | - type: main_score |
| 15126 | value: 82.79864240805654 |
| 15127 | - type: precision |
| 15128 | value: 81.21485800128767 |
| 15129 | - type: recall |
| 15130 | value: 86.47971957936905 |
| 15131 | task: |
| 15132 | type: BitextMining |
| 15133 | - dataset: |
| 15134 | config: bel_Cyrl-rus_Cyrl |
| 15135 | name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl) |
| 15136 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15137 | split: test |
| 15138 | type: mteb/NTREX |
| 15139 | metrics: |
| 15140 | - type: accuracy |
| 15141 | value: 94.84226339509264 |
| 15142 | - type: f1 |
| 15143 | value: 93.56399067465667 |
| 15144 | - type: main_score |
| 15145 | value: 93.56399067465667 |
| 15146 | - type: precision |
| 15147 | value: 93.01619095309631 |
| 15148 | - type: recall |
| 15149 | value: 94.84226339509264 |
| 15150 | task: |
| 15151 | type: BitextMining |
| 15152 | - dataset: |
| 15153 | config: ben_Beng-rus_Cyrl |
| 15154 | name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl) |
| 15155 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15156 | split: test |
| 15157 | type: mteb/NTREX |
| 15158 | metrics: |
| 15159 | - type: accuracy |
| 15160 | value: 92.18828242363544 |
| 15161 | - type: f1 |
| 15162 | value: 90.42393889620612 |
| 15163 | - type: main_score |
| 15164 | value: 90.42393889620612 |
| 15165 | - type: precision |
| 15166 | value: 89.67904925153297 |
| 15167 | - type: recall |
| 15168 | value: 92.18828242363544 |
| 15169 | task: |
| 15170 | type: BitextMining |
| 15171 | - dataset: |
| 15172 | config: bos_Latn-rus_Cyrl |
| 15173 | name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl) |
| 15174 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15175 | split: test |
| 15176 | type: mteb/NTREX |
| 15177 | metrics: |
| 15178 | - type: accuracy |
| 15179 | value: 94.69203805708563 |
| 15180 | - type: f1 |
| 15181 | value: 93.37172425304624 |
| 15182 | - type: main_score |
| 15183 | value: 93.37172425304624 |
| 15184 | - type: precision |
| 15185 | value: 92.79204521067315 |
| 15186 | - type: recall |
| 15187 | value: 94.69203805708563 |
| 15188 | task: |
| 15189 | type: BitextMining |
| 15190 | - dataset: |
| 15191 | config: bul_Cyrl-rus_Cyrl |
| 15192 | name: MTEB NTREXBitextMining (bul_Cyrl-rus_Cyrl) |
| 15193 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15194 | split: test |
| 15195 | type: mteb/NTREX |
| 15196 | metrics: |
| 15197 | - type: accuracy |
| 15198 | value: 96.99549323985978 |
| 15199 | - type: f1 |
| 15200 | value: 96.13086296110833 |
| 15201 | - type: main_score |
| 15202 | value: 96.13086296110833 |
| 15203 | - type: precision |
| 15204 | value: 95.72441996327827 |
| 15205 | - type: recall |
| 15206 | value: 96.99549323985978 |
| 15207 | task: |
| 15208 | type: BitextMining |
| 15209 | - dataset: |
| 15210 | config: ces_Latn-rus_Cyrl |
| 15211 | name: MTEB NTREXBitextMining (ces_Latn-rus_Cyrl) |
| 15212 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15213 | split: test |
| 15214 | type: mteb/NTREX |
| 15215 | metrics: |
| 15216 | - type: accuracy |
| 15217 | value: 95.94391587381071 |
| 15218 | - type: f1 |
| 15219 | value: 94.90680465142157 |
| 15220 | - type: main_score |
| 15221 | value: 94.90680465142157 |
| 15222 | - type: precision |
| 15223 | value: 94.44541812719079 |
| 15224 | - type: recall |
| 15225 | value: 95.94391587381071 |
| 15226 | task: |
| 15227 | type: BitextMining |
| 15228 | - dataset: |
| 15229 | config: deu_Latn-rus_Cyrl |
| 15230 | name: MTEB NTREXBitextMining (deu_Latn-rus_Cyrl) |
| 15231 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15232 | split: test |
| 15233 | type: mteb/NTREX |
| 15234 | metrics: |
| 15235 | - type: accuracy |
| 15236 | value: 96.09414121181773 |
| 15237 | - type: f1 |
| 15238 | value: 94.94408279085295 |
| 15239 | - type: main_score |
| 15240 | value: 94.94408279085295 |
| 15241 | - type: precision |
| 15242 | value: 94.41245201135037 |
| 15243 | - type: recall |
| 15244 | value: 96.09414121181773 |
| 15245 | task: |
| 15246 | type: BitextMining |
| 15247 | - dataset: |
| 15248 | config: ell_Grek-rus_Cyrl |
| 15249 | name: MTEB NTREXBitextMining (ell_Grek-rus_Cyrl) |
| 15250 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15251 | split: test |
| 15252 | type: mteb/NTREX |
| 15253 | metrics: |
| 15254 | - type: accuracy |
| 15255 | value: 96.19429143715573 |
| 15256 | - type: f1 |
| 15257 | value: 95.12101485561676 |
| 15258 | - type: main_score |
| 15259 | value: 95.12101485561676 |
| 15260 | - type: precision |
| 15261 | value: 94.60440660991488 |
| 15262 | - type: recall |
| 15263 | value: 96.19429143715573 |
| 15264 | task: |
| 15265 | type: BitextMining |
| 15266 | - dataset: |
| 15267 | config: eng_Latn-rus_Cyrl |
| 15268 | name: MTEB NTREXBitextMining (eng_Latn-rus_Cyrl) |
| 15269 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15270 | split: test |
| 15271 | type: mteb/NTREX |
| 15272 | metrics: |
| 15273 | - type: accuracy |
| 15274 | value: 96.49474211316975 |
| 15275 | - type: f1 |
| 15276 | value: 95.46581777428045 |
| 15277 | - type: main_score |
| 15278 | value: 95.46581777428045 |
| 15279 | - type: precision |
| 15280 | value: 94.98414288098814 |
| 15281 | - type: recall |
| 15282 | value: 96.49474211316975 |
| 15283 | task: |
| 15284 | type: BitextMining |
| 15285 | - dataset: |
| 15286 | config: fas_Arab-rus_Cyrl |
| 15287 | name: MTEB NTREXBitextMining (fas_Arab-rus_Cyrl) |
| 15288 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15289 | split: test |
| 15290 | type: mteb/NTREX |
| 15291 | metrics: |
| 15292 | - type: accuracy |
| 15293 | value: 94.44166249374061 |
| 15294 | - type: f1 |
| 15295 | value: 92.92383018972905 |
| 15296 | - type: main_score |
| 15297 | value: 92.92383018972905 |
| 15298 | - type: precision |
| 15299 | value: 92.21957936905358 |
| 15300 | - type: recall |
| 15301 | value: 94.44166249374061 |
| 15302 | task: |
| 15303 | type: BitextMining |
| 15304 | - dataset: |
| 15305 | config: fin_Latn-rus_Cyrl |
| 15306 | name: MTEB NTREXBitextMining (fin_Latn-rus_Cyrl) |
| 15307 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15308 | split: test |
| 15309 | type: mteb/NTREX |
| 15310 | metrics: |
| 15311 | - type: accuracy |
| 15312 | value: 92.18828242363544 |
| 15313 | - type: f1 |
| 15314 | value: 90.2980661468393 |
| 15315 | - type: main_score |
| 15316 | value: 90.2980661468393 |
| 15317 | - type: precision |
| 15318 | value: 89.42580537472877 |
| 15319 | - type: recall |
| 15320 | value: 92.18828242363544 |
| 15321 | task: |
| 15322 | type: BitextMining |
| 15323 | - dataset: |
| 15324 | config: fra_Latn-rus_Cyrl |
| 15325 | name: MTEB NTREXBitextMining (fra_Latn-rus_Cyrl) |
| 15326 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15327 | split: test |
| 15328 | type: mteb/NTREX |
| 15329 | metrics: |
| 15330 | - type: accuracy |
| 15331 | value: 95.84376564847271 |
| 15332 | - type: f1 |
| 15333 | value: 94.81054915706895 |
| 15334 | - type: main_score |
| 15335 | value: 94.81054915706895 |
| 15336 | - type: precision |
| 15337 | value: 94.31369276136427 |
| 15338 | - type: recall |
| 15339 | value: 95.84376564847271 |
| 15340 | task: |
| 15341 | type: BitextMining |
| 15342 | - dataset: |
| 15343 | config: heb_Hebr-rus_Cyrl |
| 15344 | name: MTEB NTREXBitextMining (heb_Hebr-rus_Cyrl) |
| 15345 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15346 | split: test |
| 15347 | type: mteb/NTREX |
| 15348 | metrics: |
| 15349 | - type: accuracy |
| 15350 | value: 94.89233850776164 |
| 15351 | - type: f1 |
| 15352 | value: 93.42513770655985 |
| 15353 | - type: main_score |
| 15354 | value: 93.42513770655985 |
| 15355 | - type: precision |
| 15356 | value: 92.73493573693875 |
| 15357 | - type: recall |
| 15358 | value: 94.89233850776164 |
| 15359 | task: |
| 15360 | type: BitextMining |
| 15361 | - dataset: |
| 15362 | config: hin_Deva-rus_Cyrl |
| 15363 | name: MTEB NTREXBitextMining (hin_Deva-rus_Cyrl) |
| 15364 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15365 | split: test |
| 15366 | type: mteb/NTREX |
| 15367 | metrics: |
| 15368 | - type: accuracy |
| 15369 | value: 93.23985978968453 |
| 15370 | - type: f1 |
| 15371 | value: 91.52816526376867 |
| 15372 | - type: main_score |
| 15373 | value: 91.52816526376867 |
| 15374 | - type: precision |
| 15375 | value: 90.76745946425466 |
| 15376 | - type: recall |
| 15377 | value: 93.23985978968453 |
| 15378 | task: |
| 15379 | type: BitextMining |
| 15380 | - dataset: |
| 15381 | config: hrv_Latn-rus_Cyrl |
| 15382 | name: MTEB NTREXBitextMining (hrv_Latn-rus_Cyrl) |
| 15383 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15384 | split: test |
| 15385 | type: mteb/NTREX |
| 15386 | metrics: |
| 15387 | - type: accuracy |
| 15388 | value: 93.99098647971958 |
| 15389 | - type: f1 |
| 15390 | value: 92.36354531797697 |
| 15391 | - type: main_score |
| 15392 | value: 92.36354531797697 |
| 15393 | - type: precision |
| 15394 | value: 91.63228970439788 |
| 15395 | - type: recall |
| 15396 | value: 93.99098647971958 |
| 15397 | task: |
| 15398 | type: BitextMining |
| 15399 | - dataset: |
| 15400 | config: hun_Latn-rus_Cyrl |
| 15401 | name: MTEB NTREXBitextMining (hun_Latn-rus_Cyrl) |
| 15402 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15403 | split: test |
| 15404 | type: mteb/NTREX |
| 15405 | metrics: |
| 15406 | - type: accuracy |
| 15407 | value: 93.64046069103655 |
| 15408 | - type: f1 |
| 15409 | value: 92.05224503421799 |
| 15410 | - type: main_score |
| 15411 | value: 92.05224503421799 |
| 15412 | - type: precision |
| 15413 | value: 91.33998616973079 |
| 15414 | - type: recall |
| 15415 | value: 93.64046069103655 |
| 15416 | task: |
| 15417 | type: BitextMining |
| 15418 | - dataset: |
| 15419 | config: ind_Latn-rus_Cyrl |
| 15420 | name: MTEB NTREXBitextMining (ind_Latn-rus_Cyrl) |
| 15421 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15422 | split: test |
| 15423 | type: mteb/NTREX |
| 15424 | metrics: |
| 15425 | - type: accuracy |
| 15426 | value: 91.68753129694541 |
| 15427 | - type: f1 |
| 15428 | value: 89.26222667334335 |
| 15429 | - type: main_score |
| 15430 | value: 89.26222667334335 |
| 15431 | - type: precision |
| 15432 | value: 88.14638624603572 |
| 15433 | - type: recall |
| 15434 | value: 91.68753129694541 |
| 15435 | task: |
| 15436 | type: BitextMining |
| 15437 | - dataset: |
| 15438 | config: jpn_Jpan-rus_Cyrl |
| 15439 | name: MTEB NTREXBitextMining (jpn_Jpan-rus_Cyrl) |
| 15440 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15441 | split: test |
| 15442 | type: mteb/NTREX |
| 15443 | metrics: |
| 15444 | - type: accuracy |
| 15445 | value: 91.28693039559339 |
| 15446 | - type: f1 |
| 15447 | value: 89.21161763348957 |
| 15448 | - type: main_score |
| 15449 | value: 89.21161763348957 |
| 15450 | - type: precision |
| 15451 | value: 88.31188340952988 |
| 15452 | - type: recall |
| 15453 | value: 91.28693039559339 |
| 15454 | task: |
| 15455 | type: BitextMining |
| 15456 | - dataset: |
| 15457 | config: kor_Hang-rus_Cyrl |
| 15458 | name: MTEB NTREXBitextMining (kor_Hang-rus_Cyrl) |
| 15459 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15460 | split: test |
| 15461 | type: mteb/NTREX |
| 15462 | metrics: |
| 15463 | - type: accuracy |
| 15464 | value: 89.53430145217827 |
| 15465 | - type: f1 |
| 15466 | value: 86.88322165788365 |
| 15467 | - type: main_score |
| 15468 | value: 86.88322165788365 |
| 15469 | - type: precision |
| 15470 | value: 85.73950211030831 |
| 15471 | - type: recall |
| 15472 | value: 89.53430145217827 |
| 15473 | task: |
| 15474 | type: BitextMining |
| 15475 | - dataset: |
| 15476 | config: lit_Latn-rus_Cyrl |
| 15477 | name: MTEB NTREXBitextMining (lit_Latn-rus_Cyrl) |
| 15478 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15479 | split: test |
| 15480 | type: mteb/NTREX |
| 15481 | metrics: |
| 15482 | - type: accuracy |
| 15483 | value: 90.28542814221332 |
| 15484 | - type: f1 |
| 15485 | value: 88.10249103814452 |
| 15486 | - type: main_score |
| 15487 | value: 88.10249103814452 |
| 15488 | - type: precision |
| 15489 | value: 87.17689323973752 |
| 15490 | - type: recall |
| 15491 | value: 90.28542814221332 |
| 15492 | task: |
| 15493 | type: BitextMining |
| 15494 | - dataset: |
| 15495 | config: mkd_Cyrl-rus_Cyrl |
| 15496 | name: MTEB NTREXBitextMining (mkd_Cyrl-rus_Cyrl) |
| 15497 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15498 | split: test |
| 15499 | type: mteb/NTREX |
| 15500 | metrics: |
| 15501 | - type: accuracy |
| 15502 | value: 95.04256384576865 |
| 15503 | - type: f1 |
| 15504 | value: 93.65643703650713 |
| 15505 | - type: main_score |
| 15506 | value: 93.65643703650713 |
| 15507 | - type: precision |
| 15508 | value: 93.02036387915207 |
| 15509 | - type: recall |
| 15510 | value: 95.04256384576865 |
| 15511 | task: |
| 15512 | type: BitextMining |
| 15513 | - dataset: |
| 15514 | config: nld_Latn-rus_Cyrl |
| 15515 | name: MTEB NTREXBitextMining (nld_Latn-rus_Cyrl) |
| 15516 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15517 | split: test |
| 15518 | type: mteb/NTREX |
| 15519 | metrics: |
| 15520 | - type: accuracy |
| 15521 | value: 95.39308963445168 |
| 15522 | - type: f1 |
| 15523 | value: 94.16207644800535 |
| 15524 | - type: main_score |
| 15525 | value: 94.16207644800535 |
| 15526 | - type: precision |
| 15527 | value: 93.582516632091 |
| 15528 | - type: recall |
| 15529 | value: 95.39308963445168 |
| 15530 | task: |
| 15531 | type: BitextMining |
| 15532 | - dataset: |
| 15533 | config: pol_Latn-rus_Cyrl |
| 15534 | name: MTEB NTREXBitextMining (pol_Latn-rus_Cyrl) |
| 15535 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15536 | split: test |
| 15537 | type: mteb/NTREX |
| 15538 | metrics: |
| 15539 | - type: accuracy |
| 15540 | value: 95.7436154231347 |
| 15541 | - type: f1 |
| 15542 | value: 94.5067601402103 |
| 15543 | - type: main_score |
| 15544 | value: 94.5067601402103 |
| 15545 | - type: precision |
| 15546 | value: 93.91587381071608 |
| 15547 | - type: recall |
| 15548 | value: 95.7436154231347 |
| 15549 | task: |
| 15550 | type: BitextMining |
| 15551 | - dataset: |
| 15552 | config: por_Latn-rus_Cyrl |
| 15553 | name: MTEB NTREXBitextMining (por_Latn-rus_Cyrl) |
| 15554 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15555 | split: test |
| 15556 | type: mteb/NTREX |
| 15557 | metrics: |
| 15558 | - type: accuracy |
| 15559 | value: 65.89884827240861 |
| 15560 | - type: f1 |
| 15561 | value: 64.61805459419219 |
| 15562 | - type: main_score |
| 15563 | value: 64.61805459419219 |
| 15564 | - type: precision |
| 15565 | value: 64.07119451106485 |
| 15566 | - type: recall |
| 15567 | value: 65.89884827240861 |
| 15568 | task: |
| 15569 | type: BitextMining |
| 15570 | - dataset: |
| 15571 | config: rus_Cyrl-arb_Arab |
| 15572 | name: MTEB NTREXBitextMining (rus_Cyrl-arb_Arab) |
| 15573 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15574 | split: test |
| 15575 | type: mteb/NTREX |
| 15576 | metrics: |
| 15577 | - type: accuracy |
| 15578 | value: 94.2413620430646 |
| 15579 | - type: f1 |
| 15580 | value: 92.67663399861698 |
| 15581 | - type: main_score |
| 15582 | value: 92.67663399861698 |
| 15583 | - type: precision |
| 15584 | value: 91.94625271240193 |
| 15585 | - type: recall |
| 15586 | value: 94.2413620430646 |
| 15587 | task: |
| 15588 | type: BitextMining |
| 15589 | - dataset: |
| 15590 | config: rus_Cyrl-bel_Cyrl |
| 15591 | name: MTEB NTREXBitextMining (rus_Cyrl-bel_Cyrl) |
| 15592 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15593 | split: test |
| 15594 | type: mteb/NTREX |
| 15595 | metrics: |
| 15596 | - type: accuracy |
| 15597 | value: 94.89233850776164 |
| 15598 | - type: f1 |
| 15599 | value: 93.40343849106993 |
| 15600 | - type: main_score |
| 15601 | value: 93.40343849106993 |
| 15602 | - type: precision |
| 15603 | value: 92.74077783341679 |
| 15604 | - type: recall |
| 15605 | value: 94.89233850776164 |
| 15606 | task: |
| 15607 | type: BitextMining |
| 15608 | - dataset: |
| 15609 | config: rus_Cyrl-ben_Beng |
| 15610 | name: MTEB NTREXBitextMining (rus_Cyrl-ben_Beng) |
| 15611 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15612 | split: test |
| 15613 | type: mteb/NTREX |
| 15614 | metrics: |
| 15615 | - type: accuracy |
| 15616 | value: 94.2914371557336 |
| 15617 | - type: f1 |
| 15618 | value: 92.62226673343348 |
| 15619 | - type: main_score |
| 15620 | value: 92.62226673343348 |
| 15621 | - type: precision |
| 15622 | value: 91.84610248706393 |
| 15623 | - type: recall |
| 15624 | value: 94.2914371557336 |
| 15625 | task: |
| 15626 | type: BitextMining |
| 15627 | - dataset: |
| 15628 | config: rus_Cyrl-bos_Latn |
| 15629 | name: MTEB NTREXBitextMining (rus_Cyrl-bos_Latn) |
| 15630 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15631 | split: test |
| 15632 | type: mteb/NTREX |
| 15633 | metrics: |
| 15634 | - type: accuracy |
| 15635 | value: 95.69354031046569 |
| 15636 | - type: f1 |
| 15637 | value: 94.50418051319403 |
| 15638 | - type: main_score |
| 15639 | value: 94.50418051319403 |
| 15640 | - type: precision |
| 15641 | value: 93.95843765648473 |
| 15642 | - type: recall |
| 15643 | value: 95.69354031046569 |
| 15644 | task: |
| 15645 | type: BitextMining |
| 15646 | - dataset: |
| 15647 | config: rus_Cyrl-bul_Cyrl |
| 15648 | name: MTEB NTREXBitextMining (rus_Cyrl-bul_Cyrl) |
| 15649 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15650 | split: test |
| 15651 | type: mteb/NTREX |
| 15652 | metrics: |
| 15653 | - type: accuracy |
| 15654 | value: 95.89384076114172 |
| 15655 | - type: f1 |
| 15656 | value: 94.66199298948423 |
| 15657 | - type: main_score |
| 15658 | value: 94.66199298948423 |
| 15659 | - type: precision |
| 15660 | value: 94.08028709731263 |
| 15661 | - type: recall |
| 15662 | value: 95.89384076114172 |
| 15663 | task: |
| 15664 | type: BitextMining |
| 15665 | - dataset: |
| 15666 | config: rus_Cyrl-ces_Latn |
| 15667 | name: MTEB NTREXBitextMining (rus_Cyrl-ces_Latn) |
| 15668 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15669 | split: test |
| 15670 | type: mteb/NTREX |
| 15671 | metrics: |
| 15672 | - type: accuracy |
| 15673 | value: 93.94091136705057 |
| 15674 | - type: f1 |
| 15675 | value: 92.3746731207923 |
| 15676 | - type: main_score |
| 15677 | value: 92.3746731207923 |
| 15678 | - type: precision |
| 15679 | value: 91.66207644800535 |
| 15680 | - type: recall |
| 15681 | value: 93.94091136705057 |
| 15682 | task: |
| 15683 | type: BitextMining |
| 15684 | - dataset: |
| 15685 | config: rus_Cyrl-deu_Latn |
| 15686 | name: MTEB NTREXBitextMining (rus_Cyrl-deu_Latn) |
| 15687 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15688 | split: test |
| 15689 | type: mteb/NTREX |
| 15690 | metrics: |
| 15691 | - type: accuracy |
| 15692 | value: 95.94391587381071 |
| 15693 | - type: f1 |
| 15694 | value: 94.76214321482223 |
| 15695 | - type: main_score |
| 15696 | value: 94.76214321482223 |
| 15697 | - type: precision |
| 15698 | value: 94.20380570856285 |
| 15699 | - type: recall |
| 15700 | value: 95.94391587381071 |
| 15701 | task: |
| 15702 | type: BitextMining |
| 15703 | - dataset: |
| 15704 | config: rus_Cyrl-ell_Grek |
| 15705 | name: MTEB NTREXBitextMining (rus_Cyrl-ell_Grek) |
| 15706 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15707 | split: test |
| 15708 | type: mteb/NTREX |
| 15709 | metrics: |
| 15710 | - type: accuracy |
| 15711 | value: 95.44316474712068 |
| 15712 | - type: f1 |
| 15713 | value: 94.14788849941579 |
| 15714 | - type: main_score |
| 15715 | value: 94.14788849941579 |
| 15716 | - type: precision |
| 15717 | value: 93.54197963612084 |
| 15718 | - type: recall |
| 15719 | value: 95.44316474712068 |
| 15720 | task: |
| 15721 | type: BitextMining |
| 15722 | - dataset: |
| 15723 | config: rus_Cyrl-eng_Latn |
| 15724 | name: MTEB NTREXBitextMining (rus_Cyrl-eng_Latn) |
| 15725 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15726 | split: test |
| 15727 | type: mteb/NTREX |
| 15728 | metrics: |
| 15729 | - type: accuracy |
| 15730 | value: 98.14722083124687 |
| 15731 | - type: f1 |
| 15732 | value: 97.57135703555333 |
| 15733 | - type: main_score |
| 15734 | value: 97.57135703555333 |
| 15735 | - type: precision |
| 15736 | value: 97.2959439158738 |
| 15737 | - type: recall |
| 15738 | value: 98.14722083124687 |
| 15739 | task: |
| 15740 | type: BitextMining |
| 15741 | - dataset: |
| 15742 | config: rus_Cyrl-fas_Arab |
| 15743 | name: MTEB NTREXBitextMining (rus_Cyrl-fas_Arab) |
| 15744 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15745 | split: test |
| 15746 | type: mteb/NTREX |
| 15747 | metrics: |
| 15748 | - type: accuracy |
| 15749 | value: 94.64196294441662 |
| 15750 | - type: f1 |
| 15751 | value: 93.24653647137372 |
| 15752 | - type: main_score |
| 15753 | value: 93.24653647137372 |
| 15754 | - type: precision |
| 15755 | value: 92.60724419963279 |
| 15756 | - type: recall |
| 15757 | value: 94.64196294441662 |
| 15758 | task: |
| 15759 | type: BitextMining |
| 15760 | - dataset: |
| 15761 | config: rus_Cyrl-fin_Latn |
| 15762 | name: MTEB NTREXBitextMining (rus_Cyrl-fin_Latn) |
| 15763 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15764 | split: test |
| 15765 | type: mteb/NTREX |
| 15766 | metrics: |
| 15767 | - type: accuracy |
| 15768 | value: 87.98197295943916 |
| 15769 | - type: f1 |
| 15770 | value: 85.23368385912201 |
| 15771 | - type: main_score |
| 15772 | value: 85.23368385912201 |
| 15773 | - type: precision |
| 15774 | value: 84.08159858835873 |
| 15775 | - type: recall |
| 15776 | value: 87.98197295943916 |
| 15777 | task: |
| 15778 | type: BitextMining |
| 15779 | - dataset: |
| 15780 | config: rus_Cyrl-fra_Latn |
| 15781 | name: MTEB NTREXBitextMining (rus_Cyrl-fra_Latn) |
| 15782 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15783 | split: test |
| 15784 | type: mteb/NTREX |
| 15785 | metrics: |
| 15786 | - type: accuracy |
| 15787 | value: 96.24436654982473 |
| 15788 | - type: f1 |
| 15789 | value: 95.07093974294774 |
| 15790 | - type: main_score |
| 15791 | value: 95.07093974294774 |
| 15792 | - type: precision |
| 15793 | value: 94.49591053246536 |
| 15794 | - type: recall |
| 15795 | value: 96.24436654982473 |
| 15796 | task: |
| 15797 | type: BitextMining |
| 15798 | - dataset: |
| 15799 | config: rus_Cyrl-heb_Hebr |
| 15800 | name: MTEB NTREXBitextMining (rus_Cyrl-heb_Hebr) |
| 15801 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15802 | split: test |
| 15803 | type: mteb/NTREX |
| 15804 | metrics: |
| 15805 | - type: accuracy |
| 15806 | value: 91.08662994491738 |
| 15807 | - type: f1 |
| 15808 | value: 88.5161074945752 |
| 15809 | - type: main_score |
| 15810 | value: 88.5161074945752 |
| 15811 | - type: precision |
| 15812 | value: 87.36187614755467 |
| 15813 | - type: recall |
| 15814 | value: 91.08662994491738 |
| 15815 | task: |
| 15816 | type: BitextMining |
| 15817 | - dataset: |
| 15818 | config: rus_Cyrl-hin_Deva |
| 15819 | name: MTEB NTREXBitextMining (rus_Cyrl-hin_Deva) |
| 15820 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15821 | split: test |
| 15822 | type: mteb/NTREX |
| 15823 | metrics: |
| 15824 | - type: accuracy |
| 15825 | value: 95.04256384576865 |
| 15826 | - type: f1 |
| 15827 | value: 93.66382907694876 |
| 15828 | - type: main_score |
| 15829 | value: 93.66382907694876 |
| 15830 | - type: precision |
| 15831 | value: 93.05291270238692 |
| 15832 | - type: recall |
| 15833 | value: 95.04256384576865 |
| 15834 | task: |
| 15835 | type: BitextMining |
| 15836 | - dataset: |
| 15837 | config: rus_Cyrl-hrv_Latn |
| 15838 | name: MTEB NTREXBitextMining (rus_Cyrl-hrv_Latn) |
| 15839 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15840 | split: test |
| 15841 | type: mteb/NTREX |
| 15842 | metrics: |
| 15843 | - type: accuracy |
| 15844 | value: 95.14271407110667 |
| 15845 | - type: f1 |
| 15846 | value: 93.7481221832749 |
| 15847 | - type: main_score |
| 15848 | value: 93.7481221832749 |
| 15849 | - type: precision |
| 15850 | value: 93.10930681736892 |
| 15851 | - type: recall |
| 15852 | value: 95.14271407110667 |
| 15853 | task: |
| 15854 | type: BitextMining |
| 15855 | - dataset: |
| 15856 | config: rus_Cyrl-hun_Latn |
| 15857 | name: MTEB NTREXBitextMining (rus_Cyrl-hun_Latn) |
| 15858 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15859 | split: test |
| 15860 | type: mteb/NTREX |
| 15861 | metrics: |
| 15862 | - type: accuracy |
| 15863 | value: 90.18527791687532 |
| 15864 | - type: f1 |
| 15865 | value: 87.61415933423946 |
| 15866 | - type: main_score |
| 15867 | value: 87.61415933423946 |
| 15868 | - type: precision |
| 15869 | value: 86.5166400394242 |
| 15870 | - type: recall |
| 15871 | value: 90.18527791687532 |
| 15872 | task: |
| 15873 | type: BitextMining |
| 15874 | - dataset: |
| 15875 | config: rus_Cyrl-ind_Latn |
| 15876 | name: MTEB NTREXBitextMining (rus_Cyrl-ind_Latn) |
| 15877 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15878 | split: test |
| 15879 | type: mteb/NTREX |
| 15880 | metrics: |
| 15881 | - type: accuracy |
| 15882 | value: 93.69053580370556 |
| 15883 | - type: f1 |
| 15884 | value: 91.83608746453012 |
| 15885 | - type: main_score |
| 15886 | value: 91.83608746453012 |
| 15887 | - type: precision |
| 15888 | value: 90.97145718577868 |
| 15889 | - type: recall |
| 15890 | value: 93.69053580370556 |
| 15891 | task: |
| 15892 | type: BitextMining |
| 15893 | - dataset: |
| 15894 | config: rus_Cyrl-jpn_Jpan |
| 15895 | name: MTEB NTREXBitextMining (rus_Cyrl-jpn_Jpan) |
| 15896 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15897 | split: test |
| 15898 | type: mteb/NTREX |
| 15899 | metrics: |
| 15900 | - type: accuracy |
| 15901 | value: 89.48422633950926 |
| 15902 | - type: f1 |
| 15903 | value: 86.91271033534429 |
| 15904 | - type: main_score |
| 15905 | value: 86.91271033534429 |
| 15906 | - type: precision |
| 15907 | value: 85.82671626487351 |
| 15908 | - type: recall |
| 15909 | value: 89.48422633950926 |
| 15910 | task: |
| 15911 | type: BitextMining |
| 15912 | - dataset: |
| 15913 | config: rus_Cyrl-kor_Hang |
| 15914 | name: MTEB NTREXBitextMining (rus_Cyrl-kor_Hang) |
| 15915 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15916 | split: test |
| 15917 | type: mteb/NTREX |
| 15918 | metrics: |
| 15919 | - type: accuracy |
| 15920 | value: 88.4827240861292 |
| 15921 | - type: f1 |
| 15922 | value: 85.35080398375342 |
| 15923 | - type: main_score |
| 15924 | value: 85.35080398375342 |
| 15925 | - type: precision |
| 15926 | value: 83.9588549490903 |
| 15927 | - type: recall |
| 15928 | value: 88.4827240861292 |
| 15929 | task: |
| 15930 | type: BitextMining |
| 15931 | - dataset: |
| 15932 | config: rus_Cyrl-lit_Latn |
| 15933 | name: MTEB NTREXBitextMining (rus_Cyrl-lit_Latn) |
| 15934 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15935 | split: test |
| 15936 | type: mteb/NTREX |
| 15937 | metrics: |
| 15938 | - type: accuracy |
| 15939 | value: 90.33550325488233 |
| 15940 | - type: f1 |
| 15941 | value: 87.68831819157307 |
| 15942 | - type: main_score |
| 15943 | value: 87.68831819157307 |
| 15944 | - type: precision |
| 15945 | value: 86.51524906407231 |
| 15946 | - type: recall |
| 15947 | value: 90.33550325488233 |
| 15948 | task: |
| 15949 | type: BitextMining |
| 15950 | - dataset: |
| 15951 | config: rus_Cyrl-mkd_Cyrl |
| 15952 | name: MTEB NTREXBitextMining (rus_Cyrl-mkd_Cyrl) |
| 15953 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15954 | split: test |
| 15955 | type: mteb/NTREX |
| 15956 | metrics: |
| 15957 | - type: accuracy |
| 15958 | value: 95.94391587381071 |
| 15959 | - type: f1 |
| 15960 | value: 94.90402270071775 |
| 15961 | - type: main_score |
| 15962 | value: 94.90402270071775 |
| 15963 | - type: precision |
| 15964 | value: 94.43915873810715 |
| 15965 | - type: recall |
| 15966 | value: 95.94391587381071 |
| 15967 | task: |
| 15968 | type: BitextMining |
| 15969 | - dataset: |
| 15970 | config: rus_Cyrl-nld_Latn |
| 15971 | name: MTEB NTREXBitextMining (rus_Cyrl-nld_Latn) |
| 15972 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15973 | split: test |
| 15974 | type: mteb/NTREX |
| 15975 | metrics: |
| 15976 | - type: accuracy |
| 15977 | value: 92.98948422633951 |
| 15978 | - type: f1 |
| 15979 | value: 91.04323151393756 |
| 15980 | - type: main_score |
| 15981 | value: 91.04323151393756 |
| 15982 | - type: precision |
| 15983 | value: 90.14688699716241 |
| 15984 | - type: recall |
| 15985 | value: 92.98948422633951 |
| 15986 | task: |
| 15987 | type: BitextMining |
| 15988 | - dataset: |
| 15989 | config: rus_Cyrl-pol_Latn |
| 15990 | name: MTEB NTREXBitextMining (rus_Cyrl-pol_Latn) |
| 15991 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 15992 | split: test |
| 15993 | type: mteb/NTREX |
| 15994 | metrics: |
| 15995 | - type: accuracy |
| 15996 | value: 94.34151226840261 |
| 15997 | - type: f1 |
| 15998 | value: 92.8726422967785 |
| 15999 | - type: main_score |
| 16000 | value: 92.8726422967785 |
| 16001 | - type: precision |
| 16002 | value: 92.19829744616925 |
| 16003 | - type: recall |
| 16004 | value: 94.34151226840261 |
| 16005 | task: |
| 16006 | type: BitextMining |
| 16007 | - dataset: |
| 16008 | config: rus_Cyrl-por_Latn |
| 16009 | name: MTEB NTREXBitextMining (rus_Cyrl-por_Latn) |
| 16010 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16011 | split: test |
| 16012 | type: mteb/NTREX |
| 16013 | metrics: |
| 16014 | - type: accuracy |
| 16015 | value: 86.17926890335504 |
| 16016 | - type: f1 |
| 16017 | value: 82.7304882287356 |
| 16018 | - type: main_score |
| 16019 | value: 82.7304882287356 |
| 16020 | - type: precision |
| 16021 | value: 81.28162481817964 |
| 16022 | - type: recall |
| 16023 | value: 86.17926890335504 |
| 16024 | task: |
| 16025 | type: BitextMining |
| 16026 | - dataset: |
| 16027 | config: rus_Cyrl-slk_Latn |
| 16028 | name: MTEB NTREXBitextMining (rus_Cyrl-slk_Latn) |
| 16029 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16030 | split: test |
| 16031 | type: mteb/NTREX |
| 16032 | metrics: |
| 16033 | - type: accuracy |
| 16034 | value: 92.7391086629945 |
| 16035 | - type: f1 |
| 16036 | value: 90.75112669003506 |
| 16037 | - type: main_score |
| 16038 | value: 90.75112669003506 |
| 16039 | - type: precision |
| 16040 | value: 89.8564513436822 |
| 16041 | - type: recall |
| 16042 | value: 92.7391086629945 |
| 16043 | task: |
| 16044 | type: BitextMining |
| 16045 | - dataset: |
| 16046 | config: rus_Cyrl-slv_Latn |
| 16047 | name: MTEB NTREXBitextMining (rus_Cyrl-slv_Latn) |
| 16048 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16049 | split: test |
| 16050 | type: mteb/NTREX |
| 16051 | metrics: |
| 16052 | - type: accuracy |
| 16053 | value: 92.8893340010015 |
| 16054 | - type: f1 |
| 16055 | value: 91.05992321816058 |
| 16056 | - type: main_score |
| 16057 | value: 91.05992321816058 |
| 16058 | - type: precision |
| 16059 | value: 90.22589439715128 |
| 16060 | - type: recall |
| 16061 | value: 92.8893340010015 |
| 16062 | task: |
| 16063 | type: BitextMining |
| 16064 | - dataset: |
| 16065 | config: rus_Cyrl-spa_Latn |
| 16066 | name: MTEB NTREXBitextMining (rus_Cyrl-spa_Latn) |
| 16067 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16068 | split: test |
| 16069 | type: mteb/NTREX |
| 16070 | metrics: |
| 16071 | - type: accuracy |
| 16072 | value: 96.49474211316975 |
| 16073 | - type: f1 |
| 16074 | value: 95.4715406442998 |
| 16075 | - type: main_score |
| 16076 | value: 95.4715406442998 |
| 16077 | - type: precision |
| 16078 | value: 94.9799699549324 |
| 16079 | - type: recall |
| 16080 | value: 96.49474211316975 |
| 16081 | task: |
| 16082 | type: BitextMining |
| 16083 | - dataset: |
| 16084 | config: rus_Cyrl-srp_Cyrl |
| 16085 | name: MTEB NTREXBitextMining (rus_Cyrl-srp_Cyrl) |
| 16086 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16087 | split: test |
| 16088 | type: mteb/NTREX |
| 16089 | metrics: |
| 16090 | - type: accuracy |
| 16091 | value: 81.07160741111667 |
| 16092 | - type: f1 |
| 16093 | value: 76.55687285507015 |
| 16094 | - type: main_score |
| 16095 | value: 76.55687285507015 |
| 16096 | - type: precision |
| 16097 | value: 74.71886401030116 |
| 16098 | - type: recall |
| 16099 | value: 81.07160741111667 |
| 16100 | task: |
| 16101 | type: BitextMining |
| 16102 | - dataset: |
| 16103 | config: rus_Cyrl-srp_Latn |
| 16104 | name: MTEB NTREXBitextMining (rus_Cyrl-srp_Latn) |
| 16105 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16106 | split: test |
| 16107 | type: mteb/NTREX |
| 16108 | metrics: |
| 16109 | - type: accuracy |
| 16110 | value: 95.14271407110667 |
| 16111 | - type: f1 |
| 16112 | value: 93.73302377809138 |
| 16113 | - type: main_score |
| 16114 | value: 93.73302377809138 |
| 16115 | - type: precision |
| 16116 | value: 93.06960440660991 |
| 16117 | - type: recall |
| 16118 | value: 95.14271407110667 |
| 16119 | task: |
| 16120 | type: BitextMining |
| 16121 | - dataset: |
| 16122 | config: rus_Cyrl-swa_Latn |
| 16123 | name: MTEB NTREXBitextMining (rus_Cyrl-swa_Latn) |
| 16124 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16125 | split: test |
| 16126 | type: mteb/NTREX |
| 16127 | metrics: |
| 16128 | - type: accuracy |
| 16129 | value: 94.79218828242364 |
| 16130 | - type: f1 |
| 16131 | value: 93.25988983475212 |
| 16132 | - type: main_score |
| 16133 | value: 93.25988983475212 |
| 16134 | - type: precision |
| 16135 | value: 92.53463528626273 |
| 16136 | - type: recall |
| 16137 | value: 94.79218828242364 |
| 16138 | task: |
| 16139 | type: BitextMining |
| 16140 | - dataset: |
| 16141 | config: rus_Cyrl-swe_Latn |
| 16142 | name: MTEB NTREXBitextMining (rus_Cyrl-swe_Latn) |
| 16143 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16144 | split: test |
| 16145 | type: mteb/NTREX |
| 16146 | metrics: |
| 16147 | - type: accuracy |
| 16148 | value: 95.04256384576865 |
| 16149 | - type: f1 |
| 16150 | value: 93.58704723752295 |
| 16151 | - type: main_score |
| 16152 | value: 93.58704723752295 |
| 16153 | - type: precision |
| 16154 | value: 92.91437155733601 |
| 16155 | - type: recall |
| 16156 | value: 95.04256384576865 |
| 16157 | task: |
| 16158 | type: BitextMining |
| 16159 | - dataset: |
| 16160 | config: rus_Cyrl-tam_Taml |
| 16161 | name: MTEB NTREXBitextMining (rus_Cyrl-tam_Taml) |
| 16162 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16163 | split: test |
| 16164 | type: mteb/NTREX |
| 16165 | metrics: |
| 16166 | - type: accuracy |
| 16167 | value: 93.28993490235354 |
| 16168 | - type: f1 |
| 16169 | value: 91.63912535469872 |
| 16170 | - type: main_score |
| 16171 | value: 91.63912535469872 |
| 16172 | - type: precision |
| 16173 | value: 90.87738750983617 |
| 16174 | - type: recall |
| 16175 | value: 93.28993490235354 |
| 16176 | task: |
| 16177 | type: BitextMining |
| 16178 | - dataset: |
| 16179 | config: rus_Cyrl-tur_Latn |
| 16180 | name: MTEB NTREXBitextMining (rus_Cyrl-tur_Latn) |
| 16181 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16182 | split: test |
| 16183 | type: mteb/NTREX |
| 16184 | metrics: |
| 16185 | - type: accuracy |
| 16186 | value: 93.74061091637456 |
| 16187 | - type: f1 |
| 16188 | value: 91.96628275746953 |
| 16189 | - type: main_score |
| 16190 | value: 91.96628275746953 |
| 16191 | - type: precision |
| 16192 | value: 91.15923885828742 |
| 16193 | - type: recall |
| 16194 | value: 93.74061091637456 |
| 16195 | task: |
| 16196 | type: BitextMining |
| 16197 | - dataset: |
| 16198 | config: rus_Cyrl-ukr_Cyrl |
| 16199 | name: MTEB NTREXBitextMining (rus_Cyrl-ukr_Cyrl) |
| 16200 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16201 | split: test |
| 16202 | type: mteb/NTREX |
| 16203 | metrics: |
| 16204 | - type: accuracy |
| 16205 | value: 95.99399098647972 |
| 16206 | - type: f1 |
| 16207 | value: 94.89567684860624 |
| 16208 | - type: main_score |
| 16209 | value: 94.89567684860624 |
| 16210 | - type: precision |
| 16211 | value: 94.37072275079286 |
| 16212 | - type: recall |
| 16213 | value: 95.99399098647972 |
| 16214 | task: |
| 16215 | type: BitextMining |
| 16216 | - dataset: |
| 16217 | config: rus_Cyrl-vie_Latn |
| 16218 | name: MTEB NTREXBitextMining (rus_Cyrl-vie_Latn) |
| 16219 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16220 | split: test |
| 16221 | type: mteb/NTREX |
| 16222 | metrics: |
| 16223 | - type: accuracy |
| 16224 | value: 91.4371557336004 |
| 16225 | - type: f1 |
| 16226 | value: 88.98681355366382 |
| 16227 | - type: main_score |
| 16228 | value: 88.98681355366382 |
| 16229 | - type: precision |
| 16230 | value: 87.89183775663496 |
| 16231 | - type: recall |
| 16232 | value: 91.4371557336004 |
| 16233 | task: |
| 16234 | type: BitextMining |
| 16235 | - dataset: |
| 16236 | config: rus_Cyrl-zho_Hant |
| 16237 | name: MTEB NTREXBitextMining (rus_Cyrl-zho_Hant) |
| 16238 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16239 | split: test |
| 16240 | type: mteb/NTREX |
| 16241 | metrics: |
| 16242 | - type: accuracy |
| 16243 | value: 92.7891837756635 |
| 16244 | - type: f1 |
| 16245 | value: 90.79047142141783 |
| 16246 | - type: main_score |
| 16247 | value: 90.79047142141783 |
| 16248 | - type: precision |
| 16249 | value: 89.86980470706058 |
| 16250 | - type: recall |
| 16251 | value: 92.7891837756635 |
| 16252 | task: |
| 16253 | type: BitextMining |
| 16254 | - dataset: |
| 16255 | config: rus_Cyrl-zul_Latn |
| 16256 | name: MTEB NTREXBitextMining (rus_Cyrl-zul_Latn) |
| 16257 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16258 | split: test |
| 16259 | type: mteb/NTREX |
| 16260 | metrics: |
| 16261 | - type: accuracy |
| 16262 | value: 87.43114672008012 |
| 16263 | - type: f1 |
| 16264 | value: 84.04618833011422 |
| 16265 | - type: main_score |
| 16266 | value: 84.04618833011422 |
| 16267 | - type: precision |
| 16268 | value: 82.52259341393041 |
| 16269 | - type: recall |
| 16270 | value: 87.43114672008012 |
| 16271 | task: |
| 16272 | type: BitextMining |
| 16273 | - dataset: |
| 16274 | config: slk_Latn-rus_Cyrl |
| 16275 | name: MTEB NTREXBitextMining (slk_Latn-rus_Cyrl) |
| 16276 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16277 | split: test |
| 16278 | type: mteb/NTREX |
| 16279 | metrics: |
| 16280 | - type: accuracy |
| 16281 | value: 95.34301452178268 |
| 16282 | - type: f1 |
| 16283 | value: 94.20392493502158 |
| 16284 | - type: main_score |
| 16285 | value: 94.20392493502158 |
| 16286 | - type: precision |
| 16287 | value: 93.67384409948257 |
| 16288 | - type: recall |
| 16289 | value: 95.34301452178268 |
| 16290 | task: |
| 16291 | type: BitextMining |
| 16292 | - dataset: |
| 16293 | config: slv_Latn-rus_Cyrl |
| 16294 | name: MTEB NTREXBitextMining (slv_Latn-rus_Cyrl) |
| 16295 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16296 | split: test |
| 16297 | type: mteb/NTREX |
| 16298 | metrics: |
| 16299 | - type: accuracy |
| 16300 | value: 92.23835753630446 |
| 16301 | - type: f1 |
| 16302 | value: 90.5061759305625 |
| 16303 | - type: main_score |
| 16304 | value: 90.5061759305625 |
| 16305 | - type: precision |
| 16306 | value: 89.74231188051918 |
| 16307 | - type: recall |
| 16308 | value: 92.23835753630446 |
| 16309 | task: |
| 16310 | type: BitextMining |
| 16311 | - dataset: |
| 16312 | config: spa_Latn-rus_Cyrl |
| 16313 | name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl) |
| 16314 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16315 | split: test |
| 16316 | type: mteb/NTREX |
| 16317 | metrics: |
| 16318 | - type: accuracy |
| 16319 | value: 96.54481722583876 |
| 16320 | - type: f1 |
| 16321 | value: 95.54665331330328 |
| 16322 | - type: main_score |
| 16323 | value: 95.54665331330328 |
| 16324 | - type: precision |
| 16325 | value: 95.06342847604739 |
| 16326 | - type: recall |
| 16327 | value: 96.54481722583876 |
| 16328 | task: |
| 16329 | type: BitextMining |
| 16330 | - dataset: |
| 16331 | config: srp_Cyrl-rus_Cyrl |
| 16332 | name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl) |
| 16333 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16334 | split: test |
| 16335 | type: mteb/NTREX |
| 16336 | metrics: |
| 16337 | - type: accuracy |
| 16338 | value: 83.62543815723585 |
| 16339 | - type: f1 |
| 16340 | value: 80.77095672699816 |
| 16341 | - type: main_score |
| 16342 | value: 80.77095672699816 |
| 16343 | - type: precision |
| 16344 | value: 79.74674313056886 |
| 16345 | - type: recall |
| 16346 | value: 83.62543815723585 |
| 16347 | task: |
| 16348 | type: BitextMining |
| 16349 | - dataset: |
| 16350 | config: srp_Latn-rus_Cyrl |
| 16351 | name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl) |
| 16352 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16353 | split: test |
| 16354 | type: mteb/NTREX |
| 16355 | metrics: |
| 16356 | - type: accuracy |
| 16357 | value: 94.44166249374061 |
| 16358 | - type: f1 |
| 16359 | value: 93.00733206591994 |
| 16360 | - type: main_score |
| 16361 | value: 93.00733206591994 |
| 16362 | - type: precision |
| 16363 | value: 92.37203026762366 |
| 16364 | - type: recall |
| 16365 | value: 94.44166249374061 |
| 16366 | task: |
| 16367 | type: BitextMining |
| 16368 | - dataset: |
| 16369 | config: swa_Latn-rus_Cyrl |
| 16370 | name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl) |
| 16371 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16372 | split: test |
| 16373 | type: mteb/NTREX |
| 16374 | metrics: |
| 16375 | - type: accuracy |
| 16376 | value: 90.23535302954431 |
| 16377 | - type: f1 |
| 16378 | value: 87.89596482636041 |
| 16379 | - type: main_score |
| 16380 | value: 87.89596482636041 |
| 16381 | - type: precision |
| 16382 | value: 86.87060227370694 |
| 16383 | - type: recall |
| 16384 | value: 90.23535302954431 |
| 16385 | task: |
| 16386 | type: BitextMining |
| 16387 | - dataset: |
| 16388 | config: swe_Latn-rus_Cyrl |
| 16389 | name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl) |
| 16390 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16391 | split: test |
| 16392 | type: mteb/NTREX |
| 16393 | metrics: |
| 16394 | - type: accuracy |
| 16395 | value: 95.44316474712068 |
| 16396 | - type: f1 |
| 16397 | value: 94.1896177599733 |
| 16398 | - type: main_score |
| 16399 | value: 94.1896177599733 |
| 16400 | - type: precision |
| 16401 | value: 93.61542313470206 |
| 16402 | - type: recall |
| 16403 | value: 95.44316474712068 |
| 16404 | task: |
| 16405 | type: BitextMining |
| 16406 | - dataset: |
| 16407 | config: tam_Taml-rus_Cyrl |
| 16408 | name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl) |
| 16409 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16410 | split: test |
| 16411 | type: mteb/NTREX |
| 16412 | metrics: |
| 16413 | - type: accuracy |
| 16414 | value: 89.68452679018529 |
| 16415 | - type: f1 |
| 16416 | value: 87.37341160650037 |
| 16417 | - type: main_score |
| 16418 | value: 87.37341160650037 |
| 16419 | - type: precision |
| 16420 | value: 86.38389402285247 |
| 16421 | - type: recall |
| 16422 | value: 89.68452679018529 |
| 16423 | task: |
| 16424 | type: BitextMining |
| 16425 | - dataset: |
| 16426 | config: tur_Latn-rus_Cyrl |
| 16427 | name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl) |
| 16428 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16429 | split: test |
| 16430 | type: mteb/NTREX |
| 16431 | metrics: |
| 16432 | - type: accuracy |
| 16433 | value: 93.89083625438157 |
| 16434 | - type: f1 |
| 16435 | value: 92.33892505424804 |
| 16436 | - type: main_score |
| 16437 | value: 92.33892505424804 |
| 16438 | - type: precision |
| 16439 | value: 91.63125640842216 |
| 16440 | - type: recall |
| 16441 | value: 93.89083625438157 |
| 16442 | task: |
| 16443 | type: BitextMining |
| 16444 | - dataset: |
| 16445 | config: ukr_Cyrl-rus_Cyrl |
| 16446 | name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl) |
| 16447 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16448 | split: test |
| 16449 | type: mteb/NTREX |
| 16450 | metrics: |
| 16451 | - type: accuracy |
| 16452 | value: 96.14421632448673 |
| 16453 | - type: f1 |
| 16454 | value: 95.11028447433054 |
| 16455 | - type: main_score |
| 16456 | value: 95.11028447433054 |
| 16457 | - type: precision |
| 16458 | value: 94.62944416624937 |
| 16459 | - type: recall |
| 16460 | value: 96.14421632448673 |
| 16461 | task: |
| 16462 | type: BitextMining |
| 16463 | - dataset: |
| 16464 | config: vie_Latn-rus_Cyrl |
| 16465 | name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl) |
| 16466 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16467 | split: test |
| 16468 | type: mteb/NTREX |
| 16469 | metrics: |
| 16470 | - type: accuracy |
| 16471 | value: 93.79068602904357 |
| 16472 | - type: f1 |
| 16473 | value: 92.14989150392256 |
| 16474 | - type: main_score |
| 16475 | value: 92.14989150392256 |
| 16476 | - type: precision |
| 16477 | value: 91.39292271740945 |
| 16478 | - type: recall |
| 16479 | value: 93.79068602904357 |
| 16480 | task: |
| 16481 | type: BitextMining |
| 16482 | - dataset: |
| 16483 | config: zho_Hant-rus_Cyrl |
| 16484 | name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl) |
| 16485 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16486 | split: test |
| 16487 | type: mteb/NTREX |
| 16488 | metrics: |
| 16489 | - type: accuracy |
| 16490 | value: 89.13370055082625 |
| 16491 | - type: f1 |
| 16492 | value: 86.51514618639217 |
| 16493 | - type: main_score |
| 16494 | value: 86.51514618639217 |
| 16495 | - type: precision |
| 16496 | value: 85.383920035898 |
| 16497 | - type: recall |
| 16498 | value: 89.13370055082625 |
| 16499 | task: |
| 16500 | type: BitextMining |
| 16501 | - dataset: |
| 16502 | config: zul_Latn-rus_Cyrl |
| 16503 | name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl) |
| 16504 | revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33 |
| 16505 | split: test |
| 16506 | type: mteb/NTREX |
| 16507 | metrics: |
| 16508 | - type: accuracy |
| 16509 | value: 81.17175763645467 |
| 16510 | - type: f1 |
| 16511 | value: 77.72331766047338 |
| 16512 | - type: main_score |
| 16513 | value: 77.72331766047338 |
| 16514 | - type: precision |
| 16515 | value: 76.24629555848075 |
| 16516 | - type: recall |
| 16517 | value: 81.17175763645467 |
| 16518 | task: |
| 16519 | type: BitextMining |
| 16520 | - dataset: |
| 16521 | config: ru |
| 16522 | name: MTEB OpusparcusPC (ru) |
| 16523 | revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
| 16524 | split: test.full |
| 16525 | type: GEM/opusparcus |
| 16526 | metrics: |
| 16527 | - type: cosine_accuracy |
| 16528 | value: 73.09136420525657 |
| 16529 | - type: cosine_accuracy_threshold |
| 16530 | value: 87.70400881767273 |
| 16531 | - type: cosine_ap |
| 16532 | value: 86.51938550599533 |
| 16533 | - type: cosine_f1 |
| 16534 | value: 80.84358523725834 |
| 16535 | - type: cosine_f1_threshold |
| 16536 | value: 86.90648078918457 |
| 16537 | - type: cosine_precision |
| 16538 | value: 73.24840764331209 |
| 16539 | - type: cosine_recall |
| 16540 | value: 90.19607843137256 |
| 16541 | - type: dot_accuracy |
| 16542 | value: 73.09136420525657 |
| 16543 | - type: dot_accuracy_threshold |
| 16544 | value: 87.7040147781372 |
| 16545 | - type: dot_ap |
| 16546 | value: 86.51934769946833 |
| 16547 | - type: dot_f1 |
| 16548 | value: 80.84358523725834 |
| 16549 | - type: dot_f1_threshold |
| 16550 | value: 86.90648078918457 |
| 16551 | - type: dot_precision |
| 16552 | value: 73.24840764331209 |
| 16553 | - type: dot_recall |
| 16554 | value: 90.19607843137256 |
| 16555 | - type: euclidean_accuracy |
| 16556 | value: 73.09136420525657 |
| 16557 | - type: euclidean_accuracy_threshold |
| 16558 | value: 49.590304493904114 |
| 16559 | - type: euclidean_ap |
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| 17483 | value: 27.725692256711188 |
| 17484 | - type: nauc_recall_at_3_std |
| 17485 | value: -8.533124289305709 |
| 17486 | - type: nauc_recall_at_5_diff1 |
| 17487 | value: 32.006964557701686 |
| 17488 | - type: nauc_recall_at_5_max |
| 17489 | value: 31.493370659289806 |
| 17490 | - type: nauc_recall_at_5_std |
| 17491 | value: -4.8639793547793255 |
| 17492 | - type: ndcg_at_1 |
| 17493 | value: 60.461 |
| 17494 | - type: ndcg_at_10 |
| 17495 | value: 68.529 |
| 17496 | - type: ndcg_at_100 |
| 17497 | value: 71.664 |
| 17498 | - type: ndcg_at_1000 |
| 17499 | value: 72.396 |
| 17500 | - type: ndcg_at_20 |
| 17501 | value: 70.344 |
| 17502 | - type: ndcg_at_3 |
| 17503 | value: 61.550000000000004 |
| 17504 | - type: ndcg_at_5 |
| 17505 | value: 64.948 |
| 17506 | - type: precision_at_1 |
| 17507 | value: 60.461 |
| 17508 | - type: precision_at_10 |
| 17509 | value: 13.28 |
| 17510 | - type: precision_at_100 |
| 17511 | value: 1.555 |
| 17512 | - type: precision_at_1000 |
| 17513 | value: 0.164 |
| 17514 | - type: precision_at_20 |
| 17515 | value: 7.216 |
| 17516 | - type: precision_at_3 |
| 17517 | value: 33.077 |
| 17518 | - type: precision_at_5 |
| 17519 | value: 23.014000000000003 |
| 17520 | - type: recall_at_1 |
| 17521 | value: 42.529 |
| 17522 | - type: recall_at_10 |
| 17523 | value: 81.169 |
| 17524 | - type: recall_at_100 |
| 17525 | value: 93.154 |
| 17526 | - type: recall_at_1000 |
| 17527 | value: 98.18299999999999 |
| 17528 | - type: recall_at_20 |
| 17529 | value: 87.132 |
| 17530 | - type: recall_at_3 |
| 17531 | value: 63.905 |
| 17532 | - type: recall_at_5 |
| 17533 | value: 71.967 |
| 17534 | task: |
| 17535 | type: Retrieval |
| 17536 | - dataset: |
| 17537 | config: default |
| 17538 | name: MTEB RuReviewsClassification (default) |
| 17539 | revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a |
| 17540 | split: test |
| 17541 | type: ai-forever/ru-reviews-classification |
| 17542 | metrics: |
| 17543 | - type: accuracy |
| 17544 | value: 61.17675781250001 |
| 17545 | - type: f1 |
| 17546 | value: 60.354535346041374 |
| 17547 | - type: f1_weighted |
| 17548 | value: 60.35437313166116 |
| 17549 | - type: main_score |
| 17550 | value: 61.17675781250001 |
| 17551 | task: |
| 17552 | type: Classification |
| 17553 | - dataset: |
| 17554 | config: default |
| 17555 | name: MTEB RuSTSBenchmarkSTS (default) |
| 17556 | revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018 |
| 17557 | split: test |
| 17558 | type: ai-forever/ru-stsbenchmark-sts |
| 17559 | metrics: |
| 17560 | - type: cosine_pearson |
| 17561 | value: 78.1301041727274 |
| 17562 | - type: cosine_spearman |
| 17563 | value: 78.08238025421747 |
| 17564 | - type: euclidean_pearson |
| 17565 | value: 77.35224254583635 |
| 17566 | - type: euclidean_spearman |
| 17567 | value: 78.08235336582496 |
| 17568 | - type: main_score |
| 17569 | value: 78.08238025421747 |
| 17570 | - type: manhattan_pearson |
| 17571 | value: 77.24138550052075 |
| 17572 | - type: manhattan_spearman |
| 17573 | value: 77.98199107904142 |
| 17574 | - type: pearson |
| 17575 | value: 78.1301041727274 |
| 17576 | - type: spearman |
| 17577 | value: 78.08238025421747 |
| 17578 | task: |
| 17579 | type: STS |
| 17580 | - dataset: |
| 17581 | config: default |
| 17582 | name: MTEB RuSciBenchGRNTIClassification (default) |
| 17583 | revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1 |
| 17584 | split: test |
| 17585 | type: ai-forever/ru-scibench-grnti-classification |
| 17586 | metrics: |
| 17587 | - type: accuracy |
| 17588 | value: 54.990234375 |
| 17589 | - type: f1 |
| 17590 | value: 53.537019057131374 |
| 17591 | - type: f1_weighted |
| 17592 | value: 53.552745354520766 |
| 17593 | - type: main_score |
| 17594 | value: 54.990234375 |
| 17595 | task: |
| 17596 | type: Classification |
| 17597 | - dataset: |
| 17598 | config: default |
| 17599 | name: MTEB RuSciBenchGRNTIClusteringP2P (default) |
| 17600 | revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1 |
| 17601 | split: test |
| 17602 | type: ai-forever/ru-scibench-grnti-classification |
| 17603 | metrics: |
| 17604 | - type: main_score |
| 17605 | value: 50.775228895355106 |
| 17606 | - type: v_measure |
| 17607 | value: 50.775228895355106 |
| 17608 | - type: v_measure_std |
| 17609 | value: 0.9533571150165796 |
| 17610 | task: |
| 17611 | type: Clustering |
| 17612 | - dataset: |
| 17613 | config: default |
| 17614 | name: MTEB RuSciBenchOECDClassification (default) |
| 17615 | revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471 |
| 17616 | split: test |
| 17617 | type: ai-forever/ru-scibench-oecd-classification |
| 17618 | metrics: |
| 17619 | - type: accuracy |
| 17620 | value: 41.71875 |
| 17621 | - type: f1 |
| 17622 | value: 39.289100975858304 |
| 17623 | - type: f1_weighted |
| 17624 | value: 39.29257829217775 |
| 17625 | - type: main_score |
| 17626 | value: 41.71875 |
| 17627 | task: |
| 17628 | type: Classification |
| 17629 | - dataset: |
| 17630 | config: default |
| 17631 | name: MTEB RuSciBenchOECDClusteringP2P (default) |
| 17632 | revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471 |
| 17633 | split: test |
| 17634 | type: ai-forever/ru-scibench-oecd-classification |
| 17635 | metrics: |
| 17636 | - type: main_score |
| 17637 | value: 45.10904808834516 |
| 17638 | - type: v_measure |
| 17639 | value: 45.10904808834516 |
| 17640 | - type: v_measure_std |
| 17641 | value: 1.0572643410157534 |
| 17642 | task: |
| 17643 | type: Clustering |
| 17644 | - dataset: |
| 17645 | config: rus_Cyrl |
| 17646 | name: MTEB SIB200Classification (rus_Cyrl) |
| 17647 | revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b |
| 17648 | split: test |
| 17649 | type: mteb/sib200 |
| 17650 | metrics: |
| 17651 | - type: accuracy |
| 17652 | value: 66.36363636363637 |
| 17653 | - type: f1 |
| 17654 | value: 64.6940336621617 |
| 17655 | - type: f1_weighted |
| 17656 | value: 66.43317771876966 |
| 17657 | - type: main_score |
| 17658 | value: 66.36363636363637 |
| 17659 | task: |
| 17660 | type: Classification |
| 17661 | - dataset: |
| 17662 | config: rus_Cyrl |
| 17663 | name: MTEB SIB200ClusteringS2S (rus_Cyrl) |
| 17664 | revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b |
| 17665 | split: test |
| 17666 | type: mteb/sib200 |
| 17667 | metrics: |
| 17668 | - type: main_score |
| 17669 | value: 33.99178497314711 |
| 17670 | - type: v_measure |
| 17671 | value: 33.99178497314711 |
| 17672 | - type: v_measure_std |
| 17673 | value: 4.036337464043786 |
| 17674 | task: |
| 17675 | type: Clustering |
| 17676 | - dataset: |
| 17677 | config: ru |
| 17678 | name: MTEB STS22.v2 (ru) |
| 17679 | revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd |
| 17680 | split: test |
| 17681 | type: mteb/sts22-crosslingual-sts |
| 17682 | metrics: |
| 17683 | - type: cosine_pearson |
| 17684 | value: 50.724322379215934 |
| 17685 | - type: cosine_spearman |
| 17686 | value: 59.90449732164651 |
| 17687 | - type: euclidean_pearson |
| 17688 | value: 50.227545226784024 |
| 17689 | - type: euclidean_spearman |
| 17690 | value: 59.898906527601085 |
| 17691 | - type: main_score |
| 17692 | value: 59.90449732164651 |
| 17693 | - type: manhattan_pearson |
| 17694 | value: 50.21762139819405 |
| 17695 | - type: manhattan_spearman |
| 17696 | value: 59.761039813759 |
| 17697 | - type: pearson |
| 17698 | value: 50.724322379215934 |
| 17699 | - type: spearman |
| 17700 | value: 59.90449732164651 |
| 17701 | task: |
| 17702 | type: STS |
| 17703 | - dataset: |
| 17704 | config: ru |
| 17705 | name: MTEB STSBenchmarkMultilingualSTS (ru) |
| 17706 | revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c |
| 17707 | split: dev |
| 17708 | type: mteb/stsb_multi_mt |
| 17709 | metrics: |
| 17710 | - type: cosine_pearson |
| 17711 | value: 78.43928769569945 |
| 17712 | - type: cosine_spearman |
| 17713 | value: 78.23961768018884 |
| 17714 | - type: euclidean_pearson |
| 17715 | value: 77.4718694027985 |
| 17716 | - type: euclidean_spearman |
| 17717 | value: 78.23887044760475 |
| 17718 | - type: main_score |
| 17719 | value: 78.23961768018884 |
| 17720 | - type: manhattan_pearson |
| 17721 | value: 77.34517128089547 |
| 17722 | - type: manhattan_spearman |
| 17723 | value: 78.1146477340426 |
| 17724 | - type: pearson |
| 17725 | value: 78.43928769569945 |
| 17726 | - type: spearman |
| 17727 | value: 78.23961768018884 |
| 17728 | task: |
| 17729 | type: STS |
| 17730 | - dataset: |
| 17731 | config: default |
| 17732 | name: MTEB SensitiveTopicsClassification (default) |
| 17733 | revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2 |
| 17734 | split: test |
| 17735 | type: ai-forever/sensitive-topics-classification |
| 17736 | metrics: |
| 17737 | - type: accuracy |
| 17738 | value: 22.8125 |
| 17739 | - type: f1 |
| 17740 | value: 17.31969589593409 |
| 17741 | - type: lrap |
| 17742 | value: 33.82412380642287 |
| 17743 | - type: main_score |
| 17744 | value: 22.8125 |
| 17745 | task: |
| 17746 | type: MultilabelClassification |
| 17747 | - dataset: |
| 17748 | config: default |
| 17749 | name: MTEB TERRa (default) |
| 17750 | revision: 7b58f24536063837d644aab9a023c62199b2a612 |
| 17751 | split: dev |
| 17752 | type: ai-forever/terra-pairclassification |
| 17753 | metrics: |
| 17754 | - type: cosine_accuracy |
| 17755 | value: 57.32899022801303 |
| 17756 | - type: cosine_accuracy_threshold |
| 17757 | value: 85.32201051712036 |
| 17758 | - type: cosine_ap |
| 17759 | value: 55.14264553720072 |
| 17760 | - type: cosine_f1 |
| 17761 | value: 66.83544303797468 |
| 17762 | - type: cosine_f1_threshold |
| 17763 | value: 85.32201051712036 |
| 17764 | - type: cosine_precision |
| 17765 | value: 54.54545454545454 |
| 17766 | - type: cosine_recall |
| 17767 | value: 86.27450980392157 |
| 17768 | - type: dot_accuracy |
| 17769 | value: 57.32899022801303 |
| 17770 | - type: dot_accuracy_threshold |
| 17771 | value: 85.32201051712036 |
| 17772 | - type: dot_ap |
| 17773 | value: 55.14264553720072 |
| 17774 | - type: dot_f1 |
| 17775 | value: 66.83544303797468 |
| 17776 | - type: dot_f1_threshold |
| 17777 | value: 85.32201051712036 |
| 17778 | - type: dot_precision |
| 17779 | value: 54.54545454545454 |
| 17780 | - type: dot_recall |
| 17781 | value: 86.27450980392157 |
| 17782 | - type: euclidean_accuracy |
| 17783 | value: 57.32899022801303 |
| 17784 | - type: euclidean_accuracy_threshold |
| 17785 | value: 54.18117046356201 |
| 17786 | - type: euclidean_ap |
| 17787 | value: 55.14264553720072 |
| 17788 | - type: euclidean_f1 |
| 17789 | value: 66.83544303797468 |
| 17790 | - type: euclidean_f1_threshold |
| 17791 | value: 54.18117046356201 |
| 17792 | - type: euclidean_precision |
| 17793 | value: 54.54545454545454 |
| 17794 | - type: euclidean_recall |
| 17795 | value: 86.27450980392157 |
| 17796 | - type: main_score |
| 17797 | value: 55.14264553720072 |
| 17798 | - type: manhattan_accuracy |
| 17799 | value: 57.32899022801303 |
| 17800 | - type: manhattan_accuracy_threshold |
| 17801 | value: 828.8480758666992 |
| 17802 | - type: manhattan_ap |
| 17803 | value: 55.077974053622555 |
| 17804 | - type: manhattan_f1 |
| 17805 | value: 66.82352941176471 |
| 17806 | - type: manhattan_f1_threshold |
| 17807 | value: 885.6784820556641 |
| 17808 | - type: manhattan_precision |
| 17809 | value: 52.20588235294118 |
| 17810 | - type: manhattan_recall |
| 17811 | value: 92.81045751633987 |
| 17812 | - type: max_ap |
| 17813 | value: 55.14264553720072 |
| 17814 | - type: max_f1 |
| 17815 | value: 66.83544303797468 |
| 17816 | - type: max_precision |
| 17817 | value: 54.54545454545454 |
| 17818 | - type: max_recall |
| 17819 | value: 92.81045751633987 |
| 17820 | - type: similarity_accuracy |
| 17821 | value: 57.32899022801303 |
| 17822 | - type: similarity_accuracy_threshold |
| 17823 | value: 85.32201051712036 |
| 17824 | - type: similarity_ap |
| 17825 | value: 55.14264553720072 |
| 17826 | - type: similarity_f1 |
| 17827 | value: 66.83544303797468 |
| 17828 | - type: similarity_f1_threshold |
| 17829 | value: 85.32201051712036 |
| 17830 | - type: similarity_precision |
| 17831 | value: 54.54545454545454 |
| 17832 | - type: similarity_recall |
| 17833 | value: 86.27450980392157 |
| 17834 | task: |
| 17835 | type: PairClassification |
| 17836 | - dataset: |
| 17837 | config: ru |
| 17838 | name: MTEB XNLI (ru) |
| 17839 | revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb |
| 17840 | split: test |
| 17841 | type: mteb/xnli |
| 17842 | metrics: |
| 17843 | - type: cosine_accuracy |
| 17844 | value: 67.6923076923077 |
| 17845 | - type: cosine_accuracy_threshold |
| 17846 | value: 87.6681923866272 |
| 17847 | - type: cosine_ap |
| 17848 | value: 73.18693800863593 |
| 17849 | - type: cosine_f1 |
| 17850 | value: 70.40641099026904 |
| 17851 | - type: cosine_f1_threshold |
| 17852 | value: 85.09706258773804 |
| 17853 | - type: cosine_precision |
| 17854 | value: 57.74647887323944 |
| 17855 | - type: cosine_recall |
| 17856 | value: 90.17595307917888 |
| 17857 | - type: dot_accuracy |
| 17858 | value: 67.6923076923077 |
| 17859 | - type: dot_accuracy_threshold |
| 17860 | value: 87.66818642616272 |
| 17861 | - type: dot_ap |
| 17862 | value: 73.18693800863593 |
| 17863 | - type: dot_f1 |
| 17864 | value: 70.40641099026904 |
| 17865 | - type: dot_f1_threshold |
| 17866 | value: 85.09706258773804 |
| 17867 | - type: dot_precision |
| 17868 | value: 57.74647887323944 |
| 17869 | - type: dot_recall |
| 17870 | value: 90.17595307917888 |
| 17871 | - type: euclidean_accuracy |
| 17872 | value: 67.6923076923077 |
| 17873 | - type: euclidean_accuracy_threshold |
| 17874 | value: 49.662476778030396 |
| 17875 | - type: euclidean_ap |
| 17876 | value: 73.18693800863593 |
| 17877 | - type: euclidean_f1 |
| 17878 | value: 70.40641099026904 |
| 17879 | - type: euclidean_f1_threshold |
| 17880 | value: 54.59475517272949 |
| 17881 | - type: euclidean_precision |
| 17882 | value: 57.74647887323944 |
| 17883 | - type: euclidean_recall |
| 17884 | value: 90.17595307917888 |
| 17885 | - type: main_score |
| 17886 | value: 73.18693800863593 |
| 17887 | - type: manhattan_accuracy |
| 17888 | value: 67.54578754578755 |
| 17889 | - type: manhattan_accuracy_threshold |
| 17890 | value: 777.1001815795898 |
| 17891 | - type: manhattan_ap |
| 17892 | value: 72.98861474758783 |
| 17893 | - type: manhattan_f1 |
| 17894 | value: 70.6842435655995 |
| 17895 | - type: manhattan_f1_threshold |
| 17896 | value: 810.3782653808594 |
| 17897 | - type: manhattan_precision |
| 17898 | value: 61.80021953896817 |
| 17899 | - type: manhattan_recall |
| 17900 | value: 82.55131964809385 |
| 17901 | - type: max_ap |
| 17902 | value: 73.18693800863593 |
| 17903 | - type: max_f1 |
| 17904 | value: 70.6842435655995 |
| 17905 | - type: max_precision |
| 17906 | value: 61.80021953896817 |
| 17907 | - type: max_recall |
| 17908 | value: 90.17595307917888 |
| 17909 | - type: similarity_accuracy |
| 17910 | value: 67.6923076923077 |
| 17911 | - type: similarity_accuracy_threshold |
| 17912 | value: 87.6681923866272 |
| 17913 | - type: similarity_ap |
| 17914 | value: 73.18693800863593 |
| 17915 | - type: similarity_f1 |
| 17916 | value: 70.40641099026904 |
| 17917 | - type: similarity_f1_threshold |
| 17918 | value: 85.09706258773804 |
| 17919 | - type: similarity_precision |
| 17920 | value: 57.74647887323944 |
| 17921 | - type: similarity_recall |
| 17922 | value: 90.17595307917888 |
| 17923 | task: |
| 17924 | type: PairClassification |
| 17925 | - dataset: |
| 17926 | config: russian |
| 17927 | name: MTEB XNLIV2 (russian) |
| 17928 | revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad |
| 17929 | split: test |
| 17930 | type: mteb/xnli2.0-multi-pair |
| 17931 | metrics: |
| 17932 | - type: cosine_accuracy |
| 17933 | value: 68.35164835164835 |
| 17934 | - type: cosine_accuracy_threshold |
| 17935 | value: 88.48621845245361 |
| 17936 | - type: cosine_ap |
| 17937 | value: 73.10205506215699 |
| 17938 | - type: cosine_f1 |
| 17939 | value: 71.28712871287128 |
| 17940 | - type: cosine_f1_threshold |
| 17941 | value: 87.00399398803711 |
| 17942 | - type: cosine_precision |
| 17943 | value: 61.67023554603854 |
| 17944 | - type: cosine_recall |
| 17945 | value: 84.4574780058651 |
| 17946 | - type: dot_accuracy |
| 17947 | value: 68.35164835164835 |
| 17948 | - type: dot_accuracy_threshold |
| 17949 | value: 88.48622441291809 |
| 17950 | - type: dot_ap |
| 17951 | value: 73.10191110714706 |
| 17952 | - type: dot_f1 |
| 17953 | value: 71.28712871287128 |
| 17954 | - type: dot_f1_threshold |
| 17955 | value: 87.00399398803711 |
| 17956 | - type: dot_precision |
| 17957 | value: 61.67023554603854 |
| 17958 | - type: dot_recall |
| 17959 | value: 84.4574780058651 |
| 17960 | - type: euclidean_accuracy |
| 17961 | value: 68.35164835164835 |
| 17962 | - type: euclidean_accuracy_threshold |
| 17963 | value: 47.98704385757446 |
| 17964 | - type: euclidean_ap |
| 17965 | value: 73.10205506215699 |
| 17966 | - type: euclidean_f1 |
| 17967 | value: 71.28712871287128 |
| 17968 | - type: euclidean_f1_threshold |
| 17969 | value: 50.982362031936646 |
| 17970 | - type: euclidean_precision |
| 17971 | value: 61.67023554603854 |
| 17972 | - type: euclidean_recall |
| 17973 | value: 84.4574780058651 |
| 17974 | - type: main_score |
| 17975 | value: 73.10205506215699 |
| 17976 | - type: manhattan_accuracy |
| 17977 | value: 67.91208791208791 |
| 17978 | - type: manhattan_accuracy_threshold |
| 17979 | value: 746.1360931396484 |
| 17980 | - type: manhattan_ap |
| 17981 | value: 72.8954736175069 |
| 17982 | - type: manhattan_f1 |
| 17983 | value: 71.1297071129707 |
| 17984 | - type: manhattan_f1_threshold |
| 17985 | value: 808.0789566040039 |
| 17986 | - type: manhattan_precision |
| 17987 | value: 60.04036326942482 |
| 17988 | - type: manhattan_recall |
| 17989 | value: 87.2434017595308 |
| 17990 | - type: max_ap |
| 17991 | value: 73.10205506215699 |
| 17992 | - type: max_f1 |
| 17993 | value: 71.28712871287128 |
| 17994 | - type: max_precision |
| 17995 | value: 61.67023554603854 |
| 17996 | - type: max_recall |
| 17997 | value: 87.2434017595308 |
| 17998 | - type: similarity_accuracy |
| 17999 | value: 68.35164835164835 |
| 18000 | - type: similarity_accuracy_threshold |
| 18001 | value: 88.48621845245361 |
| 18002 | - type: similarity_ap |
| 18003 | value: 73.10205506215699 |
| 18004 | - type: similarity_f1 |
| 18005 | value: 71.28712871287128 |
| 18006 | - type: similarity_f1_threshold |
| 18007 | value: 87.00399398803711 |
| 18008 | - type: similarity_precision |
| 18009 | value: 61.67023554603854 |
| 18010 | - type: similarity_recall |
| 18011 | value: 84.4574780058651 |
| 18012 | task: |
| 18013 | type: PairClassification |
| 18014 | - dataset: |
| 18015 | config: ru |
| 18016 | name: MTEB XQuADRetrieval (ru) |
| 18017 | revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583 |
| 18018 | split: validation |
| 18019 | type: google/xquad |
| 18020 | metrics: |
| 18021 | - type: main_score |
| 18022 | value: 95.705 |
| 18023 | - type: map_at_1 |
| 18024 | value: 90.802 |
| 18025 | - type: map_at_10 |
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| 18029 | - type: map_at_1000 |
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| 18031 | - type: map_at_20 |
| 18032 | value: 94.446 |
| 18033 | - type: map_at_3 |
| 18034 | value: 94.121 |
| 18035 | - type: map_at_5 |
| 18036 | value: 94.34 |
| 18037 | - type: mrr_at_1 |
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| 18039 | - type: mrr_at_10 |
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| 18041 | - type: mrr_at_100 |
| 18042 | value: 94.45099347521871 |
| 18043 | - type: mrr_at_1000 |
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| 18045 | - type: mrr_at_20 |
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| 18047 | - type: mrr_at_3 |
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| 18049 | - type: mrr_at_5 |
| 18050 | value: 94.34036568213786 |
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| 18053 | - type: nauc_map_at_1000_max |
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| 18089 | - type: nauc_map_at_5_max |
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| 18091 | - type: nauc_map_at_5_std |
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| 18113 | - type: nauc_mrr_at_1_max |
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| 18117 | - type: nauc_mrr_at_20_diff1 |
| 18118 | value: 87.40979818966589 |
| 18119 | - type: nauc_mrr_at_20_max |
| 18120 | value: 65.59474346926105 |
| 18121 | - type: nauc_mrr_at_20_std |
| 18122 | value: 8.944420599300914 |
| 18123 | - type: nauc_mrr_at_3_diff1 |
| 18124 | value: 86.97771892161035 |
| 18125 | - type: nauc_mrr_at_3_max |
| 18126 | value: 66.14330030122467 |
| 18127 | - type: nauc_mrr_at_3_std |
| 18128 | value: 8.62516327793521 |
| 18129 | - type: nauc_mrr_at_5_diff1 |
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| 18131 | - type: nauc_mrr_at_5_max |
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| 18133 | - type: nauc_mrr_at_5_std |
| 18134 | value: 9.780940862679724 |
| 18135 | - type: nauc_ndcg_at_1000_diff1 |
| 18136 | value: 87.37823158814116 |
| 18137 | - type: nauc_ndcg_at_1000_max |
| 18138 | value: 66.00874244792789 |
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| 18155 | - type: nauc_ndcg_at_1_max |
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| 18157 | - type: nauc_ndcg_at_1_std |
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| 18163 | - type: nauc_ndcg_at_20_std |
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| 18167 | - type: nauc_ndcg_at_3_max |
| 18168 | value: 67.97798288917929 |
| 18169 | - type: nauc_ndcg_at_3_std |
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| 18171 | - type: nauc_ndcg_at_5_diff1 |
| 18172 | value: 87.13322094568531 |
| 18173 | - type: nauc_ndcg_at_5_max |
| 18174 | value: 68.08576118683821 |
| 18175 | - type: nauc_ndcg_at_5_std |
| 18176 | value: 12.639637379592855 |
| 18177 | - type: nauc_precision_at_1000_diff1 |
| 18178 | value: 100.0 |
| 18179 | - type: nauc_precision_at_1000_max |
| 18180 | value: 100.0 |
| 18181 | - type: nauc_precision_at_1000_std |
| 18182 | value: 100.0 |
| 18183 | - type: nauc_precision_at_100_diff1 |
| 18184 | value: 100.0 |
| 18185 | - type: nauc_precision_at_100_max |
| 18186 | value: 100.0 |
| 18187 | - type: nauc_precision_at_100_std |
| 18188 | value: 100.0 |
| 18189 | - type: nauc_precision_at_10_diff1 |
| 18190 | value: 93.46711505595813 |
| 18191 | - type: nauc_precision_at_10_max |
| 18192 | value: 100.0 |
| 18193 | - type: nauc_precision_at_10_std |
| 18194 | value: 65.42573557179935 |
| 18195 | - type: nauc_precision_at_1_diff1 |
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| 18197 | - type: nauc_precision_at_1_max |
| 18198 | value: 62.92813192908893 |
| 18199 | - type: nauc_precision_at_1_std |
| 18200 | value: 6.738987385482432 |
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| 18202 | value: 91.28948674127133 |
| 18203 | - type: nauc_precision_at_20_max |
| 18204 | value: 100.0 |
| 18205 | - type: nauc_precision_at_20_std |
| 18206 | value: 90.74278258632364 |
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| 18209 | - type: nauc_precision_at_3_max |
| 18210 | value: 83.26201582412921 |
| 18211 | - type: nauc_precision_at_3_std |
| 18212 | value: 23.334013491433762 |
| 18213 | - type: nauc_precision_at_5_diff1 |
| 18214 | value: 85.0867539350284 |
| 18215 | - type: nauc_precision_at_5_max |
| 18216 | value: 96.57011448655484 |
| 18217 | - type: nauc_precision_at_5_std |
| 18218 | value: 56.46869543426768 |
| 18219 | - type: nauc_recall_at_1000_diff1 |
| 18220 | value: .nan |
| 18221 | - type: nauc_recall_at_1000_max |
| 18222 | value: .nan |
| 18223 | - type: nauc_recall_at_1000_std |
| 18224 | value: .nan |
| 18225 | - type: nauc_recall_at_100_diff1 |
| 18226 | value: .nan |
| 18227 | - type: nauc_recall_at_100_max |
| 18228 | value: .nan |
| 18229 | - type: nauc_recall_at_100_std |
| 18230 | value: .nan |
| 18231 | - type: nauc_recall_at_10_diff1 |
| 18232 | value: 93.46711505595623 |
| 18233 | - type: nauc_recall_at_10_max |
| 18234 | value: 100.0 |
| 18235 | - type: nauc_recall_at_10_std |
| 18236 | value: 65.42573557180279 |
| 18237 | - type: nauc_recall_at_1_diff1 |
| 18238 | value: 88.08395824560428 |
| 18239 | - type: nauc_recall_at_1_max |
| 18240 | value: 62.92813192908893 |
| 18241 | - type: nauc_recall_at_1_std |
| 18242 | value: 6.738987385482432 |
| 18243 | - type: nauc_recall_at_20_diff1 |
| 18244 | value: 91.28948674127474 |
| 18245 | - type: nauc_recall_at_20_max |
| 18246 | value: 100.0 |
| 18247 | - type: nauc_recall_at_20_std |
| 18248 | value: 90.74278258632704 |
| 18249 | - type: nauc_recall_at_3_diff1 |
| 18250 | value: 82.64606115071967 |
| 18251 | - type: nauc_recall_at_3_max |
| 18252 | value: 83.26201582413023 |
| 18253 | - type: nauc_recall_at_3_std |
| 18254 | value: 23.334013491434007 |
| 18255 | - type: nauc_recall_at_5_diff1 |
| 18256 | value: 85.08675393502854 |
| 18257 | - type: nauc_recall_at_5_max |
| 18258 | value: 96.57011448655487 |
| 18259 | - type: nauc_recall_at_5_std |
| 18260 | value: 56.46869543426658 |
| 18261 | - type: ndcg_at_1 |
| 18262 | value: 90.802 |
| 18263 | - type: ndcg_at_10 |
| 18264 | value: 95.705 |
| 18265 | - type: ndcg_at_100 |
| 18266 | value: 95.816 |
| 18267 | - type: ndcg_at_1000 |
| 18268 | value: 95.816 |
| 18269 | - type: ndcg_at_20 |
| 18270 | value: 95.771 |
| 18271 | - type: ndcg_at_3 |
| 18272 | value: 95.11699999999999 |
| 18273 | - type: ndcg_at_5 |
| 18274 | value: 95.506 |
| 18275 | - type: precision_at_1 |
| 18276 | value: 90.802 |
| 18277 | - type: precision_at_10 |
| 18278 | value: 9.949 |
| 18279 | - type: precision_at_100 |
| 18280 | value: 1.0 |
| 18281 | - type: precision_at_1000 |
| 18282 | value: 0.1 |
| 18283 | - type: precision_at_20 |
| 18284 | value: 4.987 |
| 18285 | - type: precision_at_3 |
| 18286 | value: 32.658 |
| 18287 | - type: precision_at_5 |
| 18288 | value: 19.781000000000002 |
| 18289 | - type: recall_at_1 |
| 18290 | value: 90.802 |
| 18291 | - type: recall_at_10 |
| 18292 | value: 99.494 |
| 18293 | - type: recall_at_100 |
| 18294 | value: 100.0 |
| 18295 | - type: recall_at_1000 |
| 18296 | value: 100.0 |
| 18297 | - type: recall_at_20 |
| 18298 | value: 99.747 |
| 18299 | - type: recall_at_3 |
| 18300 | value: 97.975 |
| 18301 | - type: recall_at_5 |
| 18302 | value: 98.90299999999999 |
| 18303 | task: |
| 18304 | type: Retrieval |
| 18305 | tags: |
| 18306 | - mteb |
| 18307 | - Sentence Transformers |
| 18308 | - sentence-similarity |
| 18309 | - sentence-transformers |
| 18310 | --- |
| 18311 | |
| 18312 | |
| 18313 | ## Multilingual-E5-small |
| 18314 | |
| 18315 | [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). |
| 18316 | Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 |
| 18317 | |
| 18318 | This model has 12 layers and the embedding size is 384. |
| 18319 | |
| 18320 | ## Usage |
| 18321 | |
| 18322 | Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
| 18323 | |
| 18324 | ```python |
| 18325 | import torch.nn.functional as F |
| 18326 | |
| 18327 | from torch import Tensor |
| 18328 | from transformers import AutoTokenizer, AutoModel |
| 18329 | |
| 18330 | |
| 18331 | def average_pool(last_hidden_states: Tensor, |
| 18332 | attention_mask: Tensor) -> Tensor: |
| 18333 | last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
| 18334 | return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
| 18335 | |
| 18336 | |
| 18337 | # Each input text should start with "query: " or "passage: ", even for non-English texts. |
| 18338 | # For tasks other than retrieval, you can simply use the "query: " prefix. |
| 18339 | input_texts = ['query: how much protein should a female eat', |
| 18340 | 'query: 南瓜的家常做法', |
| 18341 | "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.", |
| 18342 | "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"] |
| 18343 | |
| 18344 | tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') |
| 18345 | model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') |
| 18346 | |
| 18347 | # Tokenize the input texts |
| 18348 | batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
| 18349 | |
| 18350 | outputs = model(**batch_dict) |
| 18351 | embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
| 18352 | |
| 18353 | # normalize embeddings |
| 18354 | embeddings = F.normalize(embeddings, p=2, dim=1) |
| 18355 | scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| 18356 | print(scores.tolist()) |
| 18357 | ``` |
| 18358 | |
| 18359 | ## Supported Languages |
| 18360 | |
| 18361 | This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) |
| 18362 | and continually trained on a mixture of multilingual datasets. |
| 18363 | It supports 100 languages from xlm-roberta, |
| 18364 | but low-resource languages may see performance degradation. |
| 18365 | |
| 18366 | ## Training Details |
| 18367 | |
| 18368 | **Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) |
| 18369 | |
| 18370 | **First stage**: contrastive pre-training with weak supervision |
| 18371 | |
| 18372 | | Dataset | Weak supervision | # of text pairs | |
| 18373 | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| |
| 18374 | | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | |
| 18375 | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | |
| 18376 | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | |
| 18377 | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | |
| 18378 | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | |
| 18379 | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | |
| 18380 | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | |
| 18381 | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | |
| 18382 | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | |
| 18383 | |
| 18384 | **Second stage**: supervised fine-tuning |
| 18385 | |
| 18386 | | Dataset | Language | # of text pairs | |
| 18387 | |----------------------------------------------------------------------------------------|--------------|-----------------| |
| 18388 | | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | |
| 18389 | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | |
| 18390 | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | |
| 18391 | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | |
| 18392 | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | |
| 18393 | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | |
| 18394 | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 18395 | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| 18396 | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | |
| 18397 | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | |
| 18398 | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | |
| 18399 | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | |
| 18400 | |
| 18401 | For all labeled datasets, we only use its training set for fine-tuning. |
| 18402 | |
| 18403 | For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). |
| 18404 | |
| 18405 | ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) |
| 18406 | |
| 18407 | | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |
| 18408 | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | |
| 18409 | | 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 | |
| 18410 | | 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 | |
| 18411 | | 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 | |
| 18412 | | | | |
| 18413 | | 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 | |
| 18414 | | 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 | |
| 18415 | | 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 | |
| 18416 | |
| 18417 | ## MTEB Benchmark Evaluation |
| 18418 | |
| 18419 | Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
| 18420 | on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
| 18421 | |
| 18422 | ## Support for Sentence Transformers |
| 18423 | |
| 18424 | Below is an example for usage with sentence_transformers. |
| 18425 | ```python |
| 18426 | from sentence_transformers import SentenceTransformer |
| 18427 | model = SentenceTransformer('intfloat/multilingual-e5-small') |
| 18428 | input_texts = [ |
| 18429 | 'query: how much protein should a female eat', |
| 18430 | 'query: 南瓜的家常做法', |
| 18431 | "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.", |
| 18432 | "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" |
| 18433 | ] |
| 18434 | embeddings = model.encode(input_texts, normalize_embeddings=True) |
| 18435 | ``` |
| 18436 | |
| 18437 | Package requirements |
| 18438 | |
| 18439 | `pip install sentence_transformers~=2.2.2` |
| 18440 | |
| 18441 | Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
| 18442 | |
| 18443 | ## FAQ |
| 18444 | |
| 18445 | **1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
| 18446 | |
| 18447 | Yes, this is how the model is trained, otherwise you will see a performance degradation. |
| 18448 | |
| 18449 | Here are some rules of thumb: |
| 18450 | - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
| 18451 | |
| 18452 | - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. |
| 18453 | |
| 18454 | - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
| 18455 | |
| 18456 | **2. Why are my reproduced results slightly different from reported in the model card?** |
| 18457 | |
| 18458 | Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
| 18459 | |
| 18460 | **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
| 18461 | |
| 18462 | This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
| 18463 | |
| 18464 | For text embedding tasks like text retrieval or semantic similarity, |
| 18465 | what matters is the relative order of the scores instead of the absolute values, |
| 18466 | so this should not be an issue. |
| 18467 | |
| 18468 | ## Citation |
| 18469 | |
| 18470 | If you find our paper or models helpful, please consider cite as follows: |
| 18471 | |
| 18472 | ``` |
| 18473 | @article{wang2024multilingual, |
| 18474 | title={Multilingual E5 Text Embeddings: A Technical Report}, |
| 18475 | author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, |
| 18476 | journal={arXiv preprint arXiv:2402.05672}, |
| 18477 | year={2024} |
| 18478 | } |
| 18479 | ``` |
| 18480 | |
| 18481 | ## Limitations |
| 18482 | |
| 18483 | Long texts will be truncated to at most 512 tokens. |
| 18484 | |
| 18485 | |