README.md
| 1 | --- |
| 2 | tags: |
| 3 | - mteb |
| 4 | - transformers.js |
| 5 | - transformers |
| 6 | model-index: |
| 7 | - name: mxbai-angle-large-v1 |
| 8 | results: |
| 9 | - task: |
| 10 | type: Classification |
| 11 | dataset: |
| 12 | type: mteb/amazon_counterfactual |
| 13 | name: MTEB AmazonCounterfactualClassification (en) |
| 14 | config: en |
| 15 | split: test |
| 16 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 17 | metrics: |
| 18 | - type: accuracy |
| 19 | value: 75.044776119403 |
| 20 | - type: ap |
| 21 | value: 37.7362433623053 |
| 22 | - type: f1 |
| 23 | value: 68.92736573359774 |
| 24 | - task: |
| 25 | type: Classification |
| 26 | dataset: |
| 27 | type: mteb/amazon_polarity |
| 28 | name: MTEB AmazonPolarityClassification |
| 29 | config: default |
| 30 | split: test |
| 31 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 32 | metrics: |
| 33 | - type: accuracy |
| 34 | value: 93.84025000000001 |
| 35 | - type: ap |
| 36 | value: 90.93190875404055 |
| 37 | - type: f1 |
| 38 | value: 93.8297833897293 |
| 39 | - task: |
| 40 | type: Classification |
| 41 | dataset: |
| 42 | type: mteb/amazon_reviews_multi |
| 43 | name: MTEB AmazonReviewsClassification (en) |
| 44 | config: en |
| 45 | split: test |
| 46 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 47 | metrics: |
| 48 | - type: accuracy |
| 49 | value: 49.184 |
| 50 | - type: f1 |
| 51 | value: 48.74163227751588 |
| 52 | - task: |
| 53 | type: Retrieval |
| 54 | dataset: |
| 55 | type: arguana |
| 56 | name: MTEB ArguAna |
| 57 | config: default |
| 58 | split: test |
| 59 | revision: None |
| 60 | metrics: |
| 61 | - type: map_at_1 |
| 62 | value: 41.252 |
| 63 | - type: map_at_10 |
| 64 | value: 57.778 |
| 65 | - type: map_at_100 |
| 66 | value: 58.233000000000004 |
| 67 | - type: map_at_1000 |
| 68 | value: 58.23700000000001 |
| 69 | - type: map_at_3 |
| 70 | value: 53.449999999999996 |
| 71 | - type: map_at_5 |
| 72 | value: 56.376000000000005 |
| 73 | - type: mrr_at_1 |
| 74 | value: 41.679 |
| 75 | - type: mrr_at_10 |
| 76 | value: 57.92699999999999 |
| 77 | - type: mrr_at_100 |
| 78 | value: 58.389 |
| 79 | - type: mrr_at_1000 |
| 80 | value: 58.391999999999996 |
| 81 | - type: mrr_at_3 |
| 82 | value: 53.651 |
| 83 | - type: mrr_at_5 |
| 84 | value: 56.521 |
| 85 | - type: ndcg_at_1 |
| 86 | value: 41.252 |
| 87 | - type: ndcg_at_10 |
| 88 | value: 66.018 |
| 89 | - type: ndcg_at_100 |
| 90 | value: 67.774 |
| 91 | - type: ndcg_at_1000 |
| 92 | value: 67.84400000000001 |
| 93 | - type: ndcg_at_3 |
| 94 | value: 57.372 |
| 95 | - type: ndcg_at_5 |
| 96 | value: 62.646 |
| 97 | - type: precision_at_1 |
| 98 | value: 41.252 |
| 99 | - type: precision_at_10 |
| 100 | value: 9.189 |
| 101 | - type: precision_at_100 |
| 102 | value: 0.991 |
| 103 | - type: precision_at_1000 |
| 104 | value: 0.1 |
| 105 | - type: precision_at_3 |
| 106 | value: 22.902 |
| 107 | - type: precision_at_5 |
| 108 | value: 16.302 |
| 109 | - type: recall_at_1 |
| 110 | value: 41.252 |
| 111 | - type: recall_at_10 |
| 112 | value: 91.892 |
| 113 | - type: recall_at_100 |
| 114 | value: 99.14699999999999 |
| 115 | - type: recall_at_1000 |
| 116 | value: 99.644 |
| 117 | - type: recall_at_3 |
| 118 | value: 68.706 |
| 119 | - type: recall_at_5 |
| 120 | value: 81.50800000000001 |
| 121 | - task: |
| 122 | type: Clustering |
| 123 | dataset: |
| 124 | type: mteb/arxiv-clustering-p2p |
| 125 | name: MTEB ArxivClusteringP2P |
| 126 | config: default |
| 127 | split: test |
| 128 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 129 | metrics: |
| 130 | - type: v_measure |
| 131 | value: 48.97294504317859 |
| 132 | - task: |
| 133 | type: Clustering |
| 134 | dataset: |
| 135 | type: mteb/arxiv-clustering-s2s |
| 136 | name: MTEB ArxivClusteringS2S |
| 137 | config: default |
| 138 | split: test |
| 139 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 140 | metrics: |
| 141 | - type: v_measure |
| 142 | value: 42.98071077674629 |
| 143 | - task: |
| 144 | type: Reranking |
| 145 | dataset: |
| 146 | type: mteb/askubuntudupquestions-reranking |
| 147 | name: MTEB AskUbuntuDupQuestions |
| 148 | config: default |
| 149 | split: test |
| 150 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 151 | metrics: |
| 152 | - type: map |
| 153 | value: 65.16477858490782 |
| 154 | - type: mrr |
| 155 | value: 78.23583080508287 |
| 156 | - task: |
| 157 | type: STS |
| 158 | dataset: |
| 159 | type: mteb/biosses-sts |
| 160 | name: MTEB BIOSSES |
| 161 | config: default |
| 162 | split: test |
| 163 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 164 | metrics: |
| 165 | - type: cos_sim_pearson |
| 166 | value: 89.6277629421789 |
| 167 | - type: cos_sim_spearman |
| 168 | value: 88.4056288400568 |
| 169 | - type: euclidean_pearson |
| 170 | value: 87.94871847578163 |
| 171 | - type: euclidean_spearman |
| 172 | value: 88.4056288400568 |
| 173 | - type: manhattan_pearson |
| 174 | value: 87.73271254229648 |
| 175 | - type: manhattan_spearman |
| 176 | value: 87.91826833762677 |
| 177 | - task: |
| 178 | type: Classification |
| 179 | dataset: |
| 180 | type: mteb/banking77 |
| 181 | name: MTEB Banking77Classification |
| 182 | config: default |
| 183 | split: test |
| 184 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 185 | metrics: |
| 186 | - type: accuracy |
| 187 | value: 87.81818181818181 |
| 188 | - type: f1 |
| 189 | value: 87.79879337316918 |
| 190 | - task: |
| 191 | type: Clustering |
| 192 | dataset: |
| 193 | type: mteb/biorxiv-clustering-p2p |
| 194 | name: MTEB BiorxivClusteringP2P |
| 195 | config: default |
| 196 | split: test |
| 197 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 198 | metrics: |
| 199 | - type: v_measure |
| 200 | value: 39.91773608582761 |
| 201 | - task: |
| 202 | type: Clustering |
| 203 | dataset: |
| 204 | type: mteb/biorxiv-clustering-s2s |
| 205 | name: MTEB BiorxivClusteringS2S |
| 206 | config: default |
| 207 | split: test |
| 208 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 209 | metrics: |
| 210 | - type: v_measure |
| 211 | value: 36.73059477462478 |
| 212 | - task: |
| 213 | type: Retrieval |
| 214 | dataset: |
| 215 | type: BeIR/cqadupstack |
| 216 | name: MTEB CQADupstackAndroidRetrieval |
| 217 | config: default |
| 218 | split: test |
| 219 | revision: None |
| 220 | metrics: |
| 221 | - type: map_at_1 |
| 222 | value: 32.745999999999995 |
| 223 | - type: map_at_10 |
| 224 | value: 43.632 |
| 225 | - type: map_at_100 |
| 226 | value: 45.206 |
| 227 | - type: map_at_1000 |
| 228 | value: 45.341 |
| 229 | - type: map_at_3 |
| 230 | value: 39.956 |
| 231 | - type: map_at_5 |
| 232 | value: 42.031 |
| 233 | - type: mrr_at_1 |
| 234 | value: 39.485 |
| 235 | - type: mrr_at_10 |
| 236 | value: 49.537 |
| 237 | - type: mrr_at_100 |
| 238 | value: 50.249 |
| 239 | - type: mrr_at_1000 |
| 240 | value: 50.294000000000004 |
| 241 | - type: mrr_at_3 |
| 242 | value: 46.757 |
| 243 | - type: mrr_at_5 |
| 244 | value: 48.481 |
| 245 | - type: ndcg_at_1 |
| 246 | value: 39.485 |
| 247 | - type: ndcg_at_10 |
| 248 | value: 50.058 |
| 249 | - type: ndcg_at_100 |
| 250 | value: 55.586 |
| 251 | - type: ndcg_at_1000 |
| 252 | value: 57.511 |
| 253 | - type: ndcg_at_3 |
| 254 | value: 44.786 |
| 255 | - type: ndcg_at_5 |
| 256 | value: 47.339999999999996 |
| 257 | - type: precision_at_1 |
| 258 | value: 39.485 |
| 259 | - type: precision_at_10 |
| 260 | value: 9.557 |
| 261 | - type: precision_at_100 |
| 262 | value: 1.552 |
| 263 | - type: precision_at_1000 |
| 264 | value: 0.202 |
| 265 | - type: precision_at_3 |
| 266 | value: 21.412 |
| 267 | - type: precision_at_5 |
| 268 | value: 15.479000000000001 |
| 269 | - type: recall_at_1 |
| 270 | value: 32.745999999999995 |
| 271 | - type: recall_at_10 |
| 272 | value: 62.056 |
| 273 | - type: recall_at_100 |
| 274 | value: 85.088 |
| 275 | - type: recall_at_1000 |
| 276 | value: 96.952 |
| 277 | - type: recall_at_3 |
| 278 | value: 46.959 |
| 279 | - type: recall_at_5 |
| 280 | value: 54.06999999999999 |
| 281 | - task: |
| 282 | type: Retrieval |
| 283 | dataset: |
| 284 | type: BeIR/cqadupstack |
| 285 | name: MTEB CQADupstackEnglishRetrieval |
| 286 | config: default |
| 287 | split: test |
| 288 | revision: None |
| 289 | metrics: |
| 290 | - type: map_at_1 |
| 291 | value: 31.898 |
| 292 | - type: map_at_10 |
| 293 | value: 42.142 |
| 294 | - type: map_at_100 |
| 295 | value: 43.349 |
| 296 | - type: map_at_1000 |
| 297 | value: 43.483 |
| 298 | - type: map_at_3 |
| 299 | value: 39.18 |
| 300 | - type: map_at_5 |
| 301 | value: 40.733000000000004 |
| 302 | - type: mrr_at_1 |
| 303 | value: 39.617999999999995 |
| 304 | - type: mrr_at_10 |
| 305 | value: 47.922 |
| 306 | - type: mrr_at_100 |
| 307 | value: 48.547000000000004 |
| 308 | - type: mrr_at_1000 |
| 309 | value: 48.597 |
| 310 | - type: mrr_at_3 |
| 311 | value: 45.86 |
| 312 | - type: mrr_at_5 |
| 313 | value: 46.949000000000005 |
| 314 | - type: ndcg_at_1 |
| 315 | value: 39.617999999999995 |
| 316 | - type: ndcg_at_10 |
| 317 | value: 47.739 |
| 318 | - type: ndcg_at_100 |
| 319 | value: 51.934999999999995 |
| 320 | - type: ndcg_at_1000 |
| 321 | value: 54.007000000000005 |
| 322 | - type: ndcg_at_3 |
| 323 | value: 43.748 |
| 324 | - type: ndcg_at_5 |
| 325 | value: 45.345 |
| 326 | - type: precision_at_1 |
| 327 | value: 39.617999999999995 |
| 328 | - type: precision_at_10 |
| 329 | value: 8.962 |
| 330 | - type: precision_at_100 |
| 331 | value: 1.436 |
| 332 | - type: precision_at_1000 |
| 333 | value: 0.192 |
| 334 | - type: precision_at_3 |
| 335 | value: 21.083 |
| 336 | - type: precision_at_5 |
| 337 | value: 14.752 |
| 338 | - type: recall_at_1 |
| 339 | value: 31.898 |
| 340 | - type: recall_at_10 |
| 341 | value: 57.587999999999994 |
| 342 | - type: recall_at_100 |
| 343 | value: 75.323 |
| 344 | - type: recall_at_1000 |
| 345 | value: 88.304 |
| 346 | - type: recall_at_3 |
| 347 | value: 45.275 |
| 348 | - type: recall_at_5 |
| 349 | value: 49.99 |
| 350 | - task: |
| 351 | type: Retrieval |
| 352 | dataset: |
| 353 | type: BeIR/cqadupstack |
| 354 | name: MTEB CQADupstackGamingRetrieval |
| 355 | config: default |
| 356 | split: test |
| 357 | revision: None |
| 358 | metrics: |
| 359 | - type: map_at_1 |
| 360 | value: 40.458 |
| 361 | - type: map_at_10 |
| 362 | value: 52.942 |
| 363 | - type: map_at_100 |
| 364 | value: 53.974 |
| 365 | - type: map_at_1000 |
| 366 | value: 54.031 |
| 367 | - type: map_at_3 |
| 368 | value: 49.559999999999995 |
| 369 | - type: map_at_5 |
| 370 | value: 51.408 |
| 371 | - type: mrr_at_1 |
| 372 | value: 46.27 |
| 373 | - type: mrr_at_10 |
| 374 | value: 56.31699999999999 |
| 375 | - type: mrr_at_100 |
| 376 | value: 56.95099999999999 |
| 377 | - type: mrr_at_1000 |
| 378 | value: 56.98 |
| 379 | - type: mrr_at_3 |
| 380 | value: 53.835 |
| 381 | - type: mrr_at_5 |
| 382 | value: 55.252 |
| 383 | - type: ndcg_at_1 |
| 384 | value: 46.27 |
| 385 | - type: ndcg_at_10 |
| 386 | value: 58.964000000000006 |
| 387 | - type: ndcg_at_100 |
| 388 | value: 62.875 |
| 389 | - type: ndcg_at_1000 |
| 390 | value: 63.969 |
| 391 | - type: ndcg_at_3 |
| 392 | value: 53.297000000000004 |
| 393 | - type: ndcg_at_5 |
| 394 | value: 55.938 |
| 395 | - type: precision_at_1 |
| 396 | value: 46.27 |
| 397 | - type: precision_at_10 |
| 398 | value: 9.549000000000001 |
| 399 | - type: precision_at_100 |
| 400 | value: 1.2409999999999999 |
| 401 | - type: precision_at_1000 |
| 402 | value: 0.13799999999999998 |
| 403 | - type: precision_at_3 |
| 404 | value: 23.762 |
| 405 | - type: precision_at_5 |
| 406 | value: 16.262999999999998 |
| 407 | - type: recall_at_1 |
| 408 | value: 40.458 |
| 409 | - type: recall_at_10 |
| 410 | value: 73.446 |
| 411 | - type: recall_at_100 |
| 412 | value: 90.12400000000001 |
| 413 | - type: recall_at_1000 |
| 414 | value: 97.795 |
| 415 | - type: recall_at_3 |
| 416 | value: 58.123000000000005 |
| 417 | - type: recall_at_5 |
| 418 | value: 64.68 |
| 419 | - task: |
| 420 | type: Retrieval |
| 421 | dataset: |
| 422 | type: BeIR/cqadupstack |
| 423 | name: MTEB CQADupstackGisRetrieval |
| 424 | config: default |
| 425 | split: test |
| 426 | revision: None |
| 427 | metrics: |
| 428 | - type: map_at_1 |
| 429 | value: 27.443 |
| 430 | - type: map_at_10 |
| 431 | value: 36.081 |
| 432 | - type: map_at_100 |
| 433 | value: 37.163000000000004 |
| 434 | - type: map_at_1000 |
| 435 | value: 37.232 |
| 436 | - type: map_at_3 |
| 437 | value: 33.308 |
| 438 | - type: map_at_5 |
| 439 | value: 34.724 |
| 440 | - type: mrr_at_1 |
| 441 | value: 29.492 |
| 442 | - type: mrr_at_10 |
| 443 | value: 38.138 |
| 444 | - type: mrr_at_100 |
| 445 | value: 39.065 |
| 446 | - type: mrr_at_1000 |
| 447 | value: 39.119 |
| 448 | - type: mrr_at_3 |
| 449 | value: 35.593 |
| 450 | - type: mrr_at_5 |
| 451 | value: 36.785000000000004 |
| 452 | - type: ndcg_at_1 |
| 453 | value: 29.492 |
| 454 | - type: ndcg_at_10 |
| 455 | value: 41.134 |
| 456 | - type: ndcg_at_100 |
| 457 | value: 46.300999999999995 |
| 458 | - type: ndcg_at_1000 |
| 459 | value: 48.106 |
| 460 | - type: ndcg_at_3 |
| 461 | value: 35.77 |
| 462 | - type: ndcg_at_5 |
| 463 | value: 38.032 |
| 464 | - type: precision_at_1 |
| 465 | value: 29.492 |
| 466 | - type: precision_at_10 |
| 467 | value: 6.249 |
| 468 | - type: precision_at_100 |
| 469 | value: 0.9299999999999999 |
| 470 | - type: precision_at_1000 |
| 471 | value: 0.11199999999999999 |
| 472 | - type: precision_at_3 |
| 473 | value: 15.065999999999999 |
| 474 | - type: precision_at_5 |
| 475 | value: 10.373000000000001 |
| 476 | - type: recall_at_1 |
| 477 | value: 27.443 |
| 478 | - type: recall_at_10 |
| 479 | value: 54.80199999999999 |
| 480 | - type: recall_at_100 |
| 481 | value: 78.21900000000001 |
| 482 | - type: recall_at_1000 |
| 483 | value: 91.751 |
| 484 | - type: recall_at_3 |
| 485 | value: 40.211000000000006 |
| 486 | - type: recall_at_5 |
| 487 | value: 45.599000000000004 |
| 488 | - task: |
| 489 | type: Retrieval |
| 490 | dataset: |
| 491 | type: BeIR/cqadupstack |
| 492 | name: MTEB CQADupstackMathematicaRetrieval |
| 493 | config: default |
| 494 | split: test |
| 495 | revision: None |
| 496 | metrics: |
| 497 | - type: map_at_1 |
| 498 | value: 18.731 |
| 499 | - type: map_at_10 |
| 500 | value: 26.717999999999996 |
| 501 | - type: map_at_100 |
| 502 | value: 27.897 |
| 503 | - type: map_at_1000 |
| 504 | value: 28.029 |
| 505 | - type: map_at_3 |
| 506 | value: 23.91 |
| 507 | - type: map_at_5 |
| 508 | value: 25.455 |
| 509 | - type: mrr_at_1 |
| 510 | value: 23.134 |
| 511 | - type: mrr_at_10 |
| 512 | value: 31.769 |
| 513 | - type: mrr_at_100 |
| 514 | value: 32.634 |
| 515 | - type: mrr_at_1000 |
| 516 | value: 32.707 |
| 517 | - type: mrr_at_3 |
| 518 | value: 28.938999999999997 |
| 519 | - type: mrr_at_5 |
| 520 | value: 30.531000000000002 |
| 521 | - type: ndcg_at_1 |
| 522 | value: 23.134 |
| 523 | - type: ndcg_at_10 |
| 524 | value: 32.249 |
| 525 | - type: ndcg_at_100 |
| 526 | value: 37.678 |
| 527 | - type: ndcg_at_1000 |
| 528 | value: 40.589999999999996 |
| 529 | - type: ndcg_at_3 |
| 530 | value: 26.985999999999997 |
| 531 | - type: ndcg_at_5 |
| 532 | value: 29.457 |
| 533 | - type: precision_at_1 |
| 534 | value: 23.134 |
| 535 | - type: precision_at_10 |
| 536 | value: 5.8709999999999996 |
| 537 | - type: precision_at_100 |
| 538 | value: 0.988 |
| 539 | - type: precision_at_1000 |
| 540 | value: 0.13799999999999998 |
| 541 | - type: precision_at_3 |
| 542 | value: 12.852 |
| 543 | - type: precision_at_5 |
| 544 | value: 9.428 |
| 545 | - type: recall_at_1 |
| 546 | value: 18.731 |
| 547 | - type: recall_at_10 |
| 548 | value: 44.419 |
| 549 | - type: recall_at_100 |
| 550 | value: 67.851 |
| 551 | - type: recall_at_1000 |
| 552 | value: 88.103 |
| 553 | - type: recall_at_3 |
| 554 | value: 29.919 |
| 555 | - type: recall_at_5 |
| 556 | value: 36.230000000000004 |
| 557 | - task: |
| 558 | type: Retrieval |
| 559 | dataset: |
| 560 | type: BeIR/cqadupstack |
| 561 | name: MTEB CQADupstackPhysicsRetrieval |
| 562 | config: default |
| 563 | split: test |
| 564 | revision: None |
| 565 | metrics: |
| 566 | - type: map_at_1 |
| 567 | value: 30.324 |
| 568 | - type: map_at_10 |
| 569 | value: 41.265 |
| 570 | - type: map_at_100 |
| 571 | value: 42.559000000000005 |
| 572 | - type: map_at_1000 |
| 573 | value: 42.669000000000004 |
| 574 | - type: map_at_3 |
| 575 | value: 38.138 |
| 576 | - type: map_at_5 |
| 577 | value: 39.881 |
| 578 | - type: mrr_at_1 |
| 579 | value: 36.67 |
| 580 | - type: mrr_at_10 |
| 581 | value: 46.774 |
| 582 | - type: mrr_at_100 |
| 583 | value: 47.554 |
| 584 | - type: mrr_at_1000 |
| 585 | value: 47.593 |
| 586 | - type: mrr_at_3 |
| 587 | value: 44.338 |
| 588 | - type: mrr_at_5 |
| 589 | value: 45.723 |
| 590 | - type: ndcg_at_1 |
| 591 | value: 36.67 |
| 592 | - type: ndcg_at_10 |
| 593 | value: 47.367 |
| 594 | - type: ndcg_at_100 |
| 595 | value: 52.623 |
| 596 | - type: ndcg_at_1000 |
| 597 | value: 54.59 |
| 598 | - type: ndcg_at_3 |
| 599 | value: 42.323 |
| 600 | - type: ndcg_at_5 |
| 601 | value: 44.727 |
| 602 | - type: precision_at_1 |
| 603 | value: 36.67 |
| 604 | - type: precision_at_10 |
| 605 | value: 8.518 |
| 606 | - type: precision_at_100 |
| 607 | value: 1.2890000000000001 |
| 608 | - type: precision_at_1000 |
| 609 | value: 0.163 |
| 610 | - type: precision_at_3 |
| 611 | value: 19.955000000000002 |
| 612 | - type: precision_at_5 |
| 613 | value: 14.11 |
| 614 | - type: recall_at_1 |
| 615 | value: 30.324 |
| 616 | - type: recall_at_10 |
| 617 | value: 59.845000000000006 |
| 618 | - type: recall_at_100 |
| 619 | value: 81.77499999999999 |
| 620 | - type: recall_at_1000 |
| 621 | value: 94.463 |
| 622 | - type: recall_at_3 |
| 623 | value: 46.019 |
| 624 | - type: recall_at_5 |
| 625 | value: 52.163000000000004 |
| 626 | - task: |
| 627 | type: Retrieval |
| 628 | dataset: |
| 629 | type: BeIR/cqadupstack |
| 630 | name: MTEB CQADupstackProgrammersRetrieval |
| 631 | config: default |
| 632 | split: test |
| 633 | revision: None |
| 634 | metrics: |
| 635 | - type: map_at_1 |
| 636 | value: 24.229 |
| 637 | - type: map_at_10 |
| 638 | value: 35.004000000000005 |
| 639 | - type: map_at_100 |
| 640 | value: 36.409000000000006 |
| 641 | - type: map_at_1000 |
| 642 | value: 36.521 |
| 643 | - type: map_at_3 |
| 644 | value: 31.793 |
| 645 | - type: map_at_5 |
| 646 | value: 33.432 |
| 647 | - type: mrr_at_1 |
| 648 | value: 30.365 |
| 649 | - type: mrr_at_10 |
| 650 | value: 40.502 |
| 651 | - type: mrr_at_100 |
| 652 | value: 41.372 |
| 653 | - type: mrr_at_1000 |
| 654 | value: 41.435 |
| 655 | - type: mrr_at_3 |
| 656 | value: 37.804 |
| 657 | - type: mrr_at_5 |
| 658 | value: 39.226 |
| 659 | - type: ndcg_at_1 |
| 660 | value: 30.365 |
| 661 | - type: ndcg_at_10 |
| 662 | value: 41.305 |
| 663 | - type: ndcg_at_100 |
| 664 | value: 47.028999999999996 |
| 665 | - type: ndcg_at_1000 |
| 666 | value: 49.375 |
| 667 | - type: ndcg_at_3 |
| 668 | value: 35.85 |
| 669 | - type: ndcg_at_5 |
| 670 | value: 38.12 |
| 671 | - type: precision_at_1 |
| 672 | value: 30.365 |
| 673 | - type: precision_at_10 |
| 674 | value: 7.808 |
| 675 | - type: precision_at_100 |
| 676 | value: 1.228 |
| 677 | - type: precision_at_1000 |
| 678 | value: 0.161 |
| 679 | - type: precision_at_3 |
| 680 | value: 17.352 |
| 681 | - type: precision_at_5 |
| 682 | value: 12.42 |
| 683 | - type: recall_at_1 |
| 684 | value: 24.229 |
| 685 | - type: recall_at_10 |
| 686 | value: 54.673 |
| 687 | - type: recall_at_100 |
| 688 | value: 78.766 |
| 689 | - type: recall_at_1000 |
| 690 | value: 94.625 |
| 691 | - type: recall_at_3 |
| 692 | value: 39.602 |
| 693 | - type: recall_at_5 |
| 694 | value: 45.558 |
| 695 | - task: |
| 696 | type: Retrieval |
| 697 | dataset: |
| 698 | type: BeIR/cqadupstack |
| 699 | name: MTEB CQADupstackRetrieval |
| 700 | config: default |
| 701 | split: test |
| 702 | revision: None |
| 703 | metrics: |
| 704 | - type: map_at_1 |
| 705 | value: 26.695 |
| 706 | - type: map_at_10 |
| 707 | value: 36.0895 |
| 708 | - type: map_at_100 |
| 709 | value: 37.309416666666664 |
| 710 | - type: map_at_1000 |
| 711 | value: 37.42558333333334 |
| 712 | - type: map_at_3 |
| 713 | value: 33.19616666666666 |
| 714 | - type: map_at_5 |
| 715 | value: 34.78641666666667 |
| 716 | - type: mrr_at_1 |
| 717 | value: 31.486083333333337 |
| 718 | - type: mrr_at_10 |
| 719 | value: 40.34774999999999 |
| 720 | - type: mrr_at_100 |
| 721 | value: 41.17533333333333 |
| 722 | - type: mrr_at_1000 |
| 723 | value: 41.231583333333326 |
| 724 | - type: mrr_at_3 |
| 725 | value: 37.90075 |
| 726 | - type: mrr_at_5 |
| 727 | value: 39.266999999999996 |
| 728 | - type: ndcg_at_1 |
| 729 | value: 31.486083333333337 |
| 730 | - type: ndcg_at_10 |
| 731 | value: 41.60433333333334 |
| 732 | - type: ndcg_at_100 |
| 733 | value: 46.74525 |
| 734 | - type: ndcg_at_1000 |
| 735 | value: 48.96166666666667 |
| 736 | - type: ndcg_at_3 |
| 737 | value: 36.68825 |
| 738 | - type: ndcg_at_5 |
| 739 | value: 38.966499999999996 |
| 740 | - type: precision_at_1 |
| 741 | value: 31.486083333333337 |
| 742 | - type: precision_at_10 |
| 743 | value: 7.29675 |
| 744 | - type: precision_at_100 |
| 745 | value: 1.1621666666666666 |
| 746 | - type: precision_at_1000 |
| 747 | value: 0.1545 |
| 748 | - type: precision_at_3 |
| 749 | value: 16.8815 |
| 750 | - type: precision_at_5 |
| 751 | value: 11.974583333333333 |
| 752 | - type: recall_at_1 |
| 753 | value: 26.695 |
| 754 | - type: recall_at_10 |
| 755 | value: 53.651916666666665 |
| 756 | - type: recall_at_100 |
| 757 | value: 76.12083333333332 |
| 758 | - type: recall_at_1000 |
| 759 | value: 91.31191666666668 |
| 760 | - type: recall_at_3 |
| 761 | value: 40.03575 |
| 762 | - type: recall_at_5 |
| 763 | value: 45.876666666666665 |
| 764 | - task: |
| 765 | type: Retrieval |
| 766 | dataset: |
| 767 | type: BeIR/cqadupstack |
| 768 | name: MTEB CQADupstackStatsRetrieval |
| 769 | config: default |
| 770 | split: test |
| 771 | revision: None |
| 772 | metrics: |
| 773 | - type: map_at_1 |
| 774 | value: 25.668000000000003 |
| 775 | - type: map_at_10 |
| 776 | value: 32.486 |
| 777 | - type: map_at_100 |
| 778 | value: 33.371 |
| 779 | - type: map_at_1000 |
| 780 | value: 33.458 |
| 781 | - type: map_at_3 |
| 782 | value: 30.261 |
| 783 | - type: map_at_5 |
| 784 | value: 31.418000000000003 |
| 785 | - type: mrr_at_1 |
| 786 | value: 28.988000000000003 |
| 787 | - type: mrr_at_10 |
| 788 | value: 35.414 |
| 789 | - type: mrr_at_100 |
| 790 | value: 36.149 |
| 791 | - type: mrr_at_1000 |
| 792 | value: 36.215 |
| 793 | - type: mrr_at_3 |
| 794 | value: 33.333 |
| 795 | - type: mrr_at_5 |
| 796 | value: 34.43 |
| 797 | - type: ndcg_at_1 |
| 798 | value: 28.988000000000003 |
| 799 | - type: ndcg_at_10 |
| 800 | value: 36.732 |
| 801 | - type: ndcg_at_100 |
| 802 | value: 41.331 |
| 803 | - type: ndcg_at_1000 |
| 804 | value: 43.575 |
| 805 | - type: ndcg_at_3 |
| 806 | value: 32.413 |
| 807 | - type: ndcg_at_5 |
| 808 | value: 34.316 |
| 809 | - type: precision_at_1 |
| 810 | value: 28.988000000000003 |
| 811 | - type: precision_at_10 |
| 812 | value: 5.7059999999999995 |
| 813 | - type: precision_at_100 |
| 814 | value: 0.882 |
| 815 | - type: precision_at_1000 |
| 816 | value: 0.11299999999999999 |
| 817 | - type: precision_at_3 |
| 818 | value: 13.65 |
| 819 | - type: precision_at_5 |
| 820 | value: 9.417 |
| 821 | - type: recall_at_1 |
| 822 | value: 25.668000000000003 |
| 823 | - type: recall_at_10 |
| 824 | value: 47.147 |
| 825 | - type: recall_at_100 |
| 826 | value: 68.504 |
| 827 | - type: recall_at_1000 |
| 828 | value: 85.272 |
| 829 | - type: recall_at_3 |
| 830 | value: 35.19 |
| 831 | - type: recall_at_5 |
| 832 | value: 39.925 |
| 833 | - task: |
| 834 | type: Retrieval |
| 835 | dataset: |
| 836 | type: BeIR/cqadupstack |
| 837 | name: MTEB CQADupstackTexRetrieval |
| 838 | config: default |
| 839 | split: test |
| 840 | revision: None |
| 841 | metrics: |
| 842 | - type: map_at_1 |
| 843 | value: 17.256 |
| 844 | - type: map_at_10 |
| 845 | value: 24.58 |
| 846 | - type: map_at_100 |
| 847 | value: 25.773000000000003 |
| 848 | - type: map_at_1000 |
| 849 | value: 25.899 |
| 850 | - type: map_at_3 |
| 851 | value: 22.236 |
| 852 | - type: map_at_5 |
| 853 | value: 23.507 |
| 854 | - type: mrr_at_1 |
| 855 | value: 20.957 |
| 856 | - type: mrr_at_10 |
| 857 | value: 28.416000000000004 |
| 858 | - type: mrr_at_100 |
| 859 | value: 29.447000000000003 |
| 860 | - type: mrr_at_1000 |
| 861 | value: 29.524 |
| 862 | - type: mrr_at_3 |
| 863 | value: 26.245 |
| 864 | - type: mrr_at_5 |
| 865 | value: 27.451999999999998 |
| 866 | - type: ndcg_at_1 |
| 867 | value: 20.957 |
| 868 | - type: ndcg_at_10 |
| 869 | value: 29.285 |
| 870 | - type: ndcg_at_100 |
| 871 | value: 35.003 |
| 872 | - type: ndcg_at_1000 |
| 873 | value: 37.881 |
| 874 | - type: ndcg_at_3 |
| 875 | value: 25.063000000000002 |
| 876 | - type: ndcg_at_5 |
| 877 | value: 26.983 |
| 878 | - type: precision_at_1 |
| 879 | value: 20.957 |
| 880 | - type: precision_at_10 |
| 881 | value: 5.344 |
| 882 | - type: precision_at_100 |
| 883 | value: 0.958 |
| 884 | - type: precision_at_1000 |
| 885 | value: 0.13799999999999998 |
| 886 | - type: precision_at_3 |
| 887 | value: 11.918 |
| 888 | - type: precision_at_5 |
| 889 | value: 8.596 |
| 890 | - type: recall_at_1 |
| 891 | value: 17.256 |
| 892 | - type: recall_at_10 |
| 893 | value: 39.644 |
| 894 | - type: recall_at_100 |
| 895 | value: 65.279 |
| 896 | - type: recall_at_1000 |
| 897 | value: 85.693 |
| 898 | - type: recall_at_3 |
| 899 | value: 27.825 |
| 900 | - type: recall_at_5 |
| 901 | value: 32.792 |
| 902 | - task: |
| 903 | type: Retrieval |
| 904 | dataset: |
| 905 | type: BeIR/cqadupstack |
| 906 | name: MTEB CQADupstackUnixRetrieval |
| 907 | config: default |
| 908 | split: test |
| 909 | revision: None |
| 910 | metrics: |
| 911 | - type: map_at_1 |
| 912 | value: 26.700000000000003 |
| 913 | - type: map_at_10 |
| 914 | value: 36.205999999999996 |
| 915 | - type: map_at_100 |
| 916 | value: 37.316 |
| 917 | - type: map_at_1000 |
| 918 | value: 37.425000000000004 |
| 919 | - type: map_at_3 |
| 920 | value: 33.166000000000004 |
| 921 | - type: map_at_5 |
| 922 | value: 35.032999999999994 |
| 923 | - type: mrr_at_1 |
| 924 | value: 31.436999999999998 |
| 925 | - type: mrr_at_10 |
| 926 | value: 40.61 |
| 927 | - type: mrr_at_100 |
| 928 | value: 41.415 |
| 929 | - type: mrr_at_1000 |
| 930 | value: 41.48 |
| 931 | - type: mrr_at_3 |
| 932 | value: 37.966 |
| 933 | - type: mrr_at_5 |
| 934 | value: 39.599000000000004 |
| 935 | - type: ndcg_at_1 |
| 936 | value: 31.436999999999998 |
| 937 | - type: ndcg_at_10 |
| 938 | value: 41.771 |
| 939 | - type: ndcg_at_100 |
| 940 | value: 46.784 |
| 941 | - type: ndcg_at_1000 |
| 942 | value: 49.183 |
| 943 | - type: ndcg_at_3 |
| 944 | value: 36.437000000000005 |
| 945 | - type: ndcg_at_5 |
| 946 | value: 39.291 |
| 947 | - type: precision_at_1 |
| 948 | value: 31.436999999999998 |
| 949 | - type: precision_at_10 |
| 950 | value: 6.987 |
| 951 | - type: precision_at_100 |
| 952 | value: 1.072 |
| 953 | - type: precision_at_1000 |
| 954 | value: 0.13899999999999998 |
| 955 | - type: precision_at_3 |
| 956 | value: 16.448999999999998 |
| 957 | - type: precision_at_5 |
| 958 | value: 11.866 |
| 959 | - type: recall_at_1 |
| 960 | value: 26.700000000000003 |
| 961 | - type: recall_at_10 |
| 962 | value: 54.301 |
| 963 | - type: recall_at_100 |
| 964 | value: 75.871 |
| 965 | - type: recall_at_1000 |
| 966 | value: 92.529 |
| 967 | - type: recall_at_3 |
| 968 | value: 40.201 |
| 969 | - type: recall_at_5 |
| 970 | value: 47.208 |
| 971 | - task: |
| 972 | type: Retrieval |
| 973 | dataset: |
| 974 | type: BeIR/cqadupstack |
| 975 | name: MTEB CQADupstackWebmastersRetrieval |
| 976 | config: default |
| 977 | split: test |
| 978 | revision: None |
| 979 | metrics: |
| 980 | - type: map_at_1 |
| 981 | value: 24.296 |
| 982 | - type: map_at_10 |
| 983 | value: 33.116 |
| 984 | - type: map_at_100 |
| 985 | value: 34.81 |
| 986 | - type: map_at_1000 |
| 987 | value: 35.032000000000004 |
| 988 | - type: map_at_3 |
| 989 | value: 30.105999999999998 |
| 990 | - type: map_at_5 |
| 991 | value: 31.839000000000002 |
| 992 | - type: mrr_at_1 |
| 993 | value: 29.051 |
| 994 | - type: mrr_at_10 |
| 995 | value: 37.803 |
| 996 | - type: mrr_at_100 |
| 997 | value: 38.856 |
| 998 | - type: mrr_at_1000 |
| 999 | value: 38.903999999999996 |
| 1000 | - type: mrr_at_3 |
| 1001 | value: 35.211 |
| 1002 | - type: mrr_at_5 |
| 1003 | value: 36.545 |
| 1004 | - type: ndcg_at_1 |
| 1005 | value: 29.051 |
| 1006 | - type: ndcg_at_10 |
| 1007 | value: 39.007 |
| 1008 | - type: ndcg_at_100 |
| 1009 | value: 45.321 |
| 1010 | - type: ndcg_at_1000 |
| 1011 | value: 47.665 |
| 1012 | - type: ndcg_at_3 |
| 1013 | value: 34.1 |
| 1014 | - type: ndcg_at_5 |
| 1015 | value: 36.437000000000005 |
| 1016 | - type: precision_at_1 |
| 1017 | value: 29.051 |
| 1018 | - type: precision_at_10 |
| 1019 | value: 7.668 |
| 1020 | - type: precision_at_100 |
| 1021 | value: 1.542 |
| 1022 | - type: precision_at_1000 |
| 1023 | value: 0.24 |
| 1024 | - type: precision_at_3 |
| 1025 | value: 16.14 |
| 1026 | - type: precision_at_5 |
| 1027 | value: 11.897 |
| 1028 | - type: recall_at_1 |
| 1029 | value: 24.296 |
| 1030 | - type: recall_at_10 |
| 1031 | value: 49.85 |
| 1032 | - type: recall_at_100 |
| 1033 | value: 78.457 |
| 1034 | - type: recall_at_1000 |
| 1035 | value: 92.618 |
| 1036 | - type: recall_at_3 |
| 1037 | value: 36.138999999999996 |
| 1038 | - type: recall_at_5 |
| 1039 | value: 42.223 |
| 1040 | - task: |
| 1041 | type: Retrieval |
| 1042 | dataset: |
| 1043 | type: BeIR/cqadupstack |
| 1044 | name: MTEB CQADupstackWordpressRetrieval |
| 1045 | config: default |
| 1046 | split: test |
| 1047 | revision: None |
| 1048 | metrics: |
| 1049 | - type: map_at_1 |
| 1050 | value: 20.591 |
| 1051 | - type: map_at_10 |
| 1052 | value: 28.902 |
| 1053 | - type: map_at_100 |
| 1054 | value: 29.886000000000003 |
| 1055 | - type: map_at_1000 |
| 1056 | value: 29.987000000000002 |
| 1057 | - type: map_at_3 |
| 1058 | value: 26.740000000000002 |
| 1059 | - type: map_at_5 |
| 1060 | value: 27.976 |
| 1061 | - type: mrr_at_1 |
| 1062 | value: 22.366 |
| 1063 | - type: mrr_at_10 |
| 1064 | value: 30.971 |
| 1065 | - type: mrr_at_100 |
| 1066 | value: 31.865 |
| 1067 | - type: mrr_at_1000 |
| 1068 | value: 31.930999999999997 |
| 1069 | - type: mrr_at_3 |
| 1070 | value: 28.927999999999997 |
| 1071 | - type: mrr_at_5 |
| 1072 | value: 30.231 |
| 1073 | - type: ndcg_at_1 |
| 1074 | value: 22.366 |
| 1075 | - type: ndcg_at_10 |
| 1076 | value: 33.641 |
| 1077 | - type: ndcg_at_100 |
| 1078 | value: 38.477 |
| 1079 | - type: ndcg_at_1000 |
| 1080 | value: 41.088 |
| 1081 | - type: ndcg_at_3 |
| 1082 | value: 29.486 |
| 1083 | - type: ndcg_at_5 |
| 1084 | value: 31.612000000000002 |
| 1085 | - type: precision_at_1 |
| 1086 | value: 22.366 |
| 1087 | - type: precision_at_10 |
| 1088 | value: 5.3420000000000005 |
| 1089 | - type: precision_at_100 |
| 1090 | value: 0.828 |
| 1091 | - type: precision_at_1000 |
| 1092 | value: 0.11800000000000001 |
| 1093 | - type: precision_at_3 |
| 1094 | value: 12.939 |
| 1095 | - type: precision_at_5 |
| 1096 | value: 9.094 |
| 1097 | - type: recall_at_1 |
| 1098 | value: 20.591 |
| 1099 | - type: recall_at_10 |
| 1100 | value: 46.052 |
| 1101 | - type: recall_at_100 |
| 1102 | value: 68.193 |
| 1103 | - type: recall_at_1000 |
| 1104 | value: 87.638 |
| 1105 | - type: recall_at_3 |
| 1106 | value: 34.966 |
| 1107 | - type: recall_at_5 |
| 1108 | value: 40.082 |
| 1109 | - task: |
| 1110 | type: Retrieval |
| 1111 | dataset: |
| 1112 | type: climate-fever |
| 1113 | name: MTEB ClimateFEVER |
| 1114 | config: default |
| 1115 | split: test |
| 1116 | revision: None |
| 1117 | metrics: |
| 1118 | - type: map_at_1 |
| 1119 | value: 15.091 |
| 1120 | - type: map_at_10 |
| 1121 | value: 26.38 |
| 1122 | - type: map_at_100 |
| 1123 | value: 28.421999999999997 |
| 1124 | - type: map_at_1000 |
| 1125 | value: 28.621999999999996 |
| 1126 | - type: map_at_3 |
| 1127 | value: 21.597 |
| 1128 | - type: map_at_5 |
| 1129 | value: 24.12 |
| 1130 | - type: mrr_at_1 |
| 1131 | value: 34.266999999999996 |
| 1132 | - type: mrr_at_10 |
| 1133 | value: 46.864 |
| 1134 | - type: mrr_at_100 |
| 1135 | value: 47.617 |
| 1136 | - type: mrr_at_1000 |
| 1137 | value: 47.644 |
| 1138 | - type: mrr_at_3 |
| 1139 | value: 43.312 |
| 1140 | - type: mrr_at_5 |
| 1141 | value: 45.501000000000005 |
| 1142 | - type: ndcg_at_1 |
| 1143 | value: 34.266999999999996 |
| 1144 | - type: ndcg_at_10 |
| 1145 | value: 36.095 |
| 1146 | - type: ndcg_at_100 |
| 1147 | value: 43.447 |
| 1148 | - type: ndcg_at_1000 |
| 1149 | value: 46.661 |
| 1150 | - type: ndcg_at_3 |
| 1151 | value: 29.337999999999997 |
| 1152 | - type: ndcg_at_5 |
| 1153 | value: 31.824 |
| 1154 | - type: precision_at_1 |
| 1155 | value: 34.266999999999996 |
| 1156 | - type: precision_at_10 |
| 1157 | value: 11.472 |
| 1158 | - type: precision_at_100 |
| 1159 | value: 1.944 |
| 1160 | - type: precision_at_1000 |
| 1161 | value: 0.255 |
| 1162 | - type: precision_at_3 |
| 1163 | value: 21.933 |
| 1164 | - type: precision_at_5 |
| 1165 | value: 17.224999999999998 |
| 1166 | - type: recall_at_1 |
| 1167 | value: 15.091 |
| 1168 | - type: recall_at_10 |
| 1169 | value: 43.022 |
| 1170 | - type: recall_at_100 |
| 1171 | value: 68.075 |
| 1172 | - type: recall_at_1000 |
| 1173 | value: 85.76 |
| 1174 | - type: recall_at_3 |
| 1175 | value: 26.564 |
| 1176 | - type: recall_at_5 |
| 1177 | value: 33.594 |
| 1178 | - task: |
| 1179 | type: Retrieval |
| 1180 | dataset: |
| 1181 | type: dbpedia-entity |
| 1182 | name: MTEB DBPedia |
| 1183 | config: default |
| 1184 | split: test |
| 1185 | revision: None |
| 1186 | metrics: |
| 1187 | - type: map_at_1 |
| 1188 | value: 9.252 |
| 1189 | - type: map_at_10 |
| 1190 | value: 20.923 |
| 1191 | - type: map_at_100 |
| 1192 | value: 30.741000000000003 |
| 1193 | - type: map_at_1000 |
| 1194 | value: 32.542 |
| 1195 | - type: map_at_3 |
| 1196 | value: 14.442 |
| 1197 | - type: map_at_5 |
| 1198 | value: 17.399 |
| 1199 | - type: mrr_at_1 |
| 1200 | value: 70.25 |
| 1201 | - type: mrr_at_10 |
| 1202 | value: 78.17 |
| 1203 | - type: mrr_at_100 |
| 1204 | value: 78.444 |
| 1205 | - type: mrr_at_1000 |
| 1206 | value: 78.45100000000001 |
| 1207 | - type: mrr_at_3 |
| 1208 | value: 76.958 |
| 1209 | - type: mrr_at_5 |
| 1210 | value: 77.571 |
| 1211 | - type: ndcg_at_1 |
| 1212 | value: 58.375 |
| 1213 | - type: ndcg_at_10 |
| 1214 | value: 44.509 |
| 1215 | - type: ndcg_at_100 |
| 1216 | value: 49.897999999999996 |
| 1217 | - type: ndcg_at_1000 |
| 1218 | value: 57.269999999999996 |
| 1219 | - type: ndcg_at_3 |
| 1220 | value: 48.64 |
| 1221 | - type: ndcg_at_5 |
| 1222 | value: 46.697 |
| 1223 | - type: precision_at_1 |
| 1224 | value: 70.25 |
| 1225 | - type: precision_at_10 |
| 1226 | value: 36.05 |
| 1227 | - type: precision_at_100 |
| 1228 | value: 11.848 |
| 1229 | - type: precision_at_1000 |
| 1230 | value: 2.213 |
| 1231 | - type: precision_at_3 |
| 1232 | value: 52.917 |
| 1233 | - type: precision_at_5 |
| 1234 | value: 45.7 |
| 1235 | - type: recall_at_1 |
| 1236 | value: 9.252 |
| 1237 | - type: recall_at_10 |
| 1238 | value: 27.006999999999998 |
| 1239 | - type: recall_at_100 |
| 1240 | value: 57.008 |
| 1241 | - type: recall_at_1000 |
| 1242 | value: 80.697 |
| 1243 | - type: recall_at_3 |
| 1244 | value: 15.798000000000002 |
| 1245 | - type: recall_at_5 |
| 1246 | value: 20.4 |
| 1247 | - task: |
| 1248 | type: Classification |
| 1249 | dataset: |
| 1250 | type: mteb/emotion |
| 1251 | name: MTEB EmotionClassification |
| 1252 | config: default |
| 1253 | split: test |
| 1254 | revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| 1255 | metrics: |
| 1256 | - type: accuracy |
| 1257 | value: 50.88 |
| 1258 | - type: f1 |
| 1259 | value: 45.545495028653384 |
| 1260 | - task: |
| 1261 | type: Retrieval |
| 1262 | dataset: |
| 1263 | type: fever |
| 1264 | name: MTEB FEVER |
| 1265 | config: default |
| 1266 | split: test |
| 1267 | revision: None |
| 1268 | metrics: |
| 1269 | - type: map_at_1 |
| 1270 | value: 75.424 |
| 1271 | - type: map_at_10 |
| 1272 | value: 83.435 |
| 1273 | - type: map_at_100 |
| 1274 | value: 83.66900000000001 |
| 1275 | - type: map_at_1000 |
| 1276 | value: 83.685 |
| 1277 | - type: map_at_3 |
| 1278 | value: 82.39800000000001 |
| 1279 | - type: map_at_5 |
| 1280 | value: 83.07 |
| 1281 | - type: mrr_at_1 |
| 1282 | value: 81.113 |
| 1283 | - type: mrr_at_10 |
| 1284 | value: 87.77199999999999 |
| 1285 | - type: mrr_at_100 |
| 1286 | value: 87.862 |
| 1287 | - type: mrr_at_1000 |
| 1288 | value: 87.86500000000001 |
| 1289 | - type: mrr_at_3 |
| 1290 | value: 87.17099999999999 |
| 1291 | - type: mrr_at_5 |
| 1292 | value: 87.616 |
| 1293 | - type: ndcg_at_1 |
| 1294 | value: 81.113 |
| 1295 | - type: ndcg_at_10 |
| 1296 | value: 86.909 |
| 1297 | - type: ndcg_at_100 |
| 1298 | value: 87.746 |
| 1299 | - type: ndcg_at_1000 |
| 1300 | value: 88.017 |
| 1301 | - type: ndcg_at_3 |
| 1302 | value: 85.368 |
| 1303 | - type: ndcg_at_5 |
| 1304 | value: 86.28099999999999 |
| 1305 | - type: precision_at_1 |
| 1306 | value: 81.113 |
| 1307 | - type: precision_at_10 |
| 1308 | value: 10.363 |
| 1309 | - type: precision_at_100 |
| 1310 | value: 1.102 |
| 1311 | - type: precision_at_1000 |
| 1312 | value: 0.11399999999999999 |
| 1313 | - type: precision_at_3 |
| 1314 | value: 32.507999999999996 |
| 1315 | - type: precision_at_5 |
| 1316 | value: 20.138 |
| 1317 | - type: recall_at_1 |
| 1318 | value: 75.424 |
| 1319 | - type: recall_at_10 |
| 1320 | value: 93.258 |
| 1321 | - type: recall_at_100 |
| 1322 | value: 96.545 |
| 1323 | - type: recall_at_1000 |
| 1324 | value: 98.284 |
| 1325 | - type: recall_at_3 |
| 1326 | value: 89.083 |
| 1327 | - type: recall_at_5 |
| 1328 | value: 91.445 |
| 1329 | - task: |
| 1330 | type: Retrieval |
| 1331 | dataset: |
| 1332 | type: fiqa |
| 1333 | name: MTEB FiQA2018 |
| 1334 | config: default |
| 1335 | split: test |
| 1336 | revision: None |
| 1337 | metrics: |
| 1338 | - type: map_at_1 |
| 1339 | value: 22.532 |
| 1340 | - type: map_at_10 |
| 1341 | value: 37.141999999999996 |
| 1342 | - type: map_at_100 |
| 1343 | value: 39.162 |
| 1344 | - type: map_at_1000 |
| 1345 | value: 39.322 |
| 1346 | - type: map_at_3 |
| 1347 | value: 32.885 |
| 1348 | - type: map_at_5 |
| 1349 | value: 35.093999999999994 |
| 1350 | - type: mrr_at_1 |
| 1351 | value: 44.29 |
| 1352 | - type: mrr_at_10 |
| 1353 | value: 53.516 |
| 1354 | - type: mrr_at_100 |
| 1355 | value: 54.24 |
| 1356 | - type: mrr_at_1000 |
| 1357 | value: 54.273 |
| 1358 | - type: mrr_at_3 |
| 1359 | value: 51.286 |
| 1360 | - type: mrr_at_5 |
| 1361 | value: 52.413 |
| 1362 | - type: ndcg_at_1 |
| 1363 | value: 44.29 |
| 1364 | - type: ndcg_at_10 |
| 1365 | value: 45.268 |
| 1366 | - type: ndcg_at_100 |
| 1367 | value: 52.125 |
| 1368 | - type: ndcg_at_1000 |
| 1369 | value: 54.778000000000006 |
| 1370 | - type: ndcg_at_3 |
| 1371 | value: 41.829 |
| 1372 | - type: ndcg_at_5 |
| 1373 | value: 42.525 |
| 1374 | - type: precision_at_1 |
| 1375 | value: 44.29 |
| 1376 | - type: precision_at_10 |
| 1377 | value: 12.5 |
| 1378 | - type: precision_at_100 |
| 1379 | value: 1.9720000000000002 |
| 1380 | - type: precision_at_1000 |
| 1381 | value: 0.245 |
| 1382 | - type: precision_at_3 |
| 1383 | value: 28.035 |
| 1384 | - type: precision_at_5 |
| 1385 | value: 20.093 |
| 1386 | - type: recall_at_1 |
| 1387 | value: 22.532 |
| 1388 | - type: recall_at_10 |
| 1389 | value: 52.419000000000004 |
| 1390 | - type: recall_at_100 |
| 1391 | value: 77.43299999999999 |
| 1392 | - type: recall_at_1000 |
| 1393 | value: 93.379 |
| 1394 | - type: recall_at_3 |
| 1395 | value: 38.629000000000005 |
| 1396 | - type: recall_at_5 |
| 1397 | value: 43.858000000000004 |
| 1398 | - task: |
| 1399 | type: Retrieval |
| 1400 | dataset: |
| 1401 | type: hotpotqa |
| 1402 | name: MTEB HotpotQA |
| 1403 | config: default |
| 1404 | split: test |
| 1405 | revision: None |
| 1406 | metrics: |
| 1407 | - type: map_at_1 |
| 1408 | value: 39.359 |
| 1409 | - type: map_at_10 |
| 1410 | value: 63.966 |
| 1411 | - type: map_at_100 |
| 1412 | value: 64.87 |
| 1413 | - type: map_at_1000 |
| 1414 | value: 64.92599999999999 |
| 1415 | - type: map_at_3 |
| 1416 | value: 60.409 |
| 1417 | - type: map_at_5 |
| 1418 | value: 62.627 |
| 1419 | - type: mrr_at_1 |
| 1420 | value: 78.717 |
| 1421 | - type: mrr_at_10 |
| 1422 | value: 84.468 |
| 1423 | - type: mrr_at_100 |
| 1424 | value: 84.655 |
| 1425 | - type: mrr_at_1000 |
| 1426 | value: 84.661 |
| 1427 | - type: mrr_at_3 |
| 1428 | value: 83.554 |
| 1429 | - type: mrr_at_5 |
| 1430 | value: 84.133 |
| 1431 | - type: ndcg_at_1 |
| 1432 | value: 78.717 |
| 1433 | - type: ndcg_at_10 |
| 1434 | value: 72.03399999999999 |
| 1435 | - type: ndcg_at_100 |
| 1436 | value: 75.158 |
| 1437 | - type: ndcg_at_1000 |
| 1438 | value: 76.197 |
| 1439 | - type: ndcg_at_3 |
| 1440 | value: 67.049 |
| 1441 | - type: ndcg_at_5 |
| 1442 | value: 69.808 |
| 1443 | - type: precision_at_1 |
| 1444 | value: 78.717 |
| 1445 | - type: precision_at_10 |
| 1446 | value: 15.201 |
| 1447 | - type: precision_at_100 |
| 1448 | value: 1.764 |
| 1449 | - type: precision_at_1000 |
| 1450 | value: 0.19 |
| 1451 | - type: precision_at_3 |
| 1452 | value: 43.313 |
| 1453 | - type: precision_at_5 |
| 1454 | value: 28.165000000000003 |
| 1455 | - type: recall_at_1 |
| 1456 | value: 39.359 |
| 1457 | - type: recall_at_10 |
| 1458 | value: 76.003 |
| 1459 | - type: recall_at_100 |
| 1460 | value: 88.197 |
| 1461 | - type: recall_at_1000 |
| 1462 | value: 95.003 |
| 1463 | - type: recall_at_3 |
| 1464 | value: 64.97 |
| 1465 | - type: recall_at_5 |
| 1466 | value: 70.41199999999999 |
| 1467 | - task: |
| 1468 | type: Classification |
| 1469 | dataset: |
| 1470 | type: mteb/imdb |
| 1471 | name: MTEB ImdbClassification |
| 1472 | config: default |
| 1473 | split: test |
| 1474 | revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| 1475 | metrics: |
| 1476 | - type: accuracy |
| 1477 | value: 92.83200000000001 |
| 1478 | - type: ap |
| 1479 | value: 89.33560571859861 |
| 1480 | - type: f1 |
| 1481 | value: 92.82322915005167 |
| 1482 | - task: |
| 1483 | type: Retrieval |
| 1484 | dataset: |
| 1485 | type: msmarco |
| 1486 | name: MTEB MSMARCO |
| 1487 | config: default |
| 1488 | split: dev |
| 1489 | revision: None |
| 1490 | metrics: |
| 1491 | - type: map_at_1 |
| 1492 | value: 21.983 |
| 1493 | - type: map_at_10 |
| 1494 | value: 34.259 |
| 1495 | - type: map_at_100 |
| 1496 | value: 35.432 |
| 1497 | - type: map_at_1000 |
| 1498 | value: 35.482 |
| 1499 | - type: map_at_3 |
| 1500 | value: 30.275999999999996 |
| 1501 | - type: map_at_5 |
| 1502 | value: 32.566 |
| 1503 | - type: mrr_at_1 |
| 1504 | value: 22.579 |
| 1505 | - type: mrr_at_10 |
| 1506 | value: 34.882999999999996 |
| 1507 | - type: mrr_at_100 |
| 1508 | value: 35.984 |
| 1509 | - type: mrr_at_1000 |
| 1510 | value: 36.028 |
| 1511 | - type: mrr_at_3 |
| 1512 | value: 30.964999999999996 |
| 1513 | - type: mrr_at_5 |
| 1514 | value: 33.245000000000005 |
| 1515 | - type: ndcg_at_1 |
| 1516 | value: 22.564 |
| 1517 | - type: ndcg_at_10 |
| 1518 | value: 41.258 |
| 1519 | - type: ndcg_at_100 |
| 1520 | value: 46.824 |
| 1521 | - type: ndcg_at_1000 |
| 1522 | value: 48.037 |
| 1523 | - type: ndcg_at_3 |
| 1524 | value: 33.17 |
| 1525 | - type: ndcg_at_5 |
| 1526 | value: 37.263000000000005 |
| 1527 | - type: precision_at_1 |
| 1528 | value: 22.564 |
| 1529 | - type: precision_at_10 |
| 1530 | value: 6.572 |
| 1531 | - type: precision_at_100 |
| 1532 | value: 0.935 |
| 1533 | - type: precision_at_1000 |
| 1534 | value: 0.104 |
| 1535 | - type: precision_at_3 |
| 1536 | value: 14.130999999999998 |
| 1537 | - type: precision_at_5 |
| 1538 | value: 10.544 |
| 1539 | - type: recall_at_1 |
| 1540 | value: 21.983 |
| 1541 | - type: recall_at_10 |
| 1542 | value: 62.775000000000006 |
| 1543 | - type: recall_at_100 |
| 1544 | value: 88.389 |
| 1545 | - type: recall_at_1000 |
| 1546 | value: 97.603 |
| 1547 | - type: recall_at_3 |
| 1548 | value: 40.878 |
| 1549 | - type: recall_at_5 |
| 1550 | value: 50.690000000000005 |
| 1551 | - task: |
| 1552 | type: Classification |
| 1553 | dataset: |
| 1554 | type: mteb/mtop_domain |
| 1555 | name: MTEB MTOPDomainClassification (en) |
| 1556 | config: en |
| 1557 | split: test |
| 1558 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 1559 | metrics: |
| 1560 | - type: accuracy |
| 1561 | value: 93.95120839033288 |
| 1562 | - type: f1 |
| 1563 | value: 93.73824125055208 |
| 1564 | - task: |
| 1565 | type: Classification |
| 1566 | dataset: |
| 1567 | type: mteb/mtop_intent |
| 1568 | name: MTEB MTOPIntentClassification (en) |
| 1569 | config: en |
| 1570 | split: test |
| 1571 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 1572 | metrics: |
| 1573 | - type: accuracy |
| 1574 | value: 76.78978568171455 |
| 1575 | - type: f1 |
| 1576 | value: 57.50180552858304 |
| 1577 | - task: |
| 1578 | type: Classification |
| 1579 | dataset: |
| 1580 | type: mteb/amazon_massive_intent |
| 1581 | name: MTEB MassiveIntentClassification (en) |
| 1582 | config: en |
| 1583 | split: test |
| 1584 | revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| 1585 | metrics: |
| 1586 | - type: accuracy |
| 1587 | value: 76.24411566913248 |
| 1588 | - type: f1 |
| 1589 | value: 74.37851403532832 |
| 1590 | - task: |
| 1591 | type: Classification |
| 1592 | dataset: |
| 1593 | type: mteb/amazon_massive_scenario |
| 1594 | name: MTEB MassiveScenarioClassification (en) |
| 1595 | config: en |
| 1596 | split: test |
| 1597 | revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| 1598 | metrics: |
| 1599 | - type: accuracy |
| 1600 | value: 79.94620040349699 |
| 1601 | - type: f1 |
| 1602 | value: 80.21293397970435 |
| 1603 | - task: |
| 1604 | type: Clustering |
| 1605 | dataset: |
| 1606 | type: mteb/medrxiv-clustering-p2p |
| 1607 | name: MTEB MedrxivClusteringP2P |
| 1608 | config: default |
| 1609 | split: test |
| 1610 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 1611 | metrics: |
| 1612 | - type: v_measure |
| 1613 | value: 33.44403096245675 |
| 1614 | - task: |
| 1615 | type: Clustering |
| 1616 | dataset: |
| 1617 | type: mteb/medrxiv-clustering-s2s |
| 1618 | name: MTEB MedrxivClusteringS2S |
| 1619 | config: default |
| 1620 | split: test |
| 1621 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 1622 | metrics: |
| 1623 | - type: v_measure |
| 1624 | value: 31.659594631336812 |
| 1625 | - task: |
| 1626 | type: Reranking |
| 1627 | dataset: |
| 1628 | type: mteb/mind_small |
| 1629 | name: MTEB MindSmallReranking |
| 1630 | config: default |
| 1631 | split: test |
| 1632 | revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| 1633 | metrics: |
| 1634 | - type: map |
| 1635 | value: 32.53833075108798 |
| 1636 | - type: mrr |
| 1637 | value: 33.78840823218308 |
| 1638 | - task: |
| 1639 | type: Retrieval |
| 1640 | dataset: |
| 1641 | type: nfcorpus |
| 1642 | name: MTEB NFCorpus |
| 1643 | config: default |
| 1644 | split: test |
| 1645 | revision: None |
| 1646 | metrics: |
| 1647 | - type: map_at_1 |
| 1648 | value: 7.185999999999999 |
| 1649 | - type: map_at_10 |
| 1650 | value: 15.193999999999999 |
| 1651 | - type: map_at_100 |
| 1652 | value: 19.538 |
| 1653 | - type: map_at_1000 |
| 1654 | value: 21.178 |
| 1655 | - type: map_at_3 |
| 1656 | value: 11.208 |
| 1657 | - type: map_at_5 |
| 1658 | value: 12.745999999999999 |
| 1659 | - type: mrr_at_1 |
| 1660 | value: 48.916 |
| 1661 | - type: mrr_at_10 |
| 1662 | value: 58.141 |
| 1663 | - type: mrr_at_100 |
| 1664 | value: 58.656 |
| 1665 | - type: mrr_at_1000 |
| 1666 | value: 58.684999999999995 |
| 1667 | - type: mrr_at_3 |
| 1668 | value: 55.521 |
| 1669 | - type: mrr_at_5 |
| 1670 | value: 57.239 |
| 1671 | - type: ndcg_at_1 |
| 1672 | value: 47.059 |
| 1673 | - type: ndcg_at_10 |
| 1674 | value: 38.644 |
| 1675 | - type: ndcg_at_100 |
| 1676 | value: 36.272999999999996 |
| 1677 | - type: ndcg_at_1000 |
| 1678 | value: 44.996 |
| 1679 | - type: ndcg_at_3 |
| 1680 | value: 43.293 |
| 1681 | - type: ndcg_at_5 |
| 1682 | value: 40.819 |
| 1683 | - type: precision_at_1 |
| 1684 | value: 48.916 |
| 1685 | - type: precision_at_10 |
| 1686 | value: 28.607 |
| 1687 | - type: precision_at_100 |
| 1688 | value: 9.195 |
| 1689 | - type: precision_at_1000 |
| 1690 | value: 2.225 |
| 1691 | - type: precision_at_3 |
| 1692 | value: 40.454 |
| 1693 | - type: precision_at_5 |
| 1694 | value: 34.985 |
| 1695 | - type: recall_at_1 |
| 1696 | value: 7.185999999999999 |
| 1697 | - type: recall_at_10 |
| 1698 | value: 19.654 |
| 1699 | - type: recall_at_100 |
| 1700 | value: 37.224000000000004 |
| 1701 | - type: recall_at_1000 |
| 1702 | value: 68.663 |
| 1703 | - type: recall_at_3 |
| 1704 | value: 12.158 |
| 1705 | - type: recall_at_5 |
| 1706 | value: 14.674999999999999 |
| 1707 | - task: |
| 1708 | type: Retrieval |
| 1709 | dataset: |
| 1710 | type: nq |
| 1711 | name: MTEB NQ |
| 1712 | config: default |
| 1713 | split: test |
| 1714 | revision: None |
| 1715 | metrics: |
| 1716 | - type: map_at_1 |
| 1717 | value: 31.552000000000003 |
| 1718 | - type: map_at_10 |
| 1719 | value: 47.75 |
| 1720 | - type: map_at_100 |
| 1721 | value: 48.728 |
| 1722 | - type: map_at_1000 |
| 1723 | value: 48.754 |
| 1724 | - type: map_at_3 |
| 1725 | value: 43.156 |
| 1726 | - type: map_at_5 |
| 1727 | value: 45.883 |
| 1728 | - type: mrr_at_1 |
| 1729 | value: 35.66 |
| 1730 | - type: mrr_at_10 |
| 1731 | value: 50.269 |
| 1732 | - type: mrr_at_100 |
| 1733 | value: 50.974 |
| 1734 | - type: mrr_at_1000 |
| 1735 | value: 50.991 |
| 1736 | - type: mrr_at_3 |
| 1737 | value: 46.519 |
| 1738 | - type: mrr_at_5 |
| 1739 | value: 48.764 |
| 1740 | - type: ndcg_at_1 |
| 1741 | value: 35.632000000000005 |
| 1742 | - type: ndcg_at_10 |
| 1743 | value: 55.786 |
| 1744 | - type: ndcg_at_100 |
| 1745 | value: 59.748999999999995 |
| 1746 | - type: ndcg_at_1000 |
| 1747 | value: 60.339 |
| 1748 | - type: ndcg_at_3 |
| 1749 | value: 47.292 |
| 1750 | - type: ndcg_at_5 |
| 1751 | value: 51.766999999999996 |
| 1752 | - type: precision_at_1 |
| 1753 | value: 35.632000000000005 |
| 1754 | - type: precision_at_10 |
| 1755 | value: 9.267 |
| 1756 | - type: precision_at_100 |
| 1757 | value: 1.149 |
| 1758 | - type: precision_at_1000 |
| 1759 | value: 0.12 |
| 1760 | - type: precision_at_3 |
| 1761 | value: 21.601 |
| 1762 | - type: precision_at_5 |
| 1763 | value: 15.539 |
| 1764 | - type: recall_at_1 |
| 1765 | value: 31.552000000000003 |
| 1766 | - type: recall_at_10 |
| 1767 | value: 77.62400000000001 |
| 1768 | - type: recall_at_100 |
| 1769 | value: 94.527 |
| 1770 | - type: recall_at_1000 |
| 1771 | value: 98.919 |
| 1772 | - type: recall_at_3 |
| 1773 | value: 55.898 |
| 1774 | - type: recall_at_5 |
| 1775 | value: 66.121 |
| 1776 | - task: |
| 1777 | type: Retrieval |
| 1778 | dataset: |
| 1779 | type: quora |
| 1780 | name: MTEB QuoraRetrieval |
| 1781 | config: default |
| 1782 | split: test |
| 1783 | revision: None |
| 1784 | metrics: |
| 1785 | - type: map_at_1 |
| 1786 | value: 71.414 |
| 1787 | - type: map_at_10 |
| 1788 | value: 85.37400000000001 |
| 1789 | - type: map_at_100 |
| 1790 | value: 86.01100000000001 |
| 1791 | - type: map_at_1000 |
| 1792 | value: 86.027 |
| 1793 | - type: map_at_3 |
| 1794 | value: 82.562 |
| 1795 | - type: map_at_5 |
| 1796 | value: 84.284 |
| 1797 | - type: mrr_at_1 |
| 1798 | value: 82.24000000000001 |
| 1799 | - type: mrr_at_10 |
| 1800 | value: 88.225 |
| 1801 | - type: mrr_at_100 |
| 1802 | value: 88.324 |
| 1803 | - type: mrr_at_1000 |
| 1804 | value: 88.325 |
| 1805 | - type: mrr_at_3 |
| 1806 | value: 87.348 |
| 1807 | - type: mrr_at_5 |
| 1808 | value: 87.938 |
| 1809 | - type: ndcg_at_1 |
| 1810 | value: 82.24000000000001 |
| 1811 | - type: ndcg_at_10 |
| 1812 | value: 88.97699999999999 |
| 1813 | - type: ndcg_at_100 |
| 1814 | value: 90.16 |
| 1815 | - type: ndcg_at_1000 |
| 1816 | value: 90.236 |
| 1817 | - type: ndcg_at_3 |
| 1818 | value: 86.371 |
| 1819 | - type: ndcg_at_5 |
| 1820 | value: 87.746 |
| 1821 | - type: precision_at_1 |
| 1822 | value: 82.24000000000001 |
| 1823 | - type: precision_at_10 |
| 1824 | value: 13.481000000000002 |
| 1825 | - type: precision_at_100 |
| 1826 | value: 1.534 |
| 1827 | - type: precision_at_1000 |
| 1828 | value: 0.157 |
| 1829 | - type: precision_at_3 |
| 1830 | value: 37.86 |
| 1831 | - type: precision_at_5 |
| 1832 | value: 24.738 |
| 1833 | - type: recall_at_1 |
| 1834 | value: 71.414 |
| 1835 | - type: recall_at_10 |
| 1836 | value: 95.735 |
| 1837 | - type: recall_at_100 |
| 1838 | value: 99.696 |
| 1839 | - type: recall_at_1000 |
| 1840 | value: 99.979 |
| 1841 | - type: recall_at_3 |
| 1842 | value: 88.105 |
| 1843 | - type: recall_at_5 |
| 1844 | value: 92.17999999999999 |
| 1845 | - task: |
| 1846 | type: Clustering |
| 1847 | dataset: |
| 1848 | type: mteb/reddit-clustering |
| 1849 | name: MTEB RedditClustering |
| 1850 | config: default |
| 1851 | split: test |
| 1852 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 1853 | metrics: |
| 1854 | - type: v_measure |
| 1855 | value: 60.22146692057259 |
| 1856 | - task: |
| 1857 | type: Clustering |
| 1858 | dataset: |
| 1859 | type: mteb/reddit-clustering-p2p |
| 1860 | name: MTEB RedditClusteringP2P |
| 1861 | config: default |
| 1862 | split: test |
| 1863 | revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| 1864 | metrics: |
| 1865 | - type: v_measure |
| 1866 | value: 65.29273320614578 |
| 1867 | - task: |
| 1868 | type: Retrieval |
| 1869 | dataset: |
| 1870 | type: scidocs |
| 1871 | name: MTEB SCIDOCS |
| 1872 | config: default |
| 1873 | split: test |
| 1874 | revision: None |
| 1875 | metrics: |
| 1876 | - type: map_at_1 |
| 1877 | value: 5.023 |
| 1878 | - type: map_at_10 |
| 1879 | value: 14.161000000000001 |
| 1880 | - type: map_at_100 |
| 1881 | value: 16.68 |
| 1882 | - type: map_at_1000 |
| 1883 | value: 17.072000000000003 |
| 1884 | - type: map_at_3 |
| 1885 | value: 9.763 |
| 1886 | - type: map_at_5 |
| 1887 | value: 11.977 |
| 1888 | - type: mrr_at_1 |
| 1889 | value: 24.8 |
| 1890 | - type: mrr_at_10 |
| 1891 | value: 37.602999999999994 |
| 1892 | - type: mrr_at_100 |
| 1893 | value: 38.618 |
| 1894 | - type: mrr_at_1000 |
| 1895 | value: 38.659 |
| 1896 | - type: mrr_at_3 |
| 1897 | value: 34.117 |
| 1898 | - type: mrr_at_5 |
| 1899 | value: 36.082 |
| 1900 | - type: ndcg_at_1 |
| 1901 | value: 24.8 |
| 1902 | - type: ndcg_at_10 |
| 1903 | value: 23.316 |
| 1904 | - type: ndcg_at_100 |
| 1905 | value: 32.613 |
| 1906 | - type: ndcg_at_1000 |
| 1907 | value: 38.609 |
| 1908 | - type: ndcg_at_3 |
| 1909 | value: 21.697 |
| 1910 | - type: ndcg_at_5 |
| 1911 | value: 19.241 |
| 1912 | - type: precision_at_1 |
| 1913 | value: 24.8 |
| 1914 | - type: precision_at_10 |
| 1915 | value: 12.36 |
| 1916 | - type: precision_at_100 |
| 1917 | value: 2.593 |
| 1918 | - type: precision_at_1000 |
| 1919 | value: 0.402 |
| 1920 | - type: precision_at_3 |
| 1921 | value: 20.767 |
| 1922 | - type: precision_at_5 |
| 1923 | value: 17.34 |
| 1924 | - type: recall_at_1 |
| 1925 | value: 5.023 |
| 1926 | - type: recall_at_10 |
| 1927 | value: 25.069999999999997 |
| 1928 | - type: recall_at_100 |
| 1929 | value: 52.563 |
| 1930 | - type: recall_at_1000 |
| 1931 | value: 81.525 |
| 1932 | - type: recall_at_3 |
| 1933 | value: 12.613 |
| 1934 | - type: recall_at_5 |
| 1935 | value: 17.583 |
| 1936 | - task: |
| 1937 | type: STS |
| 1938 | dataset: |
| 1939 | type: mteb/sickr-sts |
| 1940 | name: MTEB SICK-R |
| 1941 | config: default |
| 1942 | split: test |
| 1943 | revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| 1944 | metrics: |
| 1945 | - type: cos_sim_pearson |
| 1946 | value: 87.71506247604255 |
| 1947 | - type: cos_sim_spearman |
| 1948 | value: 82.91813463738802 |
| 1949 | - type: euclidean_pearson |
| 1950 | value: 85.5154616194479 |
| 1951 | - type: euclidean_spearman |
| 1952 | value: 82.91815254466314 |
| 1953 | - type: manhattan_pearson |
| 1954 | value: 85.5280917850374 |
| 1955 | - type: manhattan_spearman |
| 1956 | value: 82.92276537286398 |
| 1957 | - task: |
| 1958 | type: STS |
| 1959 | dataset: |
| 1960 | type: mteb/sts12-sts |
| 1961 | name: MTEB STS12 |
| 1962 | config: default |
| 1963 | split: test |
| 1964 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 1965 | metrics: |
| 1966 | - type: cos_sim_pearson |
| 1967 | value: 87.43772054228462 |
| 1968 | - type: cos_sim_spearman |
| 1969 | value: 78.75750601716682 |
| 1970 | - type: euclidean_pearson |
| 1971 | value: 85.76074482955764 |
| 1972 | - type: euclidean_spearman |
| 1973 | value: 78.75651057223058 |
| 1974 | - type: manhattan_pearson |
| 1975 | value: 85.73390291701668 |
| 1976 | - type: manhattan_spearman |
| 1977 | value: 78.72699385957797 |
| 1978 | - task: |
| 1979 | type: STS |
| 1980 | dataset: |
| 1981 | type: mteb/sts13-sts |
| 1982 | name: MTEB STS13 |
| 1983 | config: default |
| 1984 | split: test |
| 1985 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 1986 | metrics: |
| 1987 | - type: cos_sim_pearson |
| 1988 | value: 89.58144067172472 |
| 1989 | - type: cos_sim_spearman |
| 1990 | value: 90.3524512966946 |
| 1991 | - type: euclidean_pearson |
| 1992 | value: 89.71365391594237 |
| 1993 | - type: euclidean_spearman |
| 1994 | value: 90.35239632843408 |
| 1995 | - type: manhattan_pearson |
| 1996 | value: 89.66905421746478 |
| 1997 | - type: manhattan_spearman |
| 1998 | value: 90.31508211683513 |
| 1999 | - task: |
| 2000 | type: STS |
| 2001 | dataset: |
| 2002 | type: mteb/sts14-sts |
| 2003 | name: MTEB STS14 |
| 2004 | config: default |
| 2005 | split: test |
| 2006 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 2007 | metrics: |
| 2008 | - type: cos_sim_pearson |
| 2009 | value: 87.77692637102102 |
| 2010 | - type: cos_sim_spearman |
| 2011 | value: 85.45710562643485 |
| 2012 | - type: euclidean_pearson |
| 2013 | value: 87.42456979928723 |
| 2014 | - type: euclidean_spearman |
| 2015 | value: 85.45709386240908 |
| 2016 | - type: manhattan_pearson |
| 2017 | value: 87.40754529526272 |
| 2018 | - type: manhattan_spearman |
| 2019 | value: 85.44834854173303 |
| 2020 | - task: |
| 2021 | type: STS |
| 2022 | dataset: |
| 2023 | type: mteb/sts15-sts |
| 2024 | name: MTEB STS15 |
| 2025 | config: default |
| 2026 | split: test |
| 2027 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 2028 | metrics: |
| 2029 | - type: cos_sim_pearson |
| 2030 | value: 88.28491331695997 |
| 2031 | - type: cos_sim_spearman |
| 2032 | value: 89.62037029566964 |
| 2033 | - type: euclidean_pearson |
| 2034 | value: 89.02479391362826 |
| 2035 | - type: euclidean_spearman |
| 2036 | value: 89.62036733618466 |
| 2037 | - type: manhattan_pearson |
| 2038 | value: 89.00394756040342 |
| 2039 | - type: manhattan_spearman |
| 2040 | value: 89.60867744215236 |
| 2041 | - task: |
| 2042 | type: STS |
| 2043 | dataset: |
| 2044 | type: mteb/sts16-sts |
| 2045 | name: MTEB STS16 |
| 2046 | config: default |
| 2047 | split: test |
| 2048 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 2049 | metrics: |
| 2050 | - type: cos_sim_pearson |
| 2051 | value: 85.08911381280191 |
| 2052 | - type: cos_sim_spearman |
| 2053 | value: 86.5791780765767 |
| 2054 | - type: euclidean_pearson |
| 2055 | value: 86.16063473577861 |
| 2056 | - type: euclidean_spearman |
| 2057 | value: 86.57917745378766 |
| 2058 | - type: manhattan_pearson |
| 2059 | value: 86.13677924604175 |
| 2060 | - type: manhattan_spearman |
| 2061 | value: 86.56115615768685 |
| 2062 | - task: |
| 2063 | type: STS |
| 2064 | dataset: |
| 2065 | type: mteb/sts17-crosslingual-sts |
| 2066 | name: MTEB STS17 (en-en) |
| 2067 | config: en-en |
| 2068 | split: test |
| 2069 | revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| 2070 | metrics: |
| 2071 | - type: cos_sim_pearson |
| 2072 | value: 89.58029496205235 |
| 2073 | - type: cos_sim_spearman |
| 2074 | value: 89.49551253826998 |
| 2075 | - type: euclidean_pearson |
| 2076 | value: 90.13714840963748 |
| 2077 | - type: euclidean_spearman |
| 2078 | value: 89.49551253826998 |
| 2079 | - type: manhattan_pearson |
| 2080 | value: 90.13039633601363 |
| 2081 | - type: manhattan_spearman |
| 2082 | value: 89.4513453745516 |
| 2083 | - task: |
| 2084 | type: STS |
| 2085 | dataset: |
| 2086 | type: mteb/sts22-crosslingual-sts |
| 2087 | name: MTEB STS22 (en) |
| 2088 | config: en |
| 2089 | split: test |
| 2090 | revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| 2091 | metrics: |
| 2092 | - type: cos_sim_pearson |
| 2093 | value: 69.01546399666435 |
| 2094 | - type: cos_sim_spearman |
| 2095 | value: 69.33824484595624 |
| 2096 | - type: euclidean_pearson |
| 2097 | value: 70.76511642998874 |
| 2098 | - type: euclidean_spearman |
| 2099 | value: 69.33824484595624 |
| 2100 | - type: manhattan_pearson |
| 2101 | value: 70.84320785047453 |
| 2102 | - type: manhattan_spearman |
| 2103 | value: 69.54233632223537 |
| 2104 | - task: |
| 2105 | type: STS |
| 2106 | dataset: |
| 2107 | type: mteb/stsbenchmark-sts |
| 2108 | name: MTEB STSBenchmark |
| 2109 | config: default |
| 2110 | split: test |
| 2111 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 2112 | metrics: |
| 2113 | - type: cos_sim_pearson |
| 2114 | value: 87.26389196390119 |
| 2115 | - type: cos_sim_spearman |
| 2116 | value: 89.09721478341385 |
| 2117 | - type: euclidean_pearson |
| 2118 | value: 88.97208685922517 |
| 2119 | - type: euclidean_spearman |
| 2120 | value: 89.09720927308881 |
| 2121 | - type: manhattan_pearson |
| 2122 | value: 88.97513670502573 |
| 2123 | - type: manhattan_spearman |
| 2124 | value: 89.07647853984004 |
| 2125 | - task: |
| 2126 | type: Reranking |
| 2127 | dataset: |
| 2128 | type: mteb/scidocs-reranking |
| 2129 | name: MTEB SciDocsRR |
| 2130 | config: default |
| 2131 | split: test |
| 2132 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 2133 | metrics: |
| 2134 | - type: map |
| 2135 | value: 87.53075025771936 |
| 2136 | - type: mrr |
| 2137 | value: 96.24327651288436 |
| 2138 | - task: |
| 2139 | type: Retrieval |
| 2140 | dataset: |
| 2141 | type: scifact |
| 2142 | name: MTEB SciFact |
| 2143 | config: default |
| 2144 | split: test |
| 2145 | revision: None |
| 2146 | metrics: |
| 2147 | - type: map_at_1 |
| 2148 | value: 60.428000000000004 |
| 2149 | - type: map_at_10 |
| 2150 | value: 70.088 |
| 2151 | - type: map_at_100 |
| 2152 | value: 70.589 |
| 2153 | - type: map_at_1000 |
| 2154 | value: 70.614 |
| 2155 | - type: map_at_3 |
| 2156 | value: 67.191 |
| 2157 | - type: map_at_5 |
| 2158 | value: 68.515 |
| 2159 | - type: mrr_at_1 |
| 2160 | value: 63.333 |
| 2161 | - type: mrr_at_10 |
| 2162 | value: 71.13000000000001 |
| 2163 | - type: mrr_at_100 |
| 2164 | value: 71.545 |
| 2165 | - type: mrr_at_1000 |
| 2166 | value: 71.569 |
| 2167 | - type: mrr_at_3 |
| 2168 | value: 68.944 |
| 2169 | - type: mrr_at_5 |
| 2170 | value: 70.078 |
| 2171 | - type: ndcg_at_1 |
| 2172 | value: 63.333 |
| 2173 | - type: ndcg_at_10 |
| 2174 | value: 74.72800000000001 |
| 2175 | - type: ndcg_at_100 |
| 2176 | value: 76.64999999999999 |
| 2177 | - type: ndcg_at_1000 |
| 2178 | value: 77.176 |
| 2179 | - type: ndcg_at_3 |
| 2180 | value: 69.659 |
| 2181 | - type: ndcg_at_5 |
| 2182 | value: 71.626 |
| 2183 | - type: precision_at_1 |
| 2184 | value: 63.333 |
| 2185 | - type: precision_at_10 |
| 2186 | value: 10 |
| 2187 | - type: precision_at_100 |
| 2188 | value: 1.09 |
| 2189 | - type: precision_at_1000 |
| 2190 | value: 0.11299999999999999 |
| 2191 | - type: precision_at_3 |
| 2192 | value: 27.111 |
| 2193 | - type: precision_at_5 |
| 2194 | value: 17.666999999999998 |
| 2195 | - type: recall_at_1 |
| 2196 | value: 60.428000000000004 |
| 2197 | - type: recall_at_10 |
| 2198 | value: 87.98899999999999 |
| 2199 | - type: recall_at_100 |
| 2200 | value: 96.167 |
| 2201 | - type: recall_at_1000 |
| 2202 | value: 100 |
| 2203 | - type: recall_at_3 |
| 2204 | value: 74.006 |
| 2205 | - type: recall_at_5 |
| 2206 | value: 79.05 |
| 2207 | - task: |
| 2208 | type: PairClassification |
| 2209 | dataset: |
| 2210 | type: mteb/sprintduplicatequestions-pairclassification |
| 2211 | name: MTEB SprintDuplicateQuestions |
| 2212 | config: default |
| 2213 | split: test |
| 2214 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 2215 | metrics: |
| 2216 | - type: cos_sim_accuracy |
| 2217 | value: 99.87326732673267 |
| 2218 | - type: cos_sim_ap |
| 2219 | value: 96.81770773701805 |
| 2220 | - type: cos_sim_f1 |
| 2221 | value: 93.6318407960199 |
| 2222 | - type: cos_sim_precision |
| 2223 | value: 93.16831683168317 |
| 2224 | - type: cos_sim_recall |
| 2225 | value: 94.1 |
| 2226 | - type: dot_accuracy |
| 2227 | value: 99.87326732673267 |
| 2228 | - type: dot_ap |
| 2229 | value: 96.8174218946665 |
| 2230 | - type: dot_f1 |
| 2231 | value: 93.6318407960199 |
| 2232 | - type: dot_precision |
| 2233 | value: 93.16831683168317 |
| 2234 | - type: dot_recall |
| 2235 | value: 94.1 |
| 2236 | - type: euclidean_accuracy |
| 2237 | value: 99.87326732673267 |
| 2238 | - type: euclidean_ap |
| 2239 | value: 96.81770773701807 |
| 2240 | - type: euclidean_f1 |
| 2241 | value: 93.6318407960199 |
| 2242 | - type: euclidean_precision |
| 2243 | value: 93.16831683168317 |
| 2244 | - type: euclidean_recall |
| 2245 | value: 94.1 |
| 2246 | - type: manhattan_accuracy |
| 2247 | value: 99.87227722772278 |
| 2248 | - type: manhattan_ap |
| 2249 | value: 96.83164126821747 |
| 2250 | - type: manhattan_f1 |
| 2251 | value: 93.54677338669335 |
| 2252 | - type: manhattan_precision |
| 2253 | value: 93.5935935935936 |
| 2254 | - type: manhattan_recall |
| 2255 | value: 93.5 |
| 2256 | - type: max_accuracy |
| 2257 | value: 99.87326732673267 |
| 2258 | - type: max_ap |
| 2259 | value: 96.83164126821747 |
| 2260 | - type: max_f1 |
| 2261 | value: 93.6318407960199 |
| 2262 | - task: |
| 2263 | type: Clustering |
| 2264 | dataset: |
| 2265 | type: mteb/stackexchange-clustering |
| 2266 | name: MTEB StackExchangeClustering |
| 2267 | config: default |
| 2268 | split: test |
| 2269 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 2270 | metrics: |
| 2271 | - type: v_measure |
| 2272 | value: 65.6212042420246 |
| 2273 | - task: |
| 2274 | type: Clustering |
| 2275 | dataset: |
| 2276 | type: mteb/stackexchange-clustering-p2p |
| 2277 | name: MTEB StackExchangeClusteringP2P |
| 2278 | config: default |
| 2279 | split: test |
| 2280 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 2281 | metrics: |
| 2282 | - type: v_measure |
| 2283 | value: 35.779230635982564 |
| 2284 | - task: |
| 2285 | type: Reranking |
| 2286 | dataset: |
| 2287 | type: mteb/stackoverflowdupquestions-reranking |
| 2288 | name: MTEB StackOverflowDupQuestions |
| 2289 | config: default |
| 2290 | split: test |
| 2291 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| 2292 | metrics: |
| 2293 | - type: map |
| 2294 | value: 55.217701909036286 |
| 2295 | - type: mrr |
| 2296 | value: 56.17658995416349 |
| 2297 | - task: |
| 2298 | type: Summarization |
| 2299 | dataset: |
| 2300 | type: mteb/summeval |
| 2301 | name: MTEB SummEval |
| 2302 | config: default |
| 2303 | split: test |
| 2304 | revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| 2305 | metrics: |
| 2306 | - type: cos_sim_pearson |
| 2307 | value: 30.954206018888453 |
| 2308 | - type: cos_sim_spearman |
| 2309 | value: 32.71062599450096 |
| 2310 | - type: dot_pearson |
| 2311 | value: 30.95420929056943 |
| 2312 | - type: dot_spearman |
| 2313 | value: 32.71062599450096 |
| 2314 | - task: |
| 2315 | type: Retrieval |
| 2316 | dataset: |
| 2317 | type: trec-covid |
| 2318 | name: MTEB TRECCOVID |
| 2319 | config: default |
| 2320 | split: test |
| 2321 | revision: None |
| 2322 | metrics: |
| 2323 | - type: map_at_1 |
| 2324 | value: 0.22699999999999998 |
| 2325 | - type: map_at_10 |
| 2326 | value: 1.924 |
| 2327 | - type: map_at_100 |
| 2328 | value: 10.525 |
| 2329 | - type: map_at_1000 |
| 2330 | value: 24.973 |
| 2331 | - type: map_at_3 |
| 2332 | value: 0.638 |
| 2333 | - type: map_at_5 |
| 2334 | value: 1.0659999999999998 |
| 2335 | - type: mrr_at_1 |
| 2336 | value: 84 |
| 2337 | - type: mrr_at_10 |
| 2338 | value: 91.067 |
| 2339 | - type: mrr_at_100 |
| 2340 | value: 91.067 |
| 2341 | - type: mrr_at_1000 |
| 2342 | value: 91.067 |
| 2343 | - type: mrr_at_3 |
| 2344 | value: 90.667 |
| 2345 | - type: mrr_at_5 |
| 2346 | value: 91.067 |
| 2347 | - type: ndcg_at_1 |
| 2348 | value: 81 |
| 2349 | - type: ndcg_at_10 |
| 2350 | value: 75.566 |
| 2351 | - type: ndcg_at_100 |
| 2352 | value: 56.387 |
| 2353 | - type: ndcg_at_1000 |
| 2354 | value: 49.834 |
| 2355 | - type: ndcg_at_3 |
| 2356 | value: 80.899 |
| 2357 | - type: ndcg_at_5 |
| 2358 | value: 80.75099999999999 |
| 2359 | - type: precision_at_1 |
| 2360 | value: 84 |
| 2361 | - type: precision_at_10 |
| 2362 | value: 79 |
| 2363 | - type: precision_at_100 |
| 2364 | value: 57.56 |
| 2365 | - type: precision_at_1000 |
| 2366 | value: 21.8 |
| 2367 | - type: precision_at_3 |
| 2368 | value: 84.667 |
| 2369 | - type: precision_at_5 |
| 2370 | value: 85.2 |
| 2371 | - type: recall_at_1 |
| 2372 | value: 0.22699999999999998 |
| 2373 | - type: recall_at_10 |
| 2374 | value: 2.136 |
| 2375 | - type: recall_at_100 |
| 2376 | value: 13.861 |
| 2377 | - type: recall_at_1000 |
| 2378 | value: 46.299 |
| 2379 | - type: recall_at_3 |
| 2380 | value: 0.6649999999999999 |
| 2381 | - type: recall_at_5 |
| 2382 | value: 1.145 |
| 2383 | - task: |
| 2384 | type: Retrieval |
| 2385 | dataset: |
| 2386 | type: webis-touche2020 |
| 2387 | name: MTEB Touche2020 |
| 2388 | config: default |
| 2389 | split: test |
| 2390 | revision: None |
| 2391 | metrics: |
| 2392 | - type: map_at_1 |
| 2393 | value: 2.752 |
| 2394 | - type: map_at_10 |
| 2395 | value: 9.951 |
| 2396 | - type: map_at_100 |
| 2397 | value: 16.794999999999998 |
| 2398 | - type: map_at_1000 |
| 2399 | value: 18.251 |
| 2400 | - type: map_at_3 |
| 2401 | value: 5.288 |
| 2402 | - type: map_at_5 |
| 2403 | value: 6.954000000000001 |
| 2404 | - type: mrr_at_1 |
| 2405 | value: 38.775999999999996 |
| 2406 | - type: mrr_at_10 |
| 2407 | value: 50.458000000000006 |
| 2408 | - type: mrr_at_100 |
| 2409 | value: 51.324999999999996 |
| 2410 | - type: mrr_at_1000 |
| 2411 | value: 51.339999999999996 |
| 2412 | - type: mrr_at_3 |
| 2413 | value: 46.939 |
| 2414 | - type: mrr_at_5 |
| 2415 | value: 47.857 |
| 2416 | - type: ndcg_at_1 |
| 2417 | value: 36.735 |
| 2418 | - type: ndcg_at_10 |
| 2419 | value: 25.198999999999998 |
| 2420 | - type: ndcg_at_100 |
| 2421 | value: 37.938 |
| 2422 | - type: ndcg_at_1000 |
| 2423 | value: 49.145 |
| 2424 | - type: ndcg_at_3 |
| 2425 | value: 29.348000000000003 |
| 2426 | - type: ndcg_at_5 |
| 2427 | value: 25.804 |
| 2428 | - type: precision_at_1 |
| 2429 | value: 38.775999999999996 |
| 2430 | - type: precision_at_10 |
| 2431 | value: 22.041 |
| 2432 | - type: precision_at_100 |
| 2433 | value: 7.939 |
| 2434 | - type: precision_at_1000 |
| 2435 | value: 1.555 |
| 2436 | - type: precision_at_3 |
| 2437 | value: 29.932 |
| 2438 | - type: precision_at_5 |
| 2439 | value: 24.490000000000002 |
| 2440 | - type: recall_at_1 |
| 2441 | value: 2.752 |
| 2442 | - type: recall_at_10 |
| 2443 | value: 16.197 |
| 2444 | - type: recall_at_100 |
| 2445 | value: 49.166 |
| 2446 | - type: recall_at_1000 |
| 2447 | value: 84.18900000000001 |
| 2448 | - type: recall_at_3 |
| 2449 | value: 6.438000000000001 |
| 2450 | - type: recall_at_5 |
| 2451 | value: 9.093 |
| 2452 | - task: |
| 2453 | type: Classification |
| 2454 | dataset: |
| 2455 | type: mteb/toxic_conversations_50k |
| 2456 | name: MTEB ToxicConversationsClassification |
| 2457 | config: default |
| 2458 | split: test |
| 2459 | revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| 2460 | metrics: |
| 2461 | - type: accuracy |
| 2462 | value: 71.47980000000001 |
| 2463 | - type: ap |
| 2464 | value: 14.605194452178754 |
| 2465 | - type: f1 |
| 2466 | value: 55.07362924988948 |
| 2467 | - task: |
| 2468 | type: Classification |
| 2469 | dataset: |
| 2470 | type: mteb/tweet_sentiment_extraction |
| 2471 | name: MTEB TweetSentimentExtractionClassification |
| 2472 | config: default |
| 2473 | split: test |
| 2474 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 2475 | metrics: |
| 2476 | - type: accuracy |
| 2477 | value: 59.708545557441994 |
| 2478 | - type: f1 |
| 2479 | value: 60.04751270975683 |
| 2480 | - task: |
| 2481 | type: Clustering |
| 2482 | dataset: |
| 2483 | type: mteb/twentynewsgroups-clustering |
| 2484 | name: MTEB TwentyNewsgroupsClustering |
| 2485 | config: default |
| 2486 | split: test |
| 2487 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 2488 | metrics: |
| 2489 | - type: v_measure |
| 2490 | value: 53.21105960597211 |
| 2491 | - task: |
| 2492 | type: PairClassification |
| 2493 | dataset: |
| 2494 | type: mteb/twittersemeval2015-pairclassification |
| 2495 | name: MTEB TwitterSemEval2015 |
| 2496 | config: default |
| 2497 | split: test |
| 2498 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 2499 | metrics: |
| 2500 | - type: cos_sim_accuracy |
| 2501 | value: 87.58419264469214 |
| 2502 | - type: cos_sim_ap |
| 2503 | value: 78.55300004517404 |
| 2504 | - type: cos_sim_f1 |
| 2505 | value: 71.49673530889001 |
| 2506 | - type: cos_sim_precision |
| 2507 | value: 68.20795400095831 |
| 2508 | - type: cos_sim_recall |
| 2509 | value: 75.11873350923483 |
| 2510 | - type: dot_accuracy |
| 2511 | value: 87.58419264469214 |
| 2512 | - type: dot_ap |
| 2513 | value: 78.55297659559511 |
| 2514 | - type: dot_f1 |
| 2515 | value: 71.49673530889001 |
| 2516 | - type: dot_precision |
| 2517 | value: 68.20795400095831 |
| 2518 | - type: dot_recall |
| 2519 | value: 75.11873350923483 |
| 2520 | - type: euclidean_accuracy |
| 2521 | value: 87.58419264469214 |
| 2522 | - type: euclidean_ap |
| 2523 | value: 78.55300477331477 |
| 2524 | - type: euclidean_f1 |
| 2525 | value: 71.49673530889001 |
| 2526 | - type: euclidean_precision |
| 2527 | value: 68.20795400095831 |
| 2528 | - type: euclidean_recall |
| 2529 | value: 75.11873350923483 |
| 2530 | - type: manhattan_accuracy |
| 2531 | value: 87.5663110210407 |
| 2532 | - type: manhattan_ap |
| 2533 | value: 78.49982050876562 |
| 2534 | - type: manhattan_f1 |
| 2535 | value: 71.35488740722104 |
| 2536 | - type: manhattan_precision |
| 2537 | value: 68.18946862226497 |
| 2538 | - type: manhattan_recall |
| 2539 | value: 74.82849604221636 |
| 2540 | - type: max_accuracy |
| 2541 | value: 87.58419264469214 |
| 2542 | - type: max_ap |
| 2543 | value: 78.55300477331477 |
| 2544 | - type: max_f1 |
| 2545 | value: 71.49673530889001 |
| 2546 | - task: |
| 2547 | type: PairClassification |
| 2548 | dataset: |
| 2549 | type: mteb/twitterurlcorpus-pairclassification |
| 2550 | name: MTEB TwitterURLCorpus |
| 2551 | config: default |
| 2552 | split: test |
| 2553 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 2554 | metrics: |
| 2555 | - type: cos_sim_accuracy |
| 2556 | value: 89.09069740365584 |
| 2557 | - type: cos_sim_ap |
| 2558 | value: 86.22749303724757 |
| 2559 | - type: cos_sim_f1 |
| 2560 | value: 78.36863452005407 |
| 2561 | - type: cos_sim_precision |
| 2562 | value: 76.49560117302053 |
| 2563 | - type: cos_sim_recall |
| 2564 | value: 80.33569448721897 |
| 2565 | - type: dot_accuracy |
| 2566 | value: 89.09069740365584 |
| 2567 | - type: dot_ap |
| 2568 | value: 86.22750233655673 |
| 2569 | - type: dot_f1 |
| 2570 | value: 78.36863452005407 |
| 2571 | - type: dot_precision |
| 2572 | value: 76.49560117302053 |
| 2573 | - type: dot_recall |
| 2574 | value: 80.33569448721897 |
| 2575 | - type: euclidean_accuracy |
| 2576 | value: 89.09069740365584 |
| 2577 | - type: euclidean_ap |
| 2578 | value: 86.22749355597347 |
| 2579 | - type: euclidean_f1 |
| 2580 | value: 78.36863452005407 |
| 2581 | - type: euclidean_precision |
| 2582 | value: 76.49560117302053 |
| 2583 | - type: euclidean_recall |
| 2584 | value: 80.33569448721897 |
| 2585 | - type: manhattan_accuracy |
| 2586 | value: 89.08293553770326 |
| 2587 | - type: manhattan_ap |
| 2588 | value: 86.21913616084771 |
| 2589 | - type: manhattan_f1 |
| 2590 | value: 78.3907031479847 |
| 2591 | - type: manhattan_precision |
| 2592 | value: 75.0352013517319 |
| 2593 | - type: manhattan_recall |
| 2594 | value: 82.06036341238065 |
| 2595 | - type: max_accuracy |
| 2596 | value: 89.09069740365584 |
| 2597 | - type: max_ap |
| 2598 | value: 86.22750233655673 |
| 2599 | - type: max_f1 |
| 2600 | value: 78.3907031479847 |
| 2601 | license: apache-2.0 |
| 2602 | language: |
| 2603 | - en |
| 2604 | library_name: sentence-transformers |
| 2605 | pipeline_tag: feature-extraction |
| 2606 | --- |
| 2607 | |
| 2608 | <br><br> |
| 2609 | |
| 2610 | <p align="center"> |
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| 2612 | </p> |
| 2613 | |
| 2614 | <p align="center"> |
| 2615 | <b>The crispy sentence embedding family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b> |
| 2616 | </p> |
| 2617 | |
| 2618 | <p align="center"> |
| 2619 | <sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> |
| 2620 | </p> |
| 2621 | |
| 2622 | |
| 2623 | # mixedbread-ai/mxbai-embed-large-v1 |
| 2624 | |
| 2625 | Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl). |
| 2626 | |
| 2627 | ## Quickstart |
| 2628 | |
| 2629 | Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages: ` for query if you want to use it for retrieval. Besides that you don't need any prompt. |
| 2630 | |
| 2631 | ### sentence-transformers |
| 2632 | |
| 2633 | ``` |
| 2634 | python -m pip install -U sentence-transformers |
| 2635 | ``` |
| 2636 | |
| 2637 | ```python |
| 2638 | from sentence_transformers import SentenceTransformer |
| 2639 | from sentence_transformers.util import cos_sim |
| 2640 | from sentence_transformers.quantization import quantize_embeddings |
| 2641 | |
| 2642 | # 1. Specify preffered dimensions |
| 2643 | dimensions = 512 |
| 2644 | |
| 2645 | # 2. load model |
| 2646 | model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions) |
| 2647 | |
| 2648 | # The prompt used for query retrieval tasks: |
| 2649 | # query_prompt = 'Represent this sentence for searching relevant passages: ' |
| 2650 | |
| 2651 | query = "A man is eating a piece of bread" |
| 2652 | docs = [ |
| 2653 | "A man is eating food.", |
| 2654 | "A man is eating pasta.", |
| 2655 | "The girl is carrying a baby.", |
| 2656 | "A man is riding a horse.", |
| 2657 | ] |
| 2658 | |
| 2659 | # 2. Encode |
| 2660 | query_embedding = model.encode(query, prompt_name="query") |
| 2661 | # Equivalent Alternatives: |
| 2662 | # query_embedding = model.encode(query_prompt + query) |
| 2663 | # query_embedding = model.encode(query, prompt=query_prompt) |
| 2664 | |
| 2665 | docs_embeddings = model.encode(docs) |
| 2666 | |
| 2667 | # Optional: Quantize the embeddings |
| 2668 | binary_query_embedding = quantize_embeddings(query_embedding, precision="ubinary") |
| 2669 | binary_docs_embeddings = quantize_embeddings(docs_embeddings, precision="ubinary") |
| 2670 | |
| 2671 | similarities = cos_sim(query_embedding, docs_embeddings) |
| 2672 | print('similarities:', similarities) |
| 2673 | ``` |
| 2674 | |
| 2675 | ### Transformers |
| 2676 | |
| 2677 | ```python |
| 2678 | from typing import Dict |
| 2679 | |
| 2680 | import torch |
| 2681 | import numpy as np |
| 2682 | from transformers import AutoModel, AutoTokenizer |
| 2683 | from sentence_transformers.util import cos_sim |
| 2684 | |
| 2685 | # For retrieval you need to pass this prompt. Please find our more in our blog post. |
| 2686 | def transform_query(query: str) -> str: |
| 2687 | """ For retrieval, add the prompt for query (not for documents). |
| 2688 | """ |
| 2689 | return f'Represent this sentence for searching relevant passages: {query}' |
| 2690 | |
| 2691 | # The model works really well with cls pooling (default) but also with mean pooling. |
| 2692 | def pooling(outputs: torch.Tensor, inputs: Dict, strategy: str = 'cls') -> np.ndarray: |
| 2693 | if strategy == 'cls': |
| 2694 | outputs = outputs[:, 0] |
| 2695 | elif strategy == 'mean': |
| 2696 | outputs = torch.sum( |
| 2697 | outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"], dim=1, keepdim=True) |
| 2698 | else: |
| 2699 | raise NotImplementedError |
| 2700 | return outputs.detach().cpu().numpy() |
| 2701 | |
| 2702 | # 1. load model |
| 2703 | model_id = 'mixedbread-ai/mxbai-embed-large-v1' |
| 2704 | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| 2705 | model = AutoModel.from_pretrained(model_id).cuda() |
| 2706 | |
| 2707 | |
| 2708 | docs = [ |
| 2709 | transform_query('A man is eating a piece of bread'), |
| 2710 | "A man is eating food.", |
| 2711 | "A man is eating pasta.", |
| 2712 | "The girl is carrying a baby.", |
| 2713 | "A man is riding a horse.", |
| 2714 | ] |
| 2715 | |
| 2716 | # 2. encode |
| 2717 | inputs = tokenizer(docs, padding=True, return_tensors='pt') |
| 2718 | for k, v in inputs.items(): |
| 2719 | inputs[k] = v.cuda() |
| 2720 | outputs = model(**inputs).last_hidden_state |
| 2721 | embeddings = pooling(outputs, inputs, 'cls') |
| 2722 | |
| 2723 | similarities = cos_sim(embeddings[0], embeddings[1:]) |
| 2724 | print('similarities:', similarities) |
| 2725 | ``` |
| 2726 | |
| 2727 | ### Transformers.js |
| 2728 | |
| 2729 | If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: |
| 2730 | |
| 2731 | ```sh |
| 2732 | npm i @huggingface/transformers |
| 2733 | ``` |
| 2734 | |
| 2735 | You can then use the model to compute embeddings like this: |
| 2736 | |
| 2737 | ```javascript |
| 2738 | import { pipeline, cos_sim } from "@huggingface/transformers"; |
| 2739 | |
| 2740 | // Create a feature extraction pipeline |
| 2741 | const extractor = await pipeline("feature-extraction", "mixedbread-ai/mxbai-embed-large-v1", { |
| 2742 | dtype: "fp32", // Options: "fp32", "fp16", "q8" |
| 2743 | }); |
| 2744 | |
| 2745 | // Generate sentence embeddings |
| 2746 | const docs = [ |
| 2747 | "Represent this sentence for searching relevant passages: A man is eating a piece of bread", |
| 2748 | "A man is eating food.", |
| 2749 | "A man is eating pasta.", |
| 2750 | "The girl is carrying a baby.", |
| 2751 | "A man is riding a horse.", |
| 2752 | ] |
| 2753 | const output = await extractor(docs, { pooling: "cls" }); |
| 2754 | |
| 2755 | // Compute similarity scores |
| 2756 | const [source_embeddings, ...document_embeddings ] = output.tolist(); |
| 2757 | const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x)); |
| 2758 | console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027] |
| 2759 | ``` |
| 2760 | |
| 2761 | ### Using API |
| 2762 | |
| 2763 | You can use the model via our API as follows: |
| 2764 | |
| 2765 | ```python |
| 2766 | from mixedbread_ai.client import MixedbreadAI, EncodingFormat |
| 2767 | from sklearn.metrics.pairwise import cosine_similarity |
| 2768 | import os |
| 2769 | |
| 2770 | mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") |
| 2771 | |
| 2772 | english_sentences = [ |
| 2773 | 'What is the capital of Australia?', |
| 2774 | 'Canberra is the capital of Australia.' |
| 2775 | ] |
| 2776 | |
| 2777 | res = mxbai.embeddings( |
| 2778 | input=english_sentences, |
| 2779 | model="mixedbread-ai/mxbai-embed-large-v1", |
| 2780 | normalized=True, |
| 2781 | encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8], |
| 2782 | dimensions=512 |
| 2783 | ) |
| 2784 | |
| 2785 | encoded_embeddings = res.data[0].embedding |
| 2786 | print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8) |
| 2787 | ``` |
| 2788 | |
| 2789 | The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information. |
| 2790 | |
| 2791 | ### Infinity |
| 2792 | ```bash |
| 2793 | docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ |
| 2794 | michaelf34/infinity:0.0.68 \ |
| 2795 | v2 --model-id mixedbread-ai/mxbai-embed-large-v1 --revision "main" --dtype float16 --engine torch --port 7997 |
| 2796 | ``` |
| 2797 | |
| 2798 | ## Evaluation |
| 2799 | As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2. |
| 2800 | |
| 2801 | |
| 2802 | | Model | Avg (56 datasets) | Classification (12 datasets) | Clustering (11 datasets) | PairClassification (3 datasets) | Reranking (4 datasets) | Retrieval (15 datasets) | STS (10 datasets) | Summarization (1 dataset) | |
| 2803 | | --------------------------------------------------------------------------------------------- | ----------------- | ---------------------------- | ------------------------ | ------------------------------- | ---------------------- | ----------------------- | ----------------- | ------------------------- | |
| 2804 | | **mxbai-embed-large-v1** | **64.68** | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85.00 | 32.71 | |
| 2805 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | |
| 2806 | | [mxbai-embed-2d-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-2d-large-v1) | 63.25 | 74.14 | 46.07 | 85.89 | 58.94 | 51.42 | 84.9 | 31.55 | |
| 2807 | | [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) | 62.39 | 74.12 | 43.91 | 85.15 | 55.69 | 52.81 | 82.06 | 30.08 | |
| 2808 | | [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) | 60.38 | 73.45 | 41.73 | 85.38 | 56.98 | 47.87 | 80.7 | 31.6 | |
| 2809 | | *Proprietary Models* | | | | | | | | | |
| 2810 | | [OpenAI text-embedding-3-large](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58 | 75.45 | 49.01 | 85.72 | 59.16 | 55.44 | 81.73 | 29.92 | |
| 2811 | | [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/) | 64.47 | 76.49 | 47.43 | 85.84 | 58.01 | 55.00 | 82.62 | 30.18 | |
| 2812 | | [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) | 60.99 | 70.93 | 45.90 | 84.89 | 56.32 | 49.25 | 80.97 | 30.80 | |
| 2813 | |
| 2814 | |
| 2815 | Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1). |
| 2816 | |
| 2817 | ## Matryoshka and Binary Quantization |
| 2818 | |
| 2819 | Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). <b> The model supports both approaches! </b> |
| 2820 | |
| 2821 | You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our [blog post](https://www.mixedbread.ai/blog/binary-mrl). |
| 2822 | |
| 2823 | ## Community |
| 2824 | Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. |
| 2825 | |
| 2826 | ## License |
| 2827 | Apache 2.0 |
| 2828 | |
| 2829 | ## Citation |
| 2830 | |
| 2831 | ```bibtex |
| 2832 | @online{emb2024mxbai, |
| 2833 | title={Open Source Strikes Bread - New Fluffy Embeddings Model}, |
| 2834 | author={Sean Lee and Aamir Shakir and Darius Koenig and Julius Lipp}, |
| 2835 | year={2024}, |
| 2836 | url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1}, |
| 2837 | } |
| 2838 | |
| 2839 | @article{li2023angle, |
| 2840 | title={AnglE-optimized Text Embeddings}, |
| 2841 | author={Li, Xianming and Li, Jing}, |
| 2842 | journal={arXiv preprint arXiv:2309.12871}, |
| 2843 | year={2023} |
| 2844 | } |
| 2845 | ``` |
| 2846 | |