README.md
| 1 | --- |
| 2 | pipeline_tag: sentence-similarity |
| 3 | tags: |
| 4 | - sentence-transformers |
| 5 | - feature-extraction |
| 6 | - sentence-similarity |
| 7 | - mteb |
| 8 | - arctic |
| 9 | - snowflake-arctic-embed |
| 10 | - transformers.js |
| 11 | license: apache-2.0 |
| 12 | language: |
| 13 | - af |
| 14 | - ar |
| 15 | - az |
| 16 | - be |
| 17 | - bg |
| 18 | - bn |
| 19 | - ca |
| 20 | - ceb |
| 21 | - cs |
| 22 | - cy |
| 23 | - da |
| 24 | - de |
| 25 | - el |
| 26 | - en |
| 27 | - es |
| 28 | - et |
| 29 | - eu |
| 30 | - fa |
| 31 | - fi |
| 32 | - fr |
| 33 | - gl |
| 34 | - gu |
| 35 | - he |
| 36 | - hi |
| 37 | - hr |
| 38 | - ht |
| 39 | - hu |
| 40 | - hy |
| 41 | - id |
| 42 | - is |
| 43 | - it |
| 44 | - ja |
| 45 | - jv |
| 46 | - ka |
| 47 | - kk |
| 48 | - km |
| 49 | - kn |
| 50 | - ko |
| 51 | - ky |
| 52 | - lo |
| 53 | - lt |
| 54 | - lv |
| 55 | - mk |
| 56 | - ml |
| 57 | - mn |
| 58 | - mr |
| 59 | - ms |
| 60 | - my |
| 61 | - ne |
| 62 | - nl |
| 63 | - pa |
| 64 | - pl |
| 65 | - pt |
| 66 | - qu |
| 67 | - ro |
| 68 | - ru |
| 69 | - si |
| 70 | - sk |
| 71 | - sl |
| 72 | - so |
| 73 | - sq |
| 74 | - sr |
| 75 | - sv |
| 76 | - sw |
| 77 | - ta |
| 78 | - te |
| 79 | - th |
| 80 | - tl |
| 81 | - tr |
| 82 | - uk |
| 83 | - ur |
| 84 | - vi |
| 85 | - yo |
| 86 | - zh |
| 87 | model-index: |
| 88 | - name: snowflake-arctic-embed-l-v2.0 |
| 89 | results: |
| 90 | - dataset: |
| 91 | config: en-ext |
| 92 | name: MTEB AmazonCounterfactualClassification (en-ext) |
| 93 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 94 | split: test |
| 95 | type: mteb/amazon_counterfactual |
| 96 | metrics: |
| 97 | - type: accuracy |
| 98 | value: 67.039 |
| 99 | - type: f1 |
| 100 | value: 55.1806 |
| 101 | - type: f1_weighted |
| 102 | value: 73.41149999999999 |
| 103 | - type: ap |
| 104 | value: 17.9914 |
| 105 | - type: ap_weighted |
| 106 | value: 17.9914 |
| 107 | - type: main_score |
| 108 | value: 67.039 |
| 109 | task: |
| 110 | type: Classification |
| 111 | - dataset: |
| 112 | config: en |
| 113 | name: MTEB AmazonCounterfactualClassification (en) |
| 114 | revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| 115 | split: test |
| 116 | type: mteb/amazon_counterfactual |
| 117 | metrics: |
| 118 | - type: accuracy |
| 119 | value: 65.59700000000001 |
| 120 | - type: f1 |
| 121 | value: 60.244299999999996 |
| 122 | - type: f1_weighted |
| 123 | value: 68.9975 |
| 124 | - type: ap |
| 125 | value: 29.762100000000004 |
| 126 | - type: ap_weighted |
| 127 | value: 29.762100000000004 |
| 128 | - type: main_score |
| 129 | value: 65.59700000000001 |
| 130 | task: |
| 131 | type: Classification |
| 132 | - dataset: |
| 133 | config: default |
| 134 | name: MTEB AmazonPolarityClassification (default) |
| 135 | revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| 136 | split: test |
| 137 | type: mteb/amazon_polarity |
| 138 | metrics: |
| 139 | - type: accuracy |
| 140 | value: 74.2565 |
| 141 | - type: f1 |
| 142 | value: 74.0291 |
| 143 | - type: f1_weighted |
| 144 | value: 74.0291 |
| 145 | - type: ap |
| 146 | value: 68.7595 |
| 147 | - type: ap_weighted |
| 148 | value: 68.7595 |
| 149 | - type: main_score |
| 150 | value: 74.2565 |
| 151 | task: |
| 152 | type: Classification |
| 153 | - dataset: |
| 154 | config: en |
| 155 | name: MTEB AmazonReviewsClassification (en) |
| 156 | revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| 157 | split: test |
| 158 | type: mteb/amazon_reviews_multi |
| 159 | metrics: |
| 160 | - type: accuracy |
| 161 | value: 34.946 |
| 162 | - type: f1 |
| 163 | value: 34.2853 |
| 164 | - type: f1_weighted |
| 165 | value: 34.2853 |
| 166 | - type: main_score |
| 167 | value: 34.946 |
| 168 | task: |
| 169 | type: Classification |
| 170 | - dataset: |
| 171 | config: default |
| 172 | name: MTEB ArguAna (default) |
| 173 | revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| 174 | split: test |
| 175 | type: mteb/arguana |
| 176 | metrics: |
| 177 | - type: ndcg_at_1 |
| 178 | value: 33.286 |
| 179 | - type: ndcg_at_3 |
| 180 | value: 49.051 |
| 181 | - type: ndcg_at_5 |
| 182 | value: 54.107000000000006 |
| 183 | - type: ndcg_at_10 |
| 184 | value: 59.146 |
| 185 | - type: ndcg_at_20 |
| 186 | value: 60.897999999999996 |
| 187 | - type: ndcg_at_100 |
| 188 | value: 61.78399999999999 |
| 189 | - type: ndcg_at_1000 |
| 190 | value: 61.845000000000006 |
| 191 | - type: map_at_1 |
| 192 | value: 33.286 |
| 193 | - type: map_at_3 |
| 194 | value: 45.14 |
| 195 | - type: map_at_5 |
| 196 | value: 47.939 |
| 197 | - type: map_at_10 |
| 198 | value: 50.046 |
| 199 | - type: map_at_20 |
| 200 | value: 50.56 |
| 201 | - type: map_at_100 |
| 202 | value: 50.708 |
| 203 | - type: map_at_1000 |
| 204 | value: 50.712 |
| 205 | - type: recall_at_1 |
| 206 | value: 33.286 |
| 207 | - type: recall_at_3 |
| 208 | value: 60.38400000000001 |
| 209 | - type: recall_at_5 |
| 210 | value: 72.688 |
| 211 | - type: recall_at_10 |
| 212 | value: 88.122 |
| 213 | - type: recall_at_20 |
| 214 | value: 94.808 |
| 215 | - type: recall_at_100 |
| 216 | value: 99.21799999999999 |
| 217 | - type: recall_at_1000 |
| 218 | value: 99.644 |
| 219 | - type: precision_at_1 |
| 220 | value: 33.286 |
| 221 | - type: precision_at_3 |
| 222 | value: 20.128 |
| 223 | - type: precision_at_5 |
| 224 | value: 14.538 |
| 225 | - type: precision_at_10 |
| 226 | value: 8.812000000000001 |
| 227 | - type: precision_at_20 |
| 228 | value: 4.74 |
| 229 | - type: precision_at_100 |
| 230 | value: 0.992 |
| 231 | - type: precision_at_1000 |
| 232 | value: 0.1 |
| 233 | - type: mrr_at_1 |
| 234 | value: 33.926 |
| 235 | - type: mrr_at_3 |
| 236 | value: 45.3414 |
| 237 | - type: mrr_at_5 |
| 238 | value: 48.1828 |
| 239 | - type: mrr_at_10 |
| 240 | value: 50.270700000000005 |
| 241 | - type: mrr_at_20 |
| 242 | value: 50.7844 |
| 243 | - type: mrr_at_100 |
| 244 | value: 50.9259 |
| 245 | - type: mrr_at_1000 |
| 246 | value: 50.9294 |
| 247 | - type: nauc_ndcg_at_1_max |
| 248 | value: -10.305 |
| 249 | - type: nauc_ndcg_at_1_std |
| 250 | value: -15.674199999999999 |
| 251 | - type: nauc_ndcg_at_1_diff1 |
| 252 | value: 18.6355 |
| 253 | - type: nauc_ndcg_at_3_max |
| 254 | value: -7.744 |
| 255 | - type: nauc_ndcg_at_3_std |
| 256 | value: -16.894000000000002 |
| 257 | - type: nauc_ndcg_at_3_diff1 |
| 258 | value: 15.4469 |
| 259 | - type: nauc_ndcg_at_5_max |
| 260 | value: -6.4887 |
| 261 | - type: nauc_ndcg_at_5_std |
| 262 | value: -16.1382 |
| 263 | - type: nauc_ndcg_at_5_diff1 |
| 264 | value: 13.8214 |
| 265 | - type: nauc_ndcg_at_10_max |
| 266 | value: -7.616499999999999 |
| 267 | - type: nauc_ndcg_at_10_std |
| 268 | value: -15.8073 |
| 269 | - type: nauc_ndcg_at_10_diff1 |
| 270 | value: 13.7678 |
| 271 | - type: nauc_ndcg_at_20_max |
| 272 | value: -6.9801 |
| 273 | - type: nauc_ndcg_at_20_std |
| 274 | value: -15.068699999999998 |
| 275 | - type: nauc_ndcg_at_20_diff1 |
| 276 | value: 14.2013 |
| 277 | - type: nauc_ndcg_at_100_max |
| 278 | value: -7.5221 |
| 279 | - type: nauc_ndcg_at_100_std |
| 280 | value: -15.417200000000001 |
| 281 | - type: nauc_ndcg_at_100_diff1 |
| 282 | value: 15.1072 |
| 283 | - type: nauc_ndcg_at_1000_max |
| 284 | value: -7.6931 |
| 285 | - type: nauc_ndcg_at_1000_std |
| 286 | value: -15.5367 |
| 287 | - type: nauc_ndcg_at_1000_diff1 |
| 288 | value: 15.001700000000001 |
| 289 | - type: nauc_map_at_1_max |
| 290 | value: -10.305 |
| 291 | - type: nauc_map_at_1_std |
| 292 | value: -15.674199999999999 |
| 293 | - type: nauc_map_at_1_diff1 |
| 294 | value: 18.6355 |
| 295 | - type: nauc_map_at_3_max |
| 296 | value: -8.4505 |
| 297 | - type: nauc_map_at_3_std |
| 298 | value: -16.5487 |
| 299 | - type: nauc_map_at_3_diff1 |
| 300 | value: 15.965599999999998 |
| 301 | - type: nauc_map_at_5_max |
| 302 | value: -7.8429 |
| 303 | - type: nauc_map_at_5_std |
| 304 | value: -16.1332 |
| 305 | - type: nauc_map_at_5_diff1 |
| 306 | value: 15.0893 |
| 307 | - type: nauc_map_at_10_max |
| 308 | value: -8.3186 |
| 309 | - type: nauc_map_at_10_std |
| 310 | value: -15.979399999999998 |
| 311 | - type: nauc_map_at_10_diff1 |
| 312 | value: 15.136199999999999 |
| 313 | - type: nauc_map_at_20_max |
| 314 | value: -8.1697 |
| 315 | - type: nauc_map_at_20_std |
| 316 | value: -15.8241 |
| 317 | - type: nauc_map_at_20_diff1 |
| 318 | value: 15.260599999999998 |
| 319 | - type: nauc_map_at_100_max |
| 320 | value: -8.2285 |
| 321 | - type: nauc_map_at_100_std |
| 322 | value: -15.8624 |
| 323 | - type: nauc_map_at_100_diff1 |
| 324 | value: 15.412600000000001 |
| 325 | - type: nauc_map_at_1000_max |
| 326 | value: -8.2359 |
| 327 | - type: nauc_map_at_1000_std |
| 328 | value: -15.867 |
| 329 | - type: nauc_map_at_1000_diff1 |
| 330 | value: 15.408 |
| 331 | - type: nauc_recall_at_1_max |
| 332 | value: -10.305 |
| 333 | - type: nauc_recall_at_1_std |
| 334 | value: -15.674199999999999 |
| 335 | - type: nauc_recall_at_1_diff1 |
| 336 | value: 18.6355 |
| 337 | - type: nauc_recall_at_3_max |
| 338 | value: -5.5097 |
| 339 | - type: nauc_recall_at_3_std |
| 340 | value: -17.9896 |
| 341 | - type: nauc_recall_at_3_diff1 |
| 342 | value: 13.9525 |
| 343 | - type: nauc_recall_at_5_max |
| 344 | value: -0.9383 |
| 345 | - type: nauc_recall_at_5_std |
| 346 | value: -16.035 |
| 347 | - type: nauc_recall_at_5_diff1 |
| 348 | value: 8.8431 |
| 349 | - type: nauc_recall_at_10_max |
| 350 | value: -2.8548 |
| 351 | - type: nauc_recall_at_10_std |
| 352 | value: -14.1203 |
| 353 | - type: nauc_recall_at_10_diff1 |
| 354 | value: 3.2265 |
| 355 | - type: nauc_recall_at_20_max |
| 356 | value: 14.2043 |
| 357 | - type: nauc_recall_at_20_std |
| 358 | value: 2.1298999999999997 |
| 359 | - type: nauc_recall_at_20_diff1 |
| 360 | value: -1.9900000000000002 |
| 361 | - type: nauc_recall_at_100_max |
| 362 | value: 44.0173 |
| 363 | - type: nauc_recall_at_100_std |
| 364 | value: 42.131800000000005 |
| 365 | - type: nauc_recall_at_100_diff1 |
| 366 | value: 29.9983 |
| 367 | - type: nauc_recall_at_1000_max |
| 368 | value: 25.9434 |
| 369 | - type: nauc_recall_at_1000_std |
| 370 | value: 53.9252 |
| 371 | - type: nauc_recall_at_1000_diff1 |
| 372 | value: -0.9778 |
| 373 | - type: nauc_precision_at_1_max |
| 374 | value: -10.305 |
| 375 | - type: nauc_precision_at_1_std |
| 376 | value: -15.674199999999999 |
| 377 | - type: nauc_precision_at_1_diff1 |
| 378 | value: 18.6355 |
| 379 | - type: nauc_precision_at_3_max |
| 380 | value: -5.5097 |
| 381 | - type: nauc_precision_at_3_std |
| 382 | value: -17.9896 |
| 383 | - type: nauc_precision_at_3_diff1 |
| 384 | value: 13.9525 |
| 385 | - type: nauc_precision_at_5_max |
| 386 | value: -0.9383 |
| 387 | - type: nauc_precision_at_5_std |
| 388 | value: -16.035 |
| 389 | - type: nauc_precision_at_5_diff1 |
| 390 | value: 8.8431 |
| 391 | - type: nauc_precision_at_10_max |
| 392 | value: -2.8548 |
| 393 | - type: nauc_precision_at_10_std |
| 394 | value: -14.1203 |
| 395 | - type: nauc_precision_at_10_diff1 |
| 396 | value: 3.2265 |
| 397 | - type: nauc_precision_at_20_max |
| 398 | value: 14.2043 |
| 399 | - type: nauc_precision_at_20_std |
| 400 | value: 2.1298999999999997 |
| 401 | - type: nauc_precision_at_20_diff1 |
| 402 | value: -1.9900000000000002 |
| 403 | - type: nauc_precision_at_100_max |
| 404 | value: 44.0173 |
| 405 | - type: nauc_precision_at_100_std |
| 406 | value: 42.131800000000005 |
| 407 | - type: nauc_precision_at_100_diff1 |
| 408 | value: 29.9983 |
| 409 | - type: nauc_precision_at_1000_max |
| 410 | value: 25.9434 |
| 411 | - type: nauc_precision_at_1000_std |
| 412 | value: 53.9252 |
| 413 | - type: nauc_precision_at_1000_diff1 |
| 414 | value: -0.9778 |
| 415 | - type: nauc_mrr_at_1_max |
| 416 | value: -9.833 |
| 417 | - type: nauc_mrr_at_1_std |
| 418 | value: -14.8351 |
| 419 | - type: nauc_mrr_at_1_diff1 |
| 420 | value: 16.7604 |
| 421 | - type: nauc_mrr_at_3_max |
| 422 | value: -9.0116 |
| 423 | - type: nauc_mrr_at_3_std |
| 424 | value: -16.296 |
| 425 | - type: nauc_mrr_at_3_diff1 |
| 426 | value: 14.178199999999999 |
| 427 | - type: nauc_mrr_at_5_max |
| 428 | value: -8.308300000000001 |
| 429 | - type: nauc_mrr_at_5_std |
| 430 | value: -15.751999999999999 |
| 431 | - type: nauc_mrr_at_5_diff1 |
| 432 | value: 13.306299999999998 |
| 433 | - type: nauc_mrr_at_10_max |
| 434 | value: -8.7962 |
| 435 | - type: nauc_mrr_at_10_std |
| 436 | value: -15.688099999999999 |
| 437 | - type: nauc_mrr_at_10_diff1 |
| 438 | value: 13.2589 |
| 439 | - type: nauc_mrr_at_20_max |
| 440 | value: -8.6773 |
| 441 | - type: nauc_mrr_at_20_std |
| 442 | value: -15.479499999999998 |
| 443 | - type: nauc_mrr_at_20_diff1 |
| 444 | value: 13.354 |
| 445 | - type: nauc_mrr_at_100_max |
| 446 | value: -8.7533 |
| 447 | - type: nauc_mrr_at_100_std |
| 448 | value: -15.553600000000001 |
| 449 | - type: nauc_mrr_at_100_diff1 |
| 450 | value: 13.4796 |
| 451 | - type: nauc_mrr_at_1000_max |
| 452 | value: -8.7608 |
| 453 | - type: nauc_mrr_at_1000_std |
| 454 | value: -15.5582 |
| 455 | - type: nauc_mrr_at_1000_diff1 |
| 456 | value: 13.4748 |
| 457 | - type: main_score |
| 458 | value: 59.146 |
| 459 | task: |
| 460 | type: Retrieval |
| 461 | - dataset: |
| 462 | config: default |
| 463 | name: MTEB ArxivClusteringP2P (default) |
| 464 | revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| 465 | split: test |
| 466 | type: mteb/arxiv-clustering-p2p |
| 467 | metrics: |
| 468 | - type: v_measure |
| 469 | value: 43.9715 |
| 470 | - type: v_measure_std |
| 471 | value: 13.4325 |
| 472 | - type: main_score |
| 473 | value: 43.9715 |
| 474 | task: |
| 475 | type: Clustering |
| 476 | - dataset: |
| 477 | config: default |
| 478 | name: MTEB ArxivClusteringS2S (default) |
| 479 | revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| 480 | split: test |
| 481 | type: mteb/arxiv-clustering-s2s |
| 482 | metrics: |
| 483 | - type: v_measure |
| 484 | value: 34.775800000000004 |
| 485 | - type: v_measure_std |
| 486 | value: 13.922799999999999 |
| 487 | - type: main_score |
| 488 | value: 34.775800000000004 |
| 489 | task: |
| 490 | type: Clustering |
| 491 | - dataset: |
| 492 | config: default |
| 493 | name: MTEB AskUbuntuDupQuestions (default) |
| 494 | revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| 495 | split: test |
| 496 | type: mteb/askubuntudupquestions-reranking |
| 497 | metrics: |
| 498 | - type: map |
| 499 | value: 63.3521 |
| 500 | - type: mrr |
| 501 | value: 77.5965 |
| 502 | - type: nAUC_map_max |
| 503 | value: 21.2353 |
| 504 | - type: nAUC_map_std |
| 505 | value: 17.002100000000002 |
| 506 | - type: nAUC_map_diff1 |
| 507 | value: 3.8135000000000003 |
| 508 | - type: nAUC_mrr_max |
| 509 | value: 35.058299999999996 |
| 510 | - type: nAUC_mrr_std |
| 511 | value: 20.432 |
| 512 | - type: nAUC_mrr_diff1 |
| 513 | value: 9.2584 |
| 514 | - type: main_score |
| 515 | value: 63.3521 |
| 516 | task: |
| 517 | type: Reranking |
| 518 | - dataset: |
| 519 | config: default |
| 520 | name: MTEB BIOSSES (default) |
| 521 | revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| 522 | split: test |
| 523 | type: mteb/biosses-sts |
| 524 | metrics: |
| 525 | - type: pearson |
| 526 | value: 89.8072 |
| 527 | - type: spearman |
| 528 | value: 87.2875 |
| 529 | - type: cosine_pearson |
| 530 | value: 89.8072 |
| 531 | - type: cosine_spearman |
| 532 | value: 87.2875 |
| 533 | - type: manhattan_pearson |
| 534 | value: 87.9173 |
| 535 | - type: manhattan_spearman |
| 536 | value: 86.7327 |
| 537 | - type: euclidean_pearson |
| 538 | value: 88.21600000000001 |
| 539 | - type: euclidean_spearman |
| 540 | value: 87.2875 |
| 541 | - type: main_score |
| 542 | value: 87.2875 |
| 543 | task: |
| 544 | type: STS |
| 545 | - dataset: |
| 546 | config: default |
| 547 | name: MTEB Banking77Classification (default) |
| 548 | revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| 549 | split: test |
| 550 | type: mteb/banking77 |
| 551 | metrics: |
| 552 | - type: accuracy |
| 553 | value: 81.8149 |
| 554 | - type: f1 |
| 555 | value: 81.2226 |
| 556 | - type: f1_weighted |
| 557 | value: 81.2226 |
| 558 | - type: main_score |
| 559 | value: 81.8149 |
| 560 | task: |
| 561 | type: Classification |
| 562 | - dataset: |
| 563 | config: default |
| 564 | name: MTEB BiorxivClusteringP2P (default) |
| 565 | revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| 566 | split: test |
| 567 | type: mteb/biorxiv-clustering-p2p |
| 568 | metrics: |
| 569 | - type: v_measure |
| 570 | value: 35.0927 |
| 571 | - type: v_measure_std |
| 572 | value: 0.7048 |
| 573 | - type: main_score |
| 574 | value: 35.0927 |
| 575 | task: |
| 576 | type: Clustering |
| 577 | - dataset: |
| 578 | config: default |
| 579 | name: MTEB BiorxivClusteringS2S (default) |
| 580 | revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| 581 | split: test |
| 582 | type: mteb/biorxiv-clustering-s2s |
| 583 | metrics: |
| 584 | - type: v_measure |
| 585 | value: 30.220999999999997 |
| 586 | - type: v_measure_std |
| 587 | value: 1.107 |
| 588 | - type: main_score |
| 589 | value: 30.220999999999997 |
| 590 | task: |
| 591 | type: Clustering |
| 592 | - dataset: |
| 593 | config: default |
| 594 | name: MTEB CQADupstackAndroidRetrieval (default) |
| 595 | revision: f46a197baaae43b4f621051089b82a364682dfeb |
| 596 | split: test |
| 597 | type: mteb/cqadupstack-android |
| 598 | metrics: |
| 599 | - type: ndcg_at_1 |
| 600 | value: 44.349 |
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| 3226 | name: MTEB CQADupstackUnixRetrieval (default) |
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| 3517 | name: MTEB CQADupstackWebmastersRetrieval (default) |
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| 5576 | - type: accuracy |
| 5577 | value: 72.5444 |
| 5578 | - type: f1 |
| 5579 | value: 72.4069 |
| 5580 | - type: f1_weighted |
| 5581 | value: 72.4069 |
| 5582 | - type: ap |
| 5583 | value: 66.8419 |
| 5584 | - type: ap_weighted |
| 5585 | value: 66.8419 |
| 5586 | - type: main_score |
| 5587 | value: 72.5444 |
| 5588 | task: |
| 5589 | type: Classification |
| 5590 | - dataset: |
| 5591 | config: default |
| 5592 | name: MTEB MSMARCO (default) |
| 5593 | revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| 5594 | split: dev |
| 5595 | type: mteb/msmarco |
| 5596 | metrics: |
| 5597 | - type: ndcg_at_1 |
| 5598 | value: 25.516 |
| 5599 | - type: ndcg_at_3 |
| 5600 | value: 36.687999999999995 |
| 5601 | - type: ndcg_at_5 |
| 5602 | value: 40.864 |
| 5603 | - type: ndcg_at_10 |
| 5604 | value: 44.856 |
| 5605 | - type: ndcg_at_20 |
| 5606 | value: 47.3 |
| 5607 | - type: ndcg_at_100 |
| 5608 | value: 50.062 |
| 5609 | - type: ndcg_at_1000 |
| 5610 | value: 51.085 |
| 5611 | - type: map_at_1 |
| 5612 | value: 24.782 |
| 5613 | - type: map_at_3 |
| 5614 | value: 33.668 |
| 5615 | - type: map_at_5 |
| 5616 | value: 36.010999999999996 |
| 5617 | - type: map_at_10 |
| 5618 | value: 37.702000000000005 |
| 5619 | - type: map_at_20 |
| 5620 | value: 38.391 |
| 5621 | - type: map_at_100 |
| 5622 | value: 38.798 |
| 5623 | - type: map_at_1000 |
| 5624 | value: 38.841 |
| 5625 | - type: recall_at_1 |
| 5626 | value: 24.782 |
| 5627 | - type: recall_at_3 |
| 5628 | value: 44.722 |
| 5629 | - type: recall_at_5 |
| 5630 | value: 54.769999999999996 |
| 5631 | - type: recall_at_10 |
| 5632 | value: 66.842 |
| 5633 | - type: recall_at_20 |
| 5634 | value: 76.319 |
| 5635 | - type: recall_at_100 |
| 5636 | value: 90.761 |
| 5637 | - type: recall_at_1000 |
| 5638 | value: 98.48 |
| 5639 | - type: precision_at_1 |
| 5640 | value: 25.516 |
| 5641 | - type: precision_at_3 |
| 5642 | value: 15.506 |
| 5643 | - type: precision_at_5 |
| 5644 | value: 11.413 |
| 5645 | - type: precision_at_10 |
| 5646 | value: 6.99 |
| 5647 | - type: precision_at_20 |
| 5648 | value: 4.009 |
| 5649 | - type: precision_at_100 |
| 5650 | value: 0.959 |
| 5651 | - type: precision_at_1000 |
| 5652 | value: 0.105 |
| 5653 | - type: mrr_at_1 |
| 5654 | value: 25.5014 |
| 5655 | - type: mrr_at_3 |
| 5656 | value: 34.3553 |
| 5657 | - type: mrr_at_5 |
| 5658 | value: 36.666199999999996 |
| 5659 | - type: mrr_at_10 |
| 5660 | value: 38.3084 |
| 5661 | - type: mrr_at_20 |
| 5662 | value: 38.9663 |
| 5663 | - type: mrr_at_100 |
| 5664 | value: 39.341300000000004 |
| 5665 | - type: mrr_at_1000 |
| 5666 | value: 39.3785 |
| 5667 | - type: nauc_ndcg_at_1_max |
| 5668 | value: 4.2138 |
| 5669 | - type: nauc_ndcg_at_1_std |
| 5670 | value: -24.7801 |
| 5671 | - type: nauc_ndcg_at_1_diff1 |
| 5672 | value: 37.758399999999995 |
| 5673 | - type: nauc_ndcg_at_3_max |
| 5674 | value: 5.2536 |
| 5675 | - type: nauc_ndcg_at_3_std |
| 5676 | value: -29.642200000000003 |
| 5677 | - type: nauc_ndcg_at_3_diff1 |
| 5678 | value: 32.1639 |
| 5679 | - type: nauc_ndcg_at_5_max |
| 5680 | value: 5.0839 |
| 5681 | - type: nauc_ndcg_at_5_std |
| 5682 | value: -31.3077 |
| 5683 | - type: nauc_ndcg_at_5_diff1 |
| 5684 | value: 31.5135 |
| 5685 | - type: nauc_ndcg_at_10_max |
| 5686 | value: 6.2542 |
| 5687 | - type: nauc_ndcg_at_10_std |
| 5688 | value: -30.8439 |
| 5689 | - type: nauc_ndcg_at_10_diff1 |
| 5690 | value: 31.461299999999998 |
| 5691 | - type: nauc_ndcg_at_20_max |
| 5692 | value: 6.5669 |
| 5693 | - type: nauc_ndcg_at_20_std |
| 5694 | value: -29.6288 |
| 5695 | - type: nauc_ndcg_at_20_diff1 |
| 5696 | value: 31.590200000000003 |
| 5697 | - type: nauc_ndcg_at_100_max |
| 5698 | value: 6.691800000000001 |
| 5699 | - type: nauc_ndcg_at_100_std |
| 5700 | value: -28.1768 |
| 5701 | - type: nauc_ndcg_at_100_diff1 |
| 5702 | value: 32.1699 |
| 5703 | - type: nauc_ndcg_at_1000_max |
| 5704 | value: 6.451700000000001 |
| 5705 | - type: nauc_ndcg_at_1000_std |
| 5706 | value: -28.2093 |
| 5707 | - type: nauc_ndcg_at_1000_diff1 |
| 5708 | value: 32.3573 |
| 5709 | - type: nauc_map_at_1_max |
| 5710 | value: 4.1941 |
| 5711 | - type: nauc_map_at_1_std |
| 5712 | value: -24.9531 |
| 5713 | - type: nauc_map_at_1_diff1 |
| 5714 | value: 38.099 |
| 5715 | - type: nauc_map_at_3_max |
| 5716 | value: 4.9883999999999995 |
| 5717 | - type: nauc_map_at_3_std |
| 5718 | value: -28.7062 |
| 5719 | - type: nauc_map_at_3_diff1 |
| 5720 | value: 33.5696 |
| 5721 | - type: nauc_map_at_5_max |
| 5722 | value: 4.8525 |
| 5723 | - type: nauc_map_at_5_std |
| 5724 | value: -29.6601 |
| 5725 | - type: nauc_map_at_5_diff1 |
| 5726 | value: 33.2144 |
| 5727 | - type: nauc_map_at_10_max |
| 5728 | value: 5.3533 |
| 5729 | - type: nauc_map_at_10_std |
| 5730 | value: -29.4529 |
| 5731 | - type: nauc_map_at_10_diff1 |
| 5732 | value: 33.219300000000004 |
| 5733 | - type: nauc_map_at_20_max |
| 5734 | value: 5.416300000000001 |
| 5735 | - type: nauc_map_at_20_std |
| 5736 | value: -29.1294 |
| 5737 | - type: nauc_map_at_20_diff1 |
| 5738 | value: 33.2747 |
| 5739 | - type: nauc_map_at_100_max |
| 5740 | value: 5.4547 |
| 5741 | - type: nauc_map_at_100_std |
| 5742 | value: -28.8978 |
| 5743 | - type: nauc_map_at_100_diff1 |
| 5744 | value: 33.3505 |
| 5745 | - type: nauc_map_at_1000_max |
| 5746 | value: 5.4512 |
| 5747 | - type: nauc_map_at_1000_std |
| 5748 | value: -28.8844 |
| 5749 | - type: nauc_map_at_1000_diff1 |
| 5750 | value: 33.356700000000004 |
| 5751 | - type: nauc_recall_at_1_max |
| 5752 | value: 4.1941 |
| 5753 | - type: nauc_recall_at_1_std |
| 5754 | value: -24.9531 |
| 5755 | - type: nauc_recall_at_1_diff1 |
| 5756 | value: 38.099 |
| 5757 | - type: nauc_recall_at_3_max |
| 5758 | value: 5.884799999999999 |
| 5759 | - type: nauc_recall_at_3_std |
| 5760 | value: -32.317 |
| 5761 | - type: nauc_recall_at_3_diff1 |
| 5762 | value: 28.284399999999998 |
| 5763 | - type: nauc_recall_at_5_max |
| 5764 | value: 5.4525 |
| 5765 | - type: nauc_recall_at_5_std |
| 5766 | value: -36.4055 |
| 5767 | - type: nauc_recall_at_5_diff1 |
| 5768 | value: 26.384200000000003 |
| 5769 | - type: nauc_recall_at_10_max |
| 5770 | value: 9.403400000000001 |
| 5771 | - type: nauc_recall_at_10_std |
| 5772 | value: -35.9112 |
| 5773 | - type: nauc_recall_at_10_diff1 |
| 5774 | value: 25.2415 |
| 5775 | - type: nauc_recall_at_20_max |
| 5776 | value: 12.0952 |
| 5777 | - type: nauc_recall_at_20_std |
| 5778 | value: -30.778299999999998 |
| 5779 | - type: nauc_recall_at_20_diff1 |
| 5780 | value: 24.1866 |
| 5781 | - type: nauc_recall_at_100_max |
| 5782 | value: 19.6413 |
| 5783 | - type: nauc_recall_at_100_std |
| 5784 | value: -11.9243 |
| 5785 | - type: nauc_recall_at_100_diff1 |
| 5786 | value: 24.6153 |
| 5787 | - type: nauc_recall_at_1000_max |
| 5788 | value: 48.1206 |
| 5789 | - type: nauc_recall_at_1000_std |
| 5790 | value: 48.0062 |
| 5791 | - type: nauc_recall_at_1000_diff1 |
| 5792 | value: 16.2543 |
| 5793 | - type: nauc_precision_at_1_max |
| 5794 | value: 4.2138 |
| 5795 | - type: nauc_precision_at_1_std |
| 5796 | value: -24.7801 |
| 5797 | - type: nauc_precision_at_1_diff1 |
| 5798 | value: 37.758399999999995 |
| 5799 | - type: nauc_precision_at_3_max |
| 5800 | value: 5.7985 |
| 5801 | - type: nauc_precision_at_3_std |
| 5802 | value: -31.749899999999997 |
| 5803 | - type: nauc_precision_at_3_diff1 |
| 5804 | value: 27.373399999999997 |
| 5805 | - type: nauc_precision_at_5_max |
| 5806 | value: 5.390000000000001 |
| 5807 | - type: nauc_precision_at_5_std |
| 5808 | value: -35.0586 |
| 5809 | - type: nauc_precision_at_5_diff1 |
| 5810 | value: 25.100099999999998 |
| 5811 | - type: nauc_precision_at_10_max |
| 5812 | value: 9.248199999999999 |
| 5813 | - type: nauc_precision_at_10_std |
| 5814 | value: -32.244299999999996 |
| 5815 | - type: nauc_precision_at_10_diff1 |
| 5816 | value: 22.5684 |
| 5817 | - type: nauc_precision_at_20_max |
| 5818 | value: 11.495099999999999 |
| 5819 | - type: nauc_precision_at_20_std |
| 5820 | value: -24.226300000000002 |
| 5821 | - type: nauc_precision_at_20_diff1 |
| 5822 | value: 19.6528 |
| 5823 | - type: nauc_precision_at_100_max |
| 5824 | value: 14.3649 |
| 5825 | - type: nauc_precision_at_100_std |
| 5826 | value: 0.0593 |
| 5827 | - type: nauc_precision_at_100_diff1 |
| 5828 | value: 10.9596 |
| 5829 | - type: nauc_precision_at_1000_max |
| 5830 | value: 10.9512 |
| 5831 | - type: nauc_precision_at_1000_std |
| 5832 | value: 18.288 |
| 5833 | - type: nauc_precision_at_1000_diff1 |
| 5834 | value: -3.5423000000000004 |
| 5835 | - type: nauc_mrr_at_1_max |
| 5836 | value: 4.2204 |
| 5837 | - type: nauc_mrr_at_1_std |
| 5838 | value: -24.7703 |
| 5839 | - type: nauc_mrr_at_1_diff1 |
| 5840 | value: 37.8126 |
| 5841 | - type: nauc_mrr_at_3_max |
| 5842 | value: 5.0668 |
| 5843 | - type: nauc_mrr_at_3_std |
| 5844 | value: -28.2677 |
| 5845 | - type: nauc_mrr_at_3_diff1 |
| 5846 | value: 33.3724 |
| 5847 | - type: nauc_mrr_at_5_max |
| 5848 | value: 5.0481 |
| 5849 | - type: nauc_mrr_at_5_std |
| 5850 | value: -29.133 |
| 5851 | - type: nauc_mrr_at_5_diff1 |
| 5852 | value: 33.0415 |
| 5853 | - type: nauc_mrr_at_10_max |
| 5854 | value: 5.5038 |
| 5855 | - type: nauc_mrr_at_10_std |
| 5856 | value: -28.886200000000002 |
| 5857 | - type: nauc_mrr_at_10_diff1 |
| 5858 | value: 33.0593 |
| 5859 | - type: nauc_mrr_at_20_max |
| 5860 | value: 5.5467 |
| 5861 | - type: nauc_mrr_at_20_std |
| 5862 | value: -28.5678 |
| 5863 | - type: nauc_mrr_at_20_diff1 |
| 5864 | value: 33.0916 |
| 5865 | - type: nauc_mrr_at_100_max |
| 5866 | value: 5.5636 |
| 5867 | - type: nauc_mrr_at_100_std |
| 5868 | value: -28.3877 |
| 5869 | - type: nauc_mrr_at_100_diff1 |
| 5870 | value: 33.1799 |
| 5871 | - type: nauc_mrr_at_1000_max |
| 5872 | value: 5.557 |
| 5873 | - type: nauc_mrr_at_1000_std |
| 5874 | value: -28.3796 |
| 5875 | - type: nauc_mrr_at_1000_diff1 |
| 5876 | value: 33.184999999999995 |
| 5877 | - type: main_score |
| 5878 | value: 44.856 |
| 5879 | task: |
| 5880 | type: Retrieval |
| 5881 | - dataset: |
| 5882 | config: en |
| 5883 | name: MTEB MTOPDomainClassification (en) |
| 5884 | revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| 5885 | split: test |
| 5886 | type: mteb/mtop_domain |
| 5887 | metrics: |
| 5888 | - type: accuracy |
| 5889 | value: 93.5317 |
| 5890 | - type: f1 |
| 5891 | value: 93.1956 |
| 5892 | - type: f1_weighted |
| 5893 | value: 93.5431 |
| 5894 | - type: main_score |
| 5895 | value: 93.5317 |
| 5896 | task: |
| 5897 | type: Classification |
| 5898 | - dataset: |
| 5899 | config: en |
| 5900 | name: MTEB MTOPIntentClassification (en) |
| 5901 | revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| 5902 | split: test |
| 5903 | type: mteb/mtop_intent |
| 5904 | metrics: |
| 5905 | - type: accuracy |
| 5906 | value: 67.7907 |
| 5907 | - type: f1 |
| 5908 | value: 48.2877 |
| 5909 | - type: f1_weighted |
| 5910 | value: 70.3225 |
| 5911 | - type: main_score |
| 5912 | value: 67.7907 |
| 5913 | task: |
| 5914 | type: Classification |
| 5915 | - dataset: |
| 5916 | config: en |
| 5917 | name: MTEB MassiveIntentClassification (en) |
| 5918 | revision: 4672e20407010da34463acc759c162ca9734bca6 |
| 5919 | split: test |
| 5920 | type: mteb/amazon_massive_intent |
| 5921 | metrics: |
| 5922 | - type: accuracy |
| 5923 | value: 71.456 |
| 5924 | - type: f1 |
| 5925 | value: 68.2268 |
| 5926 | - type: f1_weighted |
| 5927 | value: 70.4722 |
| 5928 | - type: main_score |
| 5929 | value: 71.456 |
| 5930 | task: |
| 5931 | type: Classification |
| 5932 | - dataset: |
| 5933 | config: en |
| 5934 | name: MTEB MassiveScenarioClassification (en) |
| 5935 | revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 |
| 5936 | split: test |
| 5937 | type: mteb/amazon_massive_scenario |
| 5938 | metrics: |
| 5939 | - type: accuracy |
| 5940 | value: 76.21719999999999 |
| 5941 | - type: f1 |
| 5942 | value: 75.14189999999999 |
| 5943 | - type: f1_weighted |
| 5944 | value: 76.0733 |
| 5945 | - type: main_score |
| 5946 | value: 76.21719999999999 |
| 5947 | task: |
| 5948 | type: Classification |
| 5949 | - dataset: |
| 5950 | config: default |
| 5951 | name: MTEB MedrxivClusteringP2P (default) |
| 5952 | revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| 5953 | split: test |
| 5954 | type: mteb/medrxiv-clustering-p2p |
| 5955 | metrics: |
| 5956 | - type: v_measure |
| 5957 | value: 31.3917 |
| 5958 | - type: v_measure_std |
| 5959 | value: 1.4778 |
| 5960 | - type: main_score |
| 5961 | value: 31.3917 |
| 5962 | task: |
| 5963 | type: Clustering |
| 5964 | - dataset: |
| 5965 | config: default |
| 5966 | name: MTEB MedrxivClusteringS2S (default) |
| 5967 | revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| 5968 | split: test |
| 5969 | type: mteb/medrxiv-clustering-s2s |
| 5970 | metrics: |
| 5971 | - type: v_measure |
| 5972 | value: 28.2408 |
| 5973 | - type: v_measure_std |
| 5974 | value: 1.1622999999999999 |
| 5975 | - type: main_score |
| 5976 | value: 28.2408 |
| 5977 | task: |
| 5978 | type: Clustering |
| 5979 | - dataset: |
| 5980 | config: default |
| 5981 | name: MTEB MindSmallReranking (default) |
| 5982 | revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 |
| 5983 | split: test |
| 5984 | type: mteb/mind_small |
| 5985 | metrics: |
| 5986 | - type: map |
| 5987 | value: 29.5796 |
| 5988 | - type: mrr |
| 5989 | value: 30.3081 |
| 5990 | - type: nAUC_map_max |
| 5991 | value: -24.9194 |
| 5992 | - type: nAUC_map_std |
| 5993 | value: -9.042 |
| 5994 | - type: nAUC_map_diff1 |
| 5995 | value: 12.1611 |
| 5996 | - type: nAUC_mrr_max |
| 5997 | value: -19.3867 |
| 5998 | - type: nAUC_mrr_std |
| 5999 | value: -6.3873 |
| 6000 | - type: nAUC_mrr_diff1 |
| 6001 | value: 11.8078 |
| 6002 | - type: main_score |
| 6003 | value: 29.5796 |
| 6004 | task: |
| 6005 | type: Reranking |
| 6006 | - dataset: |
| 6007 | config: default |
| 6008 | name: MTEB NFCorpus (default) |
| 6009 | revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| 6010 | split: test |
| 6011 | type: mteb/nfcorpus |
| 6012 | metrics: |
| 6013 | - type: ndcg_at_1 |
| 6014 | value: 45.046 |
| 6015 | - type: ndcg_at_3 |
| 6016 | value: 41.704 |
| 6017 | - type: ndcg_at_5 |
| 6018 | value: 39.296 |
| 6019 | - type: ndcg_at_10 |
| 6020 | value: 35.343999999999994 |
| 6021 | - type: ndcg_at_20 |
| 6022 | value: 32.525999999999996 |
| 6023 | - type: ndcg_at_100 |
| 6024 | value: 31.352999999999998 |
| 6025 | - type: ndcg_at_1000 |
| 6026 | value: 39.772 |
| 6027 | - type: map_at_1 |
| 6028 | value: 5.833 |
| 6029 | - type: map_at_3 |
| 6030 | value: 9.953 |
| 6031 | - type: map_at_5 |
| 6032 | value: 11.549 |
| 6033 | - type: map_at_10 |
| 6034 | value: 13.38 |
| 6035 | - type: map_at_20 |
| 6036 | value: 14.706 |
| 6037 | - type: map_at_100 |
| 6038 | value: 16.422 |
| 6039 | - type: map_at_1000 |
| 6040 | value: 17.777 |
| 6041 | - type: recall_at_1 |
| 6042 | value: 5.833 |
| 6043 | - type: recall_at_3 |
| 6044 | value: 11.112 |
| 6045 | - type: recall_at_5 |
| 6046 | value: 13.834 |
| 6047 | - type: recall_at_10 |
| 6048 | value: 16.961000000000002 |
| 6049 | - type: recall_at_20 |
| 6050 | value: 20.294999999999998 |
| 6051 | - type: recall_at_100 |
| 6052 | value: 30.253000000000004 |
| 6053 | - type: recall_at_1000 |
| 6054 | value: 60.902 |
| 6055 | - type: precision_at_1 |
| 6056 | value: 46.44 |
| 6057 | - type: precision_at_3 |
| 6058 | value: 39.009 |
| 6059 | - type: precision_at_5 |
| 6060 | value: 33.745999999999995 |
| 6061 | - type: precision_at_10 |
| 6062 | value: 25.635 |
| 6063 | - type: precision_at_20 |
| 6064 | value: 18.576 |
| 6065 | - type: precision_at_100 |
| 6066 | value: 7.731000000000001 |
| 6067 | - type: precision_at_1000 |
| 6068 | value: 2.037 |
| 6069 | - type: mrr_at_1 |
| 6070 | value: 46.7492 |
| 6071 | - type: mrr_at_3 |
| 6072 | value: 54.6956 |
| 6073 | - type: mrr_at_5 |
| 6074 | value: 55.8875 |
| 6075 | - type: mrr_at_10 |
| 6076 | value: 56.3913 |
| 6077 | - type: mrr_at_20 |
| 6078 | value: 56.6265 |
| 6079 | - type: mrr_at_100 |
| 6080 | value: 56.815599999999996 |
| 6081 | - type: mrr_at_1000 |
| 6082 | value: 56.8573 |
| 6083 | - type: nauc_ndcg_at_1_max |
| 6084 | value: 43.3685 |
| 6085 | - type: nauc_ndcg_at_1_std |
| 6086 | value: 21.6124 |
| 6087 | - type: nauc_ndcg_at_1_diff1 |
| 6088 | value: 29.0317 |
| 6089 | - type: nauc_ndcg_at_3_max |
| 6090 | value: 39.8155 |
| 6091 | - type: nauc_ndcg_at_3_std |
| 6092 | value: 23.2206 |
| 6093 | - type: nauc_ndcg_at_3_diff1 |
| 6094 | value: 20.7425 |
| 6095 | - type: nauc_ndcg_at_5_max |
| 6096 | value: 40.951 |
| 6097 | - type: nauc_ndcg_at_5_std |
| 6098 | value: 24.7184 |
| 6099 | - type: nauc_ndcg_at_5_diff1 |
| 6100 | value: 19.098599999999998 |
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| 6591 | revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
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| 6877 | task: |
| 6878 | type: Retrieval |
| 6879 | - dataset: |
| 6880 | config: default |
| 6881 | name: MTEB RedditClustering (default) |
| 6882 | revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| 6883 | split: test |
| 6884 | type: mteb/reddit-clustering |
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| 6892 | task: |
| 6893 | type: Clustering |
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| 6896 | name: MTEB RedditClusteringP2P (default) |
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| 6899 | type: mteb/reddit-clustering-p2p |
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| 6907 | task: |
| 6908 | type: Clustering |
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| 6911 | name: MTEB SCIDOCS (default) |
| 6912 | revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
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| 7094 | - type: nauc_recall_at_20_max |
| 7095 | value: 32.1047 |
| 7096 | - type: nauc_recall_at_20_std |
| 7097 | value: 24.823600000000003 |
| 7098 | - type: nauc_recall_at_20_diff1 |
| 7099 | value: 14.257700000000002 |
| 7100 | - type: nauc_recall_at_100_max |
| 7101 | value: 33.6062 |
| 7102 | - type: nauc_recall_at_100_std |
| 7103 | value: 33.8641 |
| 7104 | - type: nauc_recall_at_100_diff1 |
| 7105 | value: 14.5145 |
| 7106 | - type: nauc_recall_at_1000_max |
| 7107 | value: 26.848300000000002 |
| 7108 | - type: nauc_recall_at_1000_std |
| 7109 | value: 38.5884 |
| 7110 | - type: nauc_recall_at_1000_diff1 |
| 7111 | value: 5.6408 |
| 7112 | - type: nauc_precision_at_1_max |
| 7113 | value: 23.7575 |
| 7114 | - type: nauc_precision_at_1_std |
| 7115 | value: 4.1697 |
| 7116 | - type: nauc_precision_at_1_diff1 |
| 7117 | value: 28.3995 |
| 7118 | - type: nauc_precision_at_3_max |
| 7119 | value: 28.2504 |
| 7120 | - type: nauc_precision_at_3_std |
| 7121 | value: 10.6227 |
| 7122 | - type: nauc_precision_at_3_diff1 |
| 7123 | value: 19.5683 |
| 7124 | - type: nauc_precision_at_5_max |
| 7125 | value: 28.8134 |
| 7126 | - type: nauc_precision_at_5_std |
| 7127 | value: 12.518899999999999 |
| 7128 | - type: nauc_precision_at_5_diff1 |
| 7129 | value: 17.8036 |
| 7130 | - type: nauc_precision_at_10_max |
| 7131 | value: 30.9813 |
| 7132 | - type: nauc_precision_at_10_std |
| 7133 | value: 19.3506 |
| 7134 | - type: nauc_precision_at_10_diff1 |
| 7135 | value: 15.512 |
| 7136 | - type: nauc_precision_at_20_max |
| 7137 | value: 32.6743 |
| 7138 | - type: nauc_precision_at_20_std |
| 7139 | value: 24.9974 |
| 7140 | - type: nauc_precision_at_20_diff1 |
| 7141 | value: 14.794099999999998 |
| 7142 | - type: nauc_precision_at_100_max |
| 7143 | value: 34.413700000000006 |
| 7144 | - type: nauc_precision_at_100_std |
| 7145 | value: 34.0889 |
| 7146 | - type: nauc_precision_at_100_diff1 |
| 7147 | value: 15.252699999999999 |
| 7148 | - type: nauc_precision_at_1000_max |
| 7149 | value: 27.3954 |
| 7150 | - type: nauc_precision_at_1000_std |
| 7151 | value: 37.8895 |
| 7152 | - type: nauc_precision_at_1000_diff1 |
| 7153 | value: 6.587999999999999 |
| 7154 | - type: nauc_mrr_at_1_max |
| 7155 | value: 23.7575 |
| 7156 | - type: nauc_mrr_at_1_std |
| 7157 | value: 4.1697 |
| 7158 | - type: nauc_mrr_at_1_diff1 |
| 7159 | value: 28.3995 |
| 7160 | - type: nauc_mrr_at_3_max |
| 7161 | value: 26.8324 |
| 7162 | - type: nauc_mrr_at_3_std |
| 7163 | value: 8.646700000000001 |
| 7164 | - type: nauc_mrr_at_3_diff1 |
| 7165 | value: 25.5754 |
| 7166 | - type: nauc_mrr_at_5_max |
| 7167 | value: 26.8274 |
| 7168 | - type: nauc_mrr_at_5_std |
| 7169 | value: 8.911 |
| 7170 | - type: nauc_mrr_at_5_diff1 |
| 7171 | value: 25.106 |
| 7172 | - type: nauc_mrr_at_10_max |
| 7173 | value: 27.073399999999996 |
| 7174 | - type: nauc_mrr_at_10_std |
| 7175 | value: 9.7624 |
| 7176 | - type: nauc_mrr_at_10_diff1 |
| 7177 | value: 24.9405 |
| 7178 | - type: nauc_mrr_at_20_max |
| 7179 | value: 27.1229 |
| 7180 | - type: nauc_mrr_at_20_std |
| 7181 | value: 10.0676 |
| 7182 | - type: nauc_mrr_at_20_diff1 |
| 7183 | value: 24.8122 |
| 7184 | - type: nauc_mrr_at_100_max |
| 7185 | value: 27.1391 |
| 7186 | - type: nauc_mrr_at_100_std |
| 7187 | value: 9.9628 |
| 7188 | - type: nauc_mrr_at_100_diff1 |
| 7189 | value: 24.9507 |
| 7190 | - type: nauc_mrr_at_1000_max |
| 7191 | value: 27.114 |
| 7192 | - type: nauc_mrr_at_1000_std |
| 7193 | value: 9.9537 |
| 7194 | - type: nauc_mrr_at_1000_diff1 |
| 7195 | value: 24.9421 |
| 7196 | - type: main_score |
| 7197 | value: 20.238999999999997 |
| 7198 | task: |
| 7199 | type: Retrieval |
| 7200 | - dataset: |
| 7201 | config: default |
| 7202 | name: MTEB SICK-R (default) |
| 7203 | revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| 7204 | split: test |
| 7205 | type: mteb/sickr-sts |
| 7206 | metrics: |
| 7207 | - type: pearson |
| 7208 | value: 79.5908 |
| 7209 | - type: spearman |
| 7210 | value: 73.9888 |
| 7211 | - type: cosine_pearson |
| 7212 | value: 79.5908 |
| 7213 | - type: cosine_spearman |
| 7214 | value: 73.9888 |
| 7215 | - type: manhattan_pearson |
| 7216 | value: 77.0623 |
| 7217 | - type: manhattan_spearman |
| 7218 | value: 73.7724 |
| 7219 | - type: euclidean_pearson |
| 7220 | value: 77.30890000000001 |
| 7221 | - type: euclidean_spearman |
| 7222 | value: 73.9888 |
| 7223 | - type: main_score |
| 7224 | value: 73.9888 |
| 7225 | task: |
| 7226 | type: STS |
| 7227 | - dataset: |
| 7228 | config: default |
| 7229 | name: MTEB STS12 (default) |
| 7230 | revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| 7231 | split: test |
| 7232 | type: mteb/sts12-sts |
| 7233 | metrics: |
| 7234 | - type: pearson |
| 7235 | value: 74.0752 |
| 7236 | - type: spearman |
| 7237 | value: 71.22699999999999 |
| 7238 | - type: cosine_pearson |
| 7239 | value: 74.0752 |
| 7240 | - type: cosine_spearman |
| 7241 | value: 71.22699999999999 |
| 7242 | - type: manhattan_pearson |
| 7243 | value: 70.6037 |
| 7244 | - type: manhattan_spearman |
| 7245 | value: 70.9916 |
| 7246 | - type: euclidean_pearson |
| 7247 | value: 70.922 |
| 7248 | - type: euclidean_spearman |
| 7249 | value: 71.22699999999999 |
| 7250 | - type: main_score |
| 7251 | value: 71.22699999999999 |
| 7252 | task: |
| 7253 | type: STS |
| 7254 | - dataset: |
| 7255 | config: default |
| 7256 | name: MTEB STS13 (default) |
| 7257 | revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| 7258 | split: test |
| 7259 | type: mteb/sts13-sts |
| 7260 | metrics: |
| 7261 | - type: pearson |
| 7262 | value: 77.8946 |
| 7263 | - type: spearman |
| 7264 | value: 80.4405 |
| 7265 | - type: cosine_pearson |
| 7266 | value: 77.8946 |
| 7267 | - type: cosine_spearman |
| 7268 | value: 80.4405 |
| 7269 | - type: manhattan_pearson |
| 7270 | value: 79.6856 |
| 7271 | - type: manhattan_spearman |
| 7272 | value: 80.1236 |
| 7273 | - type: euclidean_pearson |
| 7274 | value: 80.0315 |
| 7275 | - type: euclidean_spearman |
| 7276 | value: 80.44059999999999 |
| 7277 | - type: main_score |
| 7278 | value: 80.4405 |
| 7279 | task: |
| 7280 | type: STS |
| 7281 | - dataset: |
| 7282 | config: default |
| 7283 | name: MTEB STS14 (default) |
| 7284 | revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| 7285 | split: test |
| 7286 | type: mteb/sts14-sts |
| 7287 | metrics: |
| 7288 | - type: pearson |
| 7289 | value: 76.2196 |
| 7290 | - type: spearman |
| 7291 | value: 75.10419999999999 |
| 7292 | - type: cosine_pearson |
| 7293 | value: 76.2196 |
| 7294 | - type: cosine_spearman |
| 7295 | value: 75.10419999999999 |
| 7296 | - type: manhattan_pearson |
| 7297 | value: 75.4647 |
| 7298 | - type: manhattan_spearman |
| 7299 | value: 74.81179999999999 |
| 7300 | - type: euclidean_pearson |
| 7301 | value: 75.8091 |
| 7302 | - type: euclidean_spearman |
| 7303 | value: 75.10419999999999 |
| 7304 | - type: main_score |
| 7305 | value: 75.10419999999999 |
| 7306 | task: |
| 7307 | type: STS |
| 7308 | - dataset: |
| 7309 | config: default |
| 7310 | name: MTEB STS15 (default) |
| 7311 | revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| 7312 | split: test |
| 7313 | type: mteb/sts15-sts |
| 7314 | metrics: |
| 7315 | - type: pearson |
| 7316 | value: 81.2455 |
| 7317 | - type: spearman |
| 7318 | value: 82.8681 |
| 7319 | - type: cosine_pearson |
| 7320 | value: 81.2455 |
| 7321 | - type: cosine_spearman |
| 7322 | value: 82.8681 |
| 7323 | - type: manhattan_pearson |
| 7324 | value: 82.4327 |
| 7325 | - type: manhattan_spearman |
| 7326 | value: 82.7513 |
| 7327 | - type: euclidean_pearson |
| 7328 | value: 82.5635 |
| 7329 | - type: euclidean_spearman |
| 7330 | value: 82.8681 |
| 7331 | - type: main_score |
| 7332 | value: 82.8681 |
| 7333 | task: |
| 7334 | type: STS |
| 7335 | - dataset: |
| 7336 | config: default |
| 7337 | name: MTEB STS16 (default) |
| 7338 | revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| 7339 | split: test |
| 7340 | type: mteb/sts16-sts |
| 7341 | metrics: |
| 7342 | - type: pearson |
| 7343 | value: 81.6322 |
| 7344 | - type: spearman |
| 7345 | value: 83.487 |
| 7346 | - type: cosine_pearson |
| 7347 | value: 81.6322 |
| 7348 | - type: cosine_spearman |
| 7349 | value: 83.487 |
| 7350 | - type: manhattan_pearson |
| 7351 | value: 83.0048 |
| 7352 | - type: manhattan_spearman |
| 7353 | value: 83.4064 |
| 7354 | - type: euclidean_pearson |
| 7355 | value: 83.0938 |
| 7356 | - type: euclidean_spearman |
| 7357 | value: 83.487 |
| 7358 | - type: main_score |
| 7359 | value: 83.487 |
| 7360 | task: |
| 7361 | type: STS |
| 7362 | - dataset: |
| 7363 | config: en-en |
| 7364 | name: MTEB STS17 (en-en) |
| 7365 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7366 | split: test |
| 7367 | type: mteb/sts17-crosslingual-sts |
| 7368 | metrics: |
| 7369 | - type: pearson |
| 7370 | value: 81.1124 |
| 7371 | - type: spearman |
| 7372 | value: 84.5436 |
| 7373 | - type: cosine_pearson |
| 7374 | value: 81.1124 |
| 7375 | - type: cosine_spearman |
| 7376 | value: 84.5436 |
| 7377 | - type: manhattan_pearson |
| 7378 | value: 83.5158 |
| 7379 | - type: manhattan_spearman |
| 7380 | value: 84.596 |
| 7381 | - type: euclidean_pearson |
| 7382 | value: 83.4429 |
| 7383 | - type: euclidean_spearman |
| 7384 | value: 84.5436 |
| 7385 | - type: main_score |
| 7386 | value: 84.5436 |
| 7387 | task: |
| 7388 | type: STS |
| 7389 | - dataset: |
| 7390 | config: en-tr |
| 7391 | name: MTEB STS17 (en-tr) |
| 7392 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7393 | split: test |
| 7394 | type: mteb/sts17-crosslingual-sts |
| 7395 | metrics: |
| 7396 | - type: pearson |
| 7397 | value: 62.0001 |
| 7398 | - type: spearman |
| 7399 | value: 63.631099999999996 |
| 7400 | - type: cosine_pearson |
| 7401 | value: 62.0001 |
| 7402 | - type: cosine_spearman |
| 7403 | value: 63.631099999999996 |
| 7404 | - type: manhattan_pearson |
| 7405 | value: 62.239599999999996 |
| 7406 | - type: manhattan_spearman |
| 7407 | value: 62.892199999999995 |
| 7408 | - type: euclidean_pearson |
| 7409 | value: 62.9809 |
| 7410 | - type: euclidean_spearman |
| 7411 | value: 63.631099999999996 |
| 7412 | - type: main_score |
| 7413 | value: 63.631099999999996 |
| 7414 | task: |
| 7415 | type: STS |
| 7416 | - dataset: |
| 7417 | config: it-en |
| 7418 | name: MTEB STS17 (it-en) |
| 7419 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7420 | split: test |
| 7421 | type: mteb/sts17-crosslingual-sts |
| 7422 | metrics: |
| 7423 | - type: pearson |
| 7424 | value: 75.1556 |
| 7425 | - type: spearman |
| 7426 | value: 76.8807 |
| 7427 | - type: cosine_pearson |
| 7428 | value: 75.1556 |
| 7429 | - type: cosine_spearman |
| 7430 | value: 76.8807 |
| 7431 | - type: manhattan_pearson |
| 7432 | value: 76.2428 |
| 7433 | - type: manhattan_spearman |
| 7434 | value: 76.8101 |
| 7435 | - type: euclidean_pearson |
| 7436 | value: 76.107 |
| 7437 | - type: euclidean_spearman |
| 7438 | value: 76.8807 |
| 7439 | - type: main_score |
| 7440 | value: 76.8807 |
| 7441 | task: |
| 7442 | type: STS |
| 7443 | - dataset: |
| 7444 | config: es-en |
| 7445 | name: MTEB STS17 (es-en) |
| 7446 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7447 | split: test |
| 7448 | type: mteb/sts17-crosslingual-sts |
| 7449 | metrics: |
| 7450 | - type: pearson |
| 7451 | value: 69.85719999999999 |
| 7452 | - type: spearman |
| 7453 | value: 71.0489 |
| 7454 | - type: cosine_pearson |
| 7455 | value: 69.85719999999999 |
| 7456 | - type: cosine_spearman |
| 7457 | value: 71.0489 |
| 7458 | - type: manhattan_pearson |
| 7459 | value: 71.08449999999999 |
| 7460 | - type: manhattan_spearman |
| 7461 | value: 71.0051 |
| 7462 | - type: euclidean_pearson |
| 7463 | value: 71.19760000000001 |
| 7464 | - type: euclidean_spearman |
| 7465 | value: 71.0489 |
| 7466 | - type: main_score |
| 7467 | value: 71.0489 |
| 7468 | task: |
| 7469 | type: STS |
| 7470 | - dataset: |
| 7471 | config: nl-en |
| 7472 | name: MTEB STS17 (nl-en) |
| 7473 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7474 | split: test |
| 7475 | type: mteb/sts17-crosslingual-sts |
| 7476 | metrics: |
| 7477 | - type: pearson |
| 7478 | value: 76.1131 |
| 7479 | - type: spearman |
| 7480 | value: 78.2714 |
| 7481 | - type: cosine_pearson |
| 7482 | value: 76.1131 |
| 7483 | - type: cosine_spearman |
| 7484 | value: 78.2714 |
| 7485 | - type: manhattan_pearson |
| 7486 | value: 76.70270000000001 |
| 7487 | - type: manhattan_spearman |
| 7488 | value: 77.7803 |
| 7489 | - type: euclidean_pearson |
| 7490 | value: 77.14269999999999 |
| 7491 | - type: euclidean_spearman |
| 7492 | value: 78.2714 |
| 7493 | - type: main_score |
| 7494 | value: 78.2714 |
| 7495 | task: |
| 7496 | type: STS |
| 7497 | - dataset: |
| 7498 | config: fr-en |
| 7499 | name: MTEB STS17 (fr-en) |
| 7500 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7501 | split: test |
| 7502 | type: mteb/sts17-crosslingual-sts |
| 7503 | metrics: |
| 7504 | - type: pearson |
| 7505 | value: 74.49719999999999 |
| 7506 | - type: spearman |
| 7507 | value: 76.2747 |
| 7508 | - type: cosine_pearson |
| 7509 | value: 74.49719999999999 |
| 7510 | - type: cosine_spearman |
| 7511 | value: 76.2747 |
| 7512 | - type: manhattan_pearson |
| 7513 | value: 75.071 |
| 7514 | - type: manhattan_spearman |
| 7515 | value: 75.8969 |
| 7516 | - type: euclidean_pearson |
| 7517 | value: 75.289 |
| 7518 | - type: euclidean_spearman |
| 7519 | value: 76.2747 |
| 7520 | - type: main_score |
| 7521 | value: 76.2747 |
| 7522 | task: |
| 7523 | type: STS |
| 7524 | - dataset: |
| 7525 | config: en-de |
| 7526 | name: MTEB STS17 (en-de) |
| 7527 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7528 | split: test |
| 7529 | type: mteb/sts17-crosslingual-sts |
| 7530 | metrics: |
| 7531 | - type: pearson |
| 7532 | value: 76.7073 |
| 7533 | - type: spearman |
| 7534 | value: 79.3107 |
| 7535 | - type: cosine_pearson |
| 7536 | value: 76.7073 |
| 7537 | - type: cosine_spearman |
| 7538 | value: 79.3107 |
| 7539 | - type: manhattan_pearson |
| 7540 | value: 77.9578 |
| 7541 | - type: manhattan_spearman |
| 7542 | value: 79.3195 |
| 7543 | - type: euclidean_pearson |
| 7544 | value: 77.7386 |
| 7545 | - type: euclidean_spearman |
| 7546 | value: 79.3107 |
| 7547 | - type: main_score |
| 7548 | value: 79.3107 |
| 7549 | task: |
| 7550 | type: STS |
| 7551 | - dataset: |
| 7552 | config: en-ar |
| 7553 | name: MTEB STS17 (en-ar) |
| 7554 | revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
| 7555 | split: test |
| 7556 | type: mteb/sts17-crosslingual-sts |
| 7557 | metrics: |
| 7558 | - type: pearson |
| 7559 | value: 60.5826 |
| 7560 | - type: spearman |
| 7561 | value: 61.0502 |
| 7562 | - type: cosine_pearson |
| 7563 | value: 60.5826 |
| 7564 | - type: cosine_spearman |
| 7565 | value: 61.0502 |
| 7566 | - type: manhattan_pearson |
| 7567 | value: 61.202 |
| 7568 | - type: manhattan_spearman |
| 7569 | value: 61.2039 |
| 7570 | - type: euclidean_pearson |
| 7571 | value: 61.1915 |
| 7572 | - type: euclidean_spearman |
| 7573 | value: 61.0502 |
| 7574 | - type: main_score |
| 7575 | value: 61.0502 |
| 7576 | task: |
| 7577 | type: STS |
| 7578 | - dataset: |
| 7579 | config: en |
| 7580 | name: MTEB STS22 (en) |
| 7581 | revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| 7582 | split: test |
| 7583 | type: mteb/sts22-crosslingual-sts |
| 7584 | metrics: |
| 7585 | - type: pearson |
| 7586 | value: 69.2521 |
| 7587 | - type: spearman |
| 7588 | value: 68.06219999999999 |
| 7589 | - type: cosine_pearson |
| 7590 | value: 69.2521 |
| 7591 | - type: cosine_spearman |
| 7592 | value: 68.06219999999999 |
| 7593 | - type: manhattan_pearson |
| 7594 | value: 70.5115 |
| 7595 | - type: manhattan_spearman |
| 7596 | value: 67.8705 |
| 7597 | - type: euclidean_pearson |
| 7598 | value: 70.68480000000001 |
| 7599 | - type: euclidean_spearman |
| 7600 | value: 68.06219999999999 |
| 7601 | - type: main_score |
| 7602 | value: 68.06219999999999 |
| 7603 | task: |
| 7604 | type: STS |
| 7605 | - dataset: |
| 7606 | config: pl-en |
| 7607 | name: MTEB STS22 (pl-en) |
| 7608 | revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| 7609 | split: test |
| 7610 | type: mteb/sts22-crosslingual-sts |
| 7611 | metrics: |
| 7612 | - type: pearson |
| 7613 | value: 77.97500000000001 |
| 7614 | - type: spearman |
| 7615 | value: 76.848 |
| 7616 | - type: cosine_pearson |
| 7617 | value: 77.97500000000001 |
| 7618 | - type: cosine_spearman |
| 7619 | value: 76.848 |
| 7620 | - type: manhattan_pearson |
| 7621 | value: 76.4098 |
| 7622 | - type: manhattan_spearman |
| 7623 | value: 76.6188 |
| 7624 | - type: euclidean_pearson |
| 7625 | value: 77.17500000000001 |
| 7626 | - type: euclidean_spearman |
| 7627 | value: 76.848 |
| 7628 | - type: main_score |
| 7629 | value: 76.848 |
| 7630 | task: |
| 7631 | type: STS |
| 7632 | - dataset: |
| 7633 | config: zh-en |
| 7634 | name: MTEB STS22 (zh-en) |
| 7635 | revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| 7636 | split: test |
| 7637 | type: mteb/sts22-crosslingual-sts |
| 7638 | metrics: |
| 7639 | - type: pearson |
| 7640 | value: 71.3604 |
| 7641 | - type: spearman |
| 7642 | value: 70.7891 |
| 7643 | - type: cosine_pearson |
| 7644 | value: 71.3604 |
| 7645 | - type: cosine_spearman |
| 7646 | value: 70.7891 |
| 7647 | - type: manhattan_pearson |
| 7648 | value: 73.0185 |
| 7649 | - type: manhattan_spearman |
| 7650 | value: 70.79299999999999 |
| 7651 | - type: euclidean_pearson |
| 7652 | value: 73.17620000000001 |
| 7653 | - type: euclidean_spearman |
| 7654 | value: 70.7891 |
| 7655 | - type: main_score |
| 7656 | value: 70.7891 |
| 7657 | task: |
| 7658 | type: STS |
| 7659 | - dataset: |
| 7660 | config: es-en |
| 7661 | name: MTEB STS22 (es-en) |
| 7662 | revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| 7663 | split: test |
| 7664 | type: mteb/sts22-crosslingual-sts |
| 7665 | metrics: |
| 7666 | - type: pearson |
| 7667 | value: 77.58239999999999 |
| 7668 | - type: spearman |
| 7669 | value: 78.5907 |
| 7670 | - type: cosine_pearson |
| 7671 | value: 77.58239999999999 |
| 7672 | - type: cosine_spearman |
| 7673 | value: 78.5907 |
| 7674 | - type: manhattan_pearson |
| 7675 | value: 79.25720000000001 |
| 7676 | - type: manhattan_spearman |
| 7677 | value: 78.6249 |
| 7678 | - type: euclidean_pearson |
| 7679 | value: 79.3724 |
| 7680 | - type: euclidean_spearman |
| 7681 | value: 78.5907 |
| 7682 | - type: main_score |
| 7683 | value: 78.5907 |
| 7684 | task: |
| 7685 | type: STS |
| 7686 | - dataset: |
| 7687 | config: de-en |
| 7688 | name: MTEB STS22 (de-en) |
| 7689 | revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| 7690 | split: test |
| 7691 | type: mteb/sts22-crosslingual-sts |
| 7692 | metrics: |
| 7693 | - type: pearson |
| 7694 | value: 63.324000000000005 |
| 7695 | - type: spearman |
| 7696 | value: 55.1099 |
| 7697 | - type: cosine_pearson |
| 7698 | value: 63.324000000000005 |
| 7699 | - type: cosine_spearman |
| 7700 | value: 55.1099 |
| 7701 | - type: manhattan_pearson |
| 7702 | value: 67.3128 |
| 7703 | - type: manhattan_spearman |
| 7704 | value: 56.340199999999996 |
| 7705 | - type: euclidean_pearson |
| 7706 | value: 67.12089999999999 |
| 7707 | - type: euclidean_spearman |
| 7708 | value: 55.1099 |
| 7709 | - type: main_score |
| 7710 | value: 55.1099 |
| 7711 | task: |
| 7712 | type: STS |
| 7713 | - dataset: |
| 7714 | config: default |
| 7715 | name: MTEB STSBenchmark (default) |
| 7716 | revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| 7717 | split: test |
| 7718 | type: mteb/stsbenchmark-sts |
| 7719 | metrics: |
| 7720 | - type: pearson |
| 7721 | value: 78.02329999999999 |
| 7722 | - type: spearman |
| 7723 | value: 79.1887 |
| 7724 | - type: cosine_pearson |
| 7725 | value: 78.02329999999999 |
| 7726 | - type: cosine_spearman |
| 7727 | value: 79.1887 |
| 7728 | - type: manhattan_pearson |
| 7729 | value: 78.8951 |
| 7730 | - type: manhattan_spearman |
| 7731 | value: 78.9444 |
| 7732 | - type: euclidean_pearson |
| 7733 | value: 79.1499 |
| 7734 | - type: euclidean_spearman |
| 7735 | value: 79.1888 |
| 7736 | - type: main_score |
| 7737 | value: 79.1887 |
| 7738 | task: |
| 7739 | type: STS |
| 7740 | - dataset: |
| 7741 | config: default |
| 7742 | name: MTEB SciDocsRR (default) |
| 7743 | revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| 7744 | split: test |
| 7745 | type: mteb/scidocs-reranking |
| 7746 | metrics: |
| 7747 | - type: map |
| 7748 | value: 78.7501 |
| 7749 | - type: mrr |
| 7750 | value: 93.9748 |
| 7751 | - type: nAUC_map_max |
| 7752 | value: 54.495599999999996 |
| 7753 | - type: nAUC_map_std |
| 7754 | value: 70.0377 |
| 7755 | - type: nAUC_map_diff1 |
| 7756 | value: 6.0146999999999995 |
| 7757 | - type: nAUC_mrr_max |
| 7758 | value: 81.1486 |
| 7759 | - type: nAUC_mrr_std |
| 7760 | value: 78.3478 |
| 7761 | - type: nAUC_mrr_diff1 |
| 7762 | value: 50.7613 |
| 7763 | - type: main_score |
| 7764 | value: 78.7501 |
| 7765 | task: |
| 7766 | type: Reranking |
| 7767 | - dataset: |
| 7768 | config: default |
| 7769 | name: MTEB SciFact (default) |
| 7770 | revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| 7771 | split: test |
| 7772 | type: mteb/scifact |
| 7773 | metrics: |
| 7774 | - type: ndcg_at_1 |
| 7775 | value: 58.667 |
| 7776 | - type: ndcg_at_3 |
| 7777 | value: 66.022 |
| 7778 | - type: ndcg_at_5 |
| 7779 | value: 68.508 |
| 7780 | - type: ndcg_at_10 |
| 7781 | value: 70.586 |
| 7782 | - type: ndcg_at_20 |
| 7783 | value: 71.714 |
| 7784 | - type: ndcg_at_100 |
| 7785 | value: 72.81 |
| 7786 | - type: ndcg_at_1000 |
| 7787 | value: 73.482 |
| 7788 | - type: map_at_1 |
| 7789 | value: 55.594 |
| 7790 | - type: map_at_3 |
| 7791 | value: 63.2 |
| 7792 | - type: map_at_5 |
| 7793 | value: 64.996 |
| 7794 | - type: map_at_10 |
| 7795 | value: 65.988 |
| 7796 | - type: map_at_20 |
| 7797 | value: 66.347 |
| 7798 | - type: map_at_100 |
| 7799 | value: 66.526 |
| 7800 | - type: map_at_1000 |
| 7801 | value: 66.547 |
| 7802 | - type: recall_at_1 |
| 7803 | value: 55.594 |
| 7804 | - type: recall_at_3 |
| 7805 | value: 71.22800000000001 |
| 7806 | - type: recall_at_5 |
| 7807 | value: 77.078 |
| 7808 | - type: recall_at_10 |
| 7809 | value: 83.172 |
| 7810 | - type: recall_at_20 |
| 7811 | value: 87.422 |
| 7812 | - type: recall_at_100 |
| 7813 | value: 93.167 |
| 7814 | - type: recall_at_1000 |
| 7815 | value: 98.667 |
| 7816 | - type: precision_at_1 |
| 7817 | value: 58.667 |
| 7818 | - type: precision_at_3 |
| 7819 | value: 25.778000000000002 |
| 7820 | - type: precision_at_5 |
| 7821 | value: 17.333000000000002 |
| 7822 | - type: precision_at_10 |
| 7823 | value: 9.433 |
| 7824 | - type: precision_at_20 |
| 7825 | value: 4.967 |
| 7826 | - type: precision_at_100 |
| 7827 | value: 1.06 |
| 7828 | - type: precision_at_1000 |
| 7829 | value: 0.11199999999999999 |
| 7830 | - type: mrr_at_1 |
| 7831 | value: 58.666700000000006 |
| 7832 | - type: mrr_at_3 |
| 7833 | value: 65.3889 |
| 7834 | - type: mrr_at_5 |
| 7835 | value: 66.62219999999999 |
| 7836 | - type: mrr_at_10 |
| 7837 | value: 67.3364 |
| 7838 | - type: mrr_at_20 |
| 7839 | value: 67.6046 |
| 7840 | - type: mrr_at_100 |
| 7841 | value: 67.73320000000001 |
| 7842 | - type: mrr_at_1000 |
| 7843 | value: 67.7526 |
| 7844 | - type: nauc_ndcg_at_1_max |
| 7845 | value: 60.2511 |
| 7846 | - type: nauc_ndcg_at_1_std |
| 7847 | value: 12.422 |
| 7848 | - type: nauc_ndcg_at_1_diff1 |
| 7849 | value: 74.4289 |
| 7850 | - type: nauc_ndcg_at_3_max |
| 7851 | value: 60.2109 |
| 7852 | - type: nauc_ndcg_at_3_std |
| 7853 | value: 11.0152 |
| 7854 | - type: nauc_ndcg_at_3_diff1 |
| 7855 | value: 71.0436 |
| 7856 | - type: nauc_ndcg_at_5_max |
| 7857 | value: 62.690999999999995 |
| 7858 | - type: nauc_ndcg_at_5_std |
| 7859 | value: 13.585700000000001 |
| 7860 | - type: nauc_ndcg_at_5_diff1 |
| 7861 | value: 70.4007 |
| 7862 | - type: nauc_ndcg_at_10_max |
| 7863 | value: 62.740899999999996 |
| 7864 | - type: nauc_ndcg_at_10_std |
| 7865 | value: 13.980400000000001 |
| 7866 | - type: nauc_ndcg_at_10_diff1 |
| 7867 | value: 70.0506 |
| 7868 | - type: nauc_ndcg_at_20_max |
| 7869 | value: 62.271699999999996 |
| 7870 | - type: nauc_ndcg_at_20_std |
| 7871 | value: 15.9756 |
| 7872 | - type: nauc_ndcg_at_20_diff1 |
| 7873 | value: 70.3237 |
| 7874 | - type: nauc_ndcg_at_100_max |
| 7875 | value: 62.125 |
| 7876 | - type: nauc_ndcg_at_100_std |
| 7877 | value: 15.5809 |
| 7878 | - type: nauc_ndcg_at_100_diff1 |
| 7879 | value: 70.4151 |
| 7880 | - type: nauc_ndcg_at_1000_max |
| 7881 | value: 61.9259 |
| 7882 | - type: nauc_ndcg_at_1000_std |
| 7883 | value: 15.3462 |
| 7884 | - type: nauc_ndcg_at_1000_diff1 |
| 7885 | value: 70.7346 |
| 7886 | - type: nauc_map_at_1_max |
| 7887 | value: 53.6767 |
| 7888 | - type: nauc_map_at_1_std |
| 7889 | value: 3.7751 |
| 7890 | - type: nauc_map_at_1_diff1 |
| 7891 | value: 74.60329999999999 |
| 7892 | - type: nauc_map_at_3_max |
| 7893 | value: 57.0403 |
| 7894 | - type: nauc_map_at_3_std |
| 7895 | value: 8.2272 |
| 7896 | - type: nauc_map_at_3_diff1 |
| 7897 | value: 71.7906 |
| 7898 | - type: nauc_map_at_5_max |
| 7899 | value: 59.6713 |
| 7900 | - type: nauc_map_at_5_std |
| 7901 | value: 10.8346 |
| 7902 | - type: nauc_map_at_5_diff1 |
| 7903 | value: 71.3356 |
| 7904 | - type: nauc_map_at_10_max |
| 7905 | value: 60.0086 |
| 7906 | - type: nauc_map_at_10_std |
| 7907 | value: 11.4394 |
| 7908 | - type: nauc_map_at_10_diff1 |
| 7909 | value: 71.14869999999999 |
| 7910 | - type: nauc_map_at_20_max |
| 7911 | value: 59.940599999999996 |
| 7912 | - type: nauc_map_at_20_std |
| 7913 | value: 12.0728 |
| 7914 | - type: nauc_map_at_20_diff1 |
| 7915 | value: 71.31 |
| 7916 | - type: nauc_map_at_100_max |
| 7917 | value: 59.95589999999999 |
| 7918 | - type: nauc_map_at_100_std |
| 7919 | value: 12.148299999999999 |
| 7920 | - type: nauc_map_at_100_diff1 |
| 7921 | value: 71.2142 |
| 7922 | - type: nauc_map_at_1000_max |
| 7923 | value: 59.9486 |
| 7924 | - type: nauc_map_at_1000_std |
| 7925 | value: 12.139 |
| 7926 | - type: nauc_map_at_1000_diff1 |
| 7927 | value: 71.2225 |
| 7928 | - type: nauc_recall_at_1_max |
| 7929 | value: 53.6767 |
| 7930 | - type: nauc_recall_at_1_std |
| 7931 | value: 3.7751 |
| 7932 | - type: nauc_recall_at_1_diff1 |
| 7933 | value: 74.60329999999999 |
| 7934 | - type: nauc_recall_at_3_max |
| 7935 | value: 60.4078 |
| 7936 | - type: nauc_recall_at_3_std |
| 7937 | value: 9.038300000000001 |
| 7938 | - type: nauc_recall_at_3_diff1 |
| 7939 | value: 67.60119999999999 |
| 7940 | - type: nauc_recall_at_5_max |
| 7941 | value: 68.0179 |
| 7942 | - type: nauc_recall_at_5_std |
| 7943 | value: 16.061600000000002 |
| 7944 | - type: nauc_recall_at_5_diff1 |
| 7945 | value: 65.54759999999999 |
| 7946 | - type: nauc_recall_at_10_max |
| 7947 | value: 68.7372 |
| 7948 | - type: nauc_recall_at_10_std |
| 7949 | value: 16.8637 |
| 7950 | - type: nauc_recall_at_10_diff1 |
| 7951 | value: 62.7613 |
| 7952 | - type: nauc_recall_at_20_max |
| 7953 | value: 67.1403 |
| 7954 | - type: nauc_recall_at_20_std |
| 7955 | value: 31.3919 |
| 7956 | - type: nauc_recall_at_20_diff1 |
| 7957 | value: 62.66929999999999 |
| 7958 | - type: nauc_recall_at_100_max |
| 7959 | value: 68.6366 |
| 7960 | - type: nauc_recall_at_100_std |
| 7961 | value: 32.4577 |
| 7962 | - type: nauc_recall_at_100_diff1 |
| 7963 | value: 64.52029999999999 |
| 7964 | - type: nauc_recall_at_1000_max |
| 7965 | value: 70.7166 |
| 7966 | - type: nauc_recall_at_1000_std |
| 7967 | value: 70.47149999999999 |
| 7968 | - type: nauc_recall_at_1000_diff1 |
| 7969 | value: 85.58590000000001 |
| 7970 | - type: nauc_precision_at_1_max |
| 7971 | value: 60.2511 |
| 7972 | - type: nauc_precision_at_1_std |
| 7973 | value: 12.422 |
| 7974 | - type: nauc_precision_at_1_diff1 |
| 7975 | value: 74.4289 |
| 7976 | - type: nauc_precision_at_3_max |
| 7977 | value: 58.75280000000001 |
| 7978 | - type: nauc_precision_at_3_std |
| 7979 | value: 27.605400000000003 |
| 7980 | - type: nauc_precision_at_3_diff1 |
| 7981 | value: 49.1523 |
| 7982 | - type: nauc_precision_at_5_max |
| 7983 | value: 56.4694 |
| 7984 | - type: nauc_precision_at_5_std |
| 7985 | value: 39.080799999999996 |
| 7986 | - type: nauc_precision_at_5_diff1 |
| 7987 | value: 28.8162 |
| 7988 | - type: nauc_precision_at_10_max |
| 7989 | value: 48.8894 |
| 7990 | - type: nauc_precision_at_10_std |
| 7991 | value: 43.8149 |
| 7992 | - type: nauc_precision_at_10_diff1 |
| 7993 | value: 15.0093 |
| 7994 | - type: nauc_precision_at_20_max |
| 7995 | value: 41.4059 |
| 7996 | - type: nauc_precision_at_20_std |
| 7997 | value: 50.7143 |
| 7998 | - type: nauc_precision_at_20_diff1 |
| 7999 | value: 8.3552 |
| 8000 | - type: nauc_precision_at_100_max |
| 8001 | value: 33.5064 |
| 8002 | - type: nauc_precision_at_100_std |
| 8003 | value: 52.8775 |
| 8004 | - type: nauc_precision_at_100_diff1 |
| 8005 | value: -5.0870999999999995 |
| 8006 | - type: nauc_precision_at_1000_max |
| 8007 | value: 23.9064 |
| 8008 | - type: nauc_precision_at_1000_std |
| 8009 | value: 57.784800000000004 |
| 8010 | - type: nauc_precision_at_1000_diff1 |
| 8011 | value: -20.1246 |
| 8012 | - type: nauc_mrr_at_1_max |
| 8013 | value: 60.2511 |
| 8014 | - type: nauc_mrr_at_1_std |
| 8015 | value: 12.422 |
| 8016 | - type: nauc_mrr_at_1_diff1 |
| 8017 | value: 74.4289 |
| 8018 | - type: nauc_mrr_at_3_max |
| 8019 | value: 62.663199999999996 |
| 8020 | - type: nauc_mrr_at_3_std |
| 8021 | value: 14.7348 |
| 8022 | - type: nauc_mrr_at_3_diff1 |
| 8023 | value: 72.1185 |
| 8024 | - type: nauc_mrr_at_5_max |
| 8025 | value: 63.3871 |
| 8026 | - type: nauc_mrr_at_5_std |
| 8027 | value: 15.773000000000001 |
| 8028 | - type: nauc_mrr_at_5_diff1 |
| 8029 | value: 71.6722 |
| 8030 | - type: nauc_mrr_at_10_max |
| 8031 | value: 62.8474 |
| 8032 | - type: nauc_mrr_at_10_std |
| 8033 | value: 15.1896 |
| 8034 | - type: nauc_mrr_at_10_diff1 |
| 8035 | value: 71.64110000000001 |
| 8036 | - type: nauc_mrr_at_20_max |
| 8037 | value: 62.699400000000004 |
| 8038 | - type: nauc_mrr_at_20_std |
| 8039 | value: 15.554499999999999 |
| 8040 | - type: nauc_mrr_at_20_diff1 |
| 8041 | value: 71.6049 |
| 8042 | - type: nauc_mrr_at_100_max |
| 8043 | value: 62.6665 |
| 8044 | - type: nauc_mrr_at_100_std |
| 8045 | value: 15.4586 |
| 8046 | - type: nauc_mrr_at_100_diff1 |
| 8047 | value: 71.6217 |
| 8048 | - type: nauc_mrr_at_1000_max |
| 8049 | value: 62.6641 |
| 8050 | - type: nauc_mrr_at_1000_std |
| 8051 | value: 15.4535 |
| 8052 | - type: nauc_mrr_at_1000_diff1 |
| 8053 | value: 71.6307 |
| 8054 | - type: main_score |
| 8055 | value: 70.586 |
| 8056 | task: |
| 8057 | type: Retrieval |
| 8058 | - dataset: |
| 8059 | config: default |
| 8060 | name: MTEB SprintDuplicateQuestions (default) |
| 8061 | revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| 8062 | split: test |
| 8063 | type: mteb/sprintduplicatequestions-pairclassification |
| 8064 | metrics: |
| 8065 | - type: similarity_accuracy |
| 8066 | value: 99.8416 |
| 8067 | - type: similarity_accuracy_threshold |
| 8068 | value: 74.52069999999999 |
| 8069 | - type: similarity_f1 |
| 8070 | value: 92.008 |
| 8071 | - type: similarity_f1_threshold |
| 8072 | value: 74.4529 |
| 8073 | - type: similarity_precision |
| 8074 | value: 91.9162 |
| 8075 | - type: similarity_recall |
| 8076 | value: 92.10000000000001 |
| 8077 | - type: similarity_ap |
| 8078 | value: 96.54209999999999 |
| 8079 | - type: cosine_accuracy |
| 8080 | value: 99.8416 |
| 8081 | - type: cosine_accuracy_threshold |
| 8082 | value: 74.52069999999999 |
| 8083 | - type: cosine_f1 |
| 8084 | value: 92.008 |
| 8085 | - type: cosine_f1_threshold |
| 8086 | value: 74.4529 |
| 8087 | - type: cosine_precision |
| 8088 | value: 91.9162 |
| 8089 | - type: cosine_recall |
| 8090 | value: 92.10000000000001 |
| 8091 | - type: cosine_ap |
| 8092 | value: 96.54209999999999 |
| 8093 | - type: manhattan_accuracy |
| 8094 | value: 99.8446 |
| 8095 | - type: manhattan_accuracy_threshold |
| 8096 | value: 1784.866 |
| 8097 | - type: manhattan_f1 |
| 8098 | value: 92.1539 |
| 8099 | - type: manhattan_f1_threshold |
| 8100 | value: 1787.6774 |
| 8101 | - type: manhattan_precision |
| 8102 | value: 92.1079 |
| 8103 | - type: manhattan_recall |
| 8104 | value: 92.2 |
| 8105 | - type: manhattan_ap |
| 8106 | value: 96.5207 |
| 8107 | - type: euclidean_accuracy |
| 8108 | value: 99.8416 |
| 8109 | - type: euclidean_accuracy_threshold |
| 8110 | value: 71.3853 |
| 8111 | - type: euclidean_f1 |
| 8112 | value: 92.008 |
| 8113 | - type: euclidean_f1_threshold |
| 8114 | value: 71.4803 |
| 8115 | - type: euclidean_precision |
| 8116 | value: 91.9162 |
| 8117 | - type: euclidean_recall |
| 8118 | value: 92.10000000000001 |
| 8119 | - type: euclidean_ap |
| 8120 | value: 96.54209999999999 |
| 8121 | - type: dot_accuracy |
| 8122 | value: 99.8416 |
| 8123 | - type: dot_accuracy_threshold |
| 8124 | value: 74.52069999999999 |
| 8125 | - type: dot_f1 |
| 8126 | value: 92.008 |
| 8127 | - type: dot_f1_threshold |
| 8128 | value: 74.4528 |
| 8129 | - type: dot_precision |
| 8130 | value: 91.9162 |
| 8131 | - type: dot_recall |
| 8132 | value: 92.10000000000001 |
| 8133 | - type: dot_ap |
| 8134 | value: 96.54209999999999 |
| 8135 | - type: max_accuracy |
| 8136 | value: 99.8446 |
| 8137 | - type: max_f1 |
| 8138 | value: 92.1539 |
| 8139 | - type: max_precision |
| 8140 | value: 92.1079 |
| 8141 | - type: max_recall |
| 8142 | value: 92.2 |
| 8143 | - type: max_ap |
| 8144 | value: 96.54209999999999 |
| 8145 | - type: main_score |
| 8146 | value: 96.54209999999999 |
| 8147 | task: |
| 8148 | type: PairClassification |
| 8149 | - dataset: |
| 8150 | config: default |
| 8151 | name: MTEB StackExchangeClustering (default) |
| 8152 | revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| 8153 | split: test |
| 8154 | type: mteb/stackexchange-clustering |
| 8155 | metrics: |
| 8156 | - type: v_measure |
| 8157 | value: 63.4035 |
| 8158 | - type: v_measure_std |
| 8159 | value: 4.758 |
| 8160 | - type: main_score |
| 8161 | value: 63.4035 |
| 8162 | task: |
| 8163 | type: Clustering |
| 8164 | - dataset: |
| 8165 | config: default |
| 8166 | name: MTEB StackExchangeClusteringP2P (default) |
| 8167 | revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| 8168 | split: test |
| 8169 | type: mteb/stackexchange-clustering-p2p |
| 8170 | metrics: |
| 8171 | - type: v_measure |
| 8172 | value: 36.288599999999995 |
| 8173 | - type: v_measure_std |
| 8174 | value: 1.3107 |
| 8175 | - type: main_score |
| 8176 | value: 36.288599999999995 |
| 8177 | task: |
| 8178 | type: Clustering |
| 8179 | - dataset: |
| 8180 | config: default |
| 8181 | name: MTEB StackOverflowDupQuestions (default) |
| 8182 | revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
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| 8184 | type: mteb/stackoverflowdupquestions-reranking |
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| 8228 | type: Summarization |
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| 8518 | task: |
| 8519 | type: Retrieval |
| 8520 | - dataset: |
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| 8685 | - type: nauc_recall_at_1_diff1 |
| 8686 | value: -1.4354 |
| 8687 | - type: nauc_recall_at_3_max |
| 8688 | value: -23.021900000000002 |
| 8689 | - type: nauc_recall_at_3_std |
| 8690 | value: -30.2834 |
| 8691 | - type: nauc_recall_at_3_diff1 |
| 8692 | value: -11.4226 |
| 8693 | - type: nauc_recall_at_5_max |
| 8694 | value: -20.596600000000002 |
| 8695 | - type: nauc_recall_at_5_std |
| 8696 | value: -33.219300000000004 |
| 8697 | - type: nauc_recall_at_5_diff1 |
| 8698 | value: -17.718999999999998 |
| 8699 | - type: nauc_recall_at_10_max |
| 8700 | value: -16.1214 |
| 8701 | - type: nauc_recall_at_10_std |
| 8702 | value: -23.9041 |
| 8703 | - type: nauc_recall_at_10_diff1 |
| 8704 | value: -11.047 |
| 8705 | - type: nauc_recall_at_20_max |
| 8706 | value: -25.603399999999997 |
| 8707 | - type: nauc_recall_at_20_std |
| 8708 | value: -15.8105 |
| 8709 | - type: nauc_recall_at_20_diff1 |
| 8710 | value: -14.546000000000001 |
| 8711 | - type: nauc_recall_at_100_max |
| 8712 | value: -16.389400000000002 |
| 8713 | - type: nauc_recall_at_100_std |
| 8714 | value: 28.5141 |
| 8715 | - type: nauc_recall_at_100_diff1 |
| 8716 | value: 6.1868 |
| 8717 | - type: nauc_recall_at_1000_max |
| 8718 | value: 11.022 |
| 8719 | - type: nauc_recall_at_1000_std |
| 8720 | value: 68.0021 |
| 8721 | - type: nauc_recall_at_1000_diff1 |
| 8722 | value: 8.426 |
| 8723 | - type: nauc_precision_at_1_max |
| 8724 | value: -17.1625 |
| 8725 | - type: nauc_precision_at_1_std |
| 8726 | value: -27.9451 |
| 8727 | - type: nauc_precision_at_1_diff1 |
| 8728 | value: 1.0831 |
| 8729 | - type: nauc_precision_at_3_max |
| 8730 | value: -17.2798 |
| 8731 | - type: nauc_precision_at_3_std |
| 8732 | value: -20.347199999999997 |
| 8733 | - type: nauc_precision_at_3_diff1 |
| 8734 | value: -5.2689 |
| 8735 | - type: nauc_precision_at_5_max |
| 8736 | value: -19.6408 |
| 8737 | - type: nauc_precision_at_5_std |
| 8738 | value: -24.157 |
| 8739 | - type: nauc_precision_at_5_diff1 |
| 8740 | value: -20.274900000000002 |
| 8741 | - type: nauc_precision_at_10_max |
| 8742 | value: -11.8033 |
| 8743 | - type: nauc_precision_at_10_std |
| 8744 | value: -7.2727 |
| 8745 | - type: nauc_precision_at_10_diff1 |
| 8746 | value: -9.3776 |
| 8747 | - type: nauc_precision_at_20_max |
| 8748 | value: -20.1541 |
| 8749 | - type: nauc_precision_at_20_std |
| 8750 | value: 9.0645 |
| 8751 | - type: nauc_precision_at_20_diff1 |
| 8752 | value: -16.1323 |
| 8753 | - type: nauc_precision_at_100_max |
| 8754 | value: 0.3701 |
| 8755 | - type: nauc_precision_at_100_std |
| 8756 | value: 67.6941 |
| 8757 | - type: nauc_precision_at_100_diff1 |
| 8758 | value: 8.0336 |
| 8759 | - type: nauc_precision_at_1000_max |
| 8760 | value: 38.8632 |
| 8761 | - type: nauc_precision_at_1000_std |
| 8762 | value: 38.0504 |
| 8763 | - type: nauc_precision_at_1000_diff1 |
| 8764 | value: 0.5907 |
| 8765 | - type: nauc_mrr_at_1_max |
| 8766 | value: -17.1625 |
| 8767 | - type: nauc_mrr_at_1_std |
| 8768 | value: -27.9451 |
| 8769 | - type: nauc_mrr_at_1_diff1 |
| 8770 | value: 1.0831 |
| 8771 | - type: nauc_mrr_at_3_max |
| 8772 | value: -20.479300000000002 |
| 8773 | - type: nauc_mrr_at_3_std |
| 8774 | value: -21.9225 |
| 8775 | - type: nauc_mrr_at_3_diff1 |
| 8776 | value: -1.5211000000000001 |
| 8777 | - type: nauc_mrr_at_5_max |
| 8778 | value: -24.8175 |
| 8779 | - type: nauc_mrr_at_5_std |
| 8780 | value: -23.805 |
| 8781 | - type: nauc_mrr_at_5_diff1 |
| 8782 | value: -7.9258 |
| 8783 | - type: nauc_mrr_at_10_max |
| 8784 | value: -22.53 |
| 8785 | - type: nauc_mrr_at_10_std |
| 8786 | value: -21.9391 |
| 8787 | - type: nauc_mrr_at_10_diff1 |
| 8788 | value: -5.7533 |
| 8789 | - type: nauc_mrr_at_20_max |
| 8790 | value: -22.7064 |
| 8791 | - type: nauc_mrr_at_20_std |
| 8792 | value: -22.4697 |
| 8793 | - type: nauc_mrr_at_20_diff1 |
| 8794 | value: -5.7068 |
| 8795 | - type: nauc_mrr_at_100_max |
| 8796 | value: -23.0016 |
| 8797 | - type: nauc_mrr_at_100_std |
| 8798 | value: -22.488 |
| 8799 | - type: nauc_mrr_at_100_diff1 |
| 8800 | value: -5.3738 |
| 8801 | - type: nauc_mrr_at_1000_max |
| 8802 | value: -23.0016 |
| 8803 | - type: nauc_mrr_at_1000_std |
| 8804 | value: -22.488 |
| 8805 | - type: nauc_mrr_at_1000_diff1 |
| 8806 | value: -5.3738 |
| 8807 | - type: main_score |
| 8808 | value: 29.499 |
| 8809 | task: |
| 8810 | type: Retrieval |
| 8811 | - dataset: |
| 8812 | config: default |
| 8813 | name: MTEB ToxicConversationsClassification (default) |
| 8814 | revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| 8815 | split: test |
| 8816 | type: mteb/toxic_conversations_50k |
| 8817 | metrics: |
| 8818 | - type: accuracy |
| 8819 | value: 65.8643 |
| 8820 | - type: f1 |
| 8821 | value: 50.6764 |
| 8822 | - type: f1_weighted |
| 8823 | value: 73.2472 |
| 8824 | - type: ap |
| 8825 | value: 12.2658 |
| 8826 | - type: ap_weighted |
| 8827 | value: 12.2658 |
| 8828 | - type: main_score |
| 8829 | value: 65.8643 |
| 8830 | task: |
| 8831 | type: Classification |
| 8832 | - dataset: |
| 8833 | config: default |
| 8834 | name: MTEB TweetSentimentExtractionClassification (default) |
| 8835 | revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| 8836 | split: test |
| 8837 | type: mteb/tweet_sentiment_extraction |
| 8838 | metrics: |
| 8839 | - type: accuracy |
| 8840 | value: 59.6633 |
| 8841 | - type: f1 |
| 8842 | value: 59.935700000000004 |
| 8843 | - type: f1_weighted |
| 8844 | value: 59.0249 |
| 8845 | - type: main_score |
| 8846 | value: 59.6633 |
| 8847 | task: |
| 8848 | type: Classification |
| 8849 | - dataset: |
| 8850 | config: default |
| 8851 | name: MTEB TwentyNewsgroupsClustering (default) |
| 8852 | revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| 8853 | split: test |
| 8854 | type: mteb/twentynewsgroups-clustering |
| 8855 | metrics: |
| 8856 | - type: v_measure |
| 8857 | value: 43.2311 |
| 8858 | - type: v_measure_std |
| 8859 | value: 2.3994999999999997 |
| 8860 | - type: main_score |
| 8861 | value: 43.2311 |
| 8862 | task: |
| 8863 | type: Clustering |
| 8864 | - dataset: |
| 8865 | config: default |
| 8866 | name: MTEB TwitterSemEval2015 (default) |
| 8867 | revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| 8868 | split: test |
| 8869 | type: mteb/twittersemeval2015-pairclassification |
| 8870 | metrics: |
| 8871 | - type: similarity_accuracy |
| 8872 | value: 83.8469 |
| 8873 | - type: similarity_accuracy_threshold |
| 8874 | value: 77.6695 |
| 8875 | - type: similarity_f1 |
| 8876 | value: 62.3159 |
| 8877 | - type: similarity_f1_threshold |
| 8878 | value: 71.6554 |
| 8879 | - type: similarity_precision |
| 8880 | value: 59.114599999999996 |
| 8881 | - type: similarity_recall |
| 8882 | value: 65.8839 |
| 8883 | - type: similarity_ap |
| 8884 | value: 67.00930000000001 |
| 8885 | - type: cosine_accuracy |
| 8886 | value: 83.8469 |
| 8887 | - type: cosine_accuracy_threshold |
| 8888 | value: 77.6695 |
| 8889 | - type: cosine_f1 |
| 8890 | value: 62.3159 |
| 8891 | - type: cosine_f1_threshold |
| 8892 | value: 71.6554 |
| 8893 | - type: cosine_precision |
| 8894 | value: 59.114599999999996 |
| 8895 | - type: cosine_recall |
| 8896 | value: 65.8839 |
| 8897 | - type: cosine_ap |
| 8898 | value: 67.00930000000001 |
| 8899 | - type: manhattan_accuracy |
| 8900 | value: 83.7694 |
| 8901 | - type: manhattan_accuracy_threshold |
| 8902 | value: 1677.8293999999999 |
| 8903 | - type: manhattan_f1 |
| 8904 | value: 62.1324 |
| 8905 | - type: manhattan_f1_threshold |
| 8906 | value: 1848.6641 |
| 8907 | - type: manhattan_precision |
| 8908 | value: 61.839999999999996 |
| 8909 | - type: manhattan_recall |
| 8910 | value: 62.4274 |
| 8911 | - type: manhattan_ap |
| 8912 | value: 66.8849 |
| 8913 | - type: euclidean_accuracy |
| 8914 | value: 83.8469 |
| 8915 | - type: euclidean_accuracy_threshold |
| 8916 | value: 66.8288 |
| 8917 | - type: euclidean_f1 |
| 8918 | value: 62.3159 |
| 8919 | - type: euclidean_f1_threshold |
| 8920 | value: 75.2922 |
| 8921 | - type: euclidean_precision |
| 8922 | value: 59.114599999999996 |
| 8923 | - type: euclidean_recall |
| 8924 | value: 65.8839 |
| 8925 | - type: euclidean_ap |
| 8926 | value: 67.00930000000001 |
| 8927 | - type: dot_accuracy |
| 8928 | value: 83.8469 |
| 8929 | - type: dot_accuracy_threshold |
| 8930 | value: 77.6695 |
| 8931 | - type: dot_f1 |
| 8932 | value: 62.3159 |
| 8933 | - type: dot_f1_threshold |
| 8934 | value: 71.6554 |
| 8935 | - type: dot_precision |
| 8936 | value: 59.114599999999996 |
| 8937 | - type: dot_recall |
| 8938 | value: 65.8839 |
| 8939 | - type: dot_ap |
| 8940 | value: 67.00930000000001 |
| 8941 | - type: max_accuracy |
| 8942 | value: 83.8469 |
| 8943 | - type: max_f1 |
| 8944 | value: 62.3159 |
| 8945 | - type: max_precision |
| 8946 | value: 61.839999999999996 |
| 8947 | - type: max_recall |
| 8948 | value: 65.8839 |
| 8949 | - type: max_ap |
| 8950 | value: 67.00930000000001 |
| 8951 | - type: main_score |
| 8952 | value: 67.00930000000001 |
| 8953 | task: |
| 8954 | type: PairClassification |
| 8955 | - dataset: |
| 8956 | config: default |
| 8957 | name: MTEB TwitterURLCorpus (default) |
| 8958 | revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| 8959 | split: test |
| 8960 | type: mteb/twitterurlcorpus-pairclassification |
| 8961 | metrics: |
| 8962 | - type: similarity_accuracy |
| 8963 | value: 88.8811 |
| 8964 | - type: similarity_accuracy_threshold |
| 8965 | value: 71.1053 |
| 8966 | - type: similarity_f1 |
| 8967 | value: 77.9005 |
| 8968 | - type: similarity_f1_threshold |
| 8969 | value: 67.5068 |
| 8970 | - type: similarity_precision |
| 8971 | value: 75.5609 |
| 8972 | - type: similarity_recall |
| 8973 | value: 80.3896 |
| 8974 | - type: similarity_ap |
| 8975 | value: 85.459 |
| 8976 | - type: cosine_accuracy |
| 8977 | value: 88.8811 |
| 8978 | - type: cosine_accuracy_threshold |
| 8979 | value: 71.1053 |
| 8980 | - type: cosine_f1 |
| 8981 | value: 77.9005 |
| 8982 | - type: cosine_f1_threshold |
| 8983 | value: 67.5068 |
| 8984 | - type: cosine_precision |
| 8985 | value: 75.5609 |
| 8986 | - type: cosine_recall |
| 8987 | value: 80.3896 |
| 8988 | - type: cosine_ap |
| 8989 | value: 85.459 |
| 8990 | - type: manhattan_accuracy |
| 8991 | value: 88.8598 |
| 8992 | - type: manhattan_accuracy_threshold |
| 8993 | value: 1928.9173 |
| 8994 | - type: manhattan_f1 |
| 8995 | value: 77.9172 |
| 8996 | - type: manhattan_f1_threshold |
| 8997 | value: 2007.8883999999998 |
| 8998 | - type: manhattan_precision |
| 8999 | value: 76.29310000000001 |
| 9000 | - type: manhattan_recall |
| 9001 | value: 79.6119 |
| 9002 | - type: manhattan_ap |
| 9003 | value: 85.4464 |
| 9004 | - type: euclidean_accuracy |
| 9005 | value: 88.8811 |
| 9006 | - type: euclidean_accuracy_threshold |
| 9007 | value: 76.0193 |
| 9008 | - type: euclidean_f1 |
| 9009 | value: 77.9005 |
| 9010 | - type: euclidean_f1_threshold |
| 9011 | value: 80.6141 |
| 9012 | - type: euclidean_precision |
| 9013 | value: 75.5609 |
| 9014 | - type: euclidean_recall |
| 9015 | value: 80.3896 |
| 9016 | - type: euclidean_ap |
| 9017 | value: 85.459 |
| 9018 | - type: dot_accuracy |
| 9019 | value: 88.8811 |
| 9020 | - type: dot_accuracy_threshold |
| 9021 | value: 71.1053 |
| 9022 | - type: dot_f1 |
| 9023 | value: 77.9005 |
| 9024 | - type: dot_f1_threshold |
| 9025 | value: 67.5068 |
| 9026 | - type: dot_precision |
| 9027 | value: 75.5609 |
| 9028 | - type: dot_recall |
| 9029 | value: 80.3896 |
| 9030 | - type: dot_ap |
| 9031 | value: 85.459 |
| 9032 | - type: max_accuracy |
| 9033 | value: 88.8811 |
| 9034 | - type: max_f1 |
| 9035 | value: 77.9172 |
| 9036 | - type: max_precision |
| 9037 | value: 76.29310000000001 |
| 9038 | - type: max_recall |
| 9039 | value: 80.3896 |
| 9040 | - type: max_ap |
| 9041 | value: 85.459 |
| 9042 | - type: main_score |
| 9043 | value: 85.459 |
| 9044 | task: |
| 9045 | type: PairClassification |
| 9046 | --- |
| 9047 | |
| 9048 | <h1 align="center">Snowflake's Arctic-embed-l-v2.0</h1> |
| 9049 | <h4 align="center"> |
| 9050 | <p> |
| 9051 | <a href=#news>News</a> | |
| 9052 | <a href=#models>Models</a> | |
| 9053 | <a href=#usage>Usage</a> | |
| 9054 | <a href="#evaluation">Evaluation</a> | |
| 9055 | <a href="#contact">Contact</a> | |
| 9056 | <a href="#faq">FAQ</a> |
| 9057 | <a href="#license">License</a> | |
| 9058 | <a href="#acknowledgement">Acknowledgement</a> |
| 9059 | <p> |
| 9060 | </h4> |
| 9061 | |
| 9062 | <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=18f5b1a3-da66-4f25-92d3-21da829509c3" /> |
| 9063 | |
| 9064 | ## News |
| 9065 | - 12/11/2024: Release of [Technical Report](https://arxiv.org/abs/2412.04506) |
| 9066 | - 12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. |
| 9067 | |
| 9068 | ## Models |
| 9069 | Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. |
| 9070 | Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. |
| 9071 | Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale. |
| 9072 | |
| 9073 | Key Features: |
| 9074 | |
| 9075 | 1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL. |
| 9076 | |
| 9077 | 2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale. |
| 9078 | |
| 9079 | 3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training. **Please note that like our v1.5 model, the MRL for this model is 256 dimensions, and high-quality 128-byte compression is achieved via 4-bit quantization (e.g. using a [`pq256x4fs` fast-scan FAISS index](https://github.com/facebookresearch/faiss/wiki/The-index-factory#encodings) or using the [example code published alongside our 1.5 model](https://github.com/Snowflake-Labs/arctic-embed/blob/main/compressed_embeddings_examples/score_arctic_embed_m_v1dot5_with_quantization.ipynb)).** |
| 9080 | |
| 9081 | 4. Drop-In Replacement: arctic-embed-l-v2.0 builds on [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc. |
| 9082 | |
| 9083 | 5. Long Context Support: arctic-embed-l-v2.0 builds on [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which can support a context window of up to 8192 via the use of RoPE. |
| 9084 | |
| 9085 | |
| 9086 | ### Quality Benchmarks |
| 9087 | Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF). |
| 9088 | You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed. |
| 9089 | |
| 9090 | | Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) | |
| 9091 | |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| 9092 | | **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** | |
| 9093 | | snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 | |
| 9094 | | snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 | |
| 9095 | | me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 | |
| 9096 | | bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 | |
| 9097 | | gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 | |
| 9098 | |
| 9099 | Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality. |
| 9100 | Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc. |
| 9101 | |
| 9102 | | Model | | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance | |
| 9103 | |---|---|:---:|:---:|:---:|:---:|:---:|---|---|---| |
| 9104 | | snowflake-arctic-l-v2.0 | 1024 | 55.6 | N/A | 55.8 | N/A | 52.9 | N/A | 54.3 | N/A | |
| 9105 | | snowflake-arctic-l-v2.0 | 256 | 54.3 | -0.18% | 54.3 | -2.70% | 51.9 | -1.81% | 53.4 | -1.53% | |
| 9106 | |
| 9107 | ## Usage |
| 9108 | |
| 9109 | ### Using Sentence Transformers |
| 9110 | |
| 9111 | ```python |
| 9112 | from sentence_transformers import SentenceTransformer |
| 9113 | |
| 9114 | # Load the model |
| 9115 | model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0' |
| 9116 | model = SentenceTransformer(model_name) |
| 9117 | |
| 9118 | # Define the queries and documents |
| 9119 | queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
| 9120 | documents = ['The Data Cloud!', 'Mexico City of Course!'] |
| 9121 | |
| 9122 | # Compute embeddings: use `prompt_name="query"` to encode queries! |
| 9123 | query_embeddings = model.encode(queries, prompt_name="query") |
| 9124 | document_embeddings = model.encode(documents) |
| 9125 | |
| 9126 | # Compute cosine similarity scores |
| 9127 | scores = model.similarity(query_embeddings, document_embeddings) |
| 9128 | |
| 9129 | # Output the results |
| 9130 | for query, query_scores in zip(queries, scores): |
| 9131 | doc_score_pairs = list(zip(documents, query_scores)) |
| 9132 | doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
| 9133 | print("Query:", query) |
| 9134 | for document, score in doc_score_pairs: |
| 9135 | print(score, document) |
| 9136 | |
| 9137 | ``` |
| 9138 | |
| 9139 | |
| 9140 | |
| 9141 | ### Using Huggingface Transformers |
| 9142 | |
| 9143 | |
| 9144 | You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query). |
| 9145 | |
| 9146 | ```python |
| 9147 | import torch |
| 9148 | from transformers import AutoModel, AutoTokenizer |
| 9149 | |
| 9150 | model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0' |
| 9151 | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| 9152 | model = AutoModel.from_pretrained(model_name, add_pooling_layer=False) |
| 9153 | model.eval() |
| 9154 | |
| 9155 | query_prefix = 'query: ' |
| 9156 | queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
| 9157 | queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries] |
| 9158 | query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192) |
| 9159 | |
| 9160 | documents = ['The Data Cloud!', 'Mexico City of Course!'] |
| 9161 | document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192) |
| 9162 | |
| 9163 | # Compute token embeddings |
| 9164 | with torch.no_grad(): |
| 9165 | query_embeddings = model(**query_tokens)[0][:, 0] |
| 9166 | document_embeddings = model(**document_tokens)[0][:, 0] |
| 9167 | |
| 9168 | |
| 9169 | # normalize embeddings |
| 9170 | query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1) |
| 9171 | document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1) |
| 9172 | |
| 9173 | scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1)) |
| 9174 | for query, query_scores in zip(queries, scores): |
| 9175 | doc_score_pairs = list(zip(documents, query_scores)) |
| 9176 | doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
| 9177 | #Output passages & scores |
| 9178 | print("Query:", query) |
| 9179 | for document, score in doc_score_pairs: |
| 9180 | print(score, document) |
| 9181 | ``` |
| 9182 | |
| 9183 | |
| 9184 | This should produce the following scores |
| 9185 | |
| 9186 | ``` |
| 9187 | Query: what is snowflake? |
| 9188 | tensor(0.2715) The Data Cloud! |
| 9189 | tensor(0.0661) Mexico City of Course! |
| 9190 | Query: Where can I get the best tacos? |
| 9191 | tensor(0.2797) Mexico City of Course! |
| 9192 | tensor(0.1250) The Data Cloud! |
| 9193 | ``` |
| 9194 | |
| 9195 | ### Using Huggingface Transformers.js |
| 9196 | |
| 9197 | 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: |
| 9198 | ```bash |
| 9199 | npm i @huggingface/transformers |
| 9200 | ``` |
| 9201 | |
| 9202 | You can then use the model for retrieval, as follows: |
| 9203 | |
| 9204 | ```js |
| 9205 | import { pipeline, dot } from '@huggingface/transformers'; |
| 9206 | |
| 9207 | // Create feature extraction pipeline |
| 9208 | const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', { |
| 9209 | dtype: 'q8', |
| 9210 | }); |
| 9211 | |
| 9212 | // Generate sentence embeddings |
| 9213 | const sentences = [ |
| 9214 | 'query: what is snowflake?', |
| 9215 | 'The Data Cloud!', |
| 9216 | 'Mexico City of Course!', |
| 9217 | ] |
| 9218 | const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); |
| 9219 | |
| 9220 | // Compute similarity scores |
| 9221 | const [source_embeddings, ...document_embeddings ] = output.tolist(); |
| 9222 | const similarities = document_embeddings.map(x => dot(source_embeddings, x)); |
| 9223 | console.log(similarities); // [0.24783534471401417, 0.05313122704326892] |
| 9224 | ``` |
| 9225 | |
| 9226 | |
| 9227 | ## Contact |
| 9228 | |
| 9229 | |
| 9230 | Feel free to open an issue or pull request if you have any questions or suggestions about this project. |
| 9231 | You also can email Daniel Campos(daniel.campos@snowflake.com). |
| 9232 | |
| 9233 | |
| 9234 | ## License |
| 9235 | Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. |