File size: 19,615 Bytes
305e26d ed3ef88 305e26d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: bge-m3-custom-fr
results:
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringP2P
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 56.727459716713
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringS2S
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 38.19920006179227
- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
name: MTEB AlloprofReranking
config: default
split: test
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
metrics:
- type: map
value: 65.17465797499942
- type: mrr
value: 66.51400197384653
- task:
type: Retrieval
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloprofRetrieval
config: default
split: test
revision: 2df7bee4080bedf2e97de3da6bd5c7bc9fc9c4d2
metrics:
- type: map_at_1
value: 29.836000000000002
- type: map_at_10
value: 39.916000000000004
- type: map_at_100
value: 40.816
- type: map_at_1000
value: 40.877
- type: map_at_3
value: 37.294
- type: map_at_5
value: 38.838
- type: mrr_at_1
value: 29.836000000000002
- type: mrr_at_10
value: 39.916000000000004
- type: mrr_at_100
value: 40.816
- type: mrr_at_1000
value: 40.877
- type: mrr_at_3
value: 37.294
- type: mrr_at_5
value: 38.838
- type: ndcg_at_1
value: 29.836000000000002
- type: ndcg_at_10
value: 45.097
- type: ndcg_at_100
value: 49.683
- type: ndcg_at_1000
value: 51.429
- type: ndcg_at_3
value: 39.717
- type: ndcg_at_5
value: 42.501
- type: precision_at_1
value: 29.836000000000002
- type: precision_at_10
value: 6.149
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 15.576
- type: precision_at_5
value: 10.698
- type: recall_at_1
value: 29.836000000000002
- type: recall_at_10
value: 61.485
- type: recall_at_100
value: 83.428
- type: recall_at_1000
value: 97.461
- type: recall_at_3
value: 46.727000000000004
- type: recall_at_5
value: 53.489
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.332
- type: f1
value: 40.801800929404344
- task:
type: Retrieval
dataset:
type: maastrichtlawtech/bsard
name: MTEB BSARDRetrieval
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 0.0
- type: map_at_10
value: 0.0
- type: map_at_100
value: 0.011000000000000001
- type: map_at_1000
value: 0.018000000000000002
- type: map_at_3
value: 0.0
- type: map_at_5
value: 0.0
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.011000000000000001
- type: mrr_at_1000
value: 0.018000000000000002
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 0.0
- type: ndcg_at_10
value: 0.0
- type: ndcg_at_100
value: 0.13999999999999999
- type: ndcg_at_1000
value: 0.457
- type: ndcg_at_3
value: 0.0
- type: ndcg_at_5
value: 0.0
- type: precision_at_1
value: 0.0
- type: precision_at_10
value: 0.0
- type: precision_at_100
value: 0.009000000000000001
- type: precision_at_1000
value: 0.004
- type: precision_at_3
value: 0.0
- type: precision_at_5
value: 0.0
- type: recall_at_1
value: 0.0
- type: recall_at_10
value: 0.0
- type: recall_at_100
value: 0.901
- type: recall_at_1000
value: 3.604
- type: recall_at_3
value: 0.0
- type: recall_at_5
value: 0.0
- task:
type: Clustering
dataset:
type: lyon-nlp/clustering-hal-s2s
name: MTEB HALClusteringS2S
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 24.1294565929144
- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringP2P
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.12040762356958
- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringS2S
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 36.69102548662494
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.3946132164109
- type: f1
value: 90.15608090764273
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 60.87691825869088
- type: f1
value: 43.56160799721332
- task:
type: Classification
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClassification (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 70.52132701421802
- type: f1
value: 66.7911493789742
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringP2P (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 34.60975901092521
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringS2S (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 32.8092912406207
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.70477471418964
- type: f1
value: 64.4848306188641
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.57969065232011
- type: f1
value: 73.58251655418402
- task:
type: Retrieval
dataset:
type: jinaai/mintakaqa
name: MTEB MintakaRetrieval (fr)
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 14.005
- type: map_at_10
value: 21.279999999999998
- type: map_at_100
value: 22.288
- type: map_at_1000
value: 22.404
- type: map_at_3
value: 19.151
- type: map_at_5
value: 20.322000000000003
- type: mrr_at_1
value: 14.005
- type: mrr_at_10
value: 21.279999999999998
- type: mrr_at_100
value: 22.288
- type: mrr_at_1000
value: 22.404
- type: mrr_at_3
value: 19.151
- type: mrr_at_5
value: 20.322000000000003
- type: ndcg_at_1
value: 14.005
- type: ndcg_at_10
value: 25.173000000000002
- type: ndcg_at_100
value: 30.452
- type: ndcg_at_1000
value: 34.241
- type: ndcg_at_3
value: 20.768
- type: ndcg_at_5
value: 22.869
- type: precision_at_1
value: 14.005
- type: precision_at_10
value: 3.759
- type: precision_at_100
value: 0.631
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 8.477
- type: precision_at_5
value: 6.101999999999999
- type: recall_at_1
value: 14.005
- type: recall_at_10
value: 37.592
- type: recall_at_100
value: 63.144999999999996
- type: recall_at_1000
value: 94.513
- type: recall_at_3
value: 25.430000000000003
- type: recall_at_5
value: 30.508000000000003
- task:
type: PairClassification
dataset:
type: GEM/opusparcus
name: MTEB OpusparcusPC (fr)
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 81.60762942779292
- type: cos_sim_ap
value: 93.33850264444463
- type: cos_sim_f1
value: 87.24705882352941
- type: cos_sim_precision
value: 82.91592128801432
- type: cos_sim_recall
value: 92.05561072492551
- type: dot_accuracy
value: 81.60762942779292
- type: dot_ap
value: 93.33850264444463
- type: dot_f1
value: 87.24705882352941
- type: dot_precision
value: 82.91592128801432
- type: dot_recall
value: 92.05561072492551
- type: euclidean_accuracy
value: 81.60762942779292
- type: euclidean_ap
value: 93.3384939260791
- type: euclidean_f1
value: 87.24705882352941
- type: euclidean_precision
value: 82.91592128801432
- type: euclidean_recall
value: 92.05561072492551
- type: manhattan_accuracy
value: 81.60762942779292
- type: manhattan_ap
value: 93.27064794794664
- type: manhattan_f1
value: 87.27440999537251
- type: manhattan_precision
value: 81.7157712305026
- type: manhattan_recall
value: 93.64448857994041
- type: max_accuracy
value: 81.60762942779292
- type: max_ap
value: 93.33850264444463
- type: max_f1
value: 87.27440999537251
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (fr)
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.95
- type: cos_sim_ap
value: 60.8497942066519
- type: cos_sim_f1
value: 62.53032928942807
- type: cos_sim_precision
value: 45.50958627648839
- type: cos_sim_recall
value: 99.88925802879291
- type: dot_accuracy
value: 61.95
- type: dot_ap
value: 60.83772617132806
- type: dot_f1
value: 62.53032928942807
- type: dot_precision
value: 45.50958627648839
- type: dot_recall
value: 99.88925802879291
- type: euclidean_accuracy
value: 61.95
- type: euclidean_ap
value: 60.8497942066519
- type: euclidean_f1
value: 62.53032928942807
- type: euclidean_precision
value: 45.50958627648839
- type: euclidean_recall
value: 99.88925802879291
- type: manhattan_accuracy
value: 61.9
- type: manhattan_ap
value: 60.87914286416435
- type: manhattan_f1
value: 62.491349480968864
- type: manhattan_precision
value: 45.44539506794162
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 61.95
- type: max_ap
value: 60.87914286416435
- type: max_f1
value: 62.53032928942807
- task:
type: STS
dataset:
type: Lajavaness/SICK-fr
name: MTEB SICKFr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 81.24400370393097
- type: cos_sim_spearman
value: 75.50548831172674
- type: euclidean_pearson
value: 77.81039134726188
- type: euclidean_spearman
value: 75.50504199480463
- type: manhattan_pearson
value: 77.79383923445839
- type: manhattan_spearman
value: 75.472882776806
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 80.48474973785514
- type: cos_sim_spearman
value: 81.69566405041475
- type: euclidean_pearson
value: 78.32784472269549
- type: euclidean_spearman
value: 81.69566405041475
- type: manhattan_pearson
value: 78.2856100079857
- type: manhattan_spearman
value: 81.84463256785325
- task:
type: STS
dataset:
type: PhilipMay/stsb_multi_mt
name: MTEB STSBenchmarkMultilingualSTS (fr)
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 80.68785966129913
- type: cos_sim_spearman
value: 81.29936344904975
- type: euclidean_pearson
value: 80.25462090186443
- type: euclidean_spearman
value: 81.29928746010391
- type: manhattan_pearson
value: 80.17083094559602
- type: manhattan_spearman
value: 81.18921827402406
- task:
type: Summarization
dataset:
type: lyon-nlp/summarization-summeval-fr-p2p
name: MTEB SummEvalFr
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 31.66113105701837
- type: cos_sim_spearman
value: 30.13316633681715
- type: dot_pearson
value: 31.66113064418324
- type: dot_spearman
value: 30.13316633681715
- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
name: MTEB SyntecReranking
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
- type: map
value: 85.43333333333334
- type: mrr
value: 85.43333333333334
- task:
type: Retrieval
dataset:
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
name: MTEB SyntecRetrieval
config: default
split: test
revision: aa460cd4d177e6a3c04fcd2affd95e8243289033
metrics:
- type: map_at_1
value: 65.0
- type: map_at_10
value: 75.19200000000001
- type: map_at_100
value: 75.77000000000001
- type: map_at_1000
value: 75.77000000000001
- type: map_at_3
value: 73.667
- type: map_at_5
value: 75.067
- type: mrr_at_1
value: 65.0
- type: mrr_at_10
value: 75.19200000000001
- type: mrr_at_100
value: 75.77000000000001
- type: mrr_at_1000
value: 75.77000000000001
- type: mrr_at_3
value: 73.667
- type: mrr_at_5
value: 75.067
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 79.145
- type: ndcg_at_100
value: 81.34400000000001
- type: ndcg_at_1000
value: 81.34400000000001
- type: ndcg_at_3
value: 76.333
- type: ndcg_at_5
value: 78.82900000000001
- type: precision_at_1
value: 65.0
- type: precision_at_10
value: 9.1
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.000000000000004
- type: precision_at_5
value: 18.0
- type: recall_at_1
value: 65.0
- type: recall_at_10
value: 91.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 84.0
- type: recall_at_5
value: 90.0
- task:
type: Retrieval
dataset:
type: jinaai/xpqa
name: MTEB XPQARetrieval (fr)
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 40.225
- type: map_at_10
value: 61.833000000000006
- type: map_at_100
value: 63.20400000000001
- type: map_at_1000
value: 63.27
- type: map_at_3
value: 55.593
- type: map_at_5
value: 59.65200000000001
- type: mrr_at_1
value: 63.284
- type: mrr_at_10
value: 71.351
- type: mrr_at_100
value: 71.772
- type: mrr_at_1000
value: 71.786
- type: mrr_at_3
value: 69.381
- type: mrr_at_5
value: 70.703
- type: ndcg_at_1
value: 63.284
- type: ndcg_at_10
value: 68.49199999999999
- type: ndcg_at_100
value: 72.79299999999999
- type: ndcg_at_1000
value: 73.735
- type: ndcg_at_3
value: 63.278
- type: ndcg_at_5
value: 65.19200000000001
- type: precision_at_1
value: 63.284
- type: precision_at_10
value: 15.661
- type: precision_at_100
value: 1.9349999999999998
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 38.273
- type: precision_at_5
value: 27.397
- type: recall_at_1
value: 40.225
- type: recall_at_10
value: 77.66999999999999
- type: recall_at_100
value: 93.887
- type: recall_at_1000
value: 99.70599999999999
- type: recall_at_3
value: 61.133
- type: recall_at_5
value: 69.789
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |