File size: 32,092 Bytes
aaf6411 8225489 aaf6411 8225489 aaf6411 b10f50e 0e4ce92 b10f50e c632864 8225489 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: stella-base-zh-v3-1792d
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 54.5145388936202
- type: cos_sim_spearman
value: 59.223125058197134
- type: euclidean_pearson
value: 57.819377838734695
- type: euclidean_spearman
value: 59.22310494948463
- type: manhattan_pearson
value: 57.44029759610327
- type: manhattan_spearman
value: 58.88336250854381
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 54.544243591344866
- type: cos_sim_spearman
value: 58.43052988038229
- type: euclidean_pearson
value: 62.1608405146189
- type: euclidean_spearman
value: 58.43052762862396
- type: manhattan_pearson
value: 61.88443779892169
- type: manhattan_spearman
value: 58.26899143609596
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.343999999999994
- type: f1
value: 44.46931958420461
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 68.52081000538426
- type: cos_sim_spearman
value: 70.44089935351529
- type: euclidean_pearson
value: 69.24671010626395
- type: euclidean_spearman
value: 70.44090281761693
- type: manhattan_pearson
value: 69.00737718109357
- type: manhattan_spearman
value: 70.24344902456502
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 42.86119436460332
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 39.97521728440642
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 88.34151862240452
- type: mrr
value: 90.40380952380953
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 89.06288758814637
- type: mrr
value: 90.91285714285713
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.651000000000003
- type: map_at_10
value: 38.576
- type: map_at_100
value: 40.534
- type: map_at_1000
value: 40.64
- type: map_at_3
value: 34.016000000000005
- type: map_at_5
value: 36.675999999999995
- type: mrr_at_1
value: 39.06
- type: mrr_at_10
value: 47.278
- type: mrr_at_100
value: 48.272999999999996
- type: mrr_at_1000
value: 48.314
- type: mrr_at_3
value: 44.461
- type: mrr_at_5
value: 46.107
- type: ndcg_at_1
value: 39.06
- type: ndcg_at_10
value: 45.384
- type: ndcg_at_100
value: 52.796
- type: ndcg_at_1000
value: 54.55
- type: ndcg_at_3
value: 39.497
- type: ndcg_at_5
value: 42.189
- type: precision_at_1
value: 39.06
- type: precision_at_10
value: 10.17
- type: precision_at_100
value: 1.6179999999999999
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 22.247
- type: precision_at_5
value: 16.529
- type: recall_at_1
value: 25.651000000000003
- type: recall_at_10
value: 56.82899999999999
- type: recall_at_100
value: 87.134
- type: recall_at_1000
value: 98.709
- type: recall_at_3
value: 39.461
- type: recall_at_5
value: 47.329
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.1870114251353
- type: cos_sim_ap
value: 90.42393852164342
- type: cos_sim_f1
value: 84.10685985963323
- type: cos_sim_precision
value: 81.5229317533465
- type: cos_sim_recall
value: 86.85994856207621
- type: dot_accuracy
value: 83.1870114251353
- type: dot_ap
value: 90.41339758845682
- type: dot_f1
value: 84.10685985963323
- type: dot_precision
value: 81.5229317533465
- type: dot_recall
value: 86.85994856207621
- type: euclidean_accuracy
value: 83.1870114251353
- type: euclidean_ap
value: 90.42393581056393
- type: euclidean_f1
value: 84.10685985963323
- type: euclidean_precision
value: 81.5229317533465
- type: euclidean_recall
value: 86.85994856207621
- type: manhattan_accuracy
value: 82.77811184606134
- type: manhattan_ap
value: 90.18115714681704
- type: manhattan_f1
value: 83.75083130126357
- type: manhattan_precision
value: 79.62065331928345
- type: manhattan_recall
value: 88.33294365209258
- type: max_accuracy
value: 83.1870114251353
- type: max_ap
value: 90.42393852164342
- type: max_f1
value: 84.10685985963323
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 68.388
- type: map_at_10
value: 76.819
- type: map_at_100
value: 77.153
- type: map_at_1000
value: 77.16
- type: map_at_3
value: 74.98700000000001
- type: map_at_5
value: 76.101
- type: mrr_at_1
value: 68.599
- type: mrr_at_10
value: 76.844
- type: mrr_at_100
value: 77.168
- type: mrr_at_1000
value: 77.17500000000001
- type: mrr_at_3
value: 75.044
- type: mrr_at_5
value: 76.208
- type: ndcg_at_1
value: 68.599
- type: ndcg_at_10
value: 80.613
- type: ndcg_at_100
value: 82.017
- type: ndcg_at_1000
value: 82.19300000000001
- type: ndcg_at_3
value: 76.956
- type: ndcg_at_5
value: 78.962
- type: precision_at_1
value: 68.599
- type: precision_at_10
value: 9.336
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.678000000000004
- type: precision_at_5
value: 17.619
- type: recall_at_1
value: 68.388
- type: recall_at_10
value: 92.36
- type: recall_at_100
value: 98.52499999999999
- type: recall_at_1000
value: 99.895
- type: recall_at_3
value: 82.53399999999999
- type: recall_at_5
value: 87.355
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.1
- type: map_at_10
value: 77.71000000000001
- type: map_at_100
value: 80.638
- type: map_at_1000
value: 80.679
- type: map_at_3
value: 53.187
- type: map_at_5
value: 67.735
- type: mrr_at_1
value: 87.8
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.893
- type: mrr_at_1000
value: 91.89500000000001
- type: mrr_at_3
value: 91.51700000000001
- type: mrr_at_5
value: 91.704
- type: ndcg_at_1
value: 87.8
- type: ndcg_at_10
value: 85.55
- type: ndcg_at_100
value: 88.626
- type: ndcg_at_1000
value: 89.021
- type: ndcg_at_3
value: 83.94
- type: ndcg_at_5
value: 83.259
- type: precision_at_1
value: 87.8
- type: precision_at_10
value: 41.295
- type: precision_at_100
value: 4.781
- type: precision_at_1000
value: 0.488
- type: precision_at_3
value: 75.3
- type: precision_at_5
value: 64.13
- type: recall_at_1
value: 25.1
- type: recall_at_10
value: 87.076
- type: recall_at_100
value: 97.095
- type: recall_at_1000
value: 99.129
- type: recall_at_3
value: 56.013999999999996
- type: recall_at_5
value: 73.2
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 53.300000000000004
- type: map_at_10
value: 63.01
- type: map_at_100
value: 63.574
- type: map_at_1000
value: 63.587
- type: map_at_3
value: 60.783
- type: map_at_5
value: 62.098
- type: mrr_at_1
value: 53.300000000000004
- type: mrr_at_10
value: 63.01
- type: mrr_at_100
value: 63.574
- type: mrr_at_1000
value: 63.587
- type: mrr_at_3
value: 60.783
- type: mrr_at_5
value: 62.098
- type: ndcg_at_1
value: 53.300000000000004
- type: ndcg_at_10
value: 67.876
- type: ndcg_at_100
value: 70.434
- type: ndcg_at_1000
value: 70.753
- type: ndcg_at_3
value: 63.275000000000006
- type: ndcg_at_5
value: 65.654
- type: precision_at_1
value: 53.300000000000004
- type: precision_at_10
value: 8.32
- type: precision_at_100
value: 0.9480000000000001
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.5
- type: precision_at_5
value: 15.260000000000002
- type: recall_at_1
value: 53.300000000000004
- type: recall_at_10
value: 83.2
- type: recall_at_100
value: 94.8
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 70.5
- type: recall_at_5
value: 76.3
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 49.92689495959984
- type: f1
value: 37.784780470986625
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.26641651031895
- type: ap
value: 54.50750244841821
- type: f1
value: 80.94927946681523
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 72.3980811478615
- type: cos_sim_spearman
value: 78.26906056425528
- type: euclidean_pearson
value: 77.87705501225068
- type: euclidean_spearman
value: 78.26905834518651
- type: manhattan_pearson
value: 77.77154630197
- type: manhattan_spearman
value: 78.1940918602169
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 27.48003475319453
- type: mrr
value: 26.400793650793652
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.373
- type: map_at_10
value: 73.604
- type: map_at_100
value: 73.953
- type: map_at_1000
value: 73.965
- type: map_at_3
value: 71.70100000000001
- type: map_at_5
value: 72.859
- type: mrr_at_1
value: 66.676
- type: mrr_at_10
value: 74.248
- type: mrr_at_100
value: 74.56099999999999
- type: mrr_at_1000
value: 74.572
- type: mrr_at_3
value: 72.59100000000001
- type: mrr_at_5
value: 73.592
- type: ndcg_at_1
value: 66.676
- type: ndcg_at_10
value: 77.417
- type: ndcg_at_100
value: 79.006
- type: ndcg_at_1000
value: 79.334
- type: ndcg_at_3
value: 73.787
- type: ndcg_at_5
value: 75.74
- type: precision_at_1
value: 66.676
- type: precision_at_10
value: 9.418
- type: precision_at_100
value: 1.0210000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 27.832
- type: precision_at_5
value: 17.736
- type: recall_at_1
value: 64.373
- type: recall_at_10
value: 88.565
- type: recall_at_100
value: 95.789
- type: recall_at_1000
value: 98.355
- type: recall_at_3
value: 78.914
- type: recall_at_5
value: 83.56
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.0544720914593
- type: f1
value: 69.61749470345791
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.30262273032953
- type: f1
value: 75.05097671215634
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 55.1
- type: map_at_10
value: 61.284000000000006
- type: map_at_100
value: 61.794000000000004
- type: map_at_1000
value: 61.838
- type: map_at_3
value: 59.75
- type: map_at_5
value: 60.64000000000001
- type: mrr_at_1
value: 55.300000000000004
- type: mrr_at_10
value: 61.38400000000001
- type: mrr_at_100
value: 61.894000000000005
- type: mrr_at_1000
value: 61.938
- type: mrr_at_3
value: 59.85
- type: mrr_at_5
value: 60.74
- type: ndcg_at_1
value: 55.1
- type: ndcg_at_10
value: 64.345
- type: ndcg_at_100
value: 67.148
- type: ndcg_at_1000
value: 68.36
- type: ndcg_at_3
value: 61.182
- type: ndcg_at_5
value: 62.808
- type: precision_at_1
value: 55.1
- type: precision_at_10
value: 7.3999999999999995
- type: precision_at_100
value: 0.8789999999999999
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 21.767
- type: precision_at_5
value: 13.86
- type: recall_at_1
value: 55.1
- type: recall_at_10
value: 74
- type: recall_at_100
value: 87.9
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 65.3
- type: recall_at_5
value: 69.3
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 76.21666666666667
- type: f1
value: 76.03732395559548
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 81.8083378451543
- type: cos_sim_ap
value: 85.43050139514027
- type: cos_sim_f1
value: 83.25969563082965
- type: cos_sim_precision
value: 77.79816513761469
- type: cos_sim_recall
value: 89.54593453009504
- type: dot_accuracy
value: 81.8083378451543
- type: dot_ap
value: 85.43050139514027
- type: dot_f1
value: 83.25969563082965
- type: dot_precision
value: 77.79816513761469
- type: dot_recall
value: 89.54593453009504
- type: euclidean_accuracy
value: 81.8083378451543
- type: euclidean_ap
value: 85.43050139514027
- type: euclidean_f1
value: 83.25969563082965
- type: euclidean_precision
value: 77.79816513761469
- type: euclidean_recall
value: 89.54593453009504
- type: manhattan_accuracy
value: 81.53762858689767
- type: manhattan_ap
value: 84.90556637024838
- type: manhattan_f1
value: 82.90258449304174
- type: manhattan_precision
value: 78.30985915492957
- type: manhattan_recall
value: 88.0675818373812
- type: max_accuracy
value: 81.8083378451543
- type: max_ap
value: 85.43050139514027
- type: max_f1
value: 83.25969563082965
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.53
- type: ap
value: 91.62070655043128
- type: f1
value: 93.51908163199477
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 38.451787103814375
- type: cos_sim_spearman
value: 43.97299462643919
- type: euclidean_pearson
value: 43.63298716626501
- type: euclidean_spearman
value: 43.973080252178576
- type: manhattan_pearson
value: 43.37465277323481
- type: manhattan_spearman
value: 43.71981281220414
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 37.75882451277358
- type: cos_sim_spearman
value: 40.0244327844802
- type: euclidean_pearson
value: 38.11050875514246
- type: euclidean_spearman
value: 40.02440987254504
- type: manhattan_pearson
value: 38.03186803221696
- type: manhattan_spearman
value: 39.757452890246775
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.9133992390713
- type: cos_sim_spearman
value: 66.4894937647578
- type: euclidean_pearson
value: 66.19047142189935
- type: euclidean_spearman
value: 66.4894937647578
- type: manhattan_pearson
value: 66.6960935896136
- type: manhattan_spearman
value: 66.88179996508133
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 80.55099417946924
- type: cos_sim_spearman
value: 83.05000687568048
- type: euclidean_pearson
value: 82.62744668792926
- type: euclidean_spearman
value: 83.05000687568048
- type: manhattan_pearson
value: 82.6543207325763
- type: manhattan_spearman
value: 83.06852715971705
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.48634798223672
- type: mrr
value: 76.30158461488861
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27.483999999999998
- type: map_at_10
value: 76.848
- type: map_at_100
value: 80.541
- type: map_at_1000
value: 80.607
- type: map_at_3
value: 54.111
- type: map_at_5
value: 66.46300000000001
- type: mrr_at_1
value: 90.045
- type: mrr_at_10
value: 92.552
- type: mrr_at_100
value: 92.642
- type: mrr_at_1000
value: 92.645
- type: mrr_at_3
value: 92.134
- type: mrr_at_5
value: 92.391
- type: ndcg_at_1
value: 90.045
- type: ndcg_at_10
value: 84.504
- type: ndcg_at_100
value: 88.23100000000001
- type: ndcg_at_1000
value: 88.85300000000001
- type: ndcg_at_3
value: 85.992
- type: ndcg_at_5
value: 84.548
- type: precision_at_1
value: 90.045
- type: precision_at_10
value: 41.91
- type: precision_at_100
value: 5.017
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 75.15899999999999
- type: precision_at_5
value: 62.958000000000006
- type: recall_at_1
value: 27.483999999999998
- type: recall_at_10
value: 83.408
- type: recall_at_100
value: 95.514
- type: recall_at_1000
value: 98.65
- type: recall_at_3
value: 55.822
- type: recall_at_5
value: 69.868
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 53.196
- type: f1
value: 51.51679244513836
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 67.87592101539063
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 62.4675464095125
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 57.9
- type: map_at_10
value: 68.099
- type: map_at_100
value: 68.55499999999999
- type: map_at_1000
value: 68.566
- type: map_at_3
value: 66.4
- type: map_at_5
value: 67.46
- type: mrr_at_1
value: 57.9
- type: mrr_at_10
value: 68.099
- type: mrr_at_100
value: 68.55499999999999
- type: mrr_at_1000
value: 68.566
- type: mrr_at_3
value: 66.4
- type: mrr_at_5
value: 67.46
- type: ndcg_at_1
value: 57.9
- type: ndcg_at_10
value: 72.555
- type: ndcg_at_100
value: 74.715
- type: ndcg_at_1000
value: 75.034
- type: ndcg_at_3
value: 69.102
- type: ndcg_at_5
value: 71.004
- type: precision_at_1
value: 57.9
- type: precision_at_10
value: 8.63
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.633
- type: precision_at_5
value: 16.3
- type: recall_at_1
value: 57.9
- type: recall_at_10
value: 86.3
- type: recall_at_100
value: 96.3
- type: recall_at_1000
value: 98.9
- type: recall_at_3
value: 76.9
- type: recall_at_5
value: 81.5
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 87.27000000000001
- type: ap
value: 71.10883470119464
- type: f1
value: 85.76618863591946
license: mit
---
**新闻 | News**
**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。
**[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。
**[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。
**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。
**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。
**[2023-09-11]** 开源stella-base-zh和stella-large-zh
欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！
# 1 开源清单
本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型，同时开源全量160万对话重写数据集和20万的难负例的检索数据集。
**开源模型：**
| ModelName | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score |
|---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------|
| [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d) | 0.4GB | 512 | 1792 | zh-CN | 通用文本 | 67.96 |
| [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | 通用文本 | 68.48 |
| [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | **对话文本** | 不适用 |
**开源数据：**
1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万
2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万
上述数据集均使用LLM构造，欢迎各位贡献数据集。
# 2 使用方法
## 2.1 通用编码模型使用方法
直接SentenceTransformer加载即可：
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d")
# model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
vectors = model.encode(["text1", "text2"])
```
## 2.2 dialogue编码模型使用方法
**使用场景：**
**在一段对话中，需要根据用户语句去检索相关文本，但是对话中的用户语句存在大量的指代和省略，导致直接使用通用编码模型效果不好，
可以使用本项目的专门的dialogue编码模型进行编码**
**使用要点：**
1. 对dialogue进行编码时，dialogue中的每个utterance需要是如下格式：`"{ROLE}: {TEXT}"`，然后使用`[SEP]` join一下
2. 整个对话都要送入模型进行编码，如果长度不够就删掉早期的对话，**编码后的向量本质是对话中最后一句话的重写版本的向量！！**
3. 对话用stella-dialogue-large-zh-v3-1792d编码，被检索文本使用stella-large-zh-v3-1792d进行编码，所以本场景是需要2个编码模型的
如果对使用方法还有疑惑，请到下面章节阅读该模型是如何训练的。
使用示例：
```python
from sentence_transformers import SentenceTransformer
dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d")
general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
# dialogue = ["张三: 吃饭吗", "李四: 等会去"]
dialogue = ["A: 最近去打篮球了吗", "B: 没有"]
corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"]
last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True)
corpus_vectors = general_model.encode(corpus, normalize_embeddings=True)
# 计算相似度
sims = (last_utterance_vector * corpus_vectors).sum(axis=1)
print(sims)
```
# 3 通用编码模型训练技巧分享
## hard negative
难负例挖掘也是个经典的trick了，几乎总能提升效果
## dropout-1d
dropout已经是深度学习的标配，我们可以稍微改造下使其更适合句向量的训练。
我们在训练时会尝试让每一个token-embedding都可以表征整个句子，而在推理时使用mean_pooling从而达到类似模型融合的效果。
具体操作是在mean_pooling时加入dropout_1d，torch代码如下：
```python
vector_dropout = nn.Dropout1d(0.3) # 算力有限，试了0.3和0.5 两个参数，其中0.3更优
last_hidden_state = bert_model(...)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
last_hidden = vector_dropout(last_hidden)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
```
# 4 dialogue编码模型细节
## 4.1 为什么需要一个dialogue编码模型？
参见本人历史文章：https://www.zhihu.com/pin/1674913544847077376
## 4.2 训练数据
单条数据示例：
```json
{
"dialogue": [
"A: 最近去打篮球了吗",
"B: 没有"
],
"last_utterance_rewrite": "B: 我最近没有去打篮球"
}
```
## 4.3 训练Loss
```
loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) )
```
dial_model就是要被训练的模型，本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的
existing_model就是现有训练好的**通用编码模型**，本人使用的是stella-large-zh-v3-1792d
已开源dialogue-embedding的全量训练数据，理论上可以复现本模型效果。
Loss下降情况：
<div align="center">
<img src="dial_loss.png" alt="icon" width="2000px"/>
</div>
## 4.4 效果
目前还没有专门测试集，本人简单测试了下是有效果的，部分测试结果见文件`dial_retrieval_test.xlsx`。
# 5 后续TODO
1. 更多的dial-rewrite数据
2. 不同EmbeddingDimensions的编码模型
# 6 FAQ
Q: 为什么向量维度是1792？\
A: 最初考虑发布768、1024，768+768，1024+1024，1024+768维度，但是时间有限，先做了1792就只发布1792维度的模型。理论上维度越高效果越好。
Q: 如何复现CMTEB效果？\
A: SentenceTransformer加载后直接用官方评测脚本就行，注意对于Classification任务向量需要先normalize一下
Q: 复现的CMTEB效果和本文不一致？\
A: 聚类不一致正常，官方评测代码没有设定seed，其他不一致建议检查代码或联系本人。
Q: 如何选择向量模型？\
A: 没有免费的午餐，在自己测试集上试试，本人推荐bge、e5和stella.
Q: 长度为什么只有512，能否更长？\
A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。
Q: 训练资源和算力？\
A: 亿级别的数据，单卡A100要一个月起步 |