File size: 38,895 Bytes
74e5847 e5fcf03 74e5847 074e526 74e5847 074e526 74e5847 074e526 74e5847 |
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 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: Dmeta-embedding
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 65.60825224706932
- type: cos_sim_spearman
value: 71.12862586297193
- type: euclidean_pearson
value: 70.18130275750404
- type: euclidean_spearman
value: 71.12862586297193
- type: manhattan_pearson
value: 70.14470398075396
- type: manhattan_spearman
value: 71.05226975911737
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 65.52386345655479
- type: cos_sim_spearman
value: 64.64245253181382
- type: euclidean_pearson
value: 73.20157662981914
- type: euclidean_spearman
value: 64.64245253178956
- type: manhattan_pearson
value: 73.22837571756348
- type: manhattan_spearman
value: 64.62632334391418
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.925999999999995
- type: f1
value: 42.82555191308971
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.35236446393156
- type: cos_sim_spearman
value: 72.29629643702184
- type: euclidean_pearson
value: 70.94570179874498
- type: euclidean_spearman
value: 72.29629297226953
- type: manhattan_pearson
value: 70.84463025501125
- type: manhattan_spearman
value: 72.24527021975821
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 40.24232916894152
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 39.167806226929706
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 88.48837920106357
- type: mrr
value: 90.36861111111111
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 89.17878171657071
- type: mrr
value: 91.35805555555555
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.751
- type: map_at_10
value: 38.946
- type: map_at_100
value: 40.855000000000004
- type: map_at_1000
value: 40.953
- type: map_at_3
value: 34.533
- type: map_at_5
value: 36.905
- type: mrr_at_1
value: 39.235
- type: mrr_at_10
value: 47.713
- type: mrr_at_100
value: 48.71
- type: mrr_at_1000
value: 48.747
- type: mrr_at_3
value: 45.086
- type: mrr_at_5
value: 46.498
- type: ndcg_at_1
value: 39.235
- type: ndcg_at_10
value: 45.831
- type: ndcg_at_100
value: 53.162
- type: ndcg_at_1000
value: 54.800000000000004
- type: ndcg_at_3
value: 40.188
- type: ndcg_at_5
value: 42.387
- type: precision_at_1
value: 39.235
- type: precision_at_10
value: 10.273
- type: precision_at_100
value: 1.627
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 22.772000000000002
- type: precision_at_5
value: 16.524
- type: recall_at_1
value: 25.751
- type: recall_at_10
value: 57.411
- type: recall_at_100
value: 87.44
- type: recall_at_1000
value: 98.386
- type: recall_at_3
value: 40.416000000000004
- type: recall_at_5
value: 47.238
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.59591100420926
- type: cos_sim_ap
value: 90.65538153970263
- type: cos_sim_f1
value: 84.76466651795673
- type: cos_sim_precision
value: 81.04073363190446
- type: cos_sim_recall
value: 88.84732288987608
- type: dot_accuracy
value: 83.59591100420926
- type: dot_ap
value: 90.64355541781003
- type: dot_f1
value: 84.76466651795673
- type: dot_precision
value: 81.04073363190446
- type: dot_recall
value: 88.84732288987608
- type: euclidean_accuracy
value: 83.59591100420926
- type: euclidean_ap
value: 90.6547878194287
- type: euclidean_f1
value: 84.76466651795673
- type: euclidean_precision
value: 81.04073363190446
- type: euclidean_recall
value: 88.84732288987608
- type: manhattan_accuracy
value: 83.51172579675286
- type: manhattan_ap
value: 90.59941589844144
- type: manhattan_f1
value: 84.51827242524917
- type: manhattan_precision
value: 80.28613507258574
- type: manhattan_recall
value: 89.22141688099134
- type: max_accuracy
value: 83.59591100420926
- type: max_ap
value: 90.65538153970263
- type: max_f1
value: 84.76466651795673
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 63.251000000000005
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.79299999999999
- type: map_at_1000
value: 72.80499999999999
- type: map_at_3
value: 70.293
- type: map_at_5
value: 71.571
- type: mrr_at_1
value: 63.541000000000004
- type: mrr_at_10
value: 72.502
- type: mrr_at_100
value: 72.846
- type: mrr_at_1000
value: 72.858
- type: mrr_at_3
value: 70.39
- type: mrr_at_5
value: 71.654
- type: ndcg_at_1
value: 63.541000000000004
- type: ndcg_at_10
value: 76.774
- type: ndcg_at_100
value: 78.389
- type: ndcg_at_1000
value: 78.678
- type: ndcg_at_3
value: 72.47
- type: ndcg_at_5
value: 74.748
- type: precision_at_1
value: 63.541000000000004
- type: precision_at_10
value: 9.115
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.379
- type: precision_at_5
value: 16.965
- type: recall_at_1
value: 63.251000000000005
- type: recall_at_10
value: 90.253
- type: recall_at_100
value: 97.576
- type: recall_at_1000
value: 99.789
- type: recall_at_3
value: 78.635
- type: recall_at_5
value: 84.141
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.597
- type: map_at_10
value: 72.411
- type: map_at_100
value: 75.58500000000001
- type: map_at_1000
value: 75.64800000000001
- type: map_at_3
value: 49.61
- type: map_at_5
value: 62.527
- type: mrr_at_1
value: 84.65
- type: mrr_at_10
value: 89.43900000000001
- type: mrr_at_100
value: 89.525
- type: mrr_at_1000
value: 89.529
- type: mrr_at_3
value: 89
- type: mrr_at_5
value: 89.297
- type: ndcg_at_1
value: 84.65
- type: ndcg_at_10
value: 81.47
- type: ndcg_at_100
value: 85.198
- type: ndcg_at_1000
value: 85.828
- type: ndcg_at_3
value: 79.809
- type: ndcg_at_5
value: 78.55
- type: precision_at_1
value: 84.65
- type: precision_at_10
value: 39.595
- type: precision_at_100
value: 4.707
- type: precision_at_1000
value: 0.485
- type: precision_at_3
value: 71.61699999999999
- type: precision_at_5
value: 60.45
- type: recall_at_1
value: 23.597
- type: recall_at_10
value: 83.34
- type: recall_at_100
value: 95.19800000000001
- type: recall_at_1000
value: 98.509
- type: recall_at_3
value: 52.744
- type: recall_at_5
value: 68.411
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 53.1
- type: map_at_10
value: 63.359
- type: map_at_100
value: 63.9
- type: map_at_1000
value: 63.909000000000006
- type: map_at_3
value: 60.95
- type: map_at_5
value: 62.305
- type: mrr_at_1
value: 53.1
- type: mrr_at_10
value: 63.359
- type: mrr_at_100
value: 63.9
- type: mrr_at_1000
value: 63.909000000000006
- type: mrr_at_3
value: 60.95
- type: mrr_at_5
value: 62.305
- type: ndcg_at_1
value: 53.1
- type: ndcg_at_10
value: 68.418
- type: ndcg_at_100
value: 70.88499999999999
- type: ndcg_at_1000
value: 71.135
- type: ndcg_at_3
value: 63.50599999999999
- type: ndcg_at_5
value: 65.92
- type: precision_at_1
value: 53.1
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.340000000000002
- type: recall_at_1
value: 53.1
- type: recall_at_10
value: 84.3
- type: recall_at_100
value: 95.5
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.7
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 48.303193535975375
- type: f1
value: 35.96559358693866
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 85.06566604127579
- type: ap
value: 52.0596483757231
- type: f1
value: 79.5196835127668
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 74.48499423626059
- type: cos_sim_spearman
value: 78.75806756061169
- type: euclidean_pearson
value: 78.47917601852879
- type: euclidean_spearman
value: 78.75807199272622
- type: manhattan_pearson
value: 78.40207586289772
- type: manhattan_spearman
value: 78.6911776964119
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 24.75987466552363
- type: mrr
value: 23.40515873015873
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.026999999999994
- type: map_at_10
value: 67.50699999999999
- type: map_at_100
value: 67.946
- type: map_at_1000
value: 67.96600000000001
- type: map_at_3
value: 65.503
- type: map_at_5
value: 66.649
- type: mrr_at_1
value: 60.20100000000001
- type: mrr_at_10
value: 68.271
- type: mrr_at_100
value: 68.664
- type: mrr_at_1000
value: 68.682
- type: mrr_at_3
value: 66.47800000000001
- type: mrr_at_5
value: 67.499
- type: ndcg_at_1
value: 60.20100000000001
- type: ndcg_at_10
value: 71.697
- type: ndcg_at_100
value: 73.736
- type: ndcg_at_1000
value: 74.259
- type: ndcg_at_3
value: 67.768
- type: ndcg_at_5
value: 69.72
- type: precision_at_1
value: 60.20100000000001
- type: precision_at_10
value: 8.927999999999999
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 25.883
- type: precision_at_5
value: 16.55
- type: recall_at_1
value: 58.026999999999994
- type: recall_at_10
value: 83.966
- type: recall_at_100
value: 93.313
- type: recall_at_1000
value: 97.426
- type: recall_at_3
value: 73.342
- type: recall_at_5
value: 77.997
- 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: 71.1600537995965
- type: f1
value: 68.8126216609964
- 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: 73.54068594485541
- type: f1
value: 73.46845879869848
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 54.900000000000006
- type: map_at_10
value: 61.363
- type: map_at_100
value: 61.924
- type: map_at_1000
value: 61.967000000000006
- type: map_at_3
value: 59.767
- type: map_at_5
value: 60.802
- type: mrr_at_1
value: 55.1
- type: mrr_at_10
value: 61.454
- type: mrr_at_100
value: 62.016000000000005
- type: mrr_at_1000
value: 62.059
- type: mrr_at_3
value: 59.882999999999996
- type: mrr_at_5
value: 60.893
- type: ndcg_at_1
value: 54.900000000000006
- type: ndcg_at_10
value: 64.423
- type: ndcg_at_100
value: 67.35900000000001
- type: ndcg_at_1000
value: 68.512
- type: ndcg_at_3
value: 61.224000000000004
- type: ndcg_at_5
value: 63.083
- type: precision_at_1
value: 54.900000000000006
- type: precision_at_10
value: 7.3999999999999995
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.8
- type: precision_at_5
value: 13.98
- type: recall_at_1
value: 54.900000000000006
- type: recall_at_10
value: 74
- type: recall_at_100
value: 88.2
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 65.4
- type: recall_at_5
value: 69.89999999999999
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 75.15666666666667
- type: f1
value: 74.8306375354435
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.10774228478614
- type: cos_sim_ap
value: 87.17679348388666
- type: cos_sim_f1
value: 84.59302325581395
- type: cos_sim_precision
value: 78.15577439570276
- type: cos_sim_recall
value: 92.18585005279832
- type: dot_accuracy
value: 83.10774228478614
- type: dot_ap
value: 87.17679348388666
- type: dot_f1
value: 84.59302325581395
- type: dot_precision
value: 78.15577439570276
- type: dot_recall
value: 92.18585005279832
- type: euclidean_accuracy
value: 83.10774228478614
- type: euclidean_ap
value: 87.17679348388666
- type: euclidean_f1
value: 84.59302325581395
- type: euclidean_precision
value: 78.15577439570276
- type: euclidean_recall
value: 92.18585005279832
- type: manhattan_accuracy
value: 82.67460747157553
- type: manhattan_ap
value: 86.94296334435238
- type: manhattan_f1
value: 84.32327166504382
- type: manhattan_precision
value: 78.22944896115628
- type: manhattan_recall
value: 91.4466737064414
- type: max_accuracy
value: 83.10774228478614
- type: max_ap
value: 87.17679348388666
- type: max_f1
value: 84.59302325581395
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.24999999999999
- type: ap
value: 90.98617641063584
- type: f1
value: 93.23447883650289
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.071417937737856
- type: cos_sim_spearman
value: 45.049199344455424
- type: euclidean_pearson
value: 44.913450096830786
- type: euclidean_spearman
value: 45.05733424275291
- type: manhattan_pearson
value: 44.881623825912065
- type: manhattan_spearman
value: 44.989923561416596
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.38238052689359
- type: cos_sim_spearman
value: 42.61949690594399
- type: euclidean_pearson
value: 40.61261500356766
- type: euclidean_spearman
value: 42.619626605620724
- type: manhattan_pearson
value: 40.8886109204474
- type: manhattan_spearman
value: 42.75791523010463
- 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: 62.10977863727196
- type: cos_sim_spearman
value: 63.843727112473225
- type: euclidean_pearson
value: 63.25133487817196
- type: euclidean_spearman
value: 63.843727112473225
- type: manhattan_pearson
value: 63.58749018644103
- type: manhattan_spearman
value: 63.83820575456674
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 79.30616496720054
- type: cos_sim_spearman
value: 80.767935782436
- type: euclidean_pearson
value: 80.4160642670106
- type: euclidean_spearman
value: 80.76820284024356
- type: manhattan_pearson
value: 80.27318714580251
- type: manhattan_spearman
value: 80.61030164164964
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.26242871142425
- type: mrr
value: 76.20689863623174
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.240999999999996
- type: map_at_10
value: 73.009
- type: map_at_100
value: 76.893
- type: map_at_1000
value: 76.973
- type: map_at_3
value: 51.339
- type: map_at_5
value: 63.003
- type: mrr_at_1
value: 87.458
- type: mrr_at_10
value: 90.44
- type: mrr_at_100
value: 90.558
- type: mrr_at_1000
value: 90.562
- type: mrr_at_3
value: 89.89
- type: mrr_at_5
value: 90.231
- type: ndcg_at_1
value: 87.458
- type: ndcg_at_10
value: 81.325
- type: ndcg_at_100
value: 85.61999999999999
- type: ndcg_at_1000
value: 86.394
- type: ndcg_at_3
value: 82.796
- type: ndcg_at_5
value: 81.219
- type: precision_at_1
value: 87.458
- type: precision_at_10
value: 40.534
- type: precision_at_100
value: 4.96
- type: precision_at_1000
value: 0.514
- type: precision_at_3
value: 72.444
- type: precision_at_5
value: 60.601000000000006
- type: recall_at_1
value: 26.240999999999996
- type: recall_at_10
value: 80.42
- type: recall_at_100
value: 94.118
- type: recall_at_1000
value: 98.02199999999999
- type: recall_at_3
value: 53.174
- type: recall_at_5
value: 66.739
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 52.40899999999999
- type: f1
value: 50.68532128056062
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 65.57616085176686
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 58.844999922904925
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.4
- type: map_at_10
value: 68.64
- type: map_at_100
value: 69.062
- type: map_at_1000
value: 69.073
- type: map_at_3
value: 66.567
- type: map_at_5
value: 67.89699999999999
- type: mrr_at_1
value: 58.4
- type: mrr_at_10
value: 68.64
- type: mrr_at_100
value: 69.062
- type: mrr_at_1000
value: 69.073
- type: mrr_at_3
value: 66.567
- type: mrr_at_5
value: 67.89699999999999
- type: ndcg_at_1
value: 58.4
- type: ndcg_at_10
value: 73.30600000000001
- type: ndcg_at_100
value: 75.276
- type: ndcg_at_1000
value: 75.553
- type: ndcg_at_3
value: 69.126
- type: ndcg_at_5
value: 71.519
- type: precision_at_1
value: 58.4
- type: precision_at_10
value: 8.780000000000001
- type: precision_at_100
value: 0.968
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.5
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 58.4
- type: recall_at_10
value: 87.8
- type: recall_at_100
value: 96.8
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 76.5
- type: recall_at_5
value: 82.3
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.21000000000001
- type: ap
value: 69.17460264576461
- type: f1
value: 84.68032984659226
license: apache-2.0
language:
- zh
- en
---
<div align="center">
<img src="logo.png" alt="icon" width="100px"/>
</div>
<h1 align="center">Dmeta-embedding</h1>
<h4 align="center">
<p>
<a href="https://huggingface.co/DMetaSoul/Dmeta-embedding/README.md">English</a> |
<a href="https://huggingface.co/DMetaSoul/Dmeta-embedding/README_zh.md">中文</a>
</p>
<p>
<a href=#usage>用法</a> |
<a href="#evaluation">评测（可复现）</a> |
<a href=#faq>FAQ</a> |
<a href="#contact">联系</a> |
<a href="#license">版权（免费商用）</a>
<p>
</h4>
**重磅更新：**
- **2024.02.07**, 发布了基于 Dmeta-embedding 模型的 **Embedding API** 产品，现已开启内测，[点击申请](https://dmetasoul.feishu.cn/share/base/form/shrcnu7mN1BDwKFfgGXG9Rb1yDf)即可免费获得 **4 亿 tokens** 使用额度，可编码大约 GB 级别汉字文本。
- 我们的初心。既要开源优秀的技术能力，又希望大家能够在实际业务中使用起来，用起来的技术才是好技术、能落地创造价值的技术才是值得长期投入的。帮助大家解决业务落地最后一公里的障碍，让大家把 Embedding 技术低成本的用起来，更多去关注自身的商业和产品服务，把复杂的技术部分交给我们。
- 申请和使用。[点击申请](https://dmetasoul.feishu.cn/share/base/form/shrcnu7mN1BDwKFfgGXG9Rb1yDf)，填写一个表单即可，48小时之内我们会通过 <[email protected]> 给您答复邮件。Embedding API 为了兼容大模型技术生态，使用方式跟 OpenAI 一致，具体用法我们将在答复邮件中进行说明。
- 加入社群。后续我们会不断在大模型/AIGC等方向发力，为社区带来有价值、低门槛的技术，可以[点击图片](https://huggingface.co/DMetaSoul/Dmeta-embedding/resolve/main/weixin.jpeg)，扫面二维码来加入我们的微信社群，一起在 AIGC 赛道加油呀！
------
**Dmeta-embedding** 是一款跨领域、跨任务、开箱即用的中文 Embedding 模型，适用于搜索、问答、智能客服、LLM+RAG 等各种业务场景，支持使用 Transformers/Sentence-Transformers/Langchain 等工具加载推理。
优势特点如下：
- 多任务、场景泛化性能优异，目前已取得 **[MTEB](https://huggingface.co/spaces/mteb/leaderboard) 中文榜单第二成绩**（2024.01.25）
- 模型参数大小仅 **400MB**，对比参数量超过 GB 级模型，可以极大降低推理成本
- 支持上下文窗口长度达到 **1024**，对于长文本检索、RAG 等场景更适配
## Usage
目前模型支持通过 [Sentence-Transformers](#sentence-transformers), [Langchain](#langchain), [Huggingface Transformers](#huggingface-transformers) 等主流框架进行推理，具体用法参考各个框架的示例。
### Sentence-Transformers
Dmeta-embedding 模型支持通过 [sentence-transformers](https://www.SBERT.net) 来加载推理：
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
model = SentenceTransformer('DMetaSoul/Dmeta-embedding')
embs1 = model.encode(texts1, normalize_embeddings=True)
embs2 = model.encode(texts2, normalize_embeddings=True)
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
示例输出如下：
```
查询文本：胡子长得太快怎么办？
相似文本：胡子长得快怎么办？，打分：0.9535336494445801
相似文本：怎样使胡子不浓密！，打分：0.6776421070098877
相似文本：香港买手表哪里好，打分：0.2297907918691635
相似文本：在杭州手机到哪里买，打分：0.11386542022228241
查询文本：在香港哪里买手表好
相似文本：香港买手表哪里好，打分：0.9843372106552124
相似文本：在杭州手机到哪里买，打分：0.45211508870124817
相似文本：胡子长得快怎么办？，打分：0.19985519349575043
相似文本：怎样使胡子不浓密！，打分：0.18558596074581146
```
### Langchain
Dmeta-embedding 模型支持通过 LLM 工具框架 [langchain](https://www.langchain.com/) 来加载推理：
```
pip install -U langchain
```
```python
import torch
import numpy as np
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "DMetaSoul/Dmeta-embedding"
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
embs1 = model.embed_documents(texts1)
embs2 = model.embed_documents(texts2)
embs1, embs2 = np.array(embs1), np.array(embs2)
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
### HuggingFace Transformers
Dmeta-embedding 模型支持通过 [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) 框架来加载推理：
```
pip install -U transformers
```
```python
import torch
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def cls_pooling(model_output):
return model_output[0][:, 0]
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/Dmeta-embedding')
model = AutoModel.from_pretrained('DMetaSoul/Dmeta-embedding')
model.eval()
with torch.no_grad():
inputs1 = tokenizer(texts1, padding=True, truncation=True, return_tensors='pt')
inputs2 = tokenizer(texts2, padding=True, truncation=True, return_tensors='pt')
model_output1 = model(**inputs1)
model_output2 = model(**inputs2)
embs1, embs2 = cls_pooling(model_output1), cls_pooling(model_output2)
embs1 = torch.nn.functional.normalize(embs1, p=2, dim=1).numpy()
embs2 = torch.nn.functional.normalize(embs2, p=2, dim=1).numpy()
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
## Evaluation
Dmeta-embedding 模型在 [MTEB 中文榜单](https://huggingface.co/spaces/mteb/leaderboard)取得开源第一的成绩（2024.01.25，Baichuan 榜单第一、未开源），具体关于评测数据和代码可参考 MTEB 官方[仓库](https://github.com/embeddings-benchmark/mteb)。
**MTEB Chinese**:
该[榜单数据集](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB)由智源研究院团队（BAAI）收集整理，包含 6 个经典任务共计 35 个中文数据集，涵盖了分类、检索、排序、句对、STS 等任务，是目前 Embedding 模型全方位能力评测的全球权威榜单。
| Model | Vendor | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------------------------------------------------------------------------------- | ------ |:-------------------:|:-----:|:---------:|:-----:|:------------------:|:--------------:|:---------:|:----------:|
| [Dmeta-embedding](https://huggingface.co/DMetaSoul/Dmeta-embedding) | 数元灵 | 1024 | 67.51 | 70.41 | 64.09 | 88.92 | 70 | 67.17 | 50.96 |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | 阿里达摩院 | 1024 | 66.72 | 72.49 | 57.82 | 84.41 | 71.34 | 67.4 | 53.07 |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 智源 | 1024 | 64.53 | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 智源 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | OpenAI | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 个人 | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 个人 | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
## FAQ
<details>
<summary>1. 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？</summary>
<!-- ### 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？ -->
简单来说，模型优异的泛化能力来自于预训练数据的广泛和多样，以及模型优化时面向多任务场景设计了不同优化目标。
具体来说，技术要点有：
1）首先是大规模弱标签对比学习。业界经验表明开箱即用的语言模型在 Embedding 相关任务上表现不佳，但由于监督数据标注、获取成本较高，因此大规模、高质量的弱标签学习成为一条可选技术路线。通过在互联网上论坛、新闻、问答社区、百科等半结构化数据中提取弱标签，并利用大模型进行低质过滤，得到 10 亿级别弱监督文本对数据。
2）其次是高质量监督学习。我们收集整理了大规模开源标注的语句对数据集，包含百科、教育、金融、医疗、法律、新闻、学术等多个领域共计 3000 万句对样本。同时挖掘难负样本对，借助对比学习更好的进行模型优化。
3）最后是检索任务针对性优化。考虑到搜索、问答以及 RAG 等场景是 Embedding 模型落地的重要应用阵地，为了增强模型跨领域、跨场景的效果性能，我们专门针对检索任务进行了模型优化，核心在于从问答、检索等数据中挖掘难负样本，借助稀疏和稠密检索等多种手段，构造百万级难负样本对数据集，显著提升了模型跨领域的检索性能。
</details>
<details>
<summary>2. 模型可以商用吗？</summary>
<!-- ### 模型可以商用吗 -->
我们的开源模型基于 Apache-2.0 协议，完全支持免费商用。
</details>
<details>
<summary>3. 如何复现 MTEB 评测结果？</summary>
<!-- ### 如何复现 MTEB 评测结果？ -->
我们在模型仓库中提供了脚本 mteb_eval.py，您可以直接运行此脚本来复现我们的评测结果。
</details>
<details>
<summary>4. 后续规划有哪些？</summary>
<!-- ### 后续规划有哪些？ -->
我们将不断致力于为社区提供效果优异、推理轻量、多场景开箱即用的 Embedding 模型，同时我们也会将 Embedding 逐步整合到目前已经的技术生态中，跟随社区一起成长！
</details>
## Contact
您如果在使用过程中，遇到任何问题，欢迎前往[讨论区](https://huggingface.co/DMetaSoul/Dmeta-embedding/discussions)建言献策。
您也可以联系我们：赵中昊 <[email protected]>, 肖文斌 <[email protected]>, 孙凯 <[email protected]>
同时我们也开通了微信群，可扫码加入我们，一起共建 AIGC 技术生态！
<image src="https://huggingface.co/DMetaSoul/Dmeta-embedding/resolve/main/weixin.jpeg" style="display: block; margin-left: auto; margin-right: auto; width: 256px; height: 358px;"/>
## License
Dmeta-embedding 模型采用 Apache-2.0 License，开源模型可以进行免费商用私有部署。 |