File size: 69,588 Bytes
e0396b7 |
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 |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: are the sequels better than the prequels?
sentences:
- '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
- The prequels are also not scared to take risks, making movies which are very different
from the original trilogy. The sequel saga, on the other hand, are technically
better made films, the acting is more consistent, the CGI is better and the writing
is stronger, however it falls down in many other places.
- While both public and private sectors use budgets as a key planning tool, public
bodies balance budgets, while private sector firms use budgets to predict operating
results. The public sector budget matches expenditures on mandated assets and
services with receipts of public money such as taxes and fees.
- source_sentence: are there bbqs at lake leschenaultia?
sentences:
- Vestavia Hills. The hummingbird, or, el zunzún as they are often called in the
Caribbean, have such a nickname because of their quick movements. The ruby-throated
hummingbird, the most commonly seen hummingbird in Alabama, is the inspiration
for this restaurant.
- Common causes of abdominal tenderness Abdominal tenderness is generally a sign
of inflammation or other acute processes in one or more organs. The organs are
located around the tender area. Acute processes mean sudden pressure caused by
something. For example, twisted or blocked organs can cause point tenderness.
- Located on 168 hectares of nature reserve, Lake Leschenaultia is the perfect
spot for a family day out in the Perth Hills. The Lake offers canoeing, swimming,
walk and cycle trails, as well as picnic, BBQ and camping facilities. ... There
are picnic tables set amongst lovely Wandoo trees.
- source_sentence: how much folic acid should you take prenatal?
sentences:
- Folic acid is a pregnancy superhero! Taking a prenatal vitamin with the recommended
400 micrograms (mcg) of folic acid before and during pregnancy can help prevent
birth defects of your baby's brain and spinal cord. Take it every day and go ahead
and have a bowl of fortified cereal, too.
- '[''You must be unemployed through no fault of your own, as defined by Virginia
law.'', ''You must have earned at least a minimum amount in wages before you were
unemployed.'', ''You must be able and available to work, and you must be actively
seeking employment.'']'
- Wallpaper is printed in batches of rolls. It is important to have the same batch
number, to ensure colours match exactly. The batch number is usually located on
the wallpaper label close to the pattern number. Remember batch numbers also apply
to white wallpapers, as different batches can be different shades of white.
- source_sentence: what is the difference between minerals and electrolytes?
sentences:
- 'North: Just head north of Junk Junction like so. South: Head below Lucky Landing.
East: You''re basically landing between Lonely Lodge and the Racetrack. West:
The sign is west of Snobby Shores.'
- The fasting glucose tolerance test is the simplest and fastest way to measure
blood glucose and diagnose diabetes. Fasting means that you have had nothing to
eat or drink (except water) for 8 to 12 hours before the test.
- In other words, the term “electrolyte” typically implies ionized minerals dissolved
within water and beverages. Electrolytes are typically minerals, whereas minerals
may or may not be electrolytes.
- source_sentence: how can i download youtube videos with internet download manager?
sentences:
- '[''Go to settings and then click on extensions (top left side in chrome).'',
''Minimise your browser and open the location (folder) where IDM is installed.
... '', ''Find the file “IDMGCExt. ... '', ''Drag this file to your chrome browser
and drop to install the IDM extension.'']'
- Coca-Cola might rot your teeth and load your body with sugar and calories, but
it's actually an effective and safe first line of treatment for some stomach blockages,
researchers say.
- To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
Click on the "Erase iPhone" option and confirm your selection. Wait for a while
as the "Find My iPhone" feature will remotely erase your iOS device. Needless
to say, it will also disable its lock.
datasets:
- sentence-transformers/gooaq
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
co2_eq_emissions:
emissions: 249.86917485332245
energy_consumed: 0.6428296609055844
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 1.727
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: bert-base-uncased adapter finetuned on GooAQ pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.3
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.54
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14833333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.25666666666666665
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2866666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2612531493211831
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3718333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2163485410063536
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.48
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.48
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4599999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4159999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.39
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04444293833661297
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10924065240694858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.14497857436843284
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.24069548747927993
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.45073427319400694
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6354682539682539
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3182747550673792
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.6
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.84
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.96
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.184
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09799999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.59
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8566666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9066666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7556216606985078
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.719190476190476
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.701651515151515
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09799999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11441269841269841
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21891269841269842
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3109126984126984
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40793650793650793
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2963633422018188
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33072222222222225
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23341351928423923
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.64
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.74
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.64
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31333333333333335
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.22399999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.56
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.59
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5584295792789493
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7015
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.49543351785464007
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.46
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44155458168172074
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3666904761904761
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.38140126670451624
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2866666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.244
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.17800000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.022867372385014545
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.051610132551984836
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.061993511339545566
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.07344138386002937
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22405550472948219
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3782222222222222
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08778657539162772
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.4
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.62
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.124
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07200000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.53
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.59
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.67
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5271006159134835
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4858809523809523
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4878346435046129
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.84
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.98
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.84
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.38666666666666655
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23999999999999994
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7573333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9286666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9359999999999999
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9793333333333334
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9154478750600358
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9053333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8889771382049948
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.3
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.36
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19200000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06466666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12466666666666669
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.19666666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2906666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2646043570275534
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3836031746031746
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20582501612453505
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17333333333333337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.72
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.47137188069353025
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36633333333333323
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3750999024240443
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.345
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.525
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.615
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.521095291928473
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4848333333333332
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4707221516167083
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.3673469387755102
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8571428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9387755102040817
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3673469387755102
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4965986394557823
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4489795918367347
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.39387755102040817
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03066633506656198
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1123508290418132
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1616156991422983
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2674040762687923
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.42905651691216934
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6237204405571752
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.32876348596122706
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.40210361067503925
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6074725274725276
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6891365777080062
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7630769230769231
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.40210361067503925
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26691784406070124
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2093061224489796
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14706750392464676
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.247517129041094
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.38926520351898297
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4577308064048442
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5286777529906109
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.47051450989545496
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.519487042436022
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.399348617561261
name: Cosine Map@100
---
# bert-base-uncased adapter finetuned on GooAQ pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq-peft")
# Run inference
sentences = [
'how can i download youtube videos with internet download manager?',
"['Go to settings and then click on extensions (top left side in chrome).', 'Minimise your browser and open the location (folder) where IDM is installed. ... ', 'Find the file “IDMGCExt. ... ', 'Drag this file to your chrome browser and drop to install the IDM extension.']",
"Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 |
| cosine_accuracy@3 | 0.42 | 0.78 | 0.84 | 0.4 | 0.74 | 0.46 | 0.44 | 0.54 | 0.98 | 0.36 | 0.52 | 0.56 | 0.8571 |
| cosine_accuracy@5 | 0.48 | 0.82 | 0.9 | 0.5 | 0.82 | 0.54 | 0.46 | 0.62 | 0.98 | 0.54 | 0.72 | 0.64 | 0.9388 |
| cosine_accuracy@10 | 0.54 | 0.92 | 0.96 | 0.6 | 0.84 | 0.68 | 0.5 | 0.7 | 1.0 | 0.68 | 0.8 | 0.7 | 1.0 |
| cosine_precision@1 | 0.3 | 0.48 | 0.6 | 0.22 | 0.64 | 0.22 | 0.32 | 0.4 | 0.84 | 0.3 | 0.16 | 0.38 | 0.3673 |
| cosine_precision@3 | 0.16 | 0.46 | 0.28 | 0.18 | 0.3133 | 0.1533 | 0.2867 | 0.18 | 0.3867 | 0.2 | 0.1733 | 0.2 | 0.4966 |
| cosine_precision@5 | 0.116 | 0.416 | 0.184 | 0.14 | 0.224 | 0.108 | 0.244 | 0.124 | 0.24 | 0.192 | 0.144 | 0.14 | 0.449 |
| cosine_precision@10 | 0.066 | 0.39 | 0.098 | 0.098 | 0.118 | 0.068 | 0.178 | 0.072 | 0.13 | 0.142 | 0.08 | 0.078 | 0.3939 |
| cosine_recall@1 | 0.1483 | 0.0444 | 0.59 | 0.1144 | 0.32 | 0.22 | 0.0229 | 0.4 | 0.7573 | 0.0647 | 0.16 | 0.345 | 0.0307 |
| cosine_recall@3 | 0.21 | 0.1092 | 0.8 | 0.2189 | 0.47 | 0.46 | 0.0516 | 0.53 | 0.9287 | 0.1247 | 0.52 | 0.525 | 0.1124 |
| cosine_recall@5 | 0.2567 | 0.145 | 0.8567 | 0.3109 | 0.56 | 0.54 | 0.062 | 0.59 | 0.936 | 0.1967 | 0.72 | 0.615 | 0.1616 |
| cosine_recall@10 | 0.2867 | 0.2407 | 0.9067 | 0.4079 | 0.59 | 0.68 | 0.0734 | 0.67 | 0.9793 | 0.2907 | 0.8 | 0.68 | 0.2674 |
| **cosine_ndcg@10** | **0.2613** | **0.4507** | **0.7556** | **0.2964** | **0.5584** | **0.4416** | **0.2241** | **0.5271** | **0.9154** | **0.2646** | **0.4714** | **0.5211** | **0.4291** |
| cosine_mrr@10 | 0.3718 | 0.6355 | 0.7192 | 0.3307 | 0.7015 | 0.3667 | 0.3782 | 0.4859 | 0.9053 | 0.3836 | 0.3663 | 0.4848 | 0.6237 |
| cosine_map@100 | 0.2163 | 0.3183 | 0.7017 | 0.2334 | 0.4954 | 0.3814 | 0.0878 | 0.4878 | 0.889 | 0.2058 | 0.3751 | 0.4707 | 0.3288 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4021 |
| cosine_accuracy@3 | 0.6075 |
| cosine_accuracy@5 | 0.6891 |
| cosine_accuracy@10 | 0.7631 |
| cosine_precision@1 | 0.4021 |
| cosine_precision@3 | 0.2669 |
| cosine_precision@5 | 0.2093 |
| cosine_precision@10 | 0.1471 |
| cosine_recall@1 | 0.2475 |
| cosine_recall@3 | 0.3893 |
| cosine_recall@5 | 0.4577 |
| cosine_recall@10 | 0.5287 |
| **cosine_ndcg@10** | **0.4705** |
| cosine_mrr@10 | 0.5195 |
| cosine_map@100 | 0.3993 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> |
| <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
| <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> |
| <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> |
| <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0 | 0 | - | - | 0.1046 | 0.2182 | 0.1573 | 0.0575 | 0.2597 | 0.1602 | 0.0521 | 0.0493 | 0.7310 | 0.1320 | 0.2309 | 0.1240 | 0.0970 | 0.1826 |
| 0.0010 | 1 | 28.4479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0256 | 25 | 27.0904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0512 | 50 | 19.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0768 | 75 | 12.2306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1024 | 100 | 9.0613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1279 | 125 | 7.393 | 3.7497 | 0.2787 | 0.4840 | 0.7029 | 0.2589 | 0.5208 | 0.4094 | 0.2117 | 0.4526 | 0.9042 | 0.2503 | 0.5280 | 0.4922 | 0.4132 | 0.4544 |
| 0.1535 | 150 | 6.6613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1791 | 175 | 6.1911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2047 | 200 | 5.9305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2303 | 225 | 5.6825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2559 | 250 | 5.5326 | 2.8771 | 0.2867 | 0.4619 | 0.7333 | 0.2835 | 0.5549 | 0.4056 | 0.2281 | 0.4883 | 0.9137 | 0.2555 | 0.5114 | 0.5220 | 0.4298 | 0.4673 |
| 0.2815 | 275 | 5.1671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3071 | 300 | 5.2006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3327 | 325 | 5.0447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3582 | 350 | 4.9647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3838 | 375 | 4.8521 | 2.5709 | 0.2881 | 0.4577 | 0.7438 | 0.2909 | 0.5712 | 0.4093 | 0.2273 | 0.5141 | 0.9008 | 0.2668 | 0.5117 | 0.5253 | 0.4331 | 0.4723 |
| 0.4094 | 400 | 4.8423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4350 | 425 | 4.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 450 | 4.6527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4862 | 475 | 4.61 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5118 | 500 | 4.5451 | 2.4136 | 0.2786 | 0.4464 | 0.7485 | 0.2961 | 0.5638 | 0.4368 | 0.2269 | 0.5125 | 0.8998 | 0.2680 | 0.4938 | 0.5341 | 0.4383 | 0.4726 |
| 0.5374 | 525 | 4.5357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5629 | 550 | 4.481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5885 | 575 | 4.4669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6141 | 600 | 4.3886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6397 | 625 | 4.2929 | 2.3091 | 0.2639 | 0.4475 | 0.7521 | 0.3095 | 0.5619 | 0.4448 | 0.2244 | 0.5178 | 0.9102 | 0.2655 | 0.4809 | 0.5253 | 0.4351 | 0.4722 |
| 0.6653 | 650 | 4.2558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6909 | 675 | 4.3228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7165 | 700 | 4.2496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7421 | 725 | 4.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7677 | 750 | 4.224 | 2.2440 | 0.2628 | 0.4514 | 0.7387 | 0.3028 | 0.5522 | 0.4313 | 0.2253 | 0.5266 | 0.9211 | 0.2675 | 0.4929 | 0.5232 | 0.4351 | 0.4716 |
| 0.7932 | 775 | 4.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8188 | 800 | 4.2686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8444 | 825 | 4.1657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8700 | 850 | 4.2297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8956 | 875 | 4.1709 | 2.2142 | 0.2685 | 0.4520 | 0.7569 | 0.2930 | 0.5625 | 0.4486 | 0.2229 | 0.5280 | 0.9153 | 0.2601 | 0.4862 | 0.5199 | 0.4334 | 0.4729 |
| 0.9212 | 900 | 4.0771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9468 | 925 | 4.1492 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9724 | 950 | 4.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9980 | 975 | 4.0993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 977 | - | - | 0.2613 | 0.4507 | 0.7556 | 0.2964 | 0.5584 | 0.4416 | 0.2241 | 0.5271 | 0.9154 | 0.2646 | 0.4714 | 0.5211 | 0.4291 | 0.4705 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.643 kWh
- **Carbon Emitted**: 0.250 kg of CO2
- **Hours Used**: 1.727 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.20.3
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |