File size: 191,564 Bytes
6dc336b |
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 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 |
---
base_model: Qwen/Qwen2.5-0.5B-Instruct
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1077240
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: When was the frigate the USS Alliance built?
sentences:
- 'Middle Stone Age
The Middle Stone Age (or MSA) was a period of African prehistory between the Early
Stone Age and the Later Stone Age. It is generally considered to have begun around
280,000 years ago and ended around 50–25,000 years ago.[1] The beginnings of particular
MSA stone tools have their origins as far back as 550–500,000 years ago and as
such some researchers consider this to be the beginnings of the MSA.[2] The MSA
is often mistakenly understood to be synonymous with the Middle Paleolithic of
Europe, especially due to their roughly contemporaneous time span, however, the
Middle Paleolithic of Europe represents an entirely different hominin population,
Homo neanderthalensis, than the MSA of Africa, which did not have Neanderthal
populations. Additionally, current archaeological research in Africa has yielded
much evidence to suggest that modern human behavior and cognition was beginning
to develop much earlier in Africa during the MSA than it was in Europe during
the Middle Paleolithic.[3] The MSA is associated with both anatomically modern
humans (Homo sapiens) as well as archaic Homo sapiens, sometimes referred to as
Homo helmei. Early physical evidence comes from the Gademotta Formation in Ethiopia,
the Kapthurin Formation in Kenya and Kathu Pan in South Africa.[2]'
- 'USS Alliance (1778)
Originally named Hancock, she was laid down in 1777 on the Merrimack River at
Amesbury, Massachusetts, by the partners and cousins, William and James K. Hackett,
launched on 28 April 1778, and renamed Alliance on 29 May 1778 by resolution of
the Continental Congress. Her first commanding officer was Capt. Pierre Landais,
a former officer of the French Navy who had come to the New World hoping to become
a naval counterpart of Lafayette. The frigate''s first captain was widely accepted
as such in America. Massachusetts made him an honorary citizen and the Continental
Congress gave him command of Alliance, thought to be the finest warship built
to that date on the western side of the Atlantic.'
- 'USS Frigate Bird (AMS-191)
The second ship in the Navy to be named "Frigate Bird", she was laid down 20 July
1953, as AMS-191; launched 24 October 1953, by Quincy Adams Yacht Yard, Inc.,
Quincy, Massachusetts; sponsored by Mrs. Matthew Gushing; and commissioned 13
January 1955, Lieutenant (jg) George B. Shick, Jr., in command. She was reclassified
MSC-191 on 7 February 1955.'
- source_sentence: How many bananas do Americans consume each year?
sentences:
- 'Dust Bowl
The Dust Bowl was a period of severe dust storms that greatly damaged the ecology
and agriculture of the American and Canadian prairies during the 1930s; severe
drought and a failure to apply dryland farming methods to prevent the aeolian
processes (wind erosion) caused the phenomenon.[1][2] The drought came in three
waves, 1934, 1936, and 1939–1940, but some regions of the high plains experienced
drought conditions for as many as eight years.[3] With insufficient understanding
of the ecology of the plains, farmers had conducted extensive deep plowing of
the virgin topsoil of the Great Plains during the previous decade; this had displaced
the native, deep-rooted grasses that normally trapped soil and moisture even during
periods of drought and high winds. The rapid mechanization of farm equipment,
especially small gasoline tractors, and widespread use of the combine harvester
contributed to farmers'' decisions to convert arid grassland (much of which received
no more than 10inches (~250mm) of precipitation per year) to cultivated cropland.[4]'
- 'Banana production in the United States
Commercial banana production in the United States is relatively limited in scale
and economic impact. While Americans eat 26 pounds (12kg) of bananas per person
per year, the vast majority of the fruit is imported from other countries, chiefly
Central and South America, where the US has previously occupied areas containing
banana plantations, and controlled the importation of bananas via various fruit
companies, such as Dole and Chiquita. [1]'
- 'Spinach in the United States
Per capita spinach consumption is greatest in the Northeast and Western US. About
80% of fresh-market spinach is purchased at retail and consumed at home, while
91% of processed spinach is consumed at home. Per capita spinach use is strongest
among Asians, highest among women 40 and older, and weakest among teenage girls.'
- source_sentence: Which rapper has the most Grammy wins?
sentences:
- 'Rhineland
The Rhineland (German: Rheinland, French: Rhénanie, Latinised name: Rhenania)
is the name used for a loosely defined area of Western Germany along the Rhine,
chiefly its middle section.'
- 'Grammy Award for Best Rap Album
6 wins Eminem 4 wins Kanye West 2 wins Outkast Kendrick Lamar'
- 'Latin Grammy Award records
René Pérez Joglar "Residente" and Eduardo Cabra "Visitante" with 24 awards, have
won more than any other male artist.'
- source_sentence: Is Iodine radioactive?
sentences:
- 'Double-decker bus
With the exception of coaches, double-decker buses are uncommon in the United
States. Many private operators, such as Megabus, run by Coach USA, employs double-decker
buses on its busier intercity routes.'
- 'Iodine-123
I is the most suitable isotope of iodine for the diagnostic study of thyroid diseases.
The half-life of approximately 13.13 hours is ideal for the 24-hour iodine uptake
test and I has other advantages for diagnostic imaging thyroid tissue and thyroid
cancer metastasis. The energy of the photon, 159 keV, is ideal for the NaI (sodium
iodide) crystal detector of current gamma cameras and also for the pinhole collimators.
It has much greater photon flux than I. It gives approximately 20 times the counting
rate of I for the same administered dose. The radiation burden to the thyroid
is far less (1%) than that of I. Moreover, scanning a thyroid remnant or metastasis
with I does not cause "stunning" of the tissue (with loss of uptake), because
of the low radiation burden of this isotope. (For the same reasons, I is never
used for thyroid cancer or Graves disease "treatment", and this role is reserved
for I.)'
- 'Iodine-131
Iodine-131 (131I) is an important radioisotope of iodine discovered by Glenn Seaborg
and John Livingood in 1938 at the University of California, Berkeley.[1] It has
a radioactive decay half-life of about eight days. It is associated with nuclear
energy, medical diagnostic and treatment procedures, and natural gas production.
It also plays a major role as a radioactive isotope present in nuclear fission
products, and was a significant contributor to the health hazards from open-air
atomic bomb testing in the 1950s, and from the Chernobyl disaster, as well as
being a large fraction of the contamination hazard in the first weeks in the Fukushima
nuclear crisis. This is because I-131 is a major fission product of uranium and
plutonium, comprising nearly 3% of the total products of fission (by weight).
See fission product yield for a comparison with other radioactive fission products.
I-131 is also a major fission product of uranium-233, produced from thorium.'
- source_sentence: Is a birth certificate a form of ID?
sentences:
- 'Identity documents in the United States
The birth certificate is the initial identification document issued to parents
shortly after the birth of their child. The birth certificate is typically issued
by local governments, usually the city or county where a child is born. It is
an important record, often called a "feeder document," because it establishes
U.S. citizenship through birthright citizenship, which is then used to obtain,
or is the basis for, all other identity documents.[2] By itself, the birth certificate
is usually only considered proof of citizenship but not proof of identity, since
it is issued without a photograph at birth, containing no identifying features.
A birth certificate is normally produced along with proof of identity, such as
a driver''s license or the testimony of a third party (such as a parent), to establish
identity or entitlement to a service.'
- 'Identity document
In Canada, different forms of identification documentation are used, but there
is no de jure national identity card. The Canadian passport is issued by the federal
(national) government, and the provinces and territories issue various documents
which can be used for identification purposes. The most commonly used forms of
identification within Canada are the health card and driver''s licence issued
by provincial and territorial governments. The widespread usage of these two documents
for identification purposes has made them de facto identity cards.'
- 'International Ladies'' Garment Workers'' Union
The ILGWU was founded on June 3, 1900[2] in New York City by seven local unions,
with a few thousand members between them. The union grew rapidly in the next few
years but began to stagnate as the conservative leadership favored the interests
of skilled workers, such as cutters. This did not sit well with the majority of
immigrant workers, particularly Jewish workers with a background in Bundist activities
in Tsarist Russia, or with Polish and Italian workers, many of whom had strong
socialist and anarchist leanings.'
model-index:
- name: SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052000000000000005
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12833333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.145
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.19333333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.16942887258019743
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24757142857142853
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1355707100998398
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.6
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.72
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.88
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4159999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.34
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05876239296513622
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12033972099857915
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1722048755781874
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23956661282775613
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43285369033010385
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6865000000000001
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.26928358503732847
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.66
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18666666666666668
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08599999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.63
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5142272491063538
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4404126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4252817429020436
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.096
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.062
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0968888888888889
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1971111111111111
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.274968253968254
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3351904761904762
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23653831229880148
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2642460317460318
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19230802555548754
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23333333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.23
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.35
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.37
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.45
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4053306177663136
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.512126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.34910258985509013
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: 0.34
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.068
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.044000000000000004
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.34
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.44
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3192297885891097
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2824126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2997920687164519
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: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.24
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.18
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.012493999137489852
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.03666681098754983
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.05620504095265686
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.06687360120479266
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22113733731375582
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3847142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08151129221772184
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.28
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: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.37
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.47
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.53
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.397781398000215
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3704682539682539
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.36349470659883915
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: 0.92
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.92
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.98
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.84
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.236
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.128
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7406666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8686666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8859999999999999
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.956
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8890148621063009
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8828571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8629278499278499
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.118
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.053000000000000005
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09300000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.24066666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21606068741193518
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35269047619047617
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15911734324501478
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.52
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.37083936956670244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2719126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.28287447040897273
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
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: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.235
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.355
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.465
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.58
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.39672106927390305
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35040476190476183
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3458399624509464
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.4489795918367347
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7142857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7959183673469388
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4489795918367347
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3945578231292517
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.3673469387755102
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3346938775510204
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0346506610646935
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09241187721057745
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.13780926782570618
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23921204048524203
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.37383535385863614
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6103984450923227
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3108247074769811
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.33761381475667196
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.49032967032967034
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5627629513343799
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6676609105180534
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.33761381475667196
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2139403453689168
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17194976452119312
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12513029827315542
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18011250836329812
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.29934842463906286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3551682644865234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.441603286977559
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3802306621694099
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.43513199272382946
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3136868503455821
name: Cosine Map@100
---
# SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It maps sentences & paragraphs to a 896-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:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) <!-- at revision 7ae557604adf67be50417f59c2c2f167def9a775 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 896 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model
(1): Pooling({'word_embedding_dimension': 896, '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("AlexWortega/qwen_emb_600_best_21.11")
# Run inference
sentences = [
'Is a birth certificate a form of ID?',
'Identity documents in the United States\nThe birth certificate is the initial identification document issued to parents shortly after the birth of their child. The birth certificate is typically issued by local governments, usually the city or county where a child is born. It is an important record, often called a "feeder document," because it establishes U.S. citizenship through birthright citizenship, which is then used to obtain, or is the basis for, all other identity documents.[2] By itself, the birth certificate is usually only considered proof of citizenship but not proof of identity, since it is issued without a photograph at birth, containing no identifying features. A birth certificate is normally produced along with proof of identity, such as a driver\'s license or the testimony of a third party (such as a parent), to establish identity or entitlement to a service.',
"Identity document\nIn Canada, different forms of identification documentation are used, but there is no de jure national identity card. The Canadian passport is issued by the federal (national) government, and the provinces and territories issue various documents which can be used for identification purposes. The most commonly used forms of identification within Canada are the health card and driver's licence issued by provincial and territorial governments. The widespread usage of these two documents for identification purposes has made them de facto identity cards.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]
# 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 | 0.18 | 0.6 | 0.28 | 0.18 | 0.46 | 0.22 | 0.32 | 0.28 | 0.84 | 0.26 | 0.06 | 0.26 | 0.449 |
| cosine_accuracy@3 | 0.28 | 0.72 | 0.56 | 0.28 | 0.56 | 0.34 | 0.42 | 0.4 | 0.92 | 0.38 | 0.42 | 0.38 | 0.7143 |
| cosine_accuracy@5 | 0.32 | 0.82 | 0.66 | 0.4 | 0.6 | 0.34 | 0.46 | 0.5 | 0.92 | 0.48 | 0.52 | 0.5 | 0.7959 |
| cosine_accuracy@10 | 0.4 | 0.88 | 0.82 | 0.5 | 0.68 | 0.44 | 0.52 | 0.58 | 0.98 | 0.62 | 0.68 | 0.6 | 0.9796 |
| cosine_precision@1 | 0.18 | 0.6 | 0.28 | 0.18 | 0.46 | 0.22 | 0.32 | 0.28 | 0.84 | 0.26 | 0.06 | 0.26 | 0.449 |
| cosine_precision@3 | 0.1 | 0.4333 | 0.1867 | 0.1133 | 0.2333 | 0.1133 | 0.28 | 0.1333 | 0.3667 | 0.1533 | 0.14 | 0.1333 | 0.3946 |
| cosine_precision@5 | 0.072 | 0.416 | 0.136 | 0.096 | 0.148 | 0.068 | 0.24 | 0.1 | 0.236 | 0.148 | 0.104 | 0.104 | 0.3673 |
| cosine_precision@10 | 0.052 | 0.34 | 0.086 | 0.062 | 0.09 | 0.044 | 0.18 | 0.058 | 0.128 | 0.118 | 0.068 | 0.066 | 0.3347 |
| cosine_recall@1 | 0.07 | 0.0588 | 0.26 | 0.0969 | 0.23 | 0.22 | 0.0125 | 0.27 | 0.7407 | 0.053 | 0.06 | 0.235 | 0.0347 |
| cosine_recall@3 | 0.1283 | 0.1203 | 0.52 | 0.1971 | 0.35 | 0.34 | 0.0367 | 0.37 | 0.8687 | 0.093 | 0.42 | 0.355 | 0.0924 |
| cosine_recall@5 | 0.145 | 0.1722 | 0.63 | 0.275 | 0.37 | 0.34 | 0.0562 | 0.47 | 0.886 | 0.15 | 0.52 | 0.465 | 0.1378 |
| cosine_recall@10 | 0.1933 | 0.2396 | 0.79 | 0.3352 | 0.45 | 0.44 | 0.0669 | 0.53 | 0.956 | 0.2407 | 0.68 | 0.58 | 0.2392 |
| **cosine_ndcg@10** | **0.1694** | **0.4329** | **0.5142** | **0.2365** | **0.4053** | **0.3192** | **0.2211** | **0.3978** | **0.889** | **0.2161** | **0.3708** | **0.3967** | **0.3738** |
| cosine_mrr@10 | 0.2476 | 0.6865 | 0.4404 | 0.2642 | 0.5121 | 0.2824 | 0.3847 | 0.3705 | 0.8829 | 0.3527 | 0.2719 | 0.3504 | 0.6104 |
| cosine_map@100 | 0.1356 | 0.2693 | 0.4253 | 0.1923 | 0.3491 | 0.2998 | 0.0815 | 0.3635 | 0.8629 | 0.1591 | 0.2829 | 0.3458 | 0.3108 |
#### 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 | 0.3376 |
| cosine_accuracy@3 | 0.4903 |
| cosine_accuracy@5 | 0.5628 |
| cosine_accuracy@10 | 0.6677 |
| cosine_precision@1 | 0.3376 |
| cosine_precision@3 | 0.2139 |
| cosine_precision@5 | 0.1719 |
| cosine_precision@10 | 0.1251 |
| cosine_recall@1 | 0.1801 |
| cosine_recall@3 | 0.2993 |
| cosine_recall@5 | 0.3552 |
| cosine_recall@10 | 0.4416 |
| **cosine_ndcg@10** | **0.3802** |
| cosine_mrr@10 | 0.4351 |
| cosine_map@100 | 0.3137 |
<!--
## 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
#### Unnamed Dataset
* Size: 1,077,240 training samples
* Columns: <code>query</code>, <code>response</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | response | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.76 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 141.88 tokens</li><li>max: 532 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 134.02 tokens</li><li>max: 472 tokens</li></ul> |
* Samples:
| query | response | negative |
|:--------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Was there a year 0?</code> | <code>Year zero<br>Year zero does not exist in the anno Domini system usually used to number years in the Gregorian calendar and in its predecessor, the Julian calendar. In this system, the year 1 BC is followed by AD 1. However, there is a year zero in astronomical year numbering (where it coincides with the Julian year 1 BC) and in ISO 8601:2004 (where it coincides with the Gregorian year 1 BC) as well as in all Buddhist and Hindu calendars.</code> | <code>504<br>Year 504 (DIV) was a leap year starting on Thursday (link will display the full calendar) of the Julian calendar. At the time, it was known as the Year of the Consulship of Nicomachus without colleague (or, less frequently, year 1257 "Ab urbe condita"). The denomination 504 for this year has been used since the early medieval period, when the Anno Domini calendar era became the prevalent method in Europe for naming years.</code> |
| <code>When is the dialectical method used?</code> | <code>Dialectic<br>Dialectic or dialectics (Greek: διαλεκτική, dialektikḗ; related to dialogue), also known as the dialectical method, is at base a discourse between two or more people holding different points of view about a subject but wishing to establish the truth through reasoned arguments. Dialectic resembles debate, but the concept excludes subjective elements such as emotional appeal and the modern pejorative sense of rhetoric.[1][2] Dialectic may be contrasted with the didactic method, wherein one side of the conversation teaches the other. Dialectic is alternatively known as minor logic, as opposed to major logic or critique.</code> | <code>Derek Bentley case<br>Another factor in the posthumous defence was that a "confession" recorded by Bentley, which was claimed by the prosecution to be a "verbatim record of dictated monologue", was shown by forensic linguistics methods to have been largely edited by policemen. Linguist Malcolm Coulthard showed that certain patterns, such as the frequency of the word "then" and the grammatical use of "then" after the grammatical subject ("I then" rather than "then I"), were not consistent with Bentley's use of language (his idiolect), as evidenced in court testimony. These patterns fit better the recorded testimony of the policemen involved. This is one of the earliest uses of forensic linguistics on record.</code> |
| <code>What do Grasshoppers eat?</code> | <code>Grasshopper<br>Grasshoppers are plant-eaters, with a few species at times becoming serious pests of cereals, vegetables and pasture, especially when they swarm in their millions as locusts and destroy crops over wide areas. They protect themselves from predators by camouflage; when detected, many species attempt to startle the predator with a brilliantly-coloured wing-flash while jumping and (if adult) launching themselves into the air, usually flying for only a short distance. Other species such as the rainbow grasshopper have warning coloration which deters predators. Grasshoppers are affected by parasites and various diseases, and many predatory creatures feed on both nymphs and adults. The eggs are the subject of attack by parasitoids and predators.</code> | <code>Groundhog<br>Very often the dens of groundhogs provide homes for other animals including skunks, red foxes, and cottontail rabbits. The fox and skunk feed upon field mice, grasshoppers, beetles and other creatures that destroy farm crops. In aiding these animals, the groundhog indirectly helps the farmer. In addition to providing homes for itself and other animals, the groundhog aids in soil improvement by bringing subsoil to the surface. The groundhog is also a valuable game animal and is considered a difficult sport when hunted in a fair manner. In some parts of Appalachia, they are eaten.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `gradient_accumulation_steps`: 8
- `learning_rate`: 0.0001
- `max_grad_norm`: 0.01
- `num_train_epochs`: 2
- `warmup_ratio`: 0.4
- `bf16`: True
- `dataloader_num_workers`: 8
- `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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 0.01
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.4
- `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`: 42
- `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`: True
- `dataloader_num_workers`: 8
- `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
<details><summary>Click to expand</summary>
| Epoch | Step | Training 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 |
|:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0002 | 2 | 1.8808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0005 | 4 | 1.9239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0007 | 6 | 2.0324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0010 | 8 | 2.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0012 | 10 | 2.0336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0014 | 12 | 1.9943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0017 | 14 | 1.971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0019 | 16 | 1.9206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0021 | 18 | 1.8157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0024 | 20 | 1.8605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0026 | 22 | 1.862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0029 | 24 | 1.9313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0031 | 26 | 1.8326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0033 | 28 | 1.9208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0036 | 30 | 2.4718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0038 | 32 | 2.4819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0040 | 34 | 2.4956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0043 | 36 | 2.4335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0045 | 38 | 2.4694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0048 | 40 | 2.5719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0050 | 42 | 2.4666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0052 | 44 | 2.4919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0055 | 46 | 2.4179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0057 | 48 | 2.4022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0059 | 50 | 2.39 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0062 | 52 | 2.4682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0064 | 54 | 2.3442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0067 | 56 | 2.3157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0069 | 58 | 2.2665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0071 | 60 | 2.2969 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0074 | 62 | 2.1652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0076 | 64 | 2.1243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0078 | 66 | 2.0499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0081 | 68 | 2.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0083 | 70 | 1.8372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0086 | 72 | 1.6257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0088 | 74 | 1.6398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0090 | 76 | 1.4927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0093 | 78 | 1.3491 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0095 | 80 | 1.3303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0097 | 82 | 1.3846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0100 | 84 | 1.2647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0102 | 86 | 1.1579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0105 | 88 | 1.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0107 | 90 | 0.9201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0109 | 92 | 0.8631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0112 | 94 | 0.7801 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0114 | 96 | 0.7813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0116 | 98 | 0.7898 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0119 | 100 | 0.722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0121 | 102 | 0.7595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0124 | 104 | 0.6245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0126 | 106 | 0.6036 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0128 | 108 | 0.7248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0131 | 110 | 0.637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0133 | 112 | 0.6205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0135 | 114 | 0.5956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0138 | 116 | 0.6126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0140 | 118 | 0.547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0143 | 120 | 0.5414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0145 | 122 | 0.4896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0147 | 124 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0150 | 126 | 0.5404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0152 | 128 | 0.4479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0154 | 130 | 1.1779 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0157 | 132 | 1.4533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0159 | 134 | 1.5042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0162 | 136 | 1.2167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0164 | 138 | 1.2484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0166 | 140 | 1.1236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0169 | 142 | 1.1729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0171 | 144 | 1.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0173 | 146 | 1.0314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0176 | 148 | 0.9106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0178 | 150 | 0.8994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0181 | 152 | 0.9679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0183 | 154 | 0.8576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0185 | 156 | 0.777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0188 | 158 | 0.8527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0190 | 160 | 0.864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0192 | 162 | 0.807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0195 | 164 | 0.9083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0197 | 166 | 0.7705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0200 | 168 | 0.7179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0202 | 170 | 0.7485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0204 | 172 | 0.7198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0207 | 174 | 0.7712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0209 | 176 | 0.82 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0212 | 178 | 0.7744 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0214 | 180 | 0.7668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0216 | 182 | 0.6501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0219 | 184 | 0.6327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0221 | 186 | 0.752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0223 | 188 | 0.6204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0226 | 190 | 0.6258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0228 | 192 | 0.607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0231 | 194 | 0.5688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0233 | 196 | 0.6831 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0235 | 198 | 0.5653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0238 | 200 | 0.5966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0240 | 202 | 0.5798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0242 | 204 | 0.5991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0245 | 206 | 0.5856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0247 | 208 | 0.5935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0250 | 210 | 0.5624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0252 | 212 | 0.6188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0254 | 214 | 0.5497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0257 | 216 | 0.582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0259 | 218 | 0.5912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0261 | 220 | 0.4818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0264 | 222 | 0.5686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0266 | 224 | 0.5174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0269 | 226 | 0.523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0271 | 228 | 0.5337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0273 | 230 | 0.5253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0276 | 232 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0278 | 234 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0280 | 236 | 0.5202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0283 | 238 | 0.4611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0285 | 240 | 0.4509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0288 | 242 | 0.5217 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0290 | 244 | 0.5256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0292 | 246 | 0.5529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0295 | 248 | 0.4944 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0297 | 250 | 0.568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0299 | 252 | 0.5024 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0302 | 254 | 0.5094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0304 | 256 | 0.5057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0307 | 258 | 0.5424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0309 | 260 | 0.6485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0311 | 262 | 0.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0314 | 264 | 0.475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0316 | 266 | 0.3753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0318 | 268 | 0.5117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0321 | 270 | 0.4067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0323 | 272 | 0.4706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0326 | 274 | 0.4099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0328 | 276 | 0.4251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0330 | 278 | 0.4392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0333 | 280 | 0.5373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0335 | 282 | 0.4259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0337 | 284 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0340 | 286 | 0.4774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0342 | 288 | 0.4878 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0345 | 290 | 0.5619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0347 | 292 | 0.5061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0349 | 294 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0352 | 296 | 0.5115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0354 | 298 | 0.4281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0356 | 300 | 0.4287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0359 | 302 | 0.4864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0361 | 304 | 0.4724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0364 | 306 | 0.4607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0366 | 308 | 0.3978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0368 | 310 | 0.4851 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0371 | 312 | 0.3466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0373 | 314 | 0.565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0375 | 316 | 0.4122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0378 | 318 | 0.3757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0380 | 320 | 0.4673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0383 | 322 | 0.4358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0385 | 324 | 0.4423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0387 | 326 | 0.3754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0390 | 328 | 0.4358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0392 | 330 | 0.408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0394 | 332 | 0.3901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0397 | 334 | 0.4155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0399 | 336 | 0.379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0402 | 338 | 0.373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0404 | 340 | 0.2917 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0406 | 342 | 0.3755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0409 | 344 | 0.3262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0411 | 346 | 0.4975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0414 | 348 | 0.3469 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0416 | 350 | 0.3895 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0418 | 352 | 0.4424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0421 | 354 | 0.3609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0423 | 356 | 0.434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0425 | 358 | 0.4474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0428 | 360 | 0.3514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0430 | 362 | 0.4029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0433 | 364 | 0.4438 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0435 | 366 | 0.4271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0437 | 368 | 0.3825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0440 | 370 | 0.3848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0442 | 372 | 0.4088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0444 | 374 | 0.4188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0447 | 376 | 0.4333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0449 | 378 | 0.3784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0452 | 380 | 0.4509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0454 | 382 | 0.4084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0456 | 384 | 0.371 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0459 | 386 | 0.3965 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0461 | 388 | 0.375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0463 | 390 | 0.4098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0466 | 392 | 0.4198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0468 | 394 | 0.3854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0471 | 396 | 0.3146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0473 | 398 | 0.3892 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0475 | 400 | 0.3295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0478 | 402 | 0.4124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0480 | 404 | 0.3039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0482 | 406 | 0.3353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 408 | 0.4382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0487 | 410 | 0.4013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0490 | 412 | 0.3283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0492 | 414 | 0.4264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0494 | 416 | 0.4295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0497 | 418 | 0.3451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0499 | 420 | 0.2973 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0501 | 422 | 0.3734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0504 | 424 | 0.3992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0506 | 426 | 0.3234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0509 | 428 | 0.4007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0511 | 430 | 0.4446 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0513 | 432 | 0.282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0516 | 434 | 0.3922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0518 | 436 | 0.4224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0520 | 438 | 0.3362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0523 | 440 | 0.3461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0525 | 442 | 0.344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0528 | 444 | 0.4355 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0530 | 446 | 0.3443 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0532 | 448 | 0.4363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0535 | 450 | 0.3282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0537 | 452 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0539 | 454 | 0.3279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0542 | 456 | 0.3774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0544 | 458 | 0.3888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0547 | 460 | 0.5149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0549 | 462 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0551 | 464 | 0.3943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0554 | 466 | 0.366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0556 | 468 | 0.344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0558 | 470 | 0.3681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0561 | 472 | 0.3041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0563 | 474 | 0.3857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0566 | 476 | 0.3665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0568 | 478 | 0.3871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0570 | 480 | 0.4707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0573 | 482 | 0.4031 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0575 | 484 | 0.385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0577 | 486 | 0.2868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0580 | 488 | 0.3637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0582 | 490 | 0.4484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0585 | 492 | 0.4984 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0587 | 494 | 0.3725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0589 | 496 | 0.3102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0592 | 498 | 0.3529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0594 | 500 | 0.3929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0596 | 502 | 0.3012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0599 | 504 | 0.4137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0601 | 506 | 0.3987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0604 | 508 | 0.3724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0606 | 510 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0608 | 512 | 0.389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0611 | 514 | 0.3775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0613 | 516 | 0.3429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0616 | 518 | 0.348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0618 | 520 | 0.3706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0620 | 522 | 0.3563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0623 | 524 | 0.3029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0625 | 526 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0627 | 528 | 0.3457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0630 | 530 | 0.3666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0632 | 532 | 0.3331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0635 | 534 | 0.3362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0637 | 536 | 0.3974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0639 | 538 | 0.3841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0642 | 540 | 0.3318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0644 | 542 | 0.3349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 544 | 0.461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0649 | 546 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0651 | 548 | 0.3901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0654 | 550 | 0.3292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0656 | 552 | 0.3291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0658 | 554 | 0.374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0661 | 556 | 0.3432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0663 | 558 | 0.2994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0665 | 560 | 0.3391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0668 | 562 | 0.3764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0670 | 564 | 0.2555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0673 | 566 | 0.3553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0675 | 568 | 0.3436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0677 | 570 | 0.4347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0680 | 572 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0682 | 574 | 0.2988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0684 | 576 | 0.3698 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0687 | 578 | 0.3309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0689 | 580 | 0.3529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0692 | 582 | 0.3685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0694 | 584 | 0.333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0696 | 586 | 0.3344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0699 | 588 | 0.3496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0701 | 590 | 0.3616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0703 | 592 | 0.3637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0706 | 594 | 0.3745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0708 | 596 | 0.3465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0711 | 598 | 0.4128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0713 | 600 | 0.3674 | 0.1694 | 0.4329 | 0.5142 | 0.2365 | 0.4053 | 0.3192 | 0.2211 | 0.3978 | 0.8890 | 0.2161 | 0.3708 | 0.3967 | 0.3738 | 0.3802 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.1.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
--> |