File size: 128,076 Bytes
f668efa |
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 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 |
---
base_model: microsoft/deberta-v3-small
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:32500
- loss:GISTEmbedLoss
widget:
- source_sentence: phase changes do not change
sentences:
- The major Atlantic slave trading nations, ordered by trade volume, were the Portuguese,
the British, the Spanish, the French, the Dutch, and the Danish. Several had established
outposts on the African coast where they purchased slaves from local African leaders.
- "phase changes do not change mass. Particles have mass, but mass is energy. \n\
\ phase changes do not change energy"
- According to the U.S. Census Bureau , the county is a total area of , which has
land and ( 0.2 % ) is water .
- source_sentence: what jobs can you get with a bachelor degree in anthropology?
sentences:
- To determine the atomic weight of an element, you should add up protons and neutrons.
- '[''Paleontologist*'', ''Archaeologist*'', ''University Professor*'', ''Market
Research Analyst*'', ''Primatologist.'', ''Forensic Scientist*'', ''Medical Anthropologist.'',
''Museum Technician.'']'
- The wingspan flies , the moth comes depending on the location from July to August
.
- source_sentence: Identify different forms of energy (e.g., light, sound, heat).
sentences:
- '`` Irreplaceable '''' '''' remained on the chart for thirty weeks , and was certified
double-platinum by the Recording Industry Association of America ( RIAA ) , denoting
sales of two million downloads , and had sold over 3,139,000 paid digital downloads
in the US as of October 2012 , according to Nielsen SoundScan . '''''
- On Rotten Tomatoes , the film has a rating of 63 % , based on 87 reviews , with
an average rating of 5.9/10 .
- Heat, light, and sound are all different forms of energy.
- source_sentence: what is so small it can only be seen with an electron microscope?
sentences:
- "Viruses are so small that they can be seen only with an electron microscope..\
\ Where most viruses are DNA, HIV is an RNA virus. \n HIV is so small it can only\
\ be seen with an electron microscope"
- The development of modern lasers has opened many doors to both research and applications.
A laser beam was used to measure the distance from the Earth to the moon. Lasers
are important components of CD players. As the image above illustrates, lasers
can provide precise focusing of beams to selectively destroy cancer cells in patients.
The ability of a laser to focus precisely is due to high-quality crystals that
help give rise to the laser beam. A variety of techniques are used to manufacture
pure crystals for use in lasers.
- Discussion for (a) This value is the net work done on the package. The person
actually does more work than this, because friction opposes the motion. Friction
does negative work and removes some of the energy the person expends and converts
it to thermal energy. The net work equals the sum of the work done by each individual
force. Strategy and Concept for (b) The forces acting on the package are gravity,
the normal force, the force of friction, and the applied force. The normal force
and force of gravity are each perpendicular to the displacement, and therefore
do no work. Solution for (b) The applied force does work.
- source_sentence: what aspects of your environment may relate to the epidemic of
obesity
sentences:
- Jan Kromkamp ( born August 17 , 1980 in Makkinga , Netherlands ) is a Dutch footballer
.
- When chemicals in solution react, the proper way of writing the chemical formulas
of the dissolved ionic compounds is in terms of the dissociated ions, not the
complete ionic formula. A complete ionic equation is a chemical equation in which
the dissolved ionic compounds are written as separated ions. Solubility rules
are very useful in determining which ionic compounds are dissolved and which are
not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement
reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation
includes NaCl, AgNO3, and NaNO3 written as separated ions:.
- Genetic changes in human populations occur too slowly to be responsible for the
obesity epidemic. Nevertheless, the variation in how people respond to the environment
that promotes physical inactivity and intake of high-calorie foods suggests that
genes do play a role in the development of obesity.
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.5639496482103532
name: Pearson Cosine
- type: spearman_cosine
value: 0.5729838735376087
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5739911399908657
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5731718735867141
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5732787731326285
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5730641573005449
name: Spearman Euclidean
- type: pearson_dot
value: 0.5637779615119842
name: Pearson Dot
- type: spearman_dot
value: 0.5726842638425932
name: Spearman Dot
- type: pearson_max
value: 0.5739911399908657
name: Pearson Max
- type: spearman_max
value: 0.5731718735867141
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.689453125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.9270462989807129
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.5446808510638298
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8589708805084229
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.43097643097643096
name: Cosine Precision
- type: cosine_recall
value: 0.7398843930635838
name: Cosine Recall
- type: cosine_ap
value: 0.5065548770111167
name: Cosine Ap
- type: dot_accuracy
value: 0.689453125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 712.409912109375
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.5446808510638298
name: Dot F1
- type: dot_f1_threshold
value: 660.105224609375
name: Dot F1 Threshold
- type: dot_precision
value: 0.43097643097643096
name: Dot Precision
- type: dot_recall
value: 0.7398843930635838
name: Dot Recall
- type: dot_ap
value: 0.5065544970851109
name: Dot Ap
- type: manhattan_accuracy
value: 0.693359375
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 261.6011962890625
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.5442764578833693
name: Manhattan F1
- type: manhattan_f1_threshold
value: 316.1423645019531
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.43448275862068964
name: Manhattan Precision
- type: manhattan_recall
value: 0.7283236994219653
name: Manhattan Recall
- type: manhattan_ap
value: 0.5104540022958912
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.689453125
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 10.588932037353516
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.5446808510638298
name: Euclidean F1
- type: euclidean_f1_threshold
value: 14.722663879394531
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.43097643097643096
name: Euclidean Precision
- type: euclidean_recall
value: 0.7398843930635838
name: Euclidean Recall
- type: euclidean_ap
value: 0.5065548770111167
name: Euclidean Ap
- type: max_accuracy
value: 0.693359375
name: Max Accuracy
- type: max_accuracy_threshold
value: 712.409912109375
name: Max Accuracy Threshold
- type: max_f1
value: 0.5446808510638298
name: Max F1
- type: max_f1_threshold
value: 660.105224609375
name: Max F1 Threshold
- type: max_precision
value: 0.43448275862068964
name: Max Precision
- type: max_recall
value: 0.7398843930635838
name: Max Recall
- type: max_ap
value: 0.5104540022958912
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.67578125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8426380157470703
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6866197183098591
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8136929869651794
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5873493975903614
name: Cosine Precision
- type: cosine_recall
value: 0.826271186440678
name: Cosine Recall
- type: cosine_ap
value: 0.6951783685178275
name: Cosine Ap
- type: dot_accuracy
value: 0.67578125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 647.48828125
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6866197183098591
name: Dot F1
- type: dot_f1_threshold
value: 625.2723388671875
name: Dot F1 Threshold
- type: dot_precision
value: 0.5873493975903614
name: Dot Precision
- type: dot_recall
value: 0.826271186440678
name: Dot Recall
- type: dot_ap
value: 0.6953081100663845
name: Dot Ap
- type: manhattan_accuracy
value: 0.673828125
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 337.1568603515625
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6913996627318718
name: Manhattan F1
- type: manhattan_f1_threshold
value: 378.724609375
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5742296918767507
name: Manhattan Precision
- type: manhattan_recall
value: 0.8686440677966102
name: Manhattan Recall
- type: manhattan_ap
value: 0.6981557028078741
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.67578125
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 15.551056861877441
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6866197183098591
name: Euclidean F1
- type: euclidean_f1_threshold
value: 16.921308517456055
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5873493975903614
name: Euclidean Precision
- type: euclidean_recall
value: 0.826271186440678
name: Euclidean Recall
- type: euclidean_ap
value: 0.695195292717627
name: Euclidean Ap
- type: max_accuracy
value: 0.67578125
name: Max Accuracy
- type: max_accuracy_threshold
value: 647.48828125
name: Max Accuracy Threshold
- type: max_f1
value: 0.6913996627318718
name: Max F1
- type: max_f1_threshold
value: 625.2723388671875
name: Max F1 Threshold
- type: max_precision
value: 0.5873493975903614
name: Max Precision
- type: max_recall
value: 0.8686440677966102
name: Max Recall
- type: max_ap
value: 0.6981557028078741
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v3-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). 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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): AdvancedWeightedPooling(
(alpha_dropout_layer): Dropout(p=0.05, inplace=False)
(gate_dropout_layer): Dropout(p=0.0, inplace=False)
(linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
(linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
(mha): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=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("bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp")
# Run inference
sentences = [
'what aspects of your environment may relate to the epidemic of obesity',
'Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.',
'When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:.',
]
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
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.5639 |
| **spearman_cosine** | **0.573** |
| pearson_manhattan | 0.574 |
| spearman_manhattan | 0.5732 |
| pearson_euclidean | 0.5733 |
| spearman_euclidean | 0.5731 |
| pearson_dot | 0.5638 |
| spearman_dot | 0.5727 |
| pearson_max | 0.574 |
| spearman_max | 0.5732 |
#### Binary Classification
* Dataset: `allNLI-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6895 |
| cosine_accuracy_threshold | 0.927 |
| cosine_f1 | 0.5447 |
| cosine_f1_threshold | 0.859 |
| cosine_precision | 0.431 |
| cosine_recall | 0.7399 |
| cosine_ap | 0.5066 |
| dot_accuracy | 0.6895 |
| dot_accuracy_threshold | 712.4099 |
| dot_f1 | 0.5447 |
| dot_f1_threshold | 660.1052 |
| dot_precision | 0.431 |
| dot_recall | 0.7399 |
| dot_ap | 0.5066 |
| manhattan_accuracy | 0.6934 |
| manhattan_accuracy_threshold | 261.6012 |
| manhattan_f1 | 0.5443 |
| manhattan_f1_threshold | 316.1424 |
| manhattan_precision | 0.4345 |
| manhattan_recall | 0.7283 |
| manhattan_ap | 0.5105 |
| euclidean_accuracy | 0.6895 |
| euclidean_accuracy_threshold | 10.5889 |
| euclidean_f1 | 0.5447 |
| euclidean_f1_threshold | 14.7227 |
| euclidean_precision | 0.431 |
| euclidean_recall | 0.7399 |
| euclidean_ap | 0.5066 |
| max_accuracy | 0.6934 |
| max_accuracy_threshold | 712.4099 |
| max_f1 | 0.5447 |
| max_f1_threshold | 660.1052 |
| max_precision | 0.4345 |
| max_recall | 0.7399 |
| **max_ap** | **0.5105** |
#### Binary Classification
* Dataset: `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6758 |
| cosine_accuracy_threshold | 0.8426 |
| cosine_f1 | 0.6866 |
| cosine_f1_threshold | 0.8137 |
| cosine_precision | 0.5873 |
| cosine_recall | 0.8263 |
| cosine_ap | 0.6952 |
| dot_accuracy | 0.6758 |
| dot_accuracy_threshold | 647.4883 |
| dot_f1 | 0.6866 |
| dot_f1_threshold | 625.2723 |
| dot_precision | 0.5873 |
| dot_recall | 0.8263 |
| dot_ap | 0.6953 |
| manhattan_accuracy | 0.6738 |
| manhattan_accuracy_threshold | 337.1569 |
| manhattan_f1 | 0.6914 |
| manhattan_f1_threshold | 378.7246 |
| manhattan_precision | 0.5742 |
| manhattan_recall | 0.8686 |
| manhattan_ap | 0.6982 |
| euclidean_accuracy | 0.6758 |
| euclidean_accuracy_threshold | 15.5511 |
| euclidean_f1 | 0.6866 |
| euclidean_f1_threshold | 16.9213 |
| euclidean_precision | 0.5873 |
| euclidean_recall | 0.8263 |
| euclidean_ap | 0.6952 |
| max_accuracy | 0.6758 |
| max_accuracy_threshold | 647.4883 |
| max_f1 | 0.6914 |
| max_f1_threshold | 625.2723 |
| max_precision | 0.5873 |
| max_recall | 0.8686 |
| **max_ap** | **0.6982** |
<!--
## 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: 32,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 29.39 tokens</li><li>max: 323 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 54.42 tokens</li><li>max: 423 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>In which London road is Harrod’s department store?</code> | <code>Harrods, Brompton Road, London | Shopping/Department Stores in London | LondonTown.com Opening Times Britain's most famous store and possibly the most famous store in the world, Harrods features on many tourist 'must-see' lists - and with good reason. Its humble beginnings date back to 1849, when Charles Henry Harrod opened a small East End grocer and tea merchant business that emphasised impeccable service over value. Today, it occupies a vast seven floor site in London's fashionable Knightsbridge and boasts a phenomenal range of products from pianos and cooking pans to fashion and perfumery. The luxurious Urban Retreat can be found on the sixth floor while newer departments include Superbrands, with 17 boutiques from top international designers, and Salon du Parfums, housing some of the most exceptional and exclusive perfumes in the world. The Food Hall is ostentatious to the core and mouth-wateringly exotic, and the store as a whole is well served with 27 restaurants. At Christmas time the Brompton Road windows are transformed into a magical winter wonderland and Father Christmas takes up residence at the enchanting Christmas Grotto. The summer and winter sales are calendar events in the shopping year, and although both sales are extremely crowded there are some great bargains on offer. �</code> |
| <code>e.	in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> | <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code> |
| <code>Joe Cole was unable to join West Bromwich Albion .</code> | <code>On 16th October Joe Cole took a long hard look at himself realising that he would never get the opportunity to join West Bromwich Albion and joined Coventry City instead.</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': 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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 256
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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`: 32
- `per_device_eval_batch_size`: 256
- `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`: 5e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
- `warmup_ratio`: 0.33
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: False
- `fp16`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap |
|:------:|:----:|:-------------:|:------------------------:|:-----------------:|:---------------:|
| 0.0010 | 1 | 6.0688 | - | - | - |
| 0.0020 | 2 | 7.5576 | - | - | - |
| 0.0030 | 3 | 4.6849 | - | - | - |
| 0.0039 | 4 | 5.4503 | - | - | - |
| 0.0049 | 5 | 5.6057 | - | - | - |
| 0.0059 | 6 | 6.3049 | - | - | - |
| 0.0069 | 7 | 6.8336 | - | - | - |
| 0.0079 | 8 | 5.0777 | - | - | - |
| 0.0089 | 9 | 4.8358 | - | - | - |
| 0.0098 | 10 | 4.641 | - | - | - |
| 0.0108 | 11 | 4.828 | - | - | - |
| 0.0118 | 12 | 5.2269 | - | - | - |
| 0.0128 | 13 | 5.6772 | - | - | - |
| 0.0138 | 14 | 5.1422 | - | - | - |
| 0.0148 | 15 | 6.2469 | - | - | - |
| 0.0157 | 16 | 4.6802 | - | - | - |
| 0.0167 | 17 | 4.5492 | - | - | - |
| 0.0177 | 18 | 4.8062 | - | - | - |
| 0.0187 | 19 | 7.5141 | - | - | - |
| 0.0197 | 20 | 5.5202 | - | - | - |
| 0.0207 | 21 | 6.5025 | - | - | - |
| 0.0217 | 22 | 7.318 | - | - | - |
| 0.0226 | 23 | 4.6458 | - | - | - |
| 0.0236 | 24 | 4.6191 | - | - | - |
| 0.0246 | 25 | 4.3159 | - | - | - |
| 0.0256 | 26 | 6.3677 | - | - | - |
| 0.0266 | 27 | 5.6052 | - | - | - |
| 0.0276 | 28 | 4.196 | - | - | - |
| 0.0285 | 29 | 4.4802 | - | - | - |
| 0.0295 | 30 | 4.9193 | - | - | - |
| 0.0305 | 31 | 4.0996 | - | - | - |
| 0.0315 | 32 | 5.6307 | - | - | - |
| 0.0325 | 33 | 4.5745 | - | - | - |
| 0.0335 | 34 | 4.4514 | - | - | - |
| 0.0344 | 35 | 4.0617 | - | - | - |
| 0.0354 | 36 | 5.0298 | - | - | - |
| 0.0364 | 37 | 3.9815 | - | - | - |
| 0.0374 | 38 | 4.0871 | - | - | - |
| 0.0384 | 39 | 4.2378 | - | - | - |
| 0.0394 | 40 | 3.8226 | - | - | - |
| 0.0404 | 41 | 4.3519 | - | - | - |
| 0.0413 | 42 | 3.6345 | - | - | - |
| 0.0423 | 43 | 5.0829 | - | - | - |
| 0.0433 | 44 | 4.6701 | - | - | - |
| 0.0443 | 45 | 4.1371 | - | - | - |
| 0.0453 | 46 | 4.2418 | - | - | - |
| 0.0463 | 47 | 4.4766 | - | - | - |
| 0.0472 | 48 | 4.4797 | - | - | - |
| 0.0482 | 49 | 3.8471 | - | - | - |
| 0.0492 | 50 | 4.3194 | - | - | - |
| 0.0502 | 51 | 3.9426 | - | - | - |
| 0.0512 | 52 | 3.5333 | - | - | - |
| 0.0522 | 53 | 4.2426 | - | - | - |
| 0.0531 | 54 | 3.9816 | - | - | - |
| 0.0541 | 55 | 3.663 | - | - | - |
| 0.0551 | 56 | 3.9057 | - | - | - |
| 0.0561 | 57 | 4.0345 | - | - | - |
| 0.0571 | 58 | 3.5233 | - | - | - |
| 0.0581 | 59 | 3.7999 | - | - | - |
| 0.0591 | 60 | 3.1885 | - | - | - |
| 0.0600 | 61 | 3.6013 | - | - | - |
| 0.0610 | 62 | 3.392 | - | - | - |
| 0.0620 | 63 | 3.3814 | - | - | - |
| 0.0630 | 64 | 4.0428 | - | - | - |
| 0.0640 | 65 | 3.7825 | - | - | - |
| 0.0650 | 66 | 3.4181 | - | - | - |
| 0.0659 | 67 | 3.7793 | - | - | - |
| 0.0669 | 68 | 3.8344 | - | - | - |
| 0.0679 | 69 | 3.2165 | - | - | - |
| 0.0689 | 70 | 3.3811 | - | - | - |
| 0.0699 | 71 | 3.5984 | - | - | - |
| 0.0709 | 72 | 3.8583 | - | - | - |
| 0.0719 | 73 | 3.296 | - | - | - |
| 0.0728 | 74 | 2.7661 | - | - | - |
| 0.0738 | 75 | 2.9805 | - | - | - |
| 0.0748 | 76 | 2.566 | - | - | - |
| 0.0758 | 77 | 3.258 | - | - | - |
| 0.0768 | 78 | 3.3804 | - | - | - |
| 0.0778 | 79 | 2.8828 | - | - | - |
| 0.0787 | 80 | 3.1077 | - | - | - |
| 0.0797 | 81 | 2.9441 | - | - | - |
| 0.0807 | 82 | 2.9465 | - | - | - |
| 0.0817 | 83 | 2.7088 | - | - | - |
| 0.0827 | 84 | 2.9215 | - | - | - |
| 0.0837 | 85 | 3.4698 | - | - | - |
| 0.0846 | 86 | 2.2414 | - | - | - |
| 0.0856 | 87 | 3.1601 | - | - | - |
| 0.0866 | 88 | 2.7714 | - | - | - |
| 0.0876 | 89 | 3.0311 | - | - | - |
| 0.0886 | 90 | 3.0336 | - | - | - |
| 0.0896 | 91 | 1.9358 | - | - | - |
| 0.0906 | 92 | 2.6031 | - | - | - |
| 0.0915 | 93 | 2.7515 | - | - | - |
| 0.0925 | 94 | 2.8496 | - | - | - |
| 0.0935 | 95 | 1.8015 | - | - | - |
| 0.0945 | 96 | 2.8138 | - | - | - |
| 0.0955 | 97 | 2.0597 | - | - | - |
| 0.0965 | 98 | 2.1053 | - | - | - |
| 0.0974 | 99 | 2.6785 | - | - | - |
| 0.0984 | 100 | 2.588 | - | - | - |
| 0.0994 | 101 | 2.0099 | - | - | - |
| 0.1004 | 102 | 2.7947 | - | - | - |
| 0.1014 | 103 | 2.3274 | - | - | - |
| 0.1024 | 104 | 2.2545 | - | - | - |
| 0.1033 | 105 | 2.4575 | - | - | - |
| 0.1043 | 106 | 2.4413 | - | - | - |
| 0.1053 | 107 | 2.3185 | - | - | - |
| 0.1063 | 108 | 2.1577 | - | - | - |
| 0.1073 | 109 | 2.1278 | - | - | - |
| 0.1083 | 110 | 2.0967 | - | - | - |
| 0.1093 | 111 | 2.6142 | - | - | - |
| 0.1102 | 112 | 1.8553 | - | - | - |
| 0.1112 | 113 | 2.1523 | - | - | - |
| 0.1122 | 114 | 2.1726 | - | - | - |
| 0.1132 | 115 | 1.8564 | - | - | - |
| 0.1142 | 116 | 1.8413 | - | - | - |
| 0.1152 | 117 | 2.0441 | - | - | - |
| 0.1161 | 118 | 2.2159 | - | - | - |
| 0.1171 | 119 | 2.6779 | - | - | - |
| 0.1181 | 120 | 2.2976 | - | - | - |
| 0.1191 | 121 | 1.9407 | - | - | - |
| 0.1201 | 122 | 1.9019 | - | - | - |
| 0.1211 | 123 | 2.2149 | - | - | - |
| 0.1220 | 124 | 1.6823 | - | - | - |
| 0.1230 | 125 | 1.8402 | - | - | - |
| 0.1240 | 126 | 1.6914 | - | - | - |
| 0.125 | 127 | 2.1626 | - | - | - |
| 0.1260 | 128 | 1.6414 | - | - | - |
| 0.1270 | 129 | 2.2043 | - | - | - |
| 0.1280 | 130 | 1.9987 | - | - | - |
| 0.1289 | 131 | 1.8868 | - | - | - |
| 0.1299 | 132 | 1.8262 | - | - | - |
| 0.1309 | 133 | 2.0404 | - | - | - |
| 0.1319 | 134 | 1.9134 | - | - | - |
| 0.1329 | 135 | 2.3725 | - | - | - |
| 0.1339 | 136 | 1.4127 | - | - | - |
| 0.1348 | 137 | 1.6876 | - | - | - |
| 0.1358 | 138 | 1.8376 | - | - | - |
| 0.1368 | 139 | 1.6992 | - | - | - |
| 0.1378 | 140 | 1.5032 | - | - | - |
| 0.1388 | 141 | 2.0334 | - | - | - |
| 0.1398 | 142 | 2.3581 | - | - | - |
| 0.1407 | 143 | 1.4236 | - | - | - |
| 0.1417 | 144 | 2.202 | - | - | - |
| 0.1427 | 145 | 1.7654 | - | - | - |
| 0.1437 | 146 | 1.5748 | - | - | - |
| 0.1447 | 147 | 1.7996 | - | - | - |
| 0.1457 | 148 | 1.7517 | - | - | - |
| 0.1467 | 149 | 1.8933 | - | - | - |
| 0.1476 | 150 | 1.2836 | - | - | - |
| 0.1486 | 151 | 1.7145 | - | - | - |
| 0.1496 | 152 | 1.6499 | - | - | - |
| 0.1506 | 153 | 1.8273 | 0.4057 | 0.4389 | 0.6725 |
| 0.1516 | 154 | 2.2859 | - | - | - |
| 0.1526 | 155 | 1.0833 | - | - | - |
| 0.1535 | 156 | 1.6829 | - | - | - |
| 0.1545 | 157 | 2.1464 | - | - | - |
| 0.1555 | 158 | 1.745 | - | - | - |
| 0.1565 | 159 | 1.7319 | - | - | - |
| 0.1575 | 160 | 1.6968 | - | - | - |
| 0.1585 | 161 | 1.7401 | - | - | - |
| 0.1594 | 162 | 1.729 | - | - | - |
| 0.1604 | 163 | 2.0782 | - | - | - |
| 0.1614 | 164 | 2.6545 | - | - | - |
| 0.1624 | 165 | 1.4045 | - | - | - |
| 0.1634 | 166 | 1.2937 | - | - | - |
| 0.1644 | 167 | 1.1171 | - | - | - |
| 0.1654 | 168 | 1.3537 | - | - | - |
| 0.1663 | 169 | 1.7028 | - | - | - |
| 0.1673 | 170 | 1.4143 | - | - | - |
| 0.1683 | 171 | 1.8648 | - | - | - |
| 0.1693 | 172 | 1.6768 | - | - | - |
| 0.1703 | 173 | 1.9528 | - | - | - |
| 0.1713 | 174 | 1.1718 | - | - | - |
| 0.1722 | 175 | 1.8176 | - | - | - |
| 0.1732 | 176 | 0.8439 | - | - | - |
| 0.1742 | 177 | 1.5092 | - | - | - |
| 0.1752 | 178 | 1.1947 | - | - | - |
| 0.1762 | 179 | 1.6395 | - | - | - |
| 0.1772 | 180 | 1.4394 | - | - | - |
| 0.1781 | 181 | 1.7548 | - | - | - |
| 0.1791 | 182 | 1.1181 | - | - | - |
| 0.1801 | 183 | 1.0271 | - | - | - |
| 0.1811 | 184 | 2.3108 | - | - | - |
| 0.1821 | 185 | 2.1242 | - | - | - |
| 0.1831 | 186 | 1.9822 | - | - | - |
| 0.1841 | 187 | 2.3605 | - | - | - |
| 0.1850 | 188 | 1.5251 | - | - | - |
| 0.1860 | 189 | 1.2351 | - | - | - |
| 0.1870 | 190 | 1.5859 | - | - | - |
| 0.1880 | 191 | 1.8056 | - | - | - |
| 0.1890 | 192 | 1.349 | - | - | - |
| 0.1900 | 193 | 0.893 | - | - | - |
| 0.1909 | 194 | 1.5122 | - | - | - |
| 0.1919 | 195 | 1.3875 | - | - | - |
| 0.1929 | 196 | 1.29 | - | - | - |
| 0.1939 | 197 | 2.2931 | - | - | - |
| 0.1949 | 198 | 1.2663 | - | - | - |
| 0.1959 | 199 | 1.9712 | - | - | - |
| 0.1969 | 200 | 2.3307 | - | - | - |
| 0.1978 | 201 | 1.6544 | - | - | - |
| 0.1988 | 202 | 1.638 | - | - | - |
| 0.1998 | 203 | 1.3412 | - | - | - |
| 0.2008 | 204 | 1.4454 | - | - | - |
| 0.2018 | 205 | 1.5437 | - | - | - |
| 0.2028 | 206 | 1.4921 | - | - | - |
| 0.2037 | 207 | 1.4298 | - | - | - |
| 0.2047 | 208 | 1.6174 | - | - | - |
| 0.2057 | 209 | 1.4137 | - | - | - |
| 0.2067 | 210 | 1.5652 | - | - | - |
| 0.2077 | 211 | 1.1631 | - | - | - |
| 0.2087 | 212 | 1.2351 | - | - | - |
| 0.2096 | 213 | 1.7537 | - | - | - |
| 0.2106 | 214 | 1.3186 | - | - | - |
| 0.2116 | 215 | 1.2258 | - | - | - |
| 0.2126 | 216 | 0.7695 | - | - | - |
| 0.2136 | 217 | 1.2775 | - | - | - |
| 0.2146 | 218 | 1.6795 | - | - | - |
| 0.2156 | 219 | 1.2862 | - | - | - |
| 0.2165 | 220 | 1.1723 | - | - | - |
| 0.2175 | 221 | 1.3322 | - | - | - |
| 0.2185 | 222 | 1.7564 | - | - | - |
| 0.2195 | 223 | 1.1071 | - | - | - |
| 0.2205 | 224 | 1.2011 | - | - | - |
| 0.2215 | 225 | 1.2303 | - | - | - |
| 0.2224 | 226 | 1.212 | - | - | - |
| 0.2234 | 227 | 1.0117 | - | - | - |
| 0.2244 | 228 | 1.1907 | - | - | - |
| 0.2254 | 229 | 2.1293 | - | - | - |
| 0.2264 | 230 | 1.3063 | - | - | - |
| 0.2274 | 231 | 1.2841 | - | - | - |
| 0.2283 | 232 | 1.3778 | - | - | - |
| 0.2293 | 233 | 1.2242 | - | - | - |
| 0.2303 | 234 | 0.9227 | - | - | - |
| 0.2313 | 235 | 1.2221 | - | - | - |
| 0.2323 | 236 | 2.1041 | - | - | - |
| 0.2333 | 237 | 1.3341 | - | - | - |
| 0.2343 | 238 | 1.0876 | - | - | - |
| 0.2352 | 239 | 1.3328 | - | - | - |
| 0.2362 | 240 | 1.2958 | - | - | - |
| 0.2372 | 241 | 1.1522 | - | - | - |
| 0.2382 | 242 | 1.7942 | - | - | - |
| 0.2392 | 243 | 1.1325 | - | - | - |
| 0.2402 | 244 | 1.6466 | - | - | - |
| 0.2411 | 245 | 1.4608 | - | - | - |
| 0.2421 | 246 | 0.6375 | - | - | - |
| 0.2431 | 247 | 2.0177 | - | - | - |
| 0.2441 | 248 | 1.2069 | - | - | - |
| 0.2451 | 249 | 0.7639 | - | - | - |
| 0.2461 | 250 | 1.3465 | - | - | - |
| 0.2470 | 251 | 1.064 | - | - | - |
| 0.2480 | 252 | 1.3757 | - | - | - |
| 0.2490 | 253 | 1.612 | - | - | - |
| 0.25 | 254 | 0.7917 | - | - | - |
| 0.2510 | 255 | 1.5515 | - | - | - |
| 0.2520 | 256 | 0.799 | - | - | - |
| 0.2530 | 257 | 0.9882 | - | - | - |
| 0.2539 | 258 | 1.1814 | - | - | - |
| 0.2549 | 259 | 0.6394 | - | - | - |
| 0.2559 | 260 | 1.4756 | - | - | - |
| 0.2569 | 261 | 0.5338 | - | - | - |
| 0.2579 | 262 | 0.9779 | - | - | - |
| 0.2589 | 263 | 1.5307 | - | - | - |
| 0.2598 | 264 | 1.1213 | - | - | - |
| 0.2608 | 265 | 0.9482 | - | - | - |
| 0.2618 | 266 | 0.9599 | - | - | - |
| 0.2628 | 267 | 1.4455 | - | - | - |
| 0.2638 | 268 | 1.6496 | - | - | - |
| 0.2648 | 269 | 0.7402 | - | - | - |
| 0.2657 | 270 | 0.7835 | - | - | - |
| 0.2667 | 271 | 0.7821 | - | - | - |
| 0.2677 | 272 | 1.5422 | - | - | - |
| 0.2687 | 273 | 1.0995 | - | - | - |
| 0.2697 | 274 | 1.378 | - | - | - |
| 0.2707 | 275 | 1.3562 | - | - | - |
| 0.2717 | 276 | 0.7376 | - | - | - |
| 0.2726 | 277 | 1.1678 | - | - | - |
| 0.2736 | 278 | 1.2989 | - | - | - |
| 0.2746 | 279 | 1.9559 | - | - | - |
| 0.2756 | 280 | 1.1237 | - | - | - |
| 0.2766 | 281 | 0.952 | - | - | - |
| 0.2776 | 282 | 1.6629 | - | - | - |
| 0.2785 | 283 | 1.871 | - | - | - |
| 0.2795 | 284 | 1.5946 | - | - | - |
| 0.2805 | 285 | 1.4456 | - | - | - |
| 0.2815 | 286 | 1.4085 | - | - | - |
| 0.2825 | 287 | 1.1394 | - | - | - |
| 0.2835 | 288 | 1.0315 | - | - | - |
| 0.2844 | 289 | 1.488 | - | - | - |
| 0.2854 | 290 | 1.4006 | - | - | - |
| 0.2864 | 291 | 0.9237 | - | - | - |
| 0.2874 | 292 | 1.163 | - | - | - |
| 0.2884 | 293 | 1.7037 | - | - | - |
| 0.2894 | 294 | 0.8715 | - | - | - |
| 0.2904 | 295 | 1.2101 | - | - | - |
| 0.2913 | 296 | 1.1179 | - | - | - |
| 0.2923 | 297 | 1.3986 | - | - | - |
| 0.2933 | 298 | 1.7068 | - | - | - |
| 0.2943 | 299 | 0.8695 | - | - | - |
| 0.2953 | 300 | 1.3778 | - | - | - |
| 0.2963 | 301 | 1.2834 | - | - | - |
| 0.2972 | 302 | 0.8123 | - | - | - |
| 0.2982 | 303 | 1.6521 | - | - | - |
| 0.2992 | 304 | 1.1064 | - | - | - |
| 0.3002 | 305 | 0.9578 | - | - | - |
| 0.3012 | 306 | 0.9254 | 0.4888 | 0.4789 | 0.7040 |
| 0.3022 | 307 | 0.7541 | - | - | - |
| 0.3031 | 308 | 0.7324 | - | - | - |
| 0.3041 | 309 | 0.5974 | - | - | - |
| 0.3051 | 310 | 1.1481 | - | - | - |
| 0.3061 | 311 | 1.6179 | - | - | - |
| 0.3071 | 312 | 1.4641 | - | - | - |
| 0.3081 | 313 | 1.7185 | - | - | - |
| 0.3091 | 314 | 0.9328 | - | - | - |
| 0.3100 | 315 | 0.742 | - | - | - |
| 0.3110 | 316 | 1.4173 | - | - | - |
| 0.3120 | 317 | 0.7267 | - | - | - |
| 0.3130 | 318 | 0.9494 | - | - | - |
| 0.3140 | 319 | 1.5111 | - | - | - |
| 0.3150 | 320 | 1.6949 | - | - | - |
| 0.3159 | 321 | 1.7562 | - | - | - |
| 0.3169 | 322 | 1.2532 | - | - | - |
| 0.3179 | 323 | 1.1086 | - | - | - |
| 0.3189 | 324 | 0.7377 | - | - | - |
| 0.3199 | 325 | 1.085 | - | - | - |
| 0.3209 | 326 | 0.7767 | - | - | - |
| 0.3219 | 327 | 1.4441 | - | - | - |
| 0.3228 | 328 | 0.8146 | - | - | - |
| 0.3238 | 329 | 0.7403 | - | - | - |
| 0.3248 | 330 | 0.8476 | - | - | - |
| 0.3258 | 331 | 0.7323 | - | - | - |
| 0.3268 | 332 | 1.2241 | - | - | - |
| 0.3278 | 333 | 1.5065 | - | - | - |
| 0.3287 | 334 | 0.5259 | - | - | - |
| 0.3297 | 335 | 1.3103 | - | - | - |
| 0.3307 | 336 | 0.8655 | - | - | - |
| 0.3317 | 337 | 0.7575 | - | - | - |
| 0.3327 | 338 | 1.968 | - | - | - |
| 0.3337 | 339 | 1.317 | - | - | - |
| 0.3346 | 340 | 1.1972 | - | - | - |
| 0.3356 | 341 | 1.6323 | - | - | - |
| 0.3366 | 342 | 1.0469 | - | - | - |
| 0.3376 | 343 | 1.3349 | - | - | - |
| 0.3386 | 344 | 0.9544 | - | - | - |
| 0.3396 | 345 | 1.1894 | - | - | - |
| 0.3406 | 346 | 0.7717 | - | - | - |
| 0.3415 | 347 | 1.2563 | - | - | - |
| 0.3425 | 348 | 1.2437 | - | - | - |
| 0.3435 | 349 | 0.7806 | - | - | - |
| 0.3445 | 350 | 0.8303 | - | - | - |
| 0.3455 | 351 | 1.0926 | - | - | - |
| 0.3465 | 352 | 0.6654 | - | - | - |
| 0.3474 | 353 | 1.1087 | - | - | - |
| 0.3484 | 354 | 1.1525 | - | - | - |
| 0.3494 | 355 | 1.1127 | - | - | - |
| 0.3504 | 356 | 1.4267 | - | - | - |
| 0.3514 | 357 | 0.6148 | - | - | - |
| 0.3524 | 358 | 1.0123 | - | - | - |
| 0.3533 | 359 | 1.9682 | - | - | - |
| 0.3543 | 360 | 0.8487 | - | - | - |
| 0.3553 | 361 | 1.0412 | - | - | - |
| 0.3563 | 362 | 1.0902 | - | - | - |
| 0.3573 | 363 | 0.9606 | - | - | - |
| 0.3583 | 364 | 0.9206 | - | - | - |
| 0.3593 | 365 | 1.4727 | - | - | - |
| 0.3602 | 366 | 0.9379 | - | - | - |
| 0.3612 | 367 | 0.8387 | - | - | - |
| 0.3622 | 368 | 0.9692 | - | - | - |
| 0.3632 | 369 | 1.6298 | - | - | - |
| 0.3642 | 370 | 1.0882 | - | - | - |
| 0.3652 | 371 | 1.1558 | - | - | - |
| 0.3661 | 372 | 0.9546 | - | - | - |
| 0.3671 | 373 | 1.0124 | - | - | - |
| 0.3681 | 374 | 1.3916 | - | - | - |
| 0.3691 | 375 | 0.527 | - | - | - |
| 0.3701 | 376 | 0.6387 | - | - | - |
| 0.3711 | 377 | 1.1445 | - | - | - |
| 0.3720 | 378 | 1.3309 | - | - | - |
| 0.3730 | 379 | 1.5888 | - | - | - |
| 0.3740 | 380 | 1.4422 | - | - | - |
| 0.375 | 381 | 1.7044 | - | - | - |
| 0.3760 | 382 | 0.7913 | - | - | - |
| 0.3770 | 383 | 1.3241 | - | - | - |
| 0.3780 | 384 | 0.6473 | - | - | - |
| 0.3789 | 385 | 1.221 | - | - | - |
| 0.3799 | 386 | 0.7773 | - | - | - |
| 0.3809 | 387 | 1.054 | - | - | - |
| 0.3819 | 388 | 0.9862 | - | - | - |
| 0.3829 | 389 | 0.9684 | - | - | - |
| 0.3839 | 390 | 1.3244 | - | - | - |
| 0.3848 | 391 | 1.1787 | - | - | - |
| 0.3858 | 392 | 1.4698 | - | - | - |
| 0.3868 | 393 | 1.0961 | - | - | - |
| 0.3878 | 394 | 1.1364 | - | - | - |
| 0.3888 | 395 | 0.9368 | - | - | - |
| 0.3898 | 396 | 1.1731 | - | - | - |
| 0.3907 | 397 | 0.8686 | - | - | - |
| 0.3917 | 398 | 0.7481 | - | - | - |
| 0.3927 | 399 | 0.7261 | - | - | - |
| 0.3937 | 400 | 1.2062 | - | - | - |
| 0.3947 | 401 | 0.7462 | - | - | - |
| 0.3957 | 402 | 1.0318 | - | - | - |
| 0.3967 | 403 | 1.105 | - | - | - |
| 0.3976 | 404 | 1.009 | - | - | - |
| 0.3986 | 405 | 0.5941 | - | - | - |
| 0.3996 | 406 | 1.7972 | - | - | - |
| 0.4006 | 407 | 1.0544 | - | - | - |
| 0.4016 | 408 | 1.3912 | - | - | - |
| 0.4026 | 409 | 0.8305 | - | - | - |
| 0.4035 | 410 | 0.8688 | - | - | - |
| 0.4045 | 411 | 1.0069 | - | - | - |
| 0.4055 | 412 | 1.3141 | - | - | - |
| 0.4065 | 413 | 1.1042 | - | - | - |
| 0.4075 | 414 | 1.1011 | - | - | - |
| 0.4085 | 415 | 1.1192 | - | - | - |
| 0.4094 | 416 | 1.5957 | - | - | - |
| 0.4104 | 417 | 1.164 | - | - | - |
| 0.4114 | 418 | 0.6425 | - | - | - |
| 0.4124 | 419 | 0.6068 | - | - | - |
| 0.4134 | 420 | 0.9275 | - | - | - |
| 0.4144 | 421 | 0.8836 | - | - | - |
| 0.4154 | 422 | 1.2115 | - | - | - |
| 0.4163 | 423 | 0.8367 | - | - | - |
| 0.4173 | 424 | 1.0595 | - | - | - |
| 0.4183 | 425 | 0.826 | - | - | - |
| 0.4193 | 426 | 0.707 | - | - | - |
| 0.4203 | 427 | 0.6235 | - | - | - |
| 0.4213 | 428 | 0.7719 | - | - | - |
| 0.4222 | 429 | 1.0862 | - | - | - |
| 0.4232 | 430 | 0.9311 | - | - | - |
| 0.4242 | 431 | 1.2339 | - | - | - |
| 0.4252 | 432 | 0.9891 | - | - | - |
| 0.4262 | 433 | 1.8443 | - | - | - |
| 0.4272 | 434 | 1.1799 | - | - | - |
| 0.4281 | 435 | 0.759 | - | - | - |
| 0.4291 | 436 | 1.1002 | - | - | - |
| 0.4301 | 437 | 0.9141 | - | - | - |
| 0.4311 | 438 | 0.5467 | - | - | - |
| 0.4321 | 439 | 0.7476 | - | - | - |
| 0.4331 | 440 | 1.14 | - | - | - |
| 0.4341 | 441 | 1.1504 | - | - | - |
| 0.4350 | 442 | 1.26 | - | - | - |
| 0.4360 | 443 | 1.0311 | - | - | - |
| 0.4370 | 444 | 1.0646 | - | - | - |
| 0.4380 | 445 | 0.8687 | - | - | - |
| 0.4390 | 446 | 0.6839 | - | - | - |
| 0.4400 | 447 | 1.1376 | - | - | - |
| 0.4409 | 448 | 0.9759 | - | - | - |
| 0.4419 | 449 | 0.7971 | - | - | - |
| 0.4429 | 450 | 0.9708 | - | - | - |
| 0.4439 | 451 | 0.8217 | - | - | - |
| 0.4449 | 452 | 1.3728 | - | - | - |
| 0.4459 | 453 | 0.9119 | - | - | - |
| 0.4469 | 454 | 1.012 | - | - | - |
| 0.4478 | 455 | 1.3738 | - | - | - |
| 0.4488 | 456 | 0.8219 | - | - | - |
| 0.4498 | 457 | 1.2558 | - | - | - |
| 0.4508 | 458 | 0.6247 | - | - | - |
| 0.4518 | 459 | 0.7295 | 0.5410 | 0.4920 | 0.6879 |
| 0.4528 | 460 | 0.8154 | - | - | - |
| 0.4537 | 461 | 1.1392 | - | - | - |
| 0.4547 | 462 | 0.8618 | - | - | - |
| 0.4557 | 463 | 0.9669 | - | - | - |
| 0.4567 | 464 | 0.8804 | - | - | - |
| 0.4577 | 465 | 0.8479 | - | - | - |
| 0.4587 | 466 | 0.6296 | - | - | - |
| 0.4596 | 467 | 0.8449 | - | - | - |
| 0.4606 | 468 | 0.9772 | - | - | - |
| 0.4616 | 469 | 0.6424 | - | - | - |
| 0.4626 | 470 | 0.9169 | - | - | - |
| 0.4636 | 471 | 0.7599 | - | - | - |
| 0.4646 | 472 | 0.8943 | - | - | - |
| 0.4656 | 473 | 0.9475 | - | - | - |
| 0.4665 | 474 | 1.4518 | - | - | - |
| 0.4675 | 475 | 1.274 | - | - | - |
| 0.4685 | 476 | 0.7306 | - | - | - |
| 0.4695 | 477 | 0.9238 | - | - | - |
| 0.4705 | 478 | 0.6593 | - | - | - |
| 0.4715 | 479 | 1.0183 | - | - | - |
| 0.4724 | 480 | 1.2577 | - | - | - |
| 0.4734 | 481 | 0.8738 | - | - | - |
| 0.4744 | 482 | 1.1416 | - | - | - |
| 0.4754 | 483 | 0.7135 | - | - | - |
| 0.4764 | 484 | 1.2587 | - | - | - |
| 0.4774 | 485 | 0.8823 | - | - | - |
| 0.4783 | 486 | 0.8423 | - | - | - |
| 0.4793 | 487 | 0.7704 | - | - | - |
| 0.4803 | 488 | 0.7049 | - | - | - |
| 0.4813 | 489 | 1.1893 | - | - | - |
| 0.4823 | 490 | 1.3985 | - | - | - |
| 0.4833 | 491 | 1.3567 | - | - | - |
| 0.4843 | 492 | 1.2573 | - | - | - |
| 0.4852 | 493 | 0.7671 | - | - | - |
| 0.4862 | 494 | 0.5425 | - | - | - |
| 0.4872 | 495 | 0.9372 | - | - | - |
| 0.4882 | 496 | 0.799 | - | - | - |
| 0.4892 | 497 | 0.9548 | - | - | - |
| 0.4902 | 498 | 1.0855 | - | - | - |
| 0.4911 | 499 | 1.0465 | - | - | - |
| 0.4921 | 500 | 1.1004 | - | - | - |
| 0.4931 | 501 | 0.6392 | - | - | - |
| 0.4941 | 502 | 0.7102 | - | - | - |
| 0.4951 | 503 | 1.3242 | - | - | - |
| 0.4961 | 504 | 0.6861 | - | - | - |
| 0.4970 | 505 | 0.9291 | - | - | - |
| 0.4980 | 506 | 0.8592 | - | - | - |
| 0.4990 | 507 | 0.9462 | - | - | - |
| 0.5 | 508 | 1.0167 | - | - | - |
| 0.5010 | 509 | 1.0118 | - | - | - |
| 0.5020 | 510 | 0.6741 | - | - | - |
| 0.5030 | 511 | 1.4578 | - | - | - |
| 0.5039 | 512 | 1.2959 | - | - | - |
| 0.5049 | 513 | 0.8533 | - | - | - |
| 0.5059 | 514 | 0.6685 | - | - | - |
| 0.5069 | 515 | 1.1556 | - | - | - |
| 0.5079 | 516 | 0.8177 | - | - | - |
| 0.5089 | 517 | 0.6296 | - | - | - |
| 0.5098 | 518 | 0.8407 | - | - | - |
| 0.5108 | 519 | 0.6987 | - | - | - |
| 0.5118 | 520 | 0.9888 | - | - | - |
| 0.5128 | 521 | 0.8938 | - | - | - |
| 0.5138 | 522 | 0.582 | - | - | - |
| 0.5148 | 523 | 0.6596 | - | - | - |
| 0.5157 | 524 | 0.6029 | - | - | - |
| 0.5167 | 525 | 0.9806 | - | - | - |
| 0.5177 | 526 | 0.9463 | - | - | - |
| 0.5187 | 527 | 0.7088 | - | - | - |
| 0.5197 | 528 | 0.7525 | - | - | - |
| 0.5207 | 529 | 0.7625 | - | - | - |
| 0.5217 | 530 | 0.8271 | - | - | - |
| 0.5226 | 531 | 0.6129 | - | - | - |
| 0.5236 | 532 | 1.1563 | - | - | - |
| 0.5246 | 533 | 0.8131 | - | - | - |
| 0.5256 | 534 | 0.5363 | - | - | - |
| 0.5266 | 535 | 0.8819 | - | - | - |
| 0.5276 | 536 | 0.9772 | - | - | - |
| 0.5285 | 537 | 1.2102 | - | - | - |
| 0.5295 | 538 | 1.1234 | - | - | - |
| 0.5305 | 539 | 1.1857 | - | - | - |
| 0.5315 | 540 | 0.7873 | - | - | - |
| 0.5325 | 541 | 0.5034 | - | - | - |
| 0.5335 | 542 | 1.3305 | - | - | - |
| 0.5344 | 543 | 1.1727 | - | - | - |
| 0.5354 | 544 | 1.2825 | - | - | - |
| 0.5364 | 545 | 1.0446 | - | - | - |
| 0.5374 | 546 | 0.9838 | - | - | - |
| 0.5384 | 547 | 1.2194 | - | - | - |
| 0.5394 | 548 | 0.7709 | - | - | - |
| 0.5404 | 549 | 0.748 | - | - | - |
| 0.5413 | 550 | 1.0948 | - | - | - |
| 0.5423 | 551 | 0.915 | - | - | - |
| 0.5433 | 552 | 1.537 | - | - | - |
| 0.5443 | 553 | 0.3239 | - | - | - |
| 0.5453 | 554 | 0.9592 | - | - | - |
| 0.5463 | 555 | 0.7737 | - | - | - |
| 0.5472 | 556 | 0.613 | - | - | - |
| 0.5482 | 557 | 1.3646 | - | - | - |
| 0.5492 | 558 | 0.6659 | - | - | - |
| 0.5502 | 559 | 0.5207 | - | - | - |
| 0.5512 | 560 | 0.9467 | - | - | - |
| 0.5522 | 561 | 0.5692 | - | - | - |
| 0.5531 | 562 | 1.5855 | - | - | - |
| 0.5541 | 563 | 0.8855 | - | - | - |
| 0.5551 | 564 | 1.1829 | - | - | - |
| 0.5561 | 565 | 0.978 | - | - | - |
| 0.5571 | 566 | 1.1818 | - | - | - |
| 0.5581 | 567 | 0.701 | - | - | - |
| 0.5591 | 568 | 1.0226 | - | - | - |
| 0.5600 | 569 | 0.5937 | - | - | - |
| 0.5610 | 570 | 0.8095 | - | - | - |
| 0.5620 | 571 | 1.174 | - | - | - |
| 0.5630 | 572 | 0.96 | - | - | - |
| 0.5640 | 573 | 0.8339 | - | - | - |
| 0.5650 | 574 | 0.717 | - | - | - |
| 0.5659 | 575 | 0.5938 | - | - | - |
| 0.5669 | 576 | 0.6501 | - | - | - |
| 0.5679 | 577 | 0.7003 | - | - | - |
| 0.5689 | 578 | 0.5525 | - | - | - |
| 0.5699 | 579 | 0.7003 | - | - | - |
| 0.5709 | 580 | 1.059 | - | - | - |
| 0.5719 | 581 | 0.8625 | - | - | - |
| 0.5728 | 582 | 0.5862 | - | - | - |
| 0.5738 | 583 | 0.9162 | - | - | - |
| 0.5748 | 584 | 0.926 | - | - | - |
| 0.5758 | 585 | 1.2729 | - | - | - |
| 0.5768 | 586 | 0.8935 | - | - | - |
| 0.5778 | 587 | 0.541 | - | - | - |
| 0.5787 | 588 | 1.1455 | - | - | - |
| 0.5797 | 589 | 0.7306 | - | - | - |
| 0.5807 | 590 | 0.9088 | - | - | - |
| 0.5817 | 591 | 0.9166 | - | - | - |
| 0.5827 | 592 | 0.8679 | - | - | - |
| 0.5837 | 593 | 0.9329 | - | - | - |
| 0.5846 | 594 | 1.1201 | - | - | - |
| 0.5856 | 595 | 0.6418 | - | - | - |
| 0.5866 | 596 | 1.145 | - | - | - |
| 0.5876 | 597 | 1.4041 | - | - | - |
| 0.5886 | 598 | 0.6954 | - | - | - |
| 0.5896 | 599 | 0.4567 | - | - | - |
| 0.5906 | 600 | 1.1305 | - | - | - |
| 0.5915 | 601 | 0.8077 | - | - | - |
| 0.5925 | 602 | 0.6143 | - | - | - |
| 0.5935 | 603 | 1.3139 | - | - | - |
| 0.5945 | 604 | 0.7694 | - | - | - |
| 0.5955 | 605 | 0.9622 | - | - | - |
| 0.5965 | 606 | 0.91 | - | - | - |
| 0.5974 | 607 | 1.3125 | - | - | - |
| 0.5984 | 608 | 1.0153 | - | - | - |
| 0.5994 | 609 | 0.8468 | - | - | - |
| 0.6004 | 610 | 1.1026 | - | - | - |
| 0.6014 | 611 | 0.8291 | - | - | - |
| 0.6024 | 612 | 0.7235 | 0.5680 | 0.5175 | 0.6900 |
| 0.6033 | 613 | 0.9613 | - | - | - |
| 0.6043 | 614 | 0.7124 | - | - | - |
| 0.6053 | 615 | 1.0719 | - | - | - |
| 0.6063 | 616 | 0.7233 | - | - | - |
| 0.6073 | 617 | 1.6863 | - | - | - |
| 0.6083 | 618 | 0.8665 | - | - | - |
| 0.6093 | 619 | 1.6432 | - | - | - |
| 0.6102 | 620 | 0.771 | - | - | - |
| 0.6112 | 621 | 0.6755 | - | - | - |
| 0.6122 | 622 | 0.6809 | - | - | - |
| 0.6132 | 623 | 0.5626 | - | - | - |
| 0.6142 | 624 | 0.6287 | - | - | - |
| 0.6152 | 625 | 0.5478 | - | - | - |
| 0.6161 | 626 | 0.9155 | - | - | - |
| 0.6171 | 627 | 1.8947 | - | - | - |
| 0.6181 | 628 | 1.1943 | - | - | - |
| 0.6191 | 629 | 1.2465 | - | - | - |
| 0.6201 | 630 | 0.4045 | - | - | - |
| 0.6211 | 631 | 0.5688 | - | - | - |
| 0.6220 | 632 | 0.8682 | - | - | - |
| 0.6230 | 633 | 0.901 | - | - | - |
| 0.6240 | 634 | 0.8978 | - | - | - |
| 0.625 | 635 | 0.6592 | - | - | - |
| 0.6260 | 636 | 0.6828 | - | - | - |
| 0.6270 | 637 | 0.3995 | - | - | - |
| 0.6280 | 638 | 0.8283 | - | - | - |
| 0.6289 | 639 | 0.5938 | - | - | - |
| 0.6299 | 640 | 1.256 | - | - | - |
| 0.6309 | 641 | 0.7801 | - | - | - |
| 0.6319 | 642 | 0.8975 | - | - | - |
| 0.6329 | 643 | 1.255 | - | - | - |
| 0.6339 | 644 | 1.1252 | - | - | - |
| 0.6348 | 645 | 1.2394 | - | - | - |
| 0.6358 | 646 | 0.9579 | - | - | - |
| 0.6368 | 647 | 0.8664 | - | - | - |
| 0.6378 | 648 | 0.4087 | - | - | - |
| 0.6388 | 649 | 0.4084 | - | - | - |
| 0.6398 | 650 | 0.6214 | - | - | - |
| 0.6407 | 651 | 0.7237 | - | - | - |
| 0.6417 | 652 | 0.6294 | - | - | - |
| 0.6427 | 653 | 0.8264 | - | - | - |
| 0.6437 | 654 | 0.8258 | - | - | - |
| 0.6447 | 655 | 0.6865 | - | - | - |
| 0.6457 | 656 | 0.7408 | - | - | - |
| 0.6467 | 657 | 0.6196 | - | - | - |
| 0.6476 | 658 | 0.5659 | - | - | - |
| 0.6486 | 659 | 0.6353 | - | - | - |
| 0.6496 | 660 | 0.8432 | - | - | - |
| 0.6506 | 661 | 0.6573 | - | - | - |
| 0.6516 | 662 | 0.4918 | - | - | - |
| 0.6526 | 663 | 0.8571 | - | - | - |
| 0.6535 | 664 | 0.9105 | - | - | - |
| 0.6545 | 665 | 0.7499 | - | - | - |
| 0.6555 | 666 | 0.5277 | - | - | - |
| 0.6565 | 667 | 1.3046 | - | - | - |
| 0.6575 | 668 | 0.6112 | - | - | - |
| 0.6585 | 669 | 0.754 | - | - | - |
| 0.6594 | 670 | 1.3611 | - | - | - |
| 0.6604 | 671 | 1.3654 | - | - | - |
| 0.6614 | 672 | 0.6479 | - | - | - |
| 0.6624 | 673 | 0.9422 | - | - | - |
| 0.6634 | 674 | 0.5007 | - | - | - |
| 0.6644 | 675 | 0.4789 | - | - | - |
| 0.6654 | 676 | 0.3962 | - | - | - |
| 0.6663 | 677 | 0.8648 | - | - | - |
| 0.6673 | 678 | 0.8241 | - | - | - |
| 0.6683 | 679 | 0.7923 | - | - | - |
| 0.6693 | 680 | 0.5799 | - | - | - |
| 0.6703 | 681 | 0.8422 | - | - | - |
| 0.6713 | 682 | 0.5638 | - | - | - |
| 0.6722 | 683 | 0.9627 | - | - | - |
| 0.6732 | 684 | 0.7585 | - | - | - |
| 0.6742 | 685 | 0.488 | - | - | - |
| 0.6752 | 686 | 1.0991 | - | - | - |
| 0.6762 | 687 | 0.4808 | - | - | - |
| 0.6772 | 688 | 1.3717 | - | - | - |
| 0.6781 | 689 | 0.8225 | - | - | - |
| 0.6791 | 690 | 0.9377 | - | - | - |
| 0.6801 | 691 | 0.8016 | - | - | - |
| 0.6811 | 692 | 0.6257 | - | - | - |
| 0.6821 | 693 | 1.0941 | - | - | - |
| 0.6831 | 694 | 1.0878 | - | - | - |
| 0.6841 | 695 | 0.9231 | - | - | - |
| 0.6850 | 696 | 1.3284 | - | - | - |
| 0.6860 | 697 | 0.7274 | - | - | - |
| 0.6870 | 698 | 0.7851 | - | - | - |
| 0.6880 | 699 | 0.6807 | - | - | - |
| 0.6890 | 700 | 1.1592 | - | - | - |
| 0.6900 | 701 | 0.6347 | - | - | - |
| 0.6909 | 702 | 1.1326 | - | - | - |
| 0.6919 | 703 | 1.3797 | - | - | - |
| 0.6929 | 704 | 0.7782 | - | - | - |
| 0.6939 | 705 | 1.1746 | - | - | - |
| 0.6949 | 706 | 0.7624 | - | - | - |
| 0.6959 | 707 | 0.7216 | - | - | - |
| 0.6969 | 708 | 0.4666 | - | - | - |
| 0.6978 | 709 | 0.9398 | - | - | - |
| 0.6988 | 710 | 0.9532 | - | - | - |
| 0.6998 | 711 | 0.8285 | - | - | - |
| 0.7008 | 712 | 0.7042 | - | - | - |
| 0.7018 | 713 | 1.0057 | - | - | - |
| 0.7028 | 714 | 0.6696 | - | - | - |
| 0.7037 | 715 | 0.5701 | - | - | - |
| 0.7047 | 716 | 0.7894 | - | - | - |
| 0.7057 | 717 | 0.9327 | - | - | - |
| 0.7067 | 718 | 1.0283 | - | - | - |
| 0.7077 | 719 | 0.6419 | - | - | - |
| 0.7087 | 720 | 0.7716 | - | - | - |
| 0.7096 | 721 | 0.5968 | - | - | - |
| 0.7106 | 722 | 0.6547 | - | - | - |
| 0.7116 | 723 | 0.8344 | - | - | - |
| 0.7126 | 724 | 0.5448 | - | - | - |
| 0.7136 | 725 | 0.6075 | - | - | - |
| 0.7146 | 726 | 0.6142 | - | - | - |
| 0.7156 | 727 | 0.681 | - | - | - |
| 0.7165 | 728 | 0.6137 | - | - | - |
| 0.7175 | 729 | 1.4516 | - | - | - |
| 0.7185 | 730 | 0.9083 | - | - | - |
| 0.7195 | 731 | 0.6369 | - | - | - |
| 0.7205 | 732 | 0.8102 | - | - | - |
| 0.7215 | 733 | 0.7808 | - | - | - |
| 0.7224 | 734 | 0.512 | - | - | - |
| 0.7234 | 735 | 1.3589 | - | - | - |
| 0.7244 | 736 | 0.6555 | - | - | - |
| 0.7254 | 737 | 0.8732 | - | - | - |
| 0.7264 | 738 | 0.7591 | - | - | - |
| 0.7274 | 739 | 0.8237 | - | - | - |
| 0.7283 | 740 | 1.1272 | - | - | - |
| 0.7293 | 741 | 0.5353 | - | - | - |
| 0.7303 | 742 | 1.1713 | - | - | - |
| 0.7313 | 743 | 0.7034 | - | - | - |
| 0.7323 | 744 | 0.5118 | - | - | - |
| 0.7333 | 745 | 0.7816 | - | - | - |
| 0.7343 | 746 | 0.7935 | - | - | - |
| 0.7352 | 747 | 1.0736 | - | - | - |
| 0.7362 | 748 | 0.6528 | - | - | - |
| 0.7372 | 749 | 0.5556 | - | - | - |
| 0.7382 | 750 | 0.3339 | - | - | - |
| 0.7392 | 751 | 1.0371 | - | - | - |
| 0.7402 | 752 | 1.3273 | - | - | - |
| 0.7411 | 753 | 0.5307 | - | - | - |
| 0.7421 | 754 | 0.908 | - | - | - |
| 0.7431 | 755 | 0.5248 | - | - | - |
| 0.7441 | 756 | 0.6128 | - | - | - |
| 0.7451 | 757 | 1.5065 | - | - | - |
| 0.7461 | 758 | 1.2071 | - | - | - |
| 0.7470 | 759 | 0.9983 | - | - | - |
| 0.7480 | 760 | 1.0378 | - | - | - |
| 0.7490 | 761 | 1.0732 | - | - | - |
| 0.75 | 762 | 0.8046 | - | - | - |
| 0.7510 | 763 | 1.1666 | - | - | - |
| 0.7520 | 764 | 0.7042 | - | - | - |
| 0.7530 | 765 | 1.4759 | 0.5730 | 0.5105 | 0.6982 |
| 0.7539 | 766 | 0.5595 | - | - | - |
| 0.7549 | 767 | 0.3646 | - | - | - |
| 0.7559 | 768 | 0.6791 | - | - | - |
| 0.7569 | 769 | 0.6889 | - | - | - |
| 0.7579 | 770 | 0.9715 | - | - | - |
| 0.7589 | 771 | 1.2218 | - | - | - |
| 0.7598 | 772 | 0.3957 | - | - | - |
| 0.7608 | 773 | 1.5635 | - | - | - |
| 0.7618 | 774 | 0.9081 | - | - | - |
| 0.7628 | 775 | 0.8633 | - | - | - |
| 0.7638 | 776 | 0.9482 | - | - | - |
| 0.7648 | 777 | 0.8021 | - | - | - |
| 0.7657 | 778 | 0.9083 | - | - | - |
| 0.7667 | 779 | 0.9599 | - | - | - |
| 0.7677 | 780 | 0.798 | - | - | - |
| 0.7687 | 781 | 0.5817 | - | - | - |
| 0.7697 | 782 | 0.5287 | - | - | - |
| 0.7707 | 783 | 1.0251 | - | - | - |
| 0.7717 | 784 | 0.3995 | - | - | - |
| 0.7726 | 785 | 0.8753 | - | - | - |
| 0.7736 | 786 | 1.315 | - | - | - |
| 0.7746 | 787 | 0.8053 | - | - | - |
| 0.7756 | 788 | 0.6032 | - | - | - |
| 0.7766 | 789 | 0.8059 | - | - | - |
| 0.7776 | 790 | 0.9387 | - | - | - |
| 0.7785 | 791 | 0.8321 | - | - | - |
| 0.7795 | 792 | 0.5788 | - | - | - |
| 0.7805 | 793 | 0.7874 | - | - | - |
| 0.7815 | 794 | 0.5145 | - | - | - |
| 0.7825 | 795 | 1.5847 | - | - | - |
| 0.7835 | 796 | 1.1473 | - | - | - |
| 0.7844 | 797 | 0.968 | - | - | - |
| 0.7854 | 798 | 1.2691 | - | - | - |
| 0.7864 | 799 | 0.7046 | - | - | - |
| 0.7874 | 800 | 0.9358 | - | - | - |
| 0.7884 | 801 | 0.3945 | - | - | - |
| 0.7894 | 802 | 0.861 | - | - | - |
| 0.7904 | 803 | 1.0736 | - | - | - |
| 0.7913 | 804 | 1.0279 | - | - | - |
| 0.7923 | 805 | 0.6902 | - | - | - |
| 0.7933 | 806 | 0.7344 | - | - | - |
| 0.7943 | 807 | 0.8184 | - | - | - |
| 0.7953 | 808 | 0.8121 | - | - | - |
| 0.7963 | 809 | 0.3772 | - | - | - |
| 0.7972 | 810 | 0.2367 | - | - | - |
| 0.7982 | 811 | 0.5584 | - | - | - |
| 0.7992 | 812 | 0.3465 | - | - | - |
| 0.8002 | 813 | 0.9229 | - | - | - |
| 0.8012 | 814 | 0.7351 | - | - | - |
| 0.8022 | 815 | 0.9024 | - | - | - |
| 0.8031 | 816 | 0.4958 | - | - | - |
| 0.8041 | 817 | 0.4321 | - | - | - |
| 0.8051 | 818 | 0.6945 | - | - | - |
| 0.8061 | 819 | 0.6415 | - | - | - |
| 0.8071 | 820 | 0.5615 | - | - | - |
| 0.8081 | 821 | 0.9732 | - | - | - |
| 0.8091 | 822 | 1.018 | - | - | - |
| 0.8100 | 823 | 1.1392 | - | - | - |
| 0.8110 | 824 | 0.8539 | - | - | - |
| 0.8120 | 825 | 0.7081 | - | - | - |
| 0.8130 | 826 | 1.3176 | - | - | - |
| 0.8140 | 827 | 0.7387 | - | - | - |
| 0.8150 | 828 | 1.0975 | - | - | - |
| 0.8159 | 829 | 0.5883 | - | - | - |
| 0.8169 | 830 | 0.9677 | - | - | - |
| 0.8179 | 831 | 0.3869 | - | - | - |
| 0.8189 | 832 | 0.9083 | - | - | - |
| 0.8199 | 833 | 0.6939 | - | - | - |
| 0.8209 | 834 | 0.5046 | - | - | - |
| 0.8219 | 835 | 0.3594 | - | - | - |
| 0.8228 | 836 | 0.4609 | - | - | - |
| 0.8238 | 837 | 0.8132 | - | - | - |
| 0.8248 | 838 | 0.4493 | - | - | - |
| 0.8258 | 839 | 0.8566 | - | - | - |
| 0.8268 | 840 | 0.9958 | - | - | - |
| 0.8278 | 841 | 0.936 | - | - | - |
| 0.8287 | 842 | 1.4087 | - | - | - |
| 0.8297 | 843 | 1.2214 | - | - | - |
| 0.8307 | 844 | 0.7135 | - | - | - |
| 0.8317 | 845 | 1.1881 | - | - | - |
| 0.8327 | 846 | 1.1807 | - | - | - |
| 0.8337 | 847 | 0.5896 | - | - | - |
| 0.8346 | 848 | 0.8483 | - | - | - |
| 0.8356 | 849 | 0.5409 | - | - | - |
| 0.8366 | 850 | 0.8286 | - | - | - |
| 0.8376 | 851 | 1.136 | - | - | - |
| 0.8386 | 852 | 0.819 | - | - | - |
| 0.8396 | 853 | 0.3029 | - | - | - |
| 0.8406 | 854 | 0.5538 | - | - | - |
| 0.8415 | 855 | 0.4572 | - | - | - |
| 0.8425 | 856 | 1.1516 | - | - | - |
| 0.8435 | 857 | 0.4768 | - | - | - |
| 0.8445 | 858 | 0.5357 | - | - | - |
| 0.8455 | 859 | 0.8879 | - | - | - |
| 0.8465 | 860 | 0.9121 | - | - | - |
| 0.8474 | 861 | 1.052 | - | - | - |
| 0.8484 | 862 | 0.8009 | - | - | - |
| 0.8494 | 863 | 0.2474 | - | - | - |
| 0.8504 | 864 | 0.8517 | - | - | - |
| 0.8514 | 865 | 0.5678 | - | - | - |
| 0.8524 | 866 | 0.4882 | - | - | - |
| 0.8533 | 867 | 0.638 | - | - | - |
| 0.8543 | 868 | 0.8012 | - | - | - |
| 0.8553 | 869 | 0.4172 | - | - | - |
| 0.8563 | 870 | 0.6727 | - | - | - |
| 0.8573 | 871 | 0.6383 | - | - | - |
| 0.8583 | 872 | 1.0728 | - | - | - |
| 0.8593 | 873 | 1.3336 | - | - | - |
| 0.8602 | 874 | 1.217 | - | - | - |
| 0.8612 | 875 | 0.8018 | - | - | - |
| 0.8622 | 876 | 1.2326 | - | - | - |
| 0.8632 | 877 | 1.0898 | - | - | - |
| 0.8642 | 878 | 0.7192 | - | - | - |
| 0.8652 | 879 | 0.7779 | - | - | - |
| 0.8661 | 880 | 0.9291 | - | - | - |
| 0.8671 | 881 | 0.6333 | - | - | - |
| 0.8681 | 882 | 0.9028 | - | - | - |
| 0.8691 | 883 | 0.9904 | - | - | - |
| 0.8701 | 884 | 0.5775 | - | - | - |
| 0.8711 | 885 | 0.9783 | - | - | - |
| 0.8720 | 886 | 1.111 | - | - | - |
| 0.8730 | 887 | 1.0398 | - | - | - |
| 0.8740 | 888 | 0.742 | - | - | - |
| 0.875 | 889 | 0.7606 | - | - | - |
| 0.8760 | 890 | 0.6941 | - | - | - |
| 0.8770 | 891 | 1.0219 | - | - | - |
| 0.8780 | 892 | 0.8949 | - | - | - |
| 0.8789 | 893 | 1.0013 | - | - | - |
| 0.8799 | 894 | 1.1122 | - | - | - |
| 0.8809 | 895 | 1.1711 | - | - | - |
| 0.8819 | 896 | 0.7457 | - | - | - |
| 0.8829 | 897 | 0.5431 | - | - | - |
| 0.8839 | 898 | 0.8159 | - | - | - |
| 0.8848 | 899 | 1.6812 | - | - | - |
| 0.8858 | 900 | 0.8163 | - | - | - |
| 0.8868 | 901 | 0.9315 | - | - | - |
| 0.8878 | 902 | 0.883 | - | - | - |
| 0.8888 | 903 | 0.8014 | - | - | - |
| 0.8898 | 904 | 1.4277 | - | - | - |
| 0.8907 | 905 | 0.7507 | - | - | - |
| 0.8917 | 906 | 0.9757 | - | - | - |
| 0.8927 | 907 | 0.6366 | - | - | - |
| 0.8937 | 908 | 0.7567 | - | - | - |
| 0.8947 | 909 | 0.7853 | - | - | - |
| 0.8957 | 910 | 0.7611 | - | - | - |
| 0.8967 | 911 | 0.8417 | - | - | - |
| 0.8976 | 912 | 0.5825 | - | - | - |
| 0.8986 | 913 | 1.0816 | - | - | - |
| 0.8996 | 914 | 1.1768 | - | - | - |
| 0.9006 | 915 | 0.4894 | - | - | - |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.2
- Tokenizers: 0.19.1
## 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",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
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.*
--> |