File size: 177,999 Bytes
0a4d816 |
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 |
---
language:
- pt
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25863649
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: google o que causa urina turva
sentences:
- Um cagoule, cagoul, kagoule ou kagool (do francês cagoule significa balaclava)
é o termo inglês britânico para um leve (geralmente sem forro), capa de chuva
à prova de intempéries ou anorak com um capuz, que muitas vezes vem no joelho.
O equivalente inglês canadense é quebra-vento ou K-Way".
- Causas da Urina Nublada. 1 Infecção da bexiga (Cisite) A infecção da bexiga é
uma infecção da bexiga, geralmente causada por bactérias ou, raramente, por Candida.
2 Desidratação é a perda excessiva de água corporal. 3 Gonorreia Em Mulheres Gonorreia
é uma infecção bacteriana transmitida durante o contato sexual".
- infecção vaginal ou desidratação. Se a urina é mais leitosa na aparência, isso
pode ser devido à presença de bactérias, muco, gordura ou glóbulos vermelhos ou
brancos. A propósito, a urina â-saudável deve ser amarela pálida ou de cor palha
na aparência. Se a sua urina cheira. Engraçado. É mais provável devido a algo
que você comeu".
- source_sentence: como viver a vida sem depressão
sentences:
- a depressão resulta em uma perda da qualidade de vida. Por definição, um transtorno
depressivo prejudica sua capacidade de funcionar adequadamente em seu trabalho,
participar adequadamente de relacionamentos com os outros e de atender adequadamente
às suas atividades de vida diária".
- Mantém o controle de seus sentimentos e atividades. Quando você se sente mais
deprimido, você pode começar a se afastar de atividades que você normalmente faz,
como ir para a aula ou trabalhar, visitar amigos, fazer exercícios e até mesmo
tomar banho. Você também pode começar a se sentir pior ou ter sintomas mais graves
de depressão".
- EUA Embaixadores e outras agências para sincronizar planos e executar atividades
de informação e influenciar (IIA) em toda a gama de operações militares. 4o Grupo
MIS (A) ".
- source_sentence: o que faz tadasana significa
sentences:
- 'Esta é uma refeição vegetariana (VGML) que também é preparado chinês ou oriental-estilo.
Vegetarian Lacto-Ovo Refeição (VLML) Esta é uma refeição vegetariana que também
pode conter ovos e produtos lácteos. Contém um ou mais destes ingredientes: legumes,
frutas frescas, ovos, produtos lácteos e leguminosas. Não contém qualquer tipo
de peixe ou carne".'
- Tadasana, com 'tada' que significa 'montanha', é considerado como uma das posturas
mais benéficas na ioga. Embora pareça ser bastante simples, uma pessoa tem que
passar por muita prática para alcançar a postura perfeita de tadasana. Acredita-se
que a asana também fornece benefícios físicos, mas mentais".
- 'Alafia: Uma saudação, como olá com o significado de boa saúde ou paz (como shalom).
Fanga: Uma dança de boas-vindas tradicional. Muitas vezes é escrito como funga.Ashe:
(Pronuncia-se ah-shay) O Yoruba acredita que a cinza é uma força básica que emana
do Criador que une todas as coisas vivas e não-viveres.lafia: Uma saudação, como
olá com o significado de boa saúde ou paz (como shalom). Fanga: Uma dança de boas-vindas
tradicional. Muitas vezes é escrito como funga".'
- source_sentence: qual é a coisa voando sobre a cidade esmeralda
sentences:
- '" Maior aeroporto principal para Chincoteague, Virgínia: O principal aeroporto
mais próximo de Chincoteague, Virginia é Salisbury-Ocean City Wicomico Regional
Airport (SBY / KSBY). Este aeroporto fica em Salisbury, Maryland e fica a 47 milhas
do centro de Chincoteague, VA. Se você está procurando voos domésticos para SBY,
verifique as companhias aéreas que voam para SBY".'
- 1 The Emerald City aparece no filme The Wizard of Oz (1939). 2 The Emerald City
aparece em The Wizard of Oz série. 3 Depois que a Bruxa Malvada do Ocidente é
ressuscitada por seus leais Macacos Voadores, ela lança um feitiço na Cidade Esmeralda
que o mancha".
- Isso dá a Esmeralda o valor adicional da boa sorte, da providência e como uma
ponte entre a mente humana e os escritos Divinos. Onde quer que haja alguém impactando
a mente e o espírito da humanidade de maneiras profundas, é provável que você
encontre a Esmeralda na imagem. Esmeralda vem sob o domínio da deusa Vênus".
- source_sentence: o que ajuda a síndrome de ibs
sentences:
- óleo de hortelã-revestida com antecérico é amplamente utilizado para a síndrome
do intestino irritável. Tem a intenção de reduzir a dor abdominal e inchaço da
síndrome do intestino irritável. Peppermint é considerada uma erva carminativa,
o que significa que é usado para eliminar o excesso de gás nos intestinos. Embora
novas pesquisas sejam necessárias, estudos preliminares indicam que pode aliviar
os sintomas da SII".
- diarreia ou prisão de ventre que não responde ao tratamento domiciliar".
- Este tipo de halva é feito por fritar farinha (como sêmola) em óleo, misturando-o
em um roux, e depois cozinhá-lo com um xarope açucarado. Esta variedade é popular
na Grécia, Azerbaijão, Irã, Turquia, Somália, índia, Paquistão e Afeganistão".
datasets:
- cnmoro/AllTripletsMsMarco-PTBR
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.056000000000000015
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07233333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12233333333333335
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.169
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.21633333333333332
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.17347962524637853
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22666666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.13734138567741627
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.48
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.48
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.43999999999999995
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.408
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.35999999999999993
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.035316726913150166
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10434144077897482
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15231964640086332
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2237637244339288
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4246552618150319
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6176666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.31123449548810894
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2866666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6933333333333332
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.541603756700773
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4797777777777777
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4632721572721572
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333332
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: 0.047079365079365075
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12374603174603176
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1498015873015873
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.19921428571428573
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14911410247271004
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2208571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.10914868671112705
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.76
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19599999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.122
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.49
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.61
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5140251570207169
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6078333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4296608736936407
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.32
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.10666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.32
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.58
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31757857296738545
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2347460317460317
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24643617899193362
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.26
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: 0.48
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14800000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.039136679314288055
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07088473736441431
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.08854886067737688
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.09738297754672119
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20662886108023884
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33716666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08492712298780619
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.12
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.039999999999999994
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.036000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.030000000000000006
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.27
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14834320225800574
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.11593650793650795
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12214508911612589
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.84
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21199999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.644
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7613333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.848
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.902
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.796606045632188
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7831666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7555666834462891
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.18
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: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03866666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1456666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.18566666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1754827925505982
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2969126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12469236976328293
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.54
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.54
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2899394224946307
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21268253968253967
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22184431538753369
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.096
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.34
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.43
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.44
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.495
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4215626178273768
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3998571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4072112112025905
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.30612244897959184
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4897959183673469
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6122448979591837
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7959183673469388
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.30612244897959184
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.28571428571428575
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.2714285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.017318112827283315
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.04934081962696573
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.08015471400681852
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1539608360137575
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28125127808062544
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4444930353093618
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1901047659008045
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.2789324960753532
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4438304552590267
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5286342229199373
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6181475667189953
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2789324960753532
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1927472527472527
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15767032967032968
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11534065934065933
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15311673467698103
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2664086945781836
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3266788314143574
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40487090951605337
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3415592843189738
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3829048366599387
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27719887197221665
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) dataset. It maps sentences & paragraphs to a 512-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:** [Unknown](https://huggingface.co./unknown) -->
- **Maximum Sequence Length:** inf tokens
- **Output Dimensionality:** 512 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR)
- **Language:** pt
<!-- - **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): StaticEmbedding(
(embedding): EmbeddingBag(29794, 512, mode='mean')
)
)
```
## 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("cnmoro/static-retrieval-distilbert-ptbr")
# Run inference
sentences = [
'o que ajuda a síndrome de ibs',
'óleo de hortelã-revestida com antecérico é amplamente utilizado para a síndrome do intestino irritável. Tem a intenção de reduzir a dor abdominal e inchaço da síndrome do intestino irritável. Peppermint é considerada uma erva carminativa, o que significa que é usado para eliminar o excesso de gás nos intestinos. Embora novas pesquisas sejam necessárias, estudos preliminares indicam que pode aliviar os sintomas da SII".',
'diarreia ou prisão de ventre que não responde ao tratamento domiciliar".',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]
# 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 | 0.16 | 0.48 | 0.32 | 0.16 | 0.5 | 0.08 | 0.26 | 0.06 | 0.7 | 0.18 | 0.08 | 0.34 | 0.3061 |
| cosine_accuracy@3 | 0.26 | 0.7 | 0.58 | 0.26 | 0.68 | 0.32 | 0.42 | 0.12 | 0.84 | 0.38 | 0.28 | 0.44 | 0.4898 |
| cosine_accuracy@5 | 0.34 | 0.82 | 0.72 | 0.32 | 0.76 | 0.46 | 0.46 | 0.18 | 0.9 | 0.48 | 0.36 | 0.46 | 0.6122 |
| cosine_accuracy@10 | 0.38 | 0.86 | 0.82 | 0.38 | 0.86 | 0.58 | 0.48 | 0.3 | 0.94 | 0.58 | 0.54 | 0.52 | 0.7959 |
| cosine_precision@1 | 0.16 | 0.48 | 0.32 | 0.16 | 0.5 | 0.08 | 0.26 | 0.06 | 0.7 | 0.18 | 0.08 | 0.34 | 0.3061 |
| cosine_precision@3 | 0.1 | 0.44 | 0.2 | 0.0933 | 0.2933 | 0.1067 | 0.2267 | 0.04 | 0.3067 | 0.1667 | 0.0933 | 0.1533 | 0.2857 |
| cosine_precision@5 | 0.088 | 0.408 | 0.152 | 0.072 | 0.196 | 0.092 | 0.2 | 0.036 | 0.212 | 0.14 | 0.072 | 0.096 | 0.2857 |
| cosine_precision@10 | 0.056 | 0.36 | 0.088 | 0.052 | 0.122 | 0.058 | 0.148 | 0.03 | 0.114 | 0.09 | 0.054 | 0.056 | 0.2714 |
| cosine_recall@1 | 0.0723 | 0.0353 | 0.2867 | 0.0471 | 0.25 | 0.08 | 0.0391 | 0.06 | 0.644 | 0.0387 | 0.08 | 0.34 | 0.0173 |
| cosine_recall@3 | 0.1223 | 0.1043 | 0.5467 | 0.1237 | 0.44 | 0.32 | 0.0709 | 0.11 | 0.7613 | 0.1047 | 0.28 | 0.43 | 0.0493 |
| cosine_recall@5 | 0.169 | 0.1523 | 0.6933 | 0.1498 | 0.49 | 0.46 | 0.0885 | 0.17 | 0.848 | 0.1457 | 0.36 | 0.44 | 0.0802 |
| cosine_recall@10 | 0.2163 | 0.2238 | 0.79 | 0.1992 | 0.61 | 0.58 | 0.0974 | 0.27 | 0.902 | 0.1857 | 0.54 | 0.495 | 0.154 |
| **cosine_ndcg@10** | **0.1735** | **0.4247** | **0.5416** | **0.1491** | **0.514** | **0.3176** | **0.2066** | **0.1483** | **0.7966** | **0.1755** | **0.2899** | **0.4216** | **0.2813** |
| cosine_mrr@10 | 0.2267 | 0.6177 | 0.4798 | 0.2209 | 0.6078 | 0.2347 | 0.3372 | 0.1159 | 0.7832 | 0.2969 | 0.2127 | 0.3999 | 0.4445 |
| cosine_map@100 | 0.1373 | 0.3112 | 0.4633 | 0.1091 | 0.4297 | 0.2464 | 0.0849 | 0.1221 | 0.7556 | 0.1247 | 0.2218 | 0.4072 | 0.1901 |
#### 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 | 0.2789 |
| cosine_accuracy@3 | 0.4438 |
| cosine_accuracy@5 | 0.5286 |
| cosine_accuracy@10 | 0.6181 |
| cosine_precision@1 | 0.2789 |
| cosine_precision@3 | 0.1927 |
| cosine_precision@5 | 0.1577 |
| cosine_precision@10 | 0.1153 |
| cosine_recall@1 | 0.1531 |
| cosine_recall@3 | 0.2664 |
| cosine_recall@5 | 0.3267 |
| cosine_recall@10 | 0.4049 |
| **cosine_ndcg@10** | **0.3416** |
| cosine_mrr@10 | 0.3829 |
| cosine_map@100 | 0.2772 |
<!--
## 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
#### all_triplets_ms_marco-ptbr
* Dataset: [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) at [f934503](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR/tree/f934503cfbb69901217f12c87f28767354e597ea)
* Size: 25,863,649 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 characters</li><li>mean: 35.31 characters</li><li>max: 105 characters</li></ul> | <ul><li>min: 31 characters</li><li>mean: 356.8 characters</li><li>max: 1050 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 359.92 characters</li><li>max: 1153 characters</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>partes mais quentes da califórnia em dezembro</code> | <code>as melhores praias da Califórnia para o clima quente do inverno estão ao longo da costa sul, particularmente as margens viradas para o sul. As temperaturas mais quentes acontecem em Avila Beach, Long Beach e Laguna Beach, onde os dias se dem até pelo menos 67 graus F (19 C) em média em dezembro e janeiro".</code> | <code>Outros destinos da ilha do Caribe com uma combinação de clima quente e não muita chuva em dezembro incluem Kingston, Jamaica (87 F), St. Kitts (85 F) e Nassau, Bahamas (79 F). Nos EUA continentais, o clima de férias mais quente em dezembro é mais frequentemente a Flórida. Tente afundar seus dedos na areia branca quente e macia de Nápoles e Sarasota, dois dos nossos locais de férias de inverno românticos da Flórida da Costa do Golfo da Flórida".</code> |
| <code>definição de anosmia</code> | <code>Anosmia (/aen-É-zmiÉ/) A sÉ-zmiÉ é a incapacidade de perceber o odor ou a falta de funcionamento da autaraction a perda do sentido.</code> | <code>Anemia é um termo médico que se refere a um número reduzido de glóbulos vermelhos circulantes (RBC), hemoglobina (Hb), ou ambos. Não é uma doença específica, mas sim o resultado de algum outro processo de doença ou condição.nemia é um termo médico referindo-se a um número reduzido de glóbulos vermelhos circulantes (RBC), hemoglobina (Hb), ou ambos. Não é uma doença específica, mas sim o resultado de algum outro processo ou condição de doença".</code> |
| <code>can fêmeas obter hemofilia</code> | <code>uma fêmea que herda um afetado x cromossomo torna-se um portador de hemofilia que ela pode passar o gene afetado para seus filhos, além de uma mulher que é um portador às vezes pode ter sintomas de hemofilia na verdade alguns médicos descrevem essas mulheres como tendo mulheres leves que carregam o gene da hemofilia que carregam o gene da hemofilia e têm quaisquer sintomas do transtorno deve ser verificado e cuidado por um provedor de saúde de boa qualidade cuidados médicos e enfermeiros que podem evitar que os problemas sérios que saibam que muitos.</code> | <code>Hemofilia é um X ligado ou sexo ligado a doença hereditária que significa que o defeito é realizado no cromossomo X. As fêmeas têm dois cromossomos X e os machos têm um cromossomo X e um cromossomo Y. O cromossomo X, que carrega o gene da hemofilia em homens, faz com que Fator VIII ou Fator IX esteja ausente ou deficiente (nível baixo). Cada criança de um portador de hemofilia tem 50% de chance de ser afetada pela hemofilia; seja ter hemofilia para um macho ou ser portadora de uma mulher".</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
512,
384,
256,
128,
64,
32,
16,
8
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### all_triplets_ms_marco-ptbr
* Dataset: [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) at [f934503](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR/tree/f934503cfbb69901217f12c87f28767354e597ea)
* Size: 527,832 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 characters</li><li>mean: 36.15 characters</li><li>max: 193 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 360.3 characters</li><li>max: 1097 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 365.67 characters</li><li>max: 1145 characters</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>diferença entre o ovo cozido duro e o ovo escalfado</code> | <code>o ovo é escalfado (ou cozido) quando o branco é cozido e a gema ainda é escorrendo, um ovo cozido duro é cozido em sua casca por 7 a 8 minutos até que seja cozido sólido todo o caminho. Carmen D 4 anos atrás. Os polegares para cima. 0".</code> | <code>mexidos, escalfados, fritos ou cozidos, e dado todas essas variações, a questão de longa duração que eles podem ser armazenados com segurança é uma boa a considerar. Uma bactéria chamada Salmonella enteritidis pode estar presente dentro da gema, mas ovos duros os torna seguros para comer".</code> |
| <code>quando você pode coletar segurança social se deficientes</code> | <code>Como a Segurança Social pagará benefícios de invalidez a uma pessoa com deficiência é determinada pela data em que você apresentou sua reivindicação de deficiência ao se candidatar à segurança social e/ou incapacidade da SSI.</code> | <code>Se for esse o caso, você não terá mais direito a benefícios de Deficiência da Segurança Social, mas você pode ter direito a benefícios de aposentadoria da Previdência Social uma vez que você atinja a idade de 65 anos. Se você decidir voltar ao trabalho seus benefícios não vai parar imediatamente. Você pode ganhar renda em uma base de â-trialâ para até nove meses antes de seus benefícios de Deficiência Social são revogados. Se você tentar voltar ao trabalho e descobrir que você é incapaz de lidar com isso, seus Benefícios de Segurança Social não terminará.ou pode ganhar renda em uma base de âtrialâ por até nove meses antes de seus benefícios de deficientes de segurança social são revogados. Se você tentar voltar ao trabalho e descobrir que não consegue lidar com isso, seus Benefícios de Segurança Social não terminarão".</code> |
| <code>número de contato da sede da união ocidental</code> | <code>número de telefone da União Ocidental. O número e as etapas abaixo são votados no 1 de 4 por mais de 7190 clientes da Western Union. 800-999-9660. Suporte telefônico da Western Union. Leia as principais etapas e dicas abaixo. Eles chamam você em vez dissoNão esperando em espera. Free.ress 1 e continue pressionando 0. Este número de telefone é popular entre outros clientes da Western Union, mas não se esqueça de seguir os 6 passos mais abaixo".</code> | <code>Neste artigo eu listei o número de telefone de serviço ao cliente Western Union essencial e o número de telefone de contato e números gratuitos para a Western Union. Western Union operando em muitos países, então eu listei números de telefone de atendimento ao cliente internacional Western Union. Se você é o cliente da Western Union e gosta de saber informações sobre produtos e serviços da Western Union, basta usar os seguintes números".</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
512,
384,
256,
128,
64,
32,
16,
8
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 0.2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: True
#### 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`: 512
- `per_device_eval_batch_size`: 512
- `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`: 0.2
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 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`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:------:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0000 | 1 | 66.3307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0198 | 1000 | 42.3936 | 27.4352 | 0.1314 | 0.3901 | 0.4362 | 0.0856 | 0.4261 | 0.2743 | 0.1524 | 0.1226 | 0.7497 | 0.1547 | 0.1544 | 0.4066 | 0.2984 | 0.2910 |
| 0.0396 | 2000 | 21.4189 | 17.5353 | 0.1443 | 0.4301 | 0.5087 | 0.1281 | 0.4315 | 0.2600 | 0.1859 | 0.1462 | 0.7842 | 0.1978 | 0.1944 | 0.4489 | 0.3432 | 0.3233 |
| 0.0594 | 3000 | 15.8675 | 14.6976 | 0.1579 | 0.4524 | 0.5459 | 0.1350 | 0.4307 | 0.2972 | 0.1980 | 0.1443 | 0.7807 | 0.1921 | 0.2016 | 0.4302 | 0.3561 | 0.3325 |
| 0.0792 | 4000 | 14.0655 | 13.5888 | 0.1803 | 0.4522 | 0.5321 | 0.1402 | 0.4479 | 0.2982 | 0.1914 | 0.1912 | 0.7992 | 0.2001 | 0.2143 | 0.4502 | 0.3432 | 0.3416 |
| 0.0990 | 5000 | 13.2932 | 13.0002 | 0.1926 | 0.4523 | 0.5118 | 0.1607 | 0.4451 | 0.3059 | 0.2048 | 0.2168 | 0.7903 | 0.1974 | 0.2387 | 0.4653 | 0.3520 | 0.3487 |
| 0.1188 | 6000 | 12.8258 | 12.6530 | 0.1998 | 0.4510 | 0.5437 | 0.1296 | 0.4506 | 0.3335 | 0.2100 | 0.1894 | 0.8074 | 0.1761 | 0.2423 | 0.4456 | 0.3688 | 0.3498 |
| 0.1386 | 7000 | 12.5101 | 12.3932 | 0.1775 | 0.4638 | 0.4978 | 0.1503 | 0.4547 | 0.3197 | 0.2037 | 0.1864 | 0.8178 | 0.1757 | 0.1987 | 0.4518 | 0.3382 | 0.3412 |
| 0.1584 | 8000 | 12.2601 | 12.1873 | 0.1884 | 0.4794 | 0.5263 | 0.1668 | 0.4764 | 0.3603 | 0.2115 | 0.1673 | 0.7835 | 0.1720 | 0.2266 | 0.4534 | 0.3535 | 0.3512 |
| 0.1782 | 9000 | 12.0884 | 12.0142 | 0.2139 | 0.4735 | 0.5170 | 0.1598 | 0.4498 | 0.3448 | 0.2002 | 0.1983 | 0.7901 | 0.1651 | 0.2351 | 0.4458 | 0.3240 | 0.3475 |
| 0.1980 | 10000 | 11.9352 | 11.8797 | 0.2123 | 0.4813 | 0.5146 | 0.1452 | 0.5095 | 0.3642 | 0.1983 | 0.1637 | 0.8041 | 0.1699 | 0.2384 | 0.4545 | 0.3198 | 0.3520 |
| 0.2178 | 11000 | 11.8034 | 11.7615 | 0.1776 | 0.4579 | 0.5237 | 0.1673 | 0.4808 | 0.3068 | 0.2009 | 0.1828 | 0.8173 | 0.1706 | 0.2572 | 0.4408 | 0.3205 | 0.3465 |
| 0.2376 | 12000 | 11.6906 | 11.6589 | 0.1789 | 0.4593 | 0.5512 | 0.1341 | 0.4894 | 0.3340 | 0.2106 | 0.1811 | 0.8192 | 0.1773 | 0.2381 | 0.4480 | 0.3209 | 0.3494 |
| 0.2573 | 13000 | 11.5868 | 11.5586 | 0.1877 | 0.4648 | 0.5137 | 0.1494 | 0.4939 | 0.3212 | 0.2193 | 0.2025 | 0.8120 | 0.1640 | 0.2452 | 0.4258 | 0.3561 | 0.3504 |
| 0.2771 | 14000 | 11.4752 | 11.4752 | 0.1938 | 0.4411 | 0.5186 | 0.1418 | 0.4839 | 0.3411 | 0.2106 | 0.1688 | 0.8217 | 0.1744 | 0.2768 | 0.4688 | 0.3384 | 0.3523 |
| 0.2969 | 15000 | 11.4299 | 11.3873 | 0.1989 | 0.4501 | 0.5109 | 0.1309 | 0.5037 | 0.3280 | 0.2040 | 0.1649 | 0.8035 | 0.1707 | 0.2549 | 0.4714 | 0.3308 | 0.3479 |
| 0.3167 | 16000 | 11.3369 | 11.3173 | 0.1880 | 0.4666 | 0.4988 | 0.1430 | 0.5086 | 0.3385 | 0.2054 | 0.1786 | 0.8181 | 0.1712 | 0.2766 | 0.4555 | 0.3220 | 0.3516 |
| 0.3365 | 17000 | 11.2737 | 11.2503 | 0.1748 | 0.4673 | 0.4849 | 0.1485 | 0.4902 | 0.3567 | 0.2160 | 0.1501 | 0.8059 | 0.1659 | 0.2476 | 0.4728 | 0.3121 | 0.3456 |
| 0.3563 | 18000 | 11.2138 | 11.1802 | 0.1738 | 0.4619 | 0.5408 | 0.1426 | 0.4986 | 0.3427 | 0.2193 | 0.1594 | 0.7995 | 0.1597 | 0.2567 | 0.4331 | 0.3140 | 0.3463 |
| 0.3761 | 19000 | 11.1662 | 11.1250 | 0.1625 | 0.4522 | 0.5313 | 0.1419 | 0.5093 | 0.3499 | 0.1982 | 0.1713 | 0.8000 | 0.1693 | 0.2332 | 0.4799 | 0.3353 | 0.3488 |
| 0.3959 | 20000 | 11.0674 | 11.0633 | 0.1627 | 0.4608 | 0.5167 | 0.1368 | 0.5025 | 0.3653 | 0.2090 | 0.1743 | 0.8166 | 0.1670 | 0.2281 | 0.4614 | 0.3408 | 0.3494 |
| 0.4157 | 21000 | 11.0251 | 11.0233 | 0.1730 | 0.4695 | 0.4854 | 0.1417 | 0.5211 | 0.3393 | 0.2246 | 0.1477 | 0.8146 | 0.1692 | 0.2148 | 0.4584 | 0.3356 | 0.3458 |
| 0.4355 | 22000 | 10.9932 | 10.9695 | 0.1709 | 0.4630 | 0.5161 | 0.1400 | 0.4945 | 0.3507 | 0.2226 | 0.1585 | 0.8103 | 0.1595 | 0.2355 | 0.4325 | 0.3343 | 0.3453 |
| 0.4553 | 23000 | 10.9327 | 10.9186 | 0.1803 | 0.4509 | 0.5341 | 0.1454 | 0.5241 | 0.3485 | 0.2032 | 0.1480 | 0.8056 | 0.1634 | 0.2206 | 0.4557 | 0.3266 | 0.3466 |
| 0.4751 | 24000 | 10.8936 | 10.8830 | 0.1891 | 0.4450 | 0.5202 | 0.1485 | 0.5006 | 0.3427 | 0.2079 | 0.1639 | 0.8115 | 0.1731 | 0.2213 | 0.4269 | 0.3424 | 0.3456 |
| 0.4949 | 25000 | 10.8654 | 10.8392 | 0.1610 | 0.4479 | 0.5524 | 0.1547 | 0.5002 | 0.3377 | 0.2128 | 0.1802 | 0.7996 | 0.1937 | 0.2240 | 0.4506 | 0.3097 | 0.3480 |
| 0.5147 | 26000 | 10.8168 | 10.7826 | 0.1784 | 0.4558 | 0.5211 | 0.1482 | 0.5099 | 0.3531 | 0.2165 | 0.1456 | 0.8090 | 0.1782 | 0.2367 | 0.4240 | 0.3251 | 0.3463 |
| 0.5345 | 27000 | 10.7554 | 10.7164 | 0.1841 | 0.4593 | 0.5183 | 0.1377 | 0.4843 | 0.3469 | 0.2066 | 0.1632 | 0.8099 | 0.1818 | 0.2779 | 0.4305 | 0.3270 | 0.3483 |
| 0.5543 | 28000 | 10.6605 | 10.6510 | 0.1780 | 0.4566 | 0.5328 | 0.1439 | 0.4923 | 0.3519 | 0.2152 | 0.1507 | 0.8060 | 0.1838 | 0.2585 | 0.4256 | 0.3147 | 0.3469 |
| 0.5741 | 29000 | 10.6202 | 10.5959 | 0.1866 | 0.4668 | 0.5370 | 0.1553 | 0.5118 | 0.3699 | 0.2265 | 0.1553 | 0.8090 | 0.1732 | 0.2614 | 0.4287 | 0.3193 | 0.3539 |
| 0.5939 | 30000 | 10.5399 | 10.5401 | 0.1862 | 0.4593 | 0.5237 | 0.1510 | 0.5273 | 0.3353 | 0.2101 | 0.1594 | 0.8092 | 0.1709 | 0.2643 | 0.4308 | 0.3199 | 0.3498 |
| 0.6137 | 31000 | 10.5212 | 10.4866 | 0.2000 | 0.4547 | 0.5131 | 0.1450 | 0.5213 | 0.3341 | 0.2136 | 0.1518 | 0.8094 | 0.1726 | 0.2911 | 0.4246 | 0.3388 | 0.3516 |
| 0.6335 | 32000 | 10.4767 | 10.4375 | 0.1873 | 0.4487 | 0.5162 | 0.1377 | 0.5186 | 0.3463 | 0.2184 | 0.1711 | 0.8087 | 0.1769 | 0.2871 | 0.4441 | 0.3297 | 0.3531 |
| 0.6533 | 33000 | 10.4247 | 10.4089 | 0.1949 | 0.4572 | 0.5322 | 0.1524 | 0.5286 | 0.3309 | 0.2204 | 0.1464 | 0.8006 | 0.1765 | 0.2727 | 0.4314 | 0.3323 | 0.3520 |
| 0.6731 | 34000 | 10.389 | 10.3680 | 0.1867 | 0.4628 | 0.5265 | 0.1369 | 0.5196 | 0.3411 | 0.2224 | 0.1597 | 0.8003 | 0.1702 | 0.2678 | 0.4386 | 0.3163 | 0.3499 |
| 0.6929 | 35000 | 10.3299 | 10.3354 | 0.1937 | 0.4614 | 0.5042 | 0.1430 | 0.5215 | 0.3416 | 0.2159 | 0.1488 | 0.8101 | 0.1764 | 0.2601 | 0.4525 | 0.3192 | 0.3499 |
| 0.7127 | 36000 | 10.3103 | 10.3054 | 0.1764 | 0.4555 | 0.5281 | 0.1577 | 0.5291 | 0.3338 | 0.2049 | 0.1483 | 0.7980 | 0.1660 | 0.2626 | 0.4153 | 0.3137 | 0.3453 |
| 0.7325 | 37000 | 10.2869 | 10.2670 | 0.1703 | 0.4488 | 0.5188 | 0.1560 | 0.5200 | 0.3370 | 0.2118 | 0.1513 | 0.8108 | 0.1671 | 0.2853 | 0.4057 | 0.3102 | 0.3456 |
| 0.7523 | 38000 | 10.2414 | 10.2453 | 0.1713 | 0.4556 | 0.5400 | 0.1568 | 0.5228 | 0.3359 | 0.2081 | 0.1624 | 0.8063 | 0.1636 | 0.2644 | 0.4413 | 0.3117 | 0.3492 |
| 0.7720 | 39000 | 10.231 | 10.2169 | 0.1595 | 0.4577 | 0.5599 | 0.1510 | 0.5195 | 0.3300 | 0.2070 | 0.1635 | 0.8145 | 0.1615 | 0.2846 | 0.4269 | 0.3236 | 0.3507 |
| 0.7918 | 40000 | 10.2115 | 10.1964 | 0.1734 | 0.4621 | 0.5414 | 0.1481 | 0.5300 | 0.3438 | 0.2072 | 0.1712 | 0.8062 | 0.1639 | 0.2815 | 0.4122 | 0.3000 | 0.3493 |
| 0.8116 | 41000 | 10.1947 | 10.1671 | 0.1712 | 0.4559 | 0.5450 | 0.1523 | 0.5145 | 0.3392 | 0.2198 | 0.1588 | 0.7927 | 0.1734 | 0.2826 | 0.4281 | 0.3014 | 0.3488 |
| 0.8314 | 42000 | 10.1666 | 10.1581 | 0.1648 | 0.4464 | 0.5555 | 0.1639 | 0.5014 | 0.3477 | 0.2099 | 0.1443 | 0.7988 | 0.1640 | 0.2784 | 0.4482 | 0.2983 | 0.3478 |
| 0.8512 | 43000 | 10.1528 | 10.1265 | 0.1789 | 0.4437 | 0.5328 | 0.1525 | 0.5266 | 0.3369 | 0.2016 | 0.1561 | 0.8097 | 0.1742 | 0.2863 | 0.4503 | 0.3008 | 0.3500 |
| 0.8710 | 44000 | 10.1054 | 10.1122 | 0.1716 | 0.4542 | 0.5310 | 0.1610 | 0.5359 | 0.3454 | 0.2022 | 0.1725 | 0.7948 | 0.1666 | 0.2840 | 0.4246 | 0.3149 | 0.3507 |
| 0.8908 | 45000 | 10.0878 | 10.0890 | 0.1729 | 0.4489 | 0.5533 | 0.1561 | 0.5401 | 0.3413 | 0.2135 | 0.1510 | 0.7989 | 0.1735 | 0.2950 | 0.4348 | 0.3202 | 0.3538 |
| 0.9106 | 46000 | 10.0875 | 10.0730 | 0.1776 | 0.4550 | 0.5499 | 0.1563 | 0.5313 | 0.3357 | 0.2084 | 0.1578 | 0.8058 | 0.1739 | 0.2976 | 0.4468 | 0.3176 | 0.3549 |
| 0.9304 | 47000 | 10.0615 | 10.0561 | 0.1816 | 0.4569 | 0.5310 | 0.1583 | 0.5279 | 0.3332 | 0.2058 | 0.1532 | 0.7976 | 0.1727 | 0.2813 | 0.4513 | 0.3146 | 0.3512 |
| 0.9502 | 48000 | 10.0378 | 10.0374 | 0.1916 | 0.4558 | 0.5242 | 0.1552 | 0.5368 | 0.3518 | 0.2050 | 0.1617 | 0.8065 | 0.1736 | 0.2898 | 0.4268 | 0.3109 | 0.3531 |
| 0.9700 | 49000 | 10.0393 | 10.0283 | 0.1809 | 0.4542 | 0.5319 | 0.1594 | 0.5240 | 0.3329 | 0.2070 | 0.1595 | 0.7998 | 0.1670 | 0.2885 | 0.4522 | 0.3204 | 0.3521 |
| 0.9898 | 50000 | 10.0035 | 10.0112 | 0.1721 | 0.4495 | 0.5200 | 0.1548 | 0.5294 | 0.3514 | 0.2124 | 0.1597 | 0.8063 | 0.1798 | 0.2785 | 0.4479 | 0.3322 | 0.3534 |
| 1.0096 | 51000 | 9.9575 | 10.0040 | 0.1737 | 0.4476 | 0.5422 | 0.1527 | 0.5345 | 0.3513 | 0.2076 | 0.1513 | 0.8071 | 0.1681 | 0.2715 | 0.4547 | 0.3149 | 0.3521 |
| 1.0294 | 52000 | 9.9083 | 9.9996 | 0.1668 | 0.4530 | 0.5315 | 0.1645 | 0.5212 | 0.3375 | 0.2168 | 0.1458 | 0.8046 | 0.1720 | 0.2746 | 0.4432 | 0.3234 | 0.3504 |
| 1.0492 | 53000 | 9.9229 | 9.9895 | 0.1777 | 0.4434 | 0.5348 | 0.1601 | 0.5158 | 0.3390 | 0.2130 | 0.1461 | 0.8014 | 0.1717 | 0.2808 | 0.4546 | 0.3161 | 0.3504 |
| 1.0690 | 54000 | 9.884 | 9.9758 | 0.1797 | 0.4507 | 0.5372 | 0.1685 | 0.5202 | 0.3398 | 0.2174 | 0.1739 | 0.7949 | 0.1744 | 0.2944 | 0.4334 | 0.3191 | 0.3541 |
| 1.0888 | 55000 | 9.9108 | 9.9650 | 0.1780 | 0.4458 | 0.5249 | 0.1510 | 0.5190 | 0.3492 | 0.2222 | 0.1639 | 0.7968 | 0.1895 | 0.2878 | 0.4251 | 0.3153 | 0.3514 |
| 1.1086 | 56000 | 9.9019 | 9.9556 | 0.1893 | 0.4465 | 0.5368 | 0.1514 | 0.5131 | 0.3384 | 0.2151 | 0.1609 | 0.8029 | 0.1886 | 0.2993 | 0.4280 | 0.3223 | 0.3533 |
| 1.1284 | 57000 | 9.8931 | 9.9392 | 0.1837 | 0.4409 | 0.5381 | 0.1632 | 0.5254 | 0.3332 | 0.2046 | 0.1470 | 0.8067 | 0.1915 | 0.2797 | 0.4167 | 0.3212 | 0.3501 |
| 1.1482 | 58000 | 9.8714 | 9.9229 | 0.1731 | 0.4440 | 0.5289 | 0.1477 | 0.5073 | 0.3257 | 0.2063 | 0.1631 | 0.8079 | 0.1844 | 0.3001 | 0.4391 | 0.3194 | 0.3498 |
| 1.1680 | 59000 | 9.885 | 9.9159 | 0.1756 | 0.4498 | 0.5274 | 0.1580 | 0.5156 | 0.3227 | 0.2101 | 0.1470 | 0.8042 | 0.1783 | 0.3026 | 0.4215 | 0.3237 | 0.3490 |
| 1.1878 | 60000 | 9.8824 | 9.9016 | 0.1794 | 0.4512 | 0.5261 | 0.1523 | 0.5093 | 0.3427 | 0.1964 | 0.1468 | 0.8029 | 0.1756 | 0.2898 | 0.4325 | 0.3173 | 0.3479 |
| 1.2076 | 61000 | 9.8846 | 9.8969 | 0.1768 | 0.4518 | 0.5452 | 0.1643 | 0.5087 | 0.3471 | 0.2004 | 0.1509 | 0.7959 | 0.1847 | 0.2954 | 0.4386 | 0.3099 | 0.3515 |
| 1.2274 | 62000 | 9.8534 | 9.8831 | 0.1848 | 0.4532 | 0.5422 | 0.1583 | 0.5177 | 0.3546 | 0.2087 | 0.1546 | 0.7985 | 0.1815 | 0.3024 | 0.4335 | 0.3285 | 0.3553 |
| 1.2472 | 63000 | 9.8494 | 9.8759 | 0.1776 | 0.4490 | 0.5305 | 0.1641 | 0.5138 | 0.3517 | 0.2043 | 0.1474 | 0.8040 | 0.1809 | 0.2947 | 0.4252 | 0.3183 | 0.3509 |
| 1.2670 | 64000 | 9.8514 | 9.8639 | 0.1820 | 0.4553 | 0.5386 | 0.1569 | 0.5055 | 0.3442 | 0.2116 | 0.1396 | 0.7949 | 0.1807 | 0.2820 | 0.4225 | 0.3154 | 0.3484 |
| 1.2867 | 65000 | 9.8341 | 9.8563 | 0.1772 | 0.4507 | 0.5300 | 0.1579 | 0.5072 | 0.3392 | 0.2067 | 0.1529 | 0.7961 | 0.1825 | 0.2874 | 0.4215 | 0.3195 | 0.3484 |
| 1.3065 | 66000 | 9.8417 | 9.8492 | 0.1784 | 0.4557 | 0.5251 | 0.1598 | 0.5011 | 0.3324 | 0.2183 | 0.1566 | 0.7928 | 0.1821 | 0.2873 | 0.4181 | 0.3153 | 0.3479 |
| 1.3263 | 67000 | 9.8081 | 9.8369 | 0.1831 | 0.4488 | 0.5360 | 0.1681 | 0.5046 | 0.3317 | 0.2064 | 0.1467 | 0.8013 | 0.1738 | 0.2887 | 0.4381 | 0.3043 | 0.3486 |
| 1.3461 | 68000 | 9.8001 | 9.8274 | 0.1842 | 0.4563 | 0.5387 | 0.1647 | 0.5080 | 0.3174 | 0.2089 | 0.1595 | 0.7964 | 0.1705 | 0.2918 | 0.4187 | 0.3054 | 0.3477 |
| 1.3659 | 69000 | 9.8059 | 9.8159 | 0.1827 | 0.4570 | 0.5528 | 0.1715 | 0.5207 | 0.3289 | 0.2046 | 0.1543 | 0.8094 | 0.1757 | 0.2839 | 0.4281 | 0.3025 | 0.3517 |
| 1.3857 | 70000 | 9.7848 | 9.8117 | 0.1656 | 0.4547 | 0.5381 | 0.1562 | 0.5091 | 0.3233 | 0.2127 | 0.1539 | 0.8000 | 0.1722 | 0.2885 | 0.4168 | 0.3091 | 0.3462 |
| 1.4055 | 71000 | 9.7847 | 9.8049 | 0.1786 | 0.4499 | 0.5495 | 0.1675 | 0.5194 | 0.3180 | 0.2133 | 0.1587 | 0.8025 | 0.1588 | 0.2895 | 0.4224 | 0.3056 | 0.3487 |
| 1.4253 | 72000 | 9.7587 | 9.7976 | 0.1706 | 0.4562 | 0.5425 | 0.1530 | 0.5283 | 0.3356 | 0.2125 | 0.1564 | 0.8055 | 0.1660 | 0.2939 | 0.4219 | 0.3005 | 0.3495 |
| 1.4451 | 73000 | 9.7652 | 9.7898 | 0.1787 | 0.4479 | 0.5406 | 0.1539 | 0.5281 | 0.3291 | 0.2088 | 0.1438 | 0.8058 | 0.1767 | 0.2938 | 0.4115 | 0.2960 | 0.3473 |
| 1.4649 | 74000 | 9.7507 | 9.7830 | 0.1746 | 0.4394 | 0.5426 | 0.1647 | 0.5201 | 0.3290 | 0.2131 | 0.1507 | 0.8039 | 0.1643 | 0.2856 | 0.4510 | 0.3030 | 0.3494 |
| 1.4847 | 75000 | 9.7412 | 9.7757 | 0.1701 | 0.4386 | 0.5244 | 0.1639 | 0.5140 | 0.3218 | 0.2111 | 0.1542 | 0.8086 | 0.1714 | 0.2765 | 0.4224 | 0.2973 | 0.3442 |
| 1.5045 | 76000 | 9.7412 | 9.7727 | 0.1823 | 0.4477 | 0.5337 | 0.1544 | 0.5117 | 0.3381 | 0.2074 | 0.1605 | 0.8079 | 0.1710 | 0.2820 | 0.4325 | 0.2996 | 0.3484 |
| 1.5243 | 77000 | 9.7475 | 9.7626 | 0.1743 | 0.4423 | 0.5343 | 0.1511 | 0.5142 | 0.3224 | 0.2124 | 0.1567 | 0.8076 | 0.1802 | 0.2946 | 0.4303 | 0.3044 | 0.3481 |
| 1.5441 | 78000 | 9.7512 | 9.7590 | 0.1737 | 0.4406 | 0.5323 | 0.1535 | 0.5102 | 0.3419 | 0.2099 | 0.1476 | 0.8058 | 0.1626 | 0.2877 | 0.4073 | 0.3015 | 0.3442 |
| 1.5639 | 79000 | 9.7406 | 9.7501 | 0.1735 | 0.4472 | 0.5189 | 0.1639 | 0.5148 | 0.3232 | 0.2065 | 0.1555 | 0.8015 | 0.1698 | 0.2826 | 0.4320 | 0.3047 | 0.3457 |
| 1.5837 | 80000 | 9.7409 | 9.7426 | 0.1799 | 0.4405 | 0.5225 | 0.1627 | 0.5158 | 0.3487 | 0.2051 | 0.1608 | 0.8079 | 0.1657 | 0.2857 | 0.4469 | 0.3014 | 0.3495 |
| 1.6035 | 81000 | 9.7125 | 9.7399 | 0.1781 | 0.4402 | 0.5230 | 0.1564 | 0.5153 | 0.3439 | 0.2167 | 0.1622 | 0.8070 | 0.1706 | 0.3040 | 0.4512 | 0.3071 | 0.3520 |
| 1.6233 | 82000 | 9.7164 | 9.7319 | 0.1806 | 0.4485 | 0.5317 | 0.1486 | 0.5220 | 0.3353 | 0.2087 | 0.1604 | 0.8033 | 0.1783 | 0.2899 | 0.4178 | 0.3025 | 0.3483 |
| 1.6431 | 83000 | 9.7203 | 9.7257 | 0.1766 | 0.4513 | 0.5120 | 0.1581 | 0.5108 | 0.3375 | 0.2084 | 0.1635 | 0.8085 | 0.1682 | 0.2904 | 0.4334 | 0.2932 | 0.3471 |
| 1.6629 | 84000 | 9.7035 | 9.7229 | 0.1759 | 0.4447 | 0.5391 | 0.1555 | 0.5104 | 0.3369 | 0.2067 | 0.1584 | 0.8036 | 0.1754 | 0.2943 | 0.4266 | 0.3032 | 0.3485 |
| 1.6827 | 85000 | 9.7277 | 9.7206 | 0.1757 | 0.4401 | 0.5229 | 0.1540 | 0.5188 | 0.3448 | 0.2070 | 0.1521 | 0.8078 | 0.1731 | 0.2967 | 0.4287 | 0.2984 | 0.3477 |
| 1.7025 | 86000 | 9.6992 | 9.7184 | 0.1849 | 0.4403 | 0.5276 | 0.1598 | 0.5196 | 0.3342 | 0.2110 | 0.1585 | 0.8119 | 0.1790 | 0.2887 | 0.4211 | 0.3067 | 0.3495 |
| 1.7223 | 87000 | 9.6789 | 9.7084 | 0.1744 | 0.4400 | 0.5367 | 0.1572 | 0.5068 | 0.3289 | 0.2088 | 0.1622 | 0.8087 | 0.1750 | 0.2886 | 0.4340 | 0.3095 | 0.3485 |
| 1.7421 | 88000 | 9.6939 | 9.7020 | 0.1736 | 0.4400 | 0.5423 | 0.1644 | 0.5125 | 0.3339 | 0.2064 | 0.1643 | 0.8052 | 0.1869 | 0.2921 | 0.4120 | 0.3091 | 0.3494 |
| 1.7619 | 89000 | 9.661 | 9.6965 | 0.1651 | 0.4404 | 0.5433 | 0.1625 | 0.5234 | 0.3362 | 0.2103 | 0.1682 | 0.8052 | 0.1797 | 0.2823 | 0.4291 | 0.3052 | 0.3501 |
| 1.7816 | 90000 | 9.6624 | 9.6919 | 0.1689 | 0.4438 | 0.5317 | 0.1496 | 0.5125 | 0.3421 | 0.2056 | 0.1643 | 0.8078 | 0.1750 | 0.3034 | 0.4187 | 0.3003 | 0.3480 |
| 1.8014 | 91000 | 9.666 | 9.6855 | 0.1719 | 0.4468 | 0.5395 | 0.1572 | 0.5188 | 0.3430 | 0.2032 | 0.1506 | 0.8065 | 0.1795 | 0.2888 | 0.4185 | 0.2940 | 0.3476 |
| 1.8212 | 92000 | 9.6715 | 9.6823 | 0.1703 | 0.4456 | 0.5311 | 0.1568 | 0.5193 | 0.3530 | 0.2046 | 0.1635 | 0.7988 | 0.1758 | 0.2951 | 0.4236 | 0.2994 | 0.3490 |
| 1.8410 | 93000 | 9.6597 | 9.6800 | 0.1703 | 0.4491 | 0.5255 | 0.1622 | 0.5194 | 0.3491 | 0.2137 | 0.1444 | 0.8062 | 0.1728 | 0.3083 | 0.4199 | 0.3070 | 0.3498 |
| 1.8608 | 94000 | 9.6594 | 9.6740 | 0.1668 | 0.4469 | 0.5233 | 0.1536 | 0.5194 | 0.3396 | 0.2077 | 0.1586 | 0.8095 | 0.1809 | 0.2895 | 0.4238 | 0.3000 | 0.3477 |
| 1.8806 | 95000 | 9.6565 | 9.6647 | 0.1738 | 0.4461 | 0.5312 | 0.1502 | 0.5392 | 0.3444 | 0.2074 | 0.1555 | 0.8063 | 0.1823 | 0.2979 | 0.4282 | 0.3023 | 0.3511 |
| 1.9004 | 96000 | 9.6476 | 9.6640 | 0.1759 | 0.4456 | 0.5433 | 0.1565 | 0.5318 | 0.3470 | 0.2149 | 0.1548 | 0.8047 | 0.1717 | 0.3024 | 0.4359 | 0.2953 | 0.3523 |
| 1.9202 | 97000 | 9.6588 | 9.6563 | 0.1815 | 0.4449 | 0.5431 | 0.1617 | 0.5267 | 0.3460 | 0.2061 | 0.1557 | 0.8068 | 0.1667 | 0.2997 | 0.4463 | 0.3066 | 0.3532 |
| 1.9400 | 98000 | 9.6232 | 9.6491 | 0.1769 | 0.4426 | 0.5411 | 0.1562 | 0.5255 | 0.3430 | 0.2074 | 0.1534 | 0.8108 | 0.1686 | 0.2991 | 0.4395 | 0.2915 | 0.3504 |
| 1.9598 | 99000 | 9.6412 | 9.6446 | 0.1722 | 0.4434 | 0.5368 | 0.1652 | 0.5236 | 0.3378 | 0.1998 | 0.1533 | 0.8043 | 0.1670 | 0.3053 | 0.4498 | 0.2899 | 0.3499 |
| 1.9796 | 100000 | 9.6418 | 9.6400 | 0.1740 | 0.4444 | 0.5379 | 0.1635 | 0.5284 | 0.3340 | 0.2038 | 0.1682 | 0.8013 | 0.1780 | 0.3077 | 0.4224 | 0.2877 | 0.3501 |
| 1.9994 | 101000 | 9.6363 | 9.6378 | 0.1784 | 0.4439 | 0.5349 | 0.1626 | 0.5273 | 0.3432 | 0.2168 | 0.1602 | 0.8028 | 0.1797 | 0.2987 | 0.4336 | 0.2999 | 0.3525 |
| 2.0192 | 102000 | 9.5424 | 9.6456 | 0.1817 | 0.4450 | 0.5436 | 0.1563 | 0.5333 | 0.3374 | 0.2124 | 0.1551 | 0.8045 | 0.1767 | 0.2880 | 0.4329 | 0.2923 | 0.3507 |
| 2.0390 | 103000 | 9.5632 | 9.6461 | 0.1818 | 0.4505 | 0.5405 | 0.1566 | 0.5251 | 0.3387 | 0.2047 | 0.1533 | 0.7995 | 0.1697 | 0.2860 | 0.4399 | 0.2936 | 0.3492 |
| 2.0588 | 104000 | 9.5526 | 9.6401 | 0.1775 | 0.4386 | 0.5245 | 0.1471 | 0.5212 | 0.3383 | 0.2110 | 0.1548 | 0.8061 | 0.1663 | 0.2945 | 0.4264 | 0.2995 | 0.3466 |
| 2.0786 | 105000 | 9.5694 | 9.6374 | 0.1915 | 0.4489 | 0.5283 | 0.1506 | 0.5276 | 0.3393 | 0.2016 | 0.1498 | 0.8045 | 0.1723 | 0.2938 | 0.4376 | 0.3007 | 0.3497 |
| 2.0984 | 106000 | 9.5772 | 9.6314 | 0.1728 | 0.4530 | 0.5356 | 0.1605 | 0.5278 | 0.3358 | 0.2061 | 0.1503 | 0.8050 | 0.1734 | 0.3016 | 0.4274 | 0.2991 | 0.3499 |
| 2.1182 | 107000 | 9.5735 | 9.6322 | 0.1711 | 0.4380 | 0.5450 | 0.1618 | 0.5333 | 0.3462 | 0.2026 | 0.1591 | 0.8057 | 0.1711 | 0.3005 | 0.4159 | 0.2984 | 0.3499 |
| 2.1380 | 108000 | 9.5764 | 9.6262 | 0.1738 | 0.4547 | 0.5394 | 0.1548 | 0.5330 | 0.3372 | 0.2003 | 0.1589 | 0.8026 | 0.1768 | 0.2914 | 0.4384 | 0.2877 | 0.3499 |
| 2.1578 | 109000 | 9.5918 | 9.6217 | 0.1699 | 0.4404 | 0.5272 | 0.1469 | 0.5248 | 0.3483 | 0.2020 | 0.1507 | 0.8006 | 0.1771 | 0.2851 | 0.4183 | 0.3009 | 0.3456 |
| 2.1776 | 110000 | 9.5565 | 9.6192 | 0.1700 | 0.4443 | 0.5291 | 0.1477 | 0.5296 | 0.3409 | 0.2072 | 0.1530 | 0.8042 | 0.1752 | 0.2823 | 0.4203 | 0.2976 | 0.3463 |
| 2.1974 | 111000 | 9.5725 | 9.6153 | 0.1733 | 0.4434 | 0.5258 | 0.1499 | 0.5215 | 0.3397 | 0.1976 | 0.1544 | 0.8031 | 0.1830 | 0.2749 | 0.4255 | 0.2939 | 0.3451 |
| 2.2172 | 112000 | 9.552 | 9.6102 | 0.1765 | 0.4440 | 0.5258 | 0.1539 | 0.5315 | 0.3397 | 0.1998 | 0.1561 | 0.8026 | 0.1833 | 0.2790 | 0.4262 | 0.2914 | 0.3469 |
| 2.2370 | 113000 | 9.5574 | 9.6062 | 0.1810 | 0.4425 | 0.5363 | 0.1573 | 0.5344 | 0.3341 | 0.2008 | 0.1549 | 0.8016 | 0.1767 | 0.2808 | 0.4411 | 0.2972 | 0.3491 |
| 2.2568 | 114000 | 9.5671 | 9.6021 | 0.1837 | 0.4423 | 0.5330 | 0.1547 | 0.5164 | 0.3357 | 0.2062 | 0.1572 | 0.7990 | 0.1733 | 0.2852 | 0.4280 | 0.2894 | 0.3465 |
| 2.2766 | 115000 | 9.5393 | 9.6005 | 0.1857 | 0.4413 | 0.5339 | 0.1639 | 0.5091 | 0.3312 | 0.2057 | 0.1547 | 0.8018 | 0.1820 | 0.2761 | 0.4236 | 0.2909 | 0.3462 |
| 2.2963 | 116000 | 9.5581 | 9.5972 | 0.1807 | 0.4443 | 0.5454 | 0.1488 | 0.5168 | 0.3191 | 0.2154 | 0.1558 | 0.8021 | 0.1770 | 0.2949 | 0.4140 | 0.2945 | 0.3468 |
| 2.3161 | 117000 | 9.5702 | 9.5921 | 0.1804 | 0.4424 | 0.5471 | 0.1499 | 0.5147 | 0.3227 | 0.2109 | 0.1461 | 0.8018 | 0.1783 | 0.3053 | 0.4120 | 0.2889 | 0.3462 |
| 2.3359 | 118000 | 9.5395 | 9.5915 | 0.1756 | 0.4371 | 0.5301 | 0.1582 | 0.5210 | 0.3224 | 0.2090 | 0.1507 | 0.7967 | 0.1780 | 0.2988 | 0.4034 | 0.2933 | 0.3442 |
| 2.3557 | 119000 | 9.5434 | 9.5855 | 0.1735 | 0.4458 | 0.5441 | 0.1566 | 0.5253 | 0.3281 | 0.2098 | 0.1517 | 0.7965 | 0.1736 | 0.3016 | 0.4166 | 0.2859 | 0.3468 |
| 2.3755 | 120000 | 9.5444 | 9.5812 | 0.1709 | 0.4490 | 0.5432 | 0.1534 | 0.5174 | 0.3308 | 0.2043 | 0.1503 | 0.7965 | 0.1748 | 0.2895 | 0.4206 | 0.2802 | 0.3447 |
| 2.3953 | 121000 | 9.5562 | 9.5739 | 0.1779 | 0.4413 | 0.5380 | 0.1467 | 0.5184 | 0.3371 | 0.2057 | 0.1511 | 0.7974 | 0.1821 | 0.2815 | 0.4202 | 0.2856 | 0.3448 |
| 2.4151 | 122000 | 9.5334 | 9.5738 | 0.1802 | 0.4385 | 0.5357 | 0.1537 | 0.5149 | 0.3361 | 0.2151 | 0.1503 | 0.7975 | 0.1836 | 0.3001 | 0.4133 | 0.2822 | 0.3463 |
| 2.4349 | 123000 | 9.5202 | 9.5696 | 0.1697 | 0.4451 | 0.5411 | 0.1493 | 0.5216 | 0.3337 | 0.2116 | 0.1488 | 0.7965 | 0.1804 | 0.2903 | 0.4231 | 0.2908 | 0.3463 |
| 2.4547 | 124000 | 9.5296 | 9.5683 | 0.1711 | 0.4556 | 0.5306 | 0.1466 | 0.5181 | 0.3235 | 0.2141 | 0.1570 | 0.7965 | 0.1785 | 0.2984 | 0.4201 | 0.2929 | 0.3464 |
| 2.4745 | 125000 | 9.5399 | 9.5660 | 0.1791 | 0.4487 | 0.5275 | 0.1417 | 0.5264 | 0.3305 | 0.2209 | 0.1596 | 0.7977 | 0.1770 | 0.3013 | 0.4271 | 0.2833 | 0.3478 |
| 2.4943 | 126000 | 9.5583 | 9.5641 | 0.1708 | 0.4400 | 0.5341 | 0.1489 | 0.5198 | 0.3291 | 0.2107 | 0.1515 | 0.8003 | 0.1784 | 0.3049 | 0.4282 | 0.2871 | 0.3465 |
| 2.5141 | 127000 | 9.5252 | 9.5618 | 0.1756 | 0.4424 | 0.5408 | 0.1577 | 0.5209 | 0.3244 | 0.2130 | 0.1526 | 0.8015 | 0.1785 | 0.3094 | 0.4217 | 0.2849 | 0.3480 |
| 2.5339 | 128000 | 9.5122 | 9.5577 | 0.1748 | 0.4405 | 0.5383 | 0.1501 | 0.5188 | 0.3305 | 0.2102 | 0.1446 | 0.8041 | 0.1804 | 0.3074 | 0.4184 | 0.2943 | 0.3471 |
| 2.5537 | 129000 | 9.5237 | 9.5523 | 0.1754 | 0.4396 | 0.5369 | 0.1509 | 0.5269 | 0.3246 | 0.2117 | 0.1458 | 0.8026 | 0.1799 | 0.2997 | 0.4153 | 0.2947 | 0.3465 |
| 2.5735 | 130000 | 9.5257 | 9.5510 | 0.1705 | 0.4365 | 0.5369 | 0.1560 | 0.5302 | 0.3310 | 0.2087 | 0.1559 | 0.8015 | 0.1832 | 0.3070 | 0.4243 | 0.2955 | 0.3490 |
| 2.5933 | 131000 | 9.5407 | 9.5489 | 0.1704 | 0.4386 | 0.5350 | 0.1495 | 0.5323 | 0.3302 | 0.2123 | 0.1565 | 0.8012 | 0.1846 | 0.3027 | 0.4278 | 0.2997 | 0.3493 |
| 2.6131 | 132000 | 9.5339 | 9.5449 | 0.1693 | 0.4445 | 0.5416 | 0.1621 | 0.5170 | 0.3186 | 0.2105 | 0.1551 | 0.8018 | 0.1799 | 0.2952 | 0.4263 | 0.2969 | 0.3476 |
| 2.6329 | 133000 | 9.5095 | 9.5399 | 0.1697 | 0.4392 | 0.5416 | 0.1545 | 0.5140 | 0.3332 | 0.2090 | 0.1557 | 0.7995 | 0.1758 | 0.2920 | 0.4202 | 0.3030 | 0.3467 |
| 2.6527 | 134000 | 9.5319 | 9.5397 | 0.1743 | 0.4370 | 0.5427 | 0.1635 | 0.5250 | 0.3231 | 0.2076 | 0.1504 | 0.8012 | 0.1767 | 0.2909 | 0.4205 | 0.2920 | 0.3465 |
| 2.6725 | 135000 | 9.5018 | 9.5376 | 0.1698 | 0.4358 | 0.5316 | 0.1600 | 0.5249 | 0.3199 | 0.2058 | 0.1496 | 0.8012 | 0.1859 | 0.2939 | 0.4150 | 0.2945 | 0.3452 |
| 2.6923 | 136000 | 9.4906 | 9.5338 | 0.1762 | 0.4350 | 0.5308 | 0.1525 | 0.5226 | 0.3315 | 0.2108 | 0.1667 | 0.7995 | 0.1809 | 0.2830 | 0.4364 | 0.2952 | 0.3478 |
| 2.7121 | 137000 | 9.4951 | 9.5307 | 0.1745 | 0.4356 | 0.5385 | 0.1482 | 0.5183 | 0.3339 | 0.2103 | 0.1658 | 0.7995 | 0.1786 | 0.2899 | 0.4205 | 0.2943 | 0.3468 |
| 2.7319 | 138000 | 9.498 | 9.5292 | 0.1710 | 0.4353 | 0.5363 | 0.1504 | 0.5278 | 0.3377 | 0.2045 | 0.1586 | 0.7981 | 0.1885 | 0.2882 | 0.4145 | 0.2996 | 0.3470 |
| 2.7517 | 139000 | 9.5133 | 9.5262 | 0.1705 | 0.4336 | 0.5352 | 0.1514 | 0.5250 | 0.3233 | 0.2091 | 0.1604 | 0.8016 | 0.1854 | 0.2837 | 0.4188 | 0.2966 | 0.3457 |
| 2.7715 | 140000 | 9.4934 | 9.5222 | 0.1740 | 0.4378 | 0.5279 | 0.1539 | 0.5199 | 0.3302 | 0.2128 | 0.1554 | 0.7989 | 0.1799 | 0.2885 | 0.4224 | 0.3013 | 0.3464 |
| 2.7913 | 141000 | 9.4993 | 9.5188 | 0.1754 | 0.4353 | 0.5209 | 0.1504 | 0.5287 | 0.3284 | 0.2128 | 0.1503 | 0.7972 | 0.1853 | 0.2851 | 0.4239 | 0.2956 | 0.3453 |
| 2.8110 | 142000 | 9.498 | 9.5188 | 0.1763 | 0.4313 | 0.5328 | 0.1514 | 0.5203 | 0.3260 | 0.2068 | 0.1603 | 0.8016 | 0.1812 | 0.3041 | 0.4303 | 0.2892 | 0.3470 |
| 2.8308 | 143000 | 9.477 | 9.5174 | 0.1749 | 0.4281 | 0.5437 | 0.1515 | 0.5096 | 0.3183 | 0.2025 | 0.1524 | 0.7963 | 0.1897 | 0.2938 | 0.4315 | 0.2872 | 0.3446 |
| 2.8506 | 144000 | 9.483 | 9.5132 | 0.1768 | 0.4279 | 0.5361 | 0.1424 | 0.5181 | 0.3307 | 0.2046 | 0.1506 | 0.7969 | 0.1834 | 0.2965 | 0.4301 | 0.2885 | 0.3448 |
| 2.8704 | 145000 | 9.478 | 9.5092 | 0.1870 | 0.4299 | 0.5334 | 0.1450 | 0.5128 | 0.3299 | 0.2035 | 0.1488 | 0.7981 | 0.1792 | 0.3008 | 0.4289 | 0.2886 | 0.3451 |
| 2.8902 | 146000 | 9.4904 | 9.5053 | 0.1759 | 0.4279 | 0.5370 | 0.1438 | 0.5218 | 0.3271 | 0.2077 | 0.1537 | 0.7995 | 0.1847 | 0.2832 | 0.4269 | 0.2891 | 0.3445 |
| 2.9100 | 147000 | 9.4787 | 9.5035 | 0.1744 | 0.4281 | 0.5437 | 0.1597 | 0.5050 | 0.3377 | 0.2044 | 0.1499 | 0.8003 | 0.1898 | 0.2915 | 0.4273 | 0.2928 | 0.3465 |
| 2.9298 | 148000 | 9.4861 | 9.5041 | 0.1801 | 0.4294 | 0.5303 | 0.1586 | 0.5067 | 0.3178 | 0.2086 | 0.1492 | 0.8030 | 0.1803 | 0.2837 | 0.4160 | 0.2972 | 0.3431 |
| 2.9496 | 149000 | 9.4736 | 9.5001 | 0.1758 | 0.4249 | 0.5350 | 0.1515 | 0.5103 | 0.3258 | 0.2128 | 0.1463 | 0.7983 | 0.1785 | 0.2847 | 0.4281 | 0.2936 | 0.3435 |
| 2.9694 | 150000 | 9.4847 | 9.4980 | 0.1742 | 0.4305 | 0.5362 | 0.1524 | 0.5215 | 0.3250 | 0.2097 | 0.1485 | 0.8016 | 0.1768 | 0.2911 | 0.4228 | 0.2946 | 0.3450 |
| 2.9892 | 151000 | 9.4756 | 9.4948 | 0.1694 | 0.4270 | 0.5333 | 0.1575 | 0.5128 | 0.3191 | 0.2116 | 0.1445 | 0.8015 | 0.1736 | 0.2908 | 0.4215 | 0.2889 | 0.3424 |
| 3.0090 | 152000 | 9.4206 | 9.4949 | 0.1751 | 0.4243 | 0.5332 | 0.1432 | 0.5094 | 0.3172 | 0.2100 | 0.1442 | 0.7981 | 0.1763 | 0.2852 | 0.4310 | 0.2880 | 0.3412 |
| 3.0288 | 153000 | 9.3728 | 9.4973 | 0.1746 | 0.4330 | 0.5332 | 0.1447 | 0.5212 | 0.3211 | 0.2142 | 0.1493 | 0.7968 | 0.1803 | 0.2964 | 0.4287 | 0.2886 | 0.3448 |
| 3.0486 | 154000 | 9.3962 | 9.5003 | 0.1815 | 0.4325 | 0.5341 | 0.1456 | 0.5162 | 0.3300 | 0.2175 | 0.1431 | 0.7971 | 0.1806 | 0.3010 | 0.4328 | 0.2892 | 0.3462 |
| 3.0684 | 155000 | 9.3975 | 9.4988 | 0.1784 | 0.4276 | 0.5391 | 0.1478 | 0.5187 | 0.3271 | 0.2212 | 0.1457 | 0.7987 | 0.1832 | 0.3011 | 0.4305 | 0.2866 | 0.3466 |
| 3.0882 | 156000 | 9.411 | 9.4975 | 0.1728 | 0.4266 | 0.5301 | 0.1505 | 0.5208 | 0.3275 | 0.2191 | 0.1461 | 0.7994 | 0.1829 | 0.3012 | 0.4289 | 0.2916 | 0.3460 |
| 3.1080 | 157000 | 9.3958 | 9.4955 | 0.1796 | 0.4283 | 0.5375 | 0.1498 | 0.5186 | 0.3409 | 0.2209 | 0.1503 | 0.7985 | 0.1816 | 0.3024 | 0.4372 | 0.2875 | 0.3487 |
| 3.1278 | 158000 | 9.4203 | 9.4925 | 0.1699 | 0.4338 | 0.5324 | 0.1454 | 0.5078 | 0.3324 | 0.2152 | 0.1480 | 0.7990 | 0.1780 | 0.2957 | 0.4364 | 0.2849 | 0.3445 |
| 3.1476 | 159000 | 9.416 | 9.4913 | 0.1751 | 0.4325 | 0.5301 | 0.1498 | 0.5152 | 0.3270 | 0.2179 | 0.1491 | 0.7964 | 0.1782 | 0.3020 | 0.4285 | 0.2878 | 0.3454 |
| 3.1674 | 160000 | 9.4133 | 9.4867 | 0.1757 | 0.4320 | 0.5334 | 0.1528 | 0.5177 | 0.3264 | 0.2153 | 0.1443 | 0.7896 | 0.1784 | 0.2946 | 0.4276 | 0.2933 | 0.3447 |
| 3.1872 | 161000 | 9.4188 | 9.4860 | 0.1780 | 0.4300 | 0.5357 | 0.1486 | 0.5096 | 0.3295 | 0.2221 | 0.1479 | 0.7915 | 0.1780 | 0.2941 | 0.4224 | 0.2920 | 0.3446 |
| 3.2070 | 162000 | 9.4297 | 9.4831 | 0.1826 | 0.4291 | 0.5338 | 0.1520 | 0.5032 | 0.3359 | 0.2204 | 0.1488 | 0.7951 | 0.1759 | 0.2946 | 0.4272 | 0.2887 | 0.3452 |
| 3.2268 | 163000 | 9.4151 | 9.4808 | 0.1779 | 0.4341 | 0.5256 | 0.1517 | 0.5141 | 0.3407 | 0.2200 | 0.1460 | 0.7973 | 0.1854 | 0.2971 | 0.4191 | 0.2903 | 0.3461 |
| 3.2466 | 164000 | 9.4185 | 9.4781 | 0.1748 | 0.4358 | 0.5368 | 0.1409 | 0.5137 | 0.3376 | 0.2139 | 0.1414 | 0.7974 | 0.1759 | 0.3024 | 0.4214 | 0.2890 | 0.3447 |
| 3.2664 | 165000 | 9.4227 | 9.4763 | 0.1771 | 0.4319 | 0.5236 | 0.1389 | 0.5143 | 0.3389 | 0.2091 | 0.1515 | 0.7960 | 0.1800 | 0.2955 | 0.4286 | 0.2896 | 0.3442 |
| 3.2862 | 166000 | 9.4049 | 9.4711 | 0.1804 | 0.4312 | 0.5264 | 0.1449 | 0.5098 | 0.3393 | 0.2083 | 0.1505 | 0.7963 | 0.1811 | 0.2918 | 0.4278 | 0.2897 | 0.3444 |
| 3.3059 | 167000 | 9.4249 | 9.4675 | 0.1788 | 0.4297 | 0.5298 | 0.1395 | 0.5121 | 0.3463 | 0.2096 | 0.1455 | 0.7975 | 0.1810 | 0.3020 | 0.4351 | 0.2882 | 0.3458 |
| 3.3257 | 168000 | 9.4047 | 9.4667 | 0.1660 | 0.4296 | 0.5296 | 0.1427 | 0.5152 | 0.3488 | 0.2093 | 0.1458 | 0.7975 | 0.1830 | 0.3008 | 0.4352 | 0.2869 | 0.3454 |
| 3.3455 | 169000 | 9.4124 | 9.4663 | 0.1661 | 0.4260 | 0.5325 | 0.1439 | 0.5171 | 0.3550 | 0.2122 | 0.1444 | 0.7975 | 0.1833 | 0.2994 | 0.4352 | 0.2891 | 0.3463 |
| 3.3653 | 170000 | 9.416 | 9.4636 | 0.1729 | 0.4248 | 0.5424 | 0.1578 | 0.5146 | 0.3521 | 0.2078 | 0.1463 | 0.7975 | 0.1783 | 0.3047 | 0.4292 | 0.2883 | 0.3474 |
| 3.3851 | 171000 | 9.4139 | 9.4593 | 0.1732 | 0.4275 | 0.5390 | 0.1517 | 0.5233 | 0.3433 | 0.2079 | 0.1477 | 0.7975 | 0.1750 | 0.3052 | 0.4285 | 0.2865 | 0.3466 |
| 3.4049 | 172000 | 9.3927 | 9.4585 | 0.1771 | 0.4279 | 0.5339 | 0.1522 | 0.5226 | 0.3456 | 0.2095 | 0.1468 | 0.7981 | 0.1791 | 0.3029 | 0.4300 | 0.2851 | 0.3470 |
| 3.4247 | 173000 | 9.4008 | 9.4560 | 0.1753 | 0.4289 | 0.5344 | 0.1606 | 0.5179 | 0.3410 | 0.2068 | 0.1467 | 0.7975 | 0.1796 | 0.2984 | 0.4294 | 0.2869 | 0.3464 |
| 3.4445 | 174000 | 9.403 | 9.4545 | 0.1730 | 0.4337 | 0.5372 | 0.1535 | 0.5230 | 0.3296 | 0.2030 | 0.1470 | 0.8010 | 0.1802 | 0.3080 | 0.4243 | 0.2879 | 0.3463 |
| 3.4643 | 175000 | 9.414 | 9.4498 | 0.1678 | 0.4330 | 0.5383 | 0.1588 | 0.5134 | 0.3348 | 0.2050 | 0.1472 | 0.7984 | 0.1794 | 0.2980 | 0.4165 | 0.2876 | 0.3445 |
| 3.4841 | 176000 | 9.4006 | 9.4484 | 0.1726 | 0.4367 | 0.5311 | 0.1571 | 0.5167 | 0.3191 | 0.2092 | 0.1517 | 0.7975 | 0.1840 | 0.2968 | 0.4212 | 0.2904 | 0.3449 |
| 3.5039 | 177000 | 9.4065 | 9.4452 | 0.1722 | 0.4347 | 0.5311 | 0.1524 | 0.5210 | 0.3324 | 0.2061 | 0.1525 | 0.7964 | 0.1810 | 0.3090 | 0.4310 | 0.2895 | 0.3469 |
| 3.5237 | 178000 | 9.4145 | 9.4411 | 0.1763 | 0.4360 | 0.5279 | 0.1571 | 0.5112 | 0.3257 | 0.2094 | 0.1505 | 0.7969 | 0.1768 | 0.2963 | 0.4288 | 0.2883 | 0.3447 |
| 3.5435 | 179000 | 9.4052 | 9.4404 | 0.1757 | 0.4367 | 0.5292 | 0.1549 | 0.5200 | 0.3348 | 0.2107 | 0.1527 | 0.7961 | 0.1808 | 0.2873 | 0.4250 | 0.2871 | 0.3455 |
| 3.5633 | 180000 | 9.412 | 9.4392 | 0.1723 | 0.4337 | 0.5354 | 0.1531 | 0.5181 | 0.3348 | 0.2092 | 0.1480 | 0.7967 | 0.1786 | 0.2877 | 0.4227 | 0.2907 | 0.3447 |
| 3.5831 | 181000 | 9.4105 | 9.4377 | 0.1747 | 0.4308 | 0.5334 | 0.1572 | 0.5188 | 0.3348 | 0.2101 | 0.1480 | 0.7967 | 0.1753 | 0.2894 | 0.4294 | 0.2895 | 0.3452 |
| 3.6029 | 182000 | 9.3904 | 9.4336 | 0.1703 | 0.4358 | 0.5354 | 0.1524 | 0.5229 | 0.3283 | 0.2227 | 0.1488 | 0.7999 | 0.1768 | 0.2954 | 0.4290 | 0.2889 | 0.3467 |
| 3.6227 | 183000 | 9.3784 | 9.4310 | 0.1743 | 0.4311 | 0.5379 | 0.1437 | 0.5182 | 0.3264 | 0.2198 | 0.1490 | 0.7999 | 0.1758 | 0.3012 | 0.4294 | 0.2889 | 0.3458 |
| 3.6425 | 184000 | 9.3762 | 9.4288 | 0.1713 | 0.4345 | 0.5362 | 0.1506 | 0.5136 | 0.3186 | 0.2107 | 0.1491 | 0.7973 | 0.1751 | 0.3018 | 0.4282 | 0.2898 | 0.3444 |
| 3.6623 | 185000 | 9.3958 | 9.4268 | 0.1757 | 0.4290 | 0.5420 | 0.1503 | 0.5158 | 0.3175 | 0.2067 | 0.1465 | 0.7938 | 0.1772 | 0.3020 | 0.4219 | 0.2921 | 0.3439 |
| 3.6821 | 186000 | 9.4056 | 9.4261 | 0.1790 | 0.4308 | 0.5388 | 0.1454 | 0.5162 | 0.3200 | 0.2096 | 0.1400 | 0.7949 | 0.1699 | 0.2988 | 0.4235 | 0.2867 | 0.3426 |
| 3.7019 | 187000 | 9.3616 | 9.4244 | 0.1797 | 0.4279 | 0.5428 | 0.1499 | 0.5173 | 0.3252 | 0.2150 | 0.1405 | 0.7975 | 0.1758 | 0.2900 | 0.4287 | 0.2862 | 0.3443 |
| 3.7217 | 188000 | 9.3864 | 9.4239 | 0.1794 | 0.4288 | 0.5447 | 0.1474 | 0.5225 | 0.3273 | 0.2210 | 0.1467 | 0.7975 | 0.1710 | 0.2966 | 0.4361 | 0.2804 | 0.3461 |
| 3.7415 | 189000 | 9.3842 | 9.4199 | 0.1765 | 0.4295 | 0.5306 | 0.1450 | 0.5176 | 0.3190 | 0.2218 | 0.1461 | 0.7961 | 0.1753 | 0.2959 | 0.4284 | 0.2843 | 0.3436 |
| 3.7613 | 190000 | 9.3888 | 9.4186 | 0.1770 | 0.4281 | 0.5369 | 0.1451 | 0.5140 | 0.3171 | 0.2173 | 0.1408 | 0.7953 | 0.1774 | 0.2887 | 0.4271 | 0.2833 | 0.3422 |
| 3.7811 | 191000 | 9.3769 | 9.4163 | 0.1777 | 0.4291 | 0.5417 | 0.1411 | 0.5150 | 0.3176 | 0.2103 | 0.1474 | 0.7959 | 0.1813 | 0.3013 | 0.4268 | 0.2757 | 0.3431 |
| 3.8009 | 192000 | 9.3643 | 9.4151 | 0.1773 | 0.4275 | 0.5396 | 0.1483 | 0.5170 | 0.3236 | 0.2100 | 0.1482 | 0.7959 | 0.1796 | 0.2993 | 0.4274 | 0.2766 | 0.3439 |
| 3.8206 | 193000 | 9.376 | 9.4128 | 0.1707 | 0.4300 | 0.5431 | 0.1422 | 0.5139 | 0.3277 | 0.2144 | 0.1472 | 0.7959 | 0.1823 | 0.2945 | 0.4283 | 0.2821 | 0.3440 |
| 3.8404 | 194000 | 9.396 | 9.4102 | 0.1727 | 0.4280 | 0.5418 | 0.1486 | 0.5137 | 0.3242 | 0.2071 | 0.1470 | 0.7959 | 0.1800 | 0.3001 | 0.4280 | 0.2843 | 0.3440 |
| 3.8602 | 195000 | 9.3662 | 9.4087 | 0.1741 | 0.4273 | 0.5371 | 0.1451 | 0.5116 | 0.3185 | 0.2101 | 0.1455 | 0.7959 | 0.1810 | 0.2940 | 0.4278 | 0.2840 | 0.3424 |
| 3.8800 | 196000 | 9.3727 | 9.4067 | 0.1704 | 0.4271 | 0.5393 | 0.1411 | 0.5099 | 0.3165 | 0.2047 | 0.1508 | 0.7967 | 0.1848 | 0.2946 | 0.4281 | 0.2838 | 0.3421 |
| 3.8998 | 197000 | 9.3805 | 9.4048 | 0.1716 | 0.4254 | 0.5416 | 0.1477 | 0.5192 | 0.3154 | 0.2098 | 0.1468 | 0.7953 | 0.1827 | 0.2920 | 0.4280 | 0.2874 | 0.3433 |
| 3.9196 | 198000 | 9.3799 | 9.4033 | 0.1687 | 0.4278 | 0.5393 | 0.1472 | 0.5146 | 0.3219 | 0.2083 | 0.1479 | 0.7961 | 0.1838 | 0.2918 | 0.4275 | 0.2860 | 0.3432 |
| 3.9394 | 199000 | 9.3702 | 9.3999 | 0.1681 | 0.4306 | 0.5401 | 0.1476 | 0.5098 | 0.3233 | 0.2112 | 0.1470 | 0.7975 | 0.1816 | 0.2926 | 0.4278 | 0.2814 | 0.3430 |
| 3.9592 | 200000 | 9.3646 | 9.3988 | 0.1701 | 0.4321 | 0.5401 | 0.1484 | 0.5107 | 0.3227 | 0.2135 | 0.1465 | 0.7980 | 0.1815 | 0.2930 | 0.4335 | 0.2858 | 0.3443 |
| 3.9790 | 201000 | 9.3559 | 9.3963 | 0.1696 | 0.4319 | 0.5418 | 0.1475 | 0.5135 | 0.3218 | 0.2117 | 0.1484 | 0.7975 | 0.1821 | 0.2856 | 0.4270 | 0.2853 | 0.3434 |
| 3.9988 | 202000 | 9.3566 | 9.3950 | 0.1743 | 0.4284 | 0.5432 | 0.1398 | 0.5092 | 0.3236 | 0.2113 | 0.1481 | 0.7980 | 0.1822 | 0.2784 | 0.4330 | 0.2827 | 0.3425 |
| 4.0186 | 203000 | 9.2801 | 9.3988 | 0.1709 | 0.4305 | 0.5418 | 0.1357 | 0.5223 | 0.3149 | 0.2129 | 0.1513 | 0.7975 | 0.1804 | 0.2873 | 0.4349 | 0.2820 | 0.3433 |
| 4.0384 | 204000 | 9.3024 | 9.3985 | 0.1745 | 0.4305 | 0.5418 | 0.1451 | 0.5189 | 0.3159 | 0.2081 | 0.1501 | 0.7975 | 0.1795 | 0.2869 | 0.4284 | 0.2828 | 0.3431 |
| 4.0582 | 205000 | 9.2953 | 9.3992 | 0.1743 | 0.4278 | 0.5418 | 0.1327 | 0.5162 | 0.3145 | 0.2110 | 0.1498 | 0.7975 | 0.1843 | 0.2818 | 0.4289 | 0.2825 | 0.3418 |
| 4.0780 | 206000 | 9.2922 | 9.4003 | 0.1731 | 0.4283 | 0.5416 | 0.1391 | 0.5180 | 0.3166 | 0.2110 | 0.1498 | 0.7972 | 0.1801 | 0.2796 | 0.4289 | 0.2830 | 0.3420 |
| 4.0978 | 207000 | 9.2851 | 9.3996 | 0.1740 | 0.4294 | 0.5416 | 0.1410 | 0.5147 | 0.3155 | 0.2134 | 0.1560 | 0.7975 | 0.1822 | 0.2880 | 0.4303 | 0.2820 | 0.3435 |
| 4.1176 | 208000 | 9.2913 | 9.3978 | 0.1740 | 0.4325 | 0.5416 | 0.1350 | 0.5131 | 0.3156 | 0.2129 | 0.1554 | 0.7975 | 0.1800 | 0.2876 | 0.4303 | 0.2856 | 0.3432 |
| 4.1374 | 209000 | 9.298 | 9.3966 | 0.1732 | 0.4274 | 0.5430 | 0.1387 | 0.5219 | 0.3139 | 0.2145 | 0.1507 | 0.7975 | 0.1779 | 0.2870 | 0.4275 | 0.2852 | 0.3430 |
| 4.1572 | 210000 | 9.2952 | 9.3943 | 0.1761 | 0.4262 | 0.5430 | 0.1433 | 0.5226 | 0.3231 | 0.2128 | 0.1561 | 0.7980 | 0.1806 | 0.2871 | 0.4282 | 0.2865 | 0.3449 |
| 4.1770 | 211000 | 9.3193 | 9.3924 | 0.1741 | 0.4269 | 0.5430 | 0.1331 | 0.5218 | 0.3256 | 0.2140 | 0.1503 | 0.7980 | 0.1786 | 0.2869 | 0.4284 | 0.2843 | 0.3435 |
| 4.1968 | 212000 | 9.297 | 9.3912 | 0.1744 | 0.4278 | 0.5428 | 0.1427 | 0.5217 | 0.3267 | 0.2138 | 0.1488 | 0.7980 | 0.1794 | 0.2806 | 0.4278 | 0.2831 | 0.3437 |
| 4.2166 | 213000 | 9.2984 | 9.3891 | 0.1797 | 0.4297 | 0.5430 | 0.1428 | 0.5236 | 0.3251 | 0.2128 | 0.1495 | 0.7980 | 0.1762 | 0.2791 | 0.4272 | 0.2859 | 0.3440 |
| 4.2364 | 214000 | 9.306 | 9.3881 | 0.1818 | 0.4275 | 0.5436 | 0.1457 | 0.5215 | 0.3244 | 0.2120 | 0.1498 | 0.7980 | 0.1812 | 0.2801 | 0.4278 | 0.2835 | 0.3444 |
| 4.2562 | 215000 | 9.3029 | 9.3861 | 0.1807 | 0.4290 | 0.5436 | 0.1413 | 0.5206 | 0.3244 | 0.2166 | 0.1481 | 0.7980 | 0.1829 | 0.2860 | 0.4275 | 0.2847 | 0.3449 |
| 4.2760 | 216000 | 9.2965 | 9.3848 | 0.1769 | 0.4280 | 0.5430 | 0.1471 | 0.5209 | 0.3251 | 0.2128 | 0.1555 | 0.7975 | 0.1794 | 0.2739 | 0.4270 | 0.2827 | 0.3438 |
| 4.2958 | 217000 | 9.3171 | 9.3828 | 0.1796 | 0.4285 | 0.5430 | 0.1438 | 0.5209 | 0.3231 | 0.2133 | 0.1490 | 0.7975 | 0.1819 | 0.2858 | 0.4270 | 0.2793 | 0.3440 |
| 4.3155 | 218000 | 9.3181 | 9.3824 | 0.1794 | 0.4262 | 0.5430 | 0.1496 | 0.5241 | 0.3243 | 0.2147 | 0.1481 | 0.7975 | 0.1794 | 0.2812 | 0.4275 | 0.2818 | 0.3444 |
| 4.3353 | 219000 | 9.2952 | 9.3794 | 0.1766 | 0.4265 | 0.5432 | 0.1412 | 0.5223 | 0.3243 | 0.2098 | 0.1475 | 0.7975 | 0.1777 | 0.2851 | 0.4328 | 0.2784 | 0.3433 |
| 4.3551 | 220000 | 9.32 | 9.3776 | 0.1739 | 0.4261 | 0.5432 | 0.1362 | 0.5258 | 0.3257 | 0.2106 | 0.1470 | 0.7980 | 0.1782 | 0.2815 | 0.4268 | 0.2787 | 0.3424 |
| 4.3749 | 221000 | 9.2999 | 9.3758 | 0.1767 | 0.4297 | 0.5432 | 0.1395 | 0.5175 | 0.3252 | 0.2126 | 0.1489 | 0.7980 | 0.1778 | 0.2865 | 0.4210 | 0.2801 | 0.3428 |
| 4.3947 | 222000 | 9.2954 | 9.3750 | 0.1783 | 0.4261 | 0.5432 | 0.1397 | 0.5220 | 0.3244 | 0.2116 | 0.1496 | 0.7980 | 0.1797 | 0.2929 | 0.4268 | 0.2786 | 0.3439 |
| 4.4145 | 223000 | 9.2944 | 9.3726 | 0.1795 | 0.4275 | 0.5432 | 0.1395 | 0.5172 | 0.3236 | 0.2130 | 0.1488 | 0.7971 | 0.1785 | 0.2921 | 0.4273 | 0.2796 | 0.3436 |
| 4.4343 | 224000 | 9.2851 | 9.3714 | 0.1794 | 0.4251 | 0.5432 | 0.1395 | 0.5172 | 0.3227 | 0.2136 | 0.1488 | 0.7975 | 0.1780 | 0.2921 | 0.4268 | 0.2788 | 0.3433 |
| 4.4541 | 225000 | 9.2856 | 9.3694 | 0.1761 | 0.4257 | 0.5432 | 0.1408 | 0.5218 | 0.3227 | 0.2116 | 0.1486 | 0.7971 | 0.1800 | 0.2935 | 0.4270 | 0.2794 | 0.3437 |
| 4.4739 | 226000 | 9.2967 | 9.3676 | 0.1792 | 0.4256 | 0.5418 | 0.1372 | 0.5200 | 0.3230 | 0.2100 | 0.1492 | 0.7967 | 0.1774 | 0.2939 | 0.4270 | 0.2803 | 0.3432 |
| 4.4937 | 227000 | 9.3019 | 9.3670 | 0.1798 | 0.4253 | 0.5430 | 0.1397 | 0.5200 | 0.3147 | 0.2063 | 0.1481 | 0.7967 | 0.1779 | 0.2946 | 0.4210 | 0.2792 | 0.3420 |
| 4.5135 | 228000 | 9.2938 | 9.3655 | 0.1795 | 0.4258 | 0.5423 | 0.1397 | 0.5192 | 0.3139 | 0.2094 | 0.1487 | 0.7967 | 0.1775 | 0.2943 | 0.4210 | 0.2780 | 0.3420 |
| 4.5333 | 229000 | 9.306 | 9.3643 | 0.1772 | 0.4251 | 0.5432 | 0.1393 | 0.5235 | 0.3148 | 0.2079 | 0.1487 | 0.7998 | 0.1798 | 0.2917 | 0.4216 | 0.2768 | 0.3423 |
| 4.5531 | 230000 | 9.3057 | 9.3631 | 0.1726 | 0.4250 | 0.5423 | 0.1393 | 0.5241 | 0.3148 | 0.2080 | 0.1483 | 0.7967 | 0.1795 | 0.2923 | 0.4216 | 0.2771 | 0.3417 |
| 4.5729 | 231000 | 9.3069 | 9.3615 | 0.1757 | 0.4240 | 0.5421 | 0.1500 | 0.5226 | 0.3171 | 0.2093 | 0.1481 | 0.7980 | 0.1783 | 0.2920 | 0.4216 | 0.2784 | 0.3429 |
| 4.5927 | 232000 | 9.3003 | 9.3604 | 0.1752 | 0.4255 | 0.5421 | 0.1498 | 0.5226 | 0.3185 | 0.2096 | 0.1478 | 0.7980 | 0.1801 | 0.2920 | 0.4216 | 0.2783 | 0.3432 |
| 4.6125 | 233000 | 9.3042 | 9.3594 | 0.1748 | 0.4243 | 0.5407 | 0.1453 | 0.5263 | 0.3185 | 0.2098 | 0.1472 | 0.7972 | 0.1796 | 0.2918 | 0.4216 | 0.2797 | 0.3428 |
| 4.6323 | 234000 | 9.3079 | 9.3573 | 0.1749 | 0.4256 | 0.5407 | 0.1428 | 0.5242 | 0.3185 | 0.2096 | 0.1536 | 0.7975 | 0.1793 | 0.2920 | 0.4273 | 0.2815 | 0.3437 |
| 4.6521 | 235000 | 9.284 | 9.3566 | 0.1729 | 0.4256 | 0.5407 | 0.1455 | 0.5253 | 0.3190 | 0.2079 | 0.1487 | 0.7975 | 0.1801 | 0.2936 | 0.4273 | 0.2812 | 0.3435 |
| 4.6719 | 236000 | 9.2916 | 9.3550 | 0.1755 | 0.4270 | 0.5416 | 0.1447 | 0.5216 | 0.3190 | 0.2081 | 0.1487 | 0.7975 | 0.1797 | 0.2869 | 0.4273 | 0.2823 | 0.3431 |
| 4.6917 | 237000 | 9.2871 | 9.3537 | 0.1733 | 0.4263 | 0.5421 | 0.1447 | 0.5246 | 0.3190 | 0.2097 | 0.1492 | 0.7980 | 0.1779 | 0.2917 | 0.4273 | 0.2786 | 0.3433 |
| 4.7115 | 238000 | 9.3105 | 9.3519 | 0.1729 | 0.4248 | 0.5430 | 0.1372 | 0.5194 | 0.3176 | 0.2096 | 0.1492 | 0.7980 | 0.1803 | 0.2917 | 0.4273 | 0.2799 | 0.3424 |
| 4.7313 | 239000 | 9.2935 | 9.3506 | 0.1731 | 0.4241 | 0.5421 | 0.1447 | 0.5194 | 0.3176 | 0.2078 | 0.1483 | 0.7975 | 0.1780 | 0.2903 | 0.4273 | 0.2797 | 0.3423 |
| 4.7511 | 240000 | 9.283 | 9.3497 | 0.1730 | 0.4257 | 0.5421 | 0.1388 | 0.5149 | 0.3176 | 0.2079 | 0.1486 | 0.7975 | 0.1779 | 0.2906 | 0.4273 | 0.2809 | 0.3417 |
| 4.7709 | 241000 | 9.2994 | 9.3486 | 0.1733 | 0.4257 | 0.5421 | 0.1388 | 0.5194 | 0.3176 | 0.2093 | 0.1486 | 0.7959 | 0.1798 | 0.2903 | 0.4216 | 0.2785 | 0.3416 |
| 4.7907 | 242000 | 9.2784 | 9.3475 | 0.1734 | 0.4245 | 0.5421 | 0.1433 | 0.5149 | 0.3176 | 0.2078 | 0.1486 | 0.7966 | 0.1780 | 0.2899 | 0.4200 | 0.2797 | 0.3413 |
| 4.8105 | 243000 | 9.2968 | 9.3466 | 0.1751 | 0.4245 | 0.5421 | 0.1388 | 0.5149 | 0.3176 | 0.2083 | 0.1486 | 0.7980 | 0.1779 | 0.2906 | 0.4273 | 0.2768 | 0.3416 |
| 4.8302 | 244000 | 9.2829 | 9.3455 | 0.1751 | 0.4245 | 0.5421 | 0.1446 | 0.5149 | 0.3176 | 0.2096 | 0.1486 | 0.7959 | 0.1778 | 0.2899 | 0.4273 | 0.2782 | 0.3420 |
| 4.8500 | 245000 | 9.2787 | 9.3449 | 0.1739 | 0.4245 | 0.5421 | 0.1446 | 0.5149 | 0.3176 | 0.2085 | 0.1486 | 0.7961 | 0.1779 | 0.2899 | 0.4273 | 0.2794 | 0.3420 |
| 4.8698 | 246000 | 9.2856 | 9.3439 | 0.1735 | 0.4247 | 0.5421 | 0.1491 | 0.5149 | 0.3176 | 0.2081 | 0.1483 | 0.7961 | 0.1779 | 0.2899 | 0.4216 | 0.2806 | 0.3419 |
| 4.8896 | 247000 | 9.2754 | 9.3433 | 0.1735 | 0.4247 | 0.5421 | 0.1490 | 0.5149 | 0.3176 | 0.2083 | 0.1483 | 0.7966 | 0.1779 | 0.2897 | 0.4216 | 0.2810 | 0.3419 |
| 4.9094 | 248000 | 9.2706 | 9.3427 | 0.1735 | 0.4247 | 0.5421 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7959 | 0.1774 | 0.2899 | 0.4216 | 0.2825 | 0.3418 |
| 4.9292 | 249000 | 9.3004 | 9.3422 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7975 | 0.1774 | 0.2899 | 0.4216 | 0.2811 | 0.3418 |
| 4.9490 | 250000 | 9.2861 | 9.3417 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7961 | 0.1774 | 0.2899 | 0.4216 | 0.2811 | 0.3417 |
| 4.9688 | 251000 | 9.2583 | 9.3412 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7966 | 0.1755 | 0.2899 | 0.4216 | 0.2813 | 0.3416 |
| 4.9886 | 252000 | 9.2786 | 9.3411 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1483 | 0.7966 | 0.1755 | 0.2899 | 0.4216 | 0.2813 | 0.3416 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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.*
--> |